Reset23/the-stack-v2-blamed2 · Datasets at Hugging Face

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d17616959f48f6438ed95d62e6f8cfbd17f4451e

/KerasRFCN/Utils.py

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mitulrm/FaceRFCN

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refs/heads/master

2020-05-15T20:35:54.496866

2019-08-20T02:41:35

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""" Keras RFCN Copyright (c) 2018 Licensed under the MIT License (see LICENSE for details) Written by parap1uie-s@github.com """ import sys import os import math import random import numpy as np import tensorflow as tf import scipy.misc import skimage.color import skimage.io import urllib.request import shutil import keras.backend as K from keras.callbacks import Callback ############################################################ # Bounding Boxes ############################################################ # def extract_bboxes(mask): # """Compute bounding boxes. # mask: [height, width, num_instances]. Mask pixels are either 1 or 0. # Returns: bbox array [num_instances, (y1, x1, y2, x2)]. # """ # boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32) # for i in range(mask.shape[-1]): # m = mask[:, :, i] # # Bounding box. # horizontal_indicies = np.where(np.any(m, axis=0))[0] # vertical_indicies = np.where(np.any(m, axis=1))[0] # if horizontal_indicies.shape[0]: # x1, x2 = horizontal_indicies[[0, -1]] # y1, y2 = vertical_indicies[[0, -1]] # # x2 and y2 should not be part of the box. Increment by 1. # x2 += 1 # y2 += 1 # else: # # No mask for this instance. Might happen due to # # resizing or cropping. Set bbox to zeros # x1, x2, y1, y2 = 0, 0, 0, 0 # boxes[i] = np.array([y1, x1, y2, x2]) # return boxes.astype(np.int32) def compute_iou(box, boxes, box_area, boxes_area): """Calculates IoU of the given box with the array of the given boxes. box: 1D vector [y1, x1, y2, x2] boxes: [boxes_count, (y1, x1, y2, x2)] box_area: float. the area of 'box' boxes_area: array of length boxes_count. Note: the areas are passed in rather than calculated here for efficency. Calculate once in the caller to avoid duplicate work. """ # Calculate intersection areas y1 = np.maximum(box[0], boxes[:, 0]) y2 = np.minimum(box[2], boxes[:, 2]) x1 = np.maximum(box[1], boxes[:, 1]) x2 = np.minimum(box[3], boxes[:, 3]) intersection = np.maximum(x2 - x1, 0) * np.maximum(y2 - y1, 0) union = box_area + boxes_area[:] - intersection[:] iou = intersection / union return iou def compute_overlaps(boxes1, boxes2): """Computes IoU overlaps between two sets of boxes. boxes1, boxes2: [N, (y1, x1, y2, x2)]. For better performance, pass the largest set first and the smaller second. """ # Areas of anchors and GT boxes area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1]) area2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1]) # Compute overlaps to generate matrix [boxes1 count, boxes2 count] # Each cell contains the IoU value. overlaps = np.zeros((boxes1.shape[0], boxes2.shape[0])) for i in range(overlaps.shape[1]): box2 = boxes2[i] overlaps[:, i] = compute_iou(box2, boxes1, area2[i], area1) return overlaps def non_max_suppression(boxes, scores, threshold): """Performs non-maximum supression and returns indicies of kept boxes. boxes: [N, (y1, x1, y2, x2)]. Notice that (y2, x2) lays outside the box. scores: 1-D array of box scores. threshold: Float. IoU threshold to use for filtering. """ assert boxes.shape[0] > 0 if boxes.dtype.kind != "f": boxes = boxes.astype(np.float32) # Compute box areas y1 = boxes[:, 0] x1 = boxes[:, 1] y2 = boxes[:, 2] x2 = boxes[:, 3] area = (y2 - y1) * (x2 - x1) # Get indicies of boxes sorted by scores (highest first) ixs = scores.argsort()[::-1] pick = [] while len(ixs) > 0: # Pick top box and add its index to the list i = ixs[0] pick.append(i) # Compute IoU of the picked box with the rest iou = compute_iou(boxes[i], boxes[ixs[1:]], area[i], area[ixs[1:]]) # Identify boxes with IoU over the threshold. This # returns indicies into ixs[1:], so add 1 to get # indicies into ixs. remove_ixs = np.where(iou > threshold)[0] + 1 # Remove indicies of the picked and overlapped boxes. ixs = np.delete(ixs, remove_ixs) ixs = np.delete(ixs, 0) return np.array(pick, dtype=np.int32) def apply_box_deltas(boxes, deltas): """Applies the given deltas to the given boxes. boxes: [N, (y1, x1, y2, x2)]. Note that (y2, x2) is outside the box. deltas: [N, (dy, dx, log(dh), log(dw))] """ boxes = boxes.astype(np.float32) # Convert to y, x, h, w height = boxes[:, 2] - boxes[:, 0] width = boxes[:, 3] - boxes[:, 1] center_y = boxes[:, 0] + 0.5 * height center_x = boxes[:, 1] + 0.5 * width # Apply deltas center_y += deltas[:, 0] * height center_x += deltas[:, 1] * width height *= np.exp(deltas[:, 2]) width *= np.exp(deltas[:, 3]) # Convert back to y1, x1, y2, x2 y1 = center_y - 0.5 * height x1 = center_x - 0.5 * width y2 = y1 + height x2 = x1 + width return np.stack([y1, x1, y2, x2], axis=1) def box_refinement_graph(box, gt_box): """Compute refinement needed to transform box to gt_box. box and gt_box are [N, (y1, x1, y2, x2)] """ box = tf.cast(box, tf.float32) gt_box = tf.cast(gt_box, tf.float32) height = box[:, 2] - box[:, 0] width = box[:, 3] - box[:, 1] center_y = box[:, 0] + 0.5 * height center_x = box[:, 1] + 0.5 * width gt_height = gt_box[:, 2] - gt_box[:, 0] gt_width = gt_box[:, 3] - gt_box[:, 1] gt_center_y = gt_box[:, 0] + 0.5 * gt_height gt_center_x = gt_box[:, 1] + 0.5 * gt_width dy = (gt_center_y - center_y) / height dx = (gt_center_x - center_x) / width dh = tf.log(gt_height / height) dw = tf.log(gt_width / width) result = tf.stack([dy, dx, dh, dw], axis=1) return result def box_refinement(box, gt_box): """Compute refinement needed to transform box to gt_box. box and gt_box are [N, (y1, x1, y2, x2)]. (y2, x2) is assumed to be outside the box. """ box = box.astype(np.float32) gt_box = gt_box.astype(np.float32) height = box[:, 2] - box[:, 0] width = box[:, 3] - box[:, 1] center_y = box[:, 0] + 0.5 * height center_x = box[:, 1] + 0.5 * width gt_height = gt_box[:, 2] - gt_box[:, 0] gt_width = gt_box[:, 3] - gt_box[:, 1] gt_center_y = gt_box[:, 0] + 0.5 * gt_height gt_center_x = gt_box[:, 1] + 0.5 * gt_width dy = (gt_center_y - center_y) / height dx = (gt_center_x - center_x) / width dh = np.log(gt_height / height) dw = np.log(gt_width / width) return np.stack([dy, dx, dh, dw], axis=1) ############################################################ # Dataset ############################################################ class Dataset(object): """The base class for dataset classes. To use it, create a new class that adds functions specific to the dataset you want to use. For example: class CatsAndDogsDataset(Dataset): def load_cats_and_dogs(self): ... def load_bbox(self, image_id): ... def image_reference(self, image_id): ... See COCODataset and ShapesDataset as examples. """ def __init__(self, class_map=None): self._image_ids = [] self.image_info = [] # Background is always the first class self.class_info = [{"source": "", "id": 0, "name": "BG"}] self.source_class_ids = {} def add_class(self, source, class_id, class_name): assert "." not in source, "Source name cannot contain a dot" # Does the class exist already? for info in self.class_info: if info['source'] == source and info["id"] == class_id: # source.class_id combination already available, skip return # Add the class self.class_info.append({ "source": source, "id": class_id, "name": class_name, }) def add_image(self, source, image_id, path, **kwargs): image_info = { "id": image_id, "source": source, "path": path, } image_info.update(kwargs) self.image_info.append(image_info) def image_reference(self, image_id): """Return a link to the image in its source Website or details about the image that help looking it up or debugging it. Override for your dataset, but pass to this function if you encounter images not in your dataset. """ return "" def prepare(self, class_map=None): """Prepares the Dataset class for use. TODO: class map is not supported yet. When done, it should handle mapping classes from different datasets to the same class ID. """ def clean_name(name): """Returns a shorter version of object names for cleaner display.""" return ",".join(name.split(",")[:1]) # Build (or rebuild) everything else from the info dicts. self.num_classes = len(self.class_info) self.class_ids = np.arange(self.num_classes) self.class_names = [clean_name(c["name"]) for c in self.class_info] self.num_images = len(self.image_info) self._image_ids = np.arange(self.num_images) self.class_from_source_map = {"{}.{}".format(info['source'], info['id']): id for info, id in zip(self.class_info, self.class_ids)} # Map sources to class_ids they support self.sources = list(set([i['source'] for i in self.class_info])) self.source_class_ids = {} # Loop over datasets for source in self.sources: self.source_class_ids[source] = [] # Find classes that belong to this dataset for i, info in enumerate(self.class_info): # Include BG class in all datasets if i == 0 or source == info['source']: self.source_class_ids[source].append(i) def map_source_class_id(self, source_class_id): """Takes a source class ID and returns the int class ID assigned to it. For example: dataset.map_source_class_id("coco.12") -> 23 """ return self.class_from_source_map[source_class_id] def get_source_class_id(self, class_id, source): """Map an internal class ID to the corresponding class ID in the source dataset.""" info = self.class_info[class_id] assert info['source'] == source return info['id'] def append_data(self, class_info, image_info): self.external_to_class_id = {} for i, c in enumerate(self.class_info): for ds, id in c["map"]: self.external_to_class_id[ds + str(id)] = i # Map external image IDs to internal ones. self.external_to_image_id = {} for i, info in enumerate(self.image_info): self.external_to_image_id[info["ds"] + str(info["id"])] = i @property def image_ids(self): return self._image_ids def source_image_link(self, image_id): """Returns the path or URL to the image. Override this to return a URL to the image if it's availble online for easy debugging. """ return self.image_info[image_id]["path"] def load_image(self, image_id): """Load the specified image and return a [H,W,3] Numpy array. """ # Load image image = skimage.io.imread(self.image_info[image_id]['path']) # If grayscale. Convert to RGB for consistency. if image.ndim != 3: image = skimage.color.gray2rgb(image) return image def load_bbox(self, image_id): """Load instance bbox for the given image. Different datasets use different ways to store bbox. Override this method to load instance bbox and return them in the form of am array of binary bbox of shape [height, width, instances]. Returns: bbox: A bool array of shape [height, width, instance count] with a binary bbox per instance. class_ids: a 1D array of class IDs of the instance bbox. """ # Override this function to load a bbox from your dataset. # Otherwise, it returns an empty bbox. bbox = np.empty([0, 0, 0]) class_ids = np.empty([0], np.int32) return bbox, class_ids def resize_image(image, min_dim=None, max_dim=None, padding=False): """ Resizes an image keeping the aspect ratio. min_dim: if provided, resizes the image such that it's smaller dimension == min_dim max_dim: if provided, ensures that the image longest side doesn't exceed this value. padding: If true, pads image with zeros so it's size is max_dim x max_dim Returns: image: the resized image window: (y1, x1, y2, x2). If max_dim is provided, padding might be inserted in the returned image. If so, this window is the coordinates of the image part of the full image (excluding the padding). The x2, y2 pixels are not included. scale: The scale factor used to resize the image padding: Padding added to the image [(top, bottom), (left, right), (0, 0)] """ # Default window (y1, x1, y2, x2) and default scale == 1. h, w = image.shape[:2] window = (0, 0, h, w) scale = 1 # Scale? if min_dim: # Scale up but not down scale = max(1, min_dim / min(h, w)) # Does it exceed max dim? if max_dim: image_max = max(h, w) if round(image_max * scale) > max_dim: scale = max_dim / image_max # Resize image and mask if scale != 1: image = scipy.misc.imresize( image, (round(h * scale), round(w * scale))) # Need padding? if padding: # Get new height and width h, w = image.shape[:2] top_pad = (max_dim - h) // 2 bottom_pad = max_dim - h - top_pad left_pad = (max_dim - w) // 2 right_pad = max_dim - w - left_pad padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)] image = np.pad(image, padding, mode='constant', constant_values=0) window = (top_pad, left_pad, h + top_pad, w + left_pad) return image, window, scale, padding def resize_bbox(boxes, scale, padding): """Resizes a bbox using the given scale and padding. Typically, you get the scale and padding from resize_image() to ensure both, the image and the bbox, are resized consistently. scale: bbox scaling factor padding: Padding to add to the bbox in the form [(top, bottom), (left, right), (0, 0)] """ top_pad = padding[0][0] left_pad = padding[1][0] resized_boxes = [] for box in boxes: temp_new_box = box * scale y1 = temp_new_box[0] + top_pad x1 = temp_new_box[1] + left_pad y2 = temp_new_box[2] + top_pad x2 = temp_new_box[3] + left_pad resized_boxes.append((y1,x1,y2,x2)) return np.array(resized_boxes) ############################################################ # Anchors ############################################################ def generate_anchors(scales, ratios, shape, feature_stride, anchor_stride): """ scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128] ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2] shape: [height, width] spatial shape of the feature map over which to generate anchors. feature_stride: Stride of the feature map relative to the image in pixels. anchor_stride: Stride of anchors on the feature map. For example, if the value is 2 then generate anchors for every other feature map pixel. """ # Get all combinations of scales and ratios scales, ratios = np.meshgrid(np.array(scales), np.array(ratios)) scales = scales.flatten() ratios = ratios.flatten() # Enumerate heights and widths from scales and ratios heights = scales / np.sqrt(ratios) widths = scales * np.sqrt(ratios) # Enumerate shifts in feature space shifts_y = np.arange(0, shape[0], anchor_stride) * feature_stride shifts_x = np.arange(0, shape[1], anchor_stride) * feature_stride shifts_x, shifts_y = np.meshgrid(shifts_x, shifts_y) # Enumerate combinations of shifts, widths, and heights box_widths, box_centers_x = np.meshgrid(widths, shifts_x) box_heights, box_centers_y = np.meshgrid(heights, shifts_y) # Reshape to get a list of (y, x) and a list of (h, w) box_centers = np.stack( [box_centers_y, box_centers_x], axis=2).reshape([-1, 2]) box_sizes = np.stack([box_heights, box_widths], axis=2).reshape([-1, 2]) # Convert to corner coordinates (y1, x1, y2, x2) boxes = np.concatenate([box_centers - 0.5 * box_sizes, box_centers + 0.5 * box_sizes], axis=1) return boxes def generate_pyramid_anchors(scales, ratios, feature_shapes, feature_strides, anchor_stride): """Generate anchors at different levels of a feature pyramid. Each scale is associated with a level of the pyramid, but each ratio is used in all levels of the pyramid. Returns: anchors: [N, (y1, x1, y2, x2)]. All generated anchors in one array. Sorted with the same order of the given scales. So, anchors of scale[0] come first, then anchors of scale[1], and so on. """ # Anchors # [anchor_count, (y1, x1, y2, x2)] anchors = [] for i in range(len(scales)): anchors.append(generate_anchors(scales[i], ratios, feature_shapes[i], feature_strides[i], anchor_stride)) return np.concatenate(anchors, axis=0) ############################################################ # Miscellaneous ############################################################ def trim_zeros(x): """It's common to have tensors larger than the available data and pad with zeros. This function removes rows that are all zeros. x: [rows, columns]. """ assert len(x.shape) == 2 return x[~np.all(x == 0, axis=1)] def compute_ap(gt_boxes, gt_class_ids, pred_boxes, pred_class_ids, pred_scores, iou_threshold=0.5): """Compute Average Precision at a set IoU threshold (default 0.5). Returns: mAP: Mean Average Precision precisions: List of precisions at different class score thresholds. recalls: List of recall values at different class score thresholds. overlaps: [pred_boxes, gt_boxes] IoU overlaps. """ # Trim zero padding and sort predictions by score from high to low # TODO: cleaner to do zero unpadding upstream gt_boxes = trim_zeros(gt_boxes) pred_boxes = trim_zeros(pred_boxes) pred_scores = pred_scores[:pred_boxes.shape[0]] indices = np.argsort(pred_scores)[::-1] pred_boxes = pred_boxes[indices] pred_class_ids = pred_class_ids[indices] pred_scores = pred_scores[indices] # Compute IoU overlaps [pred_boxes, gt_boxes] overlaps = compute_overlaps(pred_boxes, gt_boxes) # Loop through ground truth boxes and find matching predictions match_count = 0 pred_match = np.zeros([pred_boxes.shape[0]]) gt_match = np.zeros([gt_boxes.shape[0]]) for i in range(len(pred_boxes)): # Find best matching ground truth box sorted_ixs = np.argsort(overlaps[i])[::-1] for j in sorted_ixs: # If ground truth box is already matched, go to next one if gt_match[j] == 1: continue # If we reach IoU smaller than the threshold, end the loop iou = overlaps[i, j] if iou < iou_threshold: break # Do we have a match? if pred_class_ids[i] == gt_class_ids[j]: match_count += 1 gt_match[j] = 1 pred_match[i] = 1 break # Compute precision and recall at each prediction box step precisions = np.cumsum(pred_match) / (np.arange(len(pred_match)) + 1) recalls = np.cumsum(pred_match).astype(np.float32) / len(gt_match) # Pad with start and end values to simplify the math precisions = np.concatenate([[0], precisions, [0]]) recalls = np.concatenate([[0], recalls, [1]]) # Ensure precision values decrease but don't increase. This way, the # precision value at each recall threshold is the maximum it can be # for all following recall thresholds, as specified by the VOC paper. for i in range(len(precisions) - 2, -1, -1): precisions[i] = np.maximum(precisions[i], precisions[i + 1]) # Compute mean AP over recall range indices = np.where(recalls[:-1] != recalls[1:])[0] + 1 mAP = np.sum((recalls[indices] - recalls[indices - 1]) * precisions[indices]) return mAP, precisions, recalls, overlaps def compute_recall(pred_boxes, gt_boxes, iou): """Compute the recall at the given IoU threshold. It's an indication of how many GT boxes were found by the given prediction boxes. pred_boxes: [N, (y1, x1, y2, x2)] in image coordinates gt_boxes: [N, (y1, x1, y2, x2)] in image coordinates """ # Measure overlaps overlaps = compute_overlaps(pred_boxes, gt_boxes) iou_max = np.max(overlaps, axis=1) iou_argmax = np.argmax(overlaps, axis=1) positive_ids = np.where(iou_max >= iou)[0] matched_gt_boxes = iou_argmax[positive_ids] recall = len(set(matched_gt_boxes)) / gt_boxes.shape[0] return recall, positive_ids # ## Batch Slicing # Some custom layers support a batch size of 1 only, and require a lot of work # to support batches greater than 1. This function slices an input tensor # across the batch dimension and feeds batches of size 1. Effectively, # an easy way to support batches > 1 quickly with little code modification. # In the long run, it's more efficient to modify the code to support large # batches and getting rid of this function. Consider this a temporary solution def batch_slice(inputs, graph_fn, batch_size, names=None): """Splits inputs into slices and feeds each slice to a copy of the given computation graph and then combines the results. It allows you to run a graph on a batch of inputs even if the graph is written to support one instance only. inputs: list of tensors. All must have the same first dimension length graph_fn: A function that returns a TF tensor that's part of a graph. batch_size: number of slices to divide the data into. names: If provided, assigns names to the resulting tensors. """ if not isinstance(inputs, list): inputs = [inputs] outputs = [] for i in range(batch_size): inputs_slice = [x[i] for x in inputs] output_slice = graph_fn(*inputs_slice) if not isinstance(output_slice, (tuple, list)): output_slice = [output_slice] outputs.append(output_slice) # Change outputs from a list of slices where each is # a list of outputs to a list of outputs and each has # a list of slices outputs = list(zip(*outputs)) if names is None: names = [None] * len(outputs) result = [tf.stack(o, axis=0, name=n) for o, n in zip(outputs, names)] if len(result) == 1: result = result[0] return result ############################################################ # Data Formatting ############################################################ def compose_image_meta(image_id, image_shape, window, active_class_ids): """Takes attributes of an image and puts them in one 1D array. Use parse_image_meta() to parse the values back. image_id: An int ID of the image. Useful for debugging. image_shape: [height, width, channels] window: (y1, x1, y2, x2) in pixels. The area of the image where the real image is (excluding the padding) active_class_ids: List of class_ids available in the dataset from which the image came. Useful if training on images from multiple datasets where not all classes are present in all datasets. """ meta = np.array( [image_id] + # size=1 list(image_shape) + # size=3 list(window) + # size=4 (y1, x1, y2, x2) in image cooredinates list(active_class_ids) # size=num_classes ) return meta # Two functions (for Numpy and TF) to parse image_meta tensors. def parse_image_meta(meta): """Parses an image info Numpy array to its components. See compose_image_meta() for more details. """ image_id = meta[:, 0] image_shape = meta[:, 1:4] window = meta[:, 4:8] # (y1, x1, y2, x2) window of image in in pixels active_class_ids = meta[:, 8:] return image_id, image_shape, window, active_class_ids def parse_image_meta_graph(meta): """Parses a tensor that contains image attributes to its components. See compose_image_meta() for more details. meta: [batch, meta length] where meta length depends on NUM_CLASSES """ image_id = meta[:, 0] image_shape = meta[:, 1:4] window = meta[:, 4:8] active_class_ids = meta[:, 8:] return [image_id, image_shape, window, active_class_ids] def mold_image(images, config): """Takes RGB images with 0-255 values and subtraces the mean pixel and converts it to float. Expects image colors in RGB order. """ return images.astype(np.float32) - config.MEAN_PIXEL def unmold_image(normalized_images, config): """Takes a image normalized with mold() and returns the original.""" return (normalized_images + config.MEAN_PIXEL).astype(np.uint8) ''' class GradientsCallback(Callback): def on_batch_end(self, batch, logs): weights = self.model.trainable_weights # weight tensors gradients = self.model.optimizer.get_gradients(self.model.total_loss, weights) # gradient tensors input_tensors = self.model.inputs + self.model.sample_weights + self.model.targets + [K.learning_phase()] get_gradients = K.function(inputs=input_tensors, outputs=gradients) inputs = [x, x_off, np.ones(len(x)), y, 0] grads = get_gradients(inputs) with open('gradients.txt','w') as f: f.write('grads') ''' def log(text, array=None): """Prints a text message. And, optionally, if a Numpy array is provided it prints it's shape, min, and max values. """ if array is not None: text = text.ljust(25) text += ("shape: {:20} ".format(str(array.shape))) if array.size: text += ("min: {:10.5f} max: {:10.5f}".format(array.min(),array.max())) else: text += ("min: {:10} max: {:10}".format("","")) text += " {}".format(array.dtype) print(text) def denorm_boxes(boxes, shape): """Converts boxes from normalized coordinates to pixel coordinates. boxes: [N, (y1, x1, y2, x2)] in normalized coordinates shape: [..., (height, width)] in pixels Note: In pixel coordinates (y2, x2) is outside the box. But in normalized coordinates it's inside the box. Returns:[N, (y1, x1, y2, x2)] in pixel coordinates """ h, w = shape scale = np.array([h - 1, w - 1, h - 1, w - 1]) shift = np.array([0, 0, 1, 1]) return np.around(np.multiply(boxes, scale) + shift).astype(np.int32)

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import pymel.core as pm """ for geo in myGeos: target_shape_orig = pm.listRelatives(geo, s=1)[1] pm.transferAttributes('st1_prStoryBook01_mod_mod_base_v008a:' + geo, target_shape_orig, transferPositions=0, transferNormals=0, transferUVs=2, transferColors=0, sampleSpace=5, targetUvSpace="map1", searchMethod=3, flipUVs=0, colorBorders=1) pm.delete(target_shape_orig, ch=1) """ sel = pm.ls(sl=1) for s in sel: # get joint skinned to the mesh bind_joint = pm.listHistory(s, type="joint") print bind_joint # get skin cluster source_skinClstr = pm.listHistory(s, type="skinCluster")[0] print source_skinClstr # get deformer sets s_shape = pm.listRelatives(s, s=1)[-1] print s_shape # print s_shape # deformer_set = pm.listSets(type =2, object = s_shape)[-1] # print deformer_set # get reference or duplicate mesh # s_ref = "st1_prCaravansTajTej01_mod_mod_base_v005:"+ s # pm.sets(deformer_set, add = s_ref) # bind duplicate mesh to the joints destination_skinClstr = pm.skinCluster(bind_joint, s_ref, tsb=True, bm=0, sm=0, nw=1) # copy skin weights pm.copySkinWeights(ss=source_skinClstr, ds=destination_skinClstr, noMirror=True)

[ "69736885+utkarsh6191@users.noreply.github.com" ]

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/analysis/match_2017_stp26.py

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opensafely/post-covid-kidney-outcomes

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refs/heads/main

2023-08-31T07:49:42.396915

2023-08-30T18:40:24

444,884,435

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MIT

2022-06-16T12:04:03

2022-01-05T17:03:12

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import pandas as pd from osmatching import match match( case_csv="input_covid_matching_2017_stp26", match_csv="input_2017_matching_stp26", matches_per_case=3, match_variables={ "male": "category", "age": 0, }, index_date_variable="covid_date", replace_match_index_date_with_case="3_years_earlier", date_exclusion_variables={ "death_date": "before", "date_deregistered": "before", "krt_outcome_date": "before", }, output_suffix="_2017_stp26", output_path="output", )

[ "viyaasan@hotmail.com" ]

viyaasan@hotmail.com

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/Server/app/api_helper.py

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permissive

chauandvi4/Kid-friendlyThingTranslator

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refs/heads/main

2023-05-31T01:02:20.926999

2021-07-05T02:40:56

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import decouple from yolov3.yolo import ImageLabeler from yolov3.image_converter import string_to_image, to_rgb from word_definition.memory_dictionary import MemoryDictionary from word_definition.database_dictionary import DatabaseDictionary from env_access import env_accessor from app.model import * from fastapi import Body from google.cloud import vision from google.auth.exceptions import DefaultCredentialsError import mysql.connector import cv2 import logging import time from enum import Enum class ApiHelper: class DbId(Enum): User = env_accessor.database_user Dictionary = env_accessor.database_dictionary def __init__(self): self._create_conns() self._create_google_cloud_client() self._create_yolov3_image_labeler() self._create_memory_dictionary() self._create_database_dictionary() def _create_conns(self): self._db_conns = {} for db_id in self.DbId: db_name = db_id.value is_success = False while not is_success: try: logging.info("Connecting to %s database...", db_name) self._db_conns[db_id] = mysql.connector.connect( host=env_accessor.host_database, user=env_accessor.username_database, password=env_accessor.password_database, database=db_name ) logging.info("Connected to %s database", db_name) is_success = True except mysql.connector.Error as err: logging.error("Failed to connect to %s database: %s", db_name, str(err)) time_to_retry = 5 logging.info("Retry connecting in %d seconds", time_to_retry) time.sleep(time_to_retry) def _create_google_cloud_client(self): logging.info("Instantiating a Google Cloud Vision Client...") try: logging.info("Instantiating a Google Cloud Vision Client by default credentials...") self._gg_cloud_vision_client = vision.ImageAnnotatorClient() except DefaultCredentialsError as err: logging.warning("Failed to instantiate a Google Cloud Vision Client by default credentials: %s", str(err)) try: logging.info("Instantiating a Google Cloud Vision Client by environment variable...") self._gg_cloud_vision_client = vision.ImageAnnotatorClient.from_service_account_json( env_accessor.google_credential ) except (decouple.UndefinedValueError, FileNotFoundError) as err: logging.warning("Failed to instantiate a Google Cloud Vision Client: %s", str(err)) self._gg_cloud_vision_client = None return logging.info("Google Cloud Vision Client instantiated") def _create_yolov3_image_labeler(self): try: logging.info("Loading YOLO from disk...") self._yolov3_image_labeler = ImageLabeler( labels_path=env_accessor.path_yolov3_names, config_path=env_accessor.path_yolov3_config, weights_path=env_accessor.path_yolov3_weights ) logging.info("Loaded YOLO") except decouple.UndefinedValueError as err: logging.warning("Failed to load YOLO") self._yolov3_image_labeler = None def _create_memory_dictionary(self): try: logging.info("Loading a Memory Dictionary...") self._memory_word_dictionary = MemoryDictionary(env_accessor.path_json_definition_common_word) logging.info("Loaded a Memory Dictionary") except MemoryError as err: logging.warning("Failed to load a Memory Dictionary: %s", str(err)) self._memory_word_dictionary = None def _create_database_dictionary(self): try: logging.info("Loading a Database Dictionary...") self._database_word_dictionary = DatabaseDictionary(self._db_conns[self.DbId.Dictionary]) logging.info("Loaded a Database Dictionary") except Exception as e: logging.error("Failed to load a Database Dictionary: %s", str(e)) def is_user_login_info_exist(self, data: UserLoginSchema) -> bool: cursor = self._db_conns[self.DbId.User].cursor() sql = "SELECT email FROM Accounts WHERE email = %s AND passphrase = %s" param = (data.email, data.password) cursor.execute(sql, param) return cursor.fetchone() is not None def get_user_name(self, email: str) -> str: cursor = self._db_conns[self.DbId.User].cursor() sql = "SELECT name FROM Accounts WHERE email = %s" param = (email,) cursor.execute(sql, param) res = cursor.fetchone() return res[0] if res is not None else "" def create_user(self, user: UserSignupSchema = Body(...)) -> bool: conn = self._db_conns[self.DbId.User] cursor = conn.cursor() sql = "INSERT INTO Accounts(email, passphrase, name) VALUES (%s, %s, %s)" param = (user.email, user.password, user.name) try: cursor.execute(sql, param) conn.commit() return True except mysql.connector.errors.IntegrityError: return False def label_image_by_gg_cloud_vision(self, image_base64: str) -> str: if self._gg_cloud_vision_client is None: return "" image = to_rgb(string_to_image(image_base64)) _, encoded_image = cv2.imencode('.jpg', image) image = vision.Image(content=encoded_image.tobytes()) # Try using GG Cloud service try: # Performs label detection on the image file response = self._gg_cloud_vision_client.label_detection(image=image) labels = response.label_annotations if response.error.message: return "" return labels[0].description if labels else "" except Exception as err: logging.warning("Failed to use Google Cloud Vision: %s", str(err)) def label_image_by_yolov3(self, image_base64: str) -> str: if self._yolov3_image_labeler is None: return "" image = to_rgb(string_to_image(image_base64)) # Performs label detection on the image file return self._yolov3_image_labeler.label_image(image) def define_word_by_memory_dictionary(self, word: str) -> str: return self._memory_word_dictionary.define(word) def define_word_by_database_dictionary(self, word: str) -> str: return self._database_word_dictionary.define(word) def __del__(self): for conn in self._db_conns.values(): conn.close()

[ "phu.nguyenpfoem@hcmut.edu.vn" ]

phu.nguyenpfoem@hcmut.edu.vn

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/client/drawing/constants.py

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[]

no_license

victormorozov1/tanks-server

56412fb39b82c54284ef864262995b2905aaca0e

6f5e09969c5e5b83b57b1b2665ec7b408ebef347

refs/heads/master

2020-12-10T07:45:26.967586

2020-02-20T08:45:40

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import pygame CELL_SZ = 50 pygame.init() display_size = pygame.display.Info() SZX, SZY = display_size.current_w, display_size.current_h #SZX, SZY = 800, 800 SAVERS_NUM = 3

[ "vmn3w@yandex.ru" ]

vmn3w@yandex.ru

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/pe7.py

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[]

no_license

pushpithaDilhan/ProjectEulerPython

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refs/heads/master

2021-01-10T04:49:24.491855

2017-04-12T06:03:11

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import time s=time.time() def isprime(n): for i in range(3,int(n**0.5)+1,2): if n%i==0: return False else:return True a=[2,3,5] i=7 while len(a)!=10001: if isprime(i): a.append(i) i=i+2 print max(a) e=time.time()-s print "elapsed time is %f Seconds"%e

[ "pushpitha.14@cse.mrt.ac.lk" ]

pushpitha.14@cse.mrt.ac.lk

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/python/week6/survey_form/apps/surveyform/urls.py

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[]

no_license

alfredgarcia/codingdojo

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refs/heads/master

2020-09-13T22:33:01.581800

2017-01-04T22:04:14

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from django.conf.urls import url from . import views # from django.contrib import admin urlpatterns = [ url(r'^$', views.index), url(r'^process$', views.FormProcess), url(r'^showresults$', views.ShowResults), ]

[ "alfredg2007@yahoo.com" ]

alfredg2007@yahoo.com

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/exam/migrations/0026_auto_20210224_0016.py

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[ "MIT" ]

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sezinbhr/bulut_bil

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refs/heads/main

2023-08-02T22:08:17.467098

2021-09-15T13:27:13

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# Generated by Django 2.2.7 on 2021-02-23 21:16 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('exam', '0025_auto_20210223_2358'), ] operations = [ migrations.AlterField( model_name='question', name='correct_answer', field=models.IntegerField(default=1), ), ]

[ "05170000782@ogrenci.ege.edu.tr" ]

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/arrange_pichus.py

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[]

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bhargavsai77777/Route-Pichu

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2023-05-26T06:52:11.048883

2021-06-12T03:07:53

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#!/usr/local/bin/python3 # # arrange_pichus.py : arrange agents on a grid, avoiding conflicts # # Submitted by : [PUT YOUR NAME AND USERNAME HERE] # # Based on skeleton code in CSCI B551, Spring 2021 # import sys # Parse the map from a given filename def parse_map(filename): with open(filename, "r") as f: return [[char for char in line] for line in f.read().rstrip("\n").split("\n")] # Count total # of pichus on board def count_pichus(board): return sum([row.count('p') for row in board]) # Return a string with the board rendered in a human-pichuly format def printable_board(board): return "\n".join(["".join(row) for row in board]) '''This function validates the index that the successors function gives to add_pichu. Furthur it checks,if we can add pichu at that particular position based on the below designed conditions. This function returns a green signal to the add_pichu function if all goes well with the below defined conditions(chceking if pichu can see other pichu)''' def validate_position(board, R, C): check_dict = {'j': [0, 0, 0, 0]} for w in range(0, C): if (board[R][w] == 'p'): check_dict['j'][0] += 1 if (board[R][w] == 'X' or board[R][w] == '@'): check_dict['j'][0] = 0 for x in range(len(board[0]) - 1, C, -1): if (board[R][x] == 'p'): check_dict['j'][1] += 1 if (board[R][x] == 'X' or board[R][x] == '@'): check_dict['j'][1] = 0 for y in range(0, R): if (board[y][C] == 'p'): check_dict['j'][2] += 1 if (board[y][C] == 'X' or board[y][C] == '@'): check_dict['j'][2] = 0 for z in range(len(board) - 1, R, -1): if (board[z][C] == 'p'): check_dict['j'][3] += 1 if (board[z][C] == 'X' or board[z][C] == '@'): check_dict['j'][3] = 0 if sum(check_dict['j']) > 0: return False return True # Add a pichu to the board at the given position, and return a new board (doesn't change original) ' In this function we are calling validate_position to check if we can add pichu at the given index' def add_pichu(board, row, col): if validate_position(board,row,col)==True:return board[0:row] + [board[row][0:col] + ['p',] + board[row][col+1:]] + board[row+1:] else: return board # Get list of successors of given board state def successors(board): return [add_pichu(board, r, c) for r in range(0, len(board)) for c in range(0, len(board[0])) if board[r][c] == '.'] # check if board is a goal state def is_goal(board, k): return count_pichus(board) == k # Arrange agents on the map # # This function MUST take two parameters as input -- the house map and the value k -- # and return a tuple of the form (new_map, success), where: # - new_map is a new version of the map with k agents, # - success is True if a solution was found, and False otherwise. # '''' In the below solve function, I have just made a small modification by adding a visiting_node_list, it checks if the successsor is already visited. Basically here we are using DFS, we know that it goes to infinite loop sometimes. To avoid these, i am not visiting alreday visited nodes.''' def solve(initial_board, k): fringe = [initial_board] visiting_node_list = [] while len(fringe) > 0: for s in successors( fringe.pop() ): if s not in visiting_node_list: if is_goal(s, k): return(s,True) visiting_node_list.append(s) fringe.append(s) return ([],False) # Main Function if __name__ == "__main__": house_map = parse_map(sys.argv[1]) # This is K, the number of agents k = int(sys.argv[2]) #k = 9 print("Starting from initial board:\n" + printable_board(house_map) + "\n\nLooking for solution...\n") (newboard, success) = solve(house_map, k) print("Here's what we found:") print(printable_board(newboard) if success else "None")

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[]

no_license

codetubes/python_django

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2022-04-20T12:06:32.915015

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""" Django settings for ctblog project. Generated by 'django-admin startproject' using Django 3.0.2. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '_ec7l7zxqm8pbsx0b9^nm_)3j!*n)z@zlr7kssfiy!n-%wsa25' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'blog', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'ctblog.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'ctblog.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/' MEDIA_URL = '/media/' MEDIA_ROOT = os.path.join(BASE_DIR, "media")

[ "arman@armans-mbp.home" ]

arman@armans-mbp.home

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permissive

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2021-05-05T10:22:49.020183

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import asyncio import argparse import json import os import sys import time import aiohttp from django.core.management.base import BaseCommand, CommandError from django.db import transaction, DEFAULT_DB_ALIAS from django.db.models import signals from api_v2.models.Address import Address from api_v2.models.Attribute import Attribute from api_v2.models.Credential import Credential from api_v2.models.Name import Name from tob_api.rocketchat_hooks import log_error, log_warning, log_info from asgiref.sync import async_to_sync API_BASE_URL = os.environ.get('API_BASE_URL', 'http://localhost:8080') API_PATH = os.environ.get('API_PATH', '/api/v2') SEARCH_API_PATH = os.environ.get('SEARCH_API_PATH', '/search/credential') API_URL = "{}{}".format(API_BASE_URL, API_PATH) class Command(BaseCommand): help = "Verify the the indexes for all of the credentials." def handle(self, *args, **options): self.reprocess(*args, **options) @async_to_sync async def reprocess(self, *args, **options): self.stdout.write("Starting ...") cred_count = Credential.objects.count() self.stdout.write("Verifying the indexes for {} credentials ...".format(cred_count)) async with aiohttp.ClientSession() as http_client: current_cred = 0 for credential in Credential.objects.all().reverse().iterator(): current_cred += 1 self.stdout.write( "\nVerifying index for credential id: {} ({} of {}) ...".format( credential.id, current_cred, cred_count ) ) try: # Query search API using the wallet_id; credential.wallet_id response = await http_client.get( '{}{}'.format(API_URL, SEARCH_API_PATH), params={ 'format':'json', 'latest':'any', 'revoked':'any', 'inactive':'any','wallet_id': credential.wallet_id} ) self.stdout.write( "\t{}" .format(response.url)) if response.status != 200: raise RuntimeError( 'Credential index could not be processed: {}'.format(await response.text()) ) result_json = await response.json() except Exception as exc: raise Exception( 'Could not verify credential index. ' 'Is the OrgBook running?') from exc credentialCount = result_json["total"] if credentialCount < 1: msg = "Error - No index was found for credential id: {}, wallet_id: {}".format(credential.id, credential.wallet_id) self.stdout.write(msg) await log_error(msg) elif credentialCount > 1: msg = "Error - More than one index was found for credential id: {}, wallet_id: {}".format(credential.id, credential.wallet_id) self.stdout.write(msg) await log_error(msg) else: msg = "Index successfully verified for credential id: {}, wallet_id: {}".format(credential.id, credential.wallet_id) self.stdout.write(msg) await log_info(msg)

[ "wade.barnes@shaw.ca" ]

wade.barnes@shaw.ca

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[]

no_license

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refs/heads/master

2021-01-22T18:51:33.867916

2015-07-09T03:45:14

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UTF-8

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944

py

class Animal: def __init__(self, sex, age, height, species): self.sex = sex self.age = age self.height = height self.species = species def speak(self): return "Hello, I am a(n) {}".format(self.species) class Dog(Animal): def __init__(self, sex, age, height, species, breed): super().__init__(sex, age, height, species) self.breed = breed def speak(self): return "WOOF" class Cat(Animal): def __init__(self, sex, age, height, species, breed, color): super().__init__(sex, age, height, species) self.breed = breed self.color = color def speak(self): return "MEOW BETCH" def my_color(self): return "I'm {}".format(self.color) animal = Animal('Female', 21, '20cm', 'animal') print(animal.speak()) dog = Dog('Male', 18, '1cm','C. lupus', 'Husky') print(dog.speak()) cat = Cat('Female', 1000, '0.5cm','F. catus', 'Siamese', 'Grey') print(cat.speak()) print(cat.my_color())

[ "mendonca.kr@gmail.com" ]

mendonca.kr@gmail.com

25cff27e63ff8317998d9326921d3ac3a56f1d83

96d7fafccb9e35ab862e679fb22b0d790c2a081b

/main/urls.py

6cf0d1300fa5f12e4dca18d76131354887260a82

[]

no_license

folakemie5/notepadapp

695750be890662044c52dabee26e392e92bfb9c3

d2b304536fe45b8267e2b636b2ef0a1ce8868b35

refs/heads/main

2023-07-16T01:25:38.479877

2021-08-19T09:41:48

397,222,403

0

null

UTF-8

Python

false

212

py

from django.urls import path from . import views urlpatterns = [ path('', views.notepads, name='notelist'), path('<str:title>/<int:day>/<int:month>/<int:year>/', views.note_detail, name='note_detail') ]

[ "folakemie5@gmail.com" ]

folakemie5@gmail.com

b165157f87e70a1070f197296b0d780668b1fc40

e15cb8739449fa5fad211d9d28dd58ff74269a77

/感知机/.ipynb_checkpoints/感知机-checkpoint.py

328be683ce92468e94f1a813a31c7ecb553d85b1

[]

no_license

HG1227/ML

4815c299aa56cf72b4dc5811f6672421c638416f

1b92276690c57cf45b3e26125b7459e1bd236d16

refs/heads/master

2020-08-26T12:31:07.037477

2020-05-30T07:05:17

217,008,927

0

null

UTF-8

Python

false

3,896

py

#!/usr/bin/python # coding:utf-8 # @software: PyCharm # @file: 感知机.py # @time: 2019/12/4 import numpy as np def makeLinearSeparableData(weights, numLines): ''' numFeatures 是一个正整数，代表特征的数量 :param weights:是一个列表，里面存储的是我们用来产生随机数据的那条直线的法向量。 :param numLines:是一个正整数，表示需要创建多少个数据点。 :return:最后返回数据集合。 ''' w = np.array(weights) numFeatures = len(weights) dataSet = np.zeros((numLines, numFeatures + 1)) for i in range(numLines): x = np.random.rand(1, numFeatures) * 20 - 10 # 计算内积 innerProduct = np.sum(w * x) if innerProduct <= 0: # numpy 提供的 append 函数可以扩充一维数组， dataSet[i] = np.append(x, -1) else: dataSet[i] = np.append(x, 1) return dataSet data = makeLinearSeparableData([4,3, 2], 100) print(data) # 将数据集可视化 def plotData(dataSet): import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111) ax.set_title('Linear separable data set') ax.set_xlabel("X") ax.set_ylabel("Y") labels = np.array(dataSet[:, 2]) # where 函数是用来找出正例的行的下标 idx_1 = np.where(dataSet[:, 2] == 1) p1 = ax.scatter(dataSet[idx_1, 0], dataSet[idx_1, 1], marker='o', c='g', s=20, label=1) idx_2 = np.where(dataSet[:, 2] == -1) p2 = ax.scatter(dataSet[idx_2, 0], dataSet[idx_2, 1], marker='x', color='r', s=20, label=2) plt.legend(loc='upper right') plt.show() # plotData(data) # 训练感知机，可视化分类器及其法向量 def train(dataSet, plot=False): ''' (array, boolean) -> list Use dataSet to train a perceptron dataSet has at least 2 lines. ''' # 随机梯度下降算法 numLines = dataSet.shape[0] numFearures = dataSet.shape[1] w = np.zeros((1, numFearures - 1)) # initialize weights separated = False i = 0 while not separated and i < numLines: if dataSet[i][-1] * np.sum(w * dataSet[i, 0:-1]) <= 0: # 如果分类错误 w = w + dataSet[i][-1] * dataSet[i, 0:-1] # 更新权重向量 separated = False # 设置为未完全分开 i = 0 # 重新开始遍历每个数据点 else: i += 1 # 如果分类正确，检查下一个数据点 if plot == True: import matplotlib.pyplot as plt from matplotlib.lines import Line2D fig = plt.figure() ax = fig.add_subplot(111) ax.set_title('Linear separable data set') plt.xlabel('X') plt.ylabel('Y') labels = np.array(dataSet[:, 2]) idx_1 = np.where(dataSet[:, 2] == 1) p1 = ax.scatter(dataSet[idx_1, 0], dataSet[idx_1, 1], marker='o', color='g', label=1, s=20) idx_2 = np.where(dataSet[:, 2] == -1) p2 = ax.scatter(dataSet[idx_2, 0], dataSet[idx_2, 1], marker='x', color='r', label=2, s=20) # 为了避免求得的权重向量长度过大在散点图中无法显示，所以将它按比例缩小了。 x = w[0][0] / np.abs(w[0][0]) * 10 y = w[0][1] / np.abs(w[0][0]) * 10 # ann = ax.annotate(u"", xy=(x, y), # xytext=(0, 0), size=20, arrowprops=dict(arrowstyle="-|>")) # 用来产生两个点的 y 值，以绘制一条直线（感知机） ys = (-12 * (-w[0][0]) / w[0][1], 12 * (-w[0][0]) / w[0][1]) ax.add_line(Line2D((-12, 12), ys, linewidth=1, color='blue')) plt.legend(loc='upper right') plt.legend(loc='upper right') plt.show() return w data = makeLinearSeparableData([4, 3], 100) w = train(data, True)

[ "hongghu.@outlook.com" ]

hongghu.@outlook.com

2deb284c25b04ed8d2b413c44ceaf72090afcbc4

7af0bf24774db7703a60f8ab6300e293e9939ebd

/microbench/benchtest.py

b259b0b88499686a36a8a17d2e159641313f6c95

[]

no_license

ctalbert/microbench

ce8e9fe2498f4db56af26de216836775b04fc5e3

e71cd1770e6eb626350a2d4fc9273f7dfc582bb5

refs/heads/master

2016-09-05T21:08:08.999820

2013-03-19T08:32:27

null

0

null

UTF-8

Python

false

2,146

py

import os import sys import json import time import re import weakref import time from marionette import CommonTestCase from marionette import Marionette class MicroBenchTestCase(CommonTestCase): match_re = re.compile(r"test_(.*)\.html$") def __init__(self, marionette_weakref, methodName='run_test', htmlfile=None): self.htmlfile = htmlfile self._marionette_weakref = marionette_weakref self.marionette = None CommonTestCase.__init__(self, methodName) @classmethod def add_tests_to_suite(cls, mod_name, filepath, suite, testloader, marionette, testvars): suite.addTest(cls(weakref.ref(marionette), htmlfile=filepath)) def run_test(self): if self.marionette.session is None: self.marionette.start_session() self.marionette.test_name = os.path.basename(self.htmlfile) # TODO: This is kind of a hack - depends on how we set up the httpd server # Would be better to have marionette test runner pass in url # TODO: For some reason mozhttpd isn't loading this URL not sure why #self.url = self.marionette.baseurl + '/tests/' + os.path.basename(self.htmlfile) self.url = 'http://localhost/%s' % os.path.basename(self.htmlfile) print "DBG::URL is: %s" % self.url self.marionette.execute_script("log('TEST-START: %s');" % self.htmlfile.replace('\\', '\\\\')) self.marionette.set_context("chrome") self.marionette.navigate(self.url) # TODO: Set the timeouts by reading from the script file boilerplate: http://mxr.mozilla.org/mozilla-central/source/testing/marionette/client/marionette/marionette_test.py#186 self.marionette.set_script_timeout(10000) # TODO: Should capture timeouts in try/except results = self.marionette.execute_script('window.document.start_test();', new_sandbox=False, special_powers=True) self.marionette.execute_script("log('TEST-END: %s');" % self.htmlfile.replace('\\', '\\\\')) self.marionette.test_name = None

[ "ctalbert@mozilla.com" ]

ctalbert@mozilla.com

61ee902f9aec9bdeff25f6e72569396187f62aff

01afa0be1c3acbf562fd87bd8fec8b4101c1e461

/Mining-Massive-Dataset/week5/advanced_quiz3.py

8c96a6d7d682c7d9d8f2ec6fe73c3b09bf879b97

[]

no_license

listiani13/coursera

e4f1116cc619b62336c5bb4d2e714e7051ae775c

5c84cf7171a440261de639b53558e9767b1cd85e

refs/heads/master

2021-01-22T03:54:31.657656

2016-04-04T11:07:25

null

0

null

UTF-8

Python

false

424

py

from math import sqrt def euclidean(x, y): return sqrt((x[0] - y[0])**2 + (x[1] - y[1])**2) points = [(1, 6), (3, 7), (4, 3), (7, 7), (8, 2), (9, 5)] chosen = [(0, 0), (10, 10)] for _ in range(5): pos, mx = -1, -1 for i, p in enumerate(points): distance = min([euclidean(p, pc) for pc in chosen]) if distance > mx: mx, pos = distance, i print 'choose:', points[pos] chosen.append(points[pos]) del points[pos]

[ "wangliangpeking@gmail.com" ]

wangliangpeking@gmail.com

e24c46fd98029aac1b66598cac02ba5537c313d5

d89acc9364060c7628ef53409abdfd192c3b8837

/离散傅里叶变换/good.py

2217808d46a976025ca1ce0b2d9a486ce0a43294

[]

no_license

yl763593864/Fourier-Transform

ac6aba866f0dc1e3fae787ecfac68d68c8d0063c

285f5ad5595262cef5cb39bee8e46cba04495176

refs/heads/master

2020-06-13T23:21:42.453485

2019-09-03T03:28:37

194,820,850

2

1

null

UTF-8

Python

false

26

py

print("xxs") #hello #first

[ "yangsongtang@gmail.com" ]

yangsongtang@gmail.com

b233ce40007c4997abdcc19a19b7f787186f0880

2bc20bc0b4eef8541b66704a9a46e709a438d9fd

/ngramsNVI/create_NVI.py

e295ff48586929b0f8f2a0c84374bb693ab6c7f9

[ "MIT" ]

permissive

warwickpsych/ngramsNVI

ed1a41d1a921faa507e7ce9dc7048ad4df2d6295

4f07fc22eef973625180c6f9b4827b1324947614

refs/heads/master

2020-12-03T18:16:20.953496

2020-01-02T17:25:18

231,426,273

0

MIT

2020-01-02T17:13:18

2020-01-02T17:13:17

null

UTF-8

Python

false

8,049

py

import argparse import logging import os import string import pandas as pd from ngramsNVI.constants import PACKAGE_LOCATION from ngramsNVI.utils import rescale, download_nrgams_file logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) def load_valence_data(language): """ Load valence data from the affective word norms (ANEW). See Hills, T.T., Proto, E., Sgroi, D. et al. Historical analysis of national subjective wellbeing using millions of digitized books. Nat Hum Behav 3, 1271–1275 (2019) doi:10.1038/s41562-019-0750-z for more information on how this valence data has been gathered. For german, we correct the values so that it is in the same range as the other languages. Parameters ---------- language: str Load valence data for one of the following languages 'ita', 'eng-gb', 'eng-us', 'spa', 'fre', 'ger' Returns ------- valence_data: Pandas.DataFrame Dataframe with index of words and their associated valence score """ if language in ["eng-gb", "eng-us"]: language = "eng" valence_data = pd.read_csv("{}/data/ANEW/{}_valence.csv".format(PACKAGE_LOCATION, language), na_filter=False) if language == "ger": valence_data.rename(columns={'valence': 'valence_old'}, inplace=True) valence_data["valence"] = rescale(valence_data["valence_old"].values, -3, 3, 1, 9) return valence_data def merge_ngrams_and_ANEW_data(valence_data, ngrams_fpath): """Add valence scores from ANEW to downloaded Google ngrams data Parameters ---------- valence_data: Pandas.DataFrame DataFrame including the columns: words (ANEW words), valence (ANEW scores for each word) ngrams_fpath: str Path of nrgams file which needs to be processed Returns ------- ngrams_valence_scores: Pandas.DataFrame DataFrame for one ngrams letter with the following columns: nrgram - ANEW word found in nrgams year - year of data match_count- amount of times the word was found volume_count - amount of volumes the word was found in valence - score from ANEW """ ngrams_data = pd.read_table(ngrams_fpath, compression='gzip', names=["ngram", "year", "match_count", "volume_count"]) ngrams_data["ngram"] = ngrams_data["ngram"].str.lower() ANEW_words = [k for k in valence_data.word] ngrams_ANEW_words_data = ngrams_data[ngrams_data.ngram.isin(ANEW_words)] if len(ngrams_ANEW_words_data) > 0: ngrams_ANEW_words_by_year = ngrams_ANEW_words_data.groupby(['ngram', 'year']).sum() ngrams_valence_scores = pd.merge(ngrams_ANEW_words_by_year.reset_index(), valence_data, how='left', left_on=['ngram'], right_on=['word']) ngrams_valence_scores = ngrams_valence_scores.drop(['word'], axis=1) return ngrams_valence_scores def process_nrgams_data(temp_directory, language, valence_data, delete_files): """Process nrgrams data for all letters for a chosen language Parameters ---------- temp_directory: str Temp directory location language: str Which of the following languages to process 'ita', 'eng-gb', 'eng-us', 'spa', 'fre', 'ger' valence_data: Pandas.DataFrame DataFrame including the columns: words (ANEW words), valence (ANEW scores for each word) delete_files: bool Whether to delete the file downloaded from ngrams to save on disk space Returns ------- ngrams_valence_scores_all_letters: Pandas.DataFrame DataFrame for all nrgams letter with the following columns: nrgram - ANEW word found in nrgams year - year of data match_count- amount of times the word was found volume_count - amount of volumes the word was found in valence - score from ANEW """ letters = string.ascii_lowercase ngrams_valence_scores_processed = [] for letter in letters: logger.info("Downloading data for {} {}".format(language, letter)) ngrams_fpath = download_nrgams_file(temp_directory, language, letter) ngrams_valence_scores = merge_ngrams_and_ANEW_data(valence_data, ngrams_fpath) ngrams_valence_scores_processed.append(ngrams_valence_scores) if delete_files: os.remove(ngrams_fpath) ngrams_valence_scores_all_letters = pd.concat(ngrams_valence_scores_processed) return ngrams_valence_scores_all_letters def create_NVI(language, valence_data, delete_files=False): """ Create a National Valence Index using Google Ngrams data (http://storage.googleapis.com/books/ngrams/books/datasetsv2.html) and the affective word norms (ANEW) for one of the following languages: Italian (ita), EnglishGB (eng-gb), Engligh US (eng-us), Spanish (spa), French(fre), or German(ger). This function saves the associated files (valence scores with ngrams counts, NVI and missing words which were unable to be processed) for a language in the "data" directory Parameters ---------- language: str Which of the following languages to process 'ita', 'eng-gb', 'eng-us', 'spa', 'fre', 'ger' valence_data: Pandas.DataFrame DataFrame including the columns: words (ANEW words), valence (ANEW scores for each word) delete_files: bool Whether to delete downloaded ngrams files after processing Returns ------- None """ # Set up temporary directory to store large files temp_directory = "{}/googlebooksdata".format(PACKAGE_LOCATION) os.makedirs(temp_directory, exist_ok=True) ngrams_valence_data = process_nrgams_data(temp_directory, language, valence_data, delete_files) logger.info("Calculating valence scores") total_words_per_year = ngrams_valence_data.groupby('year').agg( match_totals=("match_count", sum)) ngrams_valence_data = pd.merge(ngrams_valence_data, total_words_per_year, how='left', on=['year']) ngrams_valence_data["val_score"] = ngrams_valence_data["valence"] * ( ngrams_valence_data["match_count"] / ngrams_valence_data["match_totals"]) # Saving valence scores for all words ngrams_valence_data.to_csv("{}/data/{}_valence_ngram_words.csv".format(PACKAGE_LOCATION, language), index=False) # Saving NVI for all words NVI_data = ngrams_valence_data[["year", "match_count", "val_score"]].groupby(['year']).sum() NVI_data.to_csv("{}/data/{}_NVI.csv".format(PACKAGE_LOCATION, language)) # Checking for any unprocessed words, as some words will not be found in google ngrams if they are compound words unprocessed_words = list(set(valence_data['word']) - set(ngrams_valence_data['ngram'])) logger.info("These words could not be processed {}".format(unprocessed_words)) with open("{}/data/{}_unprocessed_words.txt".format(PACKAGE_LOCATION, language), 'a') as file_: for word in unprocessed_words: file_.write("{}\n".format(word)) if __name__ == '__main__': parser = argparse.ArgumentParser( description='Create a National Valence Index using Google Ngrams data ' '(http://storage.googleapis.com/books/ngrams/books/datasetsv2.html) and the affective word norms ' '(ANEW) for one of the following languages: Italian (ita), EnglishGB (eng-gb), ' 'Engligh US (eng-us), Spanish (spa), French(fre), or German(ger).') parser.add_argument('-l', '--language', choices=['ita', 'eng-gb', 'eng-us', 'spa', 'fre', 'ger'], help='The language to process') parser.add_argument("-d", "--delete_files", help="Whether to delete downloaded ngrams files after processing", action='store_true') args = parser.parse_args() valence_data = load_valence_data(language=args.language) create_NVI(language=args.language, valence_data=valence_data, delete_files=args.delete_files)

[ "cseresinhe@turing.ac.uk" ]

cseresinhe@turing.ac.uk

28cb977f5c9241e22b09e7c8b0923e0e7cf2487d

9b3ca4821d0c275df3c298c270cf544c8604648c

/hello-world.py

7b2ccf80923a3bc34b194ea68a5dd164a6661d6b

[]

no_license

Noooneee/test_

520012f41c45f6a2804922955c571e858a712225

f3a0856273f885158d047b0fff7a8a25fb70b199

refs/heads/master

2022-11-09T05:57:36.854139

2020-06-27T02:55:35

275,285,519

0

null

UTF-8

Python

false

45

py

for i in range(10): print("Hello World!")

[ "umar.mohammad7@outlook.com" ]

umar.mohammad7@outlook.com

7c4c68f415ca42dd6a3d377d49232c08f9e0c006

d890428ae26410b7fa6bcc73e91cc5f1fde9f34a

/py/tump/test.py

bbfe3299a6f34dc6f98d84464976975770f91b8c

[]

no_license

ansenfeng/note

6623bcd6a8c700ecb2ce2ae9c142eda31944fe15

e8b7cd6b837645b47b56969f9b5ccdc78a5d10f5

refs/heads/master

2020-07-06T06:04:14.584965

2020-02-06T01:46:22

202,916,092

0

null

2019-08-17T18:20:47

2019-08-17T18:05:26

Python

UTF-8

Python

false

610

py

from tkinter import * root = Tk() root.title("网易云音乐") root.geometry("800x600") root.geometry("+250+100") label = Label(root,text="请输入要下载的内容:",font=('华文行楷',25)) label.grid(row=0,column=0) entry =Entry(root,font=('微软雅黑',25)) entry.grid(row=0,column=1) text = Listbox(root,font=('微软雅黑',30),width=45,height=10) text.grid(row=1,columnspan=2) button = Button(root,text="开始下载",font=('微软雅黑',30)) button.grid(row=2,column=0,sticky=W) button1 = Button(root,text="退出",font=('微软雅黑',30)) button1.grid(row=2,column=1,sticky=E) root.mainloop()

[ "noreply@github.com" ]

noreply@github.com

9e3e88e5e20b8620f29caa40607f2ce722da5e40

98f110f98055cc9d5bc1bb52807dddb98f9c3b32

/experiments/bytecode_update/compile_bytecode/extract_entry_points.py

45ed59fa43c73598055e7d39c12e0a3d62de1da9

[]

no_license

sgadrat/super-tilt-bro-server

4f50336d4729dff08261a04a22d89392062da22d

1fa884e66c1ac819ecf47ba7f9cdec7e677507fe

refs/heads/master

2023-08-18T05:09:35.481574

2023-08-10T21:16:39

166,117,711

0

null

UTF-8

Python

false

860

py

#!/usr/bin/env python import listing import sys import json entry_points = [] # opcodes hex in string as they are represented in xa listing JSR = '20' BPL = '10' BMI = '30' BVC = '50' BVS = '70' BCC = '90' BCS = 'b0' BEQ = 'f0' BNE = 'd0' def on_line(_, line): global entry_points if line['address'] >= 0xc000: if line['code'][0] != ' ': # label entry_points.append({'pc': line['address'], 'name': '{}'.format(line['code'].rstrip())}) elif line['data_repr'][:2] in [JSR]: # JSR, good chance callee will return to next line entry_points.append({'pc': line['address'] + 3}) elif line['data_repr'][:2] in [BPL, BMI, BVC, BVS, BCC, BCS, BEQ, BNE]: # branching, "no" branch is the next instruction entry_points.append({'pc': line['address'] + 2}) listing.parse_file(sys.argv[1], on_listing = on_line); print(json.dumps(entry_points))

[ "sgadrat@wontfix.it" ]

sgadrat@wontfix.it

cbc684ff9ae4dd85231ece8eaed2a8851b6264ba

deaf60a5ba012e68f8509c0df0d35a5228419b71

/找商网/zhao_shang_wang_changxin/zhao_shang_wang/spiders/spider_data.py

a34737cdfa962283d95ea12c2c4ffaafadfb4f46

[]

no_license

kokohui/con_spider

7162d8e58725d9334db5f1da34649cd1d1ef29ea

da1181b53e5cbca546d1bb749f9efc2f48e698f8

refs/heads/master

2022-03-03T19:37:33.721533

2019-08-22T10:05:32

193,631,800

0

null

UTF-8

Python

false

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# -*- coding: utf-8 -*- from ..items import ZhaoShangWangItem import scrapy from scrapy import Request from bs4 import BeautifulSoup import os import random import requests import pymysql import time import re import jieba.analyse conn = pymysql.connect(host='192.168.1.210', user='root', passwd='zhangxing888', db='ktcx_buschance', port=3306, charset='utf8') cur = conn.cursor() # 获取一个游标 class SpiderDataSpider(scrapy.Spider): name = 'spider_data' # start_urls = ['https://www.zhaosw.com/product/search/1541291/2'] def start_requests(self): sql_id = "SELECT url FROM bus_spider_data WHERE source = '找商网' and TYPE = 'gongying' AND is_del = '0' AND isuse = '0' ORDER BY create_date LIMIT 1 " cur.execute(sql_id) res_all_list = cur.fetchall() url = res_all_list[0][0] for num in range(1, 2): url_2 = 'https://www.zhaosw.com/product/search/{}/{}'.format(url, num) print(url_2) yield Request(url=url_2, callback=self.parse) def parse(self, response): detail_url = response.xpath('//*[@id="productForm"]/div[@class="m-product-list"]/a/@href')[0].extract() yield Request(url=detail_url, callback=self.parse_2) def parse_2(self, response): res_url = response.xpath('/html/body/header/div/div[4]/div/div/div/ul/li[2]/a/@href')[0].extract() yield Request(url=res_url, callback=self.parse_3) def parse_3(self, response): pro_url_list = response.xpath('//*[@id="productForm"]/div[3]/div/a/@href').extract() for pro_url in pro_url_list: yield Request(url=pro_url, callback=self.parse_detail) def parse_detail(self, response): item = ZhaoShangWangItem() mobile = '' result_count = 0 try: mobile = response.xpath('//p[@class="p3"]/span[@class="span2"]/text()')[0].extract().strip() com_name = str(response.xpath('//p[@class="p-title"]/a/text()').extract()[0]).strip() sql_count = "select count(0) from bus_user where company_name='{}'".format(com_name) cur.execute(sql_count) result = cur.fetchall() result_count = int(result[0][0]) except: print('没有手机号或公司重复') if mobile != '' and result_count == 0: print('................................................') # 数据库获取id sql_id = "SELECT one_level,two_level,three_level,keyword FROM bus_spider_data WHERE source = '找商网' and TYPE = 'gongying' AND is_del = '0' AND isuse = '0' ORDER BY create_date LIMIT 1 " cur.execute(sql_id) print('sql_id?????????????', sql_id) res_all_list = cur.fetchall() for res_all in res_all_list: one_level = res_all[0] item['one_level_id'] = str(one_level) print('id.........', item['one_level_id']) two_level = res_all[1] item['two_level_id'] = str(two_level) print('id.........', item['two_level_id']) three_level = res_all[2] item['three_level_id'] = str(three_level) print('id.........', item['three_level_id']) keywords = res_all[-1] item['keywords'] = str(keywords) # 保存商品图片 os_img_2_list = [] try: str_ran = str(random.randint(0, 999999)) os.makedirs('/home/imgServer/hc/{}'.format(str_ran)) # 将图片链接保存到硬盘 res_img = response.xpath('//*[@id="productImage"]/div[2]/ul/li/a/img/@src') for img_url in res_img: img_url = img_url.extract() img_url = 'https:' + img_url.strip() img_url = re.sub('\.\.\d+x\d+.jpg', '', img_url) print('img_url>>>>>>>>>>>>><<<<<<<<<<<<<<<<<::::::', img_url) code_img = requests.get(url=img_url).content img_name = str(random.randint(1, 999999)) with open('/home/imgServer/hc/{}/{}.jpg'.format(str_ran, img_name), 'wb') as f: f.write(code_img) os_img_2 = 'http://img.youkeduo.com.cn/hc/' + '{}/{}.jpg'.format(str_ran, img_name) os_img_2_list.append(os_img_2) os_img_2_str_1 = os_img_2_list[0] os_img_2_str = ','.join(os_img_2_list) item['list_img'] = os_img_2_str_1 item['imgs'] = os_img_2_str print('图片ok', os_img_2_list) except: print('图片错误.') # 创建时间 create_date = time.strftime('%Y.%m.%d %H:%M:%S ', time.localtime(time.time())) item['create_date'] = create_date # 价格 price = '' try: price = str(response.xpath('/html/body/main/div[4]/div[1]/div[2]/div[2]/div[1]/div/span/text()').extract()[0].strip()) if price.startswith('￥'): price = price[1:] if not price: price = '面议' print('price', price) except: print('price', price) item['price'] = price # 标题 title = '' try: title = str(response.xpath('/html/body/main/div[4]/div[1]/div[2]/div[1]/h4/text()').extract()[0]) print('title', title) except: print('title', title) item['title'] = title # way if price != '': way = '0' else: way = '1' item['way'] = way res_detail_html = response.text try: soup = BeautifulSoup(res_detail_html, 'lxml') html_1 = str(soup.find('div', class_="parameter-body")) html = str(soup.find('div', class_="introduction-body clearfix")) # print(html) strinfo = re.compile('<img.*?>') html_2 = strinfo.sub('', html) strinfo = re.compile('<br.*?>') html_3 = strinfo.sub('', html_2) strinfo = re.compile('慧聪网') html_4 = strinfo.sub('优客多', html_3) # 把下载图片添加到html div_list = ['<div id="img_detail">', '</div>'] for os_img_2_url in os_img_2_list: os_img_2_url = '<img alt="{}" src="{}">'.format(title, os_img_2_url) div_list.insert(1, os_img_2_url) div_str = '\n'.join(div_list) html_all = html_1 + html_4 + '\n' + div_str # print(html_all) except Exception as e: raise e item['detail'] = str(html_all) # units units = '' try: units = response.xpath('/html/body/main/div[4]/div[1]/div[2]/div[2]/div[1]/div/text()').extract()[-1] units = units.strip().replace('/', '').replace('\n', '') print('units', units) except: print('units', units) item['units'] = units # com_name com_name = '' try: com_name = str(response.xpath('//p[@class="p-title"]/a/text()').extract()[0]).strip() print('com_name', com_name) except: print('com_name', com_name) item['com_name'] = com_name # linkman linkman = '' try: linkman = re.findall('<span.*?>联系人：</span><span.*?>(.*?)</span>', response.text)[0] print('linkman', linkman) except: print('linkman', linkman) item['linkman'] = linkman # mobile mobile = '' try: mobile = response.xpath('//p[@class="p3"]/span[@class="span2"]/text()')[0].extract().strip() print('mobile', mobile) except: print('mobile', mobile) item['mobile'] = mobile # address address = '' try: address = re.findall('<span.*?>所在地区：</span><span.*?>(.*?)</span>', response.text)[0] print('address', address) except: print('address', address) item['address'] = address scopes = '-' try: scopes = response.xpath('//div[@class="p7-content"]/span[2]/a/text()').extract() scopes = str(scopes).strip('[').strip(']').replace("'", "").replace(",", " ") print('scopes', scopes) except: print('scopes', scopes) item['scopes'] = scopes summary = '' try: summary = response.xpath('//div[@class="p-contain"]/p[@class="p4"]/span[2]/text()')[0].extract() print('summary>>>>>>>>>>>>>>>', summary) except: print('summary', summary) item['summary'] = summary yield item

[ "2686162923@qq.com" ]

2686162923@qq.com

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/als-app/als_model.py

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sbartek/intro-to-pyspark

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refs/heads/master

2020-05-16T23:44:12.099782

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import pyspark.sql.functions as F from pyspark.ml.recommendation import ALS, ALSModel class ALSRecModel: def __init__(self, userCol, itemCol, rank=5, maxIter=2, spark=None): self.userCol = userCol self.itemCol = itemCol self.rank = rank self.maxIter = maxIter self.als = ALS( rank=self.rank, maxIter=self.maxIter, userCol=self.userCol, itemCol=self.itemCol, seed=666, implicitPrefs=True) self.model = None self.spark = spark def fit(self, user_item_sdf): self.model = self.als.fit(user_item_sdf) def transform(self): return self.model.recommendForAllUsers(10) def save(self, file_name): self.model.write().overwrite().save(file_name) def load(self, file_name): self.model = ALSModel.load(file_name)

[ "bartekskorulski@gmail.com" ]

bartekskorulski@gmail.com

ea592e1891a71f9b0b5333fa7e4f0802e01c234d

ad908dddab6bd9a87731881c071f8cefc2f25c0a

/app/recipe/views.py

6a362b235a2deb8b975ad554d945d452eeb85ecd

[ "MIT" ]

permissive

krismwas/recipe-app

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19972e6cdafa00db96306e54b9bad715cb459463

refs/heads/master

2020-04-30T16:29:56.505771

2019-04-02T07:47:48

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from rest_framework.decorators import action from rest_framework.response import Response from rest_framework import viewsets, mixins, status from rest_framework.authentication import TokenAuthentication from rest_framework.permissions import IsAuthenticated from core.models import Tag, Ingredient, Recipe from recipe import serializers class RecipeViewSet(viewsets.ModelViewSet): """Manage recipes in the database""" serializer_class = serializers.RecipeSerializer queryset = Recipe.objects.all() authentication_classes = (TokenAuthentication,) permission_classes = (IsAuthenticated,) # def get_queryset(self): # """Retrieve the recipes for the authenticated user""" # return self.queryset.filter(user=self.request.user) def _params_to_ints(self, qs): """Convert a list of string IDs to a list of integers""" return [int(str_id) for str_id in qs.split(',')] def get_queryset(self): """Retrieve the recipes for the authenticated user""" tags = self.request.query_params.get('tags') ingredients = self.request.query_params.get('ingredients') queryset = self.queryset if tags: tag_ids = self._params_to_ints(tags) queryset = queryset.filter(tags__id__in=tag_ids) if ingredients: ingredient_ids = self._params_to_ints(ingredients) queryset = queryset.filter(ingredients__id__in=ingredient_ids) return queryset.filter(user=self.request.user) def get_serializer_class(self): """Return appropriate serializer class""" if self.action == 'retrieve': return serializers.RecipeDetailSerializer elif self.action == 'upload_image': return serializers.RecipeImageSerializer return self.serializer_class def perform_create(self, serializer): """Create a new recipe""" serializer.save(user=self.request.user) @action(methods=['POST'], detail=True, url_path='upload-image') def upload_image(self, request, pk=None): """Upload an image to a recipe""" recipe = self.get_object() serializer = self.get_serializer( recipe, data=request.data ) if serializer.is_valid(): serializer.save() return Response( serializer.data, status=status.HTTP_200_OK ) return Response( serializer.errors, status=status.HTTP_400_BAD_REQUEST ) # class TagViewSet(viewsets.GenericViewSet, # mixins.ListModelMixin, # mixins.CreateModelMixin): # """Manage tags in the database""" # """ # Please note this code has been improved # by the code below with the class name of # BaseRecipeAttrViewSet # """ # authentication_classes = (TokenAuthentication,) # permission_classes = (IsAuthenticated,) # queryset = Tag.objects.all() # serializer_class = serializers.TagSerializer # # def get_queryset(self): # """Return objects for the current authenticated user only""" # return self.queryset.filter(user=self.request.user).order_by('-name') # # def perform_create(self, serializer): # """Create a new ingredient""" # serializer.save(user=self.request.user) # # # class IngredientViewSet(viewsets.GenericViewSet, # mixins.ListModelMixin, # mixins.CreateModelMixin): # """Manage ingredients in the database""" # authentication_classes = (TokenAuthentication,) # permission_classes = (IsAuthenticated,) # queryset = Ingredient.objects.all() # serializer_class = serializers.IngredientSerializer # # def get_queryset(self): # """Return objects for the current authenticated user only""" # return self.queryset.filter(user=self.request.user).order_by('-name') # # def perform_create(self, serializer): # """Create a new ingredient""" # serializer.save(user=self.request.user) class BaseRecipeAttrViewSet(viewsets.GenericViewSet, mixins.ListModelMixin, mixins.CreateModelMixin): """Base viewset for user owned recipe attributes""" authentication_classes = (TokenAuthentication,) permission_classes = (IsAuthenticated,) def get_queryset(self): """Return objects for the current authenticated user only""" return self.queryset.filter(user=self.request.user).order_by('-name') def perform_create(self, serializer): """Create a new ingredient""" serializer.save(user=self.request.user) class TagViewSet(BaseRecipeAttrViewSet): """Manage tags in the database""" queryset = Tag.objects.all() serializer_class = serializers.TagSerializer class IngredientViewSet(BaseRecipeAttrViewSet): """Manage ingredients in the database""" queryset = Ingredient.objects.all() serializer_class = serializers.IngredientSerializer

[ "chrischrismwangi@gmail.com" ]

chrischrismwangi@gmail.com

f99a9206e3aad2da48d0857fb2f4ee34a77b55d7

df94467e24ce38f566e5d96f179de4ad365e1f1d

/appmain/migrations/0109_auto_20200328_0724.py

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[]

no_license

dhchamber/PigSkinners

498f931d112692c0cef3767cd2a6ca8b1014bcb1

9accd0dbd4f8ed93720b708117d42aab662569f2

refs/heads/master

2023-04-07T14:32:37.649907

2020-05-09T15:28:50

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# Generated by Django 3.0.1 on 2020-03-28 13:24 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('appmain', '0108_auto_20200325_0730'), ] operations = [ migrations.CreateModel( name='PickRevision', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('revision', models.PositiveSmallIntegerField()), ('points', models.PositiveSmallIntegerField()), ('pick_score', models.PositiveSmallIntegerField(default=0)), ('saved', models.BooleanField(default=False)), ('entered_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='pick_rev_entered', to=settings.AUTH_USER_MODEL)), ('koth_game', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='pick_rev_koth_game', to='appmain.Game')), ('koth_team', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='pick_rev_koth_team', to='appmain.Team')), ('updated_by', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='pick_rev_updated', to=settings.AUTH_USER_MODEL)), ('user', models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, related_name='pick_revs', to=settings.AUTH_USER_MODEL)), ('wk', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='pick_rev_wk', to='appmain.Week')), ], options={ 'verbose_name': 'pick revision', 'verbose_name_plural': 'pick revisions', 'ordering': ['user', 'wk'], }, ), migrations.AddIndex( model_name='pickrevision', index=models.Index(fields=['revision', 'user', 'wk'], name='appmain_pic_revisio_2d0954_idx'), ), migrations.AddConstraint( model_name='pickrevision', constraint=models.UniqueConstraint(fields=('revision', 'user', 'wk'), name='pickrev_user_wk'), ), ]

[ "dhchamber@msn.com" ]

dhchamber@msn.com

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/techpitgram/urls.py

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[]

no_license

iKora128/techpitgram

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c9a55eaa036f6dbc75a3216508a1a75f85765c17

refs/heads/master

2020-12-14T22:52:06.441596

2020-01-19T12:39:45

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"""techpitgram URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.0/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path urlpatterns = [ path('admin/', admin.site.urls), ]

[ "nagashimadaichi@nagashimadaichinoMacBook-Pro.local" ]

nagashimadaichi@nagashimadaichinoMacBook-Pro.local

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/tensorflow/rnn_long_sequence.py

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[]

no_license

JungAnJoon/mini_1

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refs/heads/master

2020-06-19T06:13:27.391102

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# Lab 12 Character Sequence RNN import tensorflow as tf import numpy as np tf.set_random_seed(777) # reproducibility sample = " Hi, My name is Hongbeom Choi" idx2char = list(set(sample)) # index -> char char2idx = {c: i for i, c in enumerate(idx2char)} # char -> idex # hyper parameters dic_size = len(char2idx) # RNN input size (one hot size) hidden_size = len(char2idx) # RNN output size num_classes = len(char2idx) # final output size (RNN or softmax, etc.) batch_size = 1 # one sample data, one batch sequence_length = len(sample) - 1 # number of lstm rollings (unit #) learning_rate = 0.1 sample_idx = [char2idx[c] for c in sample] # char to index x_data = [sample_idx[:-1]] # X data sample (0 ~ n-1) hello: hell y_data = [sample_idx[1:]] # Y label sample (1 ~ n) hello: ello X = tf.placeholder(tf.int32, [None, sequence_length]) # X data Y = tf.placeholder(tf.int32, [None, sequence_length]) # Y label x_one_hot = tf.one_hot(X, num_classes) # one hot: 1 -> 0 1 0 0 0 0 0 0 0 0 cell = tf.contrib.rnn.BasicLSTMCell( num_units=hidden_size, state_is_tuple=True) initial_state = cell.zero_state(batch_size, tf.float32) outputs, _states = tf.nn.dynamic_rnn( cell, x_one_hot, initial_state=initial_state, dtype=tf.float32) # FC layer X_for_fc = tf.reshape(outputs, [-1, hidden_size]) outputs = tf.contrib.layers.fully_connected(X_for_fc, num_classes, activation_fn=None) # reshape out for sequence_loss outputs = tf.reshape(outputs, [batch_size, sequence_length, num_classes]) weights = tf.ones([batch_size, sequence_length]) sequence_loss = tf.contrib.seq2seq.sequence_loss( logits=outputs, targets=Y, weights=weights) loss = tf.reduce_mean(sequence_loss) train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss) prediction = tf.argmax(outputs, axis=2) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) for i in range(50): l, _ = sess.run([loss, train], feed_dict={X: x_data, Y: y_data}) result = sess.run(prediction, feed_dict={X: x_data}) # print char using dic result_str = [idx2char[c] for c in np.squeeze(result)] print(i, "loss:", l, "Prediction:", ''.join(result_str))

[ "jaj1012@naver.com" ]

jaj1012@naver.com

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/pylith/meshio/OutputMatElastic.py

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[ "MIT" ]

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youngsolar/pylith

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refs/heads/master

2020-12-26T04:04:21.884785

2014-10-06T21:42:42

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#!/usr/bin/env python # # ---------------------------------------------------------------------- # # Brad T. Aagaard, U.S. Geological Survey # Charles A. Williams, GNS Science # Matthew G. Knepley, University of Chicago # # This code was developed as part of the Computational Infrastructure # for Geodynamics (http://geodynamics.org). # # Copyright (c) 2010-2014 University of California, Davis # # See COPYING for license information. # # ---------------------------------------------------------------------- # ## @file pyre/meshio/OutputMatElastic.py ## ## @brief Python object for managing output of finite-element ## information for material state variables. ## ## Factory: output_manager from OutputManager import OutputManager # OutputMatElastic class class OutputMatElastic(OutputManager): """ Python object for managing output of finite-element information for material state variables. Factory: output_manager """ # INVENTORY ////////////////////////////////////////////////////////// class Inventory(OutputManager.Inventory): """ Python object for managing OutputMatElastic facilities and properties. """ ## @class Inventory ## Python object for managing OutputMatElastic facilities and properties. ## ## \b Properties ## @li \b cell_info_fields Names of cell info fields to output. ## @li \b cell_data_fields Names of cell data fields to output. ## ## \b Facilities ## @li None import pyre.inventory cellInfoFields = pyre.inventory.list("cell_info_fields", default=["mu", "lambda", "density"]) cellInfoFields.meta['tip'] = "Names of cell info fields to output." cellDataFields = pyre.inventory.list("cell_data_fields", default=["total_strain", "stress"]) cellDataFields.meta['tip'] = "Names of cell data fields to output." # PUBLIC METHODS ///////////////////////////////////////////////////// def __init__(self, name="outputmatelastic"): """ Constructor. """ OutputManager.__init__(self, name) return # PRIVATE METHODS //////////////////////////////////////////////////// def _configure(self): """ Set members based using inventory. """ OutputManager._configure(self) self.vertexInfoFields = [] self.vertexDataFields = [] self.cellInfoFields = self.inventory.cellInfoFields self.cellDataFields = self.inventory.cellDataFields return # FACTORIES //////////////////////////////////////////////////////////// def output_manager(): """ Factory associated with OutputManager. """ return OutputMatElastic() # End of file

[ "baagaard@usgs.gov" ]

baagaard@usgs.gov

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/app/api/mod_item/grey/deploy.py

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[ "BSD-3-Clause" ]

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TouchPal/guldan

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2023-03-08T19:55:26.286517

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# -*- coding: utf-8 -*- from flask import request from app.api.utils.request_util import parse_request from app.api.utils import response from app.api.mod_item import item_blueprint from app.api.mod_item.utils import ensure_item from app.api.mod_item.validate import validate_for_item_modify from app.api.mod_item.modify import item_modify from .utils import ensure_grey_item, invalidate_cache_for_grey def parse_grey_item_full_deploy_arguments(request): op_info = parse_request(request) return op_info def item_full_deploy(resource_id=None, item=None, grey_item=None): invalidate_cache_for_grey(item.id, item.name, grey_item.item_visibility) item_modify( item_id=resource_id, item_data=grey_item.item_data, item_type=grey_item.item_type, item_visibility=grey_item.item_visibility, in_grey=False ) @item_blueprint.route("/<int:item_id>/upgrade", methods=["POST"]) @response.dict_response_deco def full_deploy_item(item_id): item = ensure_item(item_id) grey_item = ensure_grey_item(item_id) op_info = parse_grey_item_full_deploy_arguments(request) validate_for_item_modify( op_info.user_hash, item_id ) item_full_deploy(resource_id=item_id, item=item, grey_item=grey_item) return { "msg": "OK" }

[ "alex.zheng@cootek.cn" ]

alex.zheng@cootek.cn

3e74af071c20b8fe4f410c52f75be3b2e4848392

1fa5805dc15ad2529d1b343d4fd5a4205dcc4701

/modules.py

3cb06a1155d2337dadcfc620dd5217b5cf6ea340

[]

no_license

readbeard/activation_fn

527c91246df7ace5b427f6ab9aba4c78fc001a29

fd42647c7c9bfa041e4db0ce723cd61c64a1ad04

refs/heads/master

2022-09-27T05:03:51.900709

2020-06-09T17:38:57

null

0

null

UTF-8

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4,017

py

import torch import torch.nn as nn class Antirelu(nn.Module): def __init__(self): super(Antirelu, self).__init__() def forward(self, s): return torch.min(torch.zeros(s.shape), s) class Identity(nn.Module): def __init__(self): super(Identity, self).__init__() def forward(self, s): return s class MLP(nn.Module): def __init__(self, combinator): super(MLP, self).__init__() if combinator == 'MLP1': # 104202 parameters self.mlp = torch.nn.Sequential(nn.Linear(4, 3), nn.ReLU(), nn.Dropout(0.2), nn.Linear(3, 1), ) if combinator == 'MLP1_neg': # 104202 parameters self.mlp = torch.nn.Sequential(nn.Linear(8, 5), nn.ReLU(), nn.Dropout(0.2), nn.Linear(5, 1), ) if combinator == 'MLP2': # 104970 self.mlp = torch.nn.Sequential(nn.Linear(4, 4), nn.ReLU(), nn.Dropout(0.2), nn.Linear(4, 1), ) if combinator == 'MLP3': # 104202 parameters --> same of MLP1 but w/out dropout self.mlp = torch.nn.Sequential(nn.Linear(4, 3), nn.ReLU(), nn.Linear(3, 1), ) if combinator == 'MLP4': # 104970 --> same of MLP1 but w/out dropout self.mlp = torch.nn.Sequential(nn.Linear(4, 4), nn.ReLU(), nn.Linear(4, 1), ) if combinator == 'MLP5': # 105098 self.mlp = torch.nn.Sequential(nn.Linear(4, 3), nn.ReLU(), nn.Dropout(0.2), nn.Linear(3, 2), nn.ReLU(), nn.Linear(2, 1), ) def forward(self, x): x = self.mlp(x) return x class MLP_ATT(nn.Module): def __init__(self, combinator): super(MLP_ATT, self).__init__() if combinator in ['MLP_ATT', 'MLP_ATT_b']: # 105738 parameters self.combinator = combinator if combinator == 'MLP_ATT_b': self.beta = nn.Parameter(torch.FloatTensor(4).uniform_(-0.5, 0.5)) self.mlp = torch.nn.Sequential(nn.Linear(4, 3), nn.ReLU(), nn.Dropout(0.2), nn.Linear(3, 4), ) if combinator in ['MLP_ATT_neg']: # 105738 parameters self.mlp = torch.nn.Sequential(nn.Linear(8, 5), nn.ReLU(), nn.Dropout(0.2), nn.Linear(5, 8), ) if combinator == 'MLP_ATT2': # 106890 parameters self.mlp = torch.nn.Sequential(nn.Linear(4, 4), nn.ReLU(), nn.Dropout(0.2), nn.Linear(4, 4), ) def forward(self, x): if self.combinator == 'MLP_ATT_b': x = x + self.beta # print(x.shape) x = self.mlp(x) return x

[ "bartozzi.1535211@studenti.uniroma1.it" ]

bartozzi.1535211@studenti.uniroma1.it

68202dcd675d0bb11daa99543c2774c9b913f26e

df43c1539d5e46d88213f09c008a6c28d2c0cb49

/barbers/migrations/0010_auto_20201121_1348.py

4016964d6cda0064e22ab1b23b202746256997ed

[]

no_license

doctor-evans/barbershub

345ae0f745f88ed146e4a997e62a730029e9a6a1

3fdfa45d5cfecbe76aeb854409ffcd71eae5df9d

refs/heads/master

2023-01-24T00:34:43.200586

2020-11-22T12:48:37

315,028,738

0

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UTF-8

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py

# Generated by Django 3.1.3 on 2020-11-21 12:48 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('barbers', '0009_productitem_slug'), ] operations = [ migrations.AlterField( model_name='productitem', name='slug', field=models.SlugField(unique=True), ), ]

[ "evanschan200@gmail.com" ]

evanschan200@gmail.com

6b1b084f116c65b5e5fe93e2388539fad7f97c69

8038e8005693a777be5deb462635e5ecc2f4d6a0

/Scrapper.py

7b1a5628e6c9bf6c0449f6e492c9a37e5685cd69

[]

no_license

Waruiru/twitter_data_mining

56197eecf5326ff40244bfbbea52c5b03463c357

ab7acf123cac229042ada8e0214e102f83b91f17

refs/heads/master

2020-04-25T16:35:47.039580

2018-09-12T09:58:36

null

0

null

UTF-8

Python

false

2,218

py

from tweepy import Stream import json import tweepy from tweepy import OAuthHandler from tweepy import Stream from tweepy.streaming import StreamListener from flask import Flask, render_template, request, flash, url_for, redirect app = Flask(__name__) # consumer key, consumer secret, access token, access secret. ckey = "6yMsHkLwEtLldyk2MinN8N7Mb" csecret = "NzFmWgiSWwiF0fK4ic6mnqfaPuUNg471pb2Qcx6aS89z80ho72" atoken = "1709707117-JibK1EyA7TCS3Hhuzn5rfOBKPSpepkm0jPSFHfP" asecret = "zae8WJWSXoocsXiCYt8VQ0WJxBYQmP9sbvkXYUGbiYpB0" auth = OAuthHandler(ckey, csecret) auth.set_access_token(atoken, asecret) print "service started!" class listener(StreamListener): def on_data(self, data): print "method for tweet retrieval started!" import sentiment_mod as s all_data = json.loads(data) print "data fetched, saving it to file!" outputfile = open('raw_data.txt', 'a') outputfile.write(data) outputfile.write('\n') outputfile.close() print "written to file!" tweet = all_data["text"] sentiment_value, confidence = s.sentiment(tweet) display =(tweet, sentiment_value, confidence) print display if confidence * 100 >= 80: output = open("twitter-out.txt", "a") output.write(sentiment_value) output.write('\n') output.close() savetweet = open("tweet.txt", "a") savetweet.write(tweet) savetweet.write('\n') savetweet.close() print "returning result to web!" return display return True def on_error(self, status): print "error ! ->" print(status) @app.route('/', methods=['GET']) def index(): print "get request received! Processing request" return render_template("home.html") @app.route('/post', methods=['post']) def method(): print "post request!" search_string = request.form.get('search_string') print search_string twitterStream = Stream(auth, listener()) twitterStream.filter(track=[search_string]) return if __name__ == '__main__': app.run()

[ "jjswork2@gmail.com" ]

jjswork2@gmail.com

b8349e59ee37806bbb211823d84042788796f8c0

8ca6ddae047850046e4eaf33d1820bd917c2d822

/src/app/util/mail.py

7fb672b41c2b901b60101023f854c2f81add3efa

[]

no_license

AllenSix/homekit

0582017588481c773689355c8b2164835ca5955d

29ad509893aaecf4b518c0e3468db7e2eb43d1e5

refs/heads/master

2020-05-17T22:03:06.176706

2020-01-09T14:36:11

183,989,418

0

null

UTF-8

Python

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985

py

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 18-6-20 上午10:27 # @Author : Skye # @Site : # @File : mail.py # @Software: PyCharm import smtplib from email.header import Header from email.mime.text import MIMEText from src.app.config import * def send_email(title, content, receivers): message = MIMEText(content, 'plain', 'utf-8') # 内容, 格式, 编码 message['From'] = "{}".format(MAIL_SENDER) message['To'] = receivers message['Subject'] = title try: smtpObj = smtplib.SMTP_SSL(MAIL_HOST, 465) # 启用SSL发信, 端口一般是465 # smtpObj = smtplib.SMTP(MAIL_HOST, 587) # 启用SSL发信, 端口一般是465 # smtpObj.ehlo() # smtpObj.starttls() smtpObj.login(MAIL_USER, MAIL_PASS) # 登录验证 smtpObj.sendmail(MAIL_SENDER, receivers, message.as_string()) # 发送 print("mail has been send successfully.") except smtplib.SMTPException as e: print(e)

[ "csf71106410@163.com" ]

csf71106410@163.com

258340149c613722be8d1cfed6c2f43bf4840d6b

69eb034a1d086d1b2ce1f1083df2b2fd74b9f5cc

/train_model_rmre.py

7396f5c35cbd7cd3cf08f2c365cdd892b4978ffc

[]

no_license

webdxq/genarate_blessing

2c0f6afc55f4c507750911802a80fe299a4690d6

a08a09071edf687dcb512713daea1daf00450383

refs/heads/master

2020-03-29T01:38:33.419803

2018-09-19T06:10:26

145,077,459

0

null

UTF-8

Python

false

14,938

py

#!/usr/bin/python3 #-*- coding: UTF-8 -*- import collections import numpy as np import tensorflow as tf import os import sys import chardet import re import json import time from datetime import datetime reload(sys) sys.setdefaultencoding('utf8') # os.environ['CUDA_VISIBLE_DEVICES']='1' #-------------------------------数据预处理---------------------------# # poetry_file ='../data/poetry.txt' minlen = 4 maxlen = 15 blessing_file ='/home/pingan_ai/dxq/project/gen_blessing/dataset/data/line_lyrics.txt' blessings = [] all_words = [] cantoneses = open('/home/pingan_ai/dxq/project/cantonese.txt','r').readline().split(' ') # print(cantoneses) cantonese = [re.compile(i.decode('utf-8')) for i in cantoneses] LEARNING_RATE_BASE = 0.02 MODEL_SAVE_PATH = '/media/pingan_ai/dxq/gen_blessing/new_model/' N_GPU = 2 MODEL_NAME = "blessing.ckpt" EPOCHS = 100 LEARNING_RATE_DECAY = 0.99 filename = blessing_file.split('/')[-1].split('.')[0] # print(blessing_file) can_count = 0 MOVING_AVERAGE_DECAY = 0.99 def HasReapeatWord(string): flag = False for i,char in enumerate(string): # print i s = i m = i+1 e = i+2 # print string[s],string[m],string[e] if flag: return True elif e >= (len(string)-1): return False else: if string[s] == string[m] and string[m] == string[e]: # print string[s],string[m],string[e] flag = True else: continue def IsCantonese(line): for i, patten in enumerate(cantonese): if patten.search(line)!= None: # print(line) # can_count = can_count+1 return True return False with open(blessing_file, "r") as f: for i,line in enumerate(f): if i == 0: continue # try: # print(line) line = line.decode('UTF-8') line = line.strip(u'\n') line = line.replace(u' ',u'') if u'_' in line or u'(' in line or u'（' in line or u'《' in line or u'[' in line: continue if len(line) < minlen or len(line) > maxlen: continue if IsCantonese(line): can_count = can_count+1 continue if HasReapeatWord(line): continue all_words += [word for word in line] line = u'[' + unicode(chr(len(line)+61)) +line + u']' blessings.append(line) # except Exception as e: # print('no') if i%100000== 0: print(u'处理到%d'%i) blessings = sorted(blessings,key=lambda line: len(line)) print(u'歌词总行数: %s'% len(blessings)) print(can_count) counter = collections.Counter(all_words) count_pairs = sorted(counter.items(), key=lambda x: -x[1]) print('*******************') words, _ = zip(*count_pairs) print(len(words)) for i in range(65,66+maxlen-minlen): words = words[:len(words)] + (unicode(chr(i)),) words = words[:len(words)] + (u'[',) words = words[:len(words)] + (u']',) words = words[:len(words)] + (u' ',) print(u'词表总数: %s'% len(words)) word_num_map = dict(zip(words, range(len(words)))) print(word_num_map[u'[']) print(word_num_map[u']']) print(word_num_map[u' ']) print(word_num_map[u'A']) print(word_num_map[u'L']) to_num = lambda word: word_num_map.get(word, len(words)-1) blessings_vector = [ list(map(to_num,blessing)) for blessing in blessings] to_words = lambda num: words[num] print(blessings_vector[-4:-1]) print(blessings_vector[1]) for i in blessings[-4:-1]: print(i) print(blessings[1]) with open(filename+'2id_re.json','w') as outfile: json.dump(word_num_map,outfile,ensure_ascii=False) # outfile.write('\n') with open(filename+'2word_re.json','w') as outfile2: # word2id = dict((value, key) for key,value in word_num_map.iteritems()) json.dump(words,outfile2,ensure_ascii=False) # outfile2.write('\n') batch_size = 256 n_chunk = len(blessings_vector) // batch_size # sys.exit() class DataSet(object): def __init__(self,data_size): self._data_size = data_size self._epochs_completed = 0 self._index_in_epoch = 0 self._data_index = np.arange(data_size) def next_batch(self,batch_size): start = self._index_in_epoch if start + batch_size > self._data_size: np.random.shuffle(self._data_index) self._epochs_completed = self._epochs_completed + 1 self._index_in_epoch = batch_size full_batch_features ,full_batch_labels = self.data_batch(0,batch_size) return full_batch_features ,full_batch_labels else: self._index_in_epoch += batch_size end = self._index_in_epoch full_batch_features ,full_batch_labels = self.data_batch(start,end) if self._index_in_epoch == self._data_size: self._index_in_epoch = 0 self._epochs_completed = self._epochs_completed + 1 np.random.shuffle(self._data_index) return full_batch_features,full_batch_labels def data_batch(self,start,end): batches = [] for i in range(start,end): batches.append(blessings_vector[self._data_index[i]]) length = max(map(len,batches)) # print(word_num_map[' ']) xdata = np.full((end - start,length), word_num_map[']'], np.int32) for row in range(end - start): xdata[row,:len(batches[row])] = batches[row] ydata = np.copy(xdata) ydata[:,:-1] = xdata[:,1:] return xdata,ydata #---------------------------------------RNN--------------------------------------# # 定义RNN def neural_network(input_data,model='lstm', rnn_size=128, num_layers=2): if model == 'rnn': cell_fun = tf.nn.rnn_cell.BasicRNNCell elif model == 'gru': cell_fun = tf.nn.rnn_cell.GRUCell elif model == 'lstm': cell_fun = tf.nn.rnn_cell.BasicLSTMCell cell = cell_fun(rnn_size, state_is_tuple=True) cell = tf.nn.rnn_cell.MultiRNNCell([cell] * num_layers, state_is_tuple=True) initial_state = cell.zero_state(batch_size, tf.float32) with tf.variable_scope('rnnlm'): softmax_w = tf.get_variable("softmax_w", [rnn_size, len(words)]) softmax_b = tf.get_variable("softmax_b", [len(words)]) with tf.device("/cpu:0"): embedding = tf.get_variable("embedding", [len(words), rnn_size]) inputs = tf.nn.embedding_lookup(embedding, input_data) outputs, last_state = tf.nn.dynamic_rnn(cell, inputs, initial_state=initial_state, scope='rnnlm') output = tf.reshape(outputs,[-1, rnn_size]) logits = tf.matmul(output, softmax_w) + softmax_b probs = tf.nn.softmax(logits) return logits, last_state, probs, cell, initial_state def load_model(sess, saver,ckpt_path): latest_ckpt = tf.train.latest_checkpoint(ckpt_path) if latest_ckpt: print ('resume from', latest_ckpt) saver.restore(sess, latest_ckpt) return int(latest_ckpt[latest_ckpt.rindex('-') + 1:]) else: print ('building model from scratch') sess.run(tf.global_variables_initializer()) return -1 def to_word(weights): sample = np.argmax(weights) return words[sample] def train_to_word(x): # print(u'x的长度',len(x)) x_words = map(to_words, x) # print(str(x_words).decode("unicode-escape")) outstr = ''.join(x_words) token = outstr[1] outstr = outstr[2:-1] print(u'[ '+ token +u' '+ outstr+u' ]') def AlignSentence(sentence): sentence = sentence[:-2] sentence_re = '' for i in range(len(sentence)): if not (sentence[i] >= u'\u4e00' and sentence[i]<=u'\u9fa5'): sentence_re += sentence[i]+u' ' else: sentence_re += sentence[i] # return u'[ '+sentence[i] + u' ]' print sentence_re + u' ]' def get_loss(input_data, targets, reuse_variables=None): # 沿用5.5节中定义的函数来计算神经网络的前向传播结果。 with tf.variable_scope(tf.get_variable_scope(), reuse=reuse_variables): logits, last_state, probs, _, _ = neural_network(input_data) loss = tf.contrib.legacy_seq2seq.sequence_loss_by_example( [logits], [targets], [tf.ones_like(targets, dtype=tf.float32)], len(words) ) cost = tf.reduce_mean(loss) return cost # 计算每一个变量梯度的平均值。 def average_gradients(tower_grads): average_grads = [] # 枚举所有的变量和变量在不同GPU上计算得出的梯度。 for grad_and_vars in zip(*tower_grads): # 计算所有GPU上的梯度平均值。 grads = [] for g, _ in grad_and_vars: expanded_g = tf.expand_dims(g, 0) grads.append(expanded_g) grad = tf.concat(grads, 0) grad = tf.reduce_mean(grad, 0) v = grad_and_vars[0][1] grad_and_var = (grad, v) # 将变量和它的平均梯度对应起来。 average_grads.append(grad_and_var) # 返回所有变量的平均梯度，这个将被用于变量的更新。 return average_grads # def main(argv=None): def main(argv=None): # 将简单的运算放在CPU上，只有神经网络的训练过程放在GPU上。 TRAINING_STEPS = EPOCHS*n_chunk/N_GPU with tf.Graph().as_default(), tf.device('/cpu:0'): input_data = tf.placeholder(tf.int32, [batch_size, None]) output_targets = tf.placeholder(tf.int32, [batch_size, None]) trainds = DataSet(len(blessings_vector)) targets = tf.reshape(output_targets, [-1]) global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False) learning_rate = tf.train.exponential_decay( LEARNING_RATE_BASE, global_step, 60000 / batch_size, LEARNING_RATE_DECAY) optimizer = tf.train.AdamOptimizer(learning_rate) tower_grads = [] reuse_variables = False # 将神经网络的优化过程跑在不同的GPU上。 for i in range(N_GPU): # 将优化过程指定在一个GPU上。 with tf.device('/gpu:%d' % i): with tf.name_scope('GPU_%d' % i) as scope: cur_loss = get_loss(input_data,targets,reuse_variables) # 在第一次声明变量之后，将控制变量重用的参数设置为True。这样可以 # 让不同的GPU更新同一组参数。 reuse_variables = True grads = optimizer.compute_gradients(cur_loss) tower_grads.append(grads) # 计算变量的平均梯度。 grads = average_gradients(tower_grads) for grad, var in grads: if grad is not None: tf.summary.histogram('gradients_on_average/%s' % var.op.name, grad) # 使用平均梯度更新参数。 apply_gradient_op = optimizer.apply_gradients(grads, global_step=global_step) for var in tf.trainable_variables(): tf.summary.histogram(var.op.name, var) # 计算变量的滑动平均值。 variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY, global_step) variables_to_average = (tf.trainable_variables() +tf.moving_average_variables()) variables_averages_op = variable_averages.apply(variables_to_average) # 每一轮迭代需要更新变量的取值并更新变量的滑动平均值。 train_op = tf.group(apply_gradient_op, variables_averages_op) saver = tf.train.Saver() summary_op = tf.summary.merge_all() init = tf.global_variables_initializer() with tf.Session(config=tf.ConfigProto( allow_soft_placement=True, log_device_placement=True)) as sess: # 初始化所有变量并启动队列。 init.run() summary_writer = tf.summary.FileWriter(MODEL_SAVE_PATH, sess.graph) for step in range(TRAINING_STEPS): # 执行神经网络训练操作，并记录训练操作的运行时间。 start_time = time.time() x,y = trainds.next_batch(batch_size) _, loss_value = sess.run([train_op, cur_loss],feed_dict={input_data: x, output_targets: y}) duration = time.time() - start_time # 每隔一段时间数据当前的训练进度，并统计训练速度。 if step != 0 and step % 10 == 0: # 计算使用过的训练数据个数。因为在每一次运行训练操作时，每一个GPU # 都会使用一个batch的训练数据，所以总共用到的训练数据个数为 # batch大小 × GPU个数。 num_examples_per_step = batch_size * N_GPU # num_examples_per_step为本次迭代使用到的训练数据个数， # duration为运行当前训练过程使用的时间，于是平均每秒可以处理的训 # 练数据个数为num_examples_per_step / duration。 examples_per_sec = num_examples_per_step / duration # duration为运行当前训练过程使用的时间，因为在每一个训练过程中， # 每一个GPU都会使用一个batch的训练数据，所以在单个batch上的训 # 练所需要时间为duration / GPU个数。 sec_per_batch = duration / N_GPU # 输出训练信息。 format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f sec/batch)') print (format_str % (datetime.now(), step, loss_value, examples_per_sec, sec_per_batch)) # 通过TensorBoard可视化训练过程。 summary = sess.run(summary_op) summary_writer.add_summary(summary, step) # 每隔一段时间保存当前的模型。 if step == n_chunk: checkpoint_path = os.path.join(MODEL_SAVE_PATH, MODEL_NAME) saver.save(sess, checkpoint_path, global_step=step) main() # if __name__ == '__main__': # tf.app.run()

[ "407383787@qq.com" ]

407383787@qq.com

5bd234d032a1cef724c7d19f94ecdca75497c3b5

803bab6f782099d995bcdb99d163486f4fff8c50

/test/test_pointnav_resnet_policy.py

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[ "MIT", "LicenseRef-scancode-unknown-license-reference", "CC-BY-NC-SA-3.0" ]

permissive

facebookresearch/habitat-lab

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f5b29e62df0788d70ba3618fc738fa4e947428ba

refs/heads/main

2023-08-24T14:00:02.707343

2023-08-23T04:53:48

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2023-09-14T15:20:03

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#!/usr/bin/env python3 # Copyright (c) Meta Platforms, Inc. and its affiliates. # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. import os.path import shlex import subprocess import numpy as np import pytest import torch from gym import spaces from habitat import read_write from habitat_baselines.config.default import get_config from habitat_baselines.rl.ddppo.policy import PointNavResNetPolicy ACTION_SPACE = spaces.Discrete(4) OBSERVATION_SPACES = { "depth_model": spaces.Dict( { "depth": spaces.Box( low=0, high=1, shape=(256, 256, 1), dtype=np.float32, ), "pointgoal_with_gps_compass": spaces.Box( low=np.finfo(np.float32).min, high=np.finfo(np.float32).max, shape=(2,), dtype=np.float32, ), } ), "rgb_model": spaces.Dict( { "rgb": spaces.Box( low=0, high=255, shape=(256, 256, 3), dtype=np.uint8, ), "pointgoal_with_gps_compass": spaces.Box( low=np.finfo(np.float32).min, high=np.finfo(np.float32).max, shape=(2,), dtype=np.float32, ), } ), "blind_model": spaces.Dict( { "pointgoal_with_gps_compass": spaces.Box( low=np.finfo(np.float32).min, high=np.finfo(np.float32).max, shape=(2,), dtype=np.float32, ), } ), } MODELS_DEST_DIR = "data/ddppo-models" MODELS_BASE_URL = "https://dl.fbaipublicfiles.com/habitat/data/baselines/v1/ddppo/ddppo-models" MODELS_TO_TEST = { "gibson-2plus-resnet50.pth": { "backbone": "resnet50", "observation_space": OBSERVATION_SPACES["depth_model"], "action_space": ACTION_SPACE, }, "gibson-2plus-mp3d-train-val-test-se-resneXt50-rgb.pth": { "backbone": "se_resneXt50", "observation_space": OBSERVATION_SPACES["rgb_model"], "action_space": ACTION_SPACE, }, "gibson-0plus-mp3d-train-val-test-blind.pth": { "backbone": None, "observation_space": OBSERVATION_SPACES["blind_model"], "action_space": ACTION_SPACE, }, } def _get_model_url(model_name): return f"{MODELS_BASE_URL}/{model_name}" def _get_model_path(model_name): return f"{MODELS_DEST_DIR}/{model_name}" @pytest.fixture(scope="module", autouse=True) def download_data(): for model_name in MODELS_TO_TEST: model_url = _get_model_url(model_name) model_path = _get_model_path(model_name) if not os.path.exists(model_path): print(f"Downloading {model_name}.") download_command = ( "wget --continue " + model_url + " -P " + MODELS_DEST_DIR ) subprocess.check_call(shlex.split(download_command)) assert os.path.exists( model_path ), "Download failed, no package found." @pytest.mark.parametrize( "pretrained_weights_path,backbone,observation_space,action_space", [ ( _get_model_path(model_name), params["backbone"], params["observation_space"], params["action_space"], ) for model_name, params in MODELS_TO_TEST.items() ], ) def test_pretrained_models( pretrained_weights_path, backbone, observation_space, action_space ): config = get_config( "test/config/habitat_baselines/ddppo_pointnav_test.yaml" ) with read_write(config): ddppo_config = config.habitat_baselines.rl.ddppo ddppo_config.pretrained = True ddppo_config.pretrained_weights = pretrained_weights_path if backbone is not None: ddppo_config.backbone = backbone policy = PointNavResNetPolicy.from_config( config=config, observation_space=observation_space, action_space=action_space, ) pretrained_state = torch.load(pretrained_weights_path, map_location="cpu") prefix = "actor_critic." policy.load_state_dict( { # type: ignore k[len(prefix) :]: v for k, v in pretrained_state["state_dict"].items() } )

[ "noreply@github.com" ]

noreply@github.com

1eb08df1e69d0570a4b551015f6243b3accb3169

e88106f6223882f5d5e7eee23e33490f33fe50f0

/db_create.py

8ba7d15940ac63c8b2de3128b59007a65095a800

[]

no_license

canonhui/VacHeure

4fc3b2d3f9ca8c69e423d2dfed6bd360975b1109

90f11882a94336e585c01812ef6d0f800b5d1493

refs/heads/master

2020-03-28T20:31:00.846404

2017-07-26T16:12:45

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522

py

#!/usr/bin/env python3 from migrate.versioning import api from config import SQLALCHEMY_DATABASE_URI from config import SQLALCHEMY_MIGRATE_REPO from app import db import os.path db.create_all() if not os.path.exists(SQLALCHEMY_MIGRATE_REPO): api.create(SQLALCHEMY_MIGRATE_REPO, 'database repository') api.version_control(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO) else: api.version_control(SQLALCHEMY_DATABASE_URI, SQLALCHEMY_MIGRATE_REPO, api.version(SQLALCHEMY_MIGRATE_REPO))

[ "ptt2hui@gmail.com" ]

ptt2hui@gmail.com

c7c9c7b8c9cdf0529dfe969bd1b620285d0716ae

e00226c64b0926acfd11a1ef5ec2a49e24ecdfdf

/ETI06F1.py

06170ea829d3e6cdee3563448a848f45dc0d9f88

[]

no_license

zetor6623/SPOJ_PL

a6b0bb4304828790565d8f141d5b6316421b8b78

9eff1e24ef25f7b1995e4dad2b07fd8d80a56abc

refs/heads/master

2020-04-09T09:58:57.103241

2018-12-03T21:04:58

160,253,318

0

null

UTF-8

Python

false

193

py

#!/usr/bin/python import math wejscie = [float(x) for x in raw_input().split()] r = wejscie[0] d = wejscie[1] pi = 3.141592654 pole = ((r*r)-((d*d)/4))*pi pole = round(pole,2) print pole

[ "noreply@github.com" ]

noreply@github.com

b793d9f4e13c712ddcf0d002de824cf6639c73c1

8ea4ca8746e9080b9522c6244807d42234260034

/web2pyApp/miFacebook/languages/es.py

cfe6e458d6797510d1fda9db26f6dd973738b5ff

[ "LicenseRef-scancode-public-domain" ]

permissive

bhandaribhumin/daw2

a59d4f1f64785bbbef55d2f4ca77edb02dce4578

480597ef2131853f7c0c4c61b4334257d12aef28

refs/heads/master

2020-12-31T05:39:35.951295

2014-06-18T11:36:45

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py

# coding: utf8 { '!langcode!': 'es', '!langname!': 'Español', '"update" is an optional expression like "field1=\'newvalue\'". You cannot update or delete the results of a JOIN': '"actualice" es una expresión opcional como "campo1=\'nuevo_valor\'". No se puede actualizar o eliminar resultados de un JOIN', '%s %%{row} deleted': '%s filas eliminadas', '%s %%{row} updated': '%s filas actualizadas', '%s selected': '%s seleccionado(s)', '%Y-%m-%d': '%Y-%m-%d', '%Y-%m-%d %H:%M:%S': '%d/%m/%Y %H:%M:%S', '(something like "it-it")': '(algo como "it-it")', 'A new version of web2py is available': 'Hay una nueva versión de web2py disponible', 'A new version of web2py is available: %s': 'Hay una nueva versión de web2py disponible: %s', 'About': 'Acerca de', 'about': 'acerca de', 'About application': 'Acerca de la aplicación', 'Access Control': 'Access Control', 'additional code for your application': 'código adicional para su aplicación', 'admin disabled because no admin password': ' por falta de contraseña', 'admin disabled because not supported on google app engine': 'admin deshabilitado, no es soportado en GAE', 'admin disabled because unable to access password file': 'admin deshabilitado, imposible acceder al archivo con la contraseña', 'Admin is disabled because insecure channel': 'Admin deshabilitado, el canal no es seguro', 'Admin is disabled because unsecure channel': 'Admin deshabilitado, el canal no es seguro', 'Administrative interface': 'Interfaz administrativa', 'Administrative Interface': 'Administrative Interface', 'Administrator Password:': 'Contraseña del Administrador:', 'Ajax Recipes': 'Ajax Recipes', 'and rename it (required):': 'y renombrela (requerido):', 'and rename it:': ' y renombrelo:', 'appadmin': 'appadmin', 'appadmin is disabled because insecure channel': 'admin deshabilitado, el canal no es seguro', 'application "%s" uninstalled': 'aplicación "%s" desinstalada', 'application compiled': 'aplicación compilada', 'application is compiled and cannot be designed': 'la aplicación está compilada y no puede ser modificada', 'Are you sure you want to delete file "%s"?': '¿Está seguro que desea eliminar el archivo "%s"?', 'Are you sure you want to delete this object?': 'Are you sure you want to delete this object?', 'Are you sure you want to uninstall application "%s"': '¿Está seguro que desea desinstalar la aplicación "%s"', 'Are you sure you want to uninstall application "%s"?': '¿Está seguro que desea desinstalar la aplicación "%s"?', 'ATTENTION: Login requires a secure (HTTPS) connection or running on localhost.': 'ATENCION: Inicio de sesión requiere una conexión segura (HTTPS) o localhost.', 'ATTENTION: TESTING IS NOT THREAD SAFE SO DO NOT PERFORM MULTIPLE TESTS CONCURRENTLY.': 'ATENCION: NO EJECUTE VARIAS PRUEBAS SIMULTANEAMENTE, NO SON THREAD SAFE.', 'ATTENTION: you cannot edit the running application!': 'ATENCION: no puede modificar la aplicación que se ejecuta!', 'Authentication': 'Autenticación', 'Available Databases and Tables': 'Bases de datos y tablas disponibles', 'Buy this book': 'Buy this book', 'Cache': 'Cache', 'cache': 'cache', 'Cache Keys': 'Cache Keys', 'cache, errors and sessions cleaned': 'cache, errores y sesiones eliminados', 'Cannot be empty': 'No puede estar vacío', 'Cannot compile: there are errors in your app. Debug it, correct errors and try again.': 'No se puede compilar: hay errores en su aplicación. Depure, corrija errores y vuelva a intentarlo.', 'cannot create file': 'no es posible crear archivo', 'cannot upload file "%(filename)s"': 'no es posible subir archivo "%(filename)s"', 'Change Password': 'Cambie Contraseña', 'change password': 'cambie contraseña', 'check all': 'marcar todos', 'Check to delete': 'Marque para eliminar', 'Check to delete:': 'Check to delete:', 'clean': 'limpiar', 'Clear CACHE?': 'Clear CACHE?', 'Clear DISK': 'Clear DISK', 'Clear RAM': 'Clear RAM', 'click to check for upgrades': 'haga clic para buscar actualizaciones', 'Client IP': 'IP del Cliente', 'Community': 'Community', 'compile': 'compilar', 'compiled application removed': 'aplicación compilada removida', 'Components and Plugins': 'Components and Plugins', 'Controller': 'Controlador', 'Controllers': 'Controladores', 'controllers': 'controladores', 'Copyright': 'Derechos de autor', 'create file with filename:': 'cree archivo con nombre:', 'Create new application': 'Cree una nueva aplicación', 'create new application:': 'nombre de la nueva aplicación:', 'Created By': 'Created By', 'Created On': 'Created On', 'crontab': 'crontab', 'Current request': 'Solicitud en curso', 'Current response': 'Respuesta en curso', 'Current session': 'Sesión en curso', 'currently saved or': 'actualmente guardado o', 'customize me!': 'Adaptame!', 'data uploaded': 'datos subidos', 'Database': 'base de datos', 'Database %s select': 'selección en base de datos %s', 'database administration': 'administración base de datos', 'Date and Time': 'Fecha y Hora', 'db': 'db', 'DB Model': 'Modelo "db"', 'defines tables': 'define tablas', 'Delete': 'Elimine', 'delete': 'eliminar', 'delete all checked': 'eliminar marcados', 'Delete:': 'Elimine:', 'Demo': 'Demo', 'Deploy on Google App Engine': 'Instale en Google App Engine', 'Deployment Recipes': 'Deployment Recipes', 'Description': 'Descripción', 'design': 'modificar', 'DESIGN': 'DISEÑO', 'Design for': 'Diseño para', 'DISK': 'DISK', 'Disk Cache Keys': 'Disk Cache Keys', 'Disk Cleared': 'Disk Cleared', 'Documentation': 'Documentación', "Don't know what to do?": "Don't know what to do?", 'done!': 'listo!', 'Download': 'Download', 'E-mail': 'Correo electrónico', 'edit': 'editar', 'EDIT': 'EDITAR', 'Edit': 'Editar', 'Edit application': 'Editar aplicación', 'edit controller': 'editar controlador', 'Edit current record': 'Edite el registro actual', 'Edit Profile': 'Editar Perfil', 'edit profile': 'editar perfil', 'Edit This App': 'Edite esta App', 'Editing file': 'Editando archivo', 'Editing file "%s"': 'Editando archivo "%s"', 'Email and SMS': 'Email and SMS', 'enter an integer between %(min)g and %(max)g': 'enter an integer between %(min)g and %(max)g', 'Error logs for "%(app)s"': 'Bitácora de errores en "%(app)s"', 'errors': 'errores', 'Errors': 'Errors', 'export as csv file': 'exportar como archivo CSV', 'exposes': 'expone', 'extends': 'extiende', 'failed to reload module': 'recarga del módulo ha fallado', 'FAQ': 'FAQ', 'file "%(filename)s" created': 'archivo "%(filename)s" creado', 'file "%(filename)s" deleted': 'archivo "%(filename)s" eliminado', 'file "%(filename)s" uploaded': 'archivo "%(filename)s" subido', 'file "%(filename)s" was not deleted': 'archivo "%(filename)s" no fué eliminado', 'file "%s" of %s restored': 'archivo "%s" de %s restaurado', 'file changed on disk': 'archivo modificado en el disco', 'file does not exist': 'archivo no existe', 'file saved on %(time)s': 'archivo guardado %(time)s', 'file saved on %s': 'archivo guardado %s', 'First name': 'Nombre', 'Forgot username?': 'Forgot username?', 'Forms and Validators': 'Forms and Validators', 'Free Applications': 'Free Applications', 'Friends': 'Amigos', 'Functions with no doctests will result in [passed] tests.': 'Funciones sin doctests equivalen a pruebas [aceptadas].', 'Group %(group_id)s created': 'Group %(group_id)s created', 'Group ID': 'ID de Grupo', 'Group uniquely assigned to user %(id)s': 'Group uniquely assigned to user %(id)s', 'Groups': 'Groups', 'Hello World': 'Hola Mundo', 'help': 'ayuda', 'Home': 'Inicio', 'How did you get here?': 'How did you get here?', 'htmledit': 'htmledit', 'import': 'import', 'Import/Export': 'Importar/Exportar', 'includes': 'incluye', 'Index': 'Indice', 'Inicio de sesión': 'Inicio de sesión', 'insert new': 'inserte nuevo', 'insert new %s': 'inserte nuevo %s', 'Installed applications': 'Aplicaciones instaladas', 'internal error': 'error interno', 'Internal State': 'Estado Interno', 'Introduction': 'Introduction', 'Invalid action': 'Acción inválida', 'Invalid email': 'Correo inválido', 'Invalid login': 'Invalid login', 'invalid password': 'contraseña inválida', 'Invalid Query': 'Consulta inválida', 'invalid request': 'solicitud inválida', 'invalid ticket': 'tiquete inválido', 'Is Active': 'Is Active', 'Key': 'Key', 'language file "%(filename)s" created/updated': 'archivo de lenguaje "%(filename)s" creado/actualizado', 'Language files (static strings) updated': 'Archivos de lenguaje (cadenas estáticas) actualizados', 'languages': 'lenguajes', 'Languages': 'Lenguajes', 'languages updated': 'lenguajes actualizados', 'Last name': 'Apellido', 'Last saved on:': 'Guardado en:', 'Layout': 'Diseño de página', 'Layout Plugins': 'Layout Plugins', 'Layouts': 'Layouts', 'License for': 'Licencia para', 'Live Chat': 'Live Chat', 'loading...': 'cargando...', 'Logged in': 'Logged in', 'Logged out': 'Logged out', 'Login': 'Inicio de sesión', 'login': 'inicio de sesión', 'Login to the Administrative Interface': 'Inicio de sesión para la Interfaz Administrativa', 'logout': 'fin de sesión', 'Logout': 'Fin de sesión', 'Lost Password': 'Contraseña perdida', 'Lost password?': 'Lost password?', 'lost password?': '¿olvido la contraseña?', 'Main Menu': 'Menú principal', 'Manage Cache': 'Manage Cache', 'Menu Model': 'Modelo "menu"', 'merge': 'combinar', 'Messages': 'Mensajes', 'Models': 'Modelos', 'models': 'modelos', 'Modified By': 'Modified By', 'Modified On': 'Modified On', 'Modules': 'Módulos', 'modules': 'módulos', 'My Sites': 'My Sites', 'Name': 'Nombre', 'new application "%s" created': 'nueva aplicación "%s" creada', 'New Record': 'Registro nuevo', 'new record inserted': 'nuevo registro insertado', 'next 100 rows': '100 filas siguientes', 'NO': 'NO', 'No databases in this application': 'No hay bases de datos en esta aplicación', 'Object or table name': 'Object or table name', 'Online examples': 'Ejemplos en línea', 'or import from csv file': 'o importar desde archivo CSV', 'or provide application url:': 'o provea URL de la aplicación:', 'Origin': 'Origen', 'Original/Translation': 'Original/Traducción', 'Other Plugins': 'Other Plugins', 'Other Recipes': 'Other Recipes', 'Overview': 'Overview', 'pack all': 'empaquetar todo', 'pack compiled': 'empaquete compiladas', 'Password': 'Contraseña', "Password fields don't match": "Password fields don't match", 'Peeking at file': 'Visualizando archivo', 'please input your password again': 'please input your password again', 'Plugins': 'Plugins', 'Powered by': 'Este sitio usa', 'Preface': 'Preface', 'previous 100 rows': '100 filas anteriores', 'Profile': 'Perfil', 'Profile updated': 'Perfil actualizado', 'Python': 'Python', 'Query:': 'Consulta:', 'Quick Examples': 'Quick Examples', 'RAM': 'RAM', 'RAM Cache Keys': 'RAM Cache Keys', 'Ram Cleared': 'Ram Cleared', 'Recipes': 'Recipes', 'Record': 'registro', 'Record %(id)s created': 'Record %(id)s created', 'Record Created': 'Record Created', 'record does not exist': 'el registro no existe', 'Record ID': 'ID de Registro', 'Record id': 'id de registro', 'register': 'registrese', 'Register': 'Registrese', 'Registration identifier': 'Registration identifier', 'Registration key': 'Contraseña de Registro', 'Registration successful': 'Registration successful', 'Registrese': 'Registrese', 'Remember me (for 30 days)': 'Remember me (for 30 days)', 'remove compiled': 'eliminar compiladas', 'request friendship': 'Petición de amistad', 'Reset Password key': 'Reset Password key', 'Resolve Conflict file': 'archivo Resolución de Conflicto', 'restore': 'restaurar', 'revert': 'revertir', 'Role': 'Rol', 'Rows in Table': 'Filas en la tabla', 'Rows selected': 'Filas seleccionadas', 'save': 'guardar', 'Save profile': 'Save profile', 'Saved file hash:': 'Hash del archivo guardado:', 'Search': 'Buscar', 'Search for friends': 'Search for friends', 'Semantic': 'Semantic', 'Services': 'Services', 'session expired': 'sesión expirada', 'shell': 'shell', 'site': 'sitio', 'Size of cache:': 'Size of cache:', 'some files could not be removed': 'algunos archivos no pudieron ser removidos', 'state': 'estado', 'static': 'estáticos', 'Static files': 'Archivos estáticos', 'Statistics': 'Statistics', 'Stylesheet': 'Hoja de estilo', 'Submit': 'Submit', 'submit': 'submit', 'Support': 'Support', 'Sure you want to delete this object?': '¿Está seguro que desea eliminar este objeto?', 'Table': 'tabla', 'Table name': 'Nombre de la tabla', 'test': 'probar', 'Testing application': 'Probando aplicación', 'The "query" is a condition like "db.table1.field1==\'value\'". Something like "db.table1.field1==db.table2.field2" results in a SQL JOIN.': 'La "consulta" es una condición como "db.tabla1.campo1==\'valor\'". Algo como "db.tabla1.campo1==db.tabla2.campo2" resulta en un JOIN SQL.', 'the application logic, each URL path is mapped in one exposed function in the controller': 'la lógica de la aplicación, cada ruta URL se mapea en una función expuesta en el controlador', 'The Core': 'The Core', 'the data representation, define database tables and sets': 'la representación de datos, define tablas y conjuntos de base de datos', 'The output of the file is a dictionary that was rendered by the view %s': 'La salida del archivo es un diccionario escenificado por la vista %s', 'the presentations layer, views are also known as templates': 'la capa de presentación, las vistas también son llamadas plantillas', 'The Views': 'The Views', 'There are no controllers': 'No hay controladores', 'There are no models': 'No hay modelos', 'There are no modules': 'No hay módulos', 'There are no static files': 'No hay archivos estáticos', 'There are no translators, only default language is supported': 'No hay traductores, sólo el lenguaje por defecto es soportado', 'There are no views': 'No hay vistas', 'these files are served without processing, your images go here': 'estos archivos son servidos sin procesar, sus imágenes van aquí', 'This App': 'This App', 'This is a copy of the scaffolding application': 'Esta es una copia de la aplicación de andamiaje', 'This is the %(filename)s template': 'Esta es la plantilla %(filename)s', 'Ticket': 'Tiquete', 'Time in Cache (h:m:s)': 'Time in Cache (h:m:s)', 'Timestamp': 'Timestamp', 'to previous version.': 'a la versión previa.', 'translation strings for the application': 'cadenas de caracteres de traducción para la aplicación', 'try': 'intente', 'try something like': 'intente algo como', 'Twitter': 'Twitter', 'Unable to check for upgrades': 'No es posible verificar la existencia de actualizaciones', 'unable to create application "%s"': 'no es posible crear la aplicación "%s"', 'unable to delete file "%(filename)s"': 'no es posible eliminar el archivo "%(filename)s"', 'Unable to download': 'No es posible la descarga', 'Unable to download app': 'No es posible descarga la aplicación', 'unable to parse csv file': 'no es posible analizar el archivo CSV', 'unable to uninstall "%s"': 'no es posible instalar "%s"', 'uncheck all': 'desmarcar todos', 'uninstall': 'desinstalar', 'update': 'actualizar', 'update all languages': 'actualizar todos los lenguajes', 'Update:': 'Actualice:', 'upload application:': 'subir aplicación:', 'Upload existing application': 'Suba esta aplicación', 'upload file:': 'suba archivo:', 'Use (...)&(...) for AND, (...)|(...) for OR, and ~(...) for NOT to build more complex queries.': 'Use (...)&(...) para AND, (...)|(...) para OR, y ~(...) para NOT, para crear consultas más complejas.', 'User %(id)s Logged-in': 'User %(id)s Logged-in', 'User %(id)s Logged-out': 'User %(id)s Logged-out', 'User %(id)s Profile updated': 'User %(id)s Profile updated', 'User %(id)s Registered': 'User %(id)s Registered', 'User ID': 'ID de Usuario', 'value already in database or empty': 'value already in database or empty', 'Verify Password': 'Verify Password', 'versioning': 'versiones', 'Videos': 'Videos', 'View': 'Vista', 'view': 'vista', 'Views': 'Vistas', 'views': 'vistas', 'Wall': 'Muro', 'web2py is up to date': 'web2py está actualizado', 'web2py Recent Tweets': 'Tweets Recientes de web2py', 'Welcome': 'Bienvenido', 'Welcome %s': 'Bienvenido %s', 'Welcome to web2py': 'Bienvenido a web2py', 'Welcome to web2py!': 'Welcome to web2py!', 'Which called the function %s located in the file %s': 'La cual llamó la función %s localizada en el archivo %s', 'YES': 'SI', 'You are successfully running web2py': 'Usted está ejecutando web2py exitosamente', 'You can modify this application and adapt it to your needs': 'Usted puede modificar esta aplicación y adaptarla a sus necesidades', 'You visited the url %s': 'Usted visitó la url %s', }

[ "albertogonzcat@MacBook-Pro-de-Alberto.local" ]

albertogonzcat@MacBook-Pro-de-Alberto.local

880859f1a8ae3ed7dbb4337a7cad06f3487bf0a6

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/lectures/cs532-s19/assignments/A6/correlation.py

ad8663da019d7e24bd90a1c80d3715c7f488c09d

[]

no_license

bdeme004/anwala.github.io

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ccbe10a516855cf7d1f635d93e4c4a0c6f4c4326

refs/heads/master

2020-04-20T04:51:45.788646

2019-05-01T03:14:05

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null

2019-02-01T04:07:43

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UTF-8

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py

import recommendations as recs import BJDfunctions as fn PROXY = 477 #712 PEOPLE = [471, 280, 373, 642, 330, 450, 541, 577, 864, 43, 805, 313, 504, 254, 94, 399, 5, 92, 381, 716, 49, 1, 843, 222] def topAndBottom(dataset): for item in fn.topValues(dataset): fn.printFail(item) print("\n") for item in fn.bottomValues(dataset): fn.printFail(item) print("\n") ratings = fn.load_data() item_mode = recs.transformPrefs(ratings) fn.printFail(recs.sim_pearson(item_mode, 'Star Wars (1977)', 'Grease (1978)')) fn.printFail(recs.sim_pearson(item_mode, 'Star Wars (1977)', 'While You Were Sleeping (1995)')) fn.printFail(recs.sim_pearson(item_mode, 'Star Wars (1977)', 'Sleepless in Seattle (1993)')) cats = item_mode['Wallace & Gromit: The Best of Aardman Animation (1996)'] gun = item_mode['Grease (1978)'] dogs = item_mode['While You Were Sleeping (1995)'] cats_dogs = list() cats_gun = list() corr = 0 print("\n") for item in dogs: if item in cats: cats_dogs.append((item, cats[item], dogs[item])) if item in gun: cats_gun.append((item, dogs[item], gun[item])) for item in cats_dogs: print(item) print("\n") for item in cats_gun: print(item) print("\n") print(corr) print("len cats:") print(len(cats)) print("len dogs:") print(len(dogs)) print("len gun:") print(len(gun)) print("len dogs/cats:") print(len(cats_dogs)) print("len dogs/gun:") print(len(cats_gun))

[ "43201288+bdeme004@users.noreply.github.com" ]

43201288+bdeme004@users.noreply.github.com

978f0fbbe7b7a78fae29b9164dc0c70b71df4389

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/TotalAssets/adminx.py

03dd72cf2caac5a2d61fa0b92499f602eae055df

[]

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HtWr/ITAM

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import xadmin from xadmin import views class BaseSetting(object): """主题配置""" enable_themes = True use_bootswatch = True xadmin.site.register(views.BaseAdminView, BaseSetting) class GlobalSetting(object): site_title = 'Local IT' site_footer = 'CTU-Local IT' menu_style = 'accordion' xadmin.site.register(views.CommAdminView, GlobalSetting)

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# Example on list assignment. # # References: # # [1] http://robertheaton.com/2014/02/09/pythons-pass-by-object-reference-as-explained-by-philip-k-dick/ # [2] http://stackoverflow.com/questions/12888506/assignment-operator-about-list-in-python listA = [0] listB = listA listB.append(1) print listA list1 = ["Tom", "Sam", "Jim"] list2 = list1 print id(list1) print id(list2) list3 = list1[:] print id(list3)

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import copy import Pyro4 import socket import threading from celery import bootsteps from popcorn.rpc import DISPATHCER_SERVER_OBJ_ID, GUARD_PORT, HUB_PORT from popcorn.rpc.base import BaseRPCServer, BaseRPCClient, RPCDispatcher from popcorn.utils import get_log_obj, get_pid debug, info, warn, error, critical = get_log_obj(__name__) __all__ = ['PyroServer', 'PyroClient'] DEFAULT_SERIALIZER = 'pickle' DEFAULT_SERVERTYPE = 'multiplex' # DEFAULT_SERVERTYPE = 'thread' class PyroBase(object): def __init__(self, **kwargs): Pyro4.config.SERVERTYPE = DEFAULT_SERVERTYPE Pyro4.config.SERIALIZER = DEFAULT_SERIALIZER class PyroServer(BaseRPCServer, PyroBase): def __init__(self, port): PyroBase.__init__(self) self.port = port self.daemon = None self.thread = None @property def ip(self): host = socket.gethostname() return socket.gethostbyname(host) @property def alive(self): if self.thread is not None and self.thread.is_alive(): return True return False def start_daemon(self): if self.daemon is None or self.daemon.transportServer is None: self.daemon = Pyro4.Daemon(host=self.ip, port=self.port) # init a Pyro daemon def start(self): """ Start a pyro server Fire a new thread for the server daemon loop. This mehtod is blocking till the server daemon loop is ready. """ self.start_daemon() uri = self.register(RPCDispatcher, DISPATHCER_SERVER_OBJ_ID) thread = threading.Thread(target=self.daemon.requestLoop) thread.daemon = True thread.start() while not thread.is_alive(): continue self.thread = thread info('[RPC Server] - [Start] - %s.' % uri) def stop(self): """ Stop the pyro server Notice. the step order is quite important and can not change. Step 1: stop the daemon loop Step 2: stop the socket server Step 3: unregister the dispather class """ self.daemon.shutdown() if self.thread is not None and self.thread.is_alive(): while self.thread.is_alive(): continue self.daemon.close() self.unregister(RPCDispatcher) info('[RPC Server] - [Shutdown] - exit daemon loop') def register(self, obj, obj_id=None): """ Register the obj to the server. """ try: return self.daemon.register(obj, obj_id, force=True) # register a obj with obj id except Exception as e: return self.daemon.uriFor(obj_id) def unregister(self, obj): """ Unregister the obj from the server. Ignore if unregister an unexist obj. """ try: return self.daemon.unregister(obj) except Exception as e: pass # don't care for multi unregister class PyroClient(BaseRPCClient, PyroBase): def __init__(self, server_ip, server_port): PyroBase.__init__(self) dispatcher_uri = self.get_uri(DISPATHCER_SERVER_OBJ_ID, server_ip, server_port) self.default_proxy = self.get_proxy_obj(dispatcher_uri) # get local proxy obj def call(self, path, *args, **kwargs): """ Call a remote obj or class. :param str path: the path of the callable obj. A valid one: popcorn.apps.hub:Hub.scan. More detail of path please check popcorn.utils.imports.symbol_by_name """ try: return self.default_proxy.dispatch(path, *args, **kwargs) except Exception as e: error('[RPC Client] - [Call] - [Error]: %s, %s' % (e.message, path)) def get_proxy_obj(self, uri): return Pyro4.Proxy(uri) def get_uri(self, obj_id, server_ip, port): return 'PYRO:%s@%s:%s' % (str(obj_id), str(server_ip), str(port))

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# -*- coding: utf-8 -*- """ Created on Wed Mar 3 14:31:36 2021 @author: Admin """ #根据样地点提取纹理特征 from osgeo import gdal import numpy as np import pandas as pd import os import osr from pandas import set_option class change_coordinate(): def __init__(self, dataset): self.dataset = dataset def getSRSPair(self,dataset): ''' 获得给定数据的投影参考系和地理参考系 :param dataset: GDAL地理数据 :return: 投影参考系和地理参考系 ''' prosrs = osr.SpatialReference() prosrs.ImportFromWkt(self.dataset.GetProjection()) geosrs = prosrs.CloneGeogCS() return prosrs, geosrs def lonlat2geo(self, lon, lat): ''' 将经纬度坐标转为投影坐标（具体的投影坐标系由给定数据确定） :param dataset: GDAL地理数据 :param lon: 地理坐标lon经度 :param lat: 地理坐标lat纬度 :return: 经纬度坐标(lon, lat)对应的投影坐标 ''' prosrs, geosrs = self.getSRSPair(self.dataset) ct = osr.CoordinateTransformation(geosrs, prosrs) coords = ct.TransformPoint(lon, lat) return coords[:2] def geo2imagexy(self, x, y): ''' 根据GDAL的六参数模型将给定的投影或地理坐标转为影像图上坐标（行列号） :param dataset: GDAL地理数据 :param x: 投影或地理坐标x :param y: 投影或地理坐标y :return: 影坐标或地理坐标(x, y)对应的影像图上行列号(row, col) ''' trans = self.dataset.GetGeoTransform() a = np.array([[trans[1], trans[2]], [trans[4], trans[5]]]) b = np.array([x - trans[0], y - trans[3]]) return np.linalg.solve(a, b) # 使用numpy的linalg.solve进行二元一次方程的求解 def lonlat2rowcol(self,lon,lat): ''' 根据经纬度转行列公式直接转换为行列 ''' # tp = self.lonlat2geo(lon,lat) geo = self.dataset.GetGeoTransform() # row = int((tp[0] -geo[0]) / geo[1]+0.5) # col = int((tp[1] - geo[3]) /geo[5]+0.5) row = int((lon -geo[0]) / geo[1]+0.5) col = int((lat - geo[3]) /geo[5]+0.5) return row,col class define_window(): ''' :param w 定义窗口大小 :param center_row 中心点行号 :param center_col 中心点列号 ''' def __init__(self,w): self.w = w def window_upleft_rowcol(self,center_row,center_col): upleft_row = center_row - (self.w-1)/2 upleft_col = center_col - (self.w-1)/2 return upleft_row,upleft_col class make_feature_names(): ''' 根据波段编写特征名称，返回特征名称列表 ''' def __init__(self,dataset): self.nb = dataset.RasterCount def feature(self,feature_list): names = [] for i in range(self.nb): for j in feature_list: names.append('{}{}{}'.format(j,'_',i)) return names if __name__ == '__main__': ''' 把图像与坐标放到一个文件夹下 ''' img_dir = r'./使用数据' out_path=r'./输出数据' gdal.AllRegister() img = gdal.Open(os.path.join(img_dir,'500_b0_win7_texture.tif')) ds = pd.read_excel(os.path.join(img_dir,'point.xls')) ns = img.RasterXSize nl = img.RasterYSize run_change_coordinate = change_coordinate(img)#调用坐标转换函数 w = 7 #窗口大小 run_define_window = define_window(w)#调用窗口定义函数 run_make_feature_names = make_feature_names(img)#调用特征名称函数 names = [ 'mean_1','variance_1','homogeneity_1','contrast_1','dissimilarity_1','entropy_1','sencond_moment_1','correlation_1', ] lon,lat = ds.iloc[:,1].values,ds.iloc[:,2].values ''' 定义输出列表 :all_out输出每个窗口下所有特征的值 :all_mean输出每个窗口下所有特征的平均值 :all_std输出每个窗口下所有特征的标准差 ''' all_out = [] all_mean = [] all_std = [] for i in range(len(lon)): ilon,ilat = lon[i],lat[i] ix,iy = run_change_coordinate.lonlat2rowcol(ilon,ilat) if ix<0 or ix >ns-1 or iy <0 or iy >nl-1: print('not in the image: '+str(ds.iat[i,0].value)) upleft_x,upleft_y = run_define_window.window_upleft_rowcol(ix,iy) ref = img.ReadAsArray(int(upleft_x),int(upleft_y),w,w) if len(ref.shape) == 3: df = np.zeros((w*w,len(names))) for j in range(len(names)): # print(j) df[:,j] = list(ref[j].flatten()) df = pd.DataFrame(df,columns=names) else: df = pd.DataFrame(ref.flatten()) description = df.describe() df_mean = description.iloc[1,:] df_std = description.iloc[2,:] all_out.append(df) all_mean.append(df_mean) all_std.append(df_std) out = pd.concat(all_out) out_mean = pd.concat(all_mean) out_std = pd.concat(all_std) out.to_csv(os.path.join(out_path,'out.csv')) out_mean.to_csv(os.path.join(out_path,'out_mean.csv')) out_std.to_csv(os.path.join(out_path,'out_std.csv'))

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#The string module provides sequences of various types of Python characters. It has an attribute called digits that produces the string ‘0123456789’. #Import the module and assign this string to the variable nums. Below, we have provided a list of characters called chars. #Using nums and chars, produce a list called is_num that consists of tuples. #The first element of each tuple should be the character from chars, and the second element should be a Boolean that reflects whether or not it is a Python digit. #I do not use - num = string.digits import string chars = ['h', '1', 'C', 'i', '9', 'True', '3.1', '8', 'F', '4', 'j'] #some text num = string.digits is_num = () spisok1 = [] for i in range(len(chars)): spisok = () if chars[i].isdigit(): spisok += (chars[i], True) spisok1.append(spisok) else: spisok += (chars[i], False) spisok1.append(spisok) is_num = spisok1 print(is_num)

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#-*- coding: UTF-8 -*- import xlrd import os import sys import time reload(sys) sys.setdefaultencoding('utf-8') root_dir = "excel" def is_useless_interview(feedback): if feedback is '': return True if '电话是否接通' in feedback: if '电话是否接通 : 是' in feedback: return False else: return True # print feedback return False def str_to_timestamp(interview_time): try: time_array = time.strptime(interview_time, "%Y年%m月%d日%H:%M:%S") return int(time.mktime(time_array)) except: return interview_time def method_name(): feed_back_collection = [] for parent, dirNames, file_names in os.walk(root_dir): for file_name in file_names: excel_file = os.path.join(parent, file_name) data = xlrd.open_workbook(excel_file) sheet_names = data.sheet_names() for i in range(0, len(sheet_names)): table = data.sheets()[i] first_row_items = table.row_values(0) if '电话是否接通' in first_row_items: if '回访时间' in first_row_items: column_index_of_time = first_row_items.index('回访时间') else: print file_name + ', 没有找到回访时间.' for row_item in first_row_items: print row_item continue colume_str_of_tel = '联系电话' if colume_str_of_tel in first_row_items: column_index_of_tel = first_row_items.index(colume_str_of_tel) else: print file_name + ', 没有找到联系电话.' for row_item in first_row_items: print row_item continue for j in range(1, table.nrows): interview_time = str(table.cell(j, column_index_of_time).value) if interview_time is '': continue tel = str(table.cell(j, column_index_of_tel).value).strip('.0') feed_back = '' for k in range(column_index_of_time + 1, table.ncols): feed_back_item = str(table.cell(j, k).value) if feed_back_item.strip(' ') is not '': feed_back += str(table.cell(0, k).value) + ' : ' + feed_back_item + '\n' if is_useless_interview(feed_back): continue one_feedback = {} one_feedback['tel'] = tel one_feedback['time'] = str_to_timestamp(interview_time) one_feedback['content'] = feed_back feed_back_collection.append(one_feedback) return feed_back_collection # str_to_timestamp() feed_backs = method_name() for item in feed_backs: print "insert `customerservice`(`csnote`,`userid`,`startdateline`,`dateline`,`mobile`) values('%s',1,%s,%s,'%s'); "%( item['content'],\ item['time'],\ item['time'],\ item['tel']) # print is_useless_interview('电话是否接通 : 是')

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import os import tensorflow as tf from Preprocessor import Preprocessor from train.AlexNet_NN_search_full import CNetTrainer from datasets.ImageNet import ImageNet from models.AlexNet_layers_lrelu import AlexNet from constants import IMAGENET_VAL_DIR from scipy import misc import cv2 def load_val_image(class_id, val_dir=IMAGENET_VAL_DIR): class_folders = os.listdir(val_dir) img_names = os.listdir(os.path.join(IMAGENET_VAL_DIR, class_folders[class_id])) img = misc.imread(os.path.join(IMAGENET_VAL_DIR, class_folders[class_id], img_names[0]), mode='RGB') img = cv2.resize(img, (256, 256), interpolation=cv2.INTER_CUBIC) return img target_shape = [227, 227, 3] model = AlexNet(batch_size=2000) data = ImageNet() preprocessor = Preprocessor(target_shape=target_shape) ckpt = '/Data/Logs/CNet/imagenet_SDNet_res1_default_baseline_finetune_conv_5/model.ckpt-324174' #ckpt = '/Data/Logs/CNet/imagenet_AlexNet_sorted_alex_sorted_finetune_conv_4/model.ckpt-450360' #ckpt = '/Data/Logs/CNet/imagenet_AlexNet_sorted2_alex_sorted_finetune_conv_5/model.ckpt-294132' trainer = CNetTrainer(model=model, dataset=data, pre_processor=preprocessor, num_epochs=1, tag='inv_tv', lr_policy='linear', optimizer='adam', init_lr=0.0003, end_lr=0.00003) # trainer.compute_stats(ckpt, 4, model.name) # imgs: 0, 3, 15, 26, 87, 95, 98, 146, 221, 229, 237, 259, 348, 378, 388, 422 # for i in range(87, 1000): # print(i) # img = load_val_image(i) # misc.imshow(img) for i in [26]: trainer = CNetTrainer(model=model, dataset=data, pre_processor=preprocessor, num_epochs=1, tag='inv_tv', lr_policy='linear', optimizer='adam', init_lr=0.0003, end_lr=0.00003) trainer.search_nn(load_val_image(i), ckpt, 4, model.name, i)

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# -*- coding: utf-8 -*- """ Created on Fri Apr 19 20:15:20 2019 @author: huashuo """ import json import matplotlib.pyplot as plt dic1 = {} sups = [] confs = [] lifts = [] with open('rules.json','r') as f: lines = f.readlines() for l in lines: load_dic = json.loads(l) X = load_dic['X_set'][0][0] Y = load_dic['Y_set'][0][0] sup = load_dic['sup'] conf = load_dic['conf'] lift = load_dic['lift'] sups.append(load_dic['sup']) confs.append(load_dic['conf']) lifts.append(load_dic['lift']) if X not in dic1.keys(): new = dict() new[Y] = [[sup],[conf]] dic1[X] = new else: if Y not in dic1[X].keys(): dic1[X][Y] = [[sup],[conf]] else: dic1[X][Y][0].append(sup) dic1[X][Y][1].append(conf) #for k in dic1.keys(): # print(k) plt.scatter(sups,confs,c=lifts,s=20,cmap='Reds') plt.xlabel('sup') plt.ylabel('conf') cb = plt.colorbar() cb.set_label('lift') plt.show()

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# -*- coding: utf-8 -*- # Generated by Django 1.11.7 on 2018-01-26 00:34 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('rango', '0001_initial'), ] operations = [ migrations.AlterModelOptions( name='category', options={'verbose_name_plural': 'Categories'}, ), migrations.AddField( model_name='category', name='likes', field=models.IntegerField(default=0), ), migrations.AddField( model_name='category', name='views', field=models.IntegerField(default=0), ), ]

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default_app_config = 'aboutMe.apps.AboutMeConfig'

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from .base import BaseTestCase from rest_framework import status from authors.apps.authentication.models import User from . import (new_user, data2, invalid_email, invalid_password, short_password, dup_username, user_login) class AccountTests(BaseTestCase): """handles user registration tests""" def test_new_user_registration(self): """check if new user can be registered""" response = self.register_user(new_user) self.assertEqual(response.status_code, status.HTTP_201_CREATED) self.assertIn("token", response.data) def test_user_login(self): """new user can be logged in\ and token returned on successful login""" self.verify_user(new_user) response = self.login_user(user_login) #raise Exception(response.data) self.assertEqual(response.status_code, status.HTTP_200_OK) self.assertIn("token", response.data) def test_wrong_token_header_prefix(self): """invalid prefix header provided""" self.client.credentials(HTTP_AUTHORIZATION='hgfds ' + 'poiuytfd') response = self.client.get("/api/user/", format="json") self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_for_invalid_token(self): """validates token""" self.client.credentials(HTTP_AUTHORIZATION='Token ' + 'yyuug') response = self.client.get("/api/user/", format="json") self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_no_token_in_header(self): """no token in header""" self.add_credentials(response='') response = self.client.get("/api/user/", format="json") self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN) def test_create_super_user(self): """checks for registration of a super user in the User model""" user = User.objects.create_superuser( username='ayebare', password='sampletestcase') self.assertIn(str(user), str(user.username)) def test_create_non_user(self): """check for registration of a client user in the User model""" user = User.objects.create_user( email='m16ayebare@gmail.com', username='ayebare', password='sampletestcase') self.assertIn(str(user), str(user.email)) def test_get_user_details(self): """get user details""" self.user_access() response = self.client.get('/api/user/', format='json') self.assertEqual(response.status_code, status.HTTP_200_OK) def test_update_user_details(self): """assert update route for user details is accessed""" self.user_access() response = self.client.put('/api/user/', format='json') self.assertEqual(response.status_code, status.HTTP_200_OK) def test_invalid_email_message(self): """test invalid email provided.""" response = self.register_user(invalid_email) self.assertIn(response.data["errors"]["email"][0], 'Please enter a valid email in the format xxxx@xxx.xxx') def test_invalid_password(self): """asserts invalid password provided.""" response = self.register_user(invalid_password) self.assertIn(response.data["errors"]["password"][0], 'Password should be alphanuemric (a-z,A_Z,0-9).') def test_short_password(self): """test short password provided.""" response = self.register_user(short_password) self.assertIn(response.data["errors"]["password"][0], 'Password should not be less than 8 characters.') def test_duplicate_username(self): "user with same username provided exists""" self.register_user(new_user) response = self.register_user(dup_username) self.assertIn(response.data["errors"]["username"][0], 'user with this username already exists.')

[ "ephraim.malinga@gmail.com" ]

ephraim.malinga@gmail.com

072f0c80d4043ff14c2008f0175536b215afaba2

e5e8092fbb4c9a5db269a190de6ed3463e81d550

/env/bin/django-admin

b43568894f124da0b0dcf0d018964da9127d39c3

[]

no_license

j0konda/computer_wizard

73a4210e75adfeed7c1680af073093a9015348d4

e4c4a8352922db2aeca215707ab9eddb2784a437

refs/heads/main

2023-08-11T01:37:02.650715

2021-09-28T06:05:43

410,764,984

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#!/home/kaf_pas/Job/computer_wizard/env/bin/python # -*- coding: utf-8 -*- import re import sys from django.core.management import execute_from_command_line if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(execute_from_command_line())

[ "chivanov1986@mail.ru" ]

chivanov1986@mail.ru

62b12bf8bb74f8a2775b4c0482a8770435327642

441f5001e8f2136b8ae048a7eae27d4dd148ddd8

/src/sortepy/util.py

f6b5dfbc48e7a169b6b4447e1fa687da5e0cd79f

[]

no_license

guilhermaker/sorte.py

97ac3aaea128c718d89ba4cd3c0fccab7eacefab

eeb3a1e850ebd07c70df3fa3499cc6290b2b7b0c

refs/heads/master

2020-04-13T16:54:06.590898

2017-12-07T03:06:43

2017-12-07T03:19:26

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py

# encoding=utf8 import http.cookiejar import errno import os import re import sqlite3 import urllib.request import time def get_config_path(app='sortepy'): """Obtém o caminho de configuração de acordo com o SO Por enquanto é suportado os sistemas POSIX e Windows (NT) """ # Linux, UNIX, BSD, ... if os.name == 'posix': prefixo = '.config/' profile_dir = os.environ.get("HOME") # Windows 2000, XP, Vista, 7, 8, ... elif os.name == 'nt': prefixo = '' profile_dir = os.environ.get("APPDATA") # Se nenhum SO suportado foi detectado, lança uma exceção else: raise NotImplementedError("Caminho de configuração não detectado") return os.path.join(profile_dir, prefixo + app) def makedirs(caminho): """Versão própria do makedirs() Essa versão não lança exceção se o caminho já existir """ try: os.makedirs(caminho) except OSError as e: if e.errno != errno.EEXIST: raise class Util: def __init__(self, cfg_path=None): # Se o caminho é uma string vazia, não deve ser usado nenhum cache # Definido para propósitos de teste if cfg_path == '': self.in_cache = False return # Se nenhum caminho foi passado, usa diretório de configuração padrão if cfg_path is None: try: cfg_path = get_config_path() # Pode ocorrer de não conseguir definir o diretório para cfg_path except NotImplementedError: self.in_cache = False return # Cria diretório de configuração, se não existir makedirs(cfg_path) # caminho do arquivo de cache self.cache_path = os.path.join(cfg_path, 'cache.db') # Define atributos de configuração self.pages_db = self.get_mapdb('paginas') self.in_cache = True def get_mapdb(self, name): return FileDB.open(self.cache_path, name) def download(self, url, in_cache=None): in_cache = in_cache if isinstance(in_cache, bool) else self.in_cache # Obtém a página do cache conteudo = None if in_cache: conteudo = self.cache(url) # Ou faz o download if conteudo is None: # As páginas de resultado de loterias exigem cookies cj = http.cookiejar.CookieJar() opener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor(cj)) # A adição desse cookie dobra o tempo de resposta opener.addheaders.append(("Cookie", "security=true")) page = opener.open(url) conteudo = page.read() charset = page.headers.get_param('charset') if charset is not None: conteudo = conteudo.decode(charset) else: conteudo = conteudo.decode() if in_cache: self.cache(url, conteudo) return conteudo def cache(self, url, conteudo=None): # Sem conteúdo: leitura do cache if conteudo is None: if url not in self.pages_db: return None # obtém a entrada do cache result = self.pages_db[url] # se for uma entrada suja, verifica se já venceu o tempo para ficar nesse estado if self.is_dirty(result): timestamp, _ = result.split('|', 1) if time.time() > int(timestamp) + 1800: del self.pages_db[url] return None else: return result # Do contrário: escrita no cache else: self.pages_db[url] = conteudo def blame(self, url): """Marca o resultado de uma URL como inválida. Isso é feito, registrando o horário em que esse método foi chamado. """ if self.in_cache and url in self.pages_db: self.pages_db[url] = "%d|" % int(time.time()) DIRTY_RE = re.compile(r'^[0-9]+\|') @classmethod def is_dirty(cls, s): return cls.DIRTY_RE.match(s) class FileDB: @staticmethod def open(filename, prefix=''): db = FileDB._SQLite3(filename, prefix) return db class _SQLite3(object): __version__ = 0 # por enquanto não serve para nada def __init__(self, filename, prefix=''): self._con = sqlite3.connect(filename) self._table = prefix + 'map' self._create_schema() def close(self): self._con.commit() self._con.close() def flush(self): self._con.commit() def __del__(self): try: self.close() except sqlite3.Error: pass def _create_schema(self): try: self._con.execute("CREATE TABLE %s (key TEXT PRIMARY KEY, value TEXT)" % self._table) self._write_dbversion(self.__version__) # caso a tabela 'map' já exista except sqlite3.OperationalError: pass def _read_dbversion(self): (dbversion,) = self._con.execute('PRAGMA user_version').fetchone() return dbversion def _write_dbversion(self, version): self._con.execute('PRAGMA user_version = %d' % version) def get(self, key, default=None): try: return self[key] except KeyError: return default def __setitem__(self, key, value): with self._con as con: try: con.execute("INSERT INTO %s VALUES (?, ?)" % self._table, (key, value)) except sqlite3.IntegrityError: con.execute("UPDATE %s SET value=? WHERE key=?" % self._table, (value, key)) def __getitem__(self, key): cursor = self._con.cursor() cursor.execute("SELECT value FROM %s WHERE key=?" % self._table, (key,)) result = cursor.fetchone() if result: return result[0] else: raise KeyError(key) def __delitem__(self, key): with self._con as con: con.execute("DELETE FROM %s WHERE key=?" % self._table, (key,)) def __contains__(self, key): cursor = self._con.cursor() cursor.execute("SELECT 1 FROM %s WHERE key=?" % self._table, (key,)) return cursor.fetchall() != [] def __enter__(self): return self def __exit__(self, *args): self.__del__()

[ "wagnerluis1982@gmail.com" ]

wagnerluis1982@gmail.com

8562913d19df6e29366246a74cfb3818c2b42ba8

55c250525bd7198ac905b1f2f86d16a44f73e03a

/Python/pygame/pygameweb/pygameweb/config.py

93d8fc8e758f4623cd6c55d2070b53f047f96a2d

[ "BSD-2-Clause" ]

permissive

NateWeiler/Resources

213d18ba86f7cc9d845741b8571b9e2c2c6be916

bd4a8a82a3e83a381c97d19e5df42cbababfc66c

refs/heads/master

2023-09-03T17:50:31.937137

2023-08-28T23:50:57

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2

1

null

2022-09-08T15:20:18

2020-05-27T16:18:17

null

UTF-8

Python

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py

version https://git-lfs.github.com/spec/v1 oid sha256:3ead591f9a215ef79ce67657f0809e549584a72ef37757eb3272ca4fbba1ab78 size 2948

[ "nateweiler84@gmail.com" ]

nateweiler84@gmail.com

5c2bac8afe40da95fd9e31452f0d7db092d11988

e11dd1fe3c2f5b079a77d81e35d06a0c010f0e0d

/character.py

b03eede15ab858e2251c4d6d036ddef40b7a25a4

[]

no_license

hissboombear/C.B.using_Python

79c23d25cfbfb3600351787c670013beaaf1037d

4c075a0ea240edbab9e7be267544b9ad3fce26fa

refs/heads/master

2021-05-12T10:24:11.310886

2018-11-10T17:35:00

117,351,616

1

0

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UTF-8

Python

false

284

py

print("Create your character") name = input("What is your character called?") age = input("How old is your character?") strengths = input("What are your character's strengths?") weaknesses = input("What are your character's weaknesses?") print(name, "says, 'Thats for creating me!'")

[ "hissboombear@gmail.com" ]

hissboombear@gmail.com

9eebd51cd8523865c63b5ea9bc13a91b30809bd9

0e1e643e864bcb96cf06f14f4cb559b034e114d0

/Exps_7_v3/doc3d/I_w_M_to_Wxyz_focus_Z_ok/wiColorJ/pyr_Tcrop255_pad20_jit15/Sob_k15_s001_EroM_Mae_s001/pyr_5s/L5/step10_a.py

e6c87465892d874e7e738fd489c714ca918ab17a

[]

no_license

KongBOy/kong_model2

33a94a9d2be5b0f28f9d479b3744e1d0e0ebd307

1af20b168ffccf0d5293a393a40a9fa9519410b2

refs/heads/master

2022-10-14T03:09:22.543998

2022-10-06T11:33:42

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0

null

UTF-8

Python

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############################################################################################################################################################################################################# ############################################################################################################################################################################################################# ### 把 kong_model2 加入 sys.path import os code_exe_path = os.path.realpath(__file__) ### 目前執行 step10_b.py 的 path code_exe_path_element = code_exe_path.split("\\") ### 把 path 切分等等要找出 kong_model 在第幾層 code_dir = "\\".join(code_exe_path_element[:-1]) kong_layer = code_exe_path_element.index("kong_model2") ### 找出 kong_model2 在第幾層 kong_model2_dir = "\\".join(code_exe_path_element[:kong_layer + 1]) ### 定位出 kong_model2 的 dir import sys ### 把 kong_model2 加入 sys.path sys.path.append(kong_model2_dir) sys.path.append(code_dir) # print(__file__.split("\\")[-1]) # print(" code_exe_path:", code_exe_path) # print(" code_exe_path_element:", code_exe_path_element) # print(" code_dir:", code_dir) # print(" kong_layer:", kong_layer) # print(" kong_model2_dir:", kong_model2_dir) ############################################################################################################################################################################################################# kong_to_py_layer = len(code_exe_path_element) - 1 - kong_layer ### 中間 -1 是為了長度轉index # print(" kong_to_py_layer:", kong_to_py_layer) if (kong_to_py_layer == 0): template_dir = "" elif(kong_to_py_layer == 2): template_dir = code_exe_path_element[kong_layer + 1][0:] ### [7:] 是為了去掉 step1x_，後來覺得好像改有意義的名字不去掉也行所以改 0 elif(kong_to_py_layer == 3): template_dir = code_exe_path_element[kong_layer + 1][0:] + "/" + code_exe_path_element[kong_layer + 2][0:] ### [5:] 是為了去掉 mask_ ，前面的 mask_ 是為了python 的 module 不能數字開頭，隨便加的這樣子，後來覺得自動排的順序也可以接受，所以改0 elif(kong_to_py_layer > 3): template_dir = code_exe_path_element[kong_layer + 1][0:] + "/" + code_exe_path_element[kong_layer + 2][0:] + "/" + "/".join(code_exe_path_element[kong_layer + 3: -1]) # print(" template_dir:", template_dir) ### 舉例： template_dir: 7_mask_unet/5_os_book_and_paper_have_dtd_hdr_mix_bg_tv_s04_mae ############################################################################################################################################################################################################# exp_dir = template_dir ############################################################################################################################################################################################################# from step06_a_datas_obj import * from step09_5side_L5 import * from step10_a2_loss_info_obj import * from step10_b2_exp_builder import Exp_builder rm_paths = [path for path in sys.path if code_dir in path] for rm_path in rm_paths: sys.path.remove(rm_path) rm_moduless = [module for module in sys.modules if "step09" in module] for rm_module in rm_moduless: del sys.modules[rm_module] ############################################################################################################################################################################################################# ''' exp_dir 是決定 result_dir 的 "上一層"資料夾名字喔！ exp_dir要巢狀也沒問題～比如：exp_dir = "6_mask_unet/自己命的名字"，那 result_dir 就都在： 6_mask_unet/自己命的名字/result_a 6_mask_unet/自己命的名字/result_b 6_mask_unet/自己命的名字/... ''' use_db_obj = type8_blender_kong_doc3d_in_I_gt_W_ch_norm_v2 use_loss_obj = [mae_s001_sobel_k15_s001_EroseM_loss_info_builder.set_loss_target("UNet_z").copy(), mae_s001_sobel_k15_s001_EroseM_loss_info_builder.set_loss_target("UNet_y").copy(), mae_s001_sobel_k15_s001_EroseM_loss_info_builder.set_loss_target("UNet_x").copy()] ### z, y, x 順序是看 step07_b_0b_Multi_UNet 來對應的喔 ############################################################# ### 為了resul_analyze畫空白的圖，建一個empty的 Exp_builder empty = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="為了resul_analyze畫空白的圖，建一個empty的 Exp_builder") ################################## ### 1side1 ################################## # "1" 3 6 10 15 21 28 36 45 55 # 2side1 OK 1 ch032_1side_1__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_1__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ################################## ### 1side2 ################################## # "1" 3 6 10 15 21 28 36 45 55 # 2side1 OK 1 ch032_1side_2__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 "3" 6 10 15 21 28 36 45 55 # 2side2 OK 4 ch032_1side_2__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_2__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_2__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_2__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_2__2side_2__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ################################## ### 1side3 ################################## # "1" 3 6 10 15 21 28 36 45 55 # 2side1 OK 1 ch032_1side_3__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 "3" 6 10 15 21 28 36 45 55 # 2side2 OK 4 ch032_1side_3__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_3__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_3__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_3__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_2__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 "6" 10 15 21 28 36 45 55 # 2side3 OK 10 ch032_1side_3__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_3__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_3__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_3__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_3__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_3__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_3__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_3__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_3__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_3__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_3__2side_3__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ################################## ### 1side4 ################################## # "1" 3 6 10 15 21 28 36 45 55 # 2side1 OK 1 ch032_1side_4__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 "3" 6 10 15 21 28 36 45 55 # 2side2 OK 4 ch032_1side_4__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_2__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 "6" 10 15 21 28 36 45 55 # 2side3 OK 10 ch032_1side_4__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_3__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 6 "10" 15 21 28 36 45 55 # 2side4 OK 20 ch032_1side_4__2side_4__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_4_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_4_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_4_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_4_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_4_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_4_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_4_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_4_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_4_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_4__2side_4__3side_4_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_4__2side_4__3side_4_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ################################## ### 1side5 ################################## # "1" 3 6 10 15 21 28 36 45 55 # 2side1 OK 1 ch032_1side_5__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 "3" 6 10 15 21 28 36 45 55 # 2side2 OK 4 ch032_1side_5__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_2__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 "6" 10 15 21 28 36 45 55 # 2side3 OK 10 ch032_1side_5__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_3__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 6 "10" 15 21 28 36 45 55 # 2side4 OK 20 ch032_1side_5__2side_4__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_4__3side_4_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_4__3side_4_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 6 10 "15" 21 28 36 45 55 # 2side5 OK 35 ch032_1side_5__2side_5__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_4_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_4_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_5_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_5_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_5_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_5_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_5__2side_5__3side_5_4side_5_5s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_5__2side_5__3side_5_4side_5_5s5.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ################################## ### 5side6 ################################## # "1" 3 6 10 15 21 28 36 45 55 # 2side1 OK 1 ch032_1side_6__2side_1__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_1__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_1__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 "3" 6 10 15 21 28 36 45 55 # 2side2 OK 4 ch032_1side_6__2side_2__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_2__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_2__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_2__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_2__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 "6" 10 15 21 28 36 45 55 # 2side3 OK 10 ch032_1side_6__2side_3__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_3__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_3__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_3__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_3__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_3__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_3__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_3__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_3__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_3__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_3__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 6 "10" 15 21 28 36 45 55 # 2side4 OK 20 ch032_1side_6__2side_4__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_4__3side_4_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_4__3side_4_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 6 10 "15" 21 28 36 45 55 # 2side5 OK 35 ch032_1side_6__2side_5__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_4_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_4_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_5_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_5_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_5_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_5_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_5__3side_5_4side_5_5s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_5__3side_5_4side_5_5s5.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") # 1 3 6 10 15 "21" 28 36 45 55 # 2side6 OK 56 ch032_1side_6__2side_6__3side_1_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_1_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_1_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_2_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_2_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_2_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_2_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_3_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_3_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_3_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_3_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_3_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_3_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_3_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_4_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_4_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_5_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_5_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_5_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_5_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_5_4side_5_5s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_5_4side_5_5s5.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_1_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_1_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_1_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_2_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_2_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_2_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_3_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_3_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_3_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_3_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_4_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_4_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_4_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_4_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_4_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_5_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_5_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_5_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_5_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_5_5s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_5_5s5.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_6_5s1 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s1, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s1.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_6_5s2 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s2.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_6_5s3 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s3.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_6_5s4 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s4.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_6_5s5 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s5.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ch032_1side_6__2side_6__3side_6_4side_6_5s6 = Exp_builder().set_basic("train", use_db_obj, ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6, use_loss_obj, exp_dir=exp_dir, code_exe_path=code_exe_path, describe_end=ch032_pyramid_1side_6__2side_6__3side_6_4side_6_5s6.kong_model.model_describe) .set_train_args(epochs= 1) .set_train_iter_args(it_see_fq=900, it_save_fq=900 * 2, it_down_step="half", it_down_fq=900).set_train_in_gt_use_range(use_in_range=Range(0, 1), use_gt_range=Range(0, 1)).set_result_name(result_name="") ############################################################# if(__name__ == "__main__"): print("build exps cost time:", time.time() - start_time) if len(sys.argv) < 2: ############################################################################################################ ### 直接按 F5 或打 python step10_b1_exp_obj_load_and_train_and_test.py，後面沒有接東西喔！才不會跑到下面給 step10_b_subprocss.py 用的程式碼~~~ ch032_1side_1__2side_1__3side_1_4side_1_5s1.build().run() # print('no argument') sys.exit() ### 以下是給 step10_b_subprocess.py 用的，相當於cmd打 python step10_b1_exp_obj_load_and_train_and_test.py 某個exp.build().run() eval(sys.argv[1])

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s89334roy@yahoo.com.tw

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frozenjava/python-rets

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2020-06-30T04:00:39.173067

2016-11-21T17:56:52

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class Base(object): def __init__(self, session): self.session = session @staticmethod def get_attributes(input_dict): return {k.lstrip('@'): v for k, v in input_dict.items() if k[0] == '@'}

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no_license

elack33/Google-FooBar

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""" For example, when a guard picks up the medical file for Rabbit #1235, she would first add those digits together to get 11, then add those together to get 2, her final sum." Write a function answer(x), which when given a number x, returns the final digit resulting from performing the above described repeated sum process on x. """ def answer(x): first = str(x) first_list = [] for each in first: first_list.append(int(each)) while len(first_list) > 1: sum_list = sum(first_list) first_list = [] for each in str(sum_list): first_list.append(int(each)) return first_list[0]

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a=int(input()) for i in range(1,6): print(i*a)

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Arpppit/Collabrative-Filtering

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import pandas as pd import numpy as np # import ipywidgets as w # from ipywidgets import HBox, VBox # from ipywidgets import Layout, widgets # from IPython.display import display, IFrame, HTML from utils import Util from rbm import RBM import math, re, datetime as dt, glob from urllib.parse import quote from urllib.request import Request, urlopen from google_images_download import google_images_download from PIL import Image import requests from bs4 import BeautifulSoup import html5lib #from nltk.corpus import wordnet import nltk nltk.download('wordnet') from nltk.corpus import wordnet def f(row): avg_cat_rat = dict() for i in range(len(row['category'])): if row['category'][i] not in avg_cat_rat: avg_cat_rat[row['category'][i]] = [row['rating'][i]] else: avg_cat_rat[row['category'][i]].append(row['rating'][i]) for key,value in avg_cat_rat.items(): avg_cat_rat[key] = sum(value)/len(value) return avg_cat_rat def sim_score(row): score = 0.0 match = 0 col1 = row['cat_rat'] col2 = row['user_data'] for key, value in col2.items(): if key in col1: match+=1 score += (value-col1[key])**2 if match != 0: return ((math.sqrt(score)/match) + (len(col2) - match)) else: return 100 def get_recc(att_df, cat_rating): util = Util() epochs = 50 rows = 40000 alpha = 0.01 H = 128 batch_size = 16 dir= 'etl/' ratings, attractions = util.read_data(dir) ratings = util.clean_subset(ratings, rows) rbm_att, train = util.preprocess(ratings) num_vis = len(ratings) rbm = RBM(alpha, H, num_vis) joined = ratings.set_index('attraction_id').join(attractions[["attraction_id", "category"]].set_index("attraction_id")).reset_index('attraction_id') grouped = joined.groupby('user_id') category_df = grouped['category'].apply(list).reset_index() rating_df = grouped['rating'].apply(list).reset_index() cat_rat_df = category_df.set_index('user_id').join(rating_df.set_index('user_id')) cat_rat_df['cat_rat'] = cat_rat_df.apply(f,axis=1) cat_rat_df = cat_rat_df.reset_index()[['user_id','cat_rat']] cat_rat_df['user_data'] = [cat_rating for i in range(len(cat_rat_df))] cat_rat_df['sim_score'] = cat_rat_df.apply(sim_score, axis=1) user = cat_rat_df.sort_values(['sim_score']).values[0][0] print("Similar User: {u}".format(u=user)) filename = "e"+str(epochs)+"_r"+str(rows)+"_lr"+str(alpha)+"_hu"+str(H)+"_bs"+str(batch_size) reco, weights, vb, hb = rbm.load_predict(filename,train,user) unseen, seen = rbm.calculate_scores(ratings, attractions, reco, user) rbm.export(unseen, seen, 'recommendations/'+filename, str(user)) return filename, user, rbm_att def filter_df(filename, user, low, high, province, att_df): recc_df = pd.read_csv('recommendations/'+filename+'/user{u}_unseen.csv'.format(u=user), index_col=0) recc_df.columns = ['attraction_id', 'att_name', 'att_cat', 'att_price', 'score'] recommendation = att_df[['attraction_id','name','category','city','latitude','longitude','price','province', 'rating']].set_index('attraction_id').join(recc_df[['attraction_id','score']].set_index('attraction_id'), how="inner").reset_index().sort_values("score",ascending=False) filtered = recommendation[(recommendation.province == province) & (recommendation.price >= low) & (recommendation.price >= low)] url = pd.read_json('outputs/attractions_cat.json',orient='records') url['id'] = url.index with_url = filtered.set_index('attraction_id').join(url[['id','attraction']].set_index('id'), how="inner") print(with_url.head()) return with_url def get_image(name): url = url =f'https://www.google.com/search?q={name}&hl=en-GB&source=lnms&tbm=isch&sa=X&ved=2ahUKEwi77e_zg_zzAhU64zgGHWyiCYgQ_AUoA3oECAEQBQ&biw=1920&bih=1007' res = requests.get(url) bs =BeautifulSoup(res.content, 'html5lib') table = bs.find_all('img') if len(table) >=6: return table[5].get('src') else: return table[1].get('src') # def get_image(name): # name = name.split(",")[0] # response = google_images_download.googleimagesdownload() # args_list = ["keywords", "keywords_from_file", "prefix_keywords", "suffix_keywords", # "limit", "format", "color", "color_type", "usage_rights", "size", # "exact_size", "aspect_ratio", "type", "time", "time_range", "delay", "url", "single_image", # "output_directory", "image_directory", "no_directory", "proxy", "similar_images", "specific_site", # "print_urls", "print_size", "print_paths", "metadata", "extract_metadata", "socket_timeout", # "thumbnail", "language", "prefix", "chromedriver", "related_images", "safe_search", "no_numbering", # "offset", "no_download"] # args = {} # for i in args_list: # args[i]= None # args["keywords"] = name # args['limit'] = 1 # params = response.build_url_parameters(args) # url = 'https://www.google.com/search?q=' + quote(name) + '&espv=2&biw=1366&bih=667&site=webhp&source=lnms&tbm=isch' + params + '&sa=X&ei=XosDVaCXD8TasATItgE&ved=0CAcQ_AUoAg' # try: # response.download(args) # for filename in glob.glob("downloads/{name}/*jpg".format(name=name)) + glob.glob("downloads/{name}/*png".format(name=name)): # return filename # except: # for filename in glob.glob("downloads/*jpg"): # return filename def top_recc(with_url, final): i=0 print(with_url) print(final) try: while(1): first_recc = with_url.iloc[[i]] if(first_recc['name'].values.T[0] not in final['name']): final['name'].append(first_recc['name'].values.T[0]) final['location'].append(first_recc[['latitude','longitude']].values.tolist()[0]) final['price'].append(first_recc['price'].values.T[0]) final['rating'].append(first_recc['rating'].values.T[0]) final['image'].append(get_image(first_recc['name'].values.T[0])) #final['image'].append('www.google.com/image') final['category'].append(first_recc['category'].values.T[0]) return final else: i+=1 except Exception as e: return final def find_closest(with_url, loc, tod, final): syns1 = wordnet.synsets("evening") syns2 = wordnet.synsets("night") evening = [word.lemmas()[0].name() for word in syns1] + [word.lemmas()[0].name() for word in syns2] distance = list() for i in with_url[['latitude','longitude']].values.tolist(): distance.append(math.sqrt((loc[0]-i[0])**2 + (loc[1]-i[1])**2)) with_dist = with_url with_dist["distance"] = distance sorted_d = with_dist.sort_values(['distance','price'], ascending=['True','False']) if tod == "Evening": mask = sorted_d.name.apply(lambda x: any(j in x for j in evening)) else: mask = sorted_d.name.apply(lambda x: all(j not in x for j in evening)) final = top_recc(sorted_d[mask], final) return final def final_output(days, final): time = ['MORNING', 'EVENING'] fields = ['NAME', 'CATEGORY', 'LOCATION', 'PRICE', 'RATING'] recommendations = ['Recommendation 1:', 'Recommendation 2:'] # box_layout = Layout(justify_content='space-between', # display='flex', # flex_flow='row', # align_items='stretch', # ) # column_layout = Layout(justify_content='space-between', # width='75%', # display='flex', # flex_flow='column', # ) tab = {} tab['name']=[] tab['image']=[] tab['price']=[] tab['rating']=[] tab['category']=[] tab['location']=[] for i in range(days): tab['image'].append(final['image'][i*4:(i+1)*4]) #images = [open(i, "rb").read() for i in images] tab['name'].append([re.sub('_',' ',i).capitalize() for i in final['name'][i*4:(i+1)*4]]) tab['category'].append([re.sub('_',' ',i).capitalize() for i in final['category'][i*4:(i+1)*4]]) tab['location'].append(["("+str(i[0])+","+str(i[1])+")" for i in final['location'][i*4:(i+1)*4]]) tab['price'].append([str(i) for i in final['price'][i*4:(i+1)*4]]) tab['rating'].append([str(i) for i in final['rating'][i*4:(i+1)*4]]) #print('Final Recommendations are: ',price, rating,location,category,name) return tab

[ "arppitmadankar@gmail.com" ]

arppitmadankar@gmail.com

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refs/heads/master

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#!/usr/bin/env python import argparse parser = argparse.ArgumentParser() parser.add_argument('-s', action='store', dest='simple_value', help='Store a simple value') parser.add_argument('-c', action='store_const', dest='constant_value', const='value-to-store', help='Store a constant value') parser.add_argument('-t', action='store_true', default=False, dest='boolean_switch', help='Set a switch to true') parser.add_argument('-f', action='store_false', default=False, dest='boolean_switch', help='Set a switch to false') parser.add_argument('-a', action='append', dest='collection', default=[], help='Add repeated values to a list', ) parser.add_argument('-A', action='append_const', dest='const_collection', const='value-1-to-append', default=[], help='Add different values to list') parser.add_argument('-B', action='append_const', dest='const_collection', const='value-2-to-append', help='Add different values to list') parser.add_argument('--version', action='version', version='%(prog)s 1.0') results = parser.parse_args() print 'simple_value =', results.simple_value print 'constant_value =', results.constant_value print 'boolean_switch =', results.boolean_switch print 'collection =', results.collection print 'const_collection =', results.const_collection

[ "DeathPoison.DC@gmail.com" ]

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2021-08-19T13:48:40.760731

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#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date : 2017-10-15 15:15:37 # @Author : jingray (lionel_jing@163.com) # @Link : http://www.jianshu.com/u/01fb0364467d # @Version : $Id$ import os import matplotlib.pyplot as plt import matplotlib.ticker as plticker import numpy as np def plot_uniform(x_minimum, x_maximum, tick_interval): x = range(x_minimum, x_maximum + 1) # TODO: Using x_maximum and x_minimum, calculate the height of the # rectangle that represents the uniform probability distribution # Recall that the rectangle area should be 1 for a uniform continuous # distribution y = 1/(x_maximum - x_minimum) plt.bar(x_minimum, y, bottom=0, width= (x_maximum - x_minimum), align='edge', alpha=0.5) plt.xlabel('Degrees') plt.ylabel('Probability Distribution') plt.title('Uniform Probability Distribution \n for a Spinning Bottle') plt.xticks(np.arange(min(x), max(x)+1, tick_interval)) plt.ylim(0, .3) plt.show() plot_uniform(5, 10, 1) import matplotlib.pyplot as plt import numpy as np def bar_heights(intervals, probabilities, total_probability): heights = [] # normalize probability intervals total_relative_prob = sum(probabilities) # calculate the sum of a list very concise!!! for i in range(0, len(probabilities)): bar_area = probabilities[i]*total_probability/total_relative_prob heights.append(bar_area/(intervals[i+1]-intervals[i])) return heights def plot_discrete(intervals, probabilities, total_probability): heights = bar_heights(intervals, probabilities, total_probability) freqs = np.array(heights) bins = np.array(hour_intervals) widths = bins[1:] - bins[:-1] #calculate the time interval widths, very good!!! freqs = freqs.astype(np.float) tick_interval = 1 plt.bar(bins[:-1], freqs, width=widths, align='edge', edgecolor='black', alpha=0.5) plt.xlabel('Interval') plt.ylabel('Probability Distribution') plt.title('Probability Distribution') plt.xticks(np.arange(min(bins), max(bins)+1, tick_interval)) plt.show() hour_intervals = [0, 5, 10, 16, 21, 24] probability_intervals = [1, 5, 3, 6, 1/2] accident_probability = 0.05 plot_discrete(hour_intervals,probability_intervals,accident_probability) # Robot World 1-D import matplotlib.pyplot as plt import numpy as np def initialize_robot(grid_size): #grid = [1/grid_size for i in range(0,grid_size)] grid = [1/grid_size] * grid_size return grid def grid_probability(grid, position): try: return grid[position] except: return None def output_map(grid): x_labels = range(len(grid)) #x_data = np.array(x_labels) #y_data = np.array(grid) #plt.bar(x_data, y_data, width=0.7, edgecolor='black') plt.bar(x_labels, height=grid, width=0.7, edgecolor='black') plt.xlabel('Grid Space') plt.ylabel('Probability') plt.title('Probability of the robot being at each space on the grid') plt.xticks(np.arange(min(x_labels), max(x_labels)+1, 1)) plt.show() def update_probabilities(grid, updates): #for i in range(len(updates)): # grid[updates[i][0]]=updates[i][1] for update in updates: grid[update[0]] = update[1] return grid print(update_probabilities([0.2, 0.2, 0.2, 0.2, 0.2], [[0, .25], [4, 0.15]])) #2-D Self-Driving Car World import matplotlib.pyplot as plt from pandas import DataFrame class SelfDrivingCar(): def __init__(self, rows, columns): self.grid = [] self.grid_size = [rows,columuns] self.num_elements = rows * columns def initialize_grid(self): probability = 1/self.num_elements for i in range(self.grid_size[0]): list = [] for j in range(self.grid_size[1]): list.append(probability) self.grid.append(list) return self.grid def output_probability(self, grid_point): try: return self.grid[grid_point[0]][grid_point[1]] else: return None def update_probability(self, update_list): for update in update_list: self.grid[update[0]][update[1]]=update[2] return self.grid def visualize_probability(self): if not self.grid: self.grid = [[0],[0]] else: plt.imshow(self.grid, cmap='Greys', clim=(0,.1)) plt.title('Heat Map of Grid Probabilities') plt.xlabel('grid x axis') plt.ylabel('grid y axis') plt.show() car = SelfDrivingCar(5,4) car.initialize_grid() # should output 0.05 print(car.output_probability([2,3])) # should output 0.05 print(car.output_probability([1,2])) car.update_probability([[2,3,.2], [1,2,.1]]) # should output 0.2 print(car.output_probability([2,3])) # should output 0.1 print(car.output_probability([1,2])) # should output a heat map car.visualize_probability() #Central Limit Theorem #normal distribution, also called a Gaussian distribution. #The normal distribution appears throughout self-driving car applications #especially with sensor measurements and tracking objects that move around the vehicle. # import libraries used in the notebook %matplotlib inline import numpy as np from scipy import stats from matplotlib import mlab import matplotlib.pyplot as plt # Set figure height and width fig_size = plt.rcParams["figure.figsize"] fig_size[0] = 8 fig_size[1] = 6 plt.rcParams["figure.figsize"] = fig_size x = np.linspace(-12, 12, 100) plt.title('Normal distribution \n mean = 0 \n standard deviation = ' + str(3)) plt.xlabel('value') plt.ylabel('distribution') plt.plot(x,mlab.normpdf(x, 0, 3)) ###Probability Distributions### x = np.linspace(-12, 12, 100) plt.subplot(221) plt.plot(x,mlab.normpdf(x, 0, 3)) plt.title('Normal Distribution') plt.subplot(222) plt.plot(x,stats.uniform.pdf(x,-5,10)) plt.title('Uniform Distribution') plt.subplot(223) plt.plot(x[x > -1],stats.chi2.pdf(x[x>-1],3)) plt.title('Chi2 Distribution') plt.subplot(224) plt.plot(x[x > -1],stats.lognorm.pdf(x[x > -1],3)) plt.title('Logarithmic Distribution') plt.subplots_adjust(hspace=.5) ###different probability distributions still work with the central limit theorem # ### Probability distributions def random_uniform(low_value, high_value, num_samples): return np.random.uniform(low_value, high_value, num_samples) ### Poisson Distribution def poisson_distribution(expectation, num_samples): return np.random.poisson(expectation, num_samples) def binomial_distribution(result, probability, trials): return np.random.binomial(result, probability, trials) uniform = random_uniform(1, 5, 100000) poisson = poisson_distribution(6.0, 10000) binomial = binomial_distribution(1, 0.5, 10000) ### Shows Central Limit Theorem: takes samples from a distribution and calculates the mean of each sample. # # variables: # distribution => array containing values from a population # iterations => number of times to draw samples and calculate the mean of the sample # num_samples => sample size # num_bins => controls number of bins in the histograms3 # # outputs: # (1) summary statistics of the population and the means of the samples # (2) histogram of the population and means of the samples # (3) normalized histogram of the means and line chart of equivalent normal distribution with same mean and stdeviation # (4) probability plot of the original distribution and the means of the samples # ### def sample_means_calculator(distribution, iterations, num_samples, num_bins): # take samples from the distribution and calculate the mean of each sample sample_means = [] # iterate through picking samples and calculating the mean of each sample for iteration in range(iterations): samples = [] # iterate through for the sample size chosen and randomly pick samples for sample in range(num_samples): samples.append(distribution[np.random.randint(1,len(distribution))]) # calculate the mean of the sample sample_means.append(np.mean(samples)) # Calculate summary statistics for the population and the sample means population_mean = np.average(distribution) population_median = np.median(distribution) population_deviation = np.std(distribution) sample_mean = np.mean(sample_means) sample_median = np.median(sample_means) sample_deviation = np.std(sample_means) print('population mean ', population_mean, ' \n population median ', population_median, '\n population standard deviation ', population_deviation) print('\n mean of sample means ', sample_mean, '\n median of sample means ', sample_median, '\n standard deviation of sample means ', sample_deviation) # histogram of the population and histogram of sample means fig = plt.figure(figsize=(8, 4)) ax1 = plt.subplot(121) plt.hist(distribution, bins=num_bins) plt.title('Histogram of the Population') plt.xlabel('Value') plt.ylabel('Count') ax2 = plt.subplot(122, sharex=ax1, sharey=ax1) plt.hist(sample_means, bins=num_bins) plt.title('Histogram of the Sample Means') plt.xlabel('Value') plt.ylabel('Count') plt.show() # normalized histogram of the sample means and an equivalent normal distribution with same mean and standard deviation fig = plt.figure(figsize=(8, 3)) plt.hist(sample_means, bins=num_bins, normed=True) plt.title('Normalized Histogram of Sample Means and \n Equivalent Normal Distribution') plt.xlabel('Value') plt.ylabel('Count') x = np.linspace(min(sample_means), max(sample_means), 1000) plt.plot(x,mlab.normpdf(x, sample_mean, sample_deviation)) plt.show() # probability plots showing how the sample mean distribution is more normal than the population mean fig = plt.figure(figsize=(8, 3)) ax5 = plt.subplot(121) stats.probplot(probability_distribution, plot=plt) ax6 = plt.subplot(122) stats.probplot(sample_means, plot=plt) ax5.set_title('Probability Plot of the Population') ax6.set_title('Probability Plot of the Sample Means') plt.show() ### Take samples and calculate the sample means for central limit theorem sample_means_calculator(uniform, 100000, 50, 100) sample_means_calculator(random_uniform(1,10,100000), 10000, 50, 100) sample_means_calculator(poisson_distribution(6.0,500000), 10000, 90, 100) sample_means_calculator(binomial_distribution(1, 0.5, 10000), 10000, 200, 100)

[ "lionel_jing@163.com" ]

lionel_jing@163.com

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/E-Bigay-Website/ebigay/migrations/0007_auto_20210623_0326.py

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TristanBachini/E-Bigay

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# Generated by Django 3.1.7 on 2021-06-22 19:26 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('ebigay', '0006_ayudadropoff_user'), ] operations = [ migrations.AlterField( model_name='region', name='region', field=models.CharField(choices=[('NCR', 'National Capital Region'), ('CAR', 'Cordillera Administrative Region'), ('Region I', 'Ilocos Region'), ('Region II', 'Cagayan Valley Region'), ('Region III', 'Central Luzon Region'), ('Region IV-A', 'CALABARZON Region'), ('Region IV-B', 'MIMAROPA Region'), ('Region V', 'Bicol Region'), ('Region VI', 'Western Visayas Region'), ('Region VII', 'Central Visayas Region'), ('Region VIII', 'Eastern Visayas Region'), ('Region IX', 'Zamboanga Peninsula Region'), ('Region X', 'Northern Mindanao Region'), ('Region XI', 'Davao Region'), ('Region XII', 'SOCCSKARGEN Region'), ('Region XIII', 'Caraga Region'), ('BARMM', 'Bangsamoro Autonomous Region in Muslim Mindanao')], max_length=50, null=True), ), ]

[ "angelo.villaluz0120@gmail.com" ]

angelo.villaluz0120@gmail.com

5c7e35eb9122c4cf1073839e65428b6b4db9e74f

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/webapp/guest_book/migrations/0001_initial.py

077856f4959d2b19653db3b10adac167bd62c4ed

[]

no_license

rodnoi7/python-group_3-exam_6-kaiypbek_sydykov

02bb2606b617cf2f1dbe230501e3fef87b4955a7

a4ac5b1bfe7e472bca59b2c603990c27351d2aaa

refs/heads/master

2023-05-01T01:35:42.125008

2019-09-21T13:04:38

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null

2023-04-21T20:37:32

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# Generated by Django 2.1 on 2019-09-21 06:04 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Article', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('author', models.CharField(max_length=200, verbose_name='Автор')), ('author_email', models.CharField(max_length=200, verbose_name='Email автора')), ('title', models.CharField(max_length=200, verbose_name='Заголовок')), ('text', models.TextField(max_length=3000, verbose_name='Текст статьи')), ('created_at', models.DateTimeField(auto_now_add=True, verbose_name='Время создания')), ('status', models.CharField(choices=[('Active', 'Active'), ('Deactive', 'Deactive')], default='Active', max_length=50, verbose_name='Статус')), ], ), ]

[ "sydykov.99@gmail.com" ]

sydykov.99@gmail.com

d31cbc5e81c667f85f43dbf60c55f2703673fc8c

5e66a11717a4760646c0e02bf9ffff2f82f66d18

/chemistry/qchem_make_opt_input_from_opt.py

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berquist/personal_scripts

4517678fa57e524e9765dc71f05594e34bdd9c72

d6c40ba6e5a607d26ffabf809cfdfdf3ce29bfb3

refs/heads/master

2023-07-21T08:44:36.401893

2023-07-07T19:55:55

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#!/usr/bin/env python """qchem_make_opt_input_from_opt.py: Make an input file for a Q-Chem geometry optimization based on the last possible geometry from a Q-Chem geometry optimization; this effectively 'restarts' the geometry with a new filename. The script assumes the output file being read from is called '*opt(\d*).out', where 'opt' might be followed by a number. The script will write an input file called '*opt(\d*)+1.in', with the previous number incremented by one. """ import os.path import re from collections import OrderedDict import cclib from cclib.parser.utils import PeriodicTable def make_file_iterator(filename): """Return an iterator over the contents of the given file name.""" # pylint: disable=C0103 with open(filename) as f: contents = f.read() return iter(contents.splitlines()) def getargs(): """Get command-line arguments.""" import argparse # pylint: disable=C0103 parser = argparse.ArgumentParser() parser.add_argument("outputfilename", nargs="+") parser.add_argument("--fragment", action="store_true") args = parser.parse_args() return args def parse_user_input(outputfilename): """Parse the $rem section in the repeated 'User input:' section of the output. The reason we do it this way rather than with shell tools is to handle any $section more easily and in a case-insensitive manner. """ user_input = dict() outputfile = make_file_iterator(outputfilename) line = "" while "User input:" not in line: line = next(outputfile) line = next(outputfile) assert "----" in line line = next(outputfile) while "--------------------------------------------------------------" not in line: if line.strip() == "": pass elif line[0] == "$" and line.strip().lower() != "$end": section_header = line[1:].lower() user_input[section_header] = [] elif line.strip().lower() == "$end": user_input[section_header] = "\n".join(user_input[section_header]) else: user_input[section_header].append(line) line = next(outputfile) return user_input def parse_fragments_from_molecule(molecule): """Given a $molecule section (without the $ lines), identify the charges and multiplicities of each fragment and the zero-based indices for the starting atom of each fragment. """ charges = [] multiplicities = [] start_indices = [] it = iter(molecule.splitlines()) line = next(it) # sys_charge, sys_multiplicity = line.split() counter = 0 # Gather the charges, spin multiplicities, and starting positions # of each fragment. for line in it: if "--" in line: line = next(it) charge, multiplicity = line.split() charges.append(charge) multiplicities.append(multiplicity) start_indices.append(counter) else: counter += 1 assert len(charges) == len(multiplicities) == len(start_indices) return charges, multiplicities, start_indices def form_molecule_section_from_fragments( elements, geometry, charges, multiplicities, start_indices ): """Form the Q-Chem $molecule section containing the charge, multiplicity, and atomic symbols and coordinates for multiple fragments. Returns a list that will need to be joined with newlines. """ assert len(charges) == len(multiplicities) == (len(start_indices) + 1) s = "{:3s} {:15.10f} {:15.10f} {:15.10f}" # The first elements of the charge and multiplicity lists are for # the supersystem (whole molecule). molecule_section = ["{} {}".format(charges[0], multiplicities[0])] from itertools import count for (charge, multiplicity, idx_iter) in zip(charges[1:], multiplicities[1:], count(0)): molecule_section.append("--") molecule_section.append("{} {}".format(charge, multiplicity)) idx_start = start_indices[idx_iter] try: idx_end = start_indices[idx_iter + 1] except IndexError: idx_end = len(elements) for element, coords in zip(elements[idx_start:idx_end], geometry[idx_start:idx_end]): molecule_section.append(s.format(element, *coords)) return molecule_section def form_molecule_section(elements, geometry, charge, multiplicity): """Form the Q-Chem $molecule section containing the charge, multiplicity, and atomic symbols and coordinates. Returns a list that will need to be joined with newlines. """ s = "{:3s} {:15.10f} {:15.10f} {:15.10f}" molecule_section = ["{} {}".format(charge, multiplicity)] for ( element, coords, ) in zip(elements, geometry): molecule_section.append(s.format(element, *coords)) return molecule_section if __name__ == "__main__": args = getargs() pt = PeriodicTable() for outputfilename in args.outputfilename: job = cclib.io.ccopen(outputfilename) assert isinstance(job, cclib.parser.qchemparser.QChem) try: data = job.parse() # this is to deal with the Q-Chem parser not handling # incomplete SCF cycles properly except StopIteration: print("no output made: StopIteration in {}".format(outputfilename)) continue # Determine the name of the file we're writing. assert outputfilename.endswith(".out") numstr = re.search(r"opt(\d*)", outputfilename).groups()[0] if numstr == "": optnum = 2 else: optnum = int(numstr) + 1 inputfilename = re.sub(r"opt\d*", "opt{}".format(optnum), outputfilename) inputfilename = inputfilename.replace(".out", ".in") inputfilename = os.path.basename(inputfilename) user_input = parse_user_input(outputfilename) # Form the atomic symbols and coordinates for each atom in # $molecule. element_list = [pt.element[Z] for Z in data.atomnos] last_geometry = data.atomcoords[-1] if args.fragment: charges, multiplicities, start_indices = parse_fragments_from_molecule( user_input["molecule"] ) charges.insert(0, data.charge) multiplicities.insert(0, data.mult) molecule_section = form_molecule_section_from_fragments( element_list, last_geometry, charges, multiplicities, start_indices ) else: molecule_section = form_molecule_section( element_list, last_geometry, data.charge, data.mult ) user_input["molecule"] = "\n".join(molecule_section) with open(inputfilename, "w") as fh: for section_header in user_input: fh.write("${}\n".format(section_header)) fh.write(user_input[section_header]) fh.write("\n$end\n\n") print(inputfilename)

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from django.shortcuts import render, HttpResponse from .models import GreetingRecords from .logger.logger import logger import json def home(request): recordData = GreetingRecords.objects.all() logger.info("All records are displayed") return render(request, "greetingApp/home.html", {"data": recordData}) def addData(request): if request.method == 'POST': formData = json.loads(request.body.decode()) if formData: recordData = GreetingRecords(name=formData["name"], message=formData['message']) recordData.save() logger.info(f"{recordData} Data is saved") data = [dict(item) for item in GreetingRecords.objects.all().values('id', 'name', 'message')] return HttpResponse(json.dumps(data)) else: logger.error("Data saving failed") return HttpResponse("false") def deleteRecord(request, recordID): if request.method == "DELETE": record = GreetingRecords.objects.filter(id=recordID) record.delete() logger.info(f"{record} Record Deleted successfully") data = [dict(item) for item in GreetingRecords.objects.all().values('id', 'name', 'message')] return HttpResponse(json.dumps(data)) logger.error("Record Deletion failed") return HttpResponse("false") def updateRecord(request, recordID): if request.method == "PUT": formData = json.loads(request.body.decode()) if formData: record = GreetingRecords.objects.get(id=recordID) record.name = formData["name"] record.message = formData["message"] record.save() logger.info(f"{record} Record updated successfully") data = [dict(item) for item in GreetingRecords.objects.all().values('id', 'name', 'message')] return HttpResponse(json.dumps(data)) else: logger.error("Record Updating failed") return HttpResponse("false")

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birajit95@gmail.com

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/controllers/ships.py

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LaurenFWinter/Project-04

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from flask import Blueprint, request, jsonify, abort from pony.orm import db_session from app import db from marshmallow import ValidationError from models.Ship import Ship, ShipSchema from lib.secure_route import secure_route # creating a router to this controller router = Blueprint(__name__, 'ships') # getting all of the ships @router.route('/ships', methods=['GET']) @db_session def index(): schema = ShipSchema(many=True) ships = Ship.select() return schema.dumps(ships) @router.route('/ships', methods=['POST']) @db_session @secure_route def create(): schema = ShipSchema() try: data = schema.load(request.get_json()) ship = Ship(**data) db.commit() except ValidationError as err: return jsonify({'message': 'Validation failed', 'errors': err.messages}), 422 return schema.dumps(ship), 201 @router.route('/ships/<int:ship_id>', methods=['GET']) @db_session def show(ship_id): schema = ShipSchema() ship = Ship.get(id=ship_id) if not ship: abort(404) return schema.dumps(ship) @router.route('/ships/<int:ship_id>', methods=['PUT']) @db_session @secure_route def update(ship_id): schema = ShipSchema() ship = Ship.get(id=ship_id) if not ship: abort(404) try: data = schema.load(request.get_json()) ship.set(**data) db.commit() except ValidationError as err: return jsonify({'message': 'Validation failed', 'errors': err.messages}), 422 return schema.dumps(ship) @router.route('/ships/<int:ship_id>', methods=['DELETE']) @db_session @secure_route def delete(ship_id): ship = Ship.get(id=ship_id) if not ship: abort(404) ship.delete() db.commit() return '', 204

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2023-04-06T15:34:04.847875

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from typing import List from collections import Counter,defaultdict from math import * from functools import reduce,lru_cache,total_ordering import numpy as np from heapq import * from bisect import bisect_left,bisect_right from itertools import count import queue # Definition for a binary tree node. # class TreeNode: # def __init__(self, val=0, left=None, right=None): # self.val = val # self.left = left # self.right = right class Solution: def minDiffInBST(self, root: Optional[TreeNode]) -> int: return self.min_difference(root) def min_difference(self,node): if node == None: return float("inf") node_left_min = float("inf") if node.left!=None: node_left_min = node.val-self.max_left(node.left) node_right_min = float("inf") if node.right!=None: node_right_min = self.max_right(node.right)-node.val left_min = self.min_difference(node.left) right_min = self.min_difference(node.right) return min(node_left_min,node_right_min,left_min,right_min) def max_left(self,node): if node.right == None: return node.val else: return self.max_left(node.right) def max_right(self,node): if node.left == None: return node.val else: return self.max_right(node.left) sol = Solution() # input [4,2,6,1,3] [1,0,48,null,null,12,49] [1,0] [2,0,5] [2,0,6] [5,0,13] # output output = sol.minDiffInBST(root) # answer answer = "" print(output, answer, answer == output)

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/mymqttcloud/learn_mqtt/mqtt_pub.py

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import time import paho.mqtt.client as mqtt mqtt_user = "username" mqtt_passwd = "password" mqtt_host = "127.0.0.1" mqtt_port = 1883 mqttc = mqtt.Client() # Connect try: mqttc.username_pw_set(mqtt_user, mqtt_passwd) mqttc.connect(mqtt_host, mqtt_port, 60) mqttc.loop_start() except Exception: print "Could not connect to MQTT" else: print "Connected to MQTT" # Loop while 1: mqttc.publish("test/hello","Hello World",2) time.sleep(1) # Close mqttc.loop_stop() mqttc.disconnect()

[ "glyn.hudson@openenergymonitor.org" ]

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/aug_v5.py

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[]

no_license

ZiwenYeee/Santander-Customer-Transaction-Prediction

c2bd2b8085e73e3dad46234fddb27b52289e0ec5

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refs/heads/master

2020-08-06T22:20:08.529636

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import numpy as np import pandas as pd import gc import time import lightgbm as lgb from sklearn.metrics import roc_auc_score, roc_curve from sklearn.model_selection import KFold, StratifiedKFold import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.simplefilter(action='ignore', category=FutureWarning) train = pd.read_csv('input/train.csv') test = pd.read_csv('input/test.csv') test_fake = pd.read_csv('synthetic_samples_indexes.csv') test_fake.columns = ['ID_code'] test_fake['ID_code'] = test_fake.ID_code.apply(lambda x: 'test_' + str(x)) test_fake['dis'] = 1 test = pd.merge(test, test_fake, on = ['ID_code'], how = 'left') test.dis.fillna(0, inplace=True) test_real = test.loc[test.dis == 0] test_fake = test.loc[test.dis == 1] train['flag']=1 test_real['flag']=2 test_fake['flag']=3 data=pd.concat([train,test_real]).reset_index(drop=True) print('data.shape=',data.shape) del train,test_real for var in features: data['scaled_' + var]= (data[var]-data[var].mean())/data[var].std()*5 train=data[data['flag']==1].copy() test_real=data[data['flag']==2].copy() # test_fake=data[data['flag']==3].copy() test=data[data['flag']>=2].copy() test = pd.concat([test, test_fake], axis = 0) print(train.shape,test_real.shape,test_fake.shape,test.shape) del data print(len(features)) def feature_eng(train, valid, test, origin_train, origin_test,feat): for var in feat: print(var) data = pd.concat([origin_train[['ID_code', var]], origin_test[['ID_code', var]]]) data['weight_' + var] = data[var].map(data.groupby([var])[var].count()) train['weight_' + var] = train[var].map(data.groupby([var])[var].count()) valid['weight_' + var] = valid[var].map(data.groupby([var])[var].count()) test['weight_'+ var] = test[var].map(data.groupby([var])[var].count()) train['binary_' + var] = train['weight_' + var].apply(lambda x: 1 if x > 1 else 0) * train[var] valid['binary_' + var] = valid['weight_' + var].apply(lambda x: 1 if x > 1 else 0) * valid[var] test['binary_' + var] = test['weight_' + var].apply(lambda x: 1 if x > 1 else 0) * test[var] return train, valid, test def augment(x,y,t=2): xs,xn = [],[] for i in range(t): mask = y>0 x1 = x[mask].copy() ids = np.arange(x1.shape[0]) for c in range(x1.shape[1]): np.random.shuffle(ids) x1[:,c] = x1[ids][:,c] xs.append(x1) for i in range(t//2): mask = y==0 x1 = x[mask].copy() ids = np.arange(x1.shape[0]) for c in range(x1.shape[1]): np.random.shuffle(ids) x1[:,c] = x1[ids][:,c] xn.append(x1) xs = np.vstack(xs) xn = np.vstack(xn) ys = np.ones(xs.shape[0]) yn = np.zeros(xn.shape[0]) x = np.vstack([x,xs,xn]) y = np.concatenate([y,ys,yn]) return x,y def kfold_lightgbm(x_train,x_test, feature, feature_list,test = True, params,num_folds, stratified = False): if stratified: folds = StratifiedKFold(n_splits= num_folds, shuffle=True, random_state=2) else: folds = KFold(n_splits= num_folds, shuffle=True, random_state=2) ntrain = x_train.shape[0] ntest = x_test.shape[0] oof_train = np.zeros((ntrain,)) oof_test = np.zeros((ntest,)) oof_test_skf = np.empty((num_folds, ntest)) feature_importance_df = pd.DataFrame() for n_fold, (train_idx, test_idx) in enumerate(folds.split(x_train[feature],x_train['target'])): print('\n############################# kfold = ' + str(n_fold + 1)) X_train, X_valid = x_train[feature].iloc[train_idx],x_train[feature].iloc[test_idx] y_train, y_valid = x_train['target'].iloc[train_idx],x_train['target'].iloc[test_idx] print('after kfold split, shape = ', X_train.shape) N = 1 pred_valid, pred_test = 0,0 for Ni in range(N): print('Ni = ', Ni) X_t, y_t = augment(X_train.values, y_train.values) X_t = pd.DataFrame(X_t, columns = feature) print('after augmentation, shape = ', X_t.shape) train_fe, valid_fe, test_fe = feature_eng(X_t, X_valid, x_test, x_train, test_real, feature) print('after FE, shape = ', train_fe.shape) if test: train_fe = train_fe valid_fe = valid_fe test_fe = test_fe else: train_fe = train_fe[feature_list] valid_fe = valid_fe[feature_list] test_fe = test_fe[feature_list] dtrain = lgb.Dataset(data = train_fe, label = y_t, free_raw_data = False, silent = True) dtest = lgb.Dataset(data = X_valid, label = y_valid, free_raw_data = False, silent = True) clf = lgb.train( params=params, train_set=dtrain, num_boost_round=100000, valid_sets=[dtrain, dtest], early_stopping_rounds=400, verbose_eval=4000 ) pred_valid += clf.predict(dtest.data)/N pred_test += clf.predict(x_test[train_fe.columns])/N oof_train[test_idx] = pred_valid oof_test_skf[n_fold,:] = pred_test print('Fold %2d AUC : %.6f' % (n_fold + 1, roc_auc_score(dtest.label, oof_train[test_idx]))) del clf, dtrain, dtest gc.collect() print("Full AUC score %.6f" % roc_auc_score(x_train['target'], oof_train)) oof_test[:] = oof_test_skf.mean(axis=0) return oof_train, oof_test params = {'metric': 'auc', 'learning_rate': 0.01, 'nthread': -1, 'max_depth':1, 'reg_lambda': 0.0, 'objective': 'binary', # 'colsample_bytree': 1, 'bagging_freq': 5, 'feature_fraction':0.05, 'min_data_in_leaf':80, 'min_sum_hessian_in_leaf':10, 'boost_from_average':False, 'tree_learner':'serial', 'num_leaves': 13, 'boosting_type': 'gbdt'} features = [col for col in train.columns if col not in ['target','ID_code','flag']] oof_train,oof_test = kfold_lightgbm(train, test, features,feature_list = features, test = True, params, 5, stratified = False)

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budaLi/leetcode-python-

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#-*-coding:utf8-*- #author : Lenovo #date: 2018/7/23 class Solution(object): def twoSum(self, nums, target): """ :type nums: List[int] :type target: int :rtype: List[int] """ d = {}# d is a dictionary to map the value of nums and the index in nums size = 0 for size in range(len(nums)): if not nums[size] in d: d[nums[size]] = size #if nums[size] doesn't exist in d ,create it if target - nums[size] in d: #if nums[size] and target - nums[size] are both in d # if d[target-nums[size]] < size + 1: # one situation should be minded nums[size] == target - nums[size] ans = [d[target - nums[size]] , size ]# for example [0,1,2] 0 and [0,1,2,0],0 return ans ex=Solution() e=ex.twoSum([1,2,5,7,8],16) print(e)

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from typing import Union, List from pythum.qubit import Qubit class Qublock: """An list-like block of n qubits""" def __init__(self, value: Union[int, 'Qublock', List[Qubit]]): _type = type(value) if _type is int: # Just instanciate the block with n qubits n = value value = [ Qubit() for i in range(n) ] elif issubclass(_type, Qublock): # Copy each qubit qublock = value value = [Qubit.from_qubit(qubit) for qubit in qublock.__qubits] elif _type is not list: raise ValueError("Expected an int or list of Qubits") self.__qubits = value @classmethod def from_notation(cls, value: str) -> 'Qublock': block = value.split(">") value = [ Qubit.from_notation("{0}>".format(q)) for q in block ] return cls(value) def __str__(self): return "".join(( str(qbit) for qbit in self.__qubits )) def __repr__(self): return str(self) def __in__(self, value) -> bool: return value in self.__qubits def __iter__(self): return iter(self.__qubits) def __len__(self): return len(self.__qubits) def __getitem__(self, pos: int): return self.__qubits[pos] def __setitem__(self, pos: int, qubit: Qubit): self.__qubits[pos] = qubit class Qubyte(Qublock): def __init__(self, value: Union[Qublock, List[Qubit]]=None): if value is None: value = 8 if type(value) is int and value > 8: value = 8 super().__init__(value) @classmethod def from_notation(cls, value: str) -> 'Qubyte': block = value.split(">")[:-1] value = [ Qubit.from_notation("{0}>".format(q)) for q in block ] _len = len(value) if _len < 8: value = ( Qubit() for i in range(_len, 8) ) + value elif _len > 8: value = value[-8:] return cls(value)

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from functools import wraps from json import dumps from typing import Any, Callable, Collection, Optional from ..language.ast import Node, OperationType from .visitor import visit, Visitor from .block_string import print_block_string __all__ = ["print_ast"] Strings = Collection[str] class PrintedNode: """A union type for all nodes that have been processed by the printer.""" alias: str arguments: Strings block: bool default_value: str definitions: Strings description: str directives: str fields: Strings interfaces: Strings locations: Strings name: str operation: OperationType operation_types: Strings repeatable: bool selection_set: str selections: Strings type: str type_condition: str types: Strings value: str values: Strings variable: str variable_definitions: Strings def print_ast(ast: Node) -> str: """Convert an AST into a string. The conversion is done using a set of reasonable formatting rules. """ return visit(ast, PrintAstVisitor()) def add_description(method: Callable[..., str]) -> Callable: """Decorator adding the description to the output of a static visitor method.""" @wraps(method) def wrapped(node: PrintedNode, *args: Any) -> str: return join((node.description, method(node, *args)), "\n") return wrapped class PrintAstVisitor(Visitor): @staticmethod def leave_name(node: PrintedNode, *_args: Any) -> str: return node.value @staticmethod def leave_variable(node: PrintedNode, *_args: Any) -> str: return f"${node.name}" # Document @staticmethod def leave_document(node: PrintedNode, *_args: Any) -> str: return join(node.definitions, "\n\n") + "\n" @staticmethod def leave_operation_definition(node: PrintedNode, *_args: Any) -> str: name, op, selection_set = node.name, node.operation, node.selection_set var_defs = wrap("(", join(node.variable_definitions, ", "), ")") directives = join(node.directives, " ") # Anonymous queries with no directives or variable definitions can use the # query short form. return ( join((op.value, join((name, var_defs)), directives, selection_set), " ") if (name or directives or var_defs or op != OperationType.QUERY) else selection_set ) @staticmethod def leave_variable_definition(node: PrintedNode, *_args: Any) -> str: return ( f"{node.variable}: {node.type}" f"{wrap(' = ', node.default_value)}" f"{wrap(' ', join(node.directives, ' '))}" ) @staticmethod def leave_selection_set(node: PrintedNode, *_args: Any) -> str: return block(node.selections) @staticmethod def leave_field(node: PrintedNode, *_args: Any) -> str: return join( ( wrap("", node.alias, ": ") + node.name + wrap("(", join(node.arguments, ", "), ")"), join(node.directives, " "), node.selection_set, ), " ", ) @staticmethod def leave_argument(node: PrintedNode, *_args: Any) -> str: return f"{node.name}: {node.value}" # Fragments @staticmethod def leave_fragment_spread(node: PrintedNode, *_args: Any) -> str: return f"...{node.name}{wrap(' ', join(node.directives, ' '))}" @staticmethod def leave_inline_fragment(node: PrintedNode, *_args: Any) -> str: return join( ( "...", wrap("on ", node.type_condition), join(node.directives, " "), node.selection_set, ), " ", ) @staticmethod def leave_fragment_definition(node: PrintedNode, *_args: Any) -> str: # Note: fragment variable definitions are experimental and may be changed or # removed in the future. return ( f"fragment {node.name}" f"{wrap('(', join(node.variable_definitions, ', '), ')')}" f" on {node.type_condition}" f" {wrap('', join(node.directives, ' '), ' ')}" f"{node.selection_set}" ) # Value @staticmethod def leave_int_value(node: PrintedNode, *_args: Any) -> str: return node.value @staticmethod def leave_float_value(node: PrintedNode, *_args: Any) -> str: return node.value @staticmethod def leave_string_value(node: PrintedNode, key: str, *_args: Any) -> str: if node.block: return print_block_string(node.value, "" if key == "description" else " ") return dumps(node.value) @staticmethod def leave_boolean_value(node: PrintedNode, *_args: Any) -> str: return "true" if node.value else "false" @staticmethod def leave_null_value(_node: PrintedNode, *_args: Any) -> str: return "null" @staticmethod def leave_enum_value(node: PrintedNode, *_args: Any) -> str: return node.value @staticmethod def leave_list_value(node: PrintedNode, *_args: Any) -> str: return f"[{join(node.values, ', ')}]" @staticmethod def leave_object_value(node: PrintedNode, *_args: Any) -> str: return f"{{{join(node.fields, ', ')}}}" @staticmethod def leave_object_field(node: PrintedNode, *_args: Any) -> str: return f"{node.name}: {node.value}" # Directive @staticmethod def leave_directive(node: PrintedNode, *_args: Any) -> str: return f"@{node.name}{wrap('(', join(node.arguments, ', '), ')')}" # Type @staticmethod def leave_named_type(node: PrintedNode, *_args: Any) -> str: return node.name @staticmethod def leave_list_type(node: PrintedNode, *_args: Any) -> str: return f"[{node.type}]" @staticmethod def leave_non_null_type(node: PrintedNode, *_args: Any) -> str: return f"{node.type}!" # Type System Definitions @staticmethod @add_description def leave_schema_definition(node: PrintedNode, *_args: Any) -> str: return join( ("schema", join(node.directives, " "), block(node.operation_types)), " " ) @staticmethod def leave_operation_type_definition(node: PrintedNode, *_args: Any) -> str: return f"{node.operation.value}: {node.type}" @staticmethod @add_description def leave_scalar_type_definition(node: PrintedNode, *_args: Any) -> str: return join(("scalar", node.name, join(node.directives, " ")), " ") @staticmethod @add_description def leave_object_type_definition(node: PrintedNode, *_args: Any) -> str: return join( ( "type", node.name, wrap("implements ", join(node.interfaces, " & ")), join(node.directives, " "), block(node.fields), ), " ", ) @staticmethod @add_description def leave_field_definition(node: PrintedNode, *_args: Any) -> str: args = node.arguments args = ( wrap("(\n", indent(join(args, "\n")), "\n)") if has_multiline_items(args) else wrap("(", join(args, ", "), ")") ) directives = wrap(" ", join(node.directives, " ")) return f"{node.name}{args}: {node.type}{directives}" @staticmethod @add_description def leave_input_value_definition(node: PrintedNode, *_args: Any) -> str: return join( ( f"{node.name}: {node.type}", wrap("= ", node.default_value), join(node.directives, " "), ), " ", ) @staticmethod @add_description def leave_interface_type_definition(node: PrintedNode, *_args: Any) -> str: return join( ( "interface", node.name, wrap("implements ", join(node.interfaces, " & ")), join(node.directives, " "), block(node.fields), ), " ", ) @staticmethod @add_description def leave_union_type_definition(node: PrintedNode, *_args: Any) -> str: return join( ( "union", node.name, join(node.directives, " "), "= " + join(node.types, " | ") if node.types else "", ), " ", ) @staticmethod @add_description def leave_enum_type_definition(node: PrintedNode, *_args: Any) -> str: return join( ("enum", node.name, join(node.directives, " "), block(node.values)), " " ) @staticmethod @add_description def leave_enum_value_definition(node: PrintedNode, *_args: Any) -> str: return join((node.name, join(node.directives, " ")), " ") @staticmethod @add_description def leave_input_object_type_definition(node: PrintedNode, *_args: Any) -> str: return join( ("input", node.name, join(node.directives, " "), block(node.fields)), " " ) @staticmethod @add_description def leave_directive_definition(node: PrintedNode, *_args: Any) -> str: args = node.arguments args = ( wrap("(\n", indent(join(args, "\n")), "\n)") if has_multiline_items(args) else wrap("(", join(args, ", "), ")") ) repeatable = " repeatable" if node.repeatable else "" locations = join(node.locations, " | ") return f"directive @{node.name}{args}{repeatable} on {locations}" @staticmethod def leave_schema_extension(node: PrintedNode, *_args: Any) -> str: return join( ("extend schema", join(node.directives, " "), block(node.operation_types)), " ", ) @staticmethod def leave_scalar_type_extension(node: PrintedNode, *_args: Any) -> str: return join(("extend scalar", node.name, join(node.directives, " ")), " ") @staticmethod def leave_object_type_extension(node: PrintedNode, *_args: Any) -> str: return join( ( "extend type", node.name, wrap("implements ", join(node.interfaces, " & ")), join(node.directives, " "), block(node.fields), ), " ", ) @staticmethod def leave_interface_type_extension(node: PrintedNode, *_args: Any) -> str: return join( ( "extend interface", node.name, wrap("implements ", join(node.interfaces, " & ")), join(node.directives, " "), block(node.fields), ), " ", ) @staticmethod def leave_union_type_extension(node: PrintedNode, *_args: Any) -> str: return join( ( "extend union", node.name, join(node.directives, " "), "= " + join(node.types, " | ") if node.types else "", ), " ", ) @staticmethod def leave_enum_type_extension(node: PrintedNode, *_args: Any) -> str: return join( ("extend enum", node.name, join(node.directives, " "), block(node.values)), " ", ) @staticmethod def leave_input_object_type_extension(node: PrintedNode, *_args: Any) -> str: return join( ("extend input", node.name, join(node.directives, " "), block(node.fields)), " ", ) def join(strings: Optional[Strings], separator: str = "") -> str: """Join strings in a given collection. Return an empty string if it is None or empty, otherwise join all items together separated by separator if provided. """ return separator.join(s for s in strings if s) if strings else "" def block(strings: Optional[Strings]) -> str: """Return strings inside a block. Given a collection of strings, return a string with each item on its own line, wrapped in an indented "{ }" block. """ return wrap("{\n", indent(join(strings, "\n")), "\n}") def wrap(start: str, string: Optional[str], end: str = "") -> str: """Wrap string inside other strings at start and end. If the string is not None or empty, then wrap with start and end, otherwise return an empty string. """ return f"{start}{string}{end}" if string else "" def indent(string: str) -> str: """Indent string with two spaces. If the string is not None or empty, add two spaces at the beginning of every line inside the string. """ return wrap(" ", string.replace("\n", "\n ")) def is_multiline(string: str) -> bool: """Check whether a string consists of multiple lines.""" return "\n" in string def has_multiline_items(strings: Optional[Strings]) -> bool: """Check whether one of the items in the list has multiple lines.""" return any(is_multiline(item) for item in strings) if strings else False

[ "cito@online.de" ]

cito@online.de

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/server/configurer.py

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[]

no_license

intrepiditee/IFTTT-Privacy

16bcc206ef3878b714567d643fbc45aecb1c4ee7

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refs/heads/master

2020-08-21T23:46:03.069728

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import json import threading class Configurer: def __init__(self, path, key="sensors"): with open(path, "rt") as f: configs = json.load(f) self.configs = configs[key] self.index = 0 self.lock = threading.Lock() def get(self): self.lock.acquire() config = self.configs[self.index]; self.index += 1 if self.index >= len(self.configs): self.index = 0 self.lock.release() return config

[ "jay.shijunjie@gmail.com" ]

jay.shijunjie@gmail.com

d76e46afa9347a3212afc1f391dab391766e7696

a36501f44a09ca03dd1167e1d7965f782e159097

/app/extensions/mongobeat/models.py

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[ "Apache-2.0" ]

permissive

ssfdust/full-stack-flask-smorest

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refs/heads/master

2023-08-05T08:48:03.474042

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# Copyright 2019 RedLotus <ssfdust@gmail.com> # Author: RedLotus <ssfdust@gmail.com> # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Copyright 2018 Regents of the University of Michigan # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy # of the License at http://www.apache.org/licenses/LICENSE-2.0 """ app.extensions.mongobeat ~~~~~~~~~~~~~~~~~~~~~~~~~ MongoBeat的ORM模块 """ import datetime from ast import literal_eval import celery.schedules from celery import current_app from mongoengine import ( BooleanField, DateTimeField, DictField, DynamicDocument, DynamicField, EmbeddedDocument, EmbeddedDocumentField, IntField, ListField, StringField, ) def get_periodic_task_collection(): """获取表名""" if ( hasattr(current_app.conf, "CELERY_MONGODB_SCHEDULER_COLLECTION") and current_app.conf.CELERY_MONGODB_SCHEDULER_COLLECTION ): return current_app.conf.CELERY_MONGODB_SCHEDULER_COLLECTION # pragma: no cover return "schedules" #: Authorized values for PeriodicTask.Interval.period PERIODS = ("days", "hours", "minutes", "seconds", "microseconds") class PeriodicTask(DynamicDocument): """ 周期任务的ORM :attr name: 定时名称 :attr task: 任务名称 :attr interval: 定时 :attr crontab: crontab :attr args: 参数 :attr kwargs: 键值参数 :attr queue: 队列 :attr no_changes: nochanges :attr exchange: AMPQ的交换器 :attr routing_key: AMPQ路由 :attr soft_time_limit: 软时间限制 :attr expires: 过期时间 :attr start_after: 在某时间后运行 :attr enabled: 启用 :attr last_run_at: 最后运行时间 :attr total_run_count: 总计运行次数 :attr max_run_count: 最大运行次数 :attr date_changed: 改变日期 :attr description: 描述 :attr run_immediately: 立刻运行 """ meta = {"collection": get_periodic_task_collection(), "allow_inheritance": True} class Interval(EmbeddedDocument): """ :attr every 每（周期） :attr period 周期区间 """ meta = {"allow_inheritance": True} every = IntField(min_value=0, default=0, required=True, verbose_name="周期") period = StringField(choices=PERIODS, verbose_name="每") @property def schedule(self): return celery.schedules.schedule( datetime.timedelta(**{self.period: self.every}) ) @property def period_singular(self): return self.period[:-1] def __str__(self): if self.every == 1: return "every {0.period_singular}".format(self) return "every {0.every} {0.period}".format(self) class Crontab(EmbeddedDocument): """ :attr minute 分钟 :attr hour 小时 :attr day_of_week 周 :attr day_of_month 日 :attr mouth_of_year 月 """ meta = {"allow_inheritance": True} minute = StringField(default="*", required=True, verbose_name="分钟") hour = StringField(default="*", required=True, verbose_name="小时") day_of_week = StringField(default="*", required=True, verbose_name="周") day_of_month = StringField(default="*", required=True, verbose_name="日") month_of_year = StringField(default="*", required=True, verbose_name="月") @property def schedule(self): return celery.schedules.crontab( minute=self.minute, hour=self.hour, day_of_week=self.day_of_week, day_of_month=self.day_of_month, month_of_year=self.month_of_year, ) def __str__(self): def rfield(f): return f and str(f).replace(" ", "") or "*" return "{0} {1} {2} {3} {4} (分/时/周/日/月)".format( rfield(self.minute), rfield(self.hour), rfield(self.day_of_week), rfield(self.day_of_month), rfield(self.month_of_year), ) name = StringField(unique=True, verbose_name="定时名称") task = StringField(required=True, verbose_name="任务名称") args = ListField(DynamicField(), verbose_name="参数") kwargs = DictField(verbose_name="键值参数") queue = StringField(verbose_name="队列") exchange = StringField(verbose_name="AMPQ的交换器") routing_key = StringField(verbose_name="AMPQ路由") soft_time_limit = IntField(verbose_name="软时间限制") expires = DateTimeField(verbose_name="过期时间") start_after = DateTimeField(verbose_name="在某时间后运行") enabled = BooleanField(default=False, verbose_name="启用") last_run_at = DateTimeField(verbose_name="最后运行时间") total_run_count = IntField(min_value=0, default=0, verbose_name="总计运行次数") max_run_count = IntField(min_value=0, default=0, verbose_name="最大运行次数") date_changed = DateTimeField(verbose_name="改变日期") description = StringField(verbose_name="描述") run_immediately = BooleanField(verbose_name="立刻运行") type = StringField( required=True, verbose_name="类型", choices=["crontab", "interval"] ) interval = EmbeddedDocumentField(Interval, verbose_name="定时") crontab = EmbeddedDocumentField(Crontab, verbose_name="周期") # objects = managers.PeriodicTaskManager() no_changes = False def clean(self): """透过MongoEngine验证interval和crontab不是同时存在""" if self.type == "crontab": self.interval = None else: self.crontab = None if isinstance(self.args, str): self.args = literal_eval(self.args) if isinstance(self.kwargs, str): self.kwargs = literal_eval(self.kwargs) @property def schedule(self): if self.interval: return self.interval.schedule elif self.crontab: return self.crontab.schedule else: raise Exception("must define interval or crontab schedule") def __str__(self): fmt = "{0.name}: {{no schedule}}" if self.interval: fmt = "{0.name}: {0.interval}" elif self.crontab: fmt = "{0.name}: {0.crontab}" else: raise Exception("must define interval or crontab schedule") return fmt.format(self)

[ "ssfdust@gmail.com" ]

ssfdust@gmail.com

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nmanley73/pyprimes

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# Noel Manley # Computing the primes P = [] # Loop through all the numbers to check for prime no's for i in range(2, 100): # assume number is prime isprime = True # Loop through all values from 2 up to i for j in range(2, i): # see if j divides i if i % j == 0: # If it does, i isn't prime so exit the loop isprime = False break # If it i prime then append to P if isprime: P.append(i) # Print out the list print(P)

[ "noelmanley@hotmail.com" ]

noelmanley@hotmail.com

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/Mechinal Turk Consensus Algorithm/vp/migrations/0065_auto_20160510_2234.py

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JustinHinh/AppyHour

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models, migrations class Migration(migrations.Migration): dependencies = [ ('vp', '0064_auto_20160508_1319'), ] operations = [ migrations.RemoveField( model_name='mturklocationinfostat', name='usLocaleRequired', ), migrations.AddField( model_name='mturklocationinfostat', name='localeRequired', field=models.CharField(max_length=3, null=True), ), ]

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2020-08-23T09:46:06.260211

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import numpy as np def get_random_other_index(current_index, num_indices): indices = list(range(num_indices)) random_index = current_index while random_index == current_index: random_index = np.random.randint(0, num_indices) return random_index def assign_probabilities(probabilities, ordered_indices): if len(probabilities) != len(ordered_indices): raise ValueError(f'Length mismatch: {len(probabilities)} != {len(ordered_indices)}') sorted_probabilities = np.zeros((len(probabilities),)) for i, idx in enumerate(ordered_indices): sorted_probabilities[idx] = probabilities[i] return sorted_probabilities def uniform_init(bounds): """ Uniform initialization function. """ if not isinstance(bounds, (np.ndarray, list)): raise ValueError('Bounds must be a list or numpy array') elif np.ndim(bounds) != 2: ndim = np.ndim(bounds) raise ValueError(f'Bounds must be a 2D array but got an array of dim {ndim}') dimensions = len(bounds) min_bounds = np.array([b[0] for b in bounds]) max_bounds = np.array([b[1] for b in bounds]) def init_fn(population_size): return np.random.uniform(min_bounds, max_bounds, size=(population_size, dimensions)) return init_fn

[ "romain.strock@gmail.com" ]

romain.strock@gmail.com

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/students/JonathanMauk/lesson02/series.py

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UWPCE-PythonCert-ClassRepos/Self_Paced-Online

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def fibonacci(n): """Function that returns the nth term in the Fibonacci sequence, per F(n) = (n-1) + (n-2).""" if n < 0: print("Error: the first term in the Fibonacci sequence is 0. Please try again.") else: return sum_series(n) def lucas(n): """Function that returns the nth term in the Lucas series, per F(n) = (n-1) + (n-2), and F(0) = 2 while F(1) = 1.""" if n < 0: print("Error: the first term in the Lucas series is 2. Please try again.") else: return sum_series(n, 2, 1) def sum_series(n, x=0, y=1): """ Generalized function that returns nth term in recursive sequences like Fibonacci and Lucas. Defaults to Fibonacci sequence. """ if n == 0: return x if n == 1: return y else: return sum_series(n-1, x, y) + sum_series(n-2, x, y) # Assert statements: Fibonacci edition assert fibonacci(1) assert fibonacci(5) assert fibonacci(15) # Assert statements: Lucas edition assert lucas(0) assert lucas(3) assert lucas(8) # Assert statements: sum series edition assert sum_series(4) # defaults to Fibonacci assert sum_series(5, 2, 1) # Lucas series assert sum_series(8, 7, 5) # random

[ "jorauk@uw.edu" ]

jorauk@uw.edu

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import logging log = logging.getLogger(__name__) class Codec(object): def __init__(self): pass def compress(self, stream): return stream def decompress(self, stream): return stream

[ "me@svenkreiss.com" ]

me@svenkreiss.com

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/Mutation_Modules/ASP_ABU.py

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no_license

PietroAronica/Parasol.py

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238abcdc2caee7bbfea6cfcdda1ca705766db204

refs/heads/master

2021-01-10T23:57:40.225140

2020-10-14T02:21:15

70,791,648

0

null

UTF-8

Python

false

18,941

py

# ASP to ABU Mutation import Frcmod_creator import PDBHandler import Leapy from parmed.tools.actions import * from parmed.amber.readparm import * def parmed_command(vxi='VXI', lipid='No'): bc = {} with open('Param_files/AminoAcid/ASP.param', 'r') as b: data = b.readlines()[1:] for line in data: key, value = line.split() bc[key] = float(value) b.close() fc = {} with open('Param_files/AminoAcid/ABU.param', 'r') as b: data = b.readlines()[1:] for line in data: key, value = line.split() fc[key] = float(value) b.close() for i in range(11): a = i*10 i = float(i) parm = AmberParm('Solv_{}_{}.prmtop'.format(a, 100-a)) changeLJPair(parm, ':{}@HB2'.format(vxi), ':{}@OD1'.format(vxi), '0', '0').execute() changeLJPair(parm, ':{}@HB'.format(vxi), ':{}@HG1'.format(vxi), '0', '0').execute() change(parm, 'charge', ':{}@N'.format(vxi), bc['N']+((fc['N']-bc['N'])/10)*i).execute() change(parm, 'charge', ':{}@H'.format(vxi), bc['H']+((fc['H']-bc['H'])/10)*i).execute() change(parm, 'charge', ':{}@CA'.format(vxi), bc['CA']+((fc['CA']-bc['CA'])/10)*i).execute() change(parm, 'charge', ':{}@HA'.format(vxi), bc['HA']+((fc['HA']-bc['HA'])/10)*i).execute() change(parm, 'charge', ':{}@CB'.format(vxi), bc['CB']+((fc['CB']-bc['CB'])/10)*i).execute() change(parm, 'charge', ':{}@HB'.format(vxi), bc['HB2']-(bc['HB2']/10)*i).execute() change(parm, 'charge', ':{}@HB2'.format(vxi), fc['HB2']/10*i).execute() change(parm, 'charge', ':{}@HB3'.format(vxi), bc['HB3']+((fc['HB3']-bc['HB3'])/10)*i).execute() change(parm, 'charge', ':{}@CG'.format(vxi), fc['CG']/10*i).execute() change(parm, 'charge', ':{}@HG1'.format(vxi), fc['HG1']/10*i).execute() change(parm, 'charge', ':{}@HG2'.format(vxi), fc['HG2']/10*i).execute() change(parm, 'charge', ':{}@HG3'.format(vxi), fc['HG3']/10*i).execute() change(parm, 'charge', ':{}@CG1'.format(vxi), (bc['CG']-(bc['CG']/10)*i)*(10-i)/10).execute() change(parm, 'charge', ':{}@OD1'.format(vxi), (bc['OD1']-(bc['OD1']/10)*i)*(10-i)/10).execute() change(parm, 'charge', ':{}@OD2'.format(vxi), (bc['OD2']-(bc['OD2']/10)*i)*(10-i)/10).execute() change(parm, 'charge', ':{}@C'.format(vxi), bc['C']+((fc['C']-bc['C'])/10)*i).execute() change(parm, 'charge', ':{}@O'.format(vxi), bc['O']+((fc['O']-bc['O'])/10)*i).execute() #print printDetails(parm, ':VXI') d = netCharge(parm).execute() change(parm, 'charge', ':PC', '{:.3f}'.format(-d)).execute() setOverwrite(parm).execute() parmout(parm, 'Solv_{}_{}.prmtop'.format(a, 100-a)).execute() def makevxi(struct, out, aa, vxi='VXI'): struct.residue_dict[aa].set_resname(vxi) CB = struct.residue_dict[aa].atom_dict['CB'] HB2 = struct.residue_dict[aa].atom_dict['HB2'] CG = struct.residue_dict[aa].atom_dict['CG'] pdb = open(out, 'w') try: pdb.write(struct.other_dict['Cryst1'].formatted()) except KeyError: pass for res in struct.residue_list: for atom in res.atom_list: if atom.get_name() == 'HB2' and res.get_resname() == vxi: pdb.write(atom.change_name('HB')) pdb.write(atom.superimposed1('HB2', CG)) elif atom.get_name() == 'HB3' and res.get_resname() == vxi: pdb.write(atom.formatted()) pdb.write(atom.halfway_between('CG', CB, HB2)) pdb.write(atom.superimposed1('HG1', HB2)) pdb.write(atom.superimposed2('HG2', HB2)) pdb.write(atom.superimposed3('HG3', HB2)) elif atom.get_name() == 'CG' and res.get_resname() == vxi: pdb.write(atom.change_name('CG1')) else: pdb.write(atom.formatted()) try: pdb.write(struct.other_dict[atom.get_number()].ter()) except: pass for oth in struct.other_dict: try: if oth.startswith('Conect'): pdb.write(struct.other_dict[oth].formatted()) except: pass pdb.write('END\n') def variablemake(sym='^'): var1 = sym + '1' var2 = sym + '2' var3 = sym + '3' var4 = sym + '4' var5 = sym + '5' var6 = sym + '6' var7 = sym + '7' var8 = sym + '8' var9 = sym + '9' var10 = sym + '0' var11 = sym + 'a' var12 = sym + 'b' var13 = sym + 'c' var14 = sym + 'd' var15 = sym + 'e' return var1, var2, var3, var4, var5, var6, var7, var8, var9, var10, var11, var12, var13, var14, var15 def lib_make(ff, outputfile, vxi='VXI', var=variablemake()): metcar = var[0] methyd = var[1] hydhyd1 = var[2] carcar = var[3] caroxy = var[4] hydhyd2 = var[5] ctrl = open('lyp.in', 'w') ctrl.write("source %s\n"%ff) ctrl.write("%s=loadpdb Param_files/LibPDB/ASP-ABU.pdb\n"%vxi) ctrl.write('set %s.1.1 element "N"\n'%vxi) ctrl.write('set %s.1.2 element "H"\n'%vxi) ctrl.write('set %s.1.3 element "C"\n'%vxi) ctrl.write('set %s.1.4 element "H"\n'%vxi) ctrl.write('set %s.1.5 element "C"\n'%vxi) ctrl.write('set %s.1.6 element "H"\n'%vxi) ctrl.write('set %s.1.7 element "H"\n'%vxi) ctrl.write('set %s.1.8 element "H"\n'%vxi) ctrl.write('set %s.1.9 element "C"\n'%vxi) ctrl.write('set %s.1.10 element "H"\n'%vxi) ctrl.write('set %s.1.11 element "H"\n'%vxi) ctrl.write('set %s.1.12 element "H"\n'%vxi) ctrl.write('set %s.1.13 element "C"\n'%vxi) ctrl.write('set %s.1.14 element "O"\n'%vxi) ctrl.write('set %s.1.15 element "O"\n'%vxi) ctrl.write('set %s.1.16 element "C"\n'%vxi) ctrl.write('set %s.1.17 element "O"\n'%vxi) ctrl.write('set %s.1.1 name "N"\n'%vxi) ctrl.write('set %s.1.2 name "H"\n'%vxi) ctrl.write('set %s.1.3 name "CA"\n'%vxi) ctrl.write('set %s.1.4 name "HA"\n'%vxi) ctrl.write('set %s.1.5 name "CB"\n'%vxi) ctrl.write('set %s.1.6 name "HB"\n'%vxi) ctrl.write('set %s.1.7 name "HB2"\n'%vxi) ctrl.write('set %s.1.8 name "HB3"\n'%vxi) ctrl.write('set %s.1.9 name "CG"\n'%vxi) ctrl.write('set %s.1.10 name "HG1"\n'%vxi) ctrl.write('set %s.1.11 name "HG2"\n'%vxi) ctrl.write('set %s.1.12 name "HG3"\n'%vxi) ctrl.write('set %s.1.13 name "CG1"\n'%vxi) ctrl.write('set %s.1.14 name "OD1"\n'%vxi) ctrl.write('set %s.1.15 name "OD2"\n'%vxi) ctrl.write('set %s.1.16 name "C"\n'%vxi) ctrl.write('set %s.1.17 name "O"\n'%vxi) ctrl.write('set %s.1.1 type "N"\n'%vxi) ctrl.write('set %s.1.2 type "H"\n'%vxi) ctrl.write('set %s.1.3 type "CT"\n'%vxi) ctrl.write('set %s.1.4 type "H1"\n'%vxi) ctrl.write('set %s.1.5 type "CT"\n'%vxi) ctrl.write('set %s.1.6 type "%s"\n'%(vxi, hydhyd1)) ctrl.write('set %s.1.7 type "%s"\n'%(vxi, hydhyd2)) ctrl.write('set %s.1.8 type "HC"\n'%vxi) ctrl.write('set %s.1.9 type "%s"\n'%(vxi, metcar)) ctrl.write('set %s.1.10 type "%s"\n'%(vxi, methyd)) ctrl.write('set %s.1.11 type "%s"\n'%(vxi, methyd)) ctrl.write('set %s.1.12 type "%s"\n'%(vxi, methyd)) ctrl.write('set %s.1.13 type "%s"\n'%(vxi, carcar)) ctrl.write('set %s.1.14 type "%s"\n'%(vxi, caroxy)) ctrl.write('set %s.1.15 type "%s"\n'%(vxi, caroxy)) ctrl.write('set %s.1.16 type "C"\n'%vxi) ctrl.write('set %s.1.17 type "O"\n'%vxi) ctrl.write('bond %s.1.1 %s.1.2\n'%(vxi, vxi)) ctrl.write('bond %s.1.1 %s.1.3\n'%(vxi, vxi)) ctrl.write('bond %s.1.3 %s.1.4\n'%(vxi, vxi)) ctrl.write('bond %s.1.3 %s.1.5\n'%(vxi, vxi)) ctrl.write('bond %s.1.3 %s.1.16\n'%(vxi, vxi)) ctrl.write('bond %s.1.5 %s.1.6\n'%(vxi, vxi)) ctrl.write('bond %s.1.5 %s.1.7\n'%(vxi, vxi)) ctrl.write('bond %s.1.5 %s.1.8\n'%(vxi, vxi)) ctrl.write('bond %s.1.5 %s.1.9\n'%(vxi, vxi)) ctrl.write('bond %s.1.5 %s.1.13\n'%(vxi, vxi)) ctrl.write('bond %s.1.9 %s.1.10\n'%(vxi, vxi)) ctrl.write('bond %s.1.9 %s.1.11\n'%(vxi, vxi)) ctrl.write('bond %s.1.9 %s.1.12\n'%(vxi, vxi)) ctrl.write('bond %s.1.13 %s.1.14\n'%(vxi, vxi)) ctrl.write('bond %s.1.13 %s.1.15\n'%(vxi, vxi)) ctrl.write('bond %s.1.16 %s.1.17\n'%(vxi, vxi)) ctrl.write('set %s.1 connect0 %s.1.N\n'%(vxi, vxi)) ctrl.write('set %s.1 connect1 %s.1.C\n'%(vxi, vxi)) ctrl.write('set %s name "%s"\n'%(vxi, vxi)) ctrl.write('set %s.1 name "%s"\n'%(vxi, vxi)) ctrl.write('set %s head %s.1.N\n'%(vxi, vxi)) ctrl.write('set %s tail %s.1.C\n'%(vxi, vxi)) ctrl.write('saveoff %s %s.lib\n'%(vxi, vxi)) ctrl.write("quit\n") ctrl.close() Leapy.run('lyp.in', outputfile) def all_make(): for i in range(0,110,10): Frcmod_creator.make ('{}_{}.frcmod'.format(i, 100-i)) def cal(x, y, i): num = x+((y-x)/10)*i return num def lac(y, x, i): num = x+((y-x)/10)*i return num def stock_add_to_all(var=variablemake()): metcar = var[0] methyd = var[1] hydhyd1 = var[2] carcar = var[3] caroxy = var[4] hydhyd2 = var[5] Frcmod_creator.make_hyb() Frcmod_creator.TYPE_insert(carcar, 'C', 'sp2') Frcmod_creator.TYPE_insert(caroxy, 'O', 'sp2') Frcmod_creator.TYPE_insert(hydhyd2, 'H', 'sp3') Frcmod_creator.TYPE_insert(metcar, 'C', 'sp3') Frcmod_creator.TYPE_insert(methyd, 'H', 'sp3') Frcmod_creator.TYPE_insert(hydhyd1, 'H', 'sp3') p = {} with open('Param_files/Stock/Stock.param', 'r') as b: data = b.readlines()[1:] for line in data: p[line.split()[0]] = [] for point in line.split()[1:]: p[line.split()[0]].append(float(point)) b.close() for i in range(11): a = i*10 Frcmod_creator.MASS_insert('{}_{}.frcmod'.format(a, 100-a), carcar, cal(p['C'][0], p['0_C'][0], i), cal(p['C'][1], p['0_C'][1], i)) Frcmod_creator.MASS_insert('{}_{}.frcmod'.format(a, 100-a), caroxy, cal(p['O2'][0], p['0_O'][0], i), cal(p['O2'][1], p['0_O'][1], i)) Frcmod_creator.MASS_insert('{}_{}.frcmod'.format(a, 100-a), hydhyd2, cal(p['0_H'][0], p['HC'][0], i), cal(p['0_H'][1], p['HC'][1], i)) Frcmod_creator.BOND_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}'.format('CT', carcar), cal(p['CT_C'][0], p['CT_mH'][0], i), cal(p['CT_C'][1], p['CT_mH'][1], i)) Frcmod_creator.BOND_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}'.format('CT', hydhyd2), cal(p['HC_sC2'][0], p['CT_HC'][0], i), cal(p['HC_sC2'][1], p['CT_HC'][1], i)) Frcmod_creator.BOND_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}'.format(carcar, caroxy), cal(p['C_O2'][0], p['O2_mH'][0], i), cal(p['C_O2'][1], p['O2_mH'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format('CT', carcar, caroxy), cal(p['C_C_O2'][0], p['Dritt'][0], i), cal(p['C_C_O2'][1], p['Dritt'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format(caroxy, carcar, caroxy), cal(p['O2_C_O2'][0], p['Close'][0], i), cal(p['O2_C_O2'][1], p['Close'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format('CT', 'CT', carcar), cal(p['CT_CT_C'][0], p['C_C_H'][0], i), cal(p['CT_CT_C'][1], p['C_C_H'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format(hydhyd2, 'CT', carcar), cal(p['Close'][0], p['Close'][0], i), cal(p['Close'][1], p['Close'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format('CT', 'CT', hydhyd2), cal(p['C_C_H'][0], p['C_C_H'][0], i), cal(p['C_C_H'][1], p['C_C_H'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format('HC', 'CT', carcar), lac(p['C_C_H'][0], p['C_C_H'][0], i), lac(p['C_C_H'][1], p['C_C_H'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format('HC', 'CT', hydhyd2), lac(p['H_C_H'][0], p['H_C_H'][0], i), lac(p['H_C_H'][1], p['H_C_H'][1], i)) Frcmod_creator.DIHEDRAL_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}-{}'.format('CT', 'CT', carcar, caroxy), cal(p['Ring_Dihe_2'][0], p['Ring_Dihe_2'][0], i), cal(p['Ring_Dihe_2'][1], p['Ring_Dihe_2'][1], i), cal(p['Ring_Dihe_2'][2], p['Ring_Dihe_2'][2], i), cal(p['Ring_Dihe_2'][3], p['Ring_Dihe_2'][3], i)) Frcmod_creator.DIHEDRAL_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}-{}'.format('HC', 'CT', carcar, caroxy), lac(p['Ring_Dihe_2'][0], p['Ring_Dihe_2'][0], i), lac(p['Ring_Dihe_2'][1], p['Ring_Dihe_2'][1], i), lac(p['Ring_Dihe_2'][2], p['Ring_Dihe_2'][2], i), lac(p['Ring_Dihe_2'][3], p['Ring_Dihe_2'][3], i)) Frcmod_creator.DIHEDRAL_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}-{}'.format(hydhyd2, 'CT', carcar, caroxy), cal(p['0_Dihe'][0], p['0_Dihe'][0], i), cal(p['0_Dihe'][1], p['0_Dihe'][1], i), cal(p['0_Dihe'][2], p['0_Dihe'][2], i), cal(p['0_Dihe'][3], p['0_Dihe'][3], i)) Frcmod_creator.IMPROPER_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}-{}'.format('X ', caroxy, carcar, caroxy), cal(p['Car_imp'][0], p['Imp_0'][0], i), cal(p['Car_imp'][1], p['Imp_0'][1], i), cal(p['Car_imp'][2], p['Imp_0'][2], i)) Frcmod_creator.NONBON_insert('{}_{}.frcmod'.format(a, 100-a), carcar, cal(p['C'][2], p['0_C'][2], i), cal(p['C'][3], p['0_C'][3], i)) Frcmod_creator.NONBON_insert('{}_{}.frcmod'.format(a, 100-a), caroxy, cal(p['O2'][2], p['0_O'][2], i), cal(p['O2'][3], p['0_O'][3], i)) Frcmod_creator.NONBON_insert('{}_{}.frcmod'.format(a, 100-a), hydhyd2, cal(p['0_H'][2], p['HC'][2], i), cal(p['0_H'][3], p['HC'][3], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format(hydhyd1, 'CT', carcar), lac(p['C_C_H'][0], p['C_C_H'][0], i), lac(p['C_C_H'][1], p['C_C_H'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format(metcar, 'CT', carcar), lac(p['C_C_H'][0], p['C_C_H'][0], i), lac(p['C_C_H'][1], p['C_C_H'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format(hydhyd1, 'CT', hydhyd2), lac(p['H_C_H'][0], p['H_C_H'][0], i), lac(p['H_C_H'][1], p['H_C_H'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format(hydhyd2, 'CT', metcar), lac(p['C_C_H'][0], p['C_C_H'][0], i), lac(p['C_C_H'][1], p['C_C_H'][1], i)) Frcmod_creator.DIHEDRAL_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}-{}'.format(hydhyd1, 'CT', carcar, caroxy), lac(p['Ring_Dihe_2'][0], p['Ring_Dihe_2'][0], i), lac(p['Ring_Dihe_2'][1], p['Ring_Dihe_2'][1], i), lac(p['Ring_Dihe_2'][2], p['Ring_Dihe_2'][2], i), lac(p['Ring_Dihe_2'][3], p['Ring_Dihe_2'][3], i)) Frcmod_creator.DIHEDRAL_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}-{}'.format(metcar, 'CT', carcar, caroxy), lac(p['0_Dihe'][0], p['0_Dihe'][0], i), lac(p['0_Dihe'][1], p['0_Dihe'][1], i), lac(p['0_Dihe'][2], p['0_Dihe'][2], i), lac(p['0_Dihe'][3], p['0_Dihe'][3], i)) Frcmod_creator.DIHEDRAL_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}-{}'.format(methyd, metcar, 'CT', carcar), lac(p['0_Dihe'][0], p['0_Dihe'][0], i), lac(p['0_Dihe'][1], p['0_Dihe'][1], i), lac(p['0_Dihe'][2], p['0_Dihe'][2], i), lac(p['0_Dihe'][3], p['0_Dihe'][3], i)) Frcmod_creator.DIHEDRAL_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}-{}'.format(hydhyd2, 'CT', metcar, methyd), lac(p['H_C_C_H'][0], p['0_1'][0], i), lac(p['H_C_C_H'][1], p['0_1'][1], i), lac(p['H_C_C_H'][2], p['0_1'][2], i), lac(p['H_C_C_H'][3], p['0_1'][3], i)) Frcmod_creator.MASS_insert('{}_{}.frcmod'.format(a, 100-a), metcar, lac(p['CT'][0], p['0_C'][0], i), lac(p['CT'][1], p['0_C'][1], i)) Frcmod_creator.MASS_insert('{}_{}.frcmod'.format(a, 100-a), methyd, lac(p['HC'][0], p['0_H'][0], i), lac(p['HC'][1], p['0_H'][1], i)) Frcmod_creator.MASS_insert('{}_{}.frcmod'.format(a, 100-a), hydhyd1, lac(p['0_H'][0], p['HC'][0], i), lac(p['0_H'][1], p['HC'][1], i)) Frcmod_creator.BOND_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}'.format('CT', metcar), lac(p['CT_CT'][0], p['CT_mH'][0], i), lac(p['CT_CT'][1], p['CT_mH'][1], i)) Frcmod_creator.BOND_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}'.format('CT', hydhyd1), lac(p['HC_sC'][0], p['CT_HC'][0], i), lac(p['HC_sC'][1], p['CT_HC'][1], i)) Frcmod_creator.BOND_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}'.format(metcar, methyd), lac(p['CT_HC'][0], p['HC_mH'][0], i), lac(p['CT_HC'][1], p['HC_mH'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format(hydhyd1, 'CT', metcar), lac(p['Close'][0], p['Close'][0], i), lac(p['Close'][1], p['Close'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format('CT', metcar, methyd), lac(p['C_C_H'][0], p['Dritt'][0], i), lac(p['C_C_H'][1], p['Dritt'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format(methyd, metcar, methyd), lac(p['H_C_H'][0], p['Close'][0], i), lac(p['H_C_H'][1], p['Close'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format('CT', 'CT', metcar), lac(p['C_C_C'][0], p['C_C_C'][0], i), lac(p['C_C_C'][1], p['C_C_C'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format('HC', 'CT', metcar), lac(p['C_C_H'][0], p['C_C_H'][0], i), lac(p['C_C_H'][1], p['C_C_H'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format('CT', 'CT', hydhyd1), lac(p['C_C_H'][0], p['C_C_H'][0], i), lac(p['C_C_H'][1], p['C_C_H'][1], i)) Frcmod_creator.ANGLE_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}'.format('HC', 'CT', hydhyd1), lac(p['H_C_H'][0], p['H_C_H'][0], i), lac(p['H_C_H'][1], p['H_C_H'][1], i)) Frcmod_creator.DIHEDRAL_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}-{}'.format('CT', 'CT', metcar, methyd), lac(p['C_C_C_H'][0], p['0_1'][0], i), lac(p['C_C_C_H'][1], p['0_1'][1], i), lac(p['C_C_C_H'][2], p['0_1'][2], i), lac(p['C_C_C_H'][3], p['0_1'][3], i)) Frcmod_creator.DIHEDRAL_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}-{}'.format('HC', 'CT', metcar, methyd), lac(p['H_C_C_H'][0], p['0_1'][0], i), lac(p['H_C_C_H'][1], p['0_1'][1], i), lac(p['H_C_C_H'][2], p['0_1'][2], i), lac(p['H_C_C_H'][3], p['0_1'][3], i)) Frcmod_creator.DIHEDRAL_insert('{}_{}.frcmod'.format(a, 100-a), '{}-{}-{}-{}'.format(hydhyd1, 'CT', metcar, methyd), lac(p['0_Dihe'][0], p['0_Dihe'][0], i), lac(p['0_Dihe'][1], p['0_Dihe'][1], i), lac(p['0_Dihe'][2], p['0_Dihe'][2], i), lac(p['0_Dihe'][3], p['0_Dihe'][3], i)) Frcmod_creator.NONBON_insert('{}_{}.frcmod'.format(a, 100-a), metcar, lac(p['CT'][2], p['0_C'][2], i), lac(p['CT'][3], p['0_C'][3], i)) Frcmod_creator.NONBON_insert('{}_{}.frcmod'.format(a, 100-a), methyd, lac(p['HC'][2], p['0_H'][2], i), lac(p['HC'][3], p['0_H'][3], i)) Frcmod_creator.NONBON_insert('{}_{}.frcmod'.format(a, 100-a), hydhyd1, lac(p['0_H'][2], p['HC'][2], i), lac(p['0_H'][3], p['HC'][3], i))

[ "pietro.ga.aronica@gmail.com" ]

pietro.ga.aronica@gmail.com

32a4734ac9c9ad913746b714ccef73833c5fc842

05c405652de52ada1b39b313f49d697ec9e23789

/DataMining/Task5/Ex2. StringGenerate.py

2511e76acdb32c2df7d9eb480c867f6d7d418d8b

[]

no_license

LukichevaPolina/2nd-cource

32f149cd94fdea239fe5193e16f7c4718430c771

1b7d82870c5079d93a3faf6d58d8287964c3c5c3

refs/heads/main

2023-07-16T06:58:39.331027

2021-08-25T18:23:25

null

0

null

UTF-8

Python

false

552

py

import random def StringGenerate(string, size, repetition): if not repetition and (size > len(string)): return 'Error: unable to compose a string!' result = '' if repetition: for i in range(size): result = result + string[random.randint(0, len(string) - 1)] else: for i in range(size): i = random.randint(0, len(string) - 1) result = result + string[i] string = string.replace(string[i], '', 1) return result print(StringGenerate('asaaaqwe', 7, False))

[ "63358667+LukichevaPolina@users.noreply.github.com" ]

63358667+LukichevaPolina@users.noreply.github.com

ca5c45856918da31bc8db94f507be26f36d20cb6

dda51ce6178e0c74ac4de1f814796ae12c5a3b2a

/OE_Game.py

a28fe7f6feb621d4be5807b2c7670e80d5a52762

[]

no_license

LiteBrick204/Odd_or_Even_Game_in_Python

8cb5837a14d676b0a7577e742811427cb67b4a97

8d851f0dfcd49c8bd7b6e38dd10f48fbba0e864c

refs/heads/master

2022-12-25T18:46:48.732418

2020-10-01T12:20:39

295,441,155

8

1

null

2020-10-01T12:20:41

2020-09-14T14:26:35

Python

UTF-8

Python

false

2,690

py

#!/usr/bin/env python import random def toss(u): t = random.randint(0,1) g = "bat" if bool(t) else "bowl" if u==t: return [int(input("You Won the Toss! Press 1 to bat and 2 to bowl:")),u==g] else: print("Computer won the toss and choose to",g) return [t+1,u==g] def check(C,w): if C=='n': if not w:print("Thank you for playing") quit() elif C=='y' and w: game() class GameOver(Exception): pass def game(): score = 0 C = input("Do you want to start(Y/N):") check(C,0) l = int(input("Call for toss! Press 0 for head and 1 for tail :")) B = toss(l) f = 1 c,u = 0,3 while c!=u and f==1 and C=='y': try: g = random.randint(1,6) u = int(input(">>>")) print("Computer Entered",g) if u<=0 or u>6: f = 2 raise ValueError if u==g and B[0]==1: print("YOU ARE OUT!!") print("Your Score is",score) f = -1 raise GameOver elif u==g and B[0]==2: print("The Computer is out!") batround(score+1,1) else: score+=u if B[0]==1 and B[1] else g continue except GameOver: if f==-1: batround(score+1,0) except ValueError: if f==-1: print("You can only enter numbers between 1-6 only") u=0 f=0 continue def batround(N,who): print("%s need"%("You" if who else "Computer"),N,"runs to win the match!!") u,c,f = 1,3,0 while N: try: g = random.randint(1,6) u = int(input(">>>")) print("Computer Entered",g) if u<=0 or u>6: f = 2 raise ValueError elif u==g: print("%s ARE OUT!!"%("You" if who else "Computer")) print("Remaining Score is",N) f = -1 raise GameOver elif ((who and (N-u)<=0) or (not who and (N-g)<=0)) or N==0: print("%s won the match!!" %("You" if who else "Computer")) raise GameOver elif (who and u in range(1,7)): N-=u elif not who: N-=g except ValueError: if f==2: print("You can only enter numbers between 1-6 only") u=0 f=0 except GameOver: print("GG!! Thank You for Playing with me.\nKudos and Godspeed!!!\n\t\t\t~Computer") check(input("Enter n to quit: ").strip(),1) game()

[ "user@xyz.in" ]

user@xyz.in

6944997ed36b9516e6282fd746cfd8cb8533635c

9ad3803c73867cc66956c53aa0e0f023c2e9b7a9

/src/setup.py

1ccad261b1321dc0f81274ea679a66bfc3cf97f2

[]

no_license

pcloth/api-shop

2f9d963971ab0eae8db7e48cfaac984ba104d079

6cae15e057172b33ec7d9b3317d033e7ceae71e1

refs/heads/master

2023-07-22T09:00:39.018633

2023-07-07T07:36:27

165,461,139

43

4

null

2023-03-06T04:46:13

2019-01-13T03:21:05

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UTF-8

Python

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py

#!/usr/bin/env python # coding=utf-8 from setuptools import setup, find_packages import re, ast, pathlib _version_re = re.compile(r'__version__\s+=\s+(.*)') with open('api_shop/__init__.py', 'rb') as f: version = str(ast.literal_eval(_version_re.search( f.read().decode('utf-8') ).group(1))) setup( name='api-shop', version=version, description=( 'RESTful api shop for django or flask or bottle' ), long_description=pathlib.Path('README.MD').read_text(encoding='utf-8'), long_description_content_type='text/markdown', author='pcloth', author_email='pcloth@gmail.com', maintainer='pcloth', maintainer_email='pcloth@gmail.com', license='BSD License', packages=find_packages(), include_package_data=True, exclude_package_date={'':['.gitignore']}, keywords=['api-shop', 'Flask-RESTful', 'Django REST framework', 'RESTful'], platforms=["all"], url='https://github.com/pcloth/api-shop', classifiers=[ 'Development Status :: 4 - Beta', 'Operating System :: OS Independent', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Programming Language :: Python', 'Programming Language :: Python :: Implementation', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Software Development :: Libraries' ], )

[ "pcloth@gmail.com" ]

pcloth@gmail.com

18093775e36a02a55ed210d93ea5fe0eb5127ffc

3201b061fef61be0263f5401771d2ae86955af4a

/scrapyuniversal/scrapyuniversal/items.py

8369ddf907b111ac3b9bacecf5fca2b3cfd60014

[]

no_license

echohsq/crawl_gouwu

2d56bd8b6eeb4036d0566a50b204f3c7ba16d8d5

491280b90bef2756a409b6173944fb4a4c685325

refs/heads/master

2023-08-03T05:47:39.599795

2021-09-22T10:29:19

409,160,777

0

null

UTF-8

Python

false

365

py

# Define here the models for your scraped items # # See documentation in: # https://docs.scrapy.org/en/latest/topics/items.html import scrapy from scrapy.item import Field, Item class NewsItem(Item): collection = table = 'tech_china' title = Field() url = Field() text = Field() datetime = Field() source = Field() website = Field()

[ "haoshengqiang@cnpc.com.cn" ]

haoshengqiang@cnpc.com.cn

b293f33dad83407bdb8ddb7a4f4a6e9e35487745

43dc562227787ef5662327444362bfb17bec2eb0

/opt/app.py

cb86cb2f7055e5843be78bb91992e2b243ccee01

[]

no_license

masafumi-tk/myhome-dash

1ff4572a1de571eefad3fac9bbc34a2b33c027c5

79cc4d0ca672c9a87de08d1bf7ab4e495c8b66a3

refs/heads/master

2023-08-13T09:41:29.605223

2021-09-14T13:02:15

406,361,411

0

null

UTF-8

Python

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py

# -*- coding: utf-8 -*- # Run this app with `python app.py` and # visit http://127.0.0.1:8050/ in your web browser. import dash import dash_core_components as dcc import dash_html_components as html import plotly.express as px import pandas as pd external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) # assume you have a "long-form" data frame # see https://plotly.com/python/px-arguments/ for more options df = pd.DataFrame({ "Fruit": ["Apples", "Oranges", "Bananas", "Apples", "Oranges", "Bananas"], "Amount": [4, 1, 2, 2, 4, 5], "City": ["SF", "SF", "SF", "Montreal", "Montreal", "Montreal"] }) fig = px.bar(df, x="Fruit", y="Amount", color="City", barmode="group") app.layout = html.Div(children=[ html.H1(children='Hello Dash'), html.Div(children=''' Dash: A web application framework for Python. '''), dcc.Graph( id='example-graph', figure=fig ) ]) if __name__ == '__main__': app.run_server(debug=True)

[ "masafumi.cascata@gmail.com" ]

masafumi.cascata@gmail.com

523d16ef57141f9cb5cf5a6b82ff1956faeb6860

600cfc373bb90cbcaec295de2583ed6e9b722b74

/CHARACTER_/character_web_fin/mbti/ai_mbti_analysis.py

2e8c0e483f5bed987732ecf4876c9204d18c4d59

[]

no_license

kwangilkimkenny/Story_Analysis

e92e5c38a7fb34948d801d3170e8709f4670e74b

ea7f2ed33de735918f4f3f568f7b551fbcd95a33

refs/heads/master

2022-09-02T19:25:36.283950

2022-08-24T00:10:03

239,153,819

0

null

UTF-8

Python

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py

#2020-10-11 #xgboost #Django적용을 위한 함수화처리 완료_하지만 테스트중중중.... import pandas as pd import numpy as np import re import pickle # plotting import seaborn as sns import matplotlib.pyplot as plt # Tune learning_rate from numpy import loadtxt from xgboost import XGBClassifier from sklearn.model_selection import GridSearchCV from sklearn.model_selection import StratifiedKFold # First XGBoost model for MBTI dataset from numpy import loadtxt from xgboost import XGBClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score ##### Compute list of subject with Type | list of comments from nltk.stem import PorterStemmer, WordNetLemmatizer from nltk.corpus import stopwords from nltk import word_tokenize import nltk nltk.download('wordnet') from sklearn.feature_extraction.text import TfidfTransformer from sklearn.feature_extraction.text import CountVectorizer from sklearn.manifold import TSNE #타입을 숫자로 변환 def get_types(row): t=row['type'] I = 0; N = 0 T = 0; J = 0 if t[0] == 'I': I = 1 elif t[0] == 'E': I = 0 else: print('I-E incorrect') if t[1] == 'N': N = 1 elif t[1] == 'S': N = 0 else: print('N-S incorrect') if t[2] == 'T': T = 1 elif t[2] == 'F': T = 0 else: print('T-F incorrect') if t[3] == 'J': J = 1 elif t[3] == 'P': J = 0 else: print('J-P incorrect') return pd.Series( {'IE':I, 'NS':N , 'TF': T, 'JP': J }) #딕셔너리파일 설정 b_Pers = {'I':0, 'E':1, 'N':0, 'S':1, 'F':0, 'T':1, 'J':0, 'P':1} #리스트를 두개씩 묶어서 리스트로 만듬 b_Pers_list = [{0:'I', 1:'E'}, {0:'N', 1:'S'}, {0:'F', 1:'T'}, {0:'J', 1:'P'}] def translate_personality(personality): # transform mbti to binary vector return [b_Pers[l] for l in personality] def translate_back(personality): # transform binary vector to mbti personality s = "" for i, l in enumerate(personality): s += b_Pers_list[i][l] return s # We want to remove these from the psosts unique_type_list = ['INFJ', 'ENTP', 'INTP', 'INTJ', 'ENTJ', 'ENFJ', 'INFP', 'ENFP', 'ISFP', 'ISTP', 'ISFJ', 'ISTJ', 'ESTP', 'ESFP', 'ESTJ', 'ESFJ'] unique_type_list = [x.lower() for x in unique_type_list] # Lemmatize stemmer = PorterStemmer() lemmatiser = WordNetLemmatizer() # Cache the stop words for speed cachedStopWords = stopwords.words("english") def pre_process_data(data, remove_stop_words=True, remove_mbti_profiles=True): list_personality = [] list_posts = [] len_data = len(data) i=0 for row in data.iterrows(): i+=1 if (i % 500 == 0 or i == 1 or i == len_data): print("%s of %s rows" % (i, len_data)) ##### Remove and clean comments posts = row[1].posts temp = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', ' ', posts) temp = re.sub("[^a-zA-Z]", " ", temp) temp = re.sub(' +', ' ', temp).lower() if remove_stop_words: temp = " ".join([lemmatiser.lemmatize(w) for w in temp.split(' ') if w not in cachedStopWords]) else: temp = " ".join([lemmatiser.lemmatize(w) for w in temp.split(' ')]) if remove_mbti_profiles: for t in unique_type_list: temp = temp.replace(t,"") type_labelized = translate_personality(row[1].type) list_personality.append(type_labelized) list_posts.append(temp) list_posts = np.array(list_posts) list_personality = np.array(list_personality) return list_posts, list_personality # read data # data = pd.read_csv('/Users/jongphilkim/Desktop/Django_WEB/essayfitaiproject_2020_12_09/essayai/mbti_1.csv') data = pd.read_csv('./essayai/data/mbti_1.csv') # get_types 함수 적용 data = data.join(data.apply (lambda row: get_types (row),axis=1)) # load with open('./essayai/ai_character/mbti/list_posts.pickle', 'rb') as f: list_posts = pickle.load(f) # load with open('./essayai/ai_character/mbti/list_personality.pickle', 'rb') as f: list_personality = pickle.load(f) # # Posts to a matrix of token counts cntizer = CountVectorizer(analyzer="word", max_features=1500, tokenizer=None, preprocessor=None, stop_words=None, max_df=0.7, min_df=0.1) # Learn the vocabulary dictionary and return term-document matrix print("CountVectorizer...") X_cnt = cntizer.fit_transform(list_posts) ################################################# #save!!! model X_cnt import pickle # save # with open('./essayai/ai_character/mbti/data_X_cnt.pickle', 'wb') as f: # pickle.dump(X_cnt, f, pickle.HIGHEST_PROTOCOL) # load with open('./essayai/ai_character/mbti/data_X_cnt.pickle', 'rb') as f: X_cnt = pickle.load(f) ################################################# # Transform the count matrix to a normalized tf or tf-idf representation tfizer = TfidfTransformer() print("Tf-idf...") # Learn the idf vector (fit) and transform a count matrix to a tf-idf representation X_tfidf = tfizer.fit_transform(X_cnt).toarray() # load with open('./essayai/ai_character/mbti/data.pickle', 'rb') as f: X_tfidf = pickle.load(f) def mbti_classify(text): type_indicators = [ "IE: Introversion (I) / Extroversion (E)", "NS: Intuition (N) – Sensing (S)", "FT: Feeling (F) - Thinking (T)", "JP: Judging (J) – Perceiving (P)" ] # Posts in tf-idf representation X = X_tfidf my_posts = str(text) # The type is just a dummy so that the data prep fucntion can be reused mydata = pd.DataFrame(data={'type': ['INFJ'], 'posts': [my_posts]}) my_posts, dummy = pre_process_data(mydata, remove_stop_words=True) my_X_cnt = cntizer.transform(my_posts) my_X_tfidf = tfizer.transform(my_X_cnt).toarray() # setup parameters for xgboost param = {} param['n_estimators'] = 200 param['max_depth'] = 2 param['nthread'] = 8 param['learning_rate'] = 0.2 result = [] # Let's train type indicator individually for l in range(len(type_indicators)): print("%s ..." % (type_indicators[l])) Y = list_personality[:,l] # split data into train and test sets seed = 7 test_size = 0.33 X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed) # fit model on training data model = XGBClassifier(**param) model.fit(X_train, y_train) # make predictions for my data y_pred = model.predict(my_X_tfidf) result.append(y_pred[0]) # print("* %s prediction: %s" % (type_indicators[l], y_pred)) print("The result is: ", translate_back(result)) #결과를 리스트에 담고 Result_list = list(translate_back(result)) #mbit 결과값에 따라 내용 print 하기 # read data # data = pd.read_csv('/Users/jongphilkim/Desktop/Django_WEB/essayfitaiproject/essayai/mbti_exp.csv') data = pd.read_csv('./essayai/data/mbti_exp.csv') #새로운 데이터프레임을 만들어서 계산된 값을 추가할 예정 df2 = pd.DataFrame(index=range(0,4),columns=['Type', 'Explain']) #리스트에서 한글자씩 불러와서 데이터프레임의 값을 출력하면 됨 for i in range(0, len(Result_list)): type = Result_list[i] for j in range(0, len(data)): if type == data.iloc[j,0]: break is_mbti = data.iloc[j,2] df2.iloc[i, [0,1]] = [type, is_mbti] print(df2) return df2 # my_posts = """Describe a place or environment where you are perfectly content. What do you do or experience there, and why is it meaningful to you? 644 words out of 650 Gettysburg, a small town in the middle of Pennsylvania, was the sight of the largest, bloodiest battle in the Civil War. Something about these hallowed grounds draws me back every year for a three day camping trip with my family over Labor Day weekend. Every year, once school starts, I count the days until I take that three and half hour drive from Pittsburgh to Gettysburg. Each year, we leave after school ends on Friday and arrive in Gettysburg with just enough daylight to pitch the tents and cook up a quick dinner on the campfire. As more of the extended family arrives, we circle around the campfire and find out what is new with everyone. The following morning, everyone is up by nine and helping to make breakfast which is our best meal of the day while camping. Breakfast will fuel us for the day as we hike the vast battlefields. My Uncle Mark, my twin brother, Andrew, and I like to take charge of the family tour since we have the most passion and knowledge about the battle. I have learned so much from the stories Mark tells us while walking on the tours. Through my own research during these last couple of trips, I did some of the explaining about the events that occurred during the battle 150 years ago. My fondest experience during one trip was when we decided to go off of the main path to find a carving in a rock from a soldier during the battle. Mark had read about the carving in one of his books about Gettysburg, and we were determined to locate it. After almost an hour of scanning rocks in the area, we finally found it with just enough daylight to read what it said. After a long day of exploring the battlefield, we went back to the campsite for some 'civil war' stew. There is nothing special about the stew, just meat, vegetables and gravy, but for whatever reason, it is some of the best stew I have ever eaten. For the rest of the night, we enjoy the company of our extended family. My cousins, my brother and I listen to the stories from Mark and his friends experiences' in the military. After the parents have gone to bed, we stay up talking with each other, inching closer and closer to the fire as it gets colder. Finally, we creep back into our tents, trying to be as quiet as possible to not wake our parents. The next morning we awake red-eyed from the lack of sleep and cook up another fantastic breakfast. Unfortunately, after breakfast we have to pack up and head back to Pittsburgh. It will be another year until I visit Gettysburg again. There is something about that time I spend in Gettysburg that keeps me coming back to visit. For one, it is just a fun, relaxing time I get to spend with my family. This trip also fulfills my love for the outdoors. From sitting by the campfire and falling asleep to the chirp of the crickets, that is my definition of a perfect weekend. Gettysburg is also an interesting place to go for Civil War buffs like me. While walking down the Union line or walking Pickett's Charge, I imagine how the battle would have been played out around me. Every year when I visit Gettysburg, I learn more facts and stories about the battle, soldiers and generally about the Civil War. While I am in Gettysburg, I am perfectly content, passionate about the history and just enjoying the great outdoors with my family. This drive to learn goes beyond just my passion for history but applies to all of the math, science and business classes I have taken and clubs I am involved in at school. Every day, I am genuinely excited to learn. # """ # test = mbti_classify(my_posts) # print ('check') # test # print ('check2')

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""" Test utility functions for the frame object. """ import os import unittest2 import lldb from lldbtest import * class FrameUtilsTestCase(TestBase): mydir = os.path.join("python_api", "lldbutil", "frame") def setUp(self): # Call super's setUp(). TestBase.setUp(self) # Find the line number to break inside main(). self.line = line_number('main.c', "// Find the line number here.") @python_api_test def test_frame_utils(self): """Test utility functions for the frame object.""" self.buildDefault() self.frame_utils() def frame_utils(self): exe = os.path.join(os.getcwd(), "a.out") target = self.dbg.CreateTarget(exe) self.assertTrue(target, VALID_TARGET) breakpoint = target.BreakpointCreateByLocation("main.c", self.line) self.assertTrue(breakpoint, VALID_BREAKPOINT) # Now launch the process, and do not stop at entry point. process = target.LaunchSimple(None, None, os.getcwd()) if not process: self.fail("SBTarget.LaunchProcess() failed") self.assertTrue(process.GetState() == lldb.eStateStopped, PROCESS_STOPPED) import lldbutil thread = lldbutil.get_stopped_thread(process, lldb.eStopReasonBreakpoint) frame0 = thread.GetFrameAtIndex(0) frame1 = thread.GetFrameAtIndex(1) parent = lldbutil.get_parent_frame(frame0) self.assertTrue(parent and parent.GetFrameID() == frame1.GetFrameID()) frame0_args = lldbutil.get_args_as_string(frame0) parent_args = lldbutil.get_args_as_string(parent) self.assertTrue(frame0_args and parent_args and "(int)val=1" in frame0_args) if self.TraceOn(): lldbutil.print_stacktrace(thread) print "Current frame: %s" % frame0_args print "Parent frame: %s" % parent_args if __name__ == '__main__': import atexit lldb.SBDebugger.Initialize() atexit.register(lambda: lldb.SBDebugger.Terminate()) unittest2.main()

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/generate_waveforms_script_EOB.py

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iphysresearch/GW_parameter_estimation

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2023-08-30T08:13:14.493859

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import lfigw.waveform_generator as wfg wfd = wfg.WaveformDataset(spins_aligned=False, domain='RB', extrinsic_at_train=True) wfd.Nrb = 600 wfd.approximant = 'SEOBNRv4P' wfd.load_event('data/events/GW150914_10Hz/') wfd.importance_sampling = 'uniform_distance' wfd.prior['distance'] = [100.0, 1000.0] wfd.prior['a_1'][1] = 0.88 wfd.prior['a_2'][1] = 0.88 print('Dataset properties') print('Event', wfd.event) print(wfd.prior) print('f_min', wfd.f_min) print('f_min_psd', wfd.f_min_psd) print('f_max', wfd.f_max) print('T', wfd.time_duration) print('reference time', wfd.ref_time) wfd.generate_reduced_basis(50000) wfd.generate_dataset(1000000) wfd.generate_noisy_test_data(5000) wfd.save('../waveforms/GW150914_SEOBNRv4P') wfd.save_train('../waveforms/GW150914_SEOBNRv4P') wfd.save_noisy_test_data('../waveforms/GW150914_SEOBNRv4P') print('Program complete. Waveform dataset has been saved.')

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import sys from pyspark import since, keyword_only from pyspark.ml.param.shared import * from pyspark.ml.wrapper import JavaEstimator, JavaModel, JavaTransformer, _jvm, JavaParams class ScalerModel(JavaModel): _classpath_model = 'mlonspark.ScalerModel' @staticmethod def _from_java(java_stage): """ Given a Java object, create and return a Python wrapper of it. Used for ML persistence. Meta-algorithms such as Pipeline should override this method as a classmethod. """ # Generate a default new instance from the stage_name class. py_type = ScalerModel if issubclass(py_type, JavaParams): # Load information from java_stage to the instance. py_stage = py_type() py_stage._java_obj = java_stage py_stage._resetUid(java_stage.uid()) py_stage._transfer_params_from_java() return py_stage class Scaler(JavaEstimator, HasInputCol, HasOutputCol): groupCol = Param(Params._dummy(), "groupCol", "groupCol", typeConverter=TypeConverters.toString) _classpath = 'mlonspark.Scaler' @keyword_only def __init__(self): super(Scaler, self).__init__() self._java_obj = self._new_java_obj( Scaler._classpath , self.uid ) kwargs = self._input_kwargs self.setParams(**kwargs) @keyword_only def setParams(self): kwargs = self._input_kwargs return self._set(**kwargs) def _create_model(self, java_model): return ScalerModel(java_model) def setGroupCol(self, value): return self._set(groupCol=value) def getGroupCol(self): return self.getOrDefault(self.groupCol) def setOutputCol(self, value): return self._set(outputCol=value) def getOutputCol(self): return self.getOrDefault(self.outputCol) def setInputCol(self, value): return self._set(inputCol=value) def getInputCol(self): return self.getOrDefault(self.inputCol)

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# -*- coding: utf-8 -*- """conway.py: Implementation of Conway's Game of Life. Attributes: living_cell (pygame.Color): The initial color of a cell when it becomes alive. dead_cell (pygame.Color): The color of a non-living cell. """ import sys, random import pygame living_cell = pygame.Color(150, 0, 0, 0) dead_cell = pygame.Color(0, 0, 0, 0) class State(object): """Class to hold the state of the environment. Attributes: conway (list): 2D list containing the current state of the conway en- vironment. living (int): The number of living cells. Args: width (int): The width for the conway data. height (int): The height for the conway data. """ def __init__(self, width: int, height: int): self._width = width self._height = height self._generations = 1 self.living = 0 self.conway = _seed(self._width, self._height) for i in self.conway: self.living += i.count(1) @property def width(self) -> int: """Return the width of the system. Returns: int """ return self._width @property def height(self) -> int: """Return the height of the system. Returns: int """ return self._height @property def generations(self) -> int: """Return the number of generations that have passed. Returns: int """ return self._generations def inc_generation(self): """Increment the generation counter. Post: _generations is modified. """ self._generations += 1 def colorize(conway: list, color_grid: list) -> list: """Sets colors for the conway system. Pre: color_grid must be the list as defined in tiles.tilemap. Post: Arg color_grid is modified. Args: conway (list): conway list color_grid (list): color list. Returns: list: color_grid is returned """ for y in range(0, len(conway)): for x in range(0, len(conway[y])): current = color_grid.get_current_chunk()[y][x] if conway[y][x] == 0: if current.color != dead_cell: current.color = dead_cell else: if current.color == dead_cell: current.color = living_cell else: color = current.color if color.r < 255: ncolor = pygame.Color(color.r+1, color.g, color.b, color.a) current.color = ncolor elif color.g < 255: ncolor = pygame.Color(color.r, color.g+1, color.b, color.a) current.color = ncolor elif color.b < 255: ncolor = pygame.Color(color.r, color.g, color.b+1, color.a) current.color = ncolor return color_grid def increment(conway: list) -> int: """Increment conway by one. Post Arg conway is modified. Args: conway (list): conway list Returns: int: The number of living cells. """ def alive(arr: list, xy: tuple) -> bool: """Check if a cell is alive. Alive is defined as currently living (1) or dying (-1); where dying indicates a temporary indicator. Args: arr (list): conway list xy (tuple): Position in arr defined in (x,y) Returns: boolean """ return True if arr[xy[1]][xy[0]] == -1 or arr[xy[1]][xy[0]] == 1 else False def num_neighbors(arr: list, xy: tuple) -> int: """Return the number of living neighbors. Args: arr (list): conway list xy (tuple): Position in arr using (x,y) values. Returns: int """ value = 0 for i in _moore_neighbors(arr, xy): if alive(conway, i): value += 1 return value for y in range(0, len(conway)): for x in range(0, len(conway[y])): if alive(conway, (x, y)) and \ (num_neighbors(conway, (x, y)) <= 1 or num_neighbors(conway, (x, y)) > 3): conway[y][x] = -1 elif not alive(conway, (x, y)) and num_neighbors(conway, (x, y)) == 3: conway[y][x] = 2 # Check for number of living cells while flipping the cells to their proper # states. living = 0 for y in range(0, len(conway)): for x in range(0, len(conway[y])): if conway[y][x] == -1: conway[y][x] = 0 elif conway[y][x] == 2: conway[y][x] = 1 living += 1 elif conway[y][x] == 1: living += 1 return living def update(state: State, color_grid: list) -> tuple: """Update the conway state. Pre: color_grid must be the list as defined in tiles.tilemap. Post: state is modified color_grid is modified. Args: state (conway.State): The conway state Returns: tuple (conway.State, list): State and color_grid are returned. """ state.living = increment(state.conway) colorize(state.conway, color_grid) state.inc_generation() return (state, color_grid) def _moore_neighbors(arr: list, xy: tuple) -> tuple: """Obtain a list of Moore's neighbours. Pre: arr must be a 2D list. Args: arr (list): 2d list. xy (tuple): (x,y) values coresponding to the x,y values in arr. Returns: list: A list of tuples holding the neighbor's (x,y) values. """ width = len(arr[0])-1 height = len(arr)-1 neighbors = [] for x in range(xy[0]-1, xy[0]+2): for y in range(xy[1]-1, xy[1]+2): if (x >= 0 and y >= 0) and (x <= width and y <= height): if not (xy[0] == x and xy[1] == y): neighbors.append((x, y)) return neighbors def _seed(width: int, height: int) -> list: """Create the initial environment. Args: width (int): The width of the environment. height (int): The height of the environment. Returns: list """ seeds = [[random.random() for _ in range(width)] for _ in range(height)] # For each cell, get the neighbors. # If the neighbor's value is <= 0.5 then remove else # if random value is < 0 remove. for x in range(0, width): for y in range(0, height): for i in _moore_neighbors(seeds, (x,y)): if seeds[i[1]][i[0]] < seeds[y][x]: if seeds[i[1]][i[0]] <= 0.5: seeds[i[1]][i[0]] = 0 elif random.random() < 0.5: seeds[i[1]][i[0]] = 0 # Final environment should only be 0 or 1. for y in range(0, height): for x in range(0, width): seeds[y][x] = round(seeds[y][x]) return seeds

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#!/home/apap26/PycharmProjects/magazin/venv/bin/python # EASY-INSTALL-ENTRY-SCRIPT: 'pip==19.0.3','console_scripts','pip' __requires__ = 'pip==19.0.3' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('pip==19.0.3', 'console_scripts', 'pip')() )

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import threading import time def asyncf(func, *args): thread = threading.Thread(target=func, args=(args)) thread.daemon = True thread.start() def timerf(interval, func, *args): thread = threading.Timer(interval, func, args) thread.daemon = True thread.start() if __name__ == '__main__': print("start")

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#calss header class _CHAUFFEUR(): def __init__(self,): self.name = "CHAUFFEUR" self.definitions = [u'someone whose job is to drive a car for a rich or important person: '] self.parents = [] self.childen = [] self.properties = [] self.jsondata = {} self.specie = 'nouns' def run(self, obj1 = [], obj2 = []): return self.jsondata

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N,K=map(int,input().split()) a=0 for i in range(K+1,N+1): t=N//i n=N-t*i a+=t*(i-K) if K: a+=max(0,n-K+1) else: a+=n print(a)

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from __future__ import annotations from collections import defaultdict from datetime import date, datetime, timedelta, timezone from typing import Dict, List import pytest from timetrackylib import bootstrap from timetrackylib.domain import commands from timetrackylib.services import handlers, unit_of_work from timetrackylib.adapters import repository from timetrackylib.adapters.orm import start_mappers from timetrackylib.services.unit_of_work import FakeUnitOfWork def boostrap_test_app(): return bootstrap.bootstrap(start_orm=False, uow=FakeUnitOfWork()) class TestAddtime_entry: def test_add_single_time_entry(self): bus = boostrap_test_app() nu: datetime = datetime(2021, 4, 19, 13, 0, 5, 0, tzinfo=timezone.utc) # add one bus.handle( commands.Addtime_entryCommand( 0, f"Test", # title f"Glasgow", # projectname nu.isoformat(), # Task starttime nu.isoformat(), # Task end time ) assert bus.uow.time_entrys.get_by_title(f"Test") is not None assert bus.uow.committed def test_get_time_entry_by_id(self): bus = boostrap_test_app() nu: datetime = datetime(2021, 4, 19, 13, 0, 5, 0, tzinfo=timezone.utc) # add one bus.handle( commands.Addtime_entryCommand( 99, f"Test", # title f"Glasgow", # projectname nu.isoformat(), # date added nu.isoformat(), # date edited ) ) assert bus.uow.time_entrys.get_by_id(99) is not None assert bus.uow.committed def test_get_time_entry_by_url(self): bus = boostrap_test_app() nu: datetime = datetime(2021, 4, 19, 13, 0, 5, 0, tzinfo=timezone.utc) # add one bus.handle( commands.Addtime_entryCommand( 99, f"Test", # title f"Glasgow", # projectname nu.isoformat(), # date added nu.isoformat(), # date edited ) ) assert bus.uow.time_entrys.get_by_projectname(f"Glasgow") is not None assert bus.uow.committed def test_get_all_time_entrys(self): bus = boostrap_test_app() nu: datetime = datetime(2021, 4, 19, 13, 0, 5, 0, tzinfo=timezone.utc) bus.handle( commands.Addtime_entryCommand( 99, f"Test", # title f"Glasgow", # projectname f"Drillhole5ft", # task nu.isoformat(), # task starttime nu.isoformat(), # task endtime ) ) nuto = nu + timedelta(days = 3, hours=13) bus.handle( commands.Addtime_entryCommand( 999, f"Test2", # title f"Glasgow", # projectname nu.isoformat(), # task starttime nu.isoformat(), # task endtime ) ) records = bus.uow.time_entry.get_all() assert len(records) == 2

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2023-05-07T01:37:38.456039

2021-04-01T07:32:59

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py

# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """fused layernorm""" import numpy as np from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore.common.parameter import Parameter from mindspore.common.initializer import initializer from mindspore.ops.primitive import constexpr import mindspore.common.dtype as mstype from mindspore.nn.cell import Cell __all__ = ['FusedLayerNorm'] @constexpr def get_shape_for_norm(x_shape, begin_norm_axis): print("input_shape: ", x_shape) norm_shape = x_shape[begin_norm_axis:] output_shape = (1, -1, 1, int(np.prod(norm_shape))) print("output_shape: ", output_shape) return output_shape class FusedLayerNorm(Cell): r""" Applies Layer Normalization over a mini-batch of inputs. Layer normalization is widely used in recurrent neural networks. It applies normalization over a mini-batch of inputs for each single training case as described in the paper `Layer Normalization <https://arxiv.org/pdf/1607.06450.pdf>`_. Unlike batch normalization, layer normalization performs exactly the same computation at training and testing times. It can be described using the following formula. It is applied across all channels and pixel but only one batch size. .. math:: y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta Args: normalized_shape (Union(tuple[int], list[int]): The normalization is performed over axis `begin_norm_axis ... R - 1`. begin_norm_axis (int): It first normalization dimension: normalization will be performed along dimensions `begin_norm_axis: rank(inputs)`, the value should be in [-1, rank(input)). Default: -1. begin_params_axis (int): The first parameter(beta, gamma)dimension: scale and centering parameters will have dimensions `begin_params_axis: rank(inputs)` and will be broadcast with the normalized inputs accordingly, the value should be in [-1, rank(input)). Default: -1. gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the gamma weight. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'ones'. beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the beta weight. The values of str refer to the function `initializer` including 'zeros', 'ones', 'xavier_uniform', 'he_uniform', etc. Default: 'zeros'. use_batch_nrom (bool): Whether use batchnorm to preocess. Inputs: - **input_x** (Tensor) - The shape of 'input_x' is :math:`(x_1, x_2, ..., x_R)`, and `input_shape[begin_norm_axis:]` is equal to `normalized_shape`. Outputs: Tensor, the normalized and scaled offset tensor, has the same shape and data type as the `input_x`. Examples: >>> x = Tensor(np.ones([20, 5, 10, 10]), mindspore.float32) >>> shape1 = x.shape[1:] >>> m = nn.LayerNorm(shape1, begin_norm_axis=1, begin_params_axis=1) >>> m(x) """ def __init__(self, normalized_shape, begin_norm_axis=-1, begin_params_axis=-1, gamma_init='ones', beta_init='zeros', use_batch_norm=False): super(FusedLayerNorm, self).__init__() if not isinstance(normalized_shape, (tuple, list)): raise TypeError("The type of 'normalized_shape' should be tuple[int] or list[int], but '{}' type is {}." .format(normalized_shape, type(normalized_shape))) self.normalized_shape = normalized_shape self.begin_norm_axis = begin_norm_axis self.begin_params_axis = begin_params_axis self.gamma = Parameter(initializer( gamma_init, normalized_shape), name="gamma") self.beta = Parameter(initializer( beta_init, normalized_shape), name="beta") self.layer_norm = P.LayerNorm(begin_norm_axis=self.begin_norm_axis, begin_params_axis=self.begin_params_axis) self.batch_norm = P.BatchNorm(is_training=True, epsilon=1e-5) self.use_batch_norm = use_batch_norm def construct(self, input_x): """Applies Layer Normalization over a mini-batch of inputs""" if self.use_batch_norm and self.training: ones = P.Fill()(mstype.float32, F.shape(input_x)[:self.begin_norm_axis], 1.0) zeros = P.Fill()(mstype.float32, F.shape(input_x)[:self.begin_norm_axis], 0.0) shape_x = F.shape(input_x) norm_shape = get_shape_for_norm(shape_x, self.begin_norm_axis) input_x = F.reshape(input_x, norm_shape) output, _, _, _, _, _ = self.batch_norm(input_x, ones, zeros, None, None) output = F.reshape(output, shape_x) y = output * self.gamma + self.beta else: y, _, _ = self.layer_norm(input_x, self.gamma, self.beta) return y def extend_repr(self): """Display instance object as string.""" s = 'normalized_shape={}, begin_norm_axis={}, begin_params_axis={}, gamma{}, beta={}'.format( self.normalized_shape, self.begin_norm_axis, self.begin_params_axis, self.gamma, self.beta) return s

[ "zhengnengjin@huawei.com" ]

zhengnengjin@huawei.com

e5580409284141245e84051d896e22d0fb6f3a7d

9e18a644070eb59c4b632b08acf57ef2a6cc4b39

/lib/read.py

dee895d95673e15a1959870758cf83d790dd2f7f

[]

no_license

cwon789/cwon789-Seeing_through_fog_porjection_npz

a82339808ad96597c5c658ce26c42439a358c447

f00533f749ea122174f714b3d9376fabdcb7c21a

refs/heads/main

2023-05-25T04:52:19.775202

2021-05-21T15:02:54

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import numpy as np import os import json from pyquaternion import Quaternion def read_label(file, label_dir, camera_to_velodyne=None): """Read label file and return object list""" file_name = file.split('.png')[0] object_list = get_kitti_object_list(os.path.join(label_dir, file_name + '.txt'), camera_to_velodyne=camera_to_velodyne) return object_list def decode_visible_labels(value): if value == "True": return True elif value == "False": return False else: return None def get_kitti_object_list(label_file, camera_to_velodyne=None): """Create dict for all objects of the label file, objects are labeled w.r.t KITTI definition""" kitti_object_list = [] try: with open(label_file.replace('.png', '.txt'), 'r') as file: for line in file: line = line.replace('\n', '') # remove '\n' kitti_properties = line.split(' ') object_dict = { 'identity': kitti_properties[0], 'truncated': float(kitti_properties[1]), 'occlusion': float(kitti_properties[2]), 'angle': float(kitti_properties[3]), 'xleft': int(round(float(kitti_properties[4]))), 'ytop': int(round(float(kitti_properties[5]))), 'xright': int(round(float(kitti_properties[6]))), 'ybottom': int(round(float(kitti_properties[7]))), 'height': float(kitti_properties[8]), 'width': float(kitti_properties[9]), 'length': float(kitti_properties[10]), 'posx': float(kitti_properties[11]), 'posy': float(kitti_properties[12]), 'posz': float(kitti_properties[13]), 'orient3d': float(kitti_properties[14]), 'rotx': float(kitti_properties[15]), 'roty': float(kitti_properties[16]), 'rotz': float(kitti_properties[17]), 'score': float(kitti_properties[18]), 'qx': float(kitti_properties[19]), 'qy': float(kitti_properties[20]), 'qz': float(kitti_properties[21]), 'qw': float(kitti_properties[22]), 'visibleRGB': decode_visible_labels(kitti_properties[23]), 'visibleGated': decode_visible_labels(kitti_properties[24]), 'visibleLidar': decode_visible_labels(kitti_properties[25]), 'visibleRadar': decode_visible_labels(kitti_properties[26]), } if camera_to_velodyne is not None: pos = np.asarray([object_dict['posx'], object_dict['posy'], object_dict['posz'], 1]) pos_lidar = np.matmul(camera_to_velodyne, pos.T) object_dict['posx_lidar'] = pos_lidar[0] object_dict['posy_lidar'] = pos_lidar[1] object_dict['posz_lidar'] = pos_lidar[2] kitti_object_list.append(object_dict) return kitti_object_list except: print('Problem occurred when reading label file!') return [] def load_velodyne_scan(file): """Load and parse velodyne binary file""" scan = np.fromfile(file, dtype=np.float32) return scan.reshape((-1, 4))[:, :3] #return scan def load_calib_data(path_total_dataset, name_camera_calib, tf_tree): """ :param path_total_dataset: Path to dataset root dir :param name_camera_calib: Camera calib file containing image intrinsic :param tf_tree: TF (transformation) tree containing translations from velodyne to cameras :return: """ with open(os.path.join(path_total_dataset, name_camera_calib), 'r') as f: data_camera = json.load(f) with open(os.path.join(path_total_dataset, tf_tree), 'r') as f: data_extrinsics = json.load(f) calib_dict = { 'calib_cam_stereo_left.json': 'cam_stereo_left_optical', 'calib_cam_stereo_right.json': 'cam_stereo_right_optical', 'calib_gated_bwv.json': 'bwv_cam_optical' } cam_name = calib_dict[name_camera_calib] # Scan data extrinsics for transformation from lidar to camera important_translations = ['lidar_hdl64_s3_roof', 'radar_ars300', cam_name] translations = [] for item in data_extrinsics: if item['child_frame_id'] in important_translations: translations.append(item) if item['child_frame_id'] == cam_name: T_cam = item['transform'] elif item['child_frame_id'] == 'lidar_hdl64_s3_roof': T_velodyne = item['transform'] elif item['child_frame_id'] == 'radar_ars300': T_radar = item['transform'] # Use pyquaternion to setup rotation matrices properly R_c_quaternion = Quaternion(w=T_cam['rotation']['w'] * 360 / 2 / np.pi, x=T_cam['rotation']['x'] * 360 / 2 / np.pi, y=T_cam['rotation']['y'] * 360 / 2 / np.pi, z=T_cam['rotation']['z'] * 360 / 2 / np.pi) R_v_quaternion = Quaternion(w=T_velodyne['rotation']['w'] * 360 / 2 / np.pi, x=T_velodyne['rotation']['x'] * 360 / 2 / np.pi, y=T_velodyne['rotation']['y'] * 360 / 2 / np.pi, z=T_velodyne['rotation']['z'] * 360 / 2 / np.pi) # Setup quaternion values as 3x3 orthogonal rotation matrices R_c_matrix = R_c_quaternion.rotation_matrix R_v_matrix = R_v_quaternion.rotation_matrix # Setup translation Vectors Tr_cam = np.asarray([T_cam['translation']['x'], T_cam['translation']['y'], T_cam['translation']['z']]) Tr_velodyne = np.asarray([T_velodyne['translation']['x'], T_velodyne['translation']['y'], T_velodyne['translation']['z']]) Tr_radar = np.asarray([T_radar['translation']['x'], T_radar['translation']['y'], T_radar['translation']['z']]) # Setup Translation Matrix camera to lidar -> ROS spans transformation from its children to its parents # Therefore one inversion step is needed for zero_to_camera -> <parent_child> zero_to_camera = np.zeros((3, 4)) zero_to_camera[0:3, 0:3] = R_c_matrix zero_to_camera[0:3, 3] = Tr_cam zero_to_camera = np.vstack((zero_to_camera, np.array([0, 0, 0, 1]))) zero_to_velodyne = np.zeros((3, 4)) zero_to_velodyne[0:3, 0:3] = R_v_matrix zero_to_velodyne[0:3, 3] = Tr_velodyne zero_to_velodyne = np.vstack((zero_to_velodyne, np.array([0, 0, 0, 1]))) zero_to_radar = zero_to_velodyne.copy() zero_to_radar[0:3, 3] = Tr_radar # Calculate total extrinsic transformation to camera velodyne_to_camera = np.matmul(np.linalg.inv(zero_to_camera), zero_to_velodyne) camera_to_velodyne = np.matmul(np.linalg.inv(zero_to_velodyne), zero_to_camera) radar_to_camera = np.matmul(np.linalg.inv(zero_to_camera), zero_to_radar) # Read projection matrix P and camera rectification matrix R P = np.reshape(data_camera['P'], [3, 4]) # In our case rectification matrix R has to be equal to the identity as the projection matrix P contains the # R matrix w.r.t KITTI definition R = np.identity(4) # Calculate total transformation matrix from velodyne to camera vtc = np.matmul(np.matmul(P, R), velodyne_to_camera) return velodyne_to_camera, camera_to_velodyne, P, R, vtc, radar_to_camera, zero_to_camera

[ "cwon789@naver.com" ]

cwon789@naver.com

f14510c92f51411a5b1fbf7e23edff82daf6ca18

b18ef775594417725557839e2242916b1ca4cfd6

/python/Classes/Classes: Dealing with Complex Number.py

dd425c93223e1638470a88b5e16efd229f5c1ab5

[]

no_license

sharique-ansari/Hackerrank-solutions

567a83ab6bd90bf2fbc0bcbbedd3eec75be5daf8

33c85d1671e64ae7d8ed79c03eb85122ee670dc5

refs/heads/master

2020-03-22T14:15:09.711607

2018-07-20T00:42:40

140,164,309

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import math class Complex(object): def __init__(self, real, imaginary): self.real = real self.imaginary = imaginary def __add__(self, no): return Complex(self.real + no.real, self.imaginary + no.imaginary) def __sub__(self, no): return Complex(self.real - no.real, self.imaginary - no.imaginary) def __mul__(self, no): return Complex(self.real * no.real - self.imaginary * no.imaginary, self.real * no.imaginary + self.imaginary * no.real) def __truediv__(self, no): a = (no.real ** 2 + no.imaginary ** 2) return self * Complex(no.real / a, -no.imaginary / a) def mod(self): return Complex(pow((self.real ** 2 + self.imaginary ** 2), 0.5), 0) def __str__(self): if self.imaginary == 0: result = "%.2f+0.00i" % (self.real) elif self.real == 0: if self.imaginary >= 0: result = "0.00+%.2fi" % (self.imaginary) else: result = "0.00-%.2fi" % (abs(self.imaginary)) elif self.imaginary > 0: result = "%.2f+%.2fi" % (self.real, self.imaginary) else: result = "%.2f-%.2fi" % (self.real, abs(self.imaginary)) return result if __name__ == '__main__': c = map(float, input().split()) d = map(float, input().split()) x = Complex(*c) y = Complex(*d) print(*map(str, [x + y, x - y, x * y, x / y, x.mod(), y.mod()]), sep='\n')

[ "sharique098@gmail.com" ]

sharique098@gmail.com

7b1dfacee784f7b05375188302ab051e0b005603

ad28a59209239be285d1127a87bc08893fb62cb9

/python/aad/test_concept_drift_classifier.py

76ebbe63d04d5f1a6934a18ec97cdc667445b71c

[ "MIT" ]

permissive

Karl-Wu/ad_examples

9e6f894c2414640b23ccdeb39db9e9b8352ef077

6fb0a2a72db51d82645e377945327eb9e1ecf8b8

refs/heads/master

2020-03-26T19:33:45.128414

2018-08-17T21:42:15

null

0

null

UTF-8

Python

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py

from aad.data_stream import * from common.gen_samples import read_anomaly_dataset from aad.anomaly_dataset_support import * from aad.classifier_trees import RandomForestAadWrapper """ Check data drift with a Random Forest classifier. NOTE: The classifier is trained only once in this example with the first window of data. The drift is tested for the rest of the windows *without* updating the model. To run: pythonw -m aad.test_concept_drift_classifier --debug --plot --log_file=temp/test_concept_drift_classifier.log --dataset=weather """ def test_kl_data_drift_classifier(): logger = logging.getLogger(__name__) args = get_command_args(debug=False) configure_logger(args) dataset_config = dataset_configs[args.dataset] stream_window = dataset_config[2] alpha = 0.05 n_trees = 100 X_full, y_full = read_anomaly_dataset(args.dataset) logger.debug("dataset: %s (%d, %d), stream_window: %d, alpha: %0.3f" % (args.dataset, X_full.shape[0], X_full.shape[1], stream_window, alpha)) stream = DataStream(X_full, y_full, IdServer(initial=0)) # get first window of data training_set = stream.read_next_from_stream(stream_window) x, y, ids = training_set.x, training_set.y, training_set.ids logger.debug("First window loaded (%s): %d" % (args.dataset, x.shape[0])) # train classifier with the window of data rf = RFClassifier.fit(x, y, n_estimators=n_trees) logger.debug("Random Forest classifier created with %d trees" % rf.clf.n_estimators) # prepare wrapper over the classifier which will compute KL-divergences # NOTE: rf.clf is the scikit-learn Random Forest classifier instance model = RandomForestAadWrapper(x=x, y=y, clf=rf.clf) logger.debug("Wrapper model created with %d nodes" % len(model.w)) # compute KL replacement threshold *without* p ref_kls, kl_q_alpha = model.get_KL_divergence_distribution(x, p=None, alpha=alpha) # now initialize reference p p = model.get_node_sample_distributions(x) window = 0 while not stream.empty(): window += 1 # get next window of data and check KL-divergence training_set = stream.read_next_from_stream(n=stream_window) x, y = training_set.x, training_set.y logger.debug("window %d loaded: %d" % (window, x.shape[0])) # compare KL-divergence of current data dist against reference dist p comp_kls, _ = model.get_KL_divergence_distribution(x, p=p) # find which trees exceed alpha-level threshold trees_exceeding_kl_q_alpha = model.get_trees_to_replace(comp_kls, kl_q_alpha) n_threshold = int(2 * alpha * n_trees) logger.debug("[%d] #trees_exceeding_kl_q_alpha: %d, threshold number of trees: %d\n%s" % (window, len(trees_exceeding_kl_q_alpha), n_threshold, str(list(trees_exceeding_kl_q_alpha)))) if __name__ == "__main__": test_kl_data_drift_classifier()

[ "smd.shubhomoydas@gmail.com" ]

smd.shubhomoydas@gmail.com

0afbaee6a3d11e935314a77c986fca4852eeb54e

d326cd8d4ca98e89b32e6a6bf6ecb26310cebdc1

/rosalind/bioinformatics/stronghold/tran/main.py

131ebf08d6ecc2fe6278e7b4127c11468845a825

[]

no_license

dswisher/rosalind

d6af5195cdbe03adb5a19ed60fcbf8c05beac784

4519740350e47202f7a45ce70e434f7ee15c6afc

refs/heads/master

2021-08-09T02:58:17.131164

2017-11-12T01:26:26

100,122,283

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null

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923

py

import sys from rosalind.common import util from rosalind.bioinformatics.common import fasta def compute_ratio(seq1, seq2): transitions = set(['AG', 'GA', 'CT', 'TC']) transversions = set(['AC', 'CA', 'GT', 'TG', 'AT', 'TA', 'CG', 'GC']) numTransitions = 0 numTransversions = 0 for i in xrange(len(seq1)): x = seq1[i] + seq2[i] if x in transitions: numTransitions += 1 elif x in transversions: numTransversions += 1 return float(numTransitions) / numTransversions def main(fname): seqs, _ = fasta.read(util.find_file(fname)) if len(seqs[0]) != len(seqs[1]): print "Sequences have different lengths!" sys.exit(1) print compute_ratio(seqs[0], seqs[1]) if __name__ == '__main__': if len(sys.argv) != 2: print ("You must specify the name of the data file to load!") sys.exit(1) main(sys.argv[1])

[ "big.swish@gmail.com" ]

big.swish@gmail.com