repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
ElvisLouis/code | work/ML/tensorflow/separa/pyradbas/train_exact.py | 1 | 1299 | # -*- coding: utf-8 -*-
# Author: Stefano Brilli
# Date: 24/10/2011
# E-mail: stefanobrilli@gmail.com
import rbfn
import numpy as np
import numpy.linalg as la
def train_exact(I, O, gw=1.0):
"""
Build an exact (zero error for inputs) radial basis network
I (N by M) N vector of M size
O (N by T) N ... | gpl-2.0 |
wallarelvo/mod | scripts/common.py | 1 | 7239 |
import re
import pandas as pd
import time
import shapely.geometry as geom
NFS_PATH = "/home/wallar/nfs/data/data-sim/"
# ]]NFS_PATH = "/data/drl/mod_sim_data/data-sim/"
def get_metrics(n_vehicles, cap, waiting_time, predictions):
m_file = NFS_PATH + "v{}-c{}-w{}-p{}/metrics_pnas.csv".format(
n_vehicles... | gpl-2.0 |
CalebHarada/DCT-photometry | Junk/master_flat.py | 1 | 1812 | import matplotlib.pyplot as plt
import ccdproc as cp
from ccdproc import ImageFileCollection, Combiner
from astropy import units as u
directory = '...'
save_directory = '...'
filter = '...'
# set desired filter
master_bias = cp.CCDData.read('...')
master_dark = cp.CCDData.read('...')
# must already have the... | mit |
lcpt/xc | verif/tests/materials/fiber_section/test_fiber_section_prop.py | 1 | 11322 | # -*- coding: utf-8 -*-
__author__= "Ana Ortega (AO_O) "
__copyright__= "Copyright 2016, AO_O"
__license__= "GPL"
__version__= "3.0"
__email__= "ana.ortega@ciccp.es "
''' Evaluation of several geometrical and mechanical properties of a
RC rectangular section under uniaxial bending moment.
The data are taken from Exa... | gpl-3.0 |
codrut3/tensorflow | tensorflow/contrib/learn/python/learn/estimators/kmeans.py | 15 | 10904 | # Copyright 2016 The TensorFlow Authors. 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 applica... | apache-2.0 |
unsiloai/syntaxnet-ops-hack | tensorflow/examples/get_started/regression/test.py | 8 | 3181 | # Copyright 2016 The TensorFlow Authors. 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 applica... | apache-2.0 |
tttor/csipb-jamu-prj | predictor/connectivity/similarity/compound-kernel/genetic-programming/src/plot.py | 1 | 1062 | import numpy as np
import matplotlib.pyplot as plt
# evenly sampled time at 200ms intervals
t = np.arange(0., 0.5, 1.0, 1.5, 2.0)
# red dashes, blue squares and green triangles
plt.plot(t, t, 'r--', t, t**2, 'bs', t, t**3, 'g^')
plt.show()
# x = np.array([[0.1, 0.04],
# [0.2, 0.08],
# [0... | mit |
xuewei4d/scikit-learn | sklearn/model_selection/tests/test_search.py | 4 | 80478 | """Test the search module"""
from collections.abc import Iterable, Sized
from io import StringIO
from itertools import chain, product
from functools import partial
import pickle
import sys
from types import GeneratorType
import re
import numpy as np
import scipy.sparse as sp
import pytest
from sklearn.utils._testing... | bsd-3-clause |
etherkit/OpenBeacon2 | client/win/venv/Lib/site-packages/PyInstaller/hooks/hook-matplotlib.backends.py | 2 | 3125 | #-----------------------------------------------------------------------------
# Copyright (c) 2013-2019, PyInstaller Development Team.
#
# Distributed under the terms of the GNU General Public License with exception
# for distributing bootloader.
#
# The full license is in the file COPYING.txt, distributed with this s... | gpl-3.0 |
ocanbascil/hackerrank-machine-learning | predicting-office-space-price/solution.py | 1 | 3026 | from StringIO import StringIO
import pandas as pd
from sklearn import linear_model
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error as mse
from sklearn.cross_validation import KFold
def get_data():
return pd.read_csv('data.txt', header=None, delim_whitespace=Tru... | mit |
juliojsb/sarviewer | plotters/matplotlib/cpu.py | 1 | 2136 | #!/usr/bin/env python2
"""
Author :Julio Sanz
Website :www.elarraydejota.com
Email :juliojosesb@gmail.com
Description :Generate CPU graph from cpu.dat file
Dependencies :Python 2.x, matplotlib
Usage :python cpu.py
License :GPLv3
"""
import matplotlib
matplotlib.use('Agg')
import m... | gpl-3.0 |
rafaelwerneck/kuaa | evaluation_measures/normalized_accuracy_score/plugin_normalized_accuracy_score.py | 1 | 6176 | #!/usr/bin/python
# -*- coding: utf-8 -*-
###############################################################################
# This file is part of Kuaa.
#
# Kuaa is a framework for the automation of machine learning experiments.
#
# It provides a workflow-based standardized environment for easy evaluation of
# feature d... | gpl-3.0 |
sanguinariojoe/aquagpusph | examples/2D/built_in_beam/cMake/plot_t.py | 14 | 5704 | #******************************************************************************
# *
# * ** * * * * *
# * * * * * * * * * *
... | gpl-3.0 |
maxlikely/scikit-learn | examples/linear_model/lasso_dense_vs_sparse_data.py | 13 | 1862 | """
==============================
Lasso on dense and sparse data
==============================
We show that linear_model.Lasso provides the same results for dense and sparse
data and that in the case of sparse data the speed is improved.
"""
print(__doc__)
from time import time
from scipy import sparse
from scipy ... | bsd-3-clause |
Clyde-fare/scikit-learn | sklearn/utils/fixes.py | 133 | 12882 | """Compatibility fixes for older version of python, numpy and scipy
If you add content to this file, please give the version of the package
at which the fixe is no longer needed.
"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# ... | bsd-3-clause |
mmagnus/rna-pdb-tools | rna_tools/tools/clarna_play/ClaRNAlib/cluster-doublets.py | 2 | 10101 | #!/usr/bin/python
import sys
import os
import re
import math
import random
from optparse import OptionParser
import simplejson as json
from numpy import array
from Bio import PDB
from Bio.SVDSuperimposer import SVDSuperimposer
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from StringIO impor... | gpl-3.0 |
tanishq-dubey/Atlas | src/dataPlotter.py | 1 | 2165 | import sys, serial, argparse
import numpy as np
from time import sleep
from collections import deque
import matplotlib.pyplot as plt
import matplotlib.animation as animation
# plot class
class AnalogPlot:
# constr
def __init__(self, strPort, maxLen):
# open serial port
self.ser = serial.Serial(s... | mit |
ankurankan/scikit-learn | examples/cluster/plot_digits_agglomeration.py | 377 | 1694 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Feature agglomeration
=========================================================
These images how similar features are merged together using
feature agglomeration.
"""
print(__doc__)
# Code source: Gaël Varoquaux
#... | bsd-3-clause |
tridesclous/tridesclous | doc/conf.py | 1 | 11849 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# tridesclous documentation build configuration file, created by
# sphinx-quickstart on Tue Nov 22 14:33:27 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
... | mit |
moutai/scikit-learn | sklearn/gaussian_process/tests/test_kernels.py | 24 | 11602 | """Testing for kernels for Gaussian processes."""
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Licence: BSD 3 clause
from collections import Hashable
from sklearn.externals.funcsigs import signature
import numpy as np
from sklearn.gaussian_process.kernels import _approx_fprime
from sklearn.metrics... | bsd-3-clause |
mrshu/iepy | iepy/extraction/active_learning_core.py | 1 | 10059 | from copy import copy
import inspect
import logging
import pickle
import random
import os.path
import numpy
from sklearn.cross_validation import StratifiedKFold
from sklearn.metrics import precision_recall_curve
from iepy import defaults
from iepy.extraction.relation_extraction_classifier import RelationExtractionCla... | bsd-3-clause |
cogstat/cogstat | cogstat/cogstat_stat_num.py | 1 | 26314 | # -*- coding: utf-8 -*-
"""
This module contains functions for statistical analysis that cannot be found in
other packages.
Arguments are the pandas data frame (pdf) and parameters.
Output is the result of the numerical analysis in numerical form.
"""
from zipfile import ZipFile
import json
from tempfile import Temp... | gpl-3.0 |
dsullivan7/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 213 | 3359 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_alm... | bsd-3-clause |
tacaswell/awj | test_awj.py | 1 | 2722 | import tempfile
import shutil
import pandas as pd
from pandas.util.testing import assert_frame_equal
from contextlib import contextmanager
from glob import glob
import os.path
from awj import AWJ
@contextmanager
def awj_context(max_size=None):
work_dir = tempfile.mkdtemp()
awj = AWJ(work_dir, max_size=max_si... | bsd-3-clause |
lmallin/coverage_test | python_venv/lib/python2.7/site-packages/pandas/tests/io/parser/header.py | 6 | 9126 | # -*- coding: utf-8 -*-
"""
Tests that the file header is properly handled or inferred
during parsing for all of the parsers defined in parsers.py
"""
import pytest
import numpy as np
import pandas.util.testing as tm
from pandas import DataFrame, Index, MultiIndex
from pandas.compat import StringIO, lrange, u
cla... | mit |
verilylifesciences/purplequery | purplequery/client.py | 1 | 21528 | # Copyright 2019 Verily Life Sciences LLC
#
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.
"""Fake implementation of Google BigQuery client."""
import collections
import uuid
from typing import Any, Callable, Dict, List, Optional, Tuple, cast # noqa: F401
from t... | bsd-3-clause |
gprMax/gprMax | tests/test_models.py | 1 | 10111 | # Copyright (C) 2015-2020: The University of Edinburgh
# Authors: Craig Warren and Antonis Giannopoulos
#
# This file is part of gprMax.
#
# gprMax is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundatio... | gpl-3.0 |
xyguo/scikit-learn | sklearn/datasets/twenty_newsgroups.py | 25 | 13704 | """Caching loader for the 20 newsgroups text classification dataset
The description of the dataset is available on the official website at:
http://people.csail.mit.edu/jrennie/20Newsgroups/
Quoting the introduction:
The 20 Newsgroups data set is a collection of approximately 20,000
newsgroup documents,... | bsd-3-clause |
meduz/scikit-learn | examples/linear_model/plot_logistic.py | 73 | 1568 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic function
=========================================================
Shown in the plot is how the logistic regression would, in this
synthetic dataset, classify values as either 0 or 1,
i.e. class one or tw... | bsd-3-clause |
jmschrei/scikit-learn | examples/neural_networks/plot_rbm_logistic_classification.py | 258 | 4609 | """
==============================================================
Restricted Boltzmann Machine features for digit classification
==============================================================
For greyscale image data where pixel values can be interpreted as degrees of
blackness on a white background, like handwritten... | bsd-3-clause |
ltiao/scikit-learn | sklearn/mixture/gmm.py | 7 | 30564 | """
Gaussian Mixture Models.
This implementation corresponds to frequentist (non-Bayesian) formulation
of Gaussian Mixture Models.
"""
# Author: Ron Weiss <ronweiss@gmail.com>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Bertrand Thirion <bertrand.thirion@inria.fr>
import warnings
import numpy as... | bsd-3-clause |
adykstra/mne-python | mne/tests/test_bem.py | 1 | 17079 | # Authors: Marijn van Vliet <w.m.vanvliet@gmail.com>
#
# License: BSD 3 clause
from copy import deepcopy
from os import remove, makedirs
import os.path as op
from shutil import copy
import numpy as np
import pytest
from numpy.testing import assert_equal, assert_allclose
import matplotlib.pyplot as plt
from mne impor... | bsd-3-clause |
georgetown-analytics/machine-learning | archive/code/wheat.py | 5 | 2120 | # wheat
# Classification and Clustering of Wheat Dataset
#
# Author: Author: Benjamin Bengfort <bbengfort@districtdatalabs.com>
# Created: Thu Feb 26 17:56:52 2015 -0500
#
# Copyright (C) 2015 District Data Labs
# For license information, see LICENSE.txt
#
# ID: wheat.py [] benjamin@bengfort.com $
"""
Classificat... | mit |
PatrickOReilly/scikit-learn | sklearn/feature_extraction/text.py | 6 | 50885 | # -*- coding: utf-8 -*-
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck
# Robert Layton <robertlayton@gmail.com>
# Jochen Wersdörfer <jochen@wersdoerfer.de>
# Roman Sinayev <roman.sinayev@gmail.com>
#
# License: B... | bsd-3-clause |
Vitens/epynet | epynet/baseobject.py | 2 | 2641 | import pandas as pd
import warnings
import weakref
def lazy_property(fn):
'''Decorator that makes a property lazy-evaluated.
'''
attr_name = fn.__name__
@property
def _lazy_property(self):
if attr_name not in self._values.keys():
self._values[attr_name] = fn(self)
retu... | apache-2.0 |
edoddridge/aronnax | reproductions/plot_Davis_et_al_2014.py | 2 | 3633 | import os
import os.path as p
import glob
from builtins import range
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
self_path = p.dirname(p.abspath(__file__))
root_path = p.dirname(self_path)
import sys
sys.path.append(p.join(root_path, 'test'))
sys.path.append(p.join(roo... | mit |
yuxng/Deep_ISM | ISM/lib/ism/test_seg.py | 1 | 5931 | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Test a Fast R-CNN network on an imdb (image database)."""
from ism.... | mit |
zhenv5/scikit-learn | sklearn/manifold/tests/test_mds.py | 324 | 1862 | import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_raises
from sklearn.manifold import mds
def test_smacof():
# test metric smacof using the data of "Modern Multidimensional Scaling",
# Borg & Groenen, p 154
sim = np.array([[0, 5, 3, 4],
... | bsd-3-clause |
siconos/siconos-deb | examples/Mechanics/NewtonEuler/BouncingBallNETS.py | 1 | 5094 | #!/usr/bin/env python
# Siconos is a program dedicated to modeling, simulation and control
# of non smooth dynamical systems.
#
# Copyright 2016 INRIA.
#
# 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 L... | apache-2.0 |
drakipovic/deep-learning | 1. labos/tf_logreg.py | 1 | 1773 | import tensorflow as tf
from sklearn.preprocessing import OneHotEncoder
import numpy as np
from data import sample_gmm_2d, graph_surface, eval_perf_binary, graph_data
class TFLogreg(object):
def __init__(self, D, C, param_delta=0.02, l=0.001):
self.X = tf.placeholder(tf.float32, [None, D])
... | mit |
AlexRobson/scikit-learn | examples/linear_model/plot_sparse_recovery.py | 243 | 7461 | """
============================================================
Sparse recovery: feature selection for sparse linear models
============================================================
Given a small number of observations, we want to recover which features
of X are relevant to explain y. For this :ref:`sparse linear ... | bsd-3-clause |
fabianp/scikit-learn | examples/applications/face_recognition.py | 191 | 5513 | """
===================================================
Faces recognition example using eigenfaces and SVMs
===================================================
The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:
http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (2... | bsd-3-clause |
lamastex/scalable-data-science | dbcArchives/2021/000_0-sds-3-x-projects/student-project-06_group-ParticleClustering/Parquet/02_dl_horovod_parquet.py | 1 | 24689 | # Databricks notebook source
# MAGIC %md
# MAGIC ScaDaMaLe Course [site](https://lamastex.github.io/scalable-data-science/sds/3/x/) and [book](https://lamastex.github.io/ScaDaMaLe/index.html)
# COMMAND ----------
# MAGIC %md ## UCluster with distributed learning
# COMMAND ----------
# MAGIC %md
# MAGIC This notebo... | unlicense |
aminert/scikit-learn | benchmarks/bench_plot_lasso_path.py | 301 | 4003 | """Benchmarks of Lasso regularization path computation using Lars and CD
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
from collections import defaultdict
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model import lars_pat... | bsd-3-clause |
open-mpi/netloc | tools/lsnettopo/viz-networkx.py | 1 | 2451 | #!/usr/bin/python
# Copyright (c) 2013 University of Wisconsin-La Crosse.
# All rights reserved.
#
# See COPYING in top-level directory.
#
# $HEADER$
#
# You will need the following libraries to use this sample script.
# NetworkX
# http://networkx.github.io/
# Matplotlib
# http://matpl... | bsd-3-clause |
horance-liu/tensorflow | tensorflow/contrib/timeseries/examples/lstm.py | 13 | 9268 | # Copyright 2017 The TensorFlow Authors. 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 applica... | apache-2.0 |
dermotte/liresolr | src/main/python/weights1299_analyze_data.py | 1 | 3447 | import re
import numpy as np
import matplotlib.pyplot as plt
import json
from pathlib import Path
import pandas as pd
home_directory = str(Path.home())
input_file = home_directory + '/projects/wipo2018/weights1299.txt'
state = 0
count_images = 0
count_classes = 0
current_image = ''
data = {}
# weights = []
classes = ... | gpl-2.0 |
bnaul/scikit-learn | benchmarks/bench_plot_parallel_pairwise.py | 127 | 1270 | # Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import time
import matplotlib.pyplot as plt
from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_kernels
def plot(func):
random_state = check_rand... | bsd-3-clause |
hugobowne/scikit-learn | sklearn/cross_validation.py | 4 | 67659 |
"""
The :mod:`sklearn.cross_validation` module includes utilities for cross-
validation and performance evaluation.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>,
# Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
fro... | bsd-3-clause |
indiajoe/SALTHRS | SALT_HRS_SpectrumExtraction.py | 1 | 27961 | """ Pipeline for extracting SALT - HRS instrument spectrum
See example.py for how to run this pipeline.
J.P.Ninan indiajoe@gmail.com """
import numpy as np
import numpy.ma
from astropy.io import fits
from astropy.modeling import models, fitting
import matplotlib.pyplot as... | gpl-3.0 |
cancan101/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator.py | 5 | 53629 | # Copyright 2016 The TensorFlow Authors. 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 applica... | apache-2.0 |
kabrau/PyImageRoi | source/Generate_TFRecord.py | 1 | 6507 | """
Usage:
# From tensorflow/models/
# Create train data:
python generate_tfrecord.py --csv_input=data/train_labels.csv --output_path=data/train.record
# Create test data:
python generate_tfrecord.py --csv_input=data/test_labels.csv --output_path=data/test.record
"""
from __future__ import division
from __... | mit |
mcquay239/cg-lectures | mesh.py | 1 | 4847 | #!/usr/bin/env python
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from math import sqrt, fabs
from pyhull.convex_hull import ConvexHull
class Pole:
__slots__ = ("r", "c")
def __init__(self, r, c):
self.r = r
self.c = c
class Tria... | mit |
newemailjdm/pybrain | pybrain/tools/plotting/classification.py | 25 | 5041 | """
matplotlib helpers for ClassificationDataSet and classifiers in general.
"""
__author__ = 'Werner Beroux <werner@beroux.com>'
import numpy as np
import matplotlib.pyplot as plt
class ClassificationDataSetPlot(object):
@staticmethod
def plot_module_classification_sequence_performance(module, dataset, seque... | bsd-3-clause |
lseman/pylspm | pylspm/call_mpi.py | 1 | 1511 | from mpi4py import MPI
import sys
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
import pandas as pd
import numpy as np
from .pylspm import PyLSpm
import random
from scipy.stats.stats import pearsonr
from .boot import PyLSboot
def PyLSmpi(mode, br, cores, dados, LVcsv, Mcsv, sche... | mit |
datasciencebr/serenata-de-amor | research/src/fetch_congressperson_details.py | 2 | 3759 | import datetime
import os
import requests
import re
import pandas as pd
import numpy as np
from bs4 import BeautifulSoup
class CongresspersonDetails:
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
DATA_PATH = os.path.join(BASE_DIR, 'data')
DATE = datetime.date.today().strftime('%... | mit |
Adai0808/scikit-learn | benchmarks/bench_sgd_regression.py | 283 | 5569 | """
Benchmark for SGD regression
Compares SGD regression against coordinate descent and Ridge
on synthetic data.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# License: BSD 3 clause
import numpy as np
import pylab as pl
import gc
from time import time
from sklearn.linear_model i... | bsd-3-clause |
maartenbreddels/vaex | packages/vaex-core/vaex/column.py | 1 | 24547 | import logging
import os
import warnings
import six
import numpy as np
import pyarrow as pa
import vaex
from .array_types import supported_array_types, supported_arrow_array_types, string_types, is_string_type
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
if not on_rtd:
import vaex.strings
logger = log... | mit |
colin2328/asciiclass | lectures/lec6/match-manual.py | 3 | 2055 | import csv
from sklearn import tree
import editdist
import re
def string_match_score(p1,p2,field):
s1 = p1[field]
s2 = p2[field]
return editdist.distance(s1.lower(),s2.lower())/float(len(s1))
def jaccard_score(p1,p2,field):
name1 = p1[field]
name2 = p2[field]
set1 = set(name1.lower().split())
... | mit |
jmmease/pandas | pandas/core/dtypes/generic.py | 9 | 3256 | """ define generic base classes for pandas objects """
# define abstract base classes to enable isinstance type checking on our
# objects
def create_pandas_abc_type(name, attr, comp):
@classmethod
def _check(cls, inst):
return getattr(inst, attr, '_typ') in comp
dct = dict(__instancecheck__=_chec... | bsd-3-clause |
vermouthmjl/scikit-learn | sklearn/feature_selection/__init__.py | 140 | 1302 | """
The :mod:`sklearn.feature_selection` module implements feature selection
algorithms. It currently includes univariate filter selection methods and the
recursive feature elimination algorithm.
"""
from .univariate_selection import chi2
from .univariate_selection import f_classif
from .univariate_selection import f_... | bsd-3-clause |
logpai/logparser | logparser/logmatch/regexmatch.py | 1 | 8388 | # Copyright 2018 The LogPAI Team (https://github.com/logpai).
#
# Licensed under the MIT License:
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without ... | mit |
mhue/scikit-learn | sklearn/tests/test_base.py | 216 | 7045 | # Author: Gael Varoquaux
# License: BSD 3 clause
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing impo... | bsd-3-clause |
olemke/pyatmlab | pyatmlab/graphics.py | 1 | 9077 | #!/usr/bin/env python
# coding: utf-8
"""Interact with matplotlib and other plotters
"""
import os.path
import datetime
now = datetime.datetime.now
import logging
import subprocess
import sys
import pickle
import lzma
import pathlib
import numpy
import matplotlib
import matplotlib.cbook
import matplotlib.pyplot
imp... | bsd-3-clause |
ryfeus/lambda-packs | Keras_tensorflow/source/numpy/core/tests/test_multiarray.py | 8 | 240387 | from __future__ import division, absolute_import, print_function
import collections
import tempfile
import sys
import shutil
import warnings
import operator
import io
import itertools
import ctypes
import os
if sys.version_info[0] >= 3:
import builtins
else:
import __builtin__ as builtins
from decimal import D... | mit |
stonewell/learn-curve | src/stock_data_provider/cn_a/baostock_adjfactor.py | 1 | 1821 | # coding=utf-8
import os
import csv
import baostock as bs
import pandas as pd
def convert_symbol(symbol):
parts = symbol.lower().split('.')
return '{}.{}'.format(parts[1], parts[0])
def load_adjfactor(symbol, data_path):
adj_file = os.path.join(data_path, symbol, 'adj.csv')
if os.path.exists(adj... | mit |
lhilt/scipy | scipy/interpolate/interpolate.py | 4 | 97600 | from __future__ import division, print_function, absolute_import
__all__ = ['interp1d', 'interp2d', 'lagrange', 'PPoly', 'BPoly', 'NdPPoly',
'RegularGridInterpolator', 'interpn']
import itertools
import warnings
import functools
import operator
import numpy as np
from numpy import (array, transpose, searc... | bsd-3-clause |
ankeshanand/neural-cryptography-tensorflow | src/model.py | 1 | 5593 | import tensorflow as tf
import numpy as np
import matplotlib
# OSX fix
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import seaborn as sns
from layers import conv_layer
from config import *
from utils import init_weights, gen_data
class CryptoNet(object):
def __init__(self, sess, msg_len=MSG_LEN, bat... | mit |
david-ragazzi/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/collections.py | 69 | 39876 | """
Classes for the efficient drawing of large collections of objects that
share most properties, e.g. a large number of line segments or
polygons.
The classes are not meant to be as flexible as their single element
counterparts (e.g. you may not be able to select all line styles) but
they are meant to be fast for com... | gpl-3.0 |
VaibhavAgarwalVA/sympy | sympy/plotting/tests/test_plot_implicit.py | 52 | 2912 | import warnings
from sympy import (plot_implicit, cos, Symbol, symbols, Eq, sin, re, And, Or, exp, I,
tan, pi)
from sympy.plotting.plot import unset_show
from tempfile import NamedTemporaryFile
from sympy.utilities.pytest import skip
from sympy.external import import_module
#Set plots not to show
un... | bsd-3-clause |
rsivapr/scikit-learn | sklearn/metrics/cluster/__init__.py | 312 | 1322 | """
The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the 'quality' of the model itself.
"""
from .supervised import ... | bsd-3-clause |
eggplantbren/TwinPeaks3 | prototype.py | 1 | 1617 | import numpy as np
import numpy.random as rng
import matplotlib.pyplot as plt
import copy
def randh(shape=None):
if shape is not None:
return 10.**(1.5 - 6.*rng.rand(shape))*rng.randn(shape)
return 10.**(1.5 - 6.*rng.rand())*rng.randn()
class Model:
N = 100
def __init__(self):
self.x = np.empty(Model.... | mit |
shincling/MemNN_and_Varieties | MemN2N_python/Unknown_networks/unknown_main.py | 1 | 29900 | # -*- coding: utf8 -*-
from __future__ import division
import argparse
import glob
import lasagne
import numpy as np
import theano
import theano.tensor as T
import time
from sklearn import metrics
from sklearn.preprocessing import LabelBinarizer,label_binarize
class SimpleAttentionLayer(lasagne.layers.MergeLayer):
... | bsd-3-clause |
dhruv13J/scikit-learn | sklearn/feature_extraction/image.py | 263 | 17600 | """
The :mod:`sklearn.feature_extraction.image` submodule gathers utilities to
extract features from images.
"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Olivier Grisel
# Vlad Niculae
# License: BSD 3 clause
fro... | bsd-3-clause |
msschwartz21/craniumPy | experiments/templates/TEMP-transform_after_align.py | 1 | 2245 | import deltascope as ds
import time
import glob
import re
import traceback
import os
import multiprocessing as mp
import pandas as pd
from functools import partial
outdirs = ['path\to\outdir']
deg = 2
fit_dim = ['x','z']
def transform_file(f,model=None):
print(f,'starting')
tic = time.time()
s =... | gpl-3.0 |
YihaoLu/statsmodels | statsmodels/iolib/summary2.py | 21 | 19583 | from statsmodels.compat.python import (lrange, iterkeys, iteritems, lzip,
reduce, itervalues, zip, string_types,
range)
from statsmodels.compat.collections import OrderedDict
import numpy as np
import pandas as pd
import datetime
import textw... | bsd-3-clause |
pradyu1993/scikit-learn | examples/linear_model/plot_ols.py | 3 | 1959 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Linear Regression Example
=========================================================
This example uses the only the first feature of the `diabetes` dataset, in
order to illustrate a two-dimensional plot of this regre... | bsd-3-clause |
PatrickOReilly/scikit-learn | sklearn/metrics/cluster/bicluster.py | 359 | 2797 | from __future__ import division
import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
from sklearn.utils.validation import check_consistent_length, check_array
__all__ = ["consensus_score"]
def _check_rows_and_columns(a, b):
"""Unpacks the row and column arrays and checks their shap... | bsd-3-clause |
geoalchimista/chflux | chflux/common.py | 1 | 25558 | """
Common functions used in flux calculation
(c) 2016-2017 Wu Sun <wu.sun@ucla.edu>
"""
from collections import namedtuple
import warnings
import numpy as np
from scipy import optimize
import scipy.constants.constants as sci_const
import pandas as pd
# Physical constants
# Do not modify unless you are in a differ... | bsd-3-clause |
zfrenchee/pandas | doc/source/conf.py | 1 | 19891 | # -*- coding: utf-8 -*-
#
# pandas documentation build configuration file, created by
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that ... | bsd-3-clause |
jmschrei/scikit-learn | sklearn/utils/tests/test_class_weight.py | 90 | 12846 | import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_blobs
from sklearn.utils.class_weight import compute_class_weight
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testin... | bsd-3-clause |
nyirock/mg_blast_wrapper | mg_blast_wrapper_v1.13.2.py | 1 | 25155 | #!/usr/bin/python
import getopt
import sys
from Bio import SeqIO
from Bio.SeqUtils import GC
import time# import time, gmtime, strftime
import os
import shutil
import pandas
from Bio.SeqRecord import SeqRecord
from Bio.Seq import Seq
import csv
#from datetime import datetime
import numpy as np
from scipy import stats
... | mit |
kaiserroll14/301finalproject | main/pandas/tools/util.py | 9 | 2780 | import numpy as np
import pandas.lib as lib
import pandas as pd
from pandas.compat import reduce
from pandas.core.index import Index
from pandas.core import common as com
def match(needles, haystack):
haystack = Index(haystack)
needles = Index(needles)
return haystack.get_indexer(needles)
def cartesian... | gpl-3.0 |
jblupus/PyLoyaltyProject | old/experimentos/experimentos.py | 1 | 7083 | from os import mkdir
from os.path import exists
import numpy as np
import pandas as pd
from sklearn.utils import shuffle
from old.project import CassandraUtils
from old.project import get_time
RTD_STS_KEY = 'retweetedStatus'
MT_STS_KEY = 'userMentionEntities'
PATH = '/home/joao/Dev/Data/Twitter/'
FRIENDS_PATH = '/ho... | bsd-2-clause |
TUM-LMF/fieldRNN | util/eval.py | 2 | 10249 | import numpy as np
import pandas as pd
import cPickle as pickle
import sklearn
def inverse_confusion_matrix(cm):
# inverse functino of sklearn confusion_matrix
rows, cols = cm.shape
y_pred = []
y_true = []
for r in range(rows):
for c in range(cols):
for i in range(cm[c,r]):
... | mit |
cademarkegard/airflow | airflow/hooks/base_hook.py | 18 | 2571 | # -*- coding: utf-8 -*-
#
# 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
... | apache-2.0 |
MetaMemoryT/OpenTrader | OpenTrader/OTPpnAmgc.py | 1 | 12003 | # -*-mode: python; py-indent-offset: 4; indent-tabs-mode: nil; encoding: utf-8-dos; coding: utf-8 -*-
# from http://pythonprogramming.net/advanced-matplotlib-graphing-charting-tutorial
"""
OTPpnAmgc charts a CSV file of Open High Low Close Volume values,
along with the SMA, MACD and RSI, using matplotlib.
Give the C... | lgpl-3.0 |
TomAugspurger/pandas | pandas/tests/io/formats/test_info.py | 1 | 11770 | from io import StringIO
import re
from string import ascii_uppercase as uppercase
import sys
import textwrap
import numpy as np
import pytest
from pandas.compat import PYPY
from pandas import (
CategoricalIndex,
DataFrame,
MultiIndex,
Series,
date_range,
option_context,
reset_option,
... | bsd-3-clause |
shikhar413/openmc | openmc/volume.py | 6 | 12168 | from collections import OrderedDict
from collections.abc import Iterable, Mapping
from numbers import Real, Integral
from xml.etree import ElementTree as ET
import warnings
import numpy as np
import pandas as pd
import h5py
from uncertainties import ufloat
import openmc
import openmc.checkvalue as cv
_VERSION_VOLUME... | mit |
ZENGXH/scikit-learn | sklearn/cluster/tests/test_birch.py | 342 | 5603 | """
Tests for the birch clustering algorithm.
"""
from scipy import sparse
import numpy as np
from sklearn.cluster.tests.common import generate_clustered_data
from sklearn.cluster.birch import Birch
from sklearn.cluster.hierarchical import AgglomerativeClustering
from sklearn.datasets import make_blobs
from sklearn.l... | bsd-3-clause |
VladimirTyrin/urbansim | urbansim/models/tests/test_dcm.py | 6 | 22108 | import numpy as np
import numpy.testing as npt
import pandas as pd
import pytest
import os
import tempfile
import yaml
from pandas.util import testing as pdt
from ...utils import testing
from .. import dcm
@pytest.fixture
def seed(request):
current = np.random.get_state()
def fin():
np.random.set_s... | bsd-3-clause |
valexandersaulys/prudential_insurance_kaggle | venv/lib/python2.7/site-packages/sklearn/cluster/tests/test_affinity_propagation.py | 341 | 2620 | """
Testing for Clustering methods
"""
import numpy as np
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.cluster.affinity_propagation_ import AffinityPropagation
from sklearn.cluster.affinity_propagatio... | gpl-2.0 |
carloderamo/mushroom | mushroom_rl/environments/mujoco_envs/humanoid_gait/reward_goals/reward.py | 1 | 10506 | from pathlib import Path
import numpy as np
from collections import deque
from mushroom_rl.environments.mujoco_envs.humanoid_gait.utils import convert_traj_quat_to_euler
from .trajectory import HumanoidTrajectory
class GoalRewardInterface:
"""
Interface to specify a reward function for the ``HumanoidGait`` ... | mit |
DonghoChoi/ISB_Project | local/WS_eye_duration_time.py | 2 | 7092 | #!/usr/bin/python
# Author: Dongho Choi
'''
This script
(1) read 'pages' data of a user
(2) calculate dwell time of each page
(3) retrieve fixation data corresponding to the dwell time
(4) calculate eye dwell time in which fixations are within the effective range of the screen
'''
import os.path
import datetime
impor... | gpl-3.0 |
miltonsarria/dsp-python | examples_classify/ex1.py | 1 | 2005 | import numpy as np
import matplotlib.pyplot as plt
import h5py
from sklearn.linear_model import LogisticRegression
file1='artificial_data.h5'
file2='artificial_data_test.h5'
########### cargar datos de entrenamiento y datos de prueba###########
hf = h5py.File(file1, "r")
X1 = np.array(hf.get('X1')); Y1=np.array(hf.get... | mit |
huzq/scikit-learn | maint_tools/sort_whats_new.py | 28 | 1251 | #!/usr/bin/env python
# Sorts what's new entries with per-module headings.
# Pass what's new entries on stdin.
import sys
import re
from collections import defaultdict
LABEL_ORDER = ['MajorFeature', 'Feature', 'Enhancement', 'Efficiency',
'Fix', 'API']
def entry_sort_key(s):
if s.startswith('- |'... | bsd-3-clause |
suranap/qiime | scripts/make_distance_boxplots.py | 15 | 13899 | #!/usr/bin/env python
from __future__ import division
__author__ = "Jai Ram Rideout"
__copyright__ = "Copyright 2011, The QIIME project"
__credits__ = ["Jai Ram Rideout"]
__license__ = "GPL"
__version__ = "1.9.1-dev"
__maintainer__ = "Jai Ram Rideout"
__email__ = "jai.rideout@gmail.com"
from os.path import join
from ... | gpl-2.0 |
liberatorqjw/scikit-learn | examples/datasets/plot_iris_dataset.py | 283 | 1928 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
The Iris Dataset
=========================================================
This data sets consists of 3 different types of irises'
(Setosa, Versicolour, and Virginica) petal and sepal
length, stored in a 150x4 numpy... | bsd-3-clause |
probml/pyprobml | scripts/sgd_demo_torch.py | 1 | 6230 | # We apply different SGD optimizers to a CNN on MNIST
# Based on various tutorials
#https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py
#https://github.com/CSCfi/machine-learning-scripts/blob/master/notebooks/pytorch-mnist-mlp.ipynb
import numpy as np
np.se... | mit |
henrykironde/scikit-learn | sklearn/feature_extraction/dict_vectorizer.py | 234 | 12267 | # Authors: Lars Buitinck
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from array import array
from collections import Mapping
from operator import itemgetter
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..ext... | bsd-3-clause |
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