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

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# Generated by Django 3.2.4 on 2021-06-28 22:05 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Snippet', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', models.DateTimeField(auto_now_add=True)), ('title', models.CharField(blank=True, default='', max_length=100)), ('code', models.TextField()), ('linenos', models.BooleanField(default=False)), ('language', models.CharField(choices=[('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5'), ('6', '6')], default='python', max_length=100)), ], options={ 'ordering': ['created'], }, ), ]

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import numpy as np import keras from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D,Convolution2D,ZeroPadding2D,BatchNormalization from keras import backend as K from keras.preprocessing import image from keras.applications.mobilenet import MobileNet from keras.applications.vgg16 import preprocess_input, decode_predictions from keras.models import Model import timeit import warnings warnings.filterwarnings('ignore') import numpy as np import tensorflow as tf import math import sys import time import datetime import os batch_size = 128 num_classes = 7 epochs = 100 class NISTHelper(): def __init__(self, train_img, train_label, test_img, test_label): self.i = 0 self.test_i = 0 self.training_images = train_img self.training_labels = train_label self.test_images = test_img self.test_labels = test_label def next_batch(self, batch_size): x = self.training_images[self.i:self.i + batch_size] y = self.training_labels[self.i:self.i + batch_size] self.i = (self.i + batch_size) % len(self.training_images) return x, y def test_batch(self, batch_size): x = self.test_images[self.test_i:self.test_i + batch_size] y = self.test_labels[self.test_i:self.test_i + batch_size] self.test_i = (self.test_i + batch_size) % len(self.test_images) return x, y def unison_shuffled_copies(a, b): """Returns 2 unison shuffled copies of array a and b""" assert len(a) == len(b) p = np.random.permutation(len(a)) return a[p], b[p] def log(logstr): """Prints logstr to console with current time""" print(datetime.datetime.now().isoformat() + " " + logstr) log("Loading data...") images = np.load("nist_images_32x32.npy") labels = np.load("nist_labels_32x32.npy") log("Data loaded... Shuffling...") images, labels = unison_shuffled_copies(images, labels) log("Shuffled!") split = math.ceil(len(images) * 0.8) train_imgs = images[:split] train_labels = labels[:split] test_imgs = images[split:] test_labels = labels[split:] log("Performed train-test split") print(type(test_imgs)) nist = NISTHelper(train_imgs, train_labels, test_imgs, test_labels) x = tf.placeholder(tf.float32, shape=[None, 32, 32, 1], name="x") # Input, shape = ?x32x32x1 y_true = tf.placeholder(tf.float32, shape=[None, 47], name="y_true") # Label print() print(train_imgs.shape) model = Sequential() model.add(Conv2D(32, (5, 5), padding='same', activation='relu', input_shape=(32,32,1))) model.add(Conv2D(32, (5, 5), padding='same', activation='relu')) model.add(Conv2D(32, (5, 5), padding='same', activation='relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Conv2D(64, (3, 3), padding='same', activation='relu')) model.add(Conv2D(64, (3, 3), padding='same', activation='relu')) model.add(Conv2D(64, (3, 3), padding='same', activation='relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Conv2D(128, (3, 3), padding='same', activation='relu')) model.add(Conv2D(128, (3, 3), padding='same', activation='relu')) model.add(Conv2D(128, (3, 3), padding='same', activation='relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(.5)) model.add(Dense(7, activation='softmax')) model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.SGD(), metrics=['accuracy']) model.fit(train_imgs,train_labels, batch_size=batch_size, epochs=250, verbose=1, validation_data=(test_imgs,test_labels)) model.summary() ##incorrects = np.nonzero(model.predict(x).reshape((-1,)) != y_true) ##print(incorrects) model.save("model.h5")

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import re from collections import namedtuple from contextlib import closing from datetime import datetime from logging import getLogger from time import time from serial import Serial import honcho.core.data as data from honcho.config import (DATA_TAGS, GPIO, TIMESTAMP_FMT, WXT_BAUD, WXT_PORT, WXT_SAMPLES, WXT_TIMEOUT) from honcho.core.data import log_serialized, serialize from honcho.core.gpio import powered from honcho.tasks.common import task from honcho.tasks.sbd import queue_sbd from honcho.util import average_datetimes logger = getLogger(__name__) _DATA_KEYS = ( "timestamp", "wind_direction", "wind_speed", "temperature", "humidity", "pressure", "rain_accumulation", "rain_duration", "rain_intensity", "rain_peak_intensity", "hail_accumulation", "hail_duration", "hail_intensity", "hail_peak_intensity", "heater_temperature", "heater_voltage", "supply_voltage", ) DATA_KEYS = namedtuple("DATA_KEYS", (el.upper() for el in _DATA_KEYS))(*_DATA_KEYS) CONVERSION_TO_VALUE = { DATA_KEYS.WIND_DIRECTION: float, DATA_KEYS.WIND_SPEED: float, DATA_KEYS.TEMPERATURE: float, DATA_KEYS.HUMIDITY: float, DATA_KEYS.PRESSURE: float, DATA_KEYS.RAIN_ACCUMULATION: float, DATA_KEYS.RAIN_DURATION: float, DATA_KEYS.RAIN_INTENSITY: float, DATA_KEYS.RAIN_PEAK_INTENSITY: float, DATA_KEYS.HAIL_ACCUMULATION: float, DATA_KEYS.HAIL_DURATION: float, DATA_KEYS.HAIL_INTENSITY: float, DATA_KEYS.HAIL_PEAK_INTENSITY: float, DATA_KEYS.HEATER_TEMPERATURE: float, DATA_KEYS.HEATER_VOLTAGE: float, DATA_KEYS.SUPPLY_VOLTAGE: float, } CONVERSION_TO_STRING = { DATA_KEYS.TIMESTAMP: "{0:" + TIMESTAMP_FMT + "}", DATA_KEYS.WIND_DIRECTION: "{0:.4f}", DATA_KEYS.WIND_SPEED: "{0:.4f}", DATA_KEYS.TEMPERATURE: "{0:.4f}", DATA_KEYS.HUMIDITY: "{0:.4f}", DATA_KEYS.PRESSURE: "{0:.4f}", DATA_KEYS.RAIN_ACCUMULATION: None, DATA_KEYS.RAIN_DURATION: None, DATA_KEYS.RAIN_INTENSITY: None, DATA_KEYS.RAIN_PEAK_INTENSITY: None, DATA_KEYS.HAIL_ACCUMULATION: None, DATA_KEYS.HAIL_DURATION: None, DATA_KEYS.HAIL_INTENSITY: None, DATA_KEYS.HAIL_PEAK_INTENSITY: None, DATA_KEYS.HEATER_TEMPERATURE: "{0:.4f}", DATA_KEYS.HEATER_VOLTAGE: "{0:.4f}", DATA_KEYS.SUPPLY_VOLTAGE: "{0:.4f}", } WeatherSample = namedtuple("WeatherSample", DATA_KEYS) LINE_PATTERN = ( r"0R0," r"Dm=(?P<wind_direction>[\d\.]+).," r"Sm=(?P<wind_speed>[\d\.]+).," r"Ta=(?P<temperature>[\+\-\d\.]+).," r"Ua=(?P<humidity>[\d\.]+).," r"Pa=(?P<pressure>[\d\.]+).," r"Rc=(?P<rain_accumulation>[\d\.]+).," r"Rd=(?P<rain_duration>[\d\.]+).," r"Ri=(?P<rain_intensity>[\d\.]+).," r"Hc=(?P<hail_accumulation>[\d\.]+).," r"Hd=(?P<hail_duration>[\d\.]+).," r"Hi=(?P<hail_intensity>[\d\.]+).," r"Rp=(?P<rain_peak_intensity>[\d\.]+).," r"Hp=(?P<hail_peak_intensity>[\d\.]+).," r"Th=(?P<heater_temperature>[\+\-\d\.]+).," r"Vh=(?P<heater_voltage>[\+\-\d\.]+).," r"Vs=(?P<supply_voltage>[\+\-\d\.]+)." ) def parse_sample(s): row = re.search(LINE_PATTERN, s).groupdict() sample = WeatherSample( timestamp=datetime.now(), **dict((key, CONVERSION_TO_VALUE[key](value)) for key, value in row.items()) ) return sample def get_samples(n=12): logger.debug("Getting {0} samples".format(n)) samples = [] start_time = time() with powered([GPIO.WXT]): with closing(Serial(WXT_PORT, WXT_BAUD, timeout=60)) as serial: while len(samples) < n and time() - start_time < WXT_TIMEOUT: line = serial.readline() logger.debug("Read line from vaisala: {0}".format(line)) if re.search(LINE_PATTERN, line): samples.append(parse_sample(line)) logger.debug("{0} of {1} samples collected".format(len(samples), n)) return samples def average_samples(samples): logger.debug("Averaging {0} samples".format(len(samples))) n = len(samples) timestamp = average_datetimes([sample.timestamp for sample in samples]) averaged = WeatherSample( timestamp=timestamp, **dict( (key, sum(getattr(sample, key) for sample in samples) / float(n)) for key in DATA_KEYS[1:] ) ) return averaged def print_samples(samples): data.print_samples(samples, CONVERSION_TO_STRING) @task def execute(): samples = get_samples(n=WXT_SAMPLES) average = average_samples(samples) serialized = serialize(average, CONVERSION_TO_STRING) log_serialized(serialized, DATA_TAGS.WXT) queue_sbd(serialized, DATA_TAGS.WXT)

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#!/Users/arieldelgado/Desktop/unsplash_bynder/env/bin/python3 # -*- coding: utf-8 -*- import re import sys from setuptools.command.easy_install import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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# -*- coding: utf-8 -*- # Generated by Django 1.10 on 2018-03-01 01:58 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('employers', '0010_auto_20180228_0624'), ] operations = [ migrations.AddField( model_name='listing', name='charge_id', field=models.CharField(default='', max_length=255), preserve_default=False, ), ]

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#!/usr/bin/env python3 """Pattoo Agent class. Description: This script: 1) Processes a variety of information from agents 2) Posts the data using HTTP to a server listed in the configuration file """ # Standard libraries import os import socket import json from collections import defaultdict # pip3 libraries import requests # Pattoo libraries from pattoo import log from pattoo import general from pattoo import data as pattoo_data from pattoo import configuration class Data(object): """Pattoo agent that gathers data.""" def __init__(self, _data): """Initialize the class. Args: _data: ConfigAgent configuration object agent_name: Name of agent Returns: None """ # Initialize key variables self._data = _data # Get the agent_name if 'agent_program' in self._data: self._agent_name = self._data['agent_program'] else: self._agent_name = '' # Get the agent ID config = configuration.ConfigAgent(self._agent_name) agent_id = pattoo_data.get_agent_id(config) # Construct URL for server if config.api_server_https() is True: prefix = 'https://' else: prefix = 'http://' self._url = ( '{}{}:{}/{}/receive/{}'.format( prefix, config.api_server_name(), config.api_server_port(), config.api_server_uri(), agent_id)) # Create the cache directory self._cache_dir = config.agent_cache_directory() if os.path.exists(self._cache_dir) is False: os.mkdir(self._cache_dir) # All cache files created by this agent will end with this suffix. devicehash = general.hashstring(self._data['agent_hostname'], sha=1) self._cache_filename_suffix = '{}_{}.json'.format(agent_id, devicehash) def post(self, save=True, data=None): """Post data to central server. Args: save: When True, save data to cache directory if postinf fails data: Data to post. If None, then uses self._data (For testing) Returns: success: True: if successful """ # Initialize key variables success = False response = False timestamp = self._data['timestamp'] # Create data to post if data is None: data = self._data # Post data save to cache if this fails try: result = requests.post(self._url, json=data) response = True except: if save is True: # Create a unique very long filename to reduce risk of filename = '{}/{}_{}'.format( self._cache_dir, timestamp, self._cache_filename_suffix) # Save data with open(filename, 'w') as f_handle: json.dump(data, f_handle) # Define success if response is True: if result.status_code == 200: success = True # Log message if success is True: log_message = ( 'Agent "{}" successfully contacted server {}' ''.format(self._agent_name, self._url)) log.log2info(1027, log_message) else: log_message = ( 'Agent "{}" failed to contact server {}' ''.format(self._agent_name, self._url)) log.log2warning(1028, log_message) # Return return success def purge(self): """Purge data from cache by posting to central server. Args: None Returns: success: "True: if successful """ # Initialize key variables agent_id = self._data['agent_id'] # Add files in cache directory to list only if they match the # cache suffix all_filenames = [filename for filename in os.listdir( self._cache_dir) if os.path.isfile( os.path.join(self._cache_dir, filename))] filenames = [ filename for filename in all_filenames if filename.endswith( self._cache_filename_suffix)] # Read cache file for filename in filenames: # Only post files for our own UID value if agent_id not in filename: continue # Get the full filepath for the cache file and post filepath = os.path.join(self._cache_dir, filename) with open(filepath, 'r') as f_handle: try: data = json.load(f_handle) except: # Log removal log_message = ( 'Error reading previously cached agent data file {} ' 'for agent {}. May be corrupted.' ''.format(filepath, self._agent_name)) log.log2die(1064, log_message) # Post file success = self.post(save=False, data=data) # Delete file if successful if success is True: os.remove(filepath) # Log removal log_message = ( 'Purging cache file {} after successfully ' 'contacting server {}' ''.format(filepath, self._url)) log.log2info(1029, log_message)

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def a(): print('a() starts') b() d() print('a() returns') def b(): print('b() starts') c() print('b() returns') def c(): print('c() starts') print('c() returns') def d(): print('d() starts') print('d() returns') a()

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from selenium import webdriver from selenium.webdriver import ActionChains from selenium.webdriver.common.keys import Keys from selenium.webdriver.chrome.options import Options #chromeOptions=Options() #chromeOptions.add_experimental_option("prefs",{"download.default_directory": "C:\Downloadedfiles"}) driver=webdriver.Chrome(executable_path="C:\Drivers\chromedriver.exe") #driver=webdriver.Firefox(executable_path="C:\Drivers\geckodriver.exe") driver.maximize_window() driver.get("http://demo.automationtesting.in/FileDownload.html") #driver.find_element_by_xpath("//*[@id='textbox']").send_keys("welcome to selenium tutorials") #driver.find_element_by_xpath("//*[@id='createTxt']").click() #driver.find_element_by_xpath("//*[@id='link-to-download']").click() driver.find_element_by_xpath("//*[@id='pdfbox']").send_keys("welcome to selenium tutorials") driver.find_element_by_xpath("//*[@id='createPdf']").click() driver.find_element_by_xpath("//*[@id='pdf-link-to-download']").click()

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import itertools from typing import Type import numpy as np from nose.tools import raises from webdnn.graph.axis import Axis from webdnn.graph.operators.concat import Concat from webdnn.graph.order import Order, OrderC, OrderNC, OrderCN, OrderNHWC, OrderHWNC, OrderHWCN, OrderCNHW, \ OrderCHWN, OrderNCHW from webdnn.graph.variable import Variable def main(order1: Type[Order], order2: Type[Order], concat_axis: Axis): default_order = { 1: OrderC, 2: OrderNC, 4: OrderNHWC } op = Concat(None, axis=concat_axis) x1 = Variable(np.arange(order1.ndim) + 1, default_order[order1.ndim]) x2 = Variable(np.arange(order2.ndim) + 1, default_order[order2.ndim]) x1.change_order(order1) x2.change_order(order2) y, = op(x1, x2) for axis in y.order.axes: if axis == concat_axis: assert y.shape_dict[axis] == x1.shape_dict[axis] + x2.shape_dict[axis] else: assert y.shape_dict[axis] == x1.shape_dict[axis] # FIXME 各orderをテストにわけられないか def test_every_order(): orders = [OrderC, OrderNC, OrderCN, OrderNHWC, OrderHWNC, OrderHWCN, OrderNCHW, OrderCNHW, OrderCHWN] axes = [Axis.N, Axis.H, Axis.W, Axis.C] for order1, order2, axis in itertools.product(orders, orders, axes): if set(order1.axes) != set(order2.axes) or axis not in order1.axes: continue main(order1, order2, axis) @raises(AssertionError) def test_invalid_size(): op = Concat(None, axis=Axis.C) v1 = Variable((2, 3, 4, 5), OrderNHWC) v2 = Variable((2, 3, 7, 6), OrderNHWC) v3, = op(v1, v2)

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import random import time chance = random.randint(1,2) print(''' ----------인생역전프로그램---------- 축하합니다. 당신은 인생역전 프로그램에서 최후의 1인이 되어 마지막 선택을 해야합니다. 당신 앞에는 천사카드와 악마카드가 뒤집어져 있습니다. 천사카드를 고르면 상금 10억원을 타게 될 것이고 악마카드를 고르면 모든것을 잃고 빈손으로 돌아가게 됩니다. ''') ans = 'yes' while ans != 'no': choice = int(input('어떤 카드를 고르실까요? 1,2')) if choice == chance: time.sleep(2) print(''' ---------- | 천사 | ---------- ''') print('천사카드. 축하합니다. 100억을 받게 되셨습니다.') ans = input('다시 선택하시겠습니까? yes or no') else: time.sleep(2) print(''' ---------- | 악마 | ---------- ''') print('악마카드. 아쉽다...') ans = input('다시 선택하시겠습니까? yes or no')

[ "ggamini7@gmail.com" ]

ggamini7@gmail.com

69e45ca1b5115c546bb8ab6ec2b5ec62ba9dd980

aabdb61eeea7b93076017f264b214161231ff3a5

/polls/models.py

a8a4c8474c0ce0d0a24477bba8a6755a26c14dfa

[]

no_license

scottbenton/django-getting-started

644c07b33bf9dc3304a0c55b535d5ade4ac67273

d25a34e0edd2d54d505f2f877197d3cb3bbe1ace

refs/heads/master

2023-03-24T07:14:55.211998

2021-03-21T03:27:46

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import datetime from django.db import models from django.utils import timezone # Create your models here. class Question(models.Model): question_text = models.CharField(max_length=200) pub_date = models.DateTimeField('date published') def was_published_recently(self): return self.pub_date >= (timezone.now() - datetime.timedelta(days=1)) def __str__(self): return self.question_text class Choice(models.Model): question = models.ForeignKey(Question, on_delete=models.CASCADE) choice_text = models.CharField(max_length=200) votes = models.IntegerField(default=0) def __str__(self): return self.choice_text

[ "scott@scottbenton.dev" ]

scott@scottbenton.dev

57ddc7252c0f15fd814ffcf0c64541feb898d1de

a46116d41839b4aa9d0eac6154f67fd9a50142e9

/387-First_Unique_Character_in_a_String.py

0b20136094c8d8762aa94f96fc45c3ff49d853d1

[]

no_license

weichuntsai0217/leetcode

85a600385d0d9a5d6d35788b0fccfd325b3e9157

f2ea7165d40b392b8f8892ba7152438001900a4d

refs/heads/master

2020-04-17T23:53:14.151694

2016-10-19T09:04:10

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py

class Solution(object): def firstUniqChar(self, s): """ :type s: str :rtype: int """ freq = {} for i, c in enumerate(s): if c not in freq: freq[c] = [i, 1] else: freq[c][1] += 1 first = len(s) for c in freq: if freq[c][1] == 1 and freq[c][0] < first: first = freq[c][0] return first if first != len(s) else -1

[ "jimmy_tsai@trend.com.tw" ]

jimmy_tsai@trend.com.tw

17709bfd1c3d21385da4ef5150b7684988173639

38dd9951c21efc2a60a60a076f5e9a2c4a5cd423

/fantasm/state.py

8660dd4abfaa9e7d88895ad52cf82016b6a63998

[]

no_license

awolf/Foojal

9a6886a3a2294e6cf566b67b37d71ee398f442e0

0227b9621c7b0d5103ee483cebd2f9e5275dbd0f

refs/heads/master

2020-05-31T13:55:45.358657

2011-10-15T16:04:38

2,582,406

1

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py

""" Fantasm: A taskqueue-based Finite State Machine for App Engine Python Docs and examples: http://code.google.com/p/fantasm/ Copyright 2010 VendAsta Technologies Inc. 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. """ from google.appengine.api.taskqueue.taskqueue import Task, TaskAlreadyExistsError, TombstonedTaskError from fantasm import constants from fantasm.transition import Transition from fantasm.exceptions import UnknownEventError, InvalidEventNameRuntimeError from fantasm.utils import knuthHash from fantasm.lock import RunOnceSemaphore class State(object): """ A state object for a machine. """ def __init__(self, name, entryAction, doAction, exitAction, machineName=None, isFinalState=False, isInitialState=False, isContinuation=False, fanInPeriod=constants.NO_FAN_IN): """ @param name: the name of the State instance @param entryAction: an FSMAction instance @param doAction: an FSMAction instance @param exitAction: an FSMAction instance @param machineName: the name of the machine this State is associated with @param isFinalState: a boolean indicating this is a terminal state @param isInitialState: a boolean indicating this is a starting state @param isContinuation: a boolean indicating this is a continuation State @param fanInPeriod: integer (seconds) representing how long these states should collect before dispatching """ assert not (exitAction and isContinuation) # TODO: revisit this with jcollins, we want to get it right assert not (exitAction and fanInPeriod > constants.NO_FAN_IN) # TODO: revisit this with jcollins self.name = name self.entryAction = entryAction self.doAction = doAction self.exitAction = exitAction self.machineName = machineName # is this really necessary? it is only for logging. self.isInitialState = isInitialState self.isFinalState = isFinalState self.isContinuation = isContinuation self.isFanIn = fanInPeriod != constants.NO_FAN_IN self.fanInPeriod = fanInPeriod self._eventToTransition = {} def addTransition(self, transition, event): """ Adds a transition for an event. @param transition: a Transition instance @param event: a string event that results in the associated Transition to execute """ assert isinstance(transition, Transition) assert isinstance(event, basestring) assert not (self.exitAction and transition.target.isContinuation) # TODO: revisit this with jcollins assert not (self.exitAction and transition.target.isFanIn) # TODO: revisit self._eventToTransition[event] = transition def getTransition(self, event): """ Gets the Transition for a given event. @param event: a string event @return: a Transition instance associated with the event @raise an UnknownEventError if event is unknown (i.e., no transition is bound to it). """ try: return self._eventToTransition[event] except KeyError: import logging logging.critical('Cannot find transition for event "%s". (Machine %s, State %s)', event, self.machineName, self.name) raise UnknownEventError(event, self.machineName, self.name) def dispatch(self, context, event, obj): """ Fires the transition and executes the next States's entry, do and exit actions. @param context: an FSMContext instance @param event: a string event to dispatch to the State @param obj: an object that the Transition can operate on @return: the event returned from the next state's main action. """ transition = self.getTransition(event) if context.currentState.exitAction: try: context.currentAction = context.currentState.exitAction context.currentState.exitAction.execute(context, obj) except Exception: context.logger.error('Error processing entry action for state. (Machine %s, State %s, exitAction %s)', context.machineName, context.currentState.name, context.currentState.exitAction.__class__) raise # join the contexts of a fan-in contextOrContexts = context if transition.target.isFanIn: taskNameBase = context.getTaskName(event, fanIn=True) contextOrContexts = context.mergeJoinDispatch(event, obj) if not contextOrContexts: context.logger.info('Fan-in resulted in 0 contexts. Terminating machine. (Machine %s, State %s)', context.machineName, context.currentState.name) obj[constants.TERMINATED_PARAM] = True transition.execute(context, obj) if context.currentState.entryAction: try: context.currentAction = context.currentState.entryAction context.currentState.entryAction.execute(contextOrContexts, obj) except Exception: context.logger.error('Error processing entry action for state. (Machine %s, State %s, entryAction %s)', context.machineName, context.currentState.name, context.currentState.entryAction.__class__) raise if context.currentState.isContinuation: try: token = context.get(constants.CONTINUATION_PARAM, None) nextToken = context.currentState.doAction.continuation(contextOrContexts, obj, token=token) if nextToken: context.continuation(nextToken) context.pop(constants.CONTINUATION_PARAM, None) # pop this off because it is really long except Exception: context.logger.error('Error processing continuation for state. (Machine %s, State %s, continuation %s)', context.machineName, context.currentState.name, context.currentState.doAction.__class__) raise # either a fan-in resulted in no contexts, or a continuation was completed if obj.get(constants.TERMINATED_PARAM): return None nextEvent = None if context.currentState.doAction: try: context.currentAction = context.currentState.doAction nextEvent = context.currentState.doAction.execute(contextOrContexts, obj) except Exception: context.logger.error('Error processing action for state. (Machine %s, State %s, Action %s)', context.machineName, context.currentState.name, context.currentState.doAction.__class__) raise if transition.target.isFanIn: # this prevents fan-in from re-counting the data if there is an Exception # or DeadlineExceeded _after_ doAction.execute(...) succeeds index = context.get(constants.INDEX_PARAM) workIndex = '%s-%d' % (taskNameBase, knuthHash(index)) semaphore = RunOnceSemaphore(workIndex, context) semaphore.writeRunOnceSemaphore(payload=obj.get(constants.TASK_NAME_PARAM)) try: # at this point we have processed the work items, delete them task = Task(name=obj.get(constants.TASK_NAME_PARAM, '') + '-cleanup', url=constants.DEFAULT_CLEANUP_URL, params={'workIndex': workIndex}) context.Queue(name=constants.DEFAULT_CLEANUP_QUEUE_NAME).add(task) except (TaskAlreadyExistsError, TombstonedTaskError): context.logger.info("Fan-in cleanup Task already exists.") if nextEvent: if not isinstance(nextEvent, str) or not constants.NAME_RE.match(nextEvent): raise InvalidEventNameRuntimeError(nextEvent, context.machineName, context.currentState.name, context.instanceName) return nextEvent

[ "adam.wolf@bluecognition.com" ]

adam.wolf@bluecognition.com

43a9a9c0d9b65b4a0aad5d154adebfbe78b142f0

9fd9a5cf4c945af1baffb4f42521a962890ce165

/dele.py

a94090117c4904a2f668417f96b1e98b1a92af44

[]

no_license

UjjwalDash/Quiz_app

332053175265eb5677a15969e0c9e945ac0bc91c

9a27527c17d4227e0744f5b1f09adb6186066b1f

refs/heads/main

2023-06-24T04:43:08.730752

2021-07-24T06:26:12

389,015,053

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from tkinter import * import sqlite3 import tkinter.messagebox mem1=Tk() mem1.title('Quiz') mem1.maxsize(1000,300) mem1.configure(bg='Powder Blue') member=Frame(mem1,bg='Powder Blue',bd=5) member.pack(side=TOP) btnfra=Frame(mem1,bg='Powder Blue',bd=5) btnfra.pack(side=BOTTOM) mno=StringVar() def reset(): mno.set('') def exi(): mem1.destroy() def delete(): a=mno.get() crr=sqlite3.connect('QUIZ.db') c=crr.cursor() c.execute('select* from quize') l=c.fetchall() d={} for row in l: d[row[0]]=row[1],row[2],row[3],row[4],row[5] try: b=d[a] A=b[0] B=b[1] C=b[2] D=b[3] Ans=b[4] try: cnx=sqlite3.connect('QUIZ.db') Cursor=cnx.cursor() Qry=("DELETE FROM quize WHERE question='%s'"%(a)) Cursor.execute(Qry) cnx.commit() Cursor.close() tkinter.messagebox.showinfo('program',"Question Deleted Successfully........") reset() except: tkinter.messagebox.showinfo('program',"Question does not exist........") except: tkinter.messagebox.showinfo("program",'No such No such Question found') question=Label(member,font=('arial 18 bold'),bg='Powder Blue',text='Question:') question.grid(row=0,column=0) questiontxt=Entry(member,font=('arial 16 bold'),textvar=mno,width=50,justify=LEFT,bd=8) questiontxt.grid(row=0,column=1) btn=Button(btnfra,text='Delete',font=('arial 16 bold'),command=delete,bd=8,pady=5, bg='Orange',width=10) btn.grid(row=4,column=0,sticky='w') btn1=Button(btnfra,text='Reset',font=('arial 16 bold'),command=reset,bd=8, pady=5,bg='Yellow',width=10) btn1.grid(row=4,column=1,sticky='w') btn2=Button(btnfra,text='Exit',font=('arial 16 bold'),command=exi,bd=8, pady=5,bg='Red',width=10) btn2.grid(row=4,column=2,sticky='w') mem1.mainloop()

[ "noreply@github.com" ]

UjjwalDash.noreply@github.com

6b20acbcf552ec7d07c9b06d8609f99fa16c7ac1

343bdaddfc66c6316e2cee490e9cedf150e3a5b7

/0001_0100/0045/0045.py

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[]

no_license

dm-alexi/acmp

af7f6b4484b78f5922f3b464406a0ba5dea0d738

3fa0016d132adfeab7937b3e8c9687a34642c93a

refs/heads/master

2021-07-09T15:14:25.857086

2020-10-20T19:08:54

201,908,038

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UTF-8

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py

with open("input.txt", "r") as f, open("output.txt", "w") as q: n = int(f.read()) s = "1" if n else "10" if n > 1: a = [0] * 10 for i in range(9, 1, -1): while n % i == 0: n //= i a[i] += 1 s = "".join((str(i) * a[i]) for i in range(2, 10)) if n == 1 else "-1" q.write(s)

[ "dm2.alexi@gmail.com" ]

dm2.alexi@gmail.com

743a6e1ca84db32348dd91efedde31759a3ada4a

0e4860fecfdd34a3255003cc8c8df086c14083dd

/python/source_code/AutomatePython/13-pdf-word/addContentToDocx.py

e226f5539fac401c7112e21e3072b634221c2153

[]

no_license

anzhihe/learning

503ab9a58f280227011da5eaa4b14b46c678e6f3

66f7f801e1395207778484e1543ea26309d4b354

refs/heads/master

2023-08-08T11:42:11.983677

2023-07-29T09:19:47

188,768,643

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null

2023-08-24T02:10:34

2019-05-27T04:04:10

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py

import docx doc = docx.Document() doc.add_paragraph('Hello world!') paraObj1 = doc.add_paragraph('This is a second paragraph.') paraObj2 = doc.add_paragraph('This is a yet another paragraph.') paraObj1.add_run(' This text is being added to the second paragraph.') doc.save('multipleParagraphs.docx')

[ "anzhihe1218@gmail.com" ]

anzhihe1218@gmail.com

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/venv/Scripts/django-admin.py

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[]

no_license

adamsagas/PythonReq2

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b2790878658e369c1dc40a135605fcbbb4d84085

refs/heads/master

2023-06-02T11:26:37.880719

2021-06-23T13:37:24

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null

UTF-8

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py

#!C:\Users\AdamSagas_jxgmrac\PycharmProjects\Git\PythonReq2\venv\Scripts\python.exe from django.core import management if __name__ == "__main__": management.execute_from_command_line()

[ "78692714+adamsagas@users.noreply.github.com" ]

78692714+adamsagas@users.noreply.github.com

82fa358282ffc2c663186533546e78ee84f2c20d

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/config/urls.py

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[ "MIT" ]

permissive

dcopm999/sibdev

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refs/heads/master

2022-11-17T10:59:23.412299

2020-07-20T12:52:42

279,256,258

0

MIT

2020-07-20T12:39:28

2020-07-13T09:23:34

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py

from django.conf import settings from django.conf.urls.static import static from django.contrib import admin from django.contrib.staticfiles.urls import staticfiles_urlpatterns from django.urls import include, path from django.views import defaults as default_views from django.views.generic import TemplateView from rest_framework.authtoken.views import obtain_auth_token urlpatterns = [ path("", TemplateView.as_view(template_name="pages/home.html"), name="home"), path( "about/", TemplateView.as_view(template_name="pages/about.html"), name="about" ), # Django Admin, use {% url 'admin:index' %} path(settings.ADMIN_URL, admin.site.urls), # User management path("users/", include("sibdev.users.urls", namespace="users")), path("accounts/", include("allauth.urls")), # Your stuff: custom urls includes go here ] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) if settings.DEBUG: # Static file serving when using Gunicorn + Uvicorn for local web socket development urlpatterns += staticfiles_urlpatterns() # API URLS urlpatterns += [ # API base url path("api/", include("config.api_router")), # DRF auth token path("auth-token/", obtain_auth_token), ] if settings.DEBUG: # This allows the error pages to be debugged during development, just visit # these url in browser to see how these error pages look like. urlpatterns += [ path( "400/", default_views.bad_request, kwargs={"exception": Exception("Bad Request!")}, ), path( "403/", default_views.permission_denied, kwargs={"exception": Exception("Permission Denied")}, ), path( "404/", default_views.page_not_found, kwargs={"exception": Exception("Page not Found")}, ), path("500/", default_views.server_error), ] if "debug_toolbar" in settings.INSTALLED_APPS: import debug_toolbar urlpatterns = [path("__debug__/", include(debug_toolbar.urls))] + urlpatterns

[ "dcopm999@gmail.com" ]

dcopm999@gmail.com

b4e044fe4e30acb0804ab1dc73629fab8472c4cf

a0d2b0dc48b8d97f5b0a08aab8e4abd07a4ea759

/markov-data-boti.py

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[ "MIT" ]

permissive

saefsten/supermarket

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refs/heads/main

2023-01-12T06:09:24.096172

2020-11-20T17:11:25

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#!/usr/bin/env python # coding: utf-8 """ Project Markov - @author: boti """ # Import pandas etc import pandas as pd import numpy as np import matplotlib.pyplot as plt import os def plotsections(df,dayofweek): totalcustomers = df['customer_no'].nunique() print(totalcustomers) print (f'Total number of customers on {dayofweek} : {str(totalcustomers)}') print (f'Total number of customers at each location on {dayofweek} :') print(df.groupby('location')['customer_no'].nunique()) df[df['location']=='spices'].resample('1h')['customer_no'].nunique().plot() df[df['location']=='fruit'].resample('1h')['customer_no'].nunique().plot() df[df['location']=='drinks'].resample('1h')['customer_no'].nunique().plot() df[df['location']=='dairy'].resample('1h')['customer_no'].nunique().plot() filename = 'plots/sections-' + dayofweek + '.jpg' plt.savefig(filename) plt.cla() df[df['location']=='checkout'].resample('1h')['customer_no'].nunique().plot() filename = 'plots/checkout-' + dayofweek + '.jpg' plt.savefig(filename) plt.cla() finalcheckoutcustomers = totalcustomers - df[df['location']=='checkout']['customer_no'].count() print(f'An additional {finalcheckoutcustomers} cutomers check out at 22:00 on {dayofweek}.') def statediagram(df): totalcustomers = df['customer_no'].nunique() states = pd.DataFrame(columns=['from','to']) for i in range (totalcustomers): route = df[df['customer_no']==i + 1]['location'].values if route[len(route)-1] != 'checkout': route = np.append(route, 'checkout') for j in range(len(route)-1): states.loc[len(states)] = route[j:j+2] return states #os.chdir('/home/boti/Spiced/git-repos/stochastic-sage-student-code/project_08/') monday = pd.read_csv('./monday.csv',sep=';',index_col='timestamp', parse_dates=True) tuesday = pd.read_csv('./tuesday.csv',sep=';',index_col='timestamp', parse_dates=True) wednesday = pd.read_csv('./wednesday.csv',sep=';',index_col='timestamp', parse_dates=True) thursday = pd.read_csv('./thursday.csv',sep=';',index_col='timestamp', parse_dates=True) friday = pd.read_csv('./friday.csv',sep=';',index_col='timestamp', parse_dates=True) plotsections(monday,'Monday') plotsections(tuesday,'Tuesday') plotsections(wednesday,'Wednesday') plotsections(thursday,'Thursday') plotsections(friday,'Friday') monstates = statediagram(monday) print(monstates.groupby(['from'])['to'].value_counts().unstack()) print(pd.crosstab(monstates['from'], monstates['to'],normalize=0))

[ "x@vitos.tv" ]

x@vitos.tv

c77fd258ec69e67562ba66cf3d8d9e89497f10d5

06fef9d2d307cca6d5143990d3032c2f566654b2

/checkpoint_log_parser.py

afdbc44171c7c8ce8d36819bb9a4f22ae7cef92a

[]

no_license

aditya-wate/checkpoint_log_parser

f9a5052cc198bcf2400dbd95df2151c7977f184f

9eccf1e0be7b87829b141493660bb373b5e86930

refs/heads/master

2020-04-06T04:55:32.478223

2017-03-21T16:37:51

71,390,283

1

0

null

UTF-8

Python

false

704

py

import re log_file = "C:\Users\\awate\Documents\Miscellaneous\server.log" #log_file = "one_line_cp.log" log_list = list() def extract_fields_to_dict(): with open(log_file) as ol: for line in ol: try: rx = \ r"([A-Za-z0-9_:;. ]*)(>eth[0-9]{1,2} )([A-Za-z0-9_:;.{}&\- ]*)" m = re.match(rx,line) event = m.group(3).split("; ") # content after the interface fields = dict(el.split(": ") for el in event) log_list.append(fields) except: continue return log_list def main(): print extract_fields_to_dict() if __name__ == '__main__': main()

[ "awate@C02T40WXG8WL.local" ]

awate@C02T40WXG8WL.local

b72973b13e4ff60b549ba28635d34749e129df63

fe8c94060b208d686ebb987fbc37c4d175c5c047

/peco.py

00e0d631726c76a99a175656d8c09ff5d6849e0a

[]

no_license

hachibeeDI/peco-chrome

aa2cbf42014a8c4536f9620a08aad8c93cfeb6d4

fc48238fa28af9d0110f59fa1030a89ed24567a5

refs/heads/master

2021-01-10T11:06:06.019871

2015-06-02T08:47:51

36,720,315

0

null

UTF-8

Python

false

377

py

# -*- coding: utf-8 -*- from __future__ import (print_function, division, absolute_import, unicode_literals, ) from subprocess import Popen, PIPE, STDOUT class Peco(object): def filter(self, source): peco_filterd = Popen(["peco", '--query', '"$LBUFFER"'], stdin=PIPE, stdout=PIPE, stderr=STDOUT, shell=True) return peco_filterd.communicate(input=source)

[ "8hachibee125@gmail.com" ]

8hachibee125@gmail.com

a28a4cb0c2674b62b154309a34f9e712e93ccdb1

f754b014aa2af7487f09040d0208a6080719088e

/3.18可视化/pandas绘图.py

7b7985b04b9a7900d77efc637539c08a44c1d6fd

[]

no_license

aallenchen2018/2019.3.20shujufenxi

09f39e0dbd20225f537d001981cc380a923d5d57

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import numpy as np import pandas as pd import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False data=pd.DataFrame(100*np.abs(np.random.rand(4,4)),index=['one','two','three','four'],columns=['HW','ZTE','ESSS','NOKIA']) ####线状图 data.plot(kind='line',figsize=(10,6),grid=True,ylim=(0,200)) #####柱状图（单个公司） fig=plt.figure(figsize=(10,10)) ax=fig.subplots(2) data['ZTE'].plot(kind='bar',ax=ax[0])###竖向柱状图 ax[0].set_xticklabels(labels=data['ZTE'].index,rotation=0) data['ZTE'].plot(kind='barh',ax=ax[1])####横向柱状图 ####柱状图2(所有公司) fig1=plt.figure(figsize=(10,10)) ax1=fig1.subplots(1,2) data.plot(kind='bar',ax=ax1[0]) data.T.plot(kind='bar',ax=ax1[1],stacked=True) plt.show() #####绘图 comp1 = np.random.normal(0,1,size=20000) comp2 = np.random.normal(10,2,size=20000) values = pd.Series(np.concatenate([comp1,comp2])) values.plot(kind='hist',bins=50,density=True) values.plot(kind='kde',style='r--')

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from turtle import Turtle, Screen from paddle import Paddle from ball import Ball from score import Scoreboard import time START_CORD1 = ((-340, 0), (-340, 20), (-340, 40)) START_CORD2 = ((340, 0), (340, 20), (340, 40)) ball = Ball() score_l=Scoreboard((-50,220)) score_r=Scoreboard((50,220)) screen = Screen() screen.listen() screen.bgcolor("black") screen.setup(700, 500) screen.tracer(0) r_paddle = Paddle((330, 0)) l_paddle = Paddle((-330, 0)) screen.onkeypress(r_paddle.move_up, 'Up') screen.onkeypress(r_paddle.move_down, 'Down') screen.onkeypress(l_paddle.move_up, 'w') screen.onkeypress(l_paddle.move_down, 's') while True: screen.update() time.sleep(ball.fast) ball.move() if ball.ycor() > 235 or ball.ycor() < -235: ball.bounce() if (ball.distance(l_paddle) < 50 and ball.xcor() < -310) or (ball.distance(r_paddle) < 50 and ball.xcor() > 310): ball.paddle_bounce() if ball.xcor()>360: score_l.score_add() ball.reset() if ball.xcor() < -360: score_r.score_add() ball.reset()

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amansaini842@gmail.com

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import numpy as np num = 5 # num = 3005290 elves = [[1,"nr"+str(x+1)] for x in range(num)] length = num i = 0 while len(elves) > 1: print(len(elves)) mod = i % len(elves) mod1 = (i+1) % len(elves) # print(123) # if elves[mod][0] == 0: print(i,elves,mod,mod1,elves[mod]) # asdasd # del elves[mod] # length -= 1 # continue elves[mod][0] += elves[mod1][0] # print(123) del elves[mod1] # print(123) i += 1 # print(i,length,elves) input() print("svar:", elves)

[ "Morten Aske Kolstad" ]

Morten Aske Kolstad

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import subprocess import re import socket import logging import os import datetime from logger import setup_logger from protocol import Protocol setup_logger() log = logging.getLogger('logger') RU = { 'encoding': 'cp866', 'startup_time': 'Время загрузки системы', 'mac': r'.{0,}Физический адрес.{0,}', 'ip': r'.{0,}IPv4-адрес.{0,}' } STARTUP_FILE_NAME = 'startup.info' PROTO_PORT_SERVER = 10788 PROTO_PORT_CLIENT = 10789 def system_startup_time(): if os.path.exists(STARTUP_FILE_NAME): with open(STARTUP_FILE_NAME, 'r') as f: return f.read() else: return update_startup_time() def update_startup_time(): with open(STARTUP_FILE_NAME, 'w') as f: time = datetime.datetime.now().isoformat() f.write(time) return time def handle(data): packet = Protocol.decode(data) if packet.code == Protocol.CODE_IFALIVE: return Protocol(Protocol.CODE_IFALIVE, packet.uid, packet.cid).encode() elif packet.code == Protocol.CODE_ASKSTARTTIME: return Protocol(Protocol.CODE_STARTTIME, packet.uid, packet.cid, system_startup_time()).encode() else: return None def main(): log.info('Starting daemon') log.info('Updating startup time') update_startup_time() sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP) sock.bind(('', PROTO_PORT_SERVER)) while True: data, addr = sock.recvfrom(1024) log.debug(f'From {addr[0]}:{addr[1]} {data}') try: answ = handle(data) log.debug(f'Response: {answ}') if answ is not None: sock.sendto(answ, (addr[0], PROTO_PORT_CLIENT)) except KeyboardInterrupt: break except Exception as err: log.error(f'Packet: {data}. Error msg: {err}') if __name__ == '__main__': main()

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""" 考虑这样一组方法，它们在被调用时检查“用户名”参数 """ class Store(object): def __init__(self): self.storage = {} def get_food(self, username, food): if username != 'admin': raise Exception('This user is not allowed to get food') return self.storage.get(food) def put_food(self, username, food): if username != 'admin': raise Exception('This user is not allowed to put food') return self.storage.put(food)

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# Our previous implementation of make_averager was not efficient. We stored all the values in the historical series and computed their sum every time averager was called. A better implementation would just store the total and the number of items so far, and compute the mean from these two numbers. def make_average_broken(): """ >>> avg = make_average_broken() >>> avg(10) # doctest +ELLIPSIS Traceback (most recent call last): ... UnboundLocalError: local variable 'count' referenced before assignment """ count = 0 total = 0 def averager(new_value): # The problem is that the statement count += 1 actually means the same as count = count + 1, when count is a number or any immutable type. So we are actually assigning to count in the body of averager, and that makes it a local variable. The same problem affects the total variable. # We did not have this problem in previous example because we never assigned to the series name; we only called series.append and invoked sum and len on it. So we took advantage of the fact that lists are mutable. # But with immutable types like numbers, strings, tuples, etc., all you can do is read, but never update. If you try to rebind them, as in count = count + 1, then you are implicitly creating a local variable count. It is no longer a free variable, and therefore it is not saved in the closure. count += 1 total += new_value return total / count return averager def make_average(): """ >>> avg = make_average() >>> avg(10) 10.0 >>> avg(11) 10.5 >>> avg(12) 11.0 """ count = 0 total = 0 def averager(new_value): # The nonlocal declaration was introduced in Python 3. It lets you flag a variable as a free variable even when it is assigned a new value within the function. If a new value is assigned to a nonlocal variable, the binding stored in the closure is changed. nonlocal count, total count += 1 total += new_value return total / count return averager if __name__ == "__main__": import doctest doctest.testmod()

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import pytest from airflow.models import DagBag @pytest.fixture() def dag_bag(): return DagBag(dag_folder="../dags", include_examples=False)

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AlexMontgomerie/ML

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2021-01-25T12:42:40.750142

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from matplotlib import pyplot as plt from sklearn import linear_model from sklearn.metrics import accuracy_score import numpy as np import seaborn as sns from sklearn.svm import SVR from sklearn.model_selection import KFold from sklearn.metrics import mean_absolute_error np.random.seed(0) NUM_FEATURE = 12 def get_data(red=True): #get red wine data red_data = np.genfromtxt('../data/winequality-red.csv',delimiter=';') red_data = np.delete(red_data,(0),axis=0) red_feature = np.array([[1 for i in range(red_data.shape[0])]]) red_y = np.array([red_data[:,11]]) red_data = np.append(red_data[:,0:11],red_feature.T,axis=1) red_data = np.append(red_data,red_y.T,axis=1) if red: return red_data #get white wine data white_data = np.genfromtxt('../data/winequality-white.csv',delimiter=';') white_data = np.delete(white_data,(0),axis=0) white_feature = np.array([[-1 for i in range(white_data.shape[0])]]) white_y = np.array([white_data[:,11]]) white_data = np.append(white_data[:,0:11],white_feature.T,axis=1) white_data = np.append(white_data,white_y.T,axis=1) #combine the 2 data = np.append(red_data,white_data,axis=0) np.random.shuffle(data) return data ####################### ### Normalise data ### def normalise(data): return (data - data.mean())/data.std() def normalise_all(data): for i in range(NUM_FEATURE): data[:,i] = normalise(data[:,i]) return data ###################### ### Split Training, Test and Validation sets ### def split_data(data,train_split=0.8): #train,test,val train,test = np.split(data, [int(train_split*data.shape[0])]) train_x,train_y = np.hsplit(train,[NUM_FEATURE]) test_x,test_y = np.hsplit(test, [NUM_FEATURE]) for i in range(NUM_FEATURE): mean = train_x[:,i].mean() std = train_x[:,i].std() #normalise training data #normalise test data test[:,i] return train_x,train_y,test_x,test_y ##### Loss Functions ##### def square_loss(y,pred_y): return ((y-pred_y)**2).mean() #N = len(y) #error_sum = 0 #for i in range(N): # error_sum += (y[i]-pred_y[i])**2 #return error/N def mae_loss(y,pred_y): return (y-pred_y).mean() def identity_loss(y,pred_y): N = len(y) error_sum = 0 for i in range(N): if y[i] != pred_y[i]: error_sum += 1 return error_sum/N ########################## ################################################ class lin_regr: def __init__(self,data,reguliser): self.data = data self.train_x = np.array([]) self.train_y = np.array([]) self.test_x = np.array([]) self.test_y = np.array([]) self.weights = np.array([]) self.reguliser = 'ridge' self.init_data() def split_data(self,train_split=0.8): #TODO: randomise data split train,test = np.split(self.data, [int(train_split*self.data.shape[0])]) #update datasets self.train_x,self.train_y = np.hsplit(train,[NUM_FEATURE]) self.test_x,self.test_y = np.hsplit(test, [NUM_FEATURE]) def init_data(self): self.split_data() #normalise all x self.train_x = normalise_all(self.train_x) self.test_x = normalise_all(self.test_x) #bias all x self.train_x = self.add_bias(self.train_x) self.test_x = self.add_bias(self.test_x) def fit(self,x,y,l=0.001): if self.reguliser=='ridge': tmp = np.matmul(x.T,x) + np.diag([l for i in range(NUM_FEATURE+1)]) tmp = np.linalg.pinv(tmp) tmp = np.matmul(tmp,x.T) self.weights = np.matmul(tmp,y) else: pinv_x = np.linalg.pinv(x) self.weights = np.matmul(pinv_x,y) def add_bias(self,x): ones = np.array([[1] for i in range(len(x[:,0]))]) return np.concatenate((x,ones),axis=1) def predict(self,x): y = np.dot(x,self.weights) return classify(y) def classify(self,y): for i in range(len(y)): for j in range(11): if y[i] > (j-0.5) and y[i] <= (j+0.5): y[i] = j if y[i] < 0: y[i] = 0 if y[i] > 10: y[i] = 10 return y def cross_validation(self,k=10,loss=square_loss): #split into K folds data = np.concatenate((self.train_x,self.train_y),axis=1) kf = KFold(n_splits=k) error_sum = 0 for train,test in kf.split(data): train_data = np.array(data)[train] test_data = np.array(data)[test] train_x, train_y = np.hsplit(train_data,[NUM_FEATURE+1]) test_x , test_y = np.hsplit(test_data,[NUM_FEATURE+1]) self.fit(train_x,train_y) pred_y = self.predict(test_x) pred_y = self.classify(pred_y) error_sum += loss(test_y,pred_y) print("cross validation error: ",error_sum/k) return error_sum/k ### Compute Linear Regression Weights ### def fit(x,y,ridge=False,l=0.001): if ridge: tmp = np.matmul(np.transpose(x),x) + np.diag([l for i in range(NUM_FEATURE+1)]) tmp = np.linalg.pinv(tmp) tmp = np.matmul(tmp,x.T) return np.matmul(tmp,y) else: pinv_x = np.linalg.pinv(x) return np.matmul(pinv_x,y) def add_bias(x): ones = np.array([[1] for i in range(len(x[:,0]))]) return np.concatenate((x,ones),axis=1) def my_lin_regr(data): train_x,train_y,test_x,test_y = split_data(data) #normalise all x train_x = normalise_all(train_x) test_x = normalise_all(test_x) #train_x = train_x[:,[1,10]] #test_x = test_x[:,[1,10]] #bias all x train_x = add_bias(train_x) test_x = add_bias(test_x) #get learned weight weights = fit(train_x,train_y,ridge=True) cross_validation(train_x,train_y,weights) pred_y = predict(train_x,weights) print("(my) linear regression accuracy: ",accuracy_score(train_y,pred_y)) def bin_classify_y(y,val): tmp = np.array([[0] for i in range(len(y))]) for i in range(len(y)): if y[i][0] >= val: tmp[i][0] = 1 else: tmp[i][0] = -1 return tmp def predict(x,weights): y = np.dot(x,weights) return classify(y) def classify(y): for i in range(len(y)): for j in range(11): if y[i] > (j-0.5) and y[i] <= (j+0.5): y[i] = j if y[i] < 0: y[i] = 0 if y[i] > 10: y[i] = 10 return y def svd_reduction(data): u,s,vh = np.linalg.svd(data,full_matrices=False) data = np.matmul(np.matmul(u,np.diag(s)),vh) return data ######################################### def sk_lin_regr(data): #data = normalise(data) train_x,train_y,test_x,test_y = split_data(data) print(train_x[0]) regr = linear_model.LinearRegression() regr.fit(train_x,train_y) pred_y = regr.predict(test_x) print("(sk) linear regression MAE: ",mean_absolute_error(test_y,pred_y)) return regr def sk_lasso_regr(data,alpha=0.1): #data = normalise(data) train_x,train_y,test_x,test_y = split_data(data) regr = linear_model.Lasso(alpha) regr.fit(train_x,train_y) pred_y = regr.predict(test_x) print("(sk) linear regression Lasso MAE: ",mean_absolute_error(test_y,pred_y)) return regr def sk_ridge_regr(data,alpha=0.1): #data = normalise(data) train_x,train_y,test_x,test_y = split_data(data) regr = linear_model.Ridge(alpha) regr.fit(train_x,train_y) pred_y = regr.predict(train_x) ''' #tuning hyperparameter lmbda = 50 prev_err = cross_validation(train_x,train_y,regr); print("Previous ERROR: ",prev_err) alpha += lmbda*0.00001 for i in range(200): regr = linear_model.Ridge(alpha) regr.fit(train_x,train_y) curr_err = cross_validation(train_x,train_y,regr); err_gradient = curr_err - prev_err print("Error: ",curr_err,", Alpha: ",alpha,", gradient: ",err_gradient) alpha = alpha - lmbda*err_gradient ''' pred_y = regr.predict(test_x) print("(sk) linear regression Ridge MAE: ",mean_absolute_error(test_y,pred_y)) return regr def sk_elastic_regr(data,alpha=0.1, l1_ratio=0.5): #data = normalise(data) train_x,train_y,test_x,test_y = split_data(data) regr = linear_model.ElasticNet(alpha, l1_ratio) regr.fit(train_x,train_y) pred_y = regr.predict(test_x) pred_y = classify(pred_y) print("(sk) linear regression Elastic Net MAE: ",mean_absolute_error(test_y,pred_y)) return regr def sk_svm(data): #split the data train_x,train_y,test_x,test_y = split_data(data) #normalise all x train_x = normalise_all(train_x) test_x = normalise_all(test_x) print(bin_classify_y(test_y,6)) #define SVR clf = SVR(C=1.0, epsilon=0.2) clf.fit(train_x,train_y) #predict y using SVC pred_y = clf.predict(test_x) pred_y = classify(pred_y) print("(sk) support vector machine for regression accuracy: {}",accuracy_score(test_y,pred_y)) return ##### Validation ##### #TODO: create K-fold cross-validation function def cross_validation(train_x,train_y,model,k=10,loss=square_loss): #split into K folds data = np.concatenate((train_x,train_y),axis=1) kf = KFold(n_splits=k) error_sum = 0 for train,test in kf.split(data): train_data = np.array(data)[train] test_data = np.array(data)[test] train_x, train_y = np.hsplit(train_data,[NUM_FEATURE]) test_x , test_y = np.hsplit(test_data,[NUM_FEATURE]) model.fit(train_x,train_y) pred_y = model.predict(test_x) error_sum += loss(test_y,pred_y) return error_sum/k if __name__=="__main__": data = get_data() model = sk_lin_regr(data) #sk_ridge_regr(data,0.05) ''' for i in range(20): alpha = pow(10,-(i-5)) print("Alpha: ", alpha) #sk_lasso_regr(data,alpha) sk_ridge_regr(data,alpha) print("\n ////////////////////// \n") for i in range(20): for j in range(10): alpha = pow(10,-i) l1_ratio = 0.1*(j+1) print("Alpha: ", alpha,", L1 Ratio: ",l1_ratio) sk_elastic_regr(data,alpha,l1_ratio) #my_lin_regr(data) #sk_svm(data) #tmp = lin_regr(data,'ridge') #tmp.cross_validation(loss=identity_loss) '''

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AlexMontgomerie@github.com

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from PacketModifier import * from Fuzzer import * from scapy.all import * import sys import afl ''' FuzzingManager.py Manages the fuzzing of packets and is external interface to other subsystems ''' class FuzzingManager: def __init__(self): self.packetModifier = PacketModifier() self.fuzzer = Fuzzer('afl_input/', 'afl_output/') afl.init() def generateFuzzValue(self, field, vrange): v = self.fuzzer.generateValue() IP(dst=v[1], src=v[1]) os._exit(0) def generateFuzzedPacket(self, packet, field, vrange, value): pass if __name__ == '__main__': fm = FuzzingManager() fv = fm.generateFuzzValue('dst', (1, 10))

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__version__ = '1.0.1' class robot(object): def myrobot(self): print('你好，请问需要什么帮助？') ji = input() if ji=='你好': print('你好') if ji=='你的名字叫什么': print('我的名字叫Robin') if ji=='你会说中文吗': print('我当然会啊！') if ji=='你会Pyhon吗？': print('我想做一条蟒蛇，把你紧紧缠住，一辈子') if ji=='你会说中文吗？': print('我说的都是标准中文') if ji=='圆周率': print('3.14159265358979') if ji=='你会编程吗？': print('当然会啊，有我不会东西吗？哈哈哈') if ji=='会Java吗？': print('编程这东西主要在于你的思想，而不是什么语言') if ji=='你很会说话': print('我嘴刚抹了蜜') if ji=='谢谢': print('不客气，应该的') if ji=='你会说什么语言': print('我会中文') if ji=='诸葛亮是谁': print(' 诸葛亮（181年~234年10月8日 [1] ），字孔明，号卧龙，琅琊阳都（今山东省沂南县）人 [2] ，三国时期蜀汉丞相，中国古') print('代杰出的政治家、军事家、文学家。') if ji=='中华人民共和国哪一年成立？': print('1949年') if ji=='1+1等于几': print('1+1=2') if ji=='再见': print('不想说拜拜，好像在和你聊一会儿啊！') def close(self): break def main(): pass if __name__ == '__main__': main()

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PickleEric/thebreeze

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refs/heads/master

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from django.apps import AppConfig class ThebreezeAppConfig(AppConfig): name = 'thebreeze_app'

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/fluidics/valves/tc_commands.py

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keyuxi/fluidics-control

f8ee6be456a0b7493004f9567e37987a95f42edf

ff7df176a788e08a7f61ece0e252f1c760f51fae

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2020-12-07T11:52:34.702159

2020-01-16T00:55:30

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import serial def tc_initialize(ser): cmd_name = ["1. INPUT1 (reads the temperature of the primary thermistor)","2. DESIRED CONTROL VALUE (set value)","3. POWER OUTPUT","4. ALARM STATUS","5. INPUT 2","6. OUTPUT CURRENT COUNTS","7. ALARM TYPE", "8. SET TYPE DEFINE (the desired control temperature or “set temp” input definition)","9. SENSOR TYPE","10. CONTROL TYPE","11. CONTROL OUTPUT POLARITY","12. POWER ON/OFF", "13. OUTPUT SHUTDOWN IF ALARM","14. FIXED DESIRED CONTROL SETTING","15. PROPORTIONAL BANDWIDTH","16. INTEGRAL GAIN","17. DERIVATIVE GAIN","18. LOW EXTERNAL SET RANGE", "19. HIGH EXTERNAL SET RANGE","20. ALARM DEADBAND","21. HIGH ALARM SETTING","22. LOW ALARM SETTING","23. CONTROL DEADBAND SETTING","24. INPUT1 OFFSET","25. INPUT2 OFFSET","26. HEAT MULTIPLIER", "27. COOL MULTIPLIER","28. OVER CURRENT COUNT COMPARE VALUE","29. ALARM LATCH ENABLE","30. COMMUNICATIONS ADDRESS (reserved command, do not use)","31. ALARM LATCH RESET","32. CHOOSE SENSOR FOR ALARM FUNCTION", "33. CHOOSE °C or °F TEMPERATURE WORKING UNITS","34. EEPROM WRITE ENABLE","35. OVER CURRENT CONTINUOUS","36. OVER CURRENT RESTART ATTEMPS","37. JP3 DISPLAY ENABLE"] read_cmd = ["01","03","02","05","06","07","41","42","43","44","45","46","47","50","51","52","53","54","55","56","57","58","59","5a","5b","5c","5d","5e","48",None,None,"4a","4b","4c","4d","5f","4e"] write_cmd = [None,None,None,None,None,None,"28","29","2a","2b","2c","2d","2e","1c","1d","1e","1f","20","21","22","23","24","25","26","27","0c","0d","0e","2f",None,"33","31","32","34","35","0f","36"] init_val = [2043,0,0,0,15566,130,0,0,1,2,1,0,1,0,500,0,0,0,10000,100,10000,0,100,0,0,20,20,14,0,None,0,0,1,1,0,300,0] for cmd_i in range(len(read_cmd)): if read_cmd[cmd_i] != None: print('\n\n%s: read %s' %(cmd_name[cmd_i], read_cmd[cmd_i])) val_back, _ = tc_talk(ser, [c for c in read_cmd[cmd_i]], 0) if write_cmd[cmd_i] != None and val_back != init_val[cmd_i]: print("Reset %s from %d to %d." % (cmd_name[cmd_i], val_back, init_val[cmd_i])) _ = tc_talk(ser, [c for c in write_cmd[cmd_i]], init_val[cmd_i]) val_back_check,_ = tc_talk(ser, [c for c in read_cmd[cmd_i]], 0) print(val_back_check) def tc_talk(ser, cmd_address, cmd_val): """ Args: cmd_address - list of 2, specify the type of cmd cmd_val - decimal value for write Returns: val_back - int, converted decimal buf - list of length 8, original msg form TC """ buf = [0,0,0,0,0,0,0,0,0,0,0,0] msg = tc_encoder(cmd_address, cmd_val) print('send: %s' % ''.join(msg)) for pn in range(0,16): ser.write((msg[pn]).encode()) for pn in range(0,12): buf[pn]=ser.read(1).decode() #print(buf[pn]) print('back: %s' % ''.join(buf)) val_back = tc_decoder(buf, msg) return val_back, buf def tc_encoder(cmd_address, cmd_val): """ Args: cmd_address - list of 2, specify the type of cmd cmd_val - int, decimal value for write Returns: cmd_str - list of char, final command to send to TC3625 """ # convert value val_hex = dec2hex(cmd_val) # device address + command address + hex value cmd_str_core = ['0', '0'] + cmd_address + val_hex # check sum cmd_str_core_ascii = [ord(char) for char in cmd_str_core] cmd_sum = hex(sum(cmd_str_core_ascii))[2:] cmd_sum = cmd_sum[-2:].zfill(2) # complete string cmd_str = ['*'] + cmd_str_core + [char.lower() for char in cmd_sum] + ['\r'] return cmd_str def tc_decoder(cmd_back, cmd_str): """ Returns: val_back - int, decimal """ if cmd_back == [0,0,0,0,0,0,0,0,0,0,0,0]: print(cmd_str) print('Cannot hear from COM.') val_back = None elif cmd_back == ['*','X','X','X','X','X','X','X','X','c','0','\r']: print(cmd_str) print('Check sum for the input is incorrect.') val_back = None else: back_sum = hex(sum([ord(char) for char in cmd_back[1:-3]]))[2:] back_sum = back_sum[-2:].zfill(2) if back_sum != (cmd_back[-3] + cmd_back[-2]): print('back checksum: %s' % back_sum) print(cmd_back) print('Checksum of the command returned from COM is incorrect.') val_back = None else: print(cmd_back[1:9]) val_back = int(''.join(cmd_back[1:9]), 16) print('value back: %d' % val_back) return val_back def hexc2dec(bufp): """ Args: bufp - list of len 8 Returns: newval - int in decimal """ newval=0 divvy=pow(16,7) #sets the word size to DDDDDDDD for pn in range (1,7): vally=ord(bufp[pn]) if(vally < 97): subby=48 else: subby=87 # ord() converts the character to the ascii number value newval += ((ord(bufp[pn])-subby)*divvy) divvy/=16 if(newval > pow(16,8)/2-1): newval=newval-pow(16,8) #distinguishes between positive and negative numbers return newval def dec2hex(val): """ Args: val - int, decimal Returns: list of len 8, hex value """ hexval = hex(val)[2:].zfill(8) return [char.lower() for char in hexval] if __name__ == "__main__": # tc = TC() # tc.talk() # tc.listen() # tc.close() ser = serial.Serial('com3', 9600, timeout=1) tc_initialize(ser) # _ = tc_talk(ser, ['1','c'], 1000) # set_temp, _ = tc_talk(ser, ['5','0'], 0) # print(set_temp/100.0) # val_back, buf = tc_talk(ser, ['0','1'], 0) # print(val_back/100.0) ser.close() print("PORT CLOSED")

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keyuxi@yeah.net

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/prueba/public/my-project/application.wsgi

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danmcode/servicios-telematicos

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refs/heads/master

2023-04-06T04:18:38.760867

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#!/usr/bin/python import sys sys.path.insert(0,"/var/www/my-project/") from app import app as application

[ "danielmuelasrivera483@gmail.com" ]

danielmuelasrivera483@gmail.com

186559069415e595b28a5f470d5ff3dad932a6ae

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/travelport/models/base_search_rsp_5.py

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[]

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tefra/xsdata-samples

c50aab4828b8c7c4448dbdab9c67d1ebc519e292

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from __future__ import annotations from dataclasses import dataclass, field from travelport.models.base_rsp_5 import BaseRsp5 from travelport.models.next_result_reference_5 import NextResultReference5 __NAMESPACE__ = "http://www.travelport.com/schema/common_v37_0" @dataclass class BaseSearchRsp5(BaseRsp5): class Meta: name = "BaseSearchRsp" next_result_reference: list[NextResultReference5] = field( default_factory=list, metadata={ "name": "NextResultReference", "type": "Element", "namespace": "http://www.travelport.com/schema/common_v37_0", "max_occurs": 999, } )

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chris@komposta.net

b6fe39655b437a466d7d0d92b5db2d795146ce61

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/Map_Gen/Engine.py

f246b7a375e09625b4be78139f669d2067bcc5bb

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permissive

Djsurry/WordRPG

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refs/heads/master

2020-04-11T14:45:20.859080

2018-12-15T02:13:45

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##-- Import --## import os import time import random import sqlite3 import pandas ##-- Custom Import --## #from Map_Gen import Biome from Map_Gen import Biome from Map_Gen import SubBiome ##-- GLOBAL VERAIBLES --## ##-- Map Width and Hight --## ##-- I am thinking maybe start with a 50 x 50 map but when all is said and done --## ##-- 1000 x 1000 will be the map size --## map_width = 100 map_hight = 100 sub_map_width = 10 sub_map_hight = 10 map_seed = [] # coords x = 1 y = 1 ##-- Clears print screen --## def clear(): if os.name == 'nt': _ = os.system('cls') else: _ = os.system('clear') ##-- This is the blueprint of the tiles or biomes --## class Tile: def __init__(self, coords_x, coords_y, abriv, name, rarity, difficulty, enterable, exit_x, exit_y, discovered=False): self.coords_x = coords_x self.coords_y = coords_y self.abriv = abriv self.name = name self.rarity = rarity ##-- How often you need to be spawned --## self.difficulty = difficulty self.enterable = enterable ##-- True or False --## self.floor = 0 ##-- in SQL it is tile_level --## self.exit_x = exit_x self.exit_y = exit_y self.discovered = discovered class SubTile: def __init__(self, from_x, from_y, coords_x, coords_y, biome_name, place_name, difficulty): self.from_x = from_x self.from_y = from_y self.coords_x = coords_x self.coords_y = coords_y self.biome_name = biome_name self.place_name = place_name self.difficulty = difficulty self.floor = 1 ##-- in SQL it is tile_level --## self.discovered = False ##-- Finds a random biome to be made in a tile --## def get_random_biome(): biome_keys = Biome.world_biomes.keys() list_of_biome_abriv = [] for bio in biome_keys: list_of_biome_abriv.append(bio) return random.choice(list_of_biome_abriv) def get_random_sub_biome(key): biome_keys = SubBiome.sub_biome[key].keys() list_of_sub_biome_abriv = [] for bio in biome_keys: list_of_sub_biome_abriv.append(bio) return random.choice(list_of_sub_biome_abriv) def set_exit_coords_x(): return random.randint(0, sub_map_width) def set_exit_coords_y(): return random.randint(0, sub_map_hight) ##-- right now there is only Common Uncommon and Rare, But maybe i will add Epic as well --## def rare_control(): while True: r_biome = get_random_biome() if Biome.world_biomes[r_biome]['rarity'] == 'Rare': num = random.randint(0,100) if num == 50: return r_biome elif Biome.world_biomes[r_biome]['rarity'] == 'Uncommon': num = random.randint(0,20) if num == 10: return r_biome elif Biome.world_biomes[r_biome]['rarity'] == 'Common': return r_biome ##-- Gets a random number to set biome difficulty --## def ran_diff(): return random.randint(1, 10) ##-- Map is placed into a class here --## def map_builder(): loading_dots = 0 clear() print('Loading.') for spot in range(0, (map_width * map_hight)): ##-- spot is index and coord_x and coord_y is finding the (x, y) coordinates from index (AKA spot) --## if spot in [10, 100, 500, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000]: loading_dots += 1 clear() print('-------------------') print('Loading' + '.' * loading_dots) print('-------------------') coord_y = int((spot / map_width)) coord_x = spot - (map_width * coord_y) biome = rare_control() map_seed.append(biome) biome_name = Biome.world_biomes[biome]['name'] biome_rarity = Biome.world_biomes[biome]['rarity'] biome_layers = Biome.world_biomes[biome]['enterable'] ##-- coords_x, coords_y, name, rarity, difficulty, enterable=False, discovered=False --## ##-- coords_x, coords_y, name, rarity, difficulty, enterable, exit_x, exit_y, discovered=False --## insert_tile(Tile(coord_x, coord_y, biome, biome_name, biome_rarity, ran_diff(), biome_layers, set_exit_coords_x(), set_exit_coords_y())) if biome_layers: for subspot in range(0, (sub_map_width * sub_map_hight)): sub_coord_y = int((subspot / sub_map_width)) sub_coord_x = subspot - (sub_map_width * sub_coord_y) # print(f"X: {coord_x}") # print(f"Y: {coord_y}") # print(f"SUB X: {sub_coord_x}") # print(f"SUB Y: {sub_coord_y}") # input("Enter") try: sub_biome_name = SubBiome.sub_biome[biome][get_random_sub_biome(biome)]['name'] except: continue insert_subtile(SubTile(coord_x, coord_y, sub_coord_x, sub_coord_y, biome_name, sub_biome_name, ran_diff())) ##-- mapfile is the varible name of the file that the map is stored in --## mapfile = 'Worldmap.db' ##-- Conn connects to the database --## conn = sqlite3.connect(mapfile) ##-- c is what lets you write c = conn.cursor() def neat_layout(): df1 = pandas.read_sql_query("SELECT * FROM tile ;", conn) df2 = pandas.read_sql_query("SELECT * FROM subTile ;", conn) print(df1) print("\n--------------------------------------------------------------------------------------------------\n") print(df2) def make_map_datebase(): with conn: c.execute("""CREATE TABLE IF NOT EXISTS tile (coords_x int, coords_y int, abriv text, place_name text, rarity text, difficulty int, enterable boolean, tile_level int, exit_x int, exit_y int, discovered boolean)""") c.execute("""CREATE TABLE IF NOT EXISTS subTile (from_x int, from_y int, coords_x int, coords_y int, biome_name text, place_name text, difficulty int, tile_level int, discovered boolean)""") def insert_tile(tile): with conn: c.execute("INSERT INTO tile VALUES (:coords_x, :coords_y, :abriv, :place_name, :rarity, :difficulty, :enterable, :tile_level, :exit_x, :exit_y, :discovered)", {'coords_x': tile.coords_x, 'coords_y': tile.coords_y, 'abriv': tile.abriv, 'place_name': tile.name, 'rarity': tile.rarity, 'difficulty': tile.difficulty, 'enterable': tile.enterable, 'tile_level': tile.floor, 'exit_x': tile.exit_x, 'exit_y': tile.exit_y, 'discovered': tile.discovered}) def insert_subtile(subtile): with conn: c.execute("INSERT INTO subTile VALUES (:from_x, :from_y, :coords_x, :coords_y, :biome_name, :place_name, :difficulty, :tile_level, :discovered)", {'from_x': subtile.from_x, 'from_y': subtile.from_y, 'coords_x': subtile.coords_x, 'coords_y': subtile.coords_y, 'biome_name': subtile.biome_name, 'place_name': subtile.place_name, 'difficulty': subtile.difficulty, 'tile_level': subtile.floor, 'discovered': subtile.discovered}) def get_tile(x,y): c.execute("SELECT * FROM tile WHERE coords_x=:coords_x AND coords_y=:coords_y;", {'coords_x': x, 'coords_y': y}) return c.fetchall() def get_subTile(from_x, from_y, x, y): c.execute("SELECT * FROM subTile WHERE from_x=:from_x AND from_y=:from_y AND coords_x=:coords_x AND coords_y=:coords_y;", {'from_x': from_x, 'from_y': from_y, 'coords_x': x, 'coords_y': y}) return c.fetchall() def get_all(): c.execute("SELECT * FROM map;") return c.fetchall() def update_tile(x,y,discovered): with conn: c.execute("""UPDATE tile SET discovered=:discovered WHERE coords_x=:coords_x AND coords_y=:coords_y""", {'coords_x': x, 'coords_y': y, 'discovered': discovered}) def update_subTile(from_x, from_y, x, y, discovered): with conn: c.execute("""UPDATE subTile SET discovered=:discovered WHERE from_x=:from_x AND from_y=:from_y AND coords_x=:coords_x AND coords_y=:coords_y""", {'from_x': from_x, 'from_y': from_y, 'coords_x': x, 'coords_y': y, 'discovered': discovered})

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/cst042_oblig2/src/src_client.py

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camillavilla1/inf_2301

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import socket, sys from Crypto.PublicKey import RSA from Crypto.Cipher import AES class Client(object): def __init__(self, ): self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) # Create a socket object def rsa_pub_key(self): """Generate new RSA-key, send public key to server""" self.new_key = RSA.generate(2048) #contain public- and private key self.public_key = self.new_key.publickey() new_pubkey = open("pem.pem", 'w') #make new pem-file for public key new_pubkey.write(self.public_key.exportKey("PEM")) #write the public key to the file new_pubkey.close() open_pem = open("pem.pem", 'r') pub_key = open_pem.read() open_pem.close() self.socket.send(pub_key) def get_aeskey(self): """Get AES key from server. Decrypt it""" self.aeskey = self.socket.recv(4096) self.decr_aes = self.new_key.decrypt(self.aeskey)[:16] #decrypt with pub/sec key self.iv = self.new_key.decrypt(self.aeskey)[16:] def new_text(self): """Get text from the server and opens new text file for the message""" self.data = self.socket.recv(4096) new_txtfile = open("text_client.txt", 'w') new_txtfile.write(self.data) new_txtfile.close() def get_msg(self): """Get message from server, decrypt message and put it in new document""" cipher = AES.new(self.decr_aes, AES.MODE_CFB, self.iv) dec_msg = cipher.decrypt(self.data) new_msg = open("text_client.txt", 'a') new_msg.write(dec_msg) def run(self, host="localhost", port=8080): host = sys.argv[1] port = int(sys.argv[2]) self.socket.connect((host, port)) self.rsa_pub_key() self.get_aeskey() self.new_text() self.get_msg() if __name__ == '__main__': client = Client() print "Client running..." client.run()

[ "dc-milla@hotmail.com" ]

dc-milla@hotmail.com

71b7d904c4b631395ebd6d5e10b3d98b8233d49e

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/smssearchreqs/models.py

8329592040210fcfb9e2c508af96b0f388ce9e3e

[]

no_license

codeht1/foxsearch

6e3e5a7de086fefba10c8cab259fa519b06aa7fc

2cc706a592949802dc8e6b3f66504ce27bc7d020

refs/heads/master

2016-09-06T06:19:31.256647

2014-10-28T17:37:50

null

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null

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py

from django.db import models # Create your models here. class smssearchreq(models.Model): mobilenum=models.DecimalField(max_digits=10,decimal_places=0) query = models.CharField(max_length=200) timestamp=models.DateTimeField(auto_now_add=True) def __unicode__(self): return "%s" %(self.mobilenum)

[ "praveenyadav1193@yahoo.com" ]

praveenyadav1193@yahoo.com

a8b8e6b8f9a3395df1433080c32261269530e44f

73a55f07a5c0bf3b3dbfc696f2f22cb2c9485c5f

/lab3/ex3_1.py

ef074fba9e7a5293646cddeae992b5f5804d5114

[]

no_license

korliakov/infa_2020_Oksanichenko

158ec38eae8693f00cf3fe1330845c36730eba93

0064c1319fe16e234ded394d7aee3b30a4b88cf9

refs/heads/master

2022-12-22T11:49:10.569667

2020-09-24T07:58:50

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import pygame import numpy as np from pygame.draw import * pygame.init() FPS = 30 screen = pygame.display.set_mode((600, 600)) WHT = (255, 255, 255) YLW = (255, 255, 0) BLC = (0, 0, 0) RED = (255, 0, 0) screen.fill(WHT) circle(screen, YLW, [300, 300], 200) circle(screen, BLC, [300, 300], 200, 1) circle(screen, BLC, [370, 250], 20) # eyes circle(screen, RED, [370, 250], 10) circle(screen, BLC, [230, 250], 30) circle(screen, RED, [230, 250], 10) x1e1 = 370 - np.sin((np.pi / 180) * 30) * 20 y1e1 = 250 - np.cos((np.pi / 180) * 30) * 20 x2e1 = x1e1 - np.cos((np.pi / 180) * 30) * 40 y2e1 = y1e1 + np.sin((np.pi / 180) * 30) * 40 x3e1 = x2e1 - np.sin((np.pi / 180) * 30) * 10 y3e1 = y2e1 - np.cos((np.pi / 180) * 30) * 10 x4e1 = x3e1 + np.cos((np.pi / 180) * 30) * 80 y4e1 = y3e1 - np.sin((np.pi / 180) * 30) * 80 x5e1 = x4e1 + np.sin((np.pi / 180) * 30) * 10 y5e1 = y4e1 + np.cos((np.pi / 180) * 30) * 10 x6e1 = x5e1 - np.cos((np.pi / 180) * 30) * 40 y6e1 = y5e1 + np.sin((np.pi / 180) * 30) * 40 Eyebrown1 = [(x1e1, y1e1), (x2e1, y2e1), (x3e1, y3e1), (x4e1, y4e1), (x5e1, y5e1), (x6e1, y6e1)] x1e2 = 230 + np.sin((np.pi / 180) * 30) * 30 y1e2 = 250 - np.cos((np.pi / 180) * 30) * 30 x2e2 = x1e2 + np.cos((np.pi / 180) * 30) * 50 y2e2 = y1e2 + np.sin((np.pi / 180) * 30) * 50 x3e2 = x2e2 + np.sin((np.pi / 180) * 30) * 15 y3e2 = y2e2 - np.cos((np.pi / 180) * 30) * 15 x4e2 = x3e2 - np.cos((np.pi / 180) * 30) * 100 y4e2 = y3e2 - np.sin((np.pi / 180) * 30) * 100 x5e2 = x4e2 - np.sin((np.pi / 180) * 30) * 15 y5e2 = y4e2 + np.cos((np.pi / 180) * 30) * 15 x6e2 = x5e2 + np.cos((np.pi / 180) * 30) * 50 y6e2 = y5e2 + np.sin((np.pi / 180) * 30) * 50 Eyebrown2 = [(x1e2, y1e2), (x2e2, y2e2), (x3e2, y3e2), (x4e2, y4e2), (x5e2, y5e2), (x6e2, y6e2)] polygon(screen, BLC, Eyebrown1) polygon(screen, BLC, Eyebrown2) x1 = 200 y1 = 400 w = 200 h = 20 rect(screen, BLC, (x1, y1, w, h)) pygame.display.update() clock = pygame.time.Clock() finished = False while not finished: clock.tick(FPS) for event in pygame.event.get(): if event.type == pygame.QUIT: finished = True pygame.quit()

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okssolotheodor@gmail.com

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import torch from torch import nn from torch.nn import functional as F class Model1(nn.Module): def __init__(self): super(Model1, self).__init__() self.fc1 = nn.Linear(773, 400) self.bn1 = nn.BatchNorm1d(400) self.fc2 = nn.Linear(400, 200) self.bn2 = nn.BatchNorm1d(200) self.fc3 = nn.Linear(200, 100) self.bn3 = nn.BatchNorm1d(100) self.fc4 = nn.Linear(100, 1) self.bn4 = nn.BatchNorm1d(1) def forward(self, x): x = F.leaky_relu(self.bn1(self.fc1(x))) x = F.leaky_relu(self.bn2(self.fc2(x))) x = F.leaky_relu(self.bn3(self.fc3(x))) x = self.bn4(self.fc4(x)) return torch.tanh(x) class Model2(nn.Module): def __init__(self): super(Model2, self).__init__() self.a1 = nn.Conv2d(12, 16, kernel_size=3, padding=1) self.a2 = nn.Conv2d(16, 16, kernel_size=3, padding=1) self.a3 = nn.Conv2d(16, 32, kernel_size=3, stride=2) self.b1 = nn.Conv2d(32, 32, kernel_size=3, padding=1) self.b2 = nn.Conv2d(32, 32, kernel_size=3, padding=1) self.b3 = nn.Conv2d(32, 64, kernel_size=3, stride=2) self.c1 = nn.Conv2d(64, 64, kernel_size=2, padding=1) self.c2 = nn.Conv2d(64, 64, kernel_size=2, padding=1) self.c3 = nn.Conv2d(64, 128, kernel_size=2, stride=2) self.d1 = nn.Conv2d(128, 128, kernel_size=1) self.d2 = nn.Conv2d(128, 128, kernel_size=1) self.d3 = nn.Conv2d(128, 128, kernel_size=1) self.last = nn.Linear(128, 1) def forward(self, x): x = F.relu(self.a1(x)) x = F.relu(self.a2(x)) x = F.relu(self.a3(x)) # 4x4 x = F.relu(self.b1(x)) x = F.relu(self.b2(x)) x = F.relu(self.b3(x)) # 2x2 x = F.relu(self.c1(x)) x = F.relu(self.c2(x)) x = F.relu(self.c3(x)) # 1x128 x = F.relu(self.d1(x)) x = F.relu(self.d2(x)) x = F.relu(self.d3(x)) x = x.view(-1, 128) x = self.last(x) # value output return torch.tanh(x)

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from flask_wtf import FlaskForm from wtforms import StringField, BooleanField from wtforms.validators import DataRequired class AddRiskForm(FlaskForm): orderid = StringField('orderid', validators=[DataRequired()]) class AddReviewForm(FlaskForm): nickname = StringField('nickname', validators=[DataRequired()]) review = StringField('review', validators=[DataRequired()])

[ "ryan.ormsby@shopify.com" ]

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import pandas as pd import numpy as np import SimpleMobileAverage class Stochastic(object): periods = 0 smoothing = 0 sma = None def __init__(self, period, smoothing): self.periods = period self.smoothing = smoothing self.sma = SimpleMobileAverage.SimpleMobileAverage(smoothing) def Calculate(self, high_prices, low_prices, close_prices): return self.getSlowStochastic(low_prices, high_prices, close_prices) def getFastStochastic(self, low_prices, high_prices, close_prices): low_min = low_prices.rolling(window=self.periods,center=False).min() high_max = high_prices.rolling(window=self.periods,center=False).max() k_fast = 100 * (close_prices - low_min)/(high_max - low_min) k_fast = k_fast.dropna() d_fast = self.sma.Calculate(k_fast) return k_fast, d_fast def getSlowStochastic(self, low_prices, high_prices, close_prices): k_fast, d_fast = self.getFastStochastic(low_prices, high_prices, close_prices) k_slow = d_fast d_slow = self.sma.Calculate(k_slow) return k_slow, d_slow

[ "root@agtemac.local" ]

root@agtemac.local

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farbodab/flatteningthecurve

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"""empty message Revision ID: 53e5dd8445f9 Revises: 0d0d42ac0c04 Create Date: 2020-05-19 18:56:45.511549 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '53e5dd8445f9' down_revision = '0d0d42ac0c04' branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('viz', sa.Column('text_bottom', sa.String(), nullable=True)) op.add_column('viz', sa.Column('text_top', sa.String(), nullable=True)) op.drop_column('viz', 'text') # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('viz', sa.Column('text', sa.VARCHAR(), autoincrement=False, nullable=True)) op.drop_column('viz', 'text_top') op.drop_column('viz', 'text_bottom') # ### end Alembic commands ###

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farbod.ab@hotmail.ca

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titilambert/monitoring-tools

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# qubit number=4 # total number=44 import pyquil from pyquil.api import local_forest_runtime, QVMConnection from pyquil import Program, get_qc from pyquil.gates import * import numpy as np conn = QVMConnection() def make_circuit()-> Program: prog = Program() # circuit begin prog += H(3) # number=19 prog += Y(2) # number=36 prog += CZ(0,3) # number=20 prog += H(3) # number=21 prog += CNOT(0,3) # number=14 prog += CNOT(0,3) # number=25 prog += CNOT(0,3) # number=28 prog += X(3) # number=29 prog += CNOT(0,3) # number=30 prog += CNOT(3,1) # number=35 prog += Y(2) # number=34 prog += CNOT(0,3) # number=27 prog += H(3) # number=22 prog += CZ(0,3) # number=23 prog += H(3) # number=24 prog += CNOT(0,3) # number=13 prog += H(3) # number=18 prog += Z(3) # number=10 prog += H(1) # number=2 prog += CNOT(2,0) # number=41 prog += Z(2) # number=42 prog += CNOT(2,0) # number=43 prog += H(2) # number=3 prog += H(3) # number=4 prog += CNOT(3,0) # number=31 prog += CNOT(3,0) # number=37 prog += Z(3) # number=38 prog += CNOT(3,0) # number=39 prog += CNOT(3,0) # number=33 prog += H(0) # number=5 prog += H(1) # number=6 prog += H(2) # number=7 prog += H(3) # number=8 prog += H(0) # number=9 # circuit end return prog def summrise_results(bitstrings) -> dict: d = {} for l in bitstrings: if d.get(l) is None: d[l] = 1 else: d[l] = d[l] + 1 return d if __name__ == '__main__': prog = make_circuit() qvm = get_qc('4q-qvm') results = qvm.run_and_measure(prog,1024) bitstrings = np.vstack([results[i] for i in qvm.qubits()]).T bitstrings = [''.join(map(str, l)) for l in bitstrings] writefile = open("../data/startPyquil3185.csv","w") print(summrise_results(bitstrings),file=writefile) writefile.close()

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wangjiyuan123@yeah.net

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import spotipy from spotipy.oauth2 import SpotifyClientCredentials import simplejson as json import pathlib from rich import print with open(pathlib.Path.home()/".spotipy"/"identity.json",'r') as f: credentials = json.load(f) sp = spotipy.Spotify(auth_manager=SpotifyClientCredentials(**credentials)) #results = sp.search(q='drei ???', limit=20,type='artist') #print(results) #for idx, track in enumerate(results['tracks']['items']): # print(idx, track['name']) # found artist uri: spotify:artist:3meJIgRw7YleJrmbpbJK6S johnsinc_uri = "spotify:artist:6lly2jn9MqaxaWRrkEzOsJ" results = sp.artist_albums(johnsinc_uri, album_type='album') albums = results['items'] while results['next']: results = sp.next(results) albums.extend(results['items']) rows = [] for album in albums: name = album['name'] this_album = {} this_album['release_date'] = album['release_date'] this_album['total_tracks'] = album['total_tracks'] this_album['uri'] = album['uri'] this_album['name'] = album['name'] rows.append(this_album) with open('albums.json','w') as f: rows = sorted(rows,key=lambda x: x['release_date']) json.dump(rows,f, indent=4)

[ "benjaminfrankmaier@gmail.com" ]

benjaminfrankmaier@gmail.com

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should-you-contribute/should-you-contribute.github.io

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from selenium import webdriver from selenium.webdriver.common.keys import Keys import unittest import time class NewVisitorTest(unittest.TestCase): def setUp(self): self.browser = webdriver.Firefox() def tearDown(self): self.browser.quit() def test_can_get_information_from_github(self): # Tux is a newcomer to open source and github. Zie wants to know whether # the github repository zie's found might be a good project to contribute # to, and has heard about this site. So zie visits it. self.browser.get("http://127.0.0.1:8000/index.html") # Tux notices that the title page and header include the phrase "Should # I Contribute?" project_name = "Should You Contribute?" self.assertIn(project_name, self.browser.title) self.assertEquals(project_name.upper(), self.browser.find_element_by_id('project_name').text) # Tux also notices a checklist on the page. The checklist is not yet filled # out - instead of #s, there are ? marks in each spot. self.assertEquals(self.browser.find_element_by_id('checklist_title').text, "CHECKLIST") self.assertEquals(6, len(self.browser.find_elements_by_class_name('fa-question-circle'))) # Tux sees that the main page is prompting hir to enter the name of the # repository. self.assertTrue(self.browser.find_element_by_id('repo_name')) # The first time Tux tries, zie enters the name wrong and sees # a message asking hir to try again. repo_input_box = self.browser.find_element_by_id('repo_name') repo_input_box.send_keys('shaunagm/terrible-idea-for-a-repo-name') # self.browser.implicitly_wait(6) repo_input_box.send_keys(Keys.ENTER) time.sleep(2) # For some reason, webdriver's explicit waiting *or* implicit waiting not working self.assertIn("That is not a valid, public Github repository.", self.browser.find_element_by_id('repo_error').text) # The second time Tux enters the repository name correctly. Zie sees a # message telling hir that the information was successfully obtained. repo_input_box.clear() working_repo = "shaunagm/WelcomeBot" repo_input_box.send_keys(working_repo) # self.browser.implicitly_wait(6) repo_input_box.send_keys(Keys.ENTER) time.sleep(2) # For some reason, webdriver's explicit waiting *or* implicit waiting not working self.assertIn("Success!", self.browser.find_element_by_id('repo_error').text) # Looking at the checklist, Tux sees that the checklist's header now contains # the name of the repository. self.assertIn(working_repo.upper(), self.browser.find_element_by_id('checklist_title').text) # Tux also sees that the ? marks have all been replaced by numbers. # In the first row, which has the prompt "How many commits have been # made in the last month?" now says something like "There have been X # commits in the last month". self.assertIn("commits in the last month.", self.browser.find_element_by_id('checklist_commits').text) # The second row, which has the prompt "How many contributors are there # to this repository?" now says something like "There are X contributors # to this repository." self.assertIn("contributors to this repository", self.browser.find_element_by_id('checklist_contribs').text) # The third row, which has the prompt "What percentage of issues have replies?" # now says something like "X% of issues have replies." self.assertIn("issues get replies. The median number of replies ", self.browser.find_element_by_id('checklist_issues').text) # The fourth row, which has the prompt "What percentage of pull requests # are merged?" now says something like "X% of pull requests have been merged." self.assertIn("pull requests have been merged.", self.browser.find_element_by_id('checklist_mergedprs').text) # The fifth row, which has the prompt "Does the repository have a README?" # now says something like "The repository has a README." self.assertIn("The repository has a readme", self.browser.find_element_by_id('checklist_files').text) # The sixth row, which has the prompt "Does the issue tracker label issues # as good for newcomers?" now says something like "The tracker has issues labeled # X" self.assertIn("The tracker has issues labeled ", self.browser.find_element_by_id('checklist_labels').text) # Tux reads down the list. Zie is confused about what the first item on the list # means, but sees a button labelled "Learn More". self.assertIn("Learn More", self.browser.find_element_by_id('checklist_commits_prompt').text) # Tux clicks the "Learn More" button. A modal window pops up containing more # information about that item. self.assertEqual(False, self.browser.find_element_by_id('commits-info').is_displayed()) self.browser.find_element_by_css_selector('#padder-row > div > div:nth-child(3) > div.col-lg-6.checklist-item.checklist-description > p > button').click() time.sleep(1) # For some reason, we need to sleep here to get the following test to work self.assertEqual('false', self.browser.find_element_by_id('commits-info').get_attribute("aria-hidden")) # Enlightened, Tux closes the modal window. self.browser.find_element_by_css_selector('#commits-info > div > div > div.modal-header > button > span').click() time.sleep(1) # For some reason, we need to sleep here to get the following test to work self.assertEqual(False, self.browser.find_element_by_id('commits-info').is_displayed()) # Tux then clicks the "Learn More" button for the next item. self.assertEqual(False, self.browser.find_element_by_id('contribs-info').is_displayed()) self.browser.find_element_by_css_selector('#padder-row > div > div:nth-child(4) > div.col-lg-6.checklist-item.checklist-description > p > button').click() time.sleep(1) # For some reason, we need to sleep here to get the following test to work self.assertEqual('false', self.browser.find_element_by_id('contribs-info').get_attribute("aria-hidden")) # A different modal window corresponding to the next item pops up. Tux # closes that too. self.browser.find_element_by_css_selector('#contribs-info > div > div > div.modal-header > button > span').click() time.sleep(1) # For some reason, we need to sleep here to get the following test to work self.assertEqual(False, self.browser.find_element_by_id('contribs-info').is_displayed()) # Tux clicks on the "Find one!" link. A new window with a guide to finding projects appears. self.browser.find_element_by_id("find_one").click() self.browser.switch_to_window(self.browser.window_handles[1]) self.assertIn("Finding a Project", self.browser.title) self.browser.switch_to_window(self.browser.window_handles[0]) self.assertIn(project_name, self.browser.title) # Tux is so excited zie decides to enter a different github repository. # When zie does, zie sees "success!" above the search bar. repo_input_box.clear() new_repo = "railsbridge/bridge_troll" repo_input_box.send_keys(new_repo) repo_input_box.send_keys(Keys.ENTER) time.sleep(2) # For some reason, webdriver's explicit waiting *or* implicit waiting not working self.assertIn("Success!", self.browser.find_element_by_id('repo_error').text) # Tux also sees the new repository name in the checklist header # and new information in the checklist data. self.assertIn(new_repo.upper(), self.browser.find_element_by_id('checklist_title').text) self.assertNotIn(working_repo.upper(), self.browser.find_element_by_id('checklist_title').text) if __name__ == '__main__': unittest.main()

[ "shaunagm@gmail.com" ]

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36,283

py

# Copyright 2017 Google Inc. All Rights Reserved. # # 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. """LSTM-based encoders and decoders for MusicVAE.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import abc # internal imports import tensorflow as tf from magenta.common import flatten_maybe_padded_sequences from magenta.common import Nade from magenta.models.music_vae import base_model from tensorflow.contrib import rnn from tensorflow.contrib import seq2seq from tensorflow.contrib.cudnn_rnn.python.layers import cudnn_rnn from tensorflow.python.framework import tensor_util from tensorflow.python.layers import core as layers_core from tensorflow.python.util import nest def rnn_cell(rnn_cell_size, dropout_keep_prob, is_training=True): """Builds an LSTMBlockCell based on the given parameters.""" dropout_keep_prob = dropout_keep_prob if is_training else 1.0 cells = [] for layer_size in rnn_cell_size: cell = rnn.LSTMBlockCell(layer_size) cell = rnn.DropoutWrapper( cell, input_keep_prob=dropout_keep_prob) cells.append(cell) return rnn.MultiRNNCell(cells) def cudnn_lstm_layer(layer_sizes, dropout_keep_prob, is_training=True, name_or_scope='rnn'): """Builds a CudnnLSTM Layer based on the given parameters.""" dropout_keep_prob = dropout_keep_prob if is_training else 1.0 for ls in layer_sizes: if ls != layer_sizes[0]: raise ValueError( 'CudnnLSTM does not support layers with differing sizes. Got: %s', layer_sizes) lstm = cudnn_rnn.CudnnLSTM( num_layers=len(layer_sizes), num_units=layer_sizes[0], direction='unidirectional', dropout=1.0 - dropout_keep_prob, name=name_or_scope) class BackwardCompatibleCudnnLSTMSaveable( tf.contrib.cudnn_rnn.CudnnLSTMSaveable): """Overrides CudnnLSTMSaveable for backward-compatibility.""" def _cudnn_to_tf_biases(self, *cu_biases): """Overrides to subtract 1.0 from `forget_bias` (see BasicLSTMCell).""" (tf_bias,) = ( super(BackwardCompatibleCudnnLSTMSaveable, self)._cudnn_to_tf_biases( *cu_biases)) i, c, f, o = tf.split(tf_bias, 4) # Non-Cudnn LSTM cells add 1.0 to the forget bias variable. return (tf.concat([i, c, f - 1.0, o], axis=0),) def _tf_to_cudnn_biases(self, *tf_biases): """Overrides to add 1.0 to `forget_bias` (see BasicLSTMCell).""" (tf_bias,) = tf_biases i, c, f, o = tf.split(tf_bias, 4) # Non-Cudnn LSTM cells add 1.0 to the forget bias variable. return ( super(BackwardCompatibleCudnnLSTMSaveable, self)._tf_to_cudnn_biases( tf.concat([i, c, f + 1.0, o], axis=0))) def _TFCanonicalNamePrefix(self, layer, is_fwd=True): """Overrides for backward-compatible variable names.""" if self._direction == 'unidirectional': return 'multi_rnn_cell/cell_%d/lstm_cell' % layer else: return ( 'cell_%d/bidirectional_rnn/%s/multi_rnn_cell/cell_0/lstm_cell' % (layer, 'fw' if is_fwd else 'bw')) lstm._saveable_cls = BackwardCompatibleCudnnLSTMSaveable # pylint:disable=protected-access return lstm def _cudnn_lstm_state(lstm_cell_state): """Convert tuple of LSTMCellStateTuples to CudnnLSTM format.""" h = tf.stack([s.h for s in lstm_cell_state]) c = tf.stack([s.c for s in lstm_cell_state]) return (h, c) def _get_final(time_major_sequence, sequence_length): final_index = tf.stack( [tf.maximum(0, sequence_length - 1), tf.range(sequence_length.shape[0])], axis=1) return tf.gather_nd(time_major_sequence, final_index) def initial_cell_state_from_embedding(cell, z, name=None): """Computes an initial RNN `cell` state from an embedding, `z`.""" flat_state_sizes = nest.flatten(cell.state_size) return nest.pack_sequence_as( cell.zero_state(batch_size=z.shape[0], dtype=tf.float32), tf.split( tf.layers.dense( z, sum(flat_state_sizes), activation=tf.tanh, kernel_initializer=tf.random_normal_initializer(stddev=0.001), name=name), flat_state_sizes, axis=1)) def _get_sampling_probability(hparams, is_training): """Returns the sampling probability as a tensor based on the hparams. Supports three sampling schedules (`hparams.sampling_schedule`): constant: `hparams.sampling_rate` is the sampling probability. Must be in the interval [0, 1]. exponential: `hparams.sampling_rate` is the base of the decay exponential. Must be in the interval (0, 1). Larger values imply a slower increase in sampling. inverse_sigmoid: `hparams.sampling_rate` is in the interval [1, inf). Larger values imply a slower increase in sampling. A constant value of 0 is returned if `hparams.sampling_schedule` is undefined. If not training and a non-0 sampling schedule is defined, a constant value of 1 is returned since this is assumed to be a test/eval job associated with a scheduled sampling trainer. Args: hparams: An HParams object containing model hyperparameters. is_training: Whether or not the model is being used for training. Raises: ValueError: On an invalid `sampling_schedule` or `sampling_rate` hparam. """ if (not hasattr(hparams, 'sampling_schedule') or not hparams.sampling_schedule or (hparams.sampling_schedule == 'constant' and hparams.sampling_rate == 0)): return tf.constant(0.0) if not is_training: # This is likely an eval/test job associated with a training job using # scheduled sampling. tf.logging.warning( 'Setting non-training sampling schedule from %s:%f to constant:1.0.', hparams.sampling_schedule, hparams.sampling_rate) hparams.sampling_schedule = 'constant' hparams.sampling_rate = 1.0 schedule = hparams.sampling_schedule rate = hparams.sampling_rate step = tf.to_float(tf.train.get_global_step()) if schedule == 'constant': if not 0 <= rate <= 1: raise ValueError( '`constant` sampling rate must be in the interval [0, 1]. Got %f.' % rate) sampling_probability = tf.constant(rate) elif schedule == 'inverse_sigmoid': if rate < 1: raise ValueError( '`inverse_sigmoid` sampling rate must be at least 1. Got %f.' % rate) k = tf.constant(rate) sampling_probability = 1.0 - k / (k + tf.exp(step / k)) elif schedule == 'exponential': if not 0 < rate < 1: raise ValueError( '`exponential` sampling rate must be in the interval (0, 1). Got %f.' % hparams.sampling_rate) k = tf.constant(rate) sampling_probability = 1.0 - tf.pow(k, step) else: raise ValueError('Invalid `sampling_schedule`: %s' % schedule) tf.summary.scalar('sampling_probability', sampling_probability) return sampling_probability class LstmEncoder(base_model.BaseEncoder): """Unidirectional LSTM Encoder.""" def build(self, hparams, is_training=True, name_or_scope='encoder'): self._is_training = is_training self._name_or_scope = name_or_scope self._use_cudnn = hparams.use_cudnn tf.logging.info('\nEncoder Cells (unidirectional):\n' ' units: %s\n', hparams.enc_rnn_size) if self._use_cudnn: self._cudnn_lstm = cudnn_lstm_layer( hparams.enc_rnn_size, hparams.dropout_keep_prob, is_training, name_or_scope=self._name_or_scope) else: self._cell = rnn_cell( hparams.enc_rnn_size, hparams.dropout_keep_prob, is_training) def encode(self, sequence, sequence_length): # Convert to time-major. sequence = tf.transpose(sequence, [1, 0, 2]) if self._use_cudnn: outputs, _ = self._cudnn_lstm( sequence, training=self._is_training) return _get_final(outputs, sequence_length) else: outputs, _ = tf.nn.dynamic_rnn( self._cell, sequence, sequence_length, dtype=tf.float32, time_major=True, scope=self._name_or_scope) return outputs[-1] class BidirectionalLstmEncoder(base_model.BaseEncoder): """Bidirectional LSTM Encoder.""" def build(self, hparams, is_training=True, name_or_scope='encoder'): self._is_training = is_training self._name_or_scope = name_or_scope self._use_cudnn = hparams.use_cudnn tf.logging.info('\nEncoder Cells (bidirectional):\n' ' units: %s\n', hparams.enc_rnn_size) if isinstance(name_or_scope, tf.VariableScope): name = name_or_scope.name reuse = name_or_scope.reuse else: name = name_or_scope reuse = None cells_fw = [] cells_bw = [] for i, layer_size in enumerate(hparams.enc_rnn_size): if self._use_cudnn: cells_fw.append(cudnn_lstm_layer( [layer_size], hparams.dropout_keep_prob, is_training, name_or_scope=tf.VariableScope( reuse, name + '/cell_%d/bidirectional_rnn/fw' % i))) cells_bw.append(cudnn_lstm_layer( [layer_size], hparams.dropout_keep_prob, is_training, name_or_scope=tf.VariableScope( reuse, name + '/cell_%d/bidirectional_rnn/bw' % i))) else: cells_fw.append( rnn_cell([layer_size], hparams.dropout_keep_prob, is_training)) cells_bw.append( rnn_cell([layer_size], hparams.dropout_keep_prob, is_training)) self._cells = (cells_fw, cells_bw) def encode(self, sequence, sequence_length): cells_fw, cells_bw = self._cells if self._use_cudnn: # Implements stacked bidirectional LSTM for variable-length sequences, # which are not supported by the CudnnLSTM layer. inputs_fw = tf.transpose(sequence, [1, 0, 2]) for lstm_fw, lstm_bw in zip(cells_fw, cells_bw): outputs_fw, _ = lstm_fw(inputs_fw, training=self._is_training) inputs_bw = tf.reverse_sequence( inputs_fw, sequence_length, seq_axis=0, batch_axis=1) outputs_bw, _ = lstm_bw(inputs_bw, training=self._is_training) outputs_bw = tf.reverse_sequence( outputs_bw, sequence_length, seq_axis=0, batch_axis=1) inputs_fw = tf.concat([outputs_fw, outputs_bw], axis=2) last_h_fw = _get_final(outputs_fw, sequence_length) # outputs_bw has already been reversed, so we can take the first element. last_h_bw = outputs_bw[0] else: _, states_fw, states_bw = rnn.stack_bidirectional_dynamic_rnn( cells_fw, cells_bw, sequence, sequence_length=sequence_length, time_major=False, dtype=tf.float32, scope=self._name_or_scope) # Note we access the outputs (h) from the states since the backward # ouputs are reversed to the input order in the returned outputs. last_h_fw = states_fw[-1][-1].h last_h_bw = states_bw[-1][-1].h return tf.concat([last_h_fw, last_h_bw], 1) class BaseLstmDecoder(base_model.BaseDecoder): """Abstract LSTM Decoder class. Implementations must define the following abstract methods: -`_sample` -`_flat_reconstruction_loss` """ def build(self, hparams, output_depth, is_training=False): self._is_training = is_training tf.logging.info('\nDecoder Cells:\n' ' units: %s\n', hparams.dec_rnn_size) self._sampling_probability = _get_sampling_probability( hparams, is_training) self._output_depth = output_depth self._output_layer = layers_core.Dense( output_depth, name='output_projection') self._dec_cell = rnn_cell( hparams.dec_rnn_size, hparams.dropout_keep_prob, is_training) self._cudnn_dec_lstm = cudnn_lstm_layer( hparams.dec_rnn_size, hparams.dropout_keep_prob, is_training, name_or_scope='decoder') if hparams.use_cudnn else None @abc.abstractmethod def _sample(self, rnn_output, temperature): """Core sampling method for a single time step. Args: rnn_output: The output from a single timestep of the RNN, sized `[batch_size, rnn_output_size]`. temperature: A scalar float specifying a sampling temperature. Returns: A batch of samples from the model. """ pass @abc.abstractmethod def _flat_reconstruction_loss(self, flat_x_target, flat_rnn_output): """Core loss calculation method for flattened outputs. Args: flat_x_target: The flattened ground truth vectors, sized `[sum(x_length), self._output_depth]`. flat_rnn_output: The flattened output from all timeputs of the RNN, sized `[sum(x_length), rnn_output_size]`. Returns: r_loss: The unreduced reconstruction losses, sized `[sum(x_length)]`. metric_map: A map of metric names to tuples, each of which contain the pair of (value_tensor, update_op) from a tf.metrics streaming metric. truths: Ground truth labels. predictions: Predicted labels. """ pass def _decode(self, z, helper, max_length=None, x_input=None): """Decodes the given batch of latent vectors vectors, which may be 0-length. Args: z: Batch of latent vectors, sized `[batch_size, z_size]`, where `z_size` may be 0 for unconditioned decoding. helper: A seq2seq.Helper to use. If a TrainingHelper is passed and a CudnnLSTM has previously been defined, it will be used instead. max_length: (Optinal) The maximum iterations to decode. x_input: (Optional) The inputs to the decoder for teacher forcing. Required if CudnnLSTM is to be used. Returns: final_output: The final seq2seq.BasicDecoderOutput. final_state: The final states of the decoder, or None if using Cudnn. """ initial_state = initial_cell_state_from_embedding( self._dec_cell, z, name='decoder/z_to_initial_state') # CudnnLSTM does not support sampling so it can only replace TrainingHelper. if self._cudnn_dec_lstm and type(helper) is seq2seq.TrainingHelper: # pylint:disable=unidiomatic-typecheck rnn_output, _ = self._cudnn_dec_lstm( tf.transpose(x_input, [1, 0, 2]), initial_state=_cudnn_lstm_state(initial_state), training=self._is_training) with tf.variable_scope('decoder'): rnn_output = self._output_layer(rnn_output) final_output = seq2seq.BasicDecoderOutput( rnn_output=tf.transpose(rnn_output, [1, 0, 2]), sample_id=None) # TODO(adarob): Return a final state for fixed-length outputs. final_state = None else: if self._cudnn_dec_lstm: tf.logging.warning( 'CudnnLSTM does not support sampling. Using `dynamic_decode` ' 'instead.') decoder = seq2seq.BasicDecoder( self._dec_cell, helper, initial_state=initial_state, output_layer=self._output_layer) final_output, final_state, _ = seq2seq.dynamic_decode( decoder, maximum_iterations=max_length, swap_memory=True, scope='decoder') return final_output, final_state def reconstruction_loss(self, x_input, x_target, x_length, z=None): """Reconstruction loss calculation. Args: x_input: Batch of decoder input sequences for teacher forcing, sized `[batch_size, max(x_length), output_depth]`. x_target: Batch of expected output sequences to compute loss against, sized `[batch_size, max(x_length), output_depth]`. x_length: Length of input/output sequences, sized `[batch_size]`. z: (Optional) Latent vectors. Required if model is conditional. Sized `[n, z_size]`. Returns: r_loss: The reconstruction loss for each sequence in the batch. metric_map: Map from metric name to tf.metrics return values for logging. truths: Ground truth labels. predictions: Predicted labels. final_state: The final states of the decoder, or None if using Cudnn. """ batch_size = x_input.shape[0].value has_z = z is not None z = tf.zeros([batch_size, 0]) if z is None else z repeated_z = tf.tile( tf.expand_dims(z, axis=1), [1, tf.shape(x_input)[1], 1]) sampling_probability_static = tensor_util.constant_value( self._sampling_probability) if sampling_probability_static == 0.0: # Use teacher forcing. x_input = tf.concat([x_input, repeated_z], axis=2) helper = seq2seq.TrainingHelper(x_input, x_length) else: # Use scheduled sampling. helper = seq2seq.ScheduledOutputTrainingHelper( inputs=x_input, sequence_length=x_length, auxiliary_inputs=repeated_z if has_z else None, sampling_probability=self._sampling_probability, next_inputs_fn=self._sample) decoder_outputs, final_state = self._decode( z, helper=helper, x_input=x_input) flat_x_target = flatten_maybe_padded_sequences(x_target, x_length) flat_rnn_output = flatten_maybe_padded_sequences( decoder_outputs.rnn_output, x_length) r_loss, metric_map, truths, predictions = self._flat_reconstruction_loss( flat_x_target, flat_rnn_output) # Sum loss over sequences. cum_x_len = tf.concat([(0,), tf.cumsum(x_length)], axis=0) r_losses = [] for i in range(batch_size): b, e = cum_x_len[i], cum_x_len[i + 1] r_losses.append(tf.reduce_sum(r_loss[b:e])) r_loss = tf.stack(r_losses) return r_loss, metric_map, truths, predictions, final_state def sample(self, n, max_length=None, z=None, temperature=1.0, start_inputs=None, end_fn=None): """Sample from decoder with an optional conditional latent vector `z`. Args: n: Scalar number of samples to return. max_length: (Optional) Scalar maximum sample length to return. Required if data representation does not include end tokens. z: (Optional) Latent vectors to sample from. Required if model is conditional. Sized `[n, z_size]`. temperature: (Optional) The softmax temperature to use when sampling, if applicable. start_inputs: (Optional) Initial inputs to use for batch. Sized `[n, output_depth]`. end_fn: (Optional) A callable that takes a batch of samples (sized `[n, output_depth]` and emits a `bool` vector shaped `[batch_size]` indicating whether each sample is an end token. Returns: samples: Sampled sequences. Sized `[n, max_length, output_depth]`. final_state: The final states of the decoder. Raises: ValueError: If `z` is provided and its first dimension does not equal `n`. """ if z is not None and z.shape[0].value != n: raise ValueError( '`z` must have a first dimension that equals `n` when given. ' 'Got: %d vs %d' % (z.shape[0].value, n)) # Use a dummy Z in unconditional case. z = tf.zeros((n, 0), tf.float32) if z is None else z # If not given, start with zeros. start_inputs = start_inputs if start_inputs is not None else tf.zeros( [n, self._output_depth], dtype=tf.float32) # In the conditional case, also concatenate the Z. start_inputs = tf.concat([start_inputs, z], axis=-1) sample_fn = lambda x: self._sample(x, temperature) end_fn = end_fn or (lambda x: False) # In the conditional case, concatenate Z to the sampled value. next_inputs_fn = lambda x: tf.concat([x, z], axis=-1) sampler = seq2seq.InferenceHelper( sample_fn, sample_shape=[self._output_depth], sample_dtype=tf.float32, start_inputs=start_inputs, end_fn=end_fn, next_inputs_fn=next_inputs_fn) decoder_outputs, final_state = self._decode( z, helper=sampler, max_length=max_length) return decoder_outputs.sample_id, final_state class CategoricalLstmDecoder(BaseLstmDecoder): """LSTM decoder with single categorical output.""" def _flat_reconstruction_loss(self, flat_x_target, flat_rnn_output): flat_logits = flat_rnn_output flat_truth = tf.argmax(flat_x_target, axis=1) flat_predictions = tf.argmax(flat_logits, axis=1) r_loss = tf.nn.softmax_cross_entropy_with_logits( labels=flat_x_target, logits=flat_logits) metric_map = { 'metrics/accuracy': tf.metrics.accuracy(flat_truth, flat_predictions), 'metrics/mean_per_class_accuracy': tf.metrics.mean_per_class_accuracy( flat_truth, flat_predictions, flat_x_target.shape[-1].value), } return r_loss, metric_map, flat_truth, flat_predictions def _sample(self, rnn_output, temperature=1.0): sampler = tf.contrib.distributions.OneHotCategorical( logits=rnn_output / temperature, dtype=tf.float32) return sampler.sample() def sample(self, n, max_length=None, z=None, temperature=None, start_inputs=None, beam_width=None, end_token=None): """Overrides BaseLstmDecoder `sample` method to add optional beam search. Args: n: Scalar number of samples to return. max_length: (Optional) Scalar maximum sample length to return. Required if data representation does not include end tokens. z: (Optional) Latent vectors to sample from. Required if model is conditional. Sized `[n, z_size]`. temperature: (Optional) The softmax temperature to use when not doing beam search. Defaults to 1.0. Ignored when `beam_width` is provided. start_inputs: (Optional) Initial inputs to use for batch. Sized `[n, output_depth]`. beam_width: (Optional) Width of beam to use for beam search. Beam search is disabled if not provided. end_token: (Optional) Scalar token signaling the end of the sequence to use for early stopping. Returns: samples: Sampled sequences. Sized `[n, max_length, output_depth]`. final_state: The final states of the decoder. Raises: ValueError: If `z` is provided and its first dimension does not equal `n`. """ if beam_width is None: end_fn = (None if end_token is None else lambda x: tf.equal(tf.argmax(x, axis=-1), end_token)) return super(CategoricalLstmDecoder, self).sample( n, max_length, z, temperature, start_inputs, end_fn) # If `end_token` is not given, use an impossible value. end_token = self._output_depth if end_token is None else end_token if z is not None and z.shape[0].value != n: raise ValueError( '`z` must have a first dimension that equals `n` when given. ' 'Got: %d vs %d' % (z.shape[0].value, n)) if temperature is not None: tf.logging.warning('`temperature` is ignored when using beam search.') # Use a dummy Z in unconditional case. z = tf.zeros((n, 0), tf.float32) if z is None else z # If not given, start with dummy `-1` token and replace with zero vectors in # `embedding_fn`. start_tokens = ( tf.argmax(start_inputs, axis=-1, output_type=tf.int32) if start_inputs is not None else -1 * tf.ones([n], dtype=tf.int32)) initial_state = initial_cell_state_from_embedding( self._dec_cell, z, name='decoder/z_to_initial_state') beam_initial_state = seq2seq.tile_batch( initial_state, multiplier=beam_width) # Tile `z` across beams. beam_z = tf.tile(tf.expand_dims(z, 1), [1, beam_width, 1]) def embedding_fn(tokens): # If tokens are the start_tokens (negative), replace with zero vectors. next_inputs = tf.cond( tf.less(tokens[0, 0], 0), lambda: tf.zeros([n, beam_width, self._output_depth]), lambda: tf.one_hot(tokens, self._output_depth)) # Concatenate `z` to next inputs. next_inputs = tf.concat([next_inputs, beam_z], axis=-1) return next_inputs decoder = seq2seq.BeamSearchDecoder( self._dec_cell, embedding_fn, start_tokens, end_token, beam_initial_state, beam_width, output_layer=self._output_layer, length_penalty_weight=0.0) final_output, final_state, _ = seq2seq.dynamic_decode( decoder, maximum_iterations=max_length, swap_memory=True, scope='decoder') # Returns samples and final states from the best beams. return (tf.one_hot(final_output.predicted_ids[:, :, 0], self._output_depth), nest.map_structure(lambda x: x[:, 0], final_state.cell_state)) class MultiOutCategoricalLstmDecoder(CategoricalLstmDecoder): """LSTM decoder with multiple categorical outputs.""" def __init__(self, output_depths): self._output_depths = output_depths def build(self, hparams, output_depth, is_training): if sum(self._output_depths) != output_depth: raise ValueError( 'Decoder output depth does not match sum of sub-decoders: %s vs %d', self._output_depths, output_depth) super(MultiOutCategoricalLstmDecoder, self).build( hparams, output_depth, is_training) def _flat_reconstruction_loss(self, flat_x_target, flat_rnn_output): split_x_target = tf.split(flat_x_target, self._output_depths, axis=-1) split_rnn_output = tf.split( flat_rnn_output, self._output_depths, axis=-1) losses = [] truths = [] predictions = [] metric_map = {} for i in range(len(self._output_depths)): l, m, t, p = ( super(MultiOutCategoricalLstmDecoder, self)._flat_reconstruction_loss( split_x_target[i], split_rnn_output[i])) losses.append(l) truths.append(t) predictions.append(p) for k, v in m.items(): metric_map['%s_%d' % (k, i)] = v return (tf.reduce_sum(losses, axis=0), metric_map, tf.stack(truths), tf.stack(predictions)) def _sample(self, rnn_output, temperature=1.0): split_logits = tf.split(rnn_output, self._output_depths, axis=-1) samples = [] for logits, output_depth in zip(split_logits, self._output_depths): sampler = tf.contrib.distributions.Categorical( logits=logits / temperature) sample_label = sampler.sample() samples.append(tf.one_hot(sample_label, output_depth, dtype=tf.float32)) return tf.concat(samples, axis=-1) class HierarchicalMultiOutLstmDecoder(base_model.BaseDecoder): """Hierarchical LSTM decoder with (optional) multiple categorical outputs.""" def __init__(self, core_decoders, output_depths): """Initializer for a HierarchicalMultiOutLstmDecoder. Args: core_decoders: The BaseDecoder implementation class(es) to use at the output layer. output_depths: A list of output depths for the core decoders. Raises: ValueError: If the number of core decoders and output depths are not equal. """ if len(core_decoders) != len(output_depths): raise ValueError( 'The number of `core_decoders` and `output_depths` provided to a ' 'HierarchicalMultiOutLstmDecoder must be equal. Got: %d != %d', len(core_decoders), len(output_depths)) self._core_decoders = core_decoders self._output_depths = output_depths def build(self, hparams, output_depth, is_training): if sum(self._output_depths) != output_depth: raise ValueError( 'Decoder output depth does not match sum of sub-decoders: %s vs %d', self._output_depths, output_depth) self.hparams = hparams self._is_training = is_training for j, (cd, od) in enumerate(zip(self._core_decoders, self._output_depths)): with tf.variable_scope('core_decoder_%d' % j): cd.build(hparams, od, is_training) def _hierarchical_decode(self, z=None): hparams = self.hparams batch_size = hparams.batch_size if z is None: learned_initial_embedding = tf.get_variable( 'learned_initial_embedding', shape=hparams.z_size, initializer=tf.random_normal_initializer(stddev=0.001)) embeddings = [tf.stack([learned_initial_embedding] * batch_size)] else: embeddings = [z] for i, h_size in enumerate(hparams.hierarchical_output_sizes): if h_size % len(embeddings) != 0: raise ValueError( 'Each size in `hierarchical_output_sizes` must be evenly divisible ' 'by the previous. Got: %d !/ %d', h_size, len(embeddings)) num_steps = h_size // len(embeddings) all_outputs = [] with tf.variable_scope('hierarchical_layer_%d' % i) as scope: cell = rnn_cell( hparams.dec_rnn_size, hparams.dropout_keep_prob, self._is_training) cudnn_cell = cudnn_lstm_layer( hparams.dec_rnn_size, hparams.dropout_keep_prob, self._is_training) for e in embeddings: e.set_shape([batch_size] + e.shape[1:].as_list()) initial_state = initial_cell_state_from_embedding( cell, e, name='e_to_initial_state') if hparams.use_cudnn: input_ = tf.zeros([num_steps, batch_size, 1]) outputs, _ = cudnn_cell( input_, initial_state=_cudnn_lstm_state(initial_state), training=self._is_training) outputs = tf.unstack(outputs) else: input_ = [tf.zeros([batch_size, 1])] * num_steps outputs, _ = tf.nn.static_rnn( cell, input_, initial_state=initial_state) all_outputs.extend(outputs) # Reuse layer next time. scope.reuse_variables() embeddings = all_outputs return embeddings def reconstruction_loss(self, x_input, x_target, x_length, z=None): embeddings = self._hierarchical_decode(z) n = len(embeddings) # TODO(adarob): Support variable length outputs. with tf.control_dependencies([ tf.assert_equal( x_length, (x_length[0] // n) * n, message='HierarchicalMultiOutLstmDecoder requires `x_length` to ' 'all be equal and divisible by the final number of embeddings.')]): x_input = tf.identity(x_input) # Split sequences into n x M subsequences where M is the number of core # models. split_x_input = [ tf.split(x, self._output_depths, axis=-1) for x in tf.split(x_input, n, axis=1)] split_x_target = [ tf.split(x, self._output_depths, axis=-1) for x in tf.split(x_target, n, axis=1)] loss_outputs = [[] for _ in self._core_decoders] # Compute reconstruction loss for the n x M split sequences. for i, e in enumerate(embeddings): for j, cd in enumerate(self._core_decoders): with tf.variable_scope('core_decoder_%d' % j, reuse=i > 0): # TODO(adarob): Sample initial inputs when using scheduled sampling. loss_outputs[j].append( cd.reconstruction_loss( split_x_input[i][j], split_x_target[i][j], x_length // n, e)) # Accumulate the split sequence losses. all_r_losses = [] all_truth = [] all_predictions = [] metric_map = {} all_final_states = [] for j, loss_outputs_j in enumerate(loss_outputs): r_losses, _, truth, predictions, final_states = zip(*loss_outputs_j) all_r_losses.append(tf.reduce_sum(r_losses, axis=0)) all_truth.append(tf.concat(truth, axis=-1)) all_predictions.append(tf.concat(predictions, axis=-1)) metric_map['metrics/accuracy_%d' % j] = tf.metrics.accuracy( all_truth[-1], all_predictions[-1]) metric_map['metrics/mean_per_class_accuracy_%d' % j] = ( tf.metrics.mean_per_class_accuracy( all_truth[-1], all_predictions[-1], self._output_depths[j])) all_final_states.append(final_states) return (tf.reduce_sum(all_r_losses, axis=0), metric_map, tf.stack(all_truth, axis=-1), tf.stack(all_predictions, axis=-1), all_final_states) def sample(self, n, max_length=None, z=None, temperature=1.0, **core_sampler_kwargs): if z is not None and z.shape[0].value != n: raise ValueError( '`z` must have a first dimension that equals `n` when given. ' 'Got: %d vs %d' % (z.shape[0].value, n)) if max_length is None: # TODO(adarob): Support variable length outputs. raise ValueError( 'HierarchicalMultiOutLstmDecoder requires `max_length` be provided ' 'during sampling.') embeddings = self._hierarchical_decode(z) sample_ids = [] final_states = [] for j, cd in enumerate(self._core_decoders): sample_ids_j = [] final_states_j = [] with tf.variable_scope('core_decoder_%d' % j) as scope: for e in embeddings: sample_ids_j_e, final_states_j_e = cd.sample( n, max_length // len(embeddings), z=e, temperature=temperature, start_inputs=( sample_ids_j[-1][:, -1] if sample_ids_j else None), **core_sampler_kwargs) sample_ids_j.append(sample_ids_j_e) final_states_j.append(final_states_j_e) scope.reuse_variables() sample_ids.append(tf.concat(sample_ids_j, axis=1)) final_states.append(final_states_j) return tf.concat(sample_ids, axis=-1), final_states class MultiLabelRnnNadeDecoder(BaseLstmDecoder): """LSTM decoder with multi-label output provided by a NADE.""" def build(self, hparams, output_depth, is_training=False): self._nade = Nade( output_depth, hparams.nade_num_hidden, name='decoder/nade') super(MultiLabelRnnNadeDecoder, self).build( hparams, output_depth, is_training) # Overwrite output layer for NADE parameterization. self._output_layer = layers_core.Dense( self._nade.num_hidden + output_depth, name='output_projection') def _flat_reconstruction_loss(self, flat_x_target, flat_rnn_output): b_enc, b_dec = tf.split( flat_rnn_output, [self._nade.num_hidden, self._output_depth], axis=1) ll, cond_probs = self._nade.log_prob( flat_x_target, b_enc=b_enc, b_dec=b_dec) r_loss = -ll flat_truth = tf.cast(flat_x_target, tf.bool) flat_predictions = tf.greater_equal(cond_probs, 0.5) metric_map = { 'metrics/accuracy': tf.metrics.mean( tf.reduce_all(tf.equal(flat_truth, flat_predictions), axis=-1)), 'metrics/recall': tf.metrics.recall(flat_truth, flat_predictions), 'metrics/precision': tf.metrics.precision(flat_truth, flat_predictions), } return r_loss, metric_map, flat_truth, flat_predictions def _sample(self, rnn_output, temperature=None): """Sample from NADE, returning the argmax if no temperature is provided.""" b_enc, b_dec = tf.split( rnn_output, [self._nade.num_hidden, self._output_depth], axis=1) sample, _ = self._nade.sample( b_enc=b_enc, b_dec=b_dec, temperature=temperature) return sample def get_default_hparams(): """Returns copy of default HParams for LSTM models.""" hparams_map = base_model.get_default_hparams().values() hparams_map.update({ 'conditional': True, 'dec_rnn_size': [512], # Decoder RNN: number of units per layer. 'enc_rnn_size': [256], # Encoder RNN: number of units per layer per dir. 'dropout_keep_prob': 1.0, # Probability all dropout keep. 'sampling_schedule': 'constant', # constant, exponential, inverse_sigmoid 'sampling_rate': 0.0, # Interpretation is based on `sampling_schedule`. 'use_cudnn': False, # Uses faster CudnnLSTM to train. For GPU only. }) return tf.contrib.training.HParams(**hparams_map)

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# Escreva um programa que faça o computador "pensar" em um número # inteiro entre 0 e 5 e peça para o usuário tentar descobrir qual # foi o número escolhido pelo computador. from random import randint from time import sleep numero_sorteado = randint(0, 5) chute_usuario = int(input("Adivinhe um numero de 1 à 5: ")) print("PROCESSANDO....") #Esse comando faz o computador adormecer por um tempo. sleep(1.5) if chute_usuario == numero_sorteado: print("Parabéns você acertou!!") else: print("Você errou!!")

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# -*- coding: utf-8 -*- """ Created on Sat Apr 3 12:08:04 2021 @author: charm """ for i in range(int(input())): n=int(input()) result=mod=div=0 mod=n%4 div=n//4 if(n>4): betn_layer=16*(div-1)#top most 4 4 4 4 of 1 row gets subracted since 60 is of subtracted 1's in it result=(div*60)-betn_layer if(mod==1): result+=20 elif(mod==2): result+=36 elif(mod==3): result+=51 result=result-(4*mod) else: if(mod==0): result=60 elif(mod==1): result=20 elif(mod==2): result=36 elif(mod==3): result=51 print(result)

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''' Implement a function is_prime(x) that returns True if x is prime, False otherwise. (1 Point) >>> is_prime(7) True >>> is_prime(15) False ''' # ... your code ... def is_prime(num): if (num == 1): return False isPrime = True for y in range(2, num - 1): if (isPrime): if (num % y == 0): isPrime = False return isPrime if __name__ == '__main__': print('is_prime') print('Test1', is_prime(1) == False) print('Test2', is_prime(2) == True) print('Test3', is_prime(3) == True) print('Test4', is_prime(4) == False) print('Test5', is_prime(5) == True)

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from django.shortcuts import render def shipping(request): """ A view to render shipping.html page """ return render(request, 'shipping/shipping.html')

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class Item: def __init__(self, name, description): self.name = name self.description = description

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import time import RPi.GPIO as GPIO def should_drive_wheels(is_granted_access): servo_pin = 12 GPIO.setmode(GPIO.BCM) GPIO.setup(servo_pin, GPIO.OUT) servo = GPIO.PWM(servo_pin, 50) # GPIO 12 for PWM with 50Hz if is_granted_access: servo.start(2.5) # Initialization set_angle(180, servo) return True servo.stop() # Stop servo.ChangeDutyCycle(0) return False def set_angle(angle, servo): duty = int(angle / 18 + 2) for i in range(0, duty, 2): servo.ChangeDutyCycle(i) time.sleep(1) time.sleep(1) servo.ChangeDutyCycle(0)

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# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- import time import logging from typing import Optional, Union, Any, Tuple try: from uamqp import ( c_uamqp, BatchMessage, constants, MessageBodyType, Message, types, SendClient, ReceiveClient, Source, utils, authentication, AMQPClient, compat, errors, Connection, __version__, ) from uamqp.message import ( MessageHeader, MessageProperties, ) uamqp_installed = True except ImportError: uamqp_installed = False from ._base import AmqpTransport from ..amqp._constants import AmqpMessageBodyType from .._constants import ( NO_RETRY_ERRORS, PROP_PARTITION_KEY, UAMQP_LIBRARY, ) from ..exceptions import ( ConnectError, OperationTimeoutError, EventHubError, AuthenticationError, ConnectionLostError, EventDataError, EventDataSendError, ) _LOGGER = logging.getLogger(__name__) if uamqp_installed: def _error_handler(error): """ Called internally when an event has failed to send so we can parse the error to determine whether we should attempt to retry sending the event again. Returns the action to take according to error type. :param error: The error received in the send attempt. :type error: Exception :rtype: ~uamqp.errors.ErrorAction """ if error.condition == b"com.microsoft:server-busy": return errors.ErrorAction(retry=True, backoff=4) if error.condition == b"com.microsoft:timeout": return errors.ErrorAction(retry=True, backoff=2) if error.condition == b"com.microsoft:operation-cancelled": return errors.ErrorAction(retry=True) if error.condition == b"com.microsoft:container-close": return errors.ErrorAction(retry=True, backoff=4) if error.condition in NO_RETRY_ERRORS: return errors.ErrorAction(retry=False) return errors.ErrorAction(retry=True) class UamqpTransport(AmqpTransport): # pylint: disable=too-many-public-methods """ Class which defines uamqp-based methods used by the producer and consumer. """ # define constants MAX_FRAME_SIZE_BYTES = constants.MAX_FRAME_SIZE_BYTES MAX_MESSAGE_LENGTH_BYTES = constants.MAX_MESSAGE_LENGTH_BYTES TIMEOUT_FACTOR = 1000 CONNECTION_CLOSING_STATES: Tuple = ( # pylint:disable=protected-access c_uamqp.ConnectionState.CLOSE_RCVD, # pylint:disable=c-extension-no-member c_uamqp.ConnectionState.CLOSE_SENT, # pylint:disable=c-extension-no-member c_uamqp.ConnectionState.DISCARDING, # pylint:disable=c-extension-no-member c_uamqp.ConnectionState.END, # pylint:disable=c-extension-no-member ) TRANSPORT_IDENTIFIER = f"{UAMQP_LIBRARY}/{__version__}" # define symbols PRODUCT_SYMBOL = types.AMQPSymbol("product") VERSION_SYMBOL = types.AMQPSymbol("version") FRAMEWORK_SYMBOL = types.AMQPSymbol("framework") PLATFORM_SYMBOL = types.AMQPSymbol("platform") USER_AGENT_SYMBOL = types.AMQPSymbol("user-agent") PROP_PARTITION_KEY_AMQP_SYMBOL = types.AMQPSymbol(PROP_PARTITION_KEY) @staticmethod def build_message(**kwargs): """ Creates a uamqp.Message with given arguments. :rtype: uamqp.Message """ return Message(**kwargs) @staticmethod def build_batch_message(**kwargs): """ Creates a uamqp.BatchMessage with given arguments. :rtype: uamqp.BatchMessage """ return BatchMessage(**kwargs) @staticmethod def to_outgoing_amqp_message(annotated_message): """ Converts an AmqpAnnotatedMessage into an Amqp Message. :param AmqpAnnotatedMessage annotated_message: AmqpAnnotatedMessage to convert. :rtype: uamqp.Message """ message_header = None header_vals = annotated_message.header.values() if annotated_message.header else None # If header and non-None header values, create outgoing header. if annotated_message.header and header_vals.count(None) != len(header_vals): message_header = MessageHeader() message_header.delivery_count = annotated_message.header.delivery_count message_header.time_to_live = annotated_message.header.time_to_live message_header.first_acquirer = annotated_message.header.first_acquirer message_header.durable = annotated_message.header.durable message_header.priority = annotated_message.header.priority message_properties = None properties_vals = annotated_message.properties.values() if annotated_message.properties else None # If properties and non-None properties values, create outgoing properties. if annotated_message.properties and properties_vals.count(None) != len(properties_vals): message_properties = MessageProperties( message_id=annotated_message.properties.message_id, user_id=annotated_message.properties.user_id, to=annotated_message.properties.to, subject=annotated_message.properties.subject, reply_to=annotated_message.properties.reply_to, correlation_id=annotated_message.properties.correlation_id, content_type=annotated_message.properties.content_type, content_encoding=annotated_message.properties.content_encoding, creation_time=int(annotated_message.properties.creation_time) if annotated_message.properties.creation_time else None, absolute_expiry_time=int(annotated_message.properties.absolute_expiry_time) if annotated_message.properties.absolute_expiry_time else None, group_id=annotated_message.properties.group_id, group_sequence=annotated_message.properties.group_sequence, reply_to_group_id=annotated_message.properties.reply_to_group_id, encoding=annotated_message._encoding # pylint: disable=protected-access ) # pylint: disable=protected-access amqp_body_type = annotated_message.body_type if amqp_body_type == AmqpMessageBodyType.DATA: amqp_body_type = MessageBodyType.Data amqp_body = list(annotated_message._data_body) elif amqp_body_type == AmqpMessageBodyType.SEQUENCE: amqp_body_type = MessageBodyType.Sequence amqp_body = list(annotated_message._sequence_body) else: amqp_body_type = MessageBodyType.Value amqp_body = annotated_message._value_body return Message( body=amqp_body, body_type=amqp_body_type, header=message_header, properties=message_properties, application_properties=annotated_message.application_properties, annotations=annotated_message.annotations, delivery_annotations=annotated_message.delivery_annotations, footer=annotated_message.footer ) @staticmethod def update_message_app_properties(message, key, value): """ Adds the given key/value to the application properties of the message. :param pyamqp.Message message: Message. :param str key: Key to set in application properties. :param str Value: Value to set for key in application properties. :rtype: pyamqp.Message """ if not message.application_properties: message.application_properties = {} message.application_properties.setdefault(key, value) return message @staticmethod def get_batch_message_encoded_size(message): """ Gets the batch message encoded size given an underlying Message. :param uamqp.BatchMessage message: Message to get encoded size of. :rtype: int """ return message.gather()[0].get_message_encoded_size() @staticmethod def get_message_encoded_size(message): """ Gets the message encoded size given an underlying Message. :param uamqp.Message message: Message to get encoded size of. :rtype: int """ return message.get_message_encoded_size() @staticmethod def get_remote_max_message_size(handler): """ Returns max peer message size. :param AMQPClient handler: Client to get remote max message size on link from. :rtype: int """ return handler.message_handler._link.peer_max_message_size # pylint:disable=protected-access @staticmethod def create_retry_policy(config): """ Creates the error retry policy. :param ~azure.eventhub._configuration.Configuration config: Configuration. """ return errors.ErrorPolicy(max_retries=config.max_retries, on_error=_error_handler) @staticmethod def create_link_properties(link_properties): """ Creates and returns the link properties. :param dict[bytes, int] link_properties: The dict of symbols and corresponding values. :rtype: dict """ return {types.AMQPSymbol(symbol): types.AMQPLong(value) for (symbol, value) in link_properties.items()} @staticmethod def create_connection(**kwargs): """ Creates and returns the uamqp Connection object. :keyword str host: The hostname, used by uamqp. :keyword JWTTokenAuth auth: The auth, used by uamqp. :keyword str endpoint: The endpoint, used by pyamqp. :keyword str container_id: Required. :keyword int max_frame_size: Required. :keyword int channel_max: Required. :keyword int idle_timeout: Required. :keyword Dict properties: Required. :keyword int remote_idle_timeout_empty_frame_send_ratio: Required. :keyword error_policy: Required. :keyword bool debug: Required. :keyword str encoding: Required. """ endpoint = kwargs.pop("endpoint") # pylint:disable=unused-variable custom_endpoint_address = kwargs.pop("custom_endpoint_address") # pylint:disable=unused-variable host = kwargs.pop("host") auth = kwargs.pop("auth") return Connection( host, auth, **kwargs ) @staticmethod def close_connection(connection): """ Closes existing connection. :param connection: uamqp or pyamqp Connection. """ connection.destroy() @staticmethod def get_connection_state(connection): """ Gets connection state. :param connection: uamqp or pyamqp Connection. """ return connection._state # pylint:disable=protected-access @staticmethod def create_send_client(*, config, **kwargs): # pylint:disable=unused-argument """ Creates and returns the uamqp SendClient. :param ~azure.eventhub._configuration.Configuration config: The configuration. :keyword str target: Required. The target. :keyword JWTTokenAuth auth: Required. :keyword int idle_timeout: Required. :keyword network_trace: Required. :keyword retry_policy: Required. :keyword keep_alive_interval: Required. :keyword str client_name: Required. :keyword dict link_properties: Required. :keyword properties: Required. """ target = kwargs.pop("target") retry_policy = kwargs.pop("retry_policy") network_trace = kwargs.pop("network_trace") return SendClient( target, debug=network_trace, error_policy=retry_policy, **kwargs ) @staticmethod def _set_msg_timeout(producer, timeout_time, last_exception, logger): if not timeout_time: return remaining_time = timeout_time - time.time() if remaining_time <= 0.0: if last_exception: error = last_exception else: error = OperationTimeoutError("Send operation timed out") logger.info("%r send operation timed out. (%r)", producer._name, error) # pylint: disable=protected-access raise error producer._handler._msg_timeout = remaining_time * 1000 # type: ignore # pylint: disable=protected-access @staticmethod def send_messages(producer, timeout_time, last_exception, logger): """ Handles sending of event data messages. :param ~azure.eventhub._producer.EventHubProducer producer: The producer with handler to send messages. :param int timeout_time: Timeout time. :param last_exception: Exception to raise if message timed out. Only used by uamqp transport. :param logger: Logger. """ # pylint: disable=protected-access producer._open() producer._unsent_events[0].on_send_complete = producer._on_outcome UamqpTransport._set_msg_timeout(producer, timeout_time, last_exception, logger) producer._handler.queue_message(*producer._unsent_events) # type: ignore producer._handler.wait() # type: ignore producer._unsent_events = producer._handler.pending_messages # type: ignore if producer._outcome != constants.MessageSendResult.Ok: if producer._outcome == constants.MessageSendResult.Timeout: producer._condition = OperationTimeoutError("Send operation timed out") if producer._condition: raise producer._condition @staticmethod def set_message_partition_key(message, partition_key, **kwargs): # pylint:disable=unused-argument # type: (Message, Optional[Union[bytes, str]], Any) -> Message """Set the partition key as an annotation on a uamqp message. :param ~uamqp.Message message: The message to update. :param str partition_key: The partition key value. :rtype: Message """ if partition_key: annotations = message.annotations if annotations is None: annotations = {} annotations[ UamqpTransport.PROP_PARTITION_KEY_AMQP_SYMBOL # TODO: see if setting non-amqp symbol is valid ] = partition_key header = MessageHeader() header.durable = True message.annotations = annotations message.header = header return message @staticmethod def add_batch(event_data_batch, outgoing_event_data, event_data): """ Add EventData to the data body of the BatchMessage. :param event_data_batch: BatchMessage to add data to. :param outgoing_event_data: Transformed EventData for sending. :param event_data: EventData to add to internal batch events. uamqp use only. :rtype: None """ # pylint: disable=protected-access event_data_batch._internal_events.append(event_data) event_data_batch._message._body_gen.append( outgoing_event_data._message ) @staticmethod def create_source(source, offset, selector): """ Creates and returns the Source. :param str source: Required. :param int offset: Required. :param bytes selector: Required. """ source = Source(source) if offset is not None: source.set_filter(selector) return source @staticmethod def create_receive_client(*, config, **kwargs): # pylint: disable=unused-argument """ Creates and returns the receive client. :param ~azure.eventhub._configuration.Configuration config: The configuration. :keyword str source: Required. The source. :keyword str offset: Required. :keyword str offset_inclusive: Required. :keyword JWTTokenAuth auth: Required. :keyword int idle_timeout: Required. :keyword network_trace: Required. :keyword retry_policy: Required. :keyword str client_name: Required. :keyword dict link_properties: Required. :keyword properties: Required. :keyword link_credit: Required. The prefetch. :keyword keep_alive_interval: Required. :keyword desired_capabilities: Required. :keyword streaming_receive: Required. :keyword message_received_callback: Required. :keyword timeout: Required. """ source = kwargs.pop("source") symbol_array = kwargs.pop("desired_capabilities") desired_capabilities = None if symbol_array: symbol_array = [types.AMQPSymbol(symbol) for symbol in symbol_array] desired_capabilities = utils.data_factory(types.AMQPArray(symbol_array)) retry_policy = kwargs.pop("retry_policy") network_trace = kwargs.pop("network_trace") link_credit = kwargs.pop("link_credit") streaming_receive = kwargs.pop("streaming_receive") message_received_callback = kwargs.pop("message_received_callback") client = ReceiveClient( source, debug=network_trace, # pylint:disable=protected-access error_policy=retry_policy, desired_capabilities=desired_capabilities, prefetch=link_credit, receive_settle_mode=constants.ReceiverSettleMode.ReceiveAndDelete, auto_complete=False, **kwargs ) # pylint:disable=protected-access client._streaming_receive = streaming_receive client._message_received_callback = (message_received_callback) return client @staticmethod def open_receive_client(*, handler, client, auth): """ Opens the receive client and returns ready status. :param ReceiveClient handler: The receive client. :param ~azure.eventhub.EventHubConsumerClient client: The consumer client. :param auth: Auth. :rtype: bool """ # pylint:disable=protected-access handler.open(connection=client._conn_manager.get_connection( client._address.hostname, auth )) @staticmethod def check_link_stolen(consumer, exception): """ Checks if link stolen and handles exception. :param consumer: The EventHubConsumer. :param exception: Exception to check. """ if ( isinstance(exception, errors.LinkDetach) and exception.condition == constants.ErrorCodes.LinkStolen # pylint: disable=no-member ): raise consumer._handle_exception(exception) # pylint: disable=protected-access @staticmethod def create_token_auth(auth_uri, get_token, token_type, config, **kwargs): """ Creates the JWTTokenAuth. :param str auth_uri: The auth uri to pass to JWTTokenAuth. :param get_token: The callback function used for getting and refreshing tokens. It should return a valid jwt token each time it is called. :param bytes token_type: Token type. :param ~azure.eventhub._configuration.Configuration config: EH config. :keyword bool update_token: Required. Whether to update token. If not updating token, then pass 300 to refresh_window. """ update_token = kwargs.pop("update_token") refresh_window = 300 if update_token: refresh_window = 0 token_auth = authentication.JWTTokenAuth( auth_uri, auth_uri, get_token, token_type=token_type, timeout=config.auth_timeout, http_proxy=config.http_proxy, transport_type=config.transport_type, custom_endpoint_hostname=config.custom_endpoint_hostname, port=config.connection_port, verify=config.connection_verify, refresh_window=refresh_window ) if update_token: token_auth.update_token() return token_auth @staticmethod def create_mgmt_client(address, mgmt_auth, config): """ Creates and returns the mgmt AMQP client. :param _Address address: Required. The Address. :param JWTTokenAuth mgmt_auth: Auth for client. :param ~azure.eventhub._configuration.Configuration config: The configuration. """ mgmt_target = f"amqps://{address.hostname}{address.path}" return AMQPClient( mgmt_target, auth=mgmt_auth, debug=config.network_tracing ) @staticmethod def open_mgmt_client(mgmt_client, conn): """ Opens the mgmt AMQP client. :param AMQPClient mgmt_client: uamqp AMQPClient. :param conn: Connection. """ mgmt_client.open(connection=conn) @staticmethod def get_updated_token(mgmt_auth): """ Return updated auth token. :param mgmt_auth: Auth. """ return mgmt_auth.token @staticmethod def mgmt_client_request(mgmt_client, mgmt_msg, **kwargs): """ Send mgmt request. :param AMQP Client mgmt_client: Client to send request with. :param str mgmt_msg: Message. :keyword bytes operation: Operation. :keyword operation_type: Op type. :keyword status_code_field: mgmt status code. :keyword description_fields: mgmt status desc. """ operation_type = kwargs.pop("operation_type") operation = kwargs.pop("operation") response = mgmt_client.mgmt_request( mgmt_msg, operation, op_type=operation_type, **kwargs ) status_code = response.application_properties[kwargs.get("status_code_field")] description = response.application_properties.get( kwargs.get("description_fields") ) # type: Optional[Union[str, bytes]] return status_code, description, response @staticmethod def get_error(status_code, description): """ Gets error corresponding to status code. :param status_code: Status code. :param str description: Description of error. """ if status_code in [401]: return errors.AuthenticationException( f"Management authentication failed. Status code: {status_code}, Description: {description!r}" ) if status_code in [404]: return ConnectError( f"Management connection failed. Status code: {status_code}, Description: {description!r}" ) return errors.AMQPConnectionError( f"Management request error. Status code: {status_code}, Description: {description!r}" ) @staticmethod def check_timeout_exception(base, exception): """ Checks if timeout exception. :param base: ClientBase. :param exception: Exception to check. """ if not base.running and isinstance( exception, compat.TimeoutException ): exception = errors.AuthenticationException( "Authorization timeout." ) return exception @staticmethod def _create_eventhub_exception(exception): if isinstance(exception, errors.AuthenticationException): error = AuthenticationError(str(exception), exception) elif isinstance(exception, errors.VendorLinkDetach): error = ConnectError(str(exception), exception) elif isinstance(exception, errors.LinkDetach): error = ConnectionLostError(str(exception), exception) elif isinstance(exception, errors.ConnectionClose): error = ConnectionLostError(str(exception), exception) elif isinstance(exception, errors.MessageHandlerError): error = ConnectionLostError(str(exception), exception) elif isinstance(exception, errors.AMQPConnectionError): error_type = ( AuthenticationError if str(exception).startswith("Unable to open authentication session") else ConnectError ) error = error_type(str(exception), exception) elif isinstance(exception, compat.TimeoutException): error = ConnectionLostError(str(exception), exception) else: error = EventHubError(str(exception), exception) return error @staticmethod def _handle_exception( exception, closable, *, is_consumer=False # pylint:disable=unused-argument ): # pylint:disable=too-many-branches, too-many-statements try: # closable is a producer/consumer object name = closable._name # pylint: disable=protected-access except AttributeError: # closable is an client object name = closable._container_id # pylint: disable=protected-access if isinstance(exception, KeyboardInterrupt): # pylint:disable=no-else-raise _LOGGER.info("%r stops due to keyboard interrupt", name) closable._close_connection() # pylint:disable=protected-access raise exception elif isinstance(exception, EventHubError): closable._close_handler() # pylint:disable=protected-access raise exception elif isinstance( exception, ( errors.MessageAccepted, errors.MessageAlreadySettled, errors.MessageModified, errors.MessageRejected, errors.MessageReleased, errors.MessageContentTooLarge, ), ): _LOGGER.info("%r Event data error (%r)", name, exception) error = EventDataError(str(exception), exception) raise error elif isinstance(exception, errors.MessageException): _LOGGER.info("%r Event data send error (%r)", name, exception) error = EventDataSendError(str(exception), exception) raise error else: if isinstance(exception, errors.AuthenticationException): if hasattr(closable, "_close_connection"): closable._close_connection() # pylint:disable=protected-access elif isinstance(exception, errors.LinkDetach): if hasattr(closable, "_close_handler"): closable._close_handler() # pylint:disable=protected-access elif isinstance(exception, errors.ConnectionClose): if hasattr(closable, "_close_connection"): closable._close_connection() # pylint:disable=protected-access elif isinstance(exception, errors.MessageHandlerError): if hasattr(closable, "_close_handler"): closable._close_handler() # pylint:disable=protected-access else: # errors.AMQPConnectionError, compat.TimeoutException if hasattr(closable, "_close_connection"): closable._close_connection() # pylint:disable=protected-access return UamqpTransport._create_eventhub_exception(exception)

[ "noreply@github.com" ]

openapi-env-test.noreply@github.com

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/firebasetri.py

8bbec63156468fc38fc367d10aa8e529b94f3c97

[]

no_license

akshaynaik797/index

5a9563fae6d6f91cea53ad16196c6f4b51a70e86

829f03adf27f53c132551cc78b08413fa980471a

refs/heads/master

2023-05-31T04:07:58.267651

2021-06-22T06:32:52

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py

import firebase_admin from firebase_admin import credentials, messaging cred = credentials.Certificate("iclaim-918b7-firebase-adminsdk-fpmv7-20c88a4f47.json") firebase_admin.initialize_app(cred) def send_to_token(): # [START send_to_token] # This registration token comes from the client FCM SDKs. registration_token = 'c8TNUgONQ-myTWY96XVZ7F:APA91bEIRZ2-WM2DcGDFCT7L9r8_i_Y1Us6VMmzmO8FugrsJEokiKsvr9qvwily3IhgeU3qLE44jN7287xEpkKjft2Bj2cSf9NAuO9GQ3E_7Tqepqb5pBsX0LfUpYT3Ac625QzJ2p69Z' # See documentation on defining a message payload. message = messaging.Message( data={6 'score': '850', 'time': '2:45', }, token=registration_token, ) # Send a message to the device corresponding to the provided # registration token. response = messaging.send(message) # Response is a message ID string. print('Successfully sent message:', response) # [END send_to_token] send_to_token()

[ "akshaynaik797@gmail.com" ]

akshaynaik797@gmail.com

c3d7754f00bdd9a1907ead98375207583f3c1a8c

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/Prob.py

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[]

no_license

Yout-bit/Dice-sum-prob

4c523f5e8f2219db122e8ca31742201af7ce4530

cb7cd78a319d9642ccc4293733986e301d5ffed9

refs/heads/main

2023-06-18T08:04:27.312155

2021-07-08T09:38:14

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from flask import Flask app = Flask(__name__) @app.route("/") def main(): d1 = int(input("Sides of first dice ")) d2 = int(input("Sides of second dice ")) d = [] dd = [] out = [] prob = [] for i in range (1, (d1+1)): d.append(i) for i in range (1, (d2+1)): dd.append(i) for i in range (len(d)): for j in range (len(dd)): out.append(d[i]+dd[j]) for i in range (2, (d1 + d2 + 1)): x = (out.count(i))/(d1*d2) prob.append([i,x]) print(""" ------------------ | Ordered by sum | ------------------ """) for i in range (len(prob)): print (str(prob[i][0]) + " probability = " + str(prob[i][1])) while True: Switches = 0 for i in range(len(prob) - 1): if prob[i][1] > prob[i + 1][1]: x = prob[i + 1] prob[i + 1] = prob[i] prob[i] = x Switches += 1 else: pass if Switches == 0: break print(""" -------------------------- | Ordered by probability | -------------------------- """) for i in range (len(prob)): print (str(prob[i][0]) + " probability = " + str(prob[i][1])) if __name__ == "__main__": app.run(debug=True, host="0.0.0.0", port=80)

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Yout-bit.noreply@github.com

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/Source Codes/AtCoder/abc110/A/4933080.py

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[]

no_license

Kawser-nerd/CLCDSA

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refs/heads/master

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2022-01-26T18:53:40

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py

A = list(map(int,input().split())) A = sorted(A) B = str(A[2]) + str(A[1]) B = int(B) print(B+A[0])

[ "kwnafi@yahoo.com" ]

kwnafi@yahoo.com

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/apps/propositions/migrations/0019_auto_20210821_0530.py

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abdulwahed-mansour/modularhistory

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refs/heads/main

2023-09-03T21:27:17.730946

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# Generated by Django 3.1.13 on 2021-08-21 05:30 from typing import TYPE_CHECKING import django.db.models.deletion from django.db import migrations, models import apps.topics.models.taggable import core.fields.m2m_foreign_key if TYPE_CHECKING: from apps.propositions.models.proposition import Proposition as _Proposition from apps.topics.models.topic import Topic def forwards_func(apps, schema_editor): Proposition: type['_Proposition'] = apps.get_model('propositions', 'Proposition') TopicRelation = apps.get_model('propositions', 'TopicRelation') p: '_Proposition' for p in Proposition.objects.all(): tag: 'Topic' for tag in p.tags.all(): TopicRelation.objects.get_or_create(topic=tag, content_object=p) class Migration(migrations.Migration): dependencies = [ ('topics', '0003_auto_20210820_2138'), ('propositions', '0018_auto_20210821_0002'), ] operations = [ migrations.AddField( model_name='quoterelation', name='deleted', field=models.DateTimeField(editable=False, null=True), ), migrations.AddField( model_name='quoterelation', name='verified', field=models.BooleanField(default=False, verbose_name='verified'), ), migrations.AlterField( model_name='proposition', name='tags', field=models.ManyToManyField( blank=True, related_name='propositions_proposition_set', to='topics.Topic', verbose_name='tags', ), ), migrations.CreateModel( name='TopicRelation', fields=[ ( 'id', models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name='ID', ), ), ('deleted', models.DateTimeField(editable=False, null=True)), ('verified', models.BooleanField(default=False, verbose_name='verified')), ('position', models.PositiveSmallIntegerField(blank=True, default=0)), ( 'content_object', core.fields.m2m_foreign_key.ManyToManyForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name='topic_relations', to='propositions.proposition', verbose_name='proposition', ), ), ( 'topic', core.fields.m2m_foreign_key.ManyToManyForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name='propositions_topicrelation_set', to='topics.topic', ), ), ], options={ 'abstract': False, }, ), migrations.AddField( model_name='proposition', name='new_tags', field=apps.topics.models.taggable.TagsField( blank=True, related_name='proposition_set', through='propositions.TopicRelation', to='topics.Topic', verbose_name='tags', ), ), migrations.RunPython(forwards_func, migrations.RunPython.noop), ]

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abdulwahed-mansour.noreply@github.com

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/api/client/src/pcluster_client/model/image_builder_image_status.py

0a1066967bbcbe0c6f06db7297463bec5398dc06

[ "Apache-2.0" ]

permissive

sean-smith/aws-parallelcluster

40292c19ca7089d8fab2903862c0e6191b7ba4f0

2def5e9f0c83cd417ae76971f7be0748dbf9eafa

refs/heads/develop

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""" ParallelCluster ParallelCluster API # noqa: E501 The version of the OpenAPI document: 3.0.0 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from pcluster_client.model_utils import ( # noqa: F401 ApiTypeError, ModelComposed, ModelNormal, ModelSimple, cached_property, change_keys_js_to_python, convert_js_args_to_python_args, date, datetime, file_type, none_type, validate_get_composed_info, ) class ImageBuilderImageStatus(ModelSimple): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. Attributes: allowed_values (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict with a capitalized key describing the allowed value and an allowed value. These dicts store the allowed enum values. validations (dict): The key is the tuple path to the attribute and the for var_name this is (var_name,). The value is a dict that stores validations for max_length, min_length, max_items, min_items, exclusive_maximum, inclusive_maximum, exclusive_minimum, inclusive_minimum, and regex. additional_properties_type (tuple): A tuple of classes accepted as additional properties values. """ allowed_values = { ('value',): { 'PENDING': "PENDING", 'CREATING': "CREATING", 'BUILDING': "BUILDING", 'TESTING': "TESTING", 'DISTRIBUTING': "DISTRIBUTING", 'INTEGRATING': "INTEGRATING", 'AVAILABLE': "AVAILABLE", 'CANCELLED': "CANCELLED", 'FAILED': "FAILED", 'DEPRECATED': "DEPRECATED", 'DELETED': "DELETED", }, } validations = { } additional_properties_type = None _nullable = False @cached_property def openapi_types(): """ This must be a method because a model may have properties that are of type self, this must run after the class is loaded Returns openapi_types (dict): The key is attribute name and the value is attribute type. """ return { 'value': (str,), } @cached_property def discriminator(): return None attribute_map = {} _composed_schemas = None required_properties = set([ '_data_store', '_check_type', '_spec_property_naming', '_path_to_item', '_configuration', '_visited_composed_classes', ]) @convert_js_args_to_python_args def __init__(self, *args, **kwargs): """ImageBuilderImageStatus - a model defined in OpenAPI Note that value can be passed either in args or in kwargs, but not in both. Args: args[0] (str):, must be one of ["PENDING", "CREATING", "BUILDING", "TESTING", "DISTRIBUTING", "INTEGRATING", "AVAILABLE", "CANCELLED", "FAILED", "DEPRECATED", "DELETED", ] # noqa: E501 Keyword Args: value (str):, must be one of ["PENDING", "CREATING", "BUILDING", "TESTING", "DISTRIBUTING", "INTEGRATING", "AVAILABLE", "CANCELLED", "FAILED", "DEPRECATED", "DELETED", ] # noqa: E501 _check_type (bool): if True, values for parameters in openapi_types will be type checked and a TypeError will be raised if the wrong type is input. Defaults to True _path_to_item (tuple/list): This is a list of keys or values to drill down to the model in received_data when deserializing a response _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _configuration (Configuration): the instance to use when deserializing a file_type parameter. If passed, type conversion is attempted If omitted no type conversion is done. _visited_composed_classes (tuple): This stores a tuple of classes that we have traveled through so that if we see that class again we will not use its discriminator again. When traveling through a discriminator, the composed schema that is is traveled through is added to this set. For example if Animal has a discriminator petType and we pass in "Dog", and the class Dog allOf includes Animal, we move through Animal once using the discriminator, and pick Dog. Then in Dog, we will make an instance of the Animal class but this time we won't travel through its discriminator because we passed in _visited_composed_classes = (Animal,) """ # required up here when default value is not given _path_to_item = kwargs.pop('_path_to_item', ()) if 'value' in kwargs: value = kwargs.pop('value') elif args: args = list(args) value = args.pop(0) else: raise ApiTypeError( "value is required, but not passed in args or kwargs and doesn't have default", path_to_item=_path_to_item, valid_classes=(self.__class__,), ) _check_type = kwargs.pop('_check_type', True) _spec_property_naming = kwargs.pop('_spec_property_naming', False) _configuration = kwargs.pop('_configuration', None) _visited_composed_classes = kwargs.pop('_visited_composed_classes', ()) if args: raise ApiTypeError( "Invalid positional arguments=%s passed to %s. Remove those invalid positional arguments." % ( args, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), ) self._data_store = {} self._check_type = _check_type self._spec_property_naming = _spec_property_naming self._path_to_item = _path_to_item self._configuration = _configuration self._visited_composed_classes = _visited_composed_classes + (self.__class__,) self.value = value if kwargs: raise ApiTypeError( "Invalid named arguments=%s passed to %s. Remove those invalid named arguments." % ( kwargs, self.__class__.__name__, ), path_to_item=_path_to_item, valid_classes=(self.__class__,), )

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charlesg3@gmail.com

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/venv/lib/python3.8/site-packages/tensorflow/python/util/all_util.py

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[]

no_license

Akira331/flask-cifar10

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refs/heads/master

2023-06-14T16:35:06.384755

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version https://git-lfs.github.com/spec/v1 oid sha256:a170c3a8c37058bfb766d86fa872bc97a2a9455eaf1276230ed371bbe5d22521 size 4693

[ "business030301@gmail.com" ]

business030301@gmail.com

dec0e0d0ae9802f6d1c1999c1944aa061b829a98

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/certificate/asgi.py

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[]

no_license

SSJ26/certificates-code

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85f6b3a4b4e7147dbc9fcb3fec4e7628268f3680

refs/heads/main

2023-03-28T15:29:48.752769

2021-03-29T18:55:57

352,591,080

0

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UTF-8

Python

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py

""" ASGI config for certificate project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.1/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'certificate.settings') application = get_asgi_application()

[ "38113204+SSJ26@users.noreply.github.com" ]

38113204+SSJ26@users.noreply.github.com

dba7be6cf6851ccc19e3ed8c1c2b5cdef9d72d93

cf91b4e409ff41debf0b50ff6c0a944afee7114c

/conanfile.py

730e47936de47bcd96d3ef6060e358a7710b8b3c

[]

no_license

bilke/conan-hello

eb835e0be0d0ec29ac444883b99314c0832355e6

3d0cd3cb4ca1b1f68ec4e31b1537d19d03aad9cc

refs/heads/master

2016-08-11T21:02:47.513065

2016-03-17T07:38:06

54,034,992

0

null

UTF-8

Python

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py

from conans import ConanFile, CMake class HelloConan(ConanFile): name = "Hello" version = "0.2" generators = "cmake" settings = "os", "compiler", "build_type", "arch" exports = ["CMakeLists.txt"] url="http://github.com/bilke/conan-hello" license="none" def source(self): self.run("git clone https://github.com/memsharded/hello.git") def build(self): cmake = CMake(self.settings) self.run("cmake . %s" % cmake.command_line) self.run("cmake --build . %s" % cmake.build_config) def package(self): self.copy("*.h", dst="include", src="hello") self.copy("*.lib", dst="lib", src="hello/lib") self.copy("*.a", dst="lib", src="hello/lib") def package_info(self): self.cpp_info.libs = ["hello"]

[ "lars.bilke@ufz.de" ]

lars.bilke@ufz.de

f7e1fdc9190476e573cb65b49eccd73e25157333

925b818801ef282b4fe65f6ace749304832d8b96

/symlearn/recursnn/recursnn_helper.py

99404e8086fadb7135e5dc1177afe6ac34e6645b

[]

no_license

renewang/Symlearn

b5f9a270fe75dfc5ac1710ccded59c2231eae5f0

1da4c4eb6af0d6d839080623b61116baa8d2299d

refs/heads/master

2022-12-26T06:24:32.431991

2018-11-20T15:28:56

89,684,591

0

null

2022-06-21T21:11:23

2017-04-28T08:19:26

Python

UTF-8

Python

false

13,437

py

from itertools import groupby from io import StringIO from sklearn.preprocessing import LabelBinarizer from symlearn.csgraph import adjmatrix from . import recursnn_utils from .recursnn_utils import (visit_inodes_inorder, calculate_spanweight, get_spanrange) import numpy as np import scipy as sp import logging logger = logging.getLogger(__name__) def computeNumericalGradient(costfunc, param, keyword=None, eps=1e-4): """ A python version of computeNumericalGradient.m from Andrew Ng's Machine Learning Open Course (PA4). The description is from the computeNumericalGradient.m file: %COMPUTENUMERICALGRADIENT Computes the gradient using "finite differences" %and gives us a numerical estimate of the gradient. % numgrad = COMPUTENUMERICALGRADIENT(J, theta) computes the numerical % gradient of the function J around theta. Calling y = J(theta) should % return the function value at theta. % Notes: The following code implements numerical gradient checking, and % returns the numerical gradient.It sets numgrad(i) to (a numerical % approximation of) the partial derivative of J with respect to the % i-th input argument, evaluated at theta. (i.e., numgrad(i) should % be the (approximately) the partial derivative of J with respect % to theta(i).) % @params costfunc is a partial bound functions whose first two arguments will bind precomputed tree and childrenmat @params learner is a recursive-auto-encoder instance potential example code: func = lambda x, y, score_func: score_func(x, y) cal_cost_obj = partial(func, X_sim, y_sim) numgrads = computeNumericalGradient(cal_cost_obj, logloss, classifier.W) assert(np.all(np.allclose(numgrads, analgrads))==True) """ numgrad = np.zeros_like(param) perturb = np.zeros_like(param) for i, (p, n) in enumerate(zip( np.nditer(perturb, order='C', op_flags=['writeonly']), np.nditer(numgrad, order='C', op_flags=['writeonly']))): p[...] = eps # ensure the change is effect assert(param.ndim == 1 or perturb[i//perturb.shape[1], i % perturb.shape[1]] == eps) if keyword: # calculate cost function with parameter - purturb loss_rhs = costfunc(**{keyword: (param - perturb)}) # calculate cost function with parameter + purturb loss_lhs = costfunc(**{keyword: (param + perturb)}) else: loss_rhs = costfunc((param - perturb)) loss_lhs = costfunc((param + perturb)) # Compute Numerical Gradient n[...] = (loss_lhs - loss_rhs)/(2*eps) p[...] = 0. # ensure the change is effect assert(param.ndim == 1 or perturb[i//perturb.shape[1], i % perturb.shape[1]] == 0.) return(numgrad) def label_binarize(y_sim, *args, **kwargs): """ @param y_sim is y label either in 1D or on-hot-coding """ if np.all(np.unique(y_sim) == np.arange(2)): # binary cases # TODO: change to sklearn.preprocessing.label_binarize, and given # classes = 2 y = np.vstack([1-y_sim, y_sim]).T else: y = LabelBinarizer().fit_transform(y_sim) return(y) class Memoize(): """ Memoize(fn) - an instance which acts like fn but memoizes its arguments Will only work on functions with non-mutable arguments Borrow from memorizing recipe: http://code.activestate.com/recipes/52201/ """ def __init__(self, fn): self.fn = fn self.memo = {} def __call__(self, keyword, *args, index=-1): key = self._compute_hash_key(*args) if key not in self.memo: self.memo[key] = self.fn(*args) if hasattr(self.memo[key], keyword): return getattr(self.memo[key], keyword)[index] else: return None def _compute_hash_key(self, *args): strbuf = StringIO() for a in args: strbuf.writelines(str(a)) return(hash(strbuf.getvalue())) def optimize_wrapper(wrapped_func, newshape): """ to return a new function which can take 1D as input and then pass to the wrapped_func for actual calculation @param wrapped_func is a function receives reshape arguments by optimize_wrapper function @param newshape is a new shape in tuple passing to wrapped_func """ def wrapper(param, *args, **kwargs): # the required transformed here param_t = param.reshape(newshape) result = wrapped_func(param_t, *args, **kwargs) return(result.ravel()) return(wrapper) def modify_partial(func, *args): """ make partial to bind 1st parameter @param func is the function object whose 1st paramter will be pre-bound @param args are the rest arguments will be passed into func """ def bind_first_arg(x): res = func(x, *args) return(res) return(bind_first_arg) def activation_gradient(activation, X): """ manually calculate f'(x) @param activation is a function object which serves as activationi function in neural network. Either is tanh or expit @param X is the input numpy array """ z = 1.0 w = 1.0 if activation.__name__ == 'tanh': z = 2.0 w = 4.0 elif activation.__name__ != 'expit': raise NotImplementedError return(w*sp.special.expit(z*X)*(1-sp.special.expit(z*X))) def compute_len_stats(len_dist): """ return lengths distribution of sentence plus cumulative sum @param len_dist is a list whose elements are corresponding sentence length """ # TODO: consider to replace with numpy.bincount len_count = np.zeros(len(np.unique(len_dist)) + 1, dtype=np.int) for i, cur_len in enumerate(np.unique(len_dist)): len_count[i + 1] = np.sum(len_dist == cur_len) return(np.unique(len_dist), len_count) def _iter_samples(word2index, embedding, **kwargs): """ prepare sample for recursive trees-based training @param word2index is a list/OrderedDict/numpy.ndarray which each entry holds the word order of sentence @param embedding is a word embedding matrix used for construct input word vectors """ params_name = [ 'input_vectors', 'traversal_order', 'inverse_inputs', 'parent_children_mapping', 'true_output', 'span_weight'] params_types = [list] * 6 hyper_params = {'error_weight': 0.1, 'alpha': 0.01, 'classification_threshold': 0.5, 'corruption_level': 0.} # TODO: improve this code for param_name in hyper_params.keys(): # TODO: check out argparser in python std if param_name in kwargs: logger.debug( "hyperparameters {} gets updated to {:.3f} (default:" "{:.3f})".format(param_name, kwargs[param_name], hyper_params[param_name])) hyper_params[param_name] = kwargs[param_name] if 'true_output' not in kwargs: params_name.remove('true_output') true_output = None else: true_output = kwargs['true_output'] if kwargs.get('trees'): trees = kwargs['trees'] iter_trees = iter(trees) else: trees = None iter_trees = None if kwargs.get('max_len'): max_len = int(kwargs['max_len']) else: max_len = np.max(list(map(len, word2index))) examples = [] index2len = [] iter_over_word2index = groupby(word2index, key=len) start = 0 for cur_len, word_orders in iter_over_word2index: if cur_len > max_len: break # allocate new list examples.append({}) tree_size = 2 * cur_len - 1 for key, cls in zip(params_name, params_types): examples[-1][key] = cls() for word_order in word_orders: if not trees and kwargs.get('autoencoder'): est_gtree, est_cost = recursnn_utils.build_greedy_tree( word_order, embedding=embedding, autoencoder=kwargs.get('autoencoder')) else: try: est_gtree = next(iter_trees) except StopIteration: if cur_len != max_len: raise RuntimeError else: pass except: raise vocab = recursnn_utils.embed_words(word_order, embedding) est_gtinfo = recursnn_utils.preprocess_input(est_gtree, vocab) assert( np.all(np.linalg.norm(est_gtinfo[2][np.sum( est_gtinfo[2], axis=1) > 0], 2, axis=1)) == 1.0) if kwargs.get('span_weight'): span_weight = est_gtinfo[3] else: span_weight = 0.5 * np.ones_like(est_gtinfo[3]) if isinstance(true_output, np.ndarray): assignments = [est_gtinfo[2], est_gtinfo[0], np.zeros_like(est_gtinfo[2]), est_gtinfo[1], true_output[start:start + tree_size], span_weight] start += tree_size else: assignments = [est_gtinfo[2], est_gtinfo[0], np.zeros_like(est_gtinfo[2]), est_gtinfo[1], span_weight] for key, val in zip(params_name, assignments): examples[-1][key].append(val) sample_size = len(examples[-1]['input_vectors']) for key, val in examples[-1].items(): if isinstance(val, list): # first, combine the matrices for all samples examples[-1][key] = np.concatenate(val, axis=0) assert(examples[-1][key].ndim <= 2) if key == 'traversal_order': examples[-1][key] += np.repeat(np.arange(sample_size) * tree_size, cur_len - 1) elif key == 'parent_children_mapping': examples[-1][key] += ( np.repeat(np.arange(sample_size) * tree_size, tree_size))[:, np.newaxis] elif key == 'true_output': if examples[-1][key].ndim == 2: examples[-1][key] = np.argmax(examples[-1][key], axis=1) index2len.append(cur_len) examples[-1].update(hyper_params) examples[-1]['sample_size'] = sample_size return((np.array(index2len), examples)) def _combine_sample(examples): """ combine sample produce by _iter_sample which are grouped by lengths into one chunk """ combined_example = {} for example in examples: for key, value in example.items(): ent = combined_example.setdefault(key, []) ent.append(value) assert(np.all(np.asarray(list(map(len, combined_example.values()))) == len(examples))) return(combined_example) def _iter_matrix_groups(matrix, **kwargs): if 'embedding' in kwargs and 'vocab_size' in kwargs: raise RuntimeError( 'embedding and vocab_size cannot be specified at the same time') elif 'embedding' not in kwargs and 'vocab_size' not in kwargs: raise RuntimeError( 'embedding and vocab_size cannot be specified at the same time') embedding = kwargs.get('embedding', None) if hasattr(embedding, 'components_'): embedding = kwargs.get('embedding').components_.T vocab_size = kwargs.get('vocab_size', None) or embedding.shape[0] if kwargs.get('traverse_func'): traverse_func = kwargs.get('traverse_func') else: traverse_func = visit_inodes_inorder mapping = recursnn_utils.retrieve_mapping(matrix) span_range = get_spanrange(matrix) traversal_order = traverse_func(matrix) # checking if indexing in mapping are attainable (not heap-ordered) if np.max(mapping) >= len(mapping): remap_lookup = np.ma.MaskedArray(np.zeros(matrix.shape[0], dtype=np.intp), mask=matrix.diagonal() == 0) remap_lookup[~remap_lookup.mask] = np.arange(np.sum( [matrix.diagonal() > 0])) for row in np.nditer(mapping, op_flags=['readwrite'], flags=['external_loop']): row[...] = remap_lookup[row] for cols in np.nditer(traversal_order, op_flags=['readwrite'], flags=['external_loop']): cols[...] = remap_lookup[cols] if kwargs.get('return_weight', False): span_weight = calculate_spanweight(matrix, span_range) node_weight = np.nan_to_num( np.clip(span_weight, 1, np.inf)[mapping] / np.sum( np.clip(span_weight, 1, np.inf)[mapping], axis=1, keepdims=True)) else: node_weight = span_range if embedding is None: # return indices word_mat = recursnn_utils.get_wordindices(matrix, vocab_size, node_weight) else: word_mat = recursnn_utils.get_wordmatrix(matrix, embedding) return(traversal_order, word_mat, node_weight, mapping)

[ "renewang@users.noreply.github.com" ]

renewang@users.noreply.github.com

729d713a868825738180938e1458012ac6cbbb8b

200fc6bfe4b6a3004836bf527e62a3dbdc93ff1c

/users/apps.py

666435576b2933cb56a397ed1c08a38f87b5cdf6

[]

no_license

risknet/reference_python_web_standard

0e77c08da300c345e22608e68e34ef1c5b665c11

e2f30f3efc1448094ce5e85562ac3c27d37f4fc4

refs/heads/master

2022-12-05T15:09:45.861554

2020-02-17T15:03:50

241,135,996

0

null

2022-11-22T05:19:47

2020-02-17T15:04:32

Python

UTF-8

Python

false

161

py

from django.apps import AppConfig class UsersConfig(AppConfig): name = 'users' # triggering signal def ready(self): import users.signals

[ "jaelee2009@gmail.com" ]

jaelee2009@gmail.com

53485b533dd5e0253e0861a3134cfd9a2f72ecee

8cb5ebd1993d23074f55db2e8aa71eb24dd8ce17

/lib/screens/custom_widgets/tasklist.py

3dbaa62152ec060c21e08cebd219fb06f8cf780b

[]

no_license

JahnsMichael/qtile

0f6bda0dbfe8aabe7fe4000b7d6e6d3fd9164416

419dc2d0f2cc3b4c63c66c98ccbca1686a5b873d

refs/heads/master

2023-07-20T22:25:41.063340

2021-09-01T15:44:02

361,193,091

0

null

UTF-8

Python

false

660

py

from libqtile.widget.tasklist import TaskList class CustomTaskList(TaskList): def __init__(self, **config): super().__init__(**config) self.add_callbacks({ 'Button1': self.select_window, 'Button2': self.kill_window, 'Button3': self.maximize_window, }) def kill_window(self): if self.clicked: window = self.clicked window.group.focus(window, False) window.cmd_kill() def maximize_window(self): if self.clicked: window = self.clicked window.group.focus(window, False) window.cmd_toggle_maximize()

[ "michael.jf.jm@gmail.com" ]

michael.jf.jm@gmail.com

a0ec26a115c3a0897cbf92beda45b68db9012d59

63a4d28c5af83dc8df0a2939a5817817658e9117

/pet/urls.py

900c720099a32d32b12bd0970064ae46e61e8e5f

[]

no_license

arv1983/KenziePet

12da6c3b94cd9e9be6b7003fea28e8f6b9fad7e6

c4a456247c6bf3a141de1bed060c231a37853e10

refs/heads/main

2023-07-17T00:47:16.405185

2021-08-06T17:11:00

397,413,149

0

null

UTF-8

Python

false

177

py

from django.urls import path from .views import GroupView urlpatterns = [ path('animals/', GroupView.as_view()), path('animals/<int:animal_id>/', GroupView.as_view()) ]

[ "ciclista-rs@hotmail.com" ]

ciclista-rs@hotmail.com

2b7c279a57035e546398ab3c7e54d5dccfe603b5

62179a165ec620ba967dbc20016e890978fbff50

/tests/common/test_builder_state.py

2353979abfefdfd4876a61cf0799d1b828e0e4e2

[ "Apache-2.0" ]

permissive

openvinotoolkit/nncf

91fcf153a96f85da166aacb7a70ca4941e4ba4a4

c027c8b43c4865d46b8de01d8350dd338ec5a874

refs/heads/develop

2023-08-24T11:25:05.704499

2023-08-23T14:44:05

263,687,600

558

157

Apache-2.0

2023-09-14T17:06:41

2020-05-13T16:41:05

Python

UTF-8

Python

false

4,436

py

# Copyright (c) 2023 Intel Corporation # 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. from typing import Any, Dict, List, Union from nncf import NNCFConfig from nncf.api.compression import CompressionAlgorithmController from nncf.api.compression import TModel from nncf.common.composite_compression import CompositeCompressionAlgorithmBuilder from nncf.common.compression import BaseCompressionAlgorithmBuilder from nncf.common.graph.transformations.layout import TransformationLayout from nncf.common.utils.registry import Registry STATE_ATTR = "state" DIFF_STATE_ATTR = STATE_ATTR + "__" class A(BaseCompressionAlgorithmBuilder): def __init__(self, config: NNCFConfig, should_init: bool = True, state_value: int = 1, name: str = "A"): setattr(self, Registry.REGISTERED_NAME_ATTR, name) super().__init__(config, should_init) self.state_value = state_value def _load_state_without_name(self, state_without_name: Dict[str, Any]): self.state_value = state_without_name.get(STATE_ATTR) def _get_state_without_name(self) -> Dict[str, Any]: return {STATE_ATTR: self.state_value} def apply_to(self, model: TModel) -> TModel: pass def _build_controller(self, model: TModel) -> CompressionAlgorithmController: pass def get_transformation_layout(self, model: TModel) -> TransformationLayout: pass def initialize(self, model: TModel) -> None: pass class CA(CompositeCompressionAlgorithmBuilder): @property def name(self) -> str: pass def add(self, child_builder) -> None: self._child_builders.append(child_builder) def apply_to(self, model: TModel) -> TModel: pass def build_controller(self, model: TModel) -> CompressionAlgorithmController: pass def get_transformation_layout(self, model: TModel) -> TransformationLayout: pass def initialize(self, model: TModel) -> None: pass def _get_mock_config(algo_name: Union[List[str], str]) -> NNCFConfig: config = NNCFConfig() config["input_info"] = {"sample_size": [1, 1]} if isinstance(algo_name, list): lst = [] for alg_n in algo_name: lst.append({"algorithm": alg_n}) config["compression"] = lst else: assert isinstance(algo_name, str) config["compression"] = {"algorithm": algo_name} return config def test_builder_state_load(): config = _get_mock_config("A") builder = A(config, True, 1) builder.state_value += 1 saved_state = builder.get_state() builder = A(config, True, 1) builder.load_state(saved_state) assert builder.state_value == 2 def test_basic_composite_builder_load(): def create_builder(): config = _get_mock_config(["A", "A2"]) c = CA(config, True) a = A(config, True, 1) b = A(config, True, 2, "A2") c.add(a) c.add(b) return c, a, b composite_bldr, bldr1, bldr2 = create_builder() bldr1.state_value += 1 bldr2.state_value += 2 saved_state = composite_bldr.get_state() composite_bldr, bldr1, bldr2 = create_builder() composite_bldr.load_state(saved_state) assert bldr1.state_value == 2 assert bldr2.state_value == 4 def test_advanced_composite_ctrl_load(): config = _get_mock_config(["A", "A2", "A3"]) composite_builder = CA(config, True) ctrl1 = A(config, True, 1) ctrl2 = A(config, True, 2, name="A2") composite_builder.add(ctrl1) composite_builder.add(ctrl2) ctrl1.state_value += 1 ctrl2.state_value += 2 saved_state = composite_builder.get_state() composite_builder = CA(config, True) ctrl1 = A(config, True, 1) ctrl3 = A(config, True, 3, name="A3") composite_builder.add(ctrl1) composite_builder.add(ctrl3) composite_builder.load_state(saved_state) assert ctrl1.state_value == 2 assert ctrl3.state_value == 3

[ "noreply@github.com" ]

openvinotoolkit.noreply@github.com

98b5212ac8776c8d2c2b282ba3d02150f45e9864

f6576c6551a1c44a0b6753587a6ce5cff978cc7c

/crawler/temp.py

0f08e16404990d70f775ad5c759c3c9a438655ba

[]

no_license

sharma-anshul/nRelate-Anshul

4cf1b6f739c82397c8c471c51d398bdaa5cf4ac7

63d8ca30c2e1812940156e69266f474d8eead7d8

refs/heads/master

2021-01-10T21:37:20.711072

2013-07-26T18:23:22

null

0

null

UTF-8

Python

false

1,102

py

import classifier import distmat import patternify def compute_classifier_domain_info(): patterns = set() # Gets a sample of links from the website starting from the homepage already_analyzed = set() links = self._get_all_internal_links(self._start_url) for i in range(depth - 1): if len(links) > 3000: break sub_links = set() cpt = 0 for link in links: print "link %d/%d [%s]" % (cpt, len(links), link) cpt += 1 if (len(sub_links) > 3000): break if not link in already_analyzed: sub_links = sub_links.union(self._get_all_internal_links(link)) already_analyzed.add(link) links = links.union(sub_links) self.log("Finished. Got %d sublinks on %d levels" % (len(links), depth)) # Identifies links pointing to content and generates patterns ones, zeros = classifier.testSVM(links) mean, distMat = distmat.getDistanceMatrix(ones) content = [] for link in distMat: if sum(distMat[link])/len(distMat) >= mean: content += [link] patterns = patternify.getPatterns(content) return patterns

[ "ansharma@seas.upenn.edu" ]

ansharma@seas.upenn.edu

f05cbb3d16420419b9e94c281c67618ca1759539

ce76b3ef70b885d7c354b6ddb8447d111548e0f1

/get_long_week/large_fact_and_good_year/want_fact/place_or_number.py

b65c0ab506ff8f0f45c1acdf99cbe05aa37dd954

[]

no_license

JingkaiTang/github-play

9bdca4115eee94a7b5e4ae9d3d6052514729ff21

51b550425a91a97480714fe9bc63cb5112f6f729

refs/heads/master

2021-01-20T20:18:21.249162

2016-08-19T07:20:12

60,834,519

0

null

UTF-8

Python

false

251

py

#! /usr/bin/env python def know_public_part_by_high_problem(str_arg): good_problem(str_arg) print('government') def good_problem(str_arg): print(str_arg) if __name__ == '__main__': know_public_part_by_high_problem('different_man')

[ "jingkaitang@gmail.com" ]

jingkaitang@gmail.com

9476c7c9346a2c3ce4257c6f31fcd18dbb75b089

548259a1cd5a0597a53030e48e9bf8437050d3fd

/blockchain_app/urls.py

3d20899ae94c366510538e8fefb545db7e7bb107

[]

no_license

jaqb8/blockchain-app

3de8e7519a773f25a6d546df7f2b773dc736d5dd

765e6478676ab16bd812157d2741660673487235

refs/heads/main

2023-07-10T08:17:56.304102

2020-11-19T14:04:15

303,366,092

0

null

UTF-8

Python

false

800

py

"""blockchain_app URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/3.1/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, include urlpatterns = [ path('admin/', admin.site.urls), path('', include('node.urls')) ]

[ "qba.anisz@gmail.com" ]

qba.anisz@gmail.com

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Bjcurty/PycharmProjects

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refs/heads/master

2022-09-05T13:24:09.740774

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# from mymodule import divide # # print(divide(10, 2)) # print(__name__) # import sys # allows python to find files locally # # error because it doesn't exist # # import sysadsf # # print(sys.path) import sys import mymodule # print(sys.modules) # print(mymodule)

[ "bradley@haulynx.com" ]

bradley@haulynx.com

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/Chapter3/3.3/3.3.3/stack.py

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[]

no_license

WustAnt/Python-Algorithm

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refs/heads/master

2022-12-07T22:19:20.130446

2020-08-21T09:25:28

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# -*- coding: utf-8 -*- # @Time : 2020/8/1 9:25 # @Author : WuatAnt # @File : stack.py # @Project : Python数据结构与算法分析 """ Stack()使用列表保存数据，默认将列表的尾部作为栈的顶端 """ class Stack: """ Stack():创建空栈 .push(item):将一个元素添加到栈的顶端 .pop():将栈顶端的元素移除，返回一个参数item .peek():返回栈顶端的元素 .isEmpty():检查栈是否为空，返回布尔值 .size():返回栈中元素的数目 """ def __init__(self): self.items = [] def isEmpty(self): return self.items == [] def push(self,item): self.items.append(item) def pop(self): return self.items.pop() def peek(self): return self.items[-1] def size(self): return len(self.items)

[ "ghostimage@163.com" ]

ghostimage@163.com

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/Python/AI learns to play FlappyBird/main.py

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[]

no_license

vafakaramzadegan/scriptshot.ir

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refs/heads/master

2023-07-11T14:30:48.815612

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''' FlappyBird AI --------------------------------- AI plays FlappyBird using Genetic Algorithm, implemented in Tensorflow and Keras. Author: Scriptshot.ir https://github.com/vafakaramzadegan/scriptshot.ir ''' import pygame from functions import functions import random from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense import numpy as np ''' init PyGame ''' pygame.init() clock = pygame.time.Clock() WINDOW_WIDTH, WINDOW_HEIGHT = 420, 768 WINDOW = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT)) pygame.display.set_caption('FlappyBird AI') FLOOR_HEIGHT = 150 # tools for loading sprites and drawing on screen toolkit = functions(WINDOW) ''' default settings for AI ''' # number of birds in each generation BIRDS_COUNT = 50 # number of neurons to mutate in each generation MUTATION_RATE = .2 # draw the guide lines SHOW_LINES = False # the data of the best brain BEST_BRAIN = None # the fittness of the best bird BEST_FITNESS = 0.0 # the index of current generation GENERATION_INDEX = 1 ''' Pipe object ''' class Pipe: img = toolkit.sprites['world']['pipe'] # the gap between two pipes (in pixels) GAP = 150 # how fast the pipes move velocity = 5 def __init__(self, x): self.x = x self.height = 0 self.top_pipe_top = 0 self.bottom_pipe_top = 0 self.PIPE_BOTTOM = self.img # we build the top pipe by flipping the bottom pipe self.PIPE_TOP = pygame.transform.flip(self.img, False, True) self.passed = False self.set_height() def set_height(self): # random height for pipes self.height = random.randrange(50, 400) self.top_pipe_top = self.height - self.PIPE_TOP.get_height() self.bottom_pipe_top = self.height + self.GAP def move(self): self.x -= self.velocity def draw(self, window): window.blit(self.PIPE_TOP, (self.x, self.top_pipe_top)) window.blit(self.PIPE_BOTTOM, (self.x, self.bottom_pipe_top)) def collide(self, bird, window): # we check for collision by using masks bird_mask = bird.get_mask() top_mask = pygame.mask.from_surface(self.PIPE_TOP) bottom_mask = pygame.mask.from_surface(self.PIPE_BOTTOM) top_offset = (self.x - bird.x, self.top_pipe_top - round(bird.y)) bottom_offset = (self.x - bird.x, self.bottom_pipe_top - round(bird.y)) # check for overlap b_point = bird_mask.overlap(bottom_mask, bottom_offset) t_point = bird_mask.overlap(top_mask, top_offset) # return True if there is a collision return True if b_point or t_point else False ''' The Brain of a bird ''' class Brain: def __init__(self): # a Keras.Sequential model is needed for our DNN self.model = Sequential() # this is the hidden layer with four neurons. # we have these three inputs: # 1) bird's y position # 2) bird's vertical distance to the top pipe # 3) bird's vertical distance to the bottom pipe # so the input_shape for the hidden layer is a tensor with three elements self.model.add(Dense(units=4, input_shape=(3, ), activation='sigmoid')) # the output layer has two neurons: # 1) jump # 2) no jump self.model.add(Dense(units=2, activation='softmax')) def loadFromFile(self, fn): data = np.load(fn, allow_pickle=True) self.setData(data) def saveToFile(self, fn): np.save(fn, self.model.get_weights()) # set the weights and biases of neurons def setData(self, brainData): brain = np.array(brainData) # mutation happens here for index, data in enumerate(brain): if random.uniform(0, 1) <= MUTATION_RATE: brain[index] = data + random.uniform(-1, 1) # set mutated weights self.model.set_weights(brain) def decide(self, data): return self.model.predict(data) def copy(self): return self.model.get_weights() ''' Bird object ''' class Bird: img = toolkit.sprites['bird']['idle'] crashed = False # number of frames it takes for the bird to change the position of its wing FLAPS_ANIMATION_TIME = 2 # how fast the bird rotates ROTATE_VEL = 15 def __init__(self, x, y): self.x = x self.y = y self.tilt = 0 self.tick = 0 self.frameIndex = 0 self.vel = 0 self.height = self.y self.brain = Brain() # indicates how long the bird survived self.fitness = .0 def jump(self): self.vel = -8 self.tick = 0 self.height = self.y def move(self): self.tick += 1 # the bird has survived for one more frame, so the fitness increases. # the fitness could be altered in various ways. self.fitness += .01 # calculate the displacement based on the time of freefall self.displacement = self.vel*(self.tick) + 1.5*self.tick**2 if self.displacement >= 16: self.displacement = (self.displacement/abs(self.displacement)) * 16 # change the vertical position by displacement value self.y = self.y + self.displacement # bird is ascending if self.displacement < 0 or self.y < self.height + 50: if self.tilt < 25: self.tilt = 25 else: # bird is descending if self.tilt > -90: self.tilt -= self.ROTATE_VEL # the bird crashes by moving out of the screen or hitting the floor if (self.y + self.img.get_height() < 0 or self.y + self.img.get_height() >= WINDOW_HEIGHT - FLOOR_HEIGHT): self.crashed = True def draw(self, window): self.frameIndex += 1 if self.frameIndex < self.FLAPS_ANIMATION_TIME: self.img = toolkit.sprites['bird']['ascend'] elif self.frameIndex < self.FLAPS_ANIMATION_TIME * 2: self.img = toolkit.sprites['bird']['idle'] elif self.frameIndex < self.FLAPS_ANIMATION_TIME * 3: self.img = toolkit.sprites['bird']['descend'] else: self.frameIndex = 0 rotated_image = pygame.transform.rotate(self.img, self.tilt) window.blit(rotated_image, (self.x, self.y)) # returns the mask of the bird for detecting collisions def get_mask(self): return pygame.mask.from_surface(self.img) ''' Background object ''' class Background: img = toolkit.sprites['world']['background'] width = img.get_width() height = img.get_height() # how fast the background moves velocity = 1 def __init__(self): self.x1 = 0 self.x2 = self.width def move(self): self.x1 -= self.velocity self.x2 -= self.velocity if self.x1 + self.width < 0: self.x1 = self.x2 + self.width if self.x2 + self.width < 0: self.x2 = self.x1 + self.width def draw(self, window): # the background moves infinitely, so we need a sequence of two images window.blit(self.img, (self.x1, 0)) window.blit(self.img, (self.x2, 0)) ''' Floor object ''' class Floor: img = toolkit.sprites['world']['floor'] width = img.get_width() height = img.get_height() # the floor moves as fast as pipes velocity = 5 def __init__(self): self.x1 = 0 self.x2 = self.width def move(self): self.x1 -= self.velocity self.x2 -= self.velocity if self.x1 + self.width < 0: self.x1 = self.x2 + self.width if self.x2 + self.width < 0: self.x2 = self.x1 + self.width def draw(self, window): window.blit(self.img, (self.x1, WINDOW_HEIGHT - FLOOR_HEIGHT)) window.blit(self.img, (self.x2, WINDOW_HEIGHT - FLOOR_HEIGHT)) ''' The main menu ''' mainmenu_active = True def mainmenu(): WINDOW.blit(toolkit.sprites['titles']['game_title'], (WINDOW_WIDTH / 2 - toolkit.sprites['titles']['game_title'].get_width() / 2, 100)) WINDOW.blit(toolkit.sprites['titles']['scriptshot'], (WINDOW_WIDTH / 2 - toolkit.sprites['titles']['scriptshot'].get_width() / 2, 180)) while True: for event in pygame.event.get(): if event.type == pygame.QUIT: pygame.quit() # play button toolkit.button(toolkit.sprites['buttons']['play'], WINDOW_WIDTH / 2 - toolkit.sprites['buttons']['play'].get_width() / 2, 300, close_mainmenu) pygame.display.update() clock.tick(30) def close_mainmenu(): global mainmenu_active mainmenu_active = False run() ''' the main function ''' def run(): global BEST_BRAIN, BEST_FITNESS, GENERATION_INDEX # the number of pipes passed in the current generation score = 0 # objects background = Background() floor = Floor() birds = [] pipes = [Pipe(500)] # all the birds in a generation have the same random starting position startPosX = random.randint(20, 100) startPosY = random.randint(20, WINDOW_HEIGHT - FLOOR_HEIGHT - 40) for i in range(BIRDS_COUNT): bird = Bird(startPosX, startPosY) if BEST_BRAIN is not None: bird.brain.setData(BEST_BRAIN) birds.append(bird) while True: clock.tick(30) # the last alive bird in a generation is the best one if len(birds) == 1: if birds[0].fitness > BEST_FITNESS: BEST_FITNESS = birds[0].fitness BEST_BRAIN = birds[0].brain.copy() # the current generation went extinct, and the next one # must start elif len(birds) == 0: GENERATION_INDEX += 1 run() for event in pygame.event.get(): if event.type == pygame.QUIT: pygame.quit() floor.move() background.move() pipe_ind = 0 if len(pipes) > 1 and bird.x > pipes[0].x + pipes[0].PIPE_TOP.get_width(): pipe_ind = 1 for x, bird in enumerate(birds): bird.move() # bird decides whether to jump or not res = bird.brain.decide([[bird.y, abs(bird.y - pipes[pipe_ind].height), abs(bird.y - pipes[pipe_ind].bottom_pipe_top)]]) # the first output neuron represents jump action if np.argmax(res) == 0: bird.jump() add_pipe = False pipes_to_remove = [] for pipe in pipes: pipe.move() for bird in birds: if (bird.crashed or pipe.collide(bird, WINDOW)): # remove crashed birds birds.pop(birds.index(bird)) # a pipe is destroyed after moving out of screen if pipe.x + pipe.PIPE_TOP.get_width() < 0: pipes_to_remove.append(pipe) # add new pipe if the current one has been passed by the birds if not pipe.passed and pipe.x < bird.x: pipe.passed = True add_pipe = True for r in pipes_to_remove: pipes.remove(r) if add_pipe: score += 1 pipes.append(Pipe(WINDOW_WIDTH)) toolkit.updateDisplay(background, floor, birds, pipes, score, not mainmenu_active, pipe_ind, GENERATION_INDEX, SHOW_LINES) if mainmenu_active: mainmenu() if __name__ == '__main__': run()

[ "noreply@github.com" ]

vafakaramzadegan.noreply@github.com

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/Algorithm/plusMinus.py

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jasper2326/HackerRank

ab0090cc9906472f29c91225c1cf8f1d3a19113c

1e5bad3bc734085fc12ce1cbd126742f2be506da

refs/heads/master

2021-05-06T09:49:22.907317

2018-03-27T08:51:59

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py

import sys n = int(raw_input().strip()) arr = map(int,raw_input().strip().split(' ')) minus = 0 plus = 0 zero = 0 for num in arr: if num > 0: plus += 1 elif num == 0: zero += 1 elif num < 0: minus += 1 print plus * 1.0 / n print minus * 1.0 / n print zero * 1.0 / n

[ "jasper_jiao@hotmail.com" ]

jasper_jiao@hotmail.com

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/data_import_pkg/tweet_parser/read_lid_spaeng.py

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[]

no_license

FilipePintoReis/PLEI_FEUP

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2023-02-18T10:46:20.152610

2021-01-11T16:48:55

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''' Contains LinceFileReader ''' class LinceFileReader: ''' This class read files from lince dataset when given a prefix. The file structure inside the prefix folder is ought to be: prefix/ train.conll test.conll dev.conll ''' @staticmethod def train_data(prefix): ''' Returns training data based on a prefix ''' train_str = f"{prefix}/train.conll" train_file = open(train_str, "r", encoding="UTF-8") train_string = train_file.read() train_file.close() return train_string @staticmethod def test_data(prefix): ''' Returns test data based on a prefix ''' test_str = f"{prefix}/test.conll" test_file = open(test_str, "r", encoding="UTF-8") test_string = test_file.read() test_file.close() return test_string @staticmethod def dev_data(prefix): ''' Returns validation data based on a prefix ''' dev_str = f"{prefix}/dev.conll" dev_file = open(dev_str, "r", encoding="UTF-8") dev_string = dev_file.read() dev_file.close() return dev_string

[ "freis@semasio.com" ]

freis@semasio.com

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/Other Codes/login.py

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[]

no_license

sakshid22/HealingManagementSystem

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846c1a61ed9f72f66fa022fe928dc9fe35280c3b

refs/heads/master

2021-05-13T21:02:27.564219

2018-01-08T03:50:00

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py

import Tkinter as tk import tkFileDialog as tkfd import tkMessageBox as tkmb #import xlrd #GUI setup root = tk.Tk() root.title("TLA Database Tool") user_label = tk.Label(root, text = "username").pack() user = tk.Entry(root, textvariable = tk.StringVar()).pack() pw_label = tk.Label(root, text = "password").pack() #pw = tk.Entry(root, show = "*", textvariable = tk.StringVar()).pack() #login_button = tk.Button(root, text = "Login", command = lambda: logintoDB(user,pw)).pack() root.mainloop()

[ "noreply@github.com" ]

sakshid22.noreply@github.com

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/langs/2/gro.py

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[]

no_license

G4te-Keep3r/HowdyHackers

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refs/heads/master

2020-08-01T12:08:10.782018

2016-11-13T20:45:50

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import sys def printFunction(lineRemaining): if lineRemaining[0] == '"' and lineRemaining[-1] == '"': if len(lineRemaining) > 2: #data to print lineRemaining = lineRemaining[1:-1] print ' '.join(lineRemaining) else: print def main(fileName): with open(fileName) as f: for line in f: data = line.split() if data[0] == 'grO': printFunction(data[1:]) else: print 'ERROR' return if __name__ == '__main__': main(sys.argv[1])

[ "juliettaylorswift@gmail.com" ]

juliettaylorswift@gmail.com

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/log_profile.py

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[]

no_license

Ewenwan/upy-profile

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3c72aa5c720c68359748a77119e89603d1d5b135

refs/heads/master

2022-04-26T02:42:50.634705

2020-04-26T16:01:50

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null

UTF-8

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import sys import time plog_field_count = 7 plog_size = 0 plog_next_idx = 0 plog = None def alloc_plog(size): global plog_size, plog plog_size = size plog = [[None]*plog_field_count for x in range(size)] def analize_plog(plog): frame_stats = {} func_stats = {} for op, frame_id, parent_frame_id, filename, lineno, func_name, now_us in plog: loc_id = (filename, lineno, func_name) # print(op, arg, id(frame), loc_id) if op == 'call': frame_stats[frame_id] = [now_us, 0] elif op == 'return' or op == 'exception': start_tm_us, callees_acc_us = frame_stats[frame_id] duration_us = now_us - start_tm_us # add total time spent in this frame to the parent frame if parent_frame_id in frame_stats: frame_stats[parent_frame_id][1] += duration_us # subtract time spent in callees from current frame duration_us -= callees_acc_us #print(op, arg, frame_id, loc_id, 'parent frame:\t', parent_frame_id, 'acc:\t', callees_acc_us, frame_stats[parent_frame_id][1] if parent_frame_id in frame_stats else 0, duration_us) del frame_stats[frame_id] if loc_id in func_stats: call_count, min_us, max_us, tot_us = func_stats[loc_id] call_count += 1 min_us = min(min_us, duration_us) max_us = max(max_us, duration_us) tot_us += duration_us else: call_count, min_us, max_us, tot_us = (1, duration_us, duration_us, duration_us) func_stats[loc_id] = call_count, min_us, max_us, tot_us else: pass return func_stats def _log_trace_func(frame, op, arg): global plog_next_idx if op == 'line': return now_us = time.ticks_us() if plog_next_idx >= plog_size: return code = frame.f_code # assigning like this `[index][:] = fields` does not replace the item in the outer list plog[plog_next_idx][:] = op, id(frame), id(frame.f_back), code.co_filename, frame.f_lineno, code.co_name, now_us plog_next_idx += 1 return _log_trace_func def test2(a): #time.sleep(0.1) pass def test1(a): test2(a) test2(a) test2(a) test2(a) test2(a) def __main__(): test1('lalala') test1('qaqaqa') alloc_plog(1000) sys.settrace(_log_trace_func) __main__() print(analize_plog(plog))

[ "dbrignoli@audioscience.com" ]

dbrignoli@audioscience.com

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no_license

marthaEstefany/DisplacedSUSY

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81de5ca0c9e9c53d64b91bbe7db4a700bb5af923

refs/heads/Run2

2021-12-05T12:03:09.003009

2018-12-02T19:40:26

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import FWCore.ParameterSet.Config as cms import copy import string from DisplacedSUSY.EMuChannel.CutDefinitions import * from DisplacedSUSY.StandardAnalysis.ElectronIdCutDefinitions import * ########################################################################################## #USE THIS CONFIG ONLY FOR ELECTRON TIGHT ID TESTS! ########################################################################################## PreselectionWithExplicitEleIdBarrel = cms.PSet( name = cms.string("PreselectionWithExplicitEleIdBarrel"), triggers = triggersMuonPhoton, cuts = cms.VPSet() ) ### jet selection (just for plotting purposes, doesn't make event cuts) PreselectionWithExplicitEleIdBarrel.cuts.extend(atLeastZero_jet_basic_selection_cuts) ### at least one good electron PreselectionWithExplicitEleIdBarrel.cuts.append(electron_eta_cut) PreselectionWithExplicitEleIdBarrel.cuts.append(electron_gap_veto) if os.environ["CMSSW_VERSION"].startswith ("CMSSW_8_0_"): PreselectionWithExplicitEleIdBarrel.cuts.append(electron_pt_42_cut) elif os.environ["CMSSW_VERSION"].startswith ("CMSSW_9_4_"): PreselectionWithExplicitEleIdBarrel.cuts.append(electron_pt_50_cut) PreselectionWithExplicitEleIdBarrel.cuts.append(electron_isEB_cut) PreselectionWithExplicitEleIdBarrel.cuts.append(electron_sigmaIetaIetaEB_cut) PreselectionWithExplicitEleIdBarrel.cuts.append(electron_deltaPhiSuperClusterEB_cut) PreselectionWithExplicitEleIdBarrel.cuts.append(electron_deltaEtaSuperClusterEB_cut) PreselectionWithExplicitEleIdBarrel.cuts.append(electron_hadronicOverEmEB_cut) if os.environ["CMSSW_VERSION"].startswith ("CMSSW_8_0_"): PreselectionWithExplicitEleIdBarrel.cuts.append(electron_abs_1overE_1overP_cut) elif os.environ["CMSSW_VERSION"].startswith ("CMSSW_9_4_"): PreselectionWithExplicitEleIdBarrel.cuts.append(electron_abs_1overE_1overP_EB_cut) PreselectionWithExplicitEleIdBarrel.cuts.append(electron_missingInnerHits_cut) PreselectionWithExplicitEleIdBarrel.cuts.append(electron_passConversionVeto_cut) ### at least one good muon PreselectionWithExplicitEleIdBarrel.cuts.append(muon_eta_cut) if os.environ["CMSSW_VERSION"].startswith ("CMSSW_8_0_"): PreselectionWithExplicitEleIdBarrel.cuts.append(muon_pt_40_cut) elif os.environ["CMSSW_VERSION"].startswith ("CMSSW_9_4_"): PreselectionWithExplicitEleIdBarrel.cuts.append(muon_pt_50_cut) PreselectionWithExplicitEleIdBarrel.cuts.append(muon_global_cut) PreselectionWithExplicitEleIdBarrel.cuts.append(muon_id_cut) PreselectionWithExplicitEleIdBarrel.cuts.append(muon_iso_cut) PreselectionWithExplicitEleIdEndcap = cms.PSet( name = cms.string("PreselectionWithExplicitEleIdEndcap"), triggers = triggersMuonPhoton, cuts = cms.VPSet() ) ### jet selection (just for plotting purposes, doesn't make event cuts) PreselectionWithExplicitEleIdEndcap.cuts.extend(atLeastZero_jet_basic_selection_cuts) ### at least one good electron PreselectionWithExplicitEleIdEndcap.cuts.append(electron_eta_cut) PreselectionWithExplicitEleIdEndcap.cuts.append(electron_gap_veto) if os.environ["CMSSW_VERSION"].startswith ("CMSSW_8_0_"): PreselectionWithExplicitEleIdEndcap.cuts.append(electron_pt_42_cut) elif os.environ["CMSSW_VERSION"].startswith ("CMSSW_9_4_"): PreselectionWithExplicitEleIdEndcap.cuts.append(electron_pt_50_cut) PreselectionWithExplicitEleIdEndcap.cuts.append(electron_isEE_cut) PreselectionWithExplicitEleIdEndcap.cuts.append(electron_sigmaIetaIetaEE_cut) PreselectionWithExplicitEleIdEndcap.cuts.append(electron_deltaPhiSuperClusterEE_cut) PreselectionWithExplicitEleIdEndcap.cuts.append(electron_deltaEtaSuperClusterEE_cut) PreselectionWithExplicitEleIdEndcap.cuts.append(electron_hadronicOverEmEE_cut) if os.environ["CMSSW_VERSION"].startswith ("CMSSW_8_0_"): PreselectionWithExplicitEleIdEndcap.cuts.append(electron_abs_1overE_1overP_cut) elif os.environ["CMSSW_VERSION"].startswith ("CMSSW_9_4_"): PreselectionWithExplicitEleIdEndcap.cuts.append(electron_abs_1overE_1overP_EE_cut) PreselectionWithExplicitEleIdEndcap.cuts.append(electron_missingInnerHits_cut) PreselectionWithExplicitEleIdEndcap.cuts.append(electron_passConversionVeto_cut) ### at least one good muon PreselectionWithExplicitEleIdEndcap.cuts.append(muon_eta_cut) if os.environ["CMSSW_VERSION"].startswith ("CMSSW_8_0_"): PreselectionWithExplicitEleIdEndcap.cuts.append(muon_pt_40_cut) elif os.environ["CMSSW_VERSION"].startswith ("CMSSW_9_4_"): PreselectionWithExplicitEleIdEndcap.cuts.append(muon_pt_50_cut) PreselectionWithExplicitEleIdEndcap.cuts.append(muon_global_cut) PreselectionWithExplicitEleIdEndcap.cuts.append(muon_id_cut) PreselectionWithExplicitEleIdEndcap.cuts.append(muon_iso_cut)

[ "juliette.alimena@cern.ch" ]

juliette.alimena@cern.ch

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[]

no_license

pythonmentor/benjamin-p11

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2022-10-02T15:46:46.619965

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from django.test import TestCase from django.urls import resolve from django.shortcuts import reverse from pur_beurre.views import home, legal_notice class UrlTestCase(TestCase): def test_home_url_view(self): found = resolve(reverse("home")) self.assertEqual(found.func, home) def test_legal_notice_url_view(self): found = resolve(reverse("legal_notice")) self.assertEqual(found.func, legal_notice)

[ "thierry@chappuis.io" ]

thierry@chappuis.io

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/cycle/bin/python-config

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[]

no_license

archi-007/bicycle-rental

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refs/heads/master

2021-09-25T15:35:11.401952

2021-04-11T21:30:32

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#!/home/archisman/Desktop/banchod/cycle/bin/python import sys import getopt import sysconfig valid_opts = ['prefix', 'exec-prefix', 'includes', 'libs', 'cflags', 'ldflags', 'help'] if sys.version_info >= (3, 2): valid_opts.insert(-1, 'extension-suffix') valid_opts.append('abiflags') if sys.version_info >= (3, 3): valid_opts.append('configdir') def exit_with_usage(code=1): sys.stderr.write("Usage: {0} [{1}]\n".format( sys.argv[0], '|'.join('--'+opt for opt in valid_opts))) sys.exit(code) try: opts, args = getopt.getopt(sys.argv[1:], '', valid_opts) except getopt.error: exit_with_usage() if not opts: exit_with_usage() pyver = sysconfig.get_config_var('VERSION') getvar = sysconfig.get_config_var opt_flags = [flag for (flag, val) in opts] if '--help' in opt_flags: exit_with_usage(code=0) for opt in opt_flags: if opt == '--prefix': print(sysconfig.get_config_var('prefix')) elif opt == '--exec-prefix': print(sysconfig.get_config_var('exec_prefix')) elif opt in ('--includes', '--cflags'): flags = ['-I' + sysconfig.get_path('include'), '-I' + sysconfig.get_path('platinclude')] if opt == '--cflags': flags.extend(getvar('CFLAGS').split()) print(' '.join(flags)) elif opt in ('--libs', '--ldflags'): abiflags = getattr(sys, 'abiflags', '') libs = ['-lpython' + pyver + abiflags] libs += getvar('LIBS').split() libs += getvar('SYSLIBS').split() # add the prefix/lib/pythonX.Y/config dir, but only if there is no # shared library in prefix/lib/. if opt == '--ldflags': if not getvar('Py_ENABLE_SHARED'): libs.insert(0, '-L' + getvar('LIBPL')) if not getvar('PYTHONFRAMEWORK'): libs.extend(getvar('LINKFORSHARED').split()) print(' '.join(libs)) elif opt == '--extension-suffix': ext_suffix = sysconfig.get_config_var('EXT_SUFFIX') if ext_suffix is None: ext_suffix = sysconfig.get_config_var('SO') print(ext_suffix) elif opt == '--abiflags': if not getattr(sys, 'abiflags', None): exit_with_usage() print(sys.abiflags) elif opt == '--configdir': print(sysconfig.get_config_var('LIBPL'))

[ "archiribhu@gmail.com" ]

archiribhu@gmail.com

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/firstpython.py

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no_license

97-shuang-chen/testrepo

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2021-02-06T19:22:19

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null

2021-02-06T19:22:20

2021-02-06T18:37:46

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# Display the output print('hello, world!')

[ "noreply@github.com" ]

97-shuang-chen.noreply@github.com

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[ "Apache-2.0" ]

permissive

AutoViML/Auto_TS

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refs/heads/master

2023-06-07T01:56:55.541110

2023-05-17T01:16:05

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Apache-2.0

2023-09-01T17:23:44

2020-02-15T15:23:32

Jupyter Notebook

UTF-8

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false

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py

class colorful: PURPLE = '\033[95m' CYAN = '\033[96m' DARKCYAN = '\033[36m' BLUE = '\033[94m' GREEN = '\033[92m' YELLOW = '\033[93m' RED = '\033[91m' BOLD = '\033[1m' UNDERLINE = '\033[4m' END = '\033[0m'

[ "rsesha2001@yahoo.com" ]

rsesha2001@yahoo.com

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/Segmentation/flood.py

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permissive

Joevaen/Scikit-image_On_CT

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2023-03-16T01:28:04.871513

2021-03-16T07:53:57

344,071,656

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py

# Mask corresponding to a flood fill.

[ "joevaen@126.com" ]

joevaen@126.com

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/stock_price/admin.py

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no_license

RyomaKawata1025/Data_processing_app

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2023-03-02T14:30:29.466553

2021-02-15T10:46:38

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from django.contrib import admin from .models import NIKKEI,SP500,USDJPY,BITCOIN,COMMON admin.site.register(NIKKEI) admin.site.register(SP500) admin.site.register(USDJPY) admin.site.register(BITCOIN) admin.site.register(COMMON)

[ "kawataryouma@kawadaryoumanoMacBook-Air.local" ]

kawataryouma@kawadaryoumanoMacBook-Air.local

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/devs/migrations/0021_remove_device_client.py

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[]

no_license

dpg3000/iec60870_5_configurator

7bfd2ae1947d2c4990a429143314e99ab6d4b614

789ed53c735d028050fcc145ac1488ad9aac6858

refs/heads/master

2022-09-14T04:58:58.835395

2020-03-02T12:58:46

null

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# Generated by Django 2.1.5 on 2019-12-05 14:05 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('devs', '0020_auto_20191205_1505'), ] operations = [ migrations.RemoveField( model_name='device', name='Client', ), ]

[ "dpou18@gmail.com" ]

dpou18@gmail.com

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/torchtimeseries/models/__init__.py

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no_license

timeseriesAI/timeseriesAI1

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refs/heads/master

2021-06-11T01:58:48.739894

2020-11-12T10:17:59

254,351,793

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from .FCN import * from .ResNet import * from .ResCNN import * from .InceptionTime import * from .layers import * from .ROCKET import *

[ "oguiza@gmail.com" ]

oguiza@gmail.com

597dc9651bdd04a071d74c5c2b3f9e4e5ffba698

394ed7b7f7d65a309a9a041a472500f160215f55

/marvel_env/bin/alembic

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[]

no_license

allyroo/marvel_project

4f5dde0a5e54d3c0525e5ebf43bddaa5d3789829

3533f937d8560fcc7e8ebecdc616f39f15375157

refs/heads/master

2023-06-09T00:15:22.507846

2021-06-28T05:19:00

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#!/Users/Allyse/Documents/coding_temple_rangers_63/week_5/marvel_inventory/marvel_env/bin/python # -*- coding: utf-8 -*- import re import sys from alembic.config import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "allyse.arucan@gmail.com" ]

allyse.arucan@gmail.com

762aed1530a3b36da97232a519cf42dab6b4cfa0

75ef339fd43451036ea7e3540fe097d3c7df212c

/catchphrases_uni.py

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no_license

snake48/PIPHRASE

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refs/heads/master

2020-05-07T06:03:53.899995

2015-09-06T18:01:59

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import time,random import unicornhat as uh cp_boat = [ [[[255, 255, 0], [255, 255, 0], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[255, 255, 0], [255, 255, 0], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 255, 0], [0, 255, 0]], [[0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 255, 0], [0, 255, 0]]], [[[255, 255, 0], [255, 255, 0], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[255, 255, 0], [255, 255, 0], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 255, 0], [0, 255, 0]], [[0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 255, 0], [0, 255, 0]]], [[[255, 255, 0], [255, 255, 0], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[255, 255, 0], [255, 255, 0], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[255, 0, 0], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 255, 0], [0, 255, 0]], [[0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 255, 0], [0, 255, 0]]], [[[255, 255, 0], [255, 255, 0], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], 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255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [255, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [255, 255, 255], [255, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [255, 0, 0], [255, 0, 0], [255, 0, 0], [255, 0, 0], [0, 255, 255], [0, 255, 255]], [[0, 0, 255], [0, 0, 255], [0, 0, 255], [255, 0, 0], [255, 0, 0], [0, 0, 255], [0, 255, 0], [0, 255, 0]], [[0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 255, 0], [0, 255, 0]]], [[[255, 255, 0], [255, 255, 0], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[255, 255, 0], [255, 255, 0], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255], [255, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [255, 255, 255], [255, 255, 255], [0, 255, 255], [0, 255, 255], [0, 255, 255]], [[0, 255, 255], [0, 255, 255], [0, 255, 255], [255, 0, 0], [255, 0, 0], [255, 0, 0], [255, 0, 0], [0, 255, 255]], [[0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [255, 0, 0], [255, 0, 0], [0, 255, 0], [0, 255, 0]], [[0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 255, 0], [0, 255, 0]]], ] cp_weather = [ [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 255, 0], [255, 255, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 255, 0], [255, 255, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 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0, 204], [102, 0, 204], [102, 0, 204], [0, 0, 0], [255, 255, 0], [255, 255, 0]], [[0, 0, 0], [102, 0, 204], [102, 0, 204], [102, 0, 204], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 128, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 255, 255], [0, 255, 0], [0, 255, 255], [0, 255, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 255, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 255, 255], [255, 128, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [102, 0, 204], [102, 0, 204], [102, 0, 204], [0, 0, 0], [0, 0, 0], [255, 255, 0], [255, 255, 0]], [[102, 0, 204], [102, 0, 204], [102, 0, 204], [102, 0, 204], [102, 0, 204], [0, 0, 0], [255, 255, 0], [255, 255, 0]], [[0, 0, 0], [102, 0, 204], [102, 0, 204], [102, 0, 204], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 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0, 0], [0, 255, 0], [0, 255, 0], [0, 255, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 255, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 255, 255], [0, 0, 0], [0, 255, 255], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [102, 0, 204], [102, 0, 204], [102, 0, 204], [0, 0, 0], [0, 0, 0], [255, 255, 0], [255, 255, 0]], [[102, 0, 204], [102, 0, 204], [102, 0, 204], [102, 0, 204], [102, 0, 204], [0, 0, 0], [255, 255, 0], [255, 255, 0]], [[0, 0, 0], [102, 0, 204], [102, 0, 204], [102, 0, 204], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 128, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 255, 0], [0, 255, 0], [0, 255, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 255, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 128, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], ] cp_cake = [ [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[255, 255, 255], [255, 255, 255], [255, 255, 255], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[255, 128, 0], [255, 128, 0], [255, 128, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[255, 128, 0], [255, 128, 0], [255, 128, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 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0], [0, 0, 255], [0, 0, 255], [0, 0, 255], [0, 0, 0]], [[255, 128, 0], [255, 128, 0], [0, 0, 0], [0, 0, 0], [255, 255, 0], [0, 0, 0], [255, 255, 0], [0, 0, 0]], [[255, 128, 0], [255, 128, 0], [255, 128, 0], [0, 0, 0], [255, 255, 0], [0, 0, 0], [255, 255, 0], [0, 0, 0]]], [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], ] cp_twowrongs = [ [[[255, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 0, 0]], [[0, 0, 0], [255, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [255, 0, 0], [0, 0, 0], [0, 0, 0], [255, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 0, 0], [255, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 0, 0], [255, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [255, 0, 0], [0, 0, 0], [0, 0, 0], [255, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [255, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 0, 0], [0, 0, 0]], [[255, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 0, 0]]], [[[255, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 0, 0]], [[0, 0, 0], [255, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [255, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [255, 0, 0], [0, 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0]]], [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 255, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 255, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 255, 0], [0, 0, 0], [0, 0, 0]], [[0, 255, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 255, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 255, 0], [0, 0, 0], [0, 255, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 255, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]], ] CATCHPHRASES= [cp_boat,cp_cake,cp_twowrongs, cp_weather] '''ANSWERS = dict ((name, eval(name)) for name in ['cp_weather','cp_boat','cp_twowrongs','cp_cake'])''' ANSWERS = {"Two wrongs don't make a right": cp_twowrongs, "When the boat comes in":cp_boat,"have your cake and eat it":cp_cake,"under the weather":cp_weather} '''uh.rotation(90) def show_catchphrase(): picked = random.choice(ANSWERS.keys()) for i in range(1): for x in ANSWERS[picked]: uh.set_pixels(x) uh.show() time.sleep(0.25) time.sleep(1) return picked show_catchphrase()'''

[ "jasper@Dads-Mac-mini.local" ]

jasper@Dads-Mac-mini.local

520dfeb3d7c17d3b5bee718128491655c2cac4a8

79b13102627bcd0bcba2fd784fb0b1aeff74993e

/gapps/gspin/forms.py

4cb6e7514983201d32b3eca1b29e4dee9a7009af

[]

no_license

gibeongideon/gspin_backend

c513869ae4cd1c51242fa38fd3024935fe3d4769

2333b5c34c24751796c0775ce2210ca6219e6554

refs/heads/master

2021-06-20T01:58:30.252945

2019-06-29T20:55:32

194,445,074

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null

2021-03-19T22:29:54

2019-06-29T20:16:13

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from django import forms from .models import Account, Balance, Bet, AccountTopUp, BetResults, GameResult, BetTime, CustomUser class AccountForm(forms.ModelForm): class Meta: model = Account fields = ['account_number', 'is_active', 'custom_user'] class BalanceForm(forms.ModelForm): class Meta: model = Balance fields = ['balance', 'account'] class BetForm(forms.ModelForm): class Meta: model = Bet fields = ['bet_amount', 'is_active', 'bet_choice', 'account', 'bet_time'] class AccountTopUpForm(forms.ModelForm): class Meta: model = AccountTopUp fields = ['topup_amount', 'account_to_topup'] class BetResultsForm(forms.ModelForm): class Meta: model = BetResults fields = ['name', 'bet_result'] class GameResultForm(forms.ModelForm): class Meta: model = GameResult fields = ['game_results', 'bet_time_result'] class BetTimeForm(forms.ModelForm): class Meta: model = BetTime fields = '__all__' class CustomUserForm(forms.ModelForm): class Meta: model = CustomUser fields = ['first_name', 'last_name', 'custom_user']

[ "kipngeno.gibeon@gmail.com" ]

kipngeno.gibeon@gmail.com

77e238f1a0830212c73060732d78d2362591aba1

152683f2e06b417b561400e3a1229426268dbdec

/nn_semisupervised_resnet_18.py

3ed619eef504d6ec1f5adff9e6cfdb7fb48c9e54

[]

no_license

bigsnarfdude/icedChainsaw

593159a5d013a167549569b4d92d0ad2a8da81a3

2158b8eaf0ccc870e444dbb74752dfd5b402e20f

refs/heads/master

2021-08-23T14:24:40.676524

2017-12-05T07:11:14

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import os import sys from itertools import chain import numpy as np import pandas as pd import torchvision.models import torch.nn.functional as F import torch.optim as optim from torch import nn from torch.utils.data import DataLoader from torchsample.callbacks import CSVLogger, LearningRateScheduler from callbacks import ModelCheckpoint, SemiSupervisedUpdater import sklearn.model_selection import paths import labels import transforms from datasets import KaggleAmazonUnsupervisedDataset, KaggleAmazonSemiSupervisedDataset, KaggleAmazonJPGDataset, mlb from ModuleTrainer import ModuleTrainer name = os.path.basename(sys.argv[0])[:-3] def generate_model(): class MyModel(nn.Module): def __init__(self, pretrained_model): super(MyModel, self).__init__() self.pretrained_model = pretrained_model self.layer1 = pretrained_model.layer1 self.layer2 = pretrained_model.layer2 self.layer3 = pretrained_model.layer3 self.layer4 = pretrained_model.layer4 pretrained_model.avgpool = nn.AvgPool2d(8) classifier = [ nn.Linear(pretrained_model.fc.in_features, 17), ] self.classifier = nn.Sequential(*classifier) pretrained_model.fc = self.classifier def forward(self, x): return self.pretrained_model(x) return MyModel(torchvision.models.resnet18(pretrained=True)) random_state = 1 labels_df = labels.get_labels_df() unsupervised_dataframe = pd.read_csv(paths.submissions + 'SOTA') kf = sklearn.model_selection.KFold(n_splits=5, shuffle=True, random_state=random_state) split = supervised_split = kf.split(labels_df) unsupervised_split = kf.split(unsupervised_dataframe) def train_net(train, val, unsupervised, model, name): unsupervised_initialization = mlb.transform(unsupervised['tags'].str.split()).astype(np.float32) unsupervised_samples = unsupervised['image_name'].as_matrix() unsupervised_initialization = unsupervised_initialization[:len(unsupervised_initialization)//2*3] unsupervised_samples = unsupervised_samples[:len(unsupervised_samples)//2*3] transformations_train = transforms.apply_chain([ transforms.to_float, transforms.augment_color(0.1), transforms.random_fliplr(), transforms.random_flipud(), transforms.augment(), torchvision.transforms.ToTensor() ]) transformations_val = transforms.apply_chain([ transforms.to_float, torchvision.transforms.ToTensor() ]) dset_train_unsupervised = KaggleAmazonUnsupervisedDataset( unsupervised_samples, paths.test_jpg, '.jpg', transformations_train, transformations_val, unsupervised_initialization ) dset_train_supervised = KaggleAmazonJPGDataset(train, paths.train_jpg, transformations_train, divide=False) dset_train = KaggleAmazonSemiSupervisedDataset(dset_train_supervised, dset_train_unsupervised, None, indices=False) train_loader = DataLoader(dset_train, batch_size=128, shuffle=True, num_workers=10, pin_memory=True) dset_val = KaggleAmazonJPGDataset(val, paths.train_jpg, transformations_val, divide=False) val_loader = DataLoader(dset_val, batch_size=128, num_workers=10, pin_memory=True) ignored_params = list(map(id, chain( model.classifier.parameters(), model.layer1.parameters(), model.layer2.parameters(), model.layer3.parameters(), model.layer4.parameters() ))) base_params = filter(lambda p: id(p) not in ignored_params, model.parameters()) optimizer = optim.Adam([ {'params': base_params}, {'params': model.layer1.parameters()}, {'params': model.layer2.parameters()}, {'params': model.layer3.parameters()}, {'params': model.layer4.parameters()}, {'params': model.classifier.parameters()} ], lr=0, weight_decay=0.0005) trainer = ModuleTrainer(model) def schedule(current_epoch, current_lrs, **logs): lrs = [1e-3, 1e-4, 0.5e-4, 1e-5, 0.5e-5] epochs = [0, 1, 8, 12, 20] for lr, epoch in zip(lrs, epochs): if current_epoch >= epoch: current_lrs[5] = lr if current_epoch >= 2: current_lrs[4] = lr * 1.0 current_lrs[3] = lr * 1.0 current_lrs[2] = lr * 1.0 current_lrs[1] = lr * 0.1 current_lrs[0] = lr * 0.05 return current_lrs trainer.set_callbacks([ ModelCheckpoint( paths.models, name, save_best_only=False, saving_strategy=lambda epoch: True ), CSVLogger(paths.logs + name), LearningRateScheduler(schedule), SemiSupervisedUpdater(trainer, dset_train_unsupervised, start_epoch=10, momentum=0.25) ]) trainer.compile(loss=nn.BCELoss(), optimizer=optimizer) trainer.fit_loader(train_loader, val_loader, nb_epoch=35, verbose=1, cuda_device=0) if __name__ == "__main__": for i, ((train_idx, val_idx), (train_idx_unsupervised, val_idx_unsupervised)) in enumerate(zip(supervised_split, unsupervised_split)): name = os.path.basename(sys.argv[0])[:-3] + '-split_' + str(i) train_net( labels_df.ix[train_idx], labels_df.ix[val_idx], unsupervised_dataframe.ix[val_idx_unsupervised], generate_model(), name )

[ "ohprecio@gmail.com" ]

ohprecio@gmail.com

0c10142f92ee52bdd943118a422ce3fba97826ec

bd00ffb02dd4470447a6ba89c01c5bf57d3eb28f

/섹션 4/8. 침몰하는 타이타닉/AA.py

92be2cc4919c71837eb15e99390a00525f00f4db

[]

no_license

yeong95/python_algorithm_inflearn

24930795882e34c31eb4d5e979533c3a578a3d09

d500f670253c56a8e62e779dde2e579a57d62266

refs/heads/master

2023-02-28T06:52:22.886154

2021-01-22T15:38:14

326,430,497

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null

UTF-8

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py

import sys import os os.chdir(r'C:\Users\CHOYEONGKYU\Desktop\파이썬 알고리즘 문제풀이(코딩테스트 대비)\섹션 4\8. 침몰하는 타이타닉') sys.stdin = open('input.txt', 'rt') n,m = map(int, input().split()) a = list(map(int, input().split())) a.sort(reverse=True) cnt=0 while a!=[]: pop = a.pop(0) tmp=len(a) for i in range(len(a)): if pop+a[i] <= m: cnt+=1 del a[i] break if len(a) == tmp: cnt+=1 print(cnt)

[ "cykpig95@gmail.com" ]

cykpig95@gmail.com

2cb43e485765050a7cd4e3a5fecc44b3c7d6801f

f2f8a2ba384dbe68861151cc15ab14c72774bb16

/evaluate.py

e70f24cc371808dc34147c117f9661afa0329a74

[ "Apache-2.0", "LicenseRef-scancode-public-domain" ]

permissive

zphang/bert

06233c65298a97475bd4c0e4a0d649dcc5da8f48

12b4afc019d6da5e0c153608c3fd553c5e7466b1

refs/heads/master

2020-04-05T01:17:18.498275

2018-11-14T20:47:26

156,430,554

0

null

2018-11-06T18:40:47

2018-11-06T18:40:46

null

UTF-8

Python

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4,093

py

import argparse import json import pandas as pd from sklearn.metrics import matthews_corrcoef, f1_score from scipy.stats import pearsonr, spearmanr import run_classifier PROCESSORS = { "cola": run_classifier.ColaProcessor, "sst": run_classifier.SstProcessor, "mrpc": run_classifier.MrpcProcessor, "stsb": run_classifier.StsbProcessor, "qqp": run_classifier.QqpProcessor, "mnli": run_classifier.MnliProcessor, "qnli": run_classifier.QnliProcessor, "rte": run_classifier.RteProcessor, "xnli": run_classifier.XnliProcessor, "snli": run_classifier.SnliProcessor, "bcs": run_classifier.BcsProcessor, } OUTPUT_MODES = { "cola": "classification", "sst": "classification", "mrpc": "classification", "stsb": "regression", "qqp": "classification", "mnli": "classification", "qnli": "classification", "rte": "classification", "xnli": "classification", "snli": "classification", "bcs": "classification", } def simple_accuracy(pred_srs, label_srs): return (pred_srs == label_srs).mean() def acc_and_f1(pred_srs, label_srs): acc = simple_accuracy(pred_srs, label_srs) f1 = f1_score(y_true=label_srs, y_pred=pred_srs) return { "acc": acc, "f1": f1, "acc_and_f1": (acc + f1) / 2, } def pearson_and_spearman(pred_srs, label_srs): pearson_corr = pearsonr(pred_srs, label_srs)[0] spearman_corr = spearmanr(pred_srs, label_srs)[0] return { "pearson": pearson_corr, "spearmanr": spearman_corr, "corr": (pearson_corr + spearman_corr) / 2, } def compute_metrics(task_name, pred_srs, label_srs): assert len(pred_srs) == len(label_srs) if task_name == "cola": return {"mcc": matthews_corrcoef(label_srs, pred_srs)} elif task_name == "sst": return {"acc": simple_accuracy(pred_srs, label_srs)} elif task_name == "mrpc": return acc_and_f1(pred_srs, label_srs) elif task_name == "stsb": return pearson_and_spearman(pred_srs, label_srs) elif task_name == "qqp": return acc_and_f1(pred_srs, label_srs) elif task_name == "mnli": return {"acc": simple_accuracy(pred_srs, label_srs)} elif task_name == "qnli": return {"acc": simple_accuracy(pred_srs, label_srs)} elif task_name == "rte": return {"acc": simple_accuracy(pred_srs, label_srs)} else: raise KeyError(task_name) def load_labels(task_name, data_dir): processor = PROCESSORS[task_name]() examples = processor.get_dev_examples(data_dir) label2idx = {label: num for (num, label) in enumerate(processor.get_labels())} label_srs = pd.Series([label2idx[example.label] for example in examples]) return label_srs def load_preds(task_name, pred_file_path): pred_df = pd.read_csv(pred_file_path, header=None, sep="\t") output_mode = OUTPUT_MODES[task_name] if output_mode == "classification": pred_srs = pred_df.idxmax(axis=1) elif output_mode == "regression": pred_srs = pred_df[:, 0] else: raise KeyError(output_mode) return pred_srs def compute_metrics_from_paths(task_name, pred_file_path, task_data_dir): pred_srs = load_preds(task_name, pred_file_path) label_srs = load_labels(task_name, task_data_dir) return compute_metrics(task_name, pred_srs, label_srs) def main(): parser = argparse.ArgumentParser(description='evaluation') parser.add_argument('--task-name', required=True) parser.add_argument('--pred-file-path', required=True) parser.add_argument('--task-data-dir', required=True) parser.add_argument('--no-print', action="store_true") parser.add_argument('--output-path', required=False, default=None) args = parser.parse_args() metrics = compute_metrics_from_paths(args.task_name, args.pred_file_path, args.task_data_dir) if not args.no_print: print(metrics) if args.output_path is not None: with open(args.output_path, "w") as f: f.write(json.dumps(metrics, indent=2)) if __name__ == "__main__": main()

[ "email@jasonphang.com" ]

email@jasonphang.com

a831882f7ba810e8c2bcbb7e4e321ee9f6afae0a

b68f83bfa9df1a9d1f5a5beda60e56742c2b9308

/dev/backend/database.py

f5908982106106bdb356fda138f552f82cdbdf05

[]

no_license

amolsurve20/gradscout

1a6be2225735473e3e8709b40430337f51870903

d424c3ea675f994ff3a260ff81e84c92a3a28b9c

refs/heads/master

2020-11-29T14:51:41.242670

2017-04-06T01:33:20

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null

UTF-8

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py

#!/usr/bin/env python """Firebase Database Interaction Module.""" from firebase import firebase class Firebase(object): """Class for Accessing Firebase Database.""" def __init__(self): """Initialize Firebase Object for Database Access.""" self.firebase = firebase.FirebaseApplication( 'https://gradscout-40fed.firebaseio.com/', None) def get_detailed_program(self, program_id): """Get the Detailed Program Details for a given Program ID. Args: program_id (int): Unique ID for Program Returns: program_dict (dict): Python Dict of Complete Program Details """ endpoint = "/programs/" + str(program_id) result = self.firebase.get(endpoint, None) return result def get_program_count(self): """Get the total count of programs available in the database. Returns: count (int): Total count of the programs in the database """ endpoint = "/programs/" result = self.firebase.get(endpoint, None) count = len(result) return count def get_program_research(self, program_id): """Get the Program Research Details for a given Program ID. Args: program_id (int): Unique ID for Program Returns: research_dict (dict): Python Dict of Research Details """ endpoint = "/programs/" + str(program_id) + '/research' result = self.firebase.get(endpoint, None) return result def get_program_admission_rate(self, program_id): """Get the Admission Rate for a given Program ID. Args: program_id (int): Unique ID for Program Returns: admission_rate (int): Admission Rate in Percentage """ endpoint = "/programs/" + str(program_id) + '/admission_rate' result = self.firebase.get(endpoint, None) return result def get_program_fees(self, program_id): """Get the Program Fees for a given Program ID. Args: program_id (int): Unique ID for Program Returns: fees_dict (dict): Python Dict of Program Fees """ endpoint = "/programs/" + str(program_id) + '/fees' result = self.firebase.get(endpoint, None) return result def get_program_acad(self, program_id): """Get the Academic Requirements for a given Program ID. Args: program_id (int): Unique ID for Program Returns: acad_dict (dict): Python Dict of Academic Requirement Details """ endpoint = "/programs/" + str(program_id) + '/academic_requirements' result = self.firebase.get(endpoint, None) return result def get_program_living(self, program_id): """Get the Living Expenses for a given Program ID. Args: program_id (int): Unique ID for Program Returns: program_dict (dict): Python Dict of Living Expense Details """ endpoint = "/programs/" + str(program_id) + '/living_expenditure' result = self.firebase.get(endpoint, None) return result def get_program_location(self, program_id): """Get the Location Details of a given Program ID. Returns: location_dict (dict): Python Dict of Location Details """ endpoint = "/programs/" + str(program_id) + '/location' result = self.firebase.get(endpoint, None) count = len(result) return count def get_program_ownership(self, program_id): """Get the Ownership Details for a given Program ID. Args: program_id (int): Unique ID for Program Returns: ownership_dict (dict): Python Dict of Ownership Details """ endpoint = "/programs/" + str(program_id) + '/ownership' result = self.firebase.get(endpoint, None) return result def get_program_details(self, program_id): """Get the Program Details for a given Program ID. Args: program_id (int): Unique ID for Program Returns: program_dict (dict): Python Dict of Program Details """ endpoint = "/programs/" + str(program_id) + '/program' result = self.firebase.get(endpoint, None) return result def get_program_university(self, program_id): """Get the University Details for a given Program ID. Args: program_id (int): Unique ID for Program Returns: program_dict (dict): Python Dict of University Details """ endpoint = "/programs/" + str(program_id) + '/university' result = self.firebase.get(endpoint, None) return result def get_program_rank(self, program_id): """Get the Rank Details for a given Program ID. Args: program_id (int): Unique ID for Program Returns: program_dict (dict): Python Dict of Rank Details """ endpoint = "/programs/" + str(program_id) + '/rank' result = self.firebase.get(endpoint, None) return result

[ "b.s.sanathkumar@gmail.com" ]

b.s.sanathkumar@gmail.com

1921b628faebff60e71ddc2dd02c19109b656a35

63cc32b3eacb6695449e3568472d33a1605b8e2d

/liga_asobal/migrations/0002_auto_20170129_1609.py

3bb0470e6d0b04adc4ddc9d6fa79753faffe7dad

[]

no_license

i32gamad/liga_asobal

5646ba782d25265084aa40d7b468a4714afae797

b51c8a8902d78d8b69ac521e912dd8fe0aed97a8

refs/heads/master

2021-01-23T07:51:12.676304

2017-01-31T12:15:00

80,517,722

0

null

UTF-8

Python

false

453

py

# -*- coding: utf-8 -*- # Generated by Django 1.10 on 2017-01-29 16:09 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('liga_asobal', '0001_initial'), ] operations = [ migrations.AlterField( model_name='equipo', name='escudo', field=models.ImageField(upload_to='static/img/'), ), ]

[ "i32gamad@uco.es" ]

i32gamad@uco.es

a9b548c471b2e055a350b837dc89d441271f681e

3597ecf8a014dbd6f7d998ab59919a94aff8011d

/api-web/src/www/application/modules/page/components.py

634a4e4b5fd6f05c5af1356a2218dfbb8e41d334

[]

no_license

duytran92-cse/nas-genomebrowser

f42b8ccbb7c5245bde4e52a0feed393f4b5f6bf1

d0240ad5edc9cfa8e7f89db52090d7d733d2bb8a

refs/heads/master

2022-10-24T05:26:01.760241

2020-06-14T19:01:35

272,264,694

0

null

UTF-8

Python

false

1,554

py

from application.models import * class PageHelper(object): def load_associated_publications(self, id): page = Page.objects.get(pk=id) # Get associtaed pubs publications = [] rows = PageAssociatedPublication.objects.filter(page_id=page.id).all() if rows: for r in rows: publications.append({ 'id': r.id, 'pmid': r.pmid, 'doi': r.doi, 'pmc': r.pmc, 'title': r.title, 'authors': r.authors, 'journal': r.journal }) return publications def load_associated_genes(self, id): page = Page.objects.get(pk=id) # Get associated genes genes = [] rows = PageAssociatedGene.objects.filter(page_id=page.id).all() if rows: for r in rows: genes.append({ 'id': r.id, 'gene_name': r.gene_name, }) return genes def load_associated_diseases(self, id): page = Page.objects.get(pk=id) # Get associated diseases diseases = [] rows = PageAssociatedDisease.objects.filter(page_id=page.id).all() if rows: for r in rows: diseases.append({ 'id': r.id, 'disease_name': r.disease_name, }) return diseases

[ "thanh.tran@etudiant.univ-lr.fr" ]

thanh.tran@etudiant.univ-lr.fr

12be76ee075152e9b002246390f3443fa75f7f06

504d4814971735c3bc84d577521404c4616bca88

/multithreading/queue_1.py

226192454b0c20bc89fe9cdfcd5e47adb5af1ef7

[]

no_license

caaden/python

203a6fec07b3993345376c2ab60246765e3d7f32

cf89a82331d14abbb376b5d199d546042e1f4f4b

refs/heads/master

2023-03-17T15:08:28.071076

2021-02-28T23:39:35

267,114,193

1

0

null

UTF-8

Python

false

1,368

py

#!/usr/bin/python3 import queue import threading import time exitFlag = 0 class myThread (threading.Thread): def __init__(self, threadID, name, q): threading.Thread.__init__(self) self.threadID = threadID self.name = name self.q = q def run(self): print ("Starting " + self.name) process_data(self.name, self.q) print ("Exiting " + self.name) def process_data(threadName, q): while not exitFlag: queueLock.acquire() if not workQueue.empty(): data = q.get() queueLock.release() print ("%s processing %s" % (threadName, data)) else: queueLock.release() time.sleep(1) threadList = ["Thread-1", "Thread-2", "Thread-3"] nameList = ["One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight","Nine"] queueLock = threading.Lock() workQueue = queue.Queue(10) threads = [] threadID = 1 # Create new threads for tName in threadList: thread = myThread(threadID, tName, workQueue) thread.start() threads.append(thread) threadID += 1 # Fill the queue queueLock.acquire() for word in nameList: workQueue.put(word) queueLock.release() # Wait for queue to empty while not workQueue.empty(): pass # Notify threads it's time to exit exitFlag = 1 # Wait for all threads to complete for t in threads: t.join() print ("Exiting Main Thread")

[ "caden@fringeai.com" ]

caden@fringeai.com

194b6313d4b7c20adfb20c53de2ca19ab668783f

91eba4efb54b28722ccbf7f8af8ee37ac4d57fd5

/transformer.py

d0f92ff2a279eb59d84c87efb230aba2f6c1cd42

[]

no_license

h2012c106/create_table_transformer

c4537fe80c9a084665a788ba1b98cd8bd1a1c78c

7e0e92dee362ba3616f64c8736aaff5f11323b91

refs/heads/master

2020-12-02T15:18:47.484966

2019-12-31T08:10:07

231,046,883

0

null

UTF-8

Python

false

6,617

py

# -*- coding: UTF-8 -*- import os import jpype import generator import collections def _get_jars_path(lib_path): res = [] for file_name in os.listdir(lib_path): if os.path.splitext(file_name)[1] == '.jar': res.append(lib_path + file_name) return [os.path.abspath(path) for path in res] class JavaEnv: def __init__(self): self.java_lib_path = './lib/' def _pre_transform(self): jvm_path = jpype.getDefaultJVMPath() jars_path = _get_jars_path(self.java_lib_path) jvm_arg = "-Djava.class.path={jars_path}".format(jars_path=":".join(jars_path)) if jpype.isJVMStarted(): jpype.shutdownJVM() jpype.startJVM(jvm_path, jvm_arg, convertStrings=True) def _set_init_class(self): assert False, 'No inherit!' def _transform(self, create_table): assert False, 'No inherit!' def _validate(self, create): assert False, 'No inherit!' def _post_transform(self): jpype.shutdownJVM() def main(self, create_table_list): self._pre_transform() self._set_init_class() try: res = [] for create_table in create_table_list: create, sql = self._transform(create_table) res.append((sql, self._validate(create))) return res finally: self._post_transform() class Transformer(JavaEnv): def __init__(self, config_file): JavaEnv.__init__(self) self._config_file = config_file def _set_init_class(self): self._statement_parser_class = jpype.JClass('com.alibaba.druid.sql.dialect.mysql.parser.MySqlStatementParser') self._row_class = jpype.JClass('com.alibaba.druid.sql.ast.statement.SQLColumnDefinition') self._key_class = jpype.JClass('com.alibaba.druid.sql.dialect.mysql.ast.MySqlKey') self._row_generator = generator.RowGenerator(self._config_file) self._key_generator = generator.KeyGenerator(self._config_file) def _transform(self, create_table): parser = self._statement_parser_class(create_table) create = parser.parseCreateTable() create_detail_list = create.getTableElementList() # deal with key and row row_list = [] key_list = [] for create_detail in create_detail_list: if isinstance(create_detail, self._row_class) \ and create_detail.getNameAsString() not in self._row_generator.get_deprecated_rows(): row_list.append(create_detail) elif isinstance(create_detail, self._key_class) and self._key_generator.use_old_key(): key_list.append(create_detail) row_list += self._row_generator.get_additional_rows(create) key_list += self._key_generator.get_new_keys(create) # deal with auto increment self._row_generator.disable_autoincrement(create, row_list) # deal with pk key_list = self._key_generator.primary_to_unique(key_list) create_detail_list.clear() for row in row_list: create_detail_list.add(row) for key in key_list: create_detail_list.add(key) # deal with table drop # choosing either of two generator is okay if self._row_generator.need_overwrite_table(): sql = u'DROP TABLE IF EXISTS {table_name};\n{create_sql};'.format(table_name=create.getName().toString(), create_sql=create.toString()) else: # not my spell error create.setIfNotExiists(True) sql = u'{create_sql};'.format(create_sql=create.toString()) return create, sql def _find_duplicate_list(self, src_list): counter = dict(collections.Counter(src_list)) return [key for key, value in counter.items() if value > 1] def _validate_row_duplicate(self, create): create_detail_list = create.getTableElementList() row_list = [detail for detail in create_detail_list if isinstance(detail, self._row_class)] row_name_duplicate_list = self._find_duplicate_list([row.getNameAsString() for row in row_list]) if len(row_name_duplicate_list) == 0: return True, None else: return False, 'duplicate row name: {li}'.format(li=row_name_duplicate_list) def _validate_key_duplicate(self, create): create_detail_list = create.getTableElementList() key_list = [detail for detail in create_detail_list if isinstance(detail, self._key_class)] key_name_duplicate_list = self._find_duplicate_list( [key.getName().toString() for key in key_list if key.getName() is not None]) if len(key_name_duplicate_list) == 0: return True, None else: return False, 'duplicate key name: {li}'.format(li=key_name_duplicate_list) def _validate_row_in_key(self, create): create_detail_list = create.getTableElementList() row_name_set = set() for detail in create_detail_list: if isinstance(detail, self._row_class): row_name_set.add(detail.getNameAsString()) err_key_dict = {} for detail in create_detail_list: if isinstance(detail, self._key_class): key_name = detail.getName().toString() if detail.getName() is not None else 'PRIMARY KEY' relative_row_set = set([row.getExpr().toString() for row in detail.getColumns()]) additional_row_set = relative_row_set - row_name_set if len(additional_row_set) > 0: err_key_dict[key_name] = list(additional_row_set) if len(err_key_dict) == 0: return True, None else: return False, ', '.join( ['key {key} contains nonexistent row: {row_list}'.format(key=key, row_list=row_list) for key, row_list in err_key_dict.items()]) def _validate(self, create): validate_list = [self._validate_row_duplicate, self._validate_key_duplicate, self._validate_row_in_key] success = True msg = [] for validate in validate_list: tmp_success, tmp_msg = validate(create) success &= tmp_success if not tmp_success: msg.append(tmp_msg) return success, '{table} error: {msg}'.format(table=create.getName().toString(), msg=' and '.join(msg)), create.getName().toString()

[ "hanxianghuang@xiaohongshu.com" ]

hanxianghuang@xiaohongshu.com

06428f4907937b0f95ba7ebdd85921a3835fb9a1

d733f3d915dd109f233f49240932e86c5f58b343

/Day23/Day23-Part1.py

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[]

no_license

MichaelShoemaker/AdventOfCode-2020

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refs/heads/main

2023-02-03T19:48:45.512523

2020-12-23T20:36:37

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0

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py

import time import typing def make_cups(file): cups = open(file,'r').read() return [int(c) for c in cups if c != '\n'] def play(cups: list, turns: int) -> list: counter = 0 for x in range(0,turns): ## print(f"-- move {x+1} --") ## print(f"cups: {cups}") #current cup if counter == 0: current_cup = cups[0] elif (cups.index(current_cup) + 1) >= len(cups): current_cup = cups[(cups.index(last_cup)+1)%len(cups)] else: current_cup = cups[cups.index(last_cup)+1] ## print(f"current cup: {current_cup}") #pick up next three into a list if cups.index(current_cup) == len(cups)-1: pick_up = cups[0:3] elif cups.index(current_cup) == len(cups)-2: pick_up = [cups[-1]] + cups[0:2] elif cups.index(current_cup) == len(cups)-3: pick_up = cups[-2:] + [cups[0]] else: first = cups.index(current_cup)+1 last = first+3 pick_up = cups[first:last] ## print(f"pick up: {pick_up}") #Remove the picked up cups from the list for i in pick_up: cups.remove(i) next_cup = current_cup -1 if next_cup == 0: next_cup = max(cups) #Make sure it isn't a picked up cup while next_cup in pick_up: lowest = min(cups) highest = max(cups) next_cup -= 1 if next_cup < lowest: next_cup = highest break ## print(f"destination: {next_cup}") #Get the index of the next cup index = cups.index(next_cup) for i in range(1,4): if index +1 > len(cups): cups.insert(index+i % len(cups)-1, pick_up[i-1]) else: cups.insert(index+i,pick_up[i-1]) ## print(cups) #time.sleep(1) last_cup = current_cup counter += 1 return cups if __name__=='__main__': print(play(make_cups('input.txt'), 100))

[ "noreply@github.com" ]

MichaelShoemaker.noreply@github.com