repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
MKIDPipeline | MKIDPipeline-master/mkidpipeline/utils/interpolating.py | import numpy as np
from scipy.interpolate import griddata
def interpolate_image(input_array, method='linear'):
"""
Seth 11/13/14
2D interpolation to smooth over missing pixels using built-in scipy methods
:param input_array: N x M array
:param method:
:return: N x M interpolated image array
... | 1,110 | 40.148148 | 92 | py |
MKIDPipeline | MKIDPipeline-master/mkidpipeline/utils/smoothing.py | import numpy as np
import ast
import matplotlib.pyplot as plt
import scipy.constants as con
from scipy.interpolate import griddata
import scipy.integrate
import astropy
import warnings
from astropy.convolution import convolve
from astropy.convolution import Gaussian2DKernel
def astropy_convolve(image, x_stddev=1.0):
... | 17,267 | 42.386935 | 138 | py |
MKIDPipeline | MKIDPipeline-master/mkidpipeline/legacy/oracle.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 29 16:28:30 2018
@author: clint
GO TO THE ENCLOSING DIRECTORY AND RUN IT FROM THE TERMINAL WITH THE FOLLOWING COMMAND:
python oracle.py
optional arguments: path to file you want to look at. It works with .h5, .bin, and .img
python oracle.py /pat... | 60,108 | 41.996423 | 251 | py |
MKIDPipeline | MKIDPipeline-master/mkidpipeline/legacy/quickLook_img.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 29 16:28:30 2018
@author: clint
GO TO THE ENCLOSING DIRECTORY AND RUN IT FROM THE TERMINAL WITH THE FOLLOWING COMMAND:
python quickLook_img.py
set the system variable $MKID_IMG_DIR to the place where you want to look for img files
set the system v... | 21,209 | 40.104651 | 163 | py |
MKIDPipeline | MKIDPipeline-master/mkidpipeline/legacy/array_operations.py | import numpy as np
from numpy import linalg
try:
from skimage.transform import rotate as imrotate
except ImportError:
from scipy.misc import imrotate
from astropy import wcs
def fit_rigid_rotation(x, y, ra, dec, x0=0, y0=0):
"""
calculate the rigid rotation from row,col positions to ra,dec positions
... | 1,766 | 28.949153 | 86 | py |
MKIDPipeline | MKIDPipeline-master/mkidpipeline/legacy/oracle_helpers.py | """
Author: Alex Walter
Date: Feb 22, 2018
Last Updated: Sept 19, 2018
This code is for analyzing the photon arrival time statistics in a bin-free way
to find a maximum likelihood estimate of Ic, Is in the presence of an incoherent background source Ir.
For example usage, see if __name__ == "__main__":
"""
import num... | 20,999 | 34.413153 | 155 | py |
MKIDPipeline | MKIDPipeline-master/mkidpipeline/legacy/__init__.py | 0 | 0 | 0 | py | |
sherpa | sherpa-master/generate_readme.py | import os
welcome_text = """SHERPA: A Python Hyperparameter Optimization Library
====================================================
.. figure:: https://docs.google.com/drawings/d/e/2PACX-1vRaTP5d5WqT4KY4V57niI4wFDkz0098zHTRzZ9n7SzzFtdN5akBd75HchBnhYI-GPv_AYH1zYa0O2_0/pub?w=522&h=150
:figwidth: 100%
:align: ... | 1,298 | 36.114286 | 151 | py |
sherpa | sherpa-master/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Note: To use the 'upload' functionality of this file, you must:
# $ pip install twine
from __future__ import print_function
import io
import os
import sys
from shutil import rmtree
import argparse
from setuptools import find_packages, setup, Command
# parser = argpar... | 3,678 | 27.968504 | 86 | py |
sherpa | sherpa-master/sherpa/core.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 31,945 | 36.060325 | 112 | py |
sherpa | sherpa-master/sherpa/schedulers.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 14,938 | 34.82494 | 80 | py |
sherpa | sherpa-master/sherpa/database.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 10,144 | 36.161172 | 177 | py |
sherpa | sherpa-master/sherpa/__init__.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 998 | 32.3 | 80 | py |
sherpa | sherpa-master/sherpa/algorithms/core.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 32,218 | 39.324155 | 128 | py |
sherpa | sherpa-master/sherpa/algorithms/bayesian_optimization.py | import numpy
import logging
import sherpa
from sherpa.algorithms import Algorithm
import pandas
from sherpa.core import Choice, Continuous, Discrete, Ordinal
import collections
import GPyOpt as gpyopt_package
import GPy
import warnings
bayesoptlogger = logging.getLogger(__name__)
class GPyOpt(Algorithm):
"""
... | 14,984 | 40.167582 | 101 | py |
sherpa | sherpa-master/sherpa/algorithms/__init__.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 894 | 39.681818 | 80 | py |
sherpa | sherpa-master/sherpa/algorithms/successive_halving.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 4,768 | 37.152 | 152 | py |
sherpa | sherpa-master/sherpa/app/app.py | import logging
import pandas
from flask import Flask
from flask import render_template, flash, redirect
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
class SherpaApp(Flask):
def __init__(self, *args, **kwargs):
Flask.__init__(self, *args, **kwargs)
self.parameter_t... | 1,916 | 29.428571 | 90 | py |
sherpa | sherpa-master/sherpa/app/__init__.py | 0 | 0 | 0 | py | |
sherpa | sherpa-master/examples/simple.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 2,694 | 37.5 | 99 | py |
sherpa | sherpa-master/examples/randomforest.py | from sklearn.datasets import load_breast_cancer
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
import time
import sherpa
import sherpa.algorithms.bayesian_optimization as bayesian_optimization
parameters = [sherpa.Discrete('n_estimators', [2, 50]),
... | 1,441 | 42.69697 | 79 | py |
sherpa | sherpa-master/examples/parallel-examples/simple.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 3,314 | 35.833333 | 170 | py |
sherpa | sherpa-master/examples/parallel-examples/bianchini/runner.py | import os
import argparse
import sherpa
from sherpa.schedulers import LocalScheduler,SGEScheduler
def run_example(FLAGS):
"""
Run parallel Sherpa optimization over a set of discrete hp combinations.
"""
# Iterate algorithm accepts dictionary containing lists of possible values.
hp_space = {'act':... | 2,758 | 40.179104 | 122 | py |
sherpa | sherpa-master/examples/parallel-examples/bianchini/bianchini.py | # Train simple network on 2D data.
# Author: Peter Sadowski
from __future__ import print_function
import numpy as np
import sherpa
from keras.models import Model
from keras.layers import Dense, Input
from keras.optimizers import SGD
def dataset_bianchini(batchsize, k=1):
'''
Synthetic data set where we can c... | 2,585 | 31.734177 | 123 | py |
sherpa | sherpa-master/examples/parallel-examples/mnistmlp/trial.py | from __future__ import print_function
import os
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import sherpa
import keras
from keras.models import Model
from keras.layers import Dense, Input, Dropout
from keras.optimizers import SGD
from keras.datasets import mnist
def define_model(params):
"""
Return compiled model... | 2,619 | 30.95122 | 93 | py |
sherpa | sherpa-master/examples/parallel-examples/mnistmlp/runner.py | import os
import argparse
import sherpa
import datetime
from sherpa.schedulers import LocalScheduler,SGEScheduler
import sherpa.algorithms.bayesian_optimization as bayesian_optimization
def run_example(FLAGS):
"""
Run parallel Sherpa optimization over a set of discrete hp combinations.
"""
parameters ... | 3,578 | 46.72 | 122 | py |
sherpa | sherpa-master/examples/parallel-examples/mnistcnnpbt/mnist_cnn.py | '''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''
from __future__ import print_function
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
CONFIG = tf.... | 3,873 | 37.74 | 109 | py |
sherpa | sherpa-master/examples/parallel-examples/mnistcnnpbt/runner.py | import sherpa
import sherpa.schedulers
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--local', help='Run locally', action='store_true',
default=True)
parser.add_argument('--max_concurrent',
help='Number of concurrent processes',
typ... | 2,716 | 44.283333 | 118 | py |
sherpa | sherpa-master/examples/parallel-examples/fashion_mnist_benchmark/runner_gpyopt.py | import os
import argparse
import sherpa
import datetime
import time
from sherpa.schedulers import LocalScheduler,SGEScheduler
import sherpa.algorithms.bayesian_optimization as bayesian_optimization
def run_example(FLAGS):
"""
Run parallel Sherpa optimization over a set of discrete hp combinations.
"""
... | 1,862 | 37.8125 | 80 | py |
sherpa | sherpa-master/examples/parallel-examples/fashion_mnist_benchmark/runner_population_based_training.py | import os
import argparse
import sherpa
import datetime
import time
from sherpa.schedulers import LocalScheduler,SGEScheduler
def run_example(FLAGS):
"""
Run parallel Sherpa optimization over a set of discrete hp combinations.
"""
parameters = [sherpa.Continuous('learning_rate', [1e-5, 5e-1], 'lo... | 1,999 | 40.666667 | 112 | py |
sherpa | sherpa-master/examples/parallel-examples/fashion_mnist_benchmark/runner_successive_halving.py | import os
import argparse
import sherpa
import datetime
import time
from sherpa.schedulers import LocalScheduler,SGEScheduler
def run_example(FLAGS):
"""
Run parallel Sherpa optimization over a set of discrete hp combinations.
"""
parameters = [sherpa.Continuous('learning_rate', [1e-5, 5e-1], 'lo... | 1,888 | 38.354167 | 98 | py |
sherpa | sherpa-master/examples/parallel-examples/fashion_mnist_benchmark/fashion_mlp.py | '''Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''
from __future__ import print_function
import os
import time
os.environ['CUDA_VISIBLE_DEVICES'] = ''
import sherpa
import keras
from ker... | 2,681 | 36.25 | 137 | py |
sherpa | sherpa-master/examples/parallel-examples/fashion_mnist_benchmark/runner_random_search.py | import os
import argparse
import sherpa
import datetime
import time
from sherpa.schedulers import LocalScheduler,SGEScheduler
def run_example(FLAGS):
"""
Run parallel Sherpa optimization over a set of discrete hp combinations.
"""
parameters = [sherpa.Continuous('learning_rate', [1e-5, 5e-1], 'lo... | 1,591 | 35.181818 | 80 | py |
sherpa | sherpa-master/tests/test_successive_halving.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 9,652 | 37.003937 | 80 | py |
sherpa | sherpa-master/tests/long_tests.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 3,945 | 34.54955 | 95 | py |
sherpa | sherpa-master/tests/test_algorithms.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 21,565 | 37.442068 | 122 | py |
sherpa | sherpa-master/tests/testing_utils.py | import pytest
import sherpa
import sherpa.core
import sherpa.schedulers
import sherpa.database
import logging
try:
import unittest.mock as mock
except ImportError:
import mock
import tempfile
import shutil
logging.basicConfig(level=logging.DEBUG)
testlogger = logging.getLogger(__name__)
def get_test_paramete... | 1,977 | 31.42623 | 65 | py |
sherpa | sherpa-master/tests/test_seed.py | import sherpa
from testing_utils import get_test_parameters
def test_rng():
sherpa.rng.randn()
sherpa.rng.seed(1234)
a = sherpa.rng.randn()
sherpa.rng.seed(1234)
b = sherpa.rng.randn()
assert a ==b
def test_random_search():
algorithm = sherpa.algorithms.RandomSearch()
sherpa.rng.seed(1... | 542 | 29.166667 | 76 | py |
sherpa | sherpa-master/tests/test_gpyopt.py | import sherpa
import pandas
import pytest
import numpy
import collections
import GPyOpt as gpyopt_package
from sherpa.algorithms.bayesian_optimization import GPyOpt
from sherpa.algorithms import Repeat
@pytest.fixture
def parameters():
parameters = [sherpa.Continuous('dropout', [0., 0.5]),
sher... | 17,804 | 40.894118 | 124 | py |
sherpa | sherpa-master/tests/test_runner.py | import sherpa
import sherpa.core
import sherpa.schedulers
import sherpa.database
try:
import unittest.mock as mock
except ImportError:
import mock
from testing_utils import *
def test_runner_update_results():
mock_db = mock.MagicMock()
mock_db.get_new_results.return_value = [{'context': {'other_metric... | 3,834 | 34.509259 | 82 | py |
sherpa | sherpa-master/tests/test_schedulers.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 5,068 | 34.697183 | 114 | py |
sherpa | sherpa-master/tests/test_database.py | import os
import pytest
import sys
import sherpa
import sherpa.core
import sherpa.schedulers
import sherpa.database
try:
import unittest.mock as mock
except ImportError:
import mock
import logging
import time
import warnings
from testing_utils import *
def test_database(test_dir):
test_trial = get_test_tr... | 3,431 | 33.32 | 79 | py |
sherpa | sherpa-master/tests/test_study.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 4,098 | 32.876033 | 182 | py |
sherpa | sherpa-master/docs/conf.py | """
SHERPA is a Python library for hyperparameter tuning of machine learning models.
Copyright (C) 2018 Lars Hertel, Peter Sadowski, and Julian Collado.
This file is part of SHERPA.
SHERPA is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the... | 6,552 | 30.965854 | 80 | py |
MetaSeg | MetaSeg-master/metaseg_io.py |
import os
import pickle
import h5py
import numpy as np
# parameters from global_defs.py, please adjust variables and paths
from global_defs import MetaSeg
metaseg = MetaSeg()
########################################################
# probs_gt_save( probs, gt, image_name, i ):
# This routine stores softmax probabil... | 4,219 | 25.878981 | 89 | py |
MetaSeg | MetaSeg-master/metrics_setup.py |
from distutils.core import setup
from Cython.Build import cythonize
from distutils.extension import Extension
import numpy as np
ext_core = Extension(
"metrics",
sources=["metrics.pyx"],
include_dirs=[np.get_include()],
extra_compile_args=["-O3"])
setup( ext_modules = cythonize([ex... | 331 | 21.133333 | 44 | py |
MetaSeg | MetaSeg-master/metrics_test.py |
import os
os.system("python3 setup.py build_ext --inplace")
from metrics import compute_metrics, test_metrics
test_metrics()
| 130 | 13.555556 | 49 | py |
MetaSeg | MetaSeg-master/metaseg_plot.py |
import os
from metaseg_io import metaseg
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from scipy.stats import pearsonr, kde
from sklearn.metrics import mean_squared_error, r2_score, roc_curve, auc
import numpy as np
def add_scatterplot_vs_iou(ious, sizes, dataset, shortname, size_fac, sc... | 10,567 | 40.443137 | 187 | py |
MetaSeg | MetaSeg-master/metaseg_eval.py |
import random
import os
import time
import sys
from PIL import Image
import numpy as np
import pandas as pd
import scipy
from sklearn import datasets, linear_model, preprocessing, model_selection
from sklearn.metrics import mean_squared_error, r2_score, roc_curve, auc
from scipy.interpolate import interp1d
from multip... | 22,375 | 34.127159 | 151 | py |
MetaSeg | MetaSeg-master/global_defs.py |
class MetaSeg:
METASEG_MODEL_NAMES = [ "xc.mscl.os8", "mn.sscl.os16" ]
METASEG_CLASS_DTYPES = [ "one_hot_classes", "probs" ]
METASEG_MODEL_NAME = METASEG_MODEL_NAMES[0]
METASEG_READ_DATA_PATH = "/home/rottmann/seg_uncertainty/metaseg_io/"
METASEG_MY_IO_PATH = "/home/rottmann/seg_uncertaint... | 2,729 | 32.292683 | 115 | py |
socks | socks-main/benchmarks/experiment_matrix.py | import logging
import numpy as np
from collections.abc import Generator
from itertools import product
from functools import reduce
from operator import mul
from operator import itemgetter
from scipy import stats
from time import perf_counter
from sacred import Experiment
def _result_accumulator(
config,
... | 6,885 | 25.898438 | 88 | py |
socks | socks-main/benchmarks/__init__.py | __all__ = ["experiment_matrix"]
| 32 | 15.5 | 31 | py |
socks | socks-main/benchmarks/kernel_linear_id/plot_sample_size_vs_time.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/kernel_linear_id/plot_dimensionality_vs_time.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/kernel_linear_id/__init__.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/kernel_linear_id/baseline.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/kernel_control_fwd/plot_sample_size_vs_accuracy.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/kernel_control_fwd/plot_lambda_vs_accuracy.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/kernel_control_fwd/plot_sigma_vs_accuracy.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/kernel_control_fwd/plot_sample_size_vs_time.py | import numpy as np
from scipy import stats
from functools import partial
from benchmarks.experiment_matrix import experiment_matrix
from benchmarks.experiment_matrix import sample_mean
import matplotlib
import matplotlib.pyplot as plt
from benchmarks.kernel_control_fwd.baseline import ex
mx = experiment_matrix(ex,... | 1,905 | 25.472222 | 76 | py |
socks | socks-main/benchmarks/kernel_control_fwd/__init__.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/kernel_control_fwd/baseline.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/kernel_sr_max/plot_sample_size_vs_time.py | import numpy as np
from scipy import stats
from functools import partial
from benchmarks.experiment_matrix import experiment_matrix
from benchmarks.experiment_matrix import sample_mean
import matplotlib
import matplotlib.pyplot as plt
from benchmarks.kernel_sr_max.baseline import ex
mx = experiment_matrix(ex, call... | 2,041 | 26.226667 | 79 | py |
socks | socks-main/benchmarks/kernel_sr_max/plot_action_sample_size_vs_time.py | import numpy as np
from scipy import stats
from functools import partial
from benchmarks.experiment_matrix import experiment_matrix
from benchmarks.experiment_matrix import sample_mean
import matplotlib
import matplotlib.pyplot as plt
from benchmarks.kernel_sr_max.baseline import ex
mx = experiment_matrix(ex, call... | 2,080 | 26.746667 | 86 | py |
socks | socks-main/benchmarks/kernel_sr_max/plot_test_sample_size_vs_time.py | import numpy as np
from scipy import stats
from functools import partial
from benchmarks.experiment_matrix import experiment_matrix
from benchmarks.experiment_matrix import sample_mean
import matplotlib
import matplotlib.pyplot as plt
from benchmarks.kernel_sr_max.baseline import ex
mx = experiment_matrix(ex, call... | 2,071 | 26.626667 | 84 | py |
socks | socks-main/benchmarks/kernel_sr_max/plot_dimensionality_vs_time.py | import numpy as np
from scipy import stats
from functools import partial
from benchmarks.experiment_matrix import experiment_matrix
from benchmarks.experiment_matrix import sample_mean
import matplotlib
import matplotlib.pyplot as plt
from benchmarks.kernel_sr_max.baseline import ex
mx = experiment_matrix(ex, call... | 2,059 | 26.466667 | 82 | py |
socks | socks-main/benchmarks/kernel_sr_max/__init__.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/kernel_sr_max/baseline.py | import numpy as np
from sacred import Experiment
from sacred import Ingredient
from time import perf_counter
from functools import lru_cache
from functools import partial
from gym.spaces import Box
from gym_socks.envs.integrator import NDIntegratorEnv
from gym_socks.sampling import random_sampler
from gym_socks.sa... | 3,855 | 20.541899 | 79 | py |
socks | socks-main/benchmarks/kernel_sr_max/plot_time_horizon_vs_time.py | import numpy as np
from scipy import stats
from functools import partial
from benchmarks.experiment_matrix import experiment_matrix
from benchmarks.experiment_matrix import sample_mean
import matplotlib
import matplotlib.pyplot as plt
from benchmarks.kernel_sr_max.baseline import ex
mx = experiment_matrix(ex, call... | 2,039 | 26.2 | 80 | py |
socks | socks-main/benchmarks/tests/test_experiment_matrix.py | import unittest
from unittest.mock import patch
import numpy as np
from functools import partial
from sacred import Experiment
from benchmarks.experiment_matrix import experiment_matrix
from benchmarks.experiment_matrix import sample_mean
class TestExperimentMatrix(unittest.TestCase):
def test_config_len(cls):... | 1,421 | 21.935484 | 88 | py |
socks | socks-main/benchmarks/tests/__init__.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/separating_kernel/__init__.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/separating_kernel/baseline.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/kernel_sr/plot_sample_size_vs_time.py | import numpy as np
from scipy import stats
from functools import partial
from benchmarks.experiment_matrix import experiment_matrix
from benchmarks.experiment_matrix import sample_mean
import matplotlib
import matplotlib.pyplot as plt
from benchmarks.kernel_sr.baseline import ex
mx = experiment_matrix(ex, callback... | 2,025 | 26.013333 | 76 | py |
socks | socks-main/benchmarks/kernel_sr/plot_test_sample_size_vs_time.py | import numpy as np
from scipy import stats
from functools import partial
from benchmarks.experiment_matrix import experiment_matrix
from benchmarks.experiment_matrix import sample_mean
import matplotlib
import matplotlib.pyplot as plt
from benchmarks.kernel_sr.baseline import ex
mx = experiment_matrix(ex, callback... | 2,055 | 26.413333 | 80 | py |
socks | socks-main/benchmarks/kernel_sr/plot_dimensionality_vs_time.py | import numpy as np
from scipy import stats
from functools import partial
from benchmarks.experiment_matrix import experiment_matrix
from benchmarks.experiment_matrix import sample_mean
import matplotlib
import matplotlib.pyplot as plt
from benchmarks.kernel_sr.baseline import ex
mx = experiment_matrix(ex, callback... | 2,043 | 26.253333 | 78 | py |
socks | socks-main/benchmarks/kernel_sr/__init__.py | 0 | 0 | 0 | py | |
socks | socks-main/benchmarks/kernel_sr/baseline.py | import numpy as np
from sacred import Experiment
from sacred import Ingredient
from time import perf_counter
from functools import lru_cache
from functools import partial
from gym.spaces import Box
from gym_socks.envs.integrator import NDIntegratorEnv
from gym_socks.sampling import random_sampler
from gym_socks.sa... | 3,256 | 20.427632 | 79 | py |
socks | socks-main/benchmarks/kernel_sr/plot_time_horizon_vs_time.py | import numpy as np
from scipy import stats
from functools import partial
from benchmarks.experiment_matrix import experiment_matrix
from benchmarks.experiment_matrix import sample_mean
import matplotlib
import matplotlib.pyplot as plt
from benchmarks.kernel_sr.baseline import ex
mx = experiment_matrix(ex, callback... | 2,023 | 25.986667 | 76 | py |
socks | socks-main/examples/reach/stoch_reach.py | # %% [markdown]
"""
# Stochastic Reachability
This example shows the stochastic reachability algorithm.
By default, the system is a double integrator (2D stochastic chain of integrators).
To run the example, use the following command:
```shell
python examples/reach/stoch_reach.py
```
"""
# %%
import gym
impo... | 3,435 | 23.898551 | 88 | py |
socks | socks-main/examples/reach/forward_reach.py | # %% [markdown]
"""
# Forward Reachability
This example demonstrates the forward reachability classifier on a set of dummy data.
Note that the data is not taken from a dynamical system, but can easily be adapted to
data taken from system observations via a simple substitution. The reason for the dummy
data is to showc... | 3,920 | 26.808511 | 89 | py |
socks | socks-main/examples/reach/stoch_reach_maximal.py | # %% [markdown]
"""
# Maximal Policies
This example demonstrates the stochastic reachability algorithm to compute the maximal
policy in the terminal (first) sense.
By default, the system is a double integrator (2D stochastic chain of integrators).
To run the example, use the following command:
```shell
python e... | 3,624 | 24.34965 | 88 | py |
socks | socks-main/examples/control/satellite_rendezvous.py | # %% [markdown]
"""
# Satellite Rendezvous and Docking
Constrained stochastic optimal control problem using CWH dynamics. Note that the
solution is currently unstable. This is partly due to the fact that the CWH dynamics are
extremely sensitive to inputs, but also due to the fact that random sampling is not
guaranteed... | 5,899 | 24.106383 | 88 | py |
socks | socks-main/examples/control/obstacle_avoid.py | # %% [markdown]
"""
# Obstacle Avoid
_Coming Soon_
To run the example, use the following command:
```shell
python examples/control/obstacle_avoid.py
```
"""
| 165 | 10.857143 | 46 | py |
socks | socks-main/examples/control/tracking.py | # %% [markdown]
"""
# Target Tracking
This example demonstrates the kernel-based stochastic optimal control algorithm and the
dynamic programming algorithm. By default, it uses a nonholonomic vehicle system
(unicycle dynamics), and seeks to track a v-shaped trajectory.
To run the example, use the following command:
... | 4,954 | 23.651741 | 87 | py |
socks | socks-main/examples/kernel/random_fourier_features.py | # %% [markdown]
"""
# Random Fourier Features
This example demonstrates the use of conditional distribution embeddings on a simple
function $x^{2}$, corrupted by Gaussian noise and using the kernel approximation
technique Random Fourier Features. This example is useful for experimenting with the
kernel choice, paramet... | 4,361 | 33.078125 | 88 | py |
socks | socks-main/examples/kernel/conditional_embedding.py | # %% [markdown]
"""
# Conditional Distribution Embedding
This example demonstrates the use of conditional distribution embeddings on a simple
function $x^{2}$, corrupted by Gaussian noise. This example is useful for
experimenting with the kernel choice, parameters, or dataset and visualizing the result.
To run the ex... | 2,240 | 25.678571 | 88 | py |
socks | socks-main/examples/kernel/derivative_approximation.py | # %% [markdown]
"""
# Derivative Approximation
This example demonstrates the use of conditional distribution embeddings to compute an
approximation of the first and second derivatives of a function,
$$f(x) = x^{3} - 4 x^{2} + 6 x - 24 + \exp(-x)$$
We use an idealized dataset, which approximates the partial derivativ... | 2,760 | 26.888889 | 88 | py |
socks | socks-main/examples/kernel/nystrom_approximation.py | # %% [markdown]
"""
# Nystrom Approximation
This example demonstrates the use of conditional distribution embeddings on a simple
function $x^{2}$, corrupted by Gaussian noise and using the Nystrom kernel approximation
technique. This example is useful for experimenting with the kernel choice, parameters,
or dataset an... | 5,209 | 30.575758 | 88 | py |
socks | socks-main/examples/kernel/maximum_mean_discrepancy.py | # %% [markdown]
"""
# Maximum Mean Discrepancy
This example demonstrates the maximum mean discrepancy using data drawn from two
different distributions. Additionally, we demonstrate the empirical witness function,
which displays the difference between the two distributions.
To run the example, use the following comma... | 2,890 | 26.533333 | 88 | py |
socks | socks-main/examples/identification/linear_id.py | # %% [markdown]
"""
# Linear System ID
This example demonstrates the linear system identification algorithm.
By default, it uses the CWH4D system dynamics. Try setting the regularization
parameter lower for higher accuracy. Note that this can introduce numerical
instability if set too low.
To run the example, use th... | 3,547 | 24.89781 | 88 | py |
socks | socks-main/.jupyter/jupyter_notebook_config.py | c.NotebookApp.contents_manager_class = "jupytext.TextFileContentsManager"
c.ContentsManager.preferred_jupytext_formats_read = "py:percent"
c.ContentsManager.sphinx_convert_rst2md = True
| 186 | 45.75 | 73 | py |
socks | socks-main/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If e... | 4,549 | 26.743902 | 222 | py |
socks | socks-main/docs/sphinxext/github_link.py | from operator import attrgetter
import inspect
import subprocess
import os
import sys
from functools import partial
REVISION_CMD = "git rev-parse --short HEAD"
def _get_git_revision():
try:
revision = subprocess.check_output(REVISION_CMD.split()).strip()
except (subprocess.CalledProcessError, OSError... | 2,643 | 30.105882 | 85 | py |
socks | socks-main/gym_socks/__init__.py | """SOCKS - A kernel-based stochastic optimal control toolbox."""
import logging
logger = logging.getLogger(__name__)
"""Default logger for gym_socks.
Used mainly for debugging. Can be diabled entirely by setting the log level of the
logger to "notset".
"""
logging.basicConfig(level=logging.INFO, format="%(levelnam... | 407 | 24.5 | 88 | py |
socks | socks-main/gym_socks/envs/core.py | from abc import ABC, abstractmethod
import gym
from gym.utils import seeding
import numpy as np
from functools import wraps
class BaseDynamicalObject(gym.Env, ABC):
"""Base dynamical object class.
Bases: :py:obj:`gym.Env`, :py:obj:`abc.ABC`
This class is **abstract**, meaning it is not meant to be in... | 4,934 | 26.569832 | 117 | py |
socks | socks-main/gym_socks/envs/planar_quad.py | """Planar quadrotor system."""
import gym
from gym_socks.envs.dynamical_system import DynamicalSystem
import numpy as np
from scipy.constants import g
from scipy.integrate import solve_ivp
class PlanarQuadrotorEnv(DynamicalSystem):
"""Planar quadrotor system.
Bases: :py:class:`gym_socks.envs.dynamical_sys... | 3,711 | 27.775194 | 307 | py |
socks | socks-main/gym_socks/envs/point_mass.py | """ND point mass system."""
import gym
from gym_socks.envs.dynamical_system import DynamicalSystem
import numpy as np
class NDPointMassEnv(DynamicalSystem):
"""ND point mass system.
Bases: :py:class:`gym_socks.envs.dynamical_system.DynamicalSystem`
A point mass is a very simple system in which the inp... | 1,595 | 26.517241 | 88 | py |
socks | socks-main/gym_socks/envs/tora.py | """TORA (translational oscillation with rotational actuation) system."""
import gym
from gym_socks.envs.dynamical_system import DynamicalSystem
import numpy as np
class TORAEnv(DynamicalSystem):
"""TORA (translational oscillation with rotational actuation) system.
Bases: :py:class:`gym_socks.envs.dynamical... | 1,808 | 29.661017 | 236 | py |
socks | socks-main/gym_socks/envs/QUAD20.py | """Quadrotor system.
This system is taken from `ARCH-COMP20 Category Report: Continuous and Hybrid Systems
with Nonlinear Dynamics <https://easychair.org/publications/open/nrdD>`_.
"""
import gym
from gym_socks.envs.dynamical_system import DynamicalSystem
import numpy as np
from scipy.constants import g
class Qu... | 5,463 | 30.222857 | 87 | py |
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