repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
nrhine1/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_02_sentiment.py | 256 | 2406 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a ... | bsd-3-clause |
AnasGhrab/scikit-learn | examples/applications/plot_out_of_core_classification.py | 255 | 13919 | """
======================================================
Out-of-core classification of text documents
======================================================
This is an example showing how scikit-learn can be used for classification
using an out-of-core approach: learning from data that doesn't fit into main
memory. ... | bsd-3-clause |
spallavolu/scikit-learn | benchmarks/bench_plot_incremental_pca.py | 374 | 6430 | """
========================
IncrementalPCA benchmark
========================
Benchmarks for IncrementalPCA
"""
import numpy as np
import gc
from time import time
from collections import defaultdict
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_lfw_people
from sklearn.decomposition import Incre... | bsd-3-clause |
heli522/scikit-learn | examples/svm/plot_rbf_parameters.py | 132 | 8096 | '''
==================
RBF SVM parameters
==================
This example illustrates the effect of the parameters ``gamma`` and ``C`` of
the Radial Basis Function (RBF) kernel SVM.
Intuitively, the ``gamma`` parameter defines how far the influence of a single
training example reaches, with low values meaning 'far' a... | bsd-3-clause |
plotly/python-api | packages/python/plotly/_plotly_utils/tests/validators/test_integer_validator.py | 2 | 4681 | # Array not ok
# ------------
import pytest
from pytest import approx
from _plotly_utils.basevalidators import IntegerValidator
import numpy as np
import pandas as pd
# ### Fixtures ###
@pytest.fixture()
def validator():
return IntegerValidator("prop", "parent")
@pytest.fixture
def validator_min_max():
retu... | mit |
yask123/scikit-learn | sklearn/pipeline.py | 61 | 21271 | """
The :mod:`sklearn.pipeline` module implements utilities to build a composite
estimator, as a chain of transforms and estimators.
"""
# Author: Edouard Duchesnay
# Gael Varoquaux
# Virgile Fritsch
# Alexandre Gramfort
# Lars Buitinck
# Licence: BSD
from collections import defaultdict... | bsd-3-clause |
ralbayaty/KaggleRetina | testing/circleDetect.py | 1 | 1170 | import numpy as np
import matplotlib.pyplot as plt
from skimage import data, color
from skimage.transform import hough_circle
from skimage.feature import peak_local_max, canny
from skimage.draw import circle_perimeter
from skimage.util import img_as_ubyte
# Load picture and detect edges
image = img_as_ubyte(data.coi... | gpl-2.0 |
gahoo/SNAP | core/gantt.py | 1 | 6208 | import dash
from dash.dependencies import Input, Output, State
from core.misc import *
from core import models
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy.exc import IntegrityError
from collections import defaultdict
import dash_core_components as dcc
import dash_html_co... | mit |
ahmadia/bokeh | bokeh/charts/builder/line_builder.py | 43 | 5360 | """This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the Line class which lets you build your Line charts just
passing the arguments to the Chart class and calling the proper functions.
"""
#-----------------------------------------------------------------... | bsd-3-clause |
StepicOrg/Stepic-API | examples/external-reports/library/models.py | 1 | 24967 | import argparse
import shutil
import os
import time
import datetime
import matplotlib.pyplot as plt
import pandas as pd
from library.api import API_HOST, get_token, fetch_objects, fetch_objects_by_pk
from library.settings import ITEM_FORMAT, OPTION_FORMAT, STEP_FORMAT, STEP_STAT_FORMAT
from library.utils import (html... | mit |
tomlof/scikit-learn | benchmarks/bench_20newsgroups.py | 377 | 3555 | from __future__ import print_function, division
from time import time
import argparse
import numpy as np
from sklearn.dummy import DummyClassifier
from sklearn.datasets import fetch_20newsgroups_vectorized
from sklearn.metrics import accuracy_score
from sklearn.utils.validation import check_array
from sklearn.ensemb... | bsd-3-clause |
sfepy/sfepy | sfepy/discrete/iga/plot_nurbs.py | 5 | 6344 | from __future__ import absolute_import
import numpy as nm
import matplotlib.pyplot as plt
from sfepy.discrete.fem.geometry_element import GeometryElement
from sfepy.mesh.mesh_generators import get_tensor_product_conn
import sfepy.postprocess.plot_dofs as pd
from sfepy.postprocess.plot_dofs import _get_axes
from sfepy... | bsd-3-clause |
diana-hep/carl | carl/distributions/transforms.py | 1 | 1770 | # Carl is free software; you can redistribute it and/or modify it
# under the terms of the Revised BSD License; see LICENSE file for
# more details.
import numpy as np
from sklearn.utils import check_random_state
from . import TheanoDistribution
class LinearTransform(TheanoDistribution):
"""Apply a linear tran... | bsd-3-clause |
jlegendary/scikit-learn | examples/mixture/plot_gmm_selection.py | 248 | 3223 | """
=================================
Gaussian Mixture Model Selection
=================================
This example shows that model selection can be performed with
Gaussian Mixture Models using information-theoretic criteria (BIC).
Model selection concerns both the covariance type
and the number of components in th... | bsd-3-clause |
dankessler/cogfusion | cogfusion/farmcogatlas.py | 1 | 3055 | #!/usr/bin/python
"""
Based on an example from https://github.com/CognitiveAtlas/cogat-python
by Vanessa Sochat
"""
try:
from cognitiveatlas.api import get_concept, get_task
except:
pass
# EXAMPLE 1: #########################################################
# We are going to retrieve all cognitive ... | mit |
viveksck/langchangetrack | langchangetrack/tsconstruction/displacements.py | 1 | 8624 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from argparse import ArgumentParser
import os
from os import path
import cPickle as pickle
import numpy as np
import scipy
import itertools
from scipy.spatial.distance import cosine, euclidean, norm
import pandas as pd
import more_itertools
from joblib import Parallel, de... | bsd-3-clause |
ankurankan/scikit-learn | examples/cluster/plot_agglomerative_clustering_metrics.py | 20 | 4491 | """
Agglomerative clustering with different metrics
===============================================
Demonstrates the effect of different metrics on the hierarchical clustering.
The example is engineered to show the effect of the choice of different
metrics. It is applied to waveforms, which can be seen as
high-dimens... | bsd-3-clause |
CallaJun/hackprince | indico/matplotlib/tests/test_delaunay.py | 14 | 7090 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import xrange
import warnings
import numpy as np
from matplotlib.testing.decorators import image_comparison, knownfailureif
from matplotlib.cbook import MatplotlibDeprecationWarning
... | lgpl-3.0 |
yashsavani/rechorder | makeTrainingSet.py | 1 | 3651 | #!/usr/bin/python
import util
import chordKMeans
import sys
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import pylab
import random
from sklearn import svm
from operator import itemgetter
'''This file generates a training set to be used in our SVM,
consisting of a list of previous-n -> curren... | mit |
knights-lab/SHOGUN | docs/shear_results_fix.py | 2 | 2157 | # usage: python me.py \
# alignment.burst.otu.txt db.tax sheared_bayes.txt
import os
import sys
import csv
import pandas as pd
import numpy as np
from scipy.sparse import csr_matrix
with open(sys.argv[1], 'r') as inf:
csv_inf = csv.reader(inf, delimiter="\t")
columns = next(csv_inf)
columns = dict(zip(co... | agpl-3.0 |
BorisJeremic/Real-ESSI-Examples | analytic_solution/test_cases/Contact/Stress_Based_Contact_Verification/HardContact_NonLinHardSoftShear/Normal_Load/Sigma_n_1/Normal_Stress_Plot.py | 72 | 2800 | #!/usr/bin/python
import h5py
import matplotlib.pylab as plt
import matplotlib as mpl
import sys
import numpy as np;
import matplotlib;
import math;
from matplotlib.ticker import MaxNLocator
plt.rcParams.update({'font.size': 28})
# set tick width
mpl.rcParams['xtick.major.size'] = 10
mpl.rcParams['xtick.major.width']... | cc0-1.0 |
mahak/spark | python/pyspark/pandas/tests/plot/test_frame_plot.py | 15 | 4733 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | apache-2.0 |
smartkit/COVITAS | V1.0_ngix:apache_mosquitto_RESTful_Solr_LIRE/octo-ninja/rest-pandas/OctoNinja/app/views.py | 1 | 1324 | # views.py
from rest_pandas import PandasView
from .models import TimeSeries
class TimeSeriesView(PandasView):
model = TimeSeries
# In response to get(), the underlying Django REST Framework ListAPIView
# will load the default queryset (self.model.objects.all()) and then pass
# it to the following func... | unlicense |
bhillmann/gingivere | tests/test_lr.py | 2 | 1117 | from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import StratifiedKFold
import numpy as np
from sklearn.metrics import classification_report
from sklearn.metrics import roc_auc_score
from tests import shelve_api
XX, yy = shelve_api.load('lr')
X = XX[2700:]
y = yy[2700:]
clf = LinearRe... | mit |
tosolveit/scikit-learn | examples/model_selection/randomized_search.py | 201 | 3214 | """
=========================================================================
Comparing randomized search and grid search for hyperparameter estimation
=========================================================================
Compare randomized search and grid search for optimizing hyperparameters of a
random forest.
... | bsd-3-clause |
equialgo/scikit-learn | sklearn/datasets/__init__.py | 5 | 3683 | """
The :mod:`sklearn.datasets` module includes utilities to load datasets,
including methods to load and fetch popular reference datasets. It also
features some artificial data generators.
"""
from .base import load_diabetes
from .base import load_digits
from .base import load_files
from .base import load_iris
from .... | bsd-3-clause |
NunoEdgarGub1/scikit-learn | examples/datasets/plot_random_dataset.py | 348 | 2254 | """
==============================================
Plot randomly generated classification dataset
==============================================
Plot several randomly generated 2D classification datasets.
This example illustrates the :func:`datasets.make_classification`
:func:`datasets.make_blobs` and :func:`datasets.... | bsd-3-clause |
COSMOGRAIL/COSMOULINE | pipe/5_pymcs_psf_scripts/6b_handcheck_psf_NU.py | 1 | 7562 | from Tkinter import *
from tkMessageBox import *
#~ try:
#~ import ImageTk
#~ import Image
#~ except:
from PIL import ImageTk
from PIL import Image
execfile("../config.py")
from kirbybase import KirbyBase, KBError
from variousfct import *
from readandreplace_fct import *
# import matplotlib.pyplot as pl... | gpl-3.0 |
mblondel/scikit-learn | sklearn/gaussian_process/tests/test_gaussian_process.py | 7 | 6830 | """
Testing for Gaussian Process module (sklearn.gaussian_process)
"""
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# Licence: BSD 3 clause
from nose.tools import raises
from nose.tools import assert_true
import numpy as np
from sklearn.gaussian_process import GaussianProcess
from sklearn.gaussian_process ... | bsd-3-clause |
rspavel/spack | var/spack/repos/builtin/packages/py-seaborn/package.py | 5 | 1184 | # Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
from spack import *
class PySeaborn(PythonPackage):
"""Seaborn: statistical data visualization.
Seaborn is a li... | lgpl-2.1 |
KennethPierce/pylearnk | pylearn2/optimization/test_batch_gradient_descent.py | 5 | 6290 | from pylearn2.optimization.batch_gradient_descent import BatchGradientDescent
import theano.tensor as T
from pylearn2.utils import sharedX
import numpy as np
from theano import config
from theano.printing import min_informative_str
def test_batch_gradient_descent():
""" Verify that batch gradient descent works... | bsd-3-clause |
zackriegman/pydnn | examples/plankton/plankton.py | 1 | 11695 | __author__ = 'isaac'
from pydnn import neuralnet as nn
from pydnn import preprocess as pp
from pydnn import tools
from pydnn import data
from pydnn import img_util
import numpy as np
import pandas as pd
from scipy.misc import imread
import os
from os.path import join
import time
config = tools.load_config('PLANKTON... | mit |
fspaolo/scikit-learn | sklearn/utils/tests/test_random.py | 20 | 3872 | from __future__ import division
import numpy as np
from scipy.misc import comb as combinations
from sklearn.utils.random import sample_without_replacement
from sklearn.utils.testing import (
assert_raises,
assert_equal,
assert_true)
######################################################################... | bsd-3-clause |
RayMick/scikit-learn | sklearn/cluster/tests/test_k_means.py | 63 | 26190 | """Testing for K-means"""
import sys
import numpy as np
from scipy import sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing i... | bsd-3-clause |
winklerand/pandas | pandas/tests/frame/test_to_csv.py | 1 | 45142 | # -*- coding: utf-8 -*-
from __future__ import print_function
import csv
import pytest
from numpy import nan
import numpy as np
from pandas.compat import (lmap, range, lrange, StringIO, u)
from pandas.core.common import _all_none
from pandas.errors import ParserError
from pandas import (DataFrame, Index, Series, Mu... | bsd-3-clause |
rs2/pandas | pandas/tests/frame/test_combine_concat.py | 2 | 8607 | from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Index, Series, Timestamp, date_range
import pandas._testing as tm
class TestDataFrameConcat:
def test_concat_multiple_frames_dtypes(self):
# GH 2759
A = DataFrame(data=np.ones((10, 2... | bsd-3-clause |
wbinventor/openmc | openmc/data/photon.py | 1 | 43051 | from collections import OrderedDict
from collections.abc import Mapping, Callable
from copy import deepcopy
from io import StringIO
from numbers import Integral, Real
import os
import h5py
import numpy as np
import pandas as pd
from scipy.interpolate import CubicSpline
from openmc.mixin import EqualityMixin
import op... | mit |
ankurankan/scikit-learn | examples/applications/topics_extraction_with_nmf.py | 106 | 2313 | """
========================================================
Topics extraction with Non-Negative Matrix Factorization
========================================================
This is a proof of concept application of Non Negative Matrix
Factorization of the term frequency matrix of a corpus of documents so
as to extra... | bsd-3-clause |
glauffer/Conditional-Entropy | old_CE.py | 1 | 2699 | ################################################################################
# #
# Program to calculate the Conditional Entropy for a single pulsating star #
# This program calculates and saves a file with periods and entropies #
# To run: type in terminal -> python3 CE.py #
# ... | gpl-2.0 |
michaelbramwell/sms-tools | lectures/04-STFT/plots-code/time-freq-compromise.py | 19 | 1255 | import numpy as np
import time, os, sys
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import stft as STFT
import utilFunctions as UF
import matplotlib.pyplot as plt
from scipy.signal import hamming
from scipy.fftpack import fft
import math
(fs, x) = UF.wavrea... | agpl-3.0 |
iulian787/spack | var/spack/repos/builtin/packages/py-inference-schema/package.py | 3 | 1603 | # Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
# Spack Project Developers. See the top-level COPYRIGHT file for details.
#
# SPDX-License-Identifier: (Apache-2.0 OR MIT)
class PyInferenceSchema(Package):
"""This package is intended to provide a uniform schema for common machine
lear... | lgpl-2.1 |
gcarq/freqtrade | tests/strategy/test_strategy_helpers.py | 1 | 3034 | import numpy as np
import pandas as pd
from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes
def generate_test_data(timeframe: str, size: int):
np.random.seed(42)
tf_mins = timeframe_to_minutes(timeframe)
base = np.random.normal(20, 2, size=size)
date = pd.period_range('2020-0... | gpl-3.0 |
seckcoder/lang-learn | python/sklearn/sklearn/feature_selection/tests/test_rfe.py | 4 | 3061 | """
Testing Recursive feature elimination
"""
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from nose.tools import assert_equal
from scipy import sparse
from sklearn.feature_selection.rfe import RFE, RFECV
from sklearn.datasets import load_iris
from sklearn.metrics import ... | unlicense |
bsipocz/bokeh | bokeh/charts/builder/tests/test_bar_builder.py | 33 | 6390 | """ This is the Bokeh charts testing interface.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with thi... | bsd-3-clause |
pratapvardhan/scikit-learn | sklearn/cross_decomposition/cca_.py | 151 | 3192 | from .pls_ import _PLS
__all__ = ['CCA']
class CCA(_PLS):
"""CCA Canonical Correlation Analysis.
CCA inherits from PLS with mode="B" and deflation_mode="canonical".
Read more in the :ref:`User Guide <cross_decomposition>`.
Parameters
----------
n_components : int, (default 2).
numb... | bsd-3-clause |
dilawar/moogli | moogli/visualization/plots/line.py | 2 | 9350 | from moogli import constants
from moogli.visualization.pipeline import SimulationDataConsumer
from PyQt4.QtGui import QWidget
from PyQt4.QtGui import QMenu
from PyQt4.QtGui import QAction
from PyQt4.QtGui import QGridLayout
from PyQt4.QtCore import pyqtSlot
from PyQt4 import QtCore
from matplotlib.lines import Line2D
f... | gpl-2.0 |
ThomasSweijen/yadesolute2 | py/post2d.py | 4 | 13849 | # encoding: utf-8
# 2009 © Václav Šmilauer <eudoxos@arcig.cz>
"""
Module for 2d postprocessing, containing classes to project points from 3d to 2d in various ways,
providing basic but flexible framework for extracting arbitrary scalar values from bodies/interactions
and plotting the results. There are 2 basic component... | gpl-2.0 |
spallavolu/scikit-learn | examples/linear_model/lasso_dense_vs_sparse_data.py | 348 | 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 |
anaruse/chainer | chainer/training/extensions/plot_report.py | 4 | 6421 | import json
from os import path
import warnings
import numpy
import six
from chainer import reporter
from chainer import serializer as serializer_module
from chainer.training import extension
from chainer.training import trigger as trigger_module
try:
import matplotlib # NOQA
_available = True
except (Im... | mit |
sguthrie/predicting-depression | scripts/find_subjects_behavior_data.py | 1 | 4298 | """
"""
import csv
import re
import sys
import argparse
import numpy as np
import matplotlib.pyplot as plt
def check_valid_value(key, val):
if key == "participant_id":
if re.match(r'sub-[0-9]{6}', val) is None:
return False
return True
# "else" statements not necessary because fun... | gpl-3.0 |
florian-f/sklearn | sklearn/cluster/setup.py | 10 | 1202 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD Style.
import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
cblas_libs, blas_info = ge... | bsd-3-clause |
dingocuster/scikit-learn | sklearn/datasets/species_distributions.py | 198 | 7923 | """
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_... | bsd-3-clause |
kpolimis/kpolimis.github.io-src | content/downloads/code/utils.py | 1 | 2245 | import os
import sys
import glob
import json
import time
import fiona
import datetime
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython.display import Image
from shapely.prepared import prep
from descartes import PolygonPatch
from shapely.geometry import Polygon
from mp... | gpl-3.0 |
mendax-grip/cfdemUtilities | vonKarmanSingh/fftMultiple.py | 2 | 4375 | # This program analyses the X and Y drag coefficient (drag and lift) from the cylinder immersed
# boundary test cases
# It can be compared visually afterward to experimental data
# Currently is not generic and can only load 2 data set, but anyway more makes it an unreadable mess
#
# USAGE : python ./FOLDERWHEREDATA-1... | lgpl-3.0 |
QISKit/qiskit-sdk-py | test/python/visualization/test_circuit_matplotlib_drawer.py | 1 | 6355 | # -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... | apache-2.0 |
nkmk/python-snippets | notebook/pandas_to_csv.py | 1 | 1404 | import pandas as pd
df = pd.read_csv('data/src/sample_pandas_normal.csv', index_col=0)
print(df)
# age state point
# name
# Alice 24 NY 64
# Bob 42 CA 92
# Charlie 18 CA 70
# Dave 68 TX 70
# Ellen 24 CA 88
# Frank 30 NY ... | mit |
khyrulimam/pemrograman-linear-optimasi-gizi-anak-kos | nutrisi.py | 1 | 1544 | import numpy as np
import pulp
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib.patches import PathPatch
from matplotlib.path import Path
import solver
bayam = 'bayam'
tempe = 'tempe'
problem_name = 'Optimasi Gizi Anak Kos'
# decision variables (variabel keputusan)
x = pulp.LpVariable(bayam... | apache-2.0 |
SanPen/GridCal | src/GridCal/Gui/GuiFunctions.py | 1 | 37446 | # This file is part of GridCal.
#
# GridCal 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 Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# GridCal is distributed in the hope that... | gpl-3.0 |
subutai/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/lines.py | 69 | 48233 | """
This module contains all the 2D line class which can draw with a
variety of line styles, markers and colors.
"""
# TODO: expose cap and join style attrs
from __future__ import division
import numpy as np
from numpy import ma
from matplotlib import verbose
import artist
from artist import Artist
from cbook import ... | agpl-3.0 |
seanli9jan/tensorflow | tensorflow/python/kernel_tests/constant_op_eager_test.py | 33 | 21448 | # Copyright 2015 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 |
erykoff/redmapper | tests/test_centeringcal.py | 1 | 2880 | import matplotlib
matplotlib.use('Agg')
import unittest
import os
import shutil
import numpy.testing as testing
import numpy as np
import fitsio
import tempfile
from numpy import random
from redmapper.configuration import Configuration
from redmapper.calibration import WcenCalibrator
class CenteringCalibratorTestCas... | apache-2.0 |
ajrichards/notebook | deep-learning/draw_simple_nn.py | 2 | 3075 | #!/usr/bin/env python
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
def draw_neural_net(ax, left, right, bottom, top, layer_sizes):
'''
Draw a neural network cartoon using matplotilb.
:usage:
>>> fig = plt.figure(figsize=(12, 12))
>>> draw_neural_net(fig.gca(),... | bsd-3-clause |
callmewillig/NetViz | Interface/interface.py | 1 | 6173 | """"
interface.py
This program will create a user interface with the capablilities to choose an
address and gain various information based on that address.
libraries(sudo aot-get install...)
python3-pyqt5(PyQt5)
matplotlib
Authors
Tommy Slota
Mathew Willig
Nicholas Miller
"""
#-----impo... | mit |
victorbergelin/scikit-learn | examples/decomposition/plot_sparse_coding.py | 247 | 3846 | """
===========================================
Sparse coding with a precomputed dictionary
===========================================
Transform a signal as a sparse combination of Ricker wavelets. This example
visually compares different sparse coding methods using the
:class:`sklearn.decomposition.SparseCoder` esti... | bsd-3-clause |
xzh86/scikit-learn | sklearn/metrics/__init__.py | 214 | 3440 | """
The :mod:`sklearn.metrics` module includes score functions, performance metrics
and pairwise metrics and distance computations.
"""
from .ranking import auc
from .ranking import average_precision_score
from .ranking import coverage_error
from .ranking import label_ranking_average_precision_score
from .ranking imp... | bsd-3-clause |
navoj/ecell4 | ecell4/egfrd/legacy/samples/hardbody/plot.py | 6 | 3594 | #!/usr/bin/env/python
import sys
import numpy
import scipy.io
from matplotlib.pylab import *
N_A = 6.0221367e23
def plot_data(N, T, fmt):
T = numpy.array(T)
mean = T.mean(1)
std_err = T.std()/math.sqrt(len(T))
#errorbar(N, mean, yerr=std_err, fmt=fmt)
print N, mean
loglog(N, mean, fmt)
... | gpl-2.0 |
jrleeman/MetPy | setup.py | 1 | 3270 | # Copyright (c) 2008,2010,2015,2016 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Setup script for installing MetPy."""
from __future__ import print_function
from setuptools import find_packages, setup
import versioneer
ver = versioneer.get_ve... | bsd-3-clause |
dnet/proxmark3 | fpga/tests/plot_edgedetect.py | 14 | 1553 | #!/usr/bin/env python
#-----------------------------------------------------------------------------
# Copyright (C) 2014 iZsh <izsh at fail0verflow.com>
#
# This code is licensed to you under the terms of the GNU GPL, version 2 or,
# at your option, any later version. See the LICENSE.txt file for the text of
# the lic... | gpl-2.0 |
PhilReinhold/pyqt_utils | plot_widgets.py | 1 | 16851 | from PyQt4 import QtGui, QtCore
import warnings
import numpy as np
import pyqtgraph as pg
pg.setConfigOption("useWeave", False)
from pyqtgraph.dockarea import Dock, DockArea
from matplotlib.figure import Figure
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg, NavigationToolbar2QTAgg
class CrosshairPlo... | mit |
clemkoa/scikit-learn | examples/ensemble/plot_adaboost_twoclass.py | 72 | 3333 | """
==================
Two-class AdaBoost
==================
This example fits an AdaBoosted decision stump on a non-linearly separable
classification dataset composed of two "Gaussian quantiles" clusters
(see :func:`sklearn.datasets.make_gaussian_quantiles`) and plots the decision
boundary and decision scores. The di... | bsd-3-clause |
canavandl/bokeh | bokeh/protocol.py | 3 | 4691 | from __future__ import absolute_import
import json
import logging
import datetime as dt
import calendar
import decimal
import numpy as np
try:
import pandas as pd
is_pandas = True
except ImportError:
is_pandas = False
try:
from dateutil.relativedelta import relativedelta
is_dateutil = True
excep... | bsd-3-clause |
petroolg/robo-spline | graph.py | 1 | 6668 | # Coordinated Spline Motion and Robot Control Project
#
# Copyright (c) 2017 Olga Petrova <olga.petrova@cvut.cz>
# Advisor: Pavel Pisa <pisa@cmp.felk.cvut.cz>
# FEE CTU Prague, Czech Republic
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentatio... | mit |
wangz19/TEST | Bone_modeling/gaussian_distribution.py | 1 | 1866 | # function is defining a gaussian distribution parameter
import numpy as np
import matplotlib.pyplot as plt
###--------------parameter
sampleSize = 1000 #total sample point
mean_E = 50. # mean value of modulus
#####----------------------gaussian distribution -------------------#######
mu, sigma = 0, 11.
E_tem... | gpl-2.0 |
mehdidc/scikit-learn | sklearn/linear_model/tests/test_theil_sen.py | 234 | 9928 | """
Testing for Theil-Sen module (sklearn.linear_model.theil_sen)
"""
# Author: Florian Wilhelm <florian.wilhelm@gmail.com>
# License: BSD 3 clause
from __future__ import division, print_function, absolute_import
import os
import sys
from contextlib import contextmanager
import numpy as np
from numpy.testing import ... | bsd-3-clause |
imk1/IMKTFBindingCode | runRandomForestClassification.py | 1 | 2267 | def makeRandomForestClassificationInputs(positivesFileName, negativesFileName, validPositivesFileName, validNegativesFileName):
# Make inputs for a random forward classifier
# ASSUMES THAT FEATURES ARE SORTED IN THE SAME WAY IN ALL FILES
positivesFeatureMat = np.loadtxt(positivesFileName)
negativesFeatureMat = ... | mit |
lheagy/simpegem | simpegEM/Utils/SrcUtils.py | 2 | 7432 | from SimPEG import *
from scipy.special import ellipk, ellipe
from scipy.constants import mu_0, pi
def MagneticDipoleVectorPotential(srcLoc, obsLoc, component, moment=1., dipoleMoment=(0., 0., 1.), mu = mu_0):
"""
Calculate the vector potential of a set of magnetic dipoles
at given locations 'ref. ... | mit |
luca76/QGIS | python/plugins/processing/algs/qgis/VectorLayerHistogram.py | 6 | 2835 | # -*- coding: utf-8 -*-
"""
***************************************************************************
EquivalentNumField.py
---------------------
Date : January 2013
Copyright : (C) 2013 by Victor Olaya
Email : volayaf at gmail dot com
*******************... | gpl-2.0 |
YinongLong/scikit-learn | sklearn/cross_validation.py | 11 | 69870 |
"""
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 |
pechersky/keras-molecules | download_dataset.py | 2 | 2215 | import os
import argparse
import urllib
import pandas
import tempfile
from progressbar import ProgressBar, Percentage, Bar, ETA, FileTransferSpeed
DEFAULTS = {
"chembl22": {
"uri": "ftp://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/latest/chembl_22_chemreps.txt.gz",
"outfile": "data/chembl22.h5"
... | mit |
jkthompson/nupic | examples/opf/tools/sp_plotter.py | 8 | 15763 | #! /usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions... | gpl-3.0 |
satishgoda/bokeh | bokeh/sampledata/gapminder.py | 41 | 2655 | from __future__ import absolute_import
import pandas as pd
from os.path import join
import sys
from . import _data_dir
'''
This module provides a pandas DataFrame instance of four
of the datasets from gapminder.org.
These are read in from csvs that have been downloaded from Bokeh's
sample data on S3. But the origina... | bsd-3-clause |
wibeasley/PyCap | test/test_project.py | 3 | 11950 | #! /usr/bin/env python
import unittest
from redcap import Project, RedcapError
skip_pd = False
try:
import pandas as pd
except ImportError:
skip_pd = True
class ProjectTests(unittest.TestCase):
"""docstring for ProjectTests"""
url = 'https://redcap.vanderbilt.edu/api/'
bad_url = 'https://redcap... | mit |
ChinaQuants/bokeh | examples/plotting/file/elements.py | 43 | 1485 | import pandas as pd
from bokeh.plotting import figure, show, output_file
from bokeh.sampledata import periodic_table
elements = periodic_table.elements
elements = elements[elements["atomic number"] <= 82]
elements = elements[~pd.isnull(elements["melting point"])]
mass = [float(x.strip("[]")) for x in elements["atomic... | bsd-3-clause |
pianomania/scikit-learn | sklearn/cluster/mean_shift_.py | 42 | 15514 | """Mean shift clustering algorithm.
Mean shift clustering aims to discover *blobs* in a smooth density of
samples. It is a centroid based algorithm, which works by updating candidates
for centroids to be the mean of the points within a given region. These
candidates are then filtered in a post-processing stage to elim... | bsd-3-clause |
eramirem/astroML | book_figures/chapter6/fig_kmeans_metallicity.py | 3 | 3200 | """
EM example: K-means
-------------------
Figure 6.13
The K-means analysis of the stellar metallicity data used in figure 6.6. Note
how the background distribution "pulls" the cluster centers away from the locus
where one would place them by eye. This is why more sophisticated models like
GMM are often better in pra... | bsd-2-clause |
backtou/longlab | gr-digital/examples/example_fll.py | 17 | 4821 | #!/usr/bin/env python
from gnuradio import gr, digital
from gnuradio import eng_notation
from gnuradio.eng_option import eng_option
from optparse import OptionParser
try:
import scipy
except ImportError:
print "Error: could not import scipy (http://www.scipy.org/)"
sys.exit(1)
try:
import pylab
excep... | gpl-3.0 |
xzh86/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 |
winklerand/pandas | pandas/tests/io/msgpack/test_pack.py | 9 | 4922 | # coding: utf-8
import pytest
import struct
from pandas import compat
from pandas.compat import u, OrderedDict
from pandas.io.msgpack import packb, unpackb, Unpacker, Packer
class TestPack(object):
def check(self, data, use_list=False):
re = unpackb(packb(data), use_list=use_list)
assert re ==... | bsd-3-clause |
alvarofierroclavero/scikit-learn | sklearn/naive_bayes.py | 128 | 28358 | # -*- coding: utf-8 -*-
"""
The :mod:`sklearn.naive_bayes` module implements Naive Bayes algorithms. These
are supervised learning methods based on applying Bayes' theorem with strong
(naive) feature independence assumptions.
"""
# Author: Vincent Michel <vincent.michel@inria.fr>
# Minor fixes by Fabian Pedre... | bsd-3-clause |
goodfeli/pylearn2 | pylearn2/train_extensions/live_monitoring.py | 13 | 11530 | """
Training extension for allowing querying of monitoring values while an
experiment executes.
"""
__authors__ = "Dustin Webb"
__copyright__ = "Copyright 2010-2012, Universite de Montreal"
__credits__ = ["Dustin Webb"]
__license__ = "3-clause BSD"
__maintainer__ = "LISA Lab"
__email__ = "pylearn-dev@googlegroups"
imp... | bsd-3-clause |
fbagirov/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 |
rishikksh20/scikit-learn | examples/manifold/plot_compare_methods.py | 52 | 3878 | """
=========================================
Comparison of Manifold Learning methods
=========================================
An illustration of dimensionality reduction on the S-curve dataset
with various manifold learning methods.
For a discussion and comparison of these algorithms, see the
:ref:`manifold module... | bsd-3-clause |
shnizzedy/SM_openSMILE | openSMILE_preprocessing/arff_csv_to_pandas.py | 1 | 6508 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
arff_csv_to_pandas.py
Functions to import openSMILE outputs to Python pandas.
Authors:
– Jon Clucas, 2016 (jon.clucas@childmind.org)
– Arno Klein, 2016 (arno.klein@childmind.org)
– Bonhwang Koo, 2016 (bonhwang.koo@childmind.org)
© 2016, Child Mind Instit... | apache-2.0 |
blaze/dask | dask/tests/test_distributed.py | 3 | 5475 | import pytest
distributed = pytest.importorskip("distributed")
import asyncio
from functools import partial
from operator import add
from tornado import gen
import dask
from dask import persist, delayed, compute
from dask.delayed import Delayed
from dask.utils import tmpdir, get_named_args
from distributed import f... | bsd-3-clause |
nicholasmalaya/arcanus | disputatio/routines/vanes/bottom.py | 2 | 4039 | #!/bin/py
#
# interpolate over data field for bottom vanes
#
#
#
import numpy as np
import matplotlib
matplotlib.use('Agg')
import itertools
import matplotlib.pyplot as plt
from scipy import integrate
from scipy.integrate import ode
radprime=3.0
radmin=0.6
def vf(t,x):
#
# Vector field function
#
the... | mit |
secimTools/SECIMTools | src/scripts/secimtools/anovaModules/volcano.py | 2 | 2710 | #Add-on packages
import matplotlib
matplotlib.use('Agg')
import numpy as np
import pandas as pd
from matplotlib.backends.backend_pdf import PdfPages
# Plotting packages
from secimtools.visualManager import module_box as box
from secimtools.visualManager import module_hist as hist
from secimtools.visualManager import m... | mit |
simon-pepin/scikit-learn | sklearn/tests/test_kernel_ridge.py | 342 | 3027 | import numpy as np
import scipy.sparse as sp
from sklearn.datasets import make_regression
from sklearn.linear_model import Ridge
from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils.testing import ignore_warnings
from sklearn.utils.testing import assert... | bsd-3-clause |
stylianos-kampakis/scikit-learn | sklearn/mixture/tests/test_gmm.py | 200 | 17427 | import unittest
import copy
import sys
from nose.tools import assert_true
import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_raises)
from scipy import stats
from sklearn import mixture
from sklearn.datasets.samples_generator import make_spd_ma... | bsd-3-clause |
Dapid/pywt | demo/benchmark.py | 2 | 1911 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import gc
import sys
import time
import numpy as np
import matplotlib.pyplot as plt
import pywt
if sys.platform == 'win32':
clock = time.clock
else:
clock = time.time
sizes = [20, 50, 100, 120, 150, 200, 250, 300, 400, 500, 600, 750,
1000, 2000, 3000,... | mit |
jbogaardt/chainladder-python | chainladder/adjustments/parallelogram.py | 1 | 4390 | # This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at https://mozilla.org/MPL/2.0/.
from sklearn.base import BaseEstimator, TransformerMixin
from chainladder.core.io import EstimatorIO
class Parallelog... | mit |
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