repo_name stringlengths 6 112 | path stringlengths 4 204 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 714 810k | license stringclasses 15
values |
|---|---|---|---|---|---|
scikit-learn-contrib/py-earth | examples/plot_feature_importance.py | 3 | 2142 | """
===========================
Plotting feature importance
===========================
A simple example showing how to compute and display
feature importances, it is also compared with the
feature importances obtained using random forests.
Feature importance is a measure of the effect of the features
on the outputs.... | bsd-3-clause |
alphatwirl/alphatwirl | alphatwirl/collector/ToDataFrameWithDatasetColumn.py | 1 | 1326 | # Tai Sakuma <tai.sakuma@gmail.com>
import pandas as pd
from .ToTupleListWithDatasetColumn import ToTupleListWithDatasetColumn
##__________________________________________________________________||
class ToDataFrameWithDatasetColumn:
def __init__(self, summaryColumnNames,
datasetColumnName = 'co... | bsd-3-clause |
FilipDominec/python-meep-utils | scripts_postpro/plot_TY.py | 1 | 4307 | #!/usr/bin/env python
#-*- coding: utf-8 -*-
simtime = 80e-12
size_y = 1400e-6
c = 3e8
maxfreq = 2e12
## Import common moduli
import numpy as np
from scipy.constants import c, hbar, pi
import matplotlib, sys, os, time
import matplotlib.pyplot as plt
## Start figure + subplot (interactive)
fig = plt.figure(figsize=(1... | gpl-2.0 |
cgalleguillosm/accasim | accasim/utils/plot_factory.py | 1 | 51706 | """
MIT License
Copyright (c) 2017 cgalleguillosm, AlessioNetti
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 limitation the rights
to use, copy, ... | mit |
grocsvs/grocsvs | src/grocsvs/stages/refine_grid_search_breakpoints.py | 1 | 13463 | # FOR GRID SEARCH CANDIDATES
import itertools
import numpy
import os
import pandas
import scipy.stats
from grocsvs import step
from grocsvs import structuralvariants
from grocsvs.stages import sv_candidates
class CombineRefinedBreakpointsStep(step.StepChunk):
@staticmethod
def get_steps(options):
y... | mit |
hugobowne/scikit-learn | sklearn/metrics/tests/test_regression.py | 272 | 6066 | from __future__ import division, print_function
import numpy as np
from itertools import product
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.... | bsd-3-clause |
AMGitsKriss/Battlegrounds | battlegrounds_write_graph.py | 2 | 1492 | import pandas as pd
#Enumerate colors.
class COLOR:
RED = "tomato"
GREEN = "yellowgreen"
BLUE = "lightblue"
NEWLINE_INDENT = "\n "
def fill(color):
return f"[style=filled fillcolor=\"{color}\"]"
def dual_label(weapon, n):
return f"[label=\"{weapon}\" taillabel=\"{n}\"]"
def solo_node(player, color):
return f... | mit |
Clyde-fare/scikit-learn | sklearn/metrics/ranking.py | 79 | 25426 | """Metrics to assess performance on classification task given scores
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.... | bsd-3-clause |
rs2/pandas | pandas/io/sql.py | 1 | 62655 | """
Collection of query wrappers / abstractions to both facilitate data
retrieval and to reduce dependency on DB-specific API.
"""
from contextlib import contextmanager
from datetime import date, datetime, time
from functools import partial
import re
from typing import Iterator, Optional, Union, overload
import warnin... | bsd-3-clause |
alanlhutchison/empirical-JTK_CYCLE-with-asymmetry | previous_files/jtk7.py | 1 | 19118 | #!/usr/bin/env python
"""
Created on April 20 2014
@author: Alan L. Hutchison, alanlhutchison@uchicago.edu, Aaron R. Dinner Group, University of Chicago
This script is one in a series of scripts for running empirical JTK_CYCLE analysis as described in
Hutchison, Maienschein-Cline, and Chiang et al. Improved statisti... | mit |
etkirsch/scikit-learn | examples/svm/plot_custom_kernel.py | 171 | 1546 | """
======================
SVM with custom kernel
======================
Simple usage of Support Vector Machines to classify a sample. It will
plot the decision surface and the support vectors.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
# import some data... | bsd-3-clause |
google/telluride_decoding | test/scaled_lda_test.py | 1 | 6747 | # Copyright 2020 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | apache-2.0 |
jeffzheng1/tensorflow | tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py | 82 | 6157 | # 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 |
DANA-Laboratory/CoolProp | Web/scripts/fluid_properties.Incompressibles.py | 3 | 6445 | from __future__ import print_function, division
import os.path
import CoolProp
import CoolProp.CoolProp
import subprocess
import sys
import numpy as np
import matplotlib
matplotlib.use('Agg') #Force mpl to use a non-GUI backend
import matplotlib.pyplot as plt
web_dir = os.path.abspath(os.path.join(os.path.dirname(__... | mit |
JaviMerino/trappy | trappy/thermal.py | 2 | 9812 | # Copyright 2015-2016 ARM Limited
#
# 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 w... | apache-2.0 |
fengzhyuan/scikit-learn | examples/semi_supervised/plot_label_propagation_versus_svm_iris.py | 286 | 2378 | """
=====================================================================
Decision boundary of label propagation versus SVM on the Iris dataset
=====================================================================
Comparison for decision boundary generated on iris dataset
between Label Propagation and SVM.
This demon... | bsd-3-clause |
ngoix/OCRF | sklearn/linear_model/ransac.py | 14 | 17163 | # coding: utf-8
# Author: Johannes Schönberger
#
# License: BSD 3 clause
import numpy as np
import warnings
from ..base import BaseEstimator, MetaEstimatorMixin, RegressorMixin, clone
from ..utils import check_random_state, check_array, check_consistent_length
from ..utils.random import sample_without_replacement
fr... | bsd-3-clause |
sonata-nfv/son-cli | setup.py | 5 | 3428 | # Copyright (c) 2015 SONATA-NFV, UBIWHERE
# 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 app... | apache-2.0 |
wavelets/zipline | zipline/utils/test_utils.py | 5 | 3103 | from contextlib import contextmanager
from logbook import FileHandler
from zipline.finance.blotter import ORDER_STATUS
from six import itervalues
import pandas as pd
def to_utc(time_str):
return pd.Timestamp(time_str, tz='US/Eastern').tz_convert('UTC')
def setup_logger(test, path='test.log'):
test.log_han... | apache-2.0 |
shikhardb/scikit-learn | sklearn/covariance/__init__.py | 389 | 1157 | """
The :mod:`sklearn.covariance` module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.
"""
from ... | bsd-3-clause |
linebp/pandas | doc/make.py | 8 | 12640 | #!/usr/bin/env python
"""
Python script for building documentation.
To build the docs you must have all optional dependencies for pandas
installed. See the installation instructions for a list of these.
<del>Note: currently latex builds do not work because of table formats that are not
supported in the latex generat... | bsd-3-clause |
lip6-mptcp/ns3mptcp | src/flow-monitor/examples/wifi-olsr-flowmon.py | 108 | 7439 | # -*- Mode: Python; -*-
# Copyright (c) 2009 INESC Porto
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 2 as
# published by the Free Software Foundation;
#
# This program is distributed in the hope that it will be useful,
#... | gpl-2.0 |
datalyze-solutions/pandas-qt | pandasqt/views/EditDialogs.py | 4 | 8445 | import re
from pandasqt.compat import QtCore, QtGui, Qt, Slot, Signal
from pandasqt.models.SupportedDtypes import SupportedDtypes
import numpy
from pandas import Timestamp
from pandas.tslib import NaTType
class DefaultValueValidator(QtGui.QValidator):
def __init__(self, parent=None):
super(DefaultValueV... | mit |
theakholic/ThinkStats2 | code/timeseries.py | 66 | 18035 | """This file contains code for use with "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function
import pandas
import numpy as np
import statsmodels.formula.api as smf
import st... | gpl-3.0 |
yanlend/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 |
simon-pepin/scikit-learn | sklearn/semi_supervised/label_propagation.py | 128 | 15312 | # coding=utf8
"""
Label propagation in the context of this module refers to a set of
semisupervised classification algorithms. In the high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.
These algorithms per... | bsd-3-clause |
thp44/delphin_6_automation | data_process/2d_1d/simon/example_sim.py | 1 | 7655 | __author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import os
import json
import pandas as pd
import xmltodict
import shutil
# RiBuild Modules
from delphin_6_automation.dat... | mit |
macks22/scikit-learn | examples/semi_supervised/plot_label_propagation_structure.py | 247 | 2432 | """
==============================================
Label Propagation learning a complex structure
==============================================
Example of LabelPropagation learning a complex internal structure
to demonstrate "manifold learning". The outer circle should be
labeled "red" and the inner circle "blue". Be... | bsd-3-clause |
jkarnows/scikit-learn | benchmarks/bench_glm.py | 297 | 1493 | """
A comparison of different methods in GLM
Data comes from a random square matrix.
"""
from datetime import datetime
import numpy as np
from sklearn import linear_model
from sklearn.utils.bench import total_seconds
if __name__ == '__main__':
import pylab as pl
n_iter = 40
time_ridge = np.empty(n_it... | bsd-3-clause |
russel1237/scikit-learn | examples/svm/plot_weighted_samples.py | 188 | 1943 | """
=====================
SVM: Weighted samples
=====================
Plot decision function of a weighted dataset, where the size of points
is proportional to its weight.
The sample weighting rescales the C parameter, which means that the classifier
puts more emphasis on getting these points right. The effect might ... | bsd-3-clause |
LiaoPan/scikit-learn | examples/exercises/plot_iris_exercise.py | 323 | 1602 | """
================================
SVM Exercise
================================
A tutorial exercise for using different SVM kernels.
This exercise is used in the :ref:`using_kernels_tut` part of the
:ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`.
"""
print(__doc__)
import numpy as np
i... | bsd-3-clause |
michigraber/scikit-learn | examples/linear_model/plot_bayesian_ridge.py | 248 | 2588 | """
=========================
Bayesian Ridge Regression
=========================
Computes a Bayesian Ridge Regression on a synthetic dataset.
See :ref:`bayesian_ridge_regression` for more information on the regressor.
Compared to the OLS (ordinary least squares) estimator, the coefficient
weights are slightly shift... | bsd-3-clause |
xzh86/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 |
jschuecker/nest-simulator | pynest/examples/plot_weight_matrices.py | 17 | 6243 | # -*- coding: utf-8 -*-
#
# plot_weight_matrices.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST 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 2 of the Li... | gpl-2.0 |
lanselin/pysal | pysal/esda/tests/test_join_counts.py | 6 | 2224 | import unittest
import numpy as np
from ..join_counts import Join_Counts
from ...weights import lat2W
from ...common import pandas
PANDAS_EXTINCT = pandas is None
class Join_Counts_Tester(unittest.TestCase):
"""Unit test for Join Counts"""
def setUp(self):
self.w = lat2W(4, 4)
self.y = np.one... | bsd-3-clause |
ah391/sc-python | datalook.py | 1 | 1722 |
# coding: utf-8
import sys
import numpy
import matplotlib.pyplot
def analyse(filename, outfile=None):
"""Load data and create plots.
Subplots with placeholders, with set lables, layout tight
"""
data = numpy.loadtxt(fname=filename, delimiter=',')
# Create a wide figure to hold the subplo... | mit |
trungnt13/scikit-learn | sklearn/decomposition/tests/test_dict_learning.py | 47 | 8095 | import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises
... | bsd-3-clause |
yilei0620/3D_Conditional_Gan | lib/data_utils.py | 1 | 1596 | import numpy as np
from sklearn import utils as skutils
from rng import np_rng, py_rng
def center_crop(x, ph, pw=None):
if pw is None:
pw = ph
h, w = x.shape[:2]
j = int(round((h - ph)/2.))
i = int(round((w - pw)/2.))
return x[j:j+ph, i:i+pw]
def patch(x, ph, pw=None):
if pw is None:
... | mit |
martatolos/DemandAnalysis | visualizations.py | 1 | 5518 | # Plots
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import re
import random
def plot_several_countries(df, ylabel, title, country_list="", save=False, num="", xticks_hourly=False, kind='bar', linestyle='-', color='mbygcr', marker='o', linewidth=4.0, fontsize=16, legend=True):
"""
This... | gpl-2.0 |
mayblue9/scikit-learn | benchmarks/bench_multilabel_metrics.py | 276 | 7138 | #!/usr/bin/env python
"""
A comparison of multilabel target formats and metrics over them
"""
from __future__ import division
from __future__ import print_function
from timeit import timeit
from functools import partial
import itertools
import argparse
import sys
import matplotlib.pyplot as plt
import scipy.sparse as... | bsd-3-clause |
Zing22/uemscode | tmp_test.py | 1 | 1634 | # -*- coding=utf-8 -*-
#### for testing steps
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.externals import joblib
from PIL import Image
from process import toBin, cropLetters
from img2feature import toFeature
from main import readAllFiles
TEMP_DIR = 'tmp/'
def test_onePi... | mit |
RPGOne/Skynet | scikit-learn-0.18.1/sklearn/linear_model/tests/test_ransac.py | 52 | 17482 | from scipy import sparse
import numpy as np
from scipy import sparse
from numpy.testing import assert_equal, assert_raises
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from sklearn.utils import check_random_state
from sklearn.utils.testing import assert_raises_rege... | bsd-3-clause |
adamhajari/spyre | tests/test_app.py | 1 | 5046 | # from spyre import server
from spyre import server
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from numpy import pi
class TestApp(server.App):
colors = [
{"label": "Green", "value": 'g'},
{"label": "Red", "value": 'r'},
{"label": "Blue", "value": 'b'},
... | mit |
jjx02230808/project0223 | benchmarks/bench_lasso.py | 297 | 3305 | """
Benchmarks of Lasso vs LassoLars
First, we fix a training set and increase the number of
samples. Then we plot the computation time as function of
the number of samples.
In the second benchmark, we increase the number of dimensions of the
training set. Then we plot the computation time as function of
the number o... | bsd-3-clause |
ChanderG/scikit-learn | sklearn/metrics/tests/test_regression.py | 272 | 6066 | from __future__ import division, print_function
import numpy as np
from itertools import product
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.... | bsd-3-clause |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/pandas/io/stata.py | 7 | 82769 | """
Module contains tools for processing Stata files into DataFrames
The StataReader below was originally written by Joe Presbrey as part of PyDTA.
It has been extended and improved by Skipper Seabold from the Statsmodels
project who also developed the StataWriter and was finally added to pandas in
a once again improv... | gpl-3.0 |
centic9/subversion-ppa | tools/dev/graph-dav-servers.py | 5 | 5465 | #!/usr/bin/env python
#
#
# 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
#... | apache-2.0 |
VirusTotal/msticpy | tests/test_ip_utils.py | 1 | 3873 | # -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------
"""IP Util... | mit |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/pandas/io/tests/test_pickle.py | 7 | 10831 | # pylint: disable=E1101,E1103,W0232
""" manage legacy pickle tests """
import nose
import os
from distutils.version import LooseVersion
import pandas as pd
from pandas import Index
from pandas.compat import u, is_platform_little_endian
import pandas
import pandas.util.testing as tm
from pandas.tseries.offsets impor... | mit |
DigitalSlideArchive/HistomicsTK | histomicstk/features/compute_intensity_features.py | 1 | 5874 | """Compute intensity features in labeled image."""
import numpy as np
import pandas as pd
import scipy.stats
from skimage.measure import regionprops
def compute_intensity_features(
im_label, im_intensity, num_hist_bins=10,
rprops=None, feature_list=None):
"""Calculate intensity features from an in... | apache-2.0 |
kgullikson88/IGRINS_Scripts | Search_Fast.py | 1 | 2751 | import sys
import os
import GenericSearch
import pandas
# Define regions contaminated by telluric residuals or other defects. We will not use those regions in the cross-correlation
badregions = [[0, 1510], # Blue end of H band (lots of water absorption)
#[1561, 1615], # CO2 band that is often poorly c... | mit |
SanPen/GridCal | src/research/opf/dc_opf_3.py | 1 | 8418 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This program implements the DC power flow as a linear program
This version uses the sparse structures and it the problem compilation is
blazing fast compared to the full matrix version
"""
from pulp import *
import numpy as np
import pandas as pd
from matplotlib import ... | gpl-3.0 |
jnez71/demos | signals/gaussian_markov_kernel.py | 1 | 1646 | #!/usr/bin/env python3
"""
Kernel of Gaussian-transition scalar Markov process?
"""
import numpy as np
from matplotlib import pyplot
npr = np.random
np.set_printoptions(suppress=True)
pyplot.rcParams["font.size"] = 16
pyplot.rcParams["axes.grid"] = True
################################################## SYSTEM
def... | mit |
HubLot/PBxplore | pbxplore/tests/test_regression.py | 2 | 21493 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Regression tests for PBxplore.
This test suite run the various PBxplore programs with various argument, and
makes sure the output is the expected one. The aim is to check that the
programs are not broken during development.
Be careful this test suite does not test th... | mit |
JPFrancoia/scikit-learn | sklearn/preprocessing/data.py | 13 | 70436 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Eric Martin <eric@ericmart.in>
# Giorgio Patrini <giorgio.patrini@anu.edu.au>
# Lic... | bsd-3-clause |
jm-begon/scikit-learn | examples/cluster/plot_kmeans_silhouette_analysis.py | 242 | 5885 | """
===============================================================================
Selecting the number of clusters with silhouette analysis on KMeans clustering
===============================================================================
Silhouette analysis can be used to study the separation distance between the... | bsd-3-clause |
carpyncho/feets | doc/source/JSAnimation/examples.py | 4 | 3126 | import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
from JSAnimation import IPython_display
def basic_animation(frames=100, interval=30):
"""Plot a basic sine wave with oscillating amplitude"""
fig = plt.figure()
ax = plt.axes(xlim=(0, 10), ylim=(-2, 2))
line, = ax.... | mit |
mizzao/ggplot | ggplot/stats/stat_function.py | 12 | 4439 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
import pandas as pd
from ggplot.utils import make_iterable_ntimes
from ggplot.utils.exceptions import GgplotError
from .stat import stat
class stat_function(stat):
"""
Superimpose a... | bsd-2-clause |
yyjiang/scikit-learn | sklearn/covariance/tests/test_covariance.py | 142 | 11068 | # 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 |
pyoceans/pocean-core | pocean/dsg/trajectory/cr.py | 1 | 10376 | #!python
# coding=utf-8
from copy import copy
from collections import OrderedDict
import numpy as np
import pandas as pd
from pocean.utils import (
create_ncvar_from_series,
dict_update,
downcast_dataframe,
generic_masked,
get_default_axes,
get_dtype,
get_mapped_axes_variables,
get_mas... | mit |
nsalomonis/AltAnalyze | AltAnalyzeViewer.py | 1 | 282646 | import os.path, sys, shutil
import os
import string, re
import subprocess
import numpy as np
import unique
import traceback
import wx
import wx.lib.scrolledpanel
import wx.grid as gridlib
try:
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore",category=UserWarning) ... | apache-2.0 |
hgrif/ds-utils | dsutils/sklearn.py | 1 | 2913 | import numpy as np
from sklearn import cross_validation
from sklearn import metrics
from sklearn import preprocessing
def multiclass_roc_auc_score(y_true, y_score, label_binarizer=None, **kwargs):
"""Compute ROC AUC score for multiclass.
:param y_true: true multiclass predictions [n_samples]
:param y_scor... | mit |
amnet04/ALECMAPREADER1 | funcionesCV_recurrentes.py | 1 | 4438 | import numpy as np
import pandas
import cv2
def cargar_imagen(archivo):
'''
Carga en variables dos matrices de la imágen, una gris y otra a color,
devuelve un diccionario con las dos versiones.
'''
imagen = {}
imagen['gris'] = cv2.imread(archivo,0)
imagen['color'] = cv2.imread(archivo)
... | mit |
schreiberx/sweet | benchmarks_plane/nonlinear_interaction/pp_plot_errors_single.py | 2 | 2935 | #! /usr/bin/env python3
import sys
import matplotlib
matplotlib.use('Agg')
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from matplotlib.lines import Line2D
from mule.postprocessing.JobsData import *
from mule.postprocessing.JobsDataConsolidate import *
if len(sys.argv) > ... | mit |
ChanChiChoi/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 |
appapantula/scikit-learn | sklearn/neighbors/graph.py | 208 | 7031 | """Nearest Neighbors graph functions"""
# Author: Jake Vanderplas <vanderplas@astro.washington.edu>
#
# License: BSD 3 clause (C) INRIA, University of Amsterdam
import warnings
from .base import KNeighborsMixin, RadiusNeighborsMixin
from .unsupervised import NearestNeighbors
def _check_params(X, metric, p, metric_... | bsd-3-clause |
nuclear-wizard/moose | python/mooseutils/VectorPostprocessorReader.py | 6 | 5970 | #* This file is part of the MOOSE framework
#* https://www.mooseframework.org
#*
#* All rights reserved, see COPYRIGHT for full restrictions
#* https://github.com/idaholab/moose/blob/master/COPYRIGHT
#*
#* Licensed under LGPL 2.1, please see LICENSE for details
#* https://www.gnu.org/licenses/lgpl-2.1.html
import os
i... | lgpl-2.1 |
jlowin/airflow | scripts/perf/scheduler_ops_metrics.py | 30 | 6536 | # -*- 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, softw... | apache-2.0 |
hasecbinusr/pysal | pysal/esda/tests/test_geary.py | 5 | 2997 | """Geary Unittest."""
import unittest
from ... import open as popen
from ... import examples
from .. import geary
import numpy as np
from ...common import pandas
PANDAS_EXTINCT = pandas is None
class Geary_Tester(unittest.TestCase):
"""Geary class for unit tests."""
def setUp(self):
self.w = popen(ex... | bsd-3-clause |
jereze/scikit-learn | sklearn/preprocessing/data.py | 68 | 57385 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Eric Martin <eric@ericmart.in>
# License: BSD 3 clause
from itertools import chain, combina... | bsd-3-clause |
friebsch/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | Chapter2_MorePyMC/separation_plot.py | 86 | 1494 | # separation plot
# Author: Cameron Davidson-Pilon,2013
# see http://mdwardlab.com/sites/default/files/GreenhillWardSacks.pdf
import matplotlib.pyplot as plt
import numpy as np
def separation_plot( p, y, **kwargs ):
"""
This function creates a separation plot for logistic and probit classification.
Se... | mit |
fw1121/BDA_py_demos | demos_ch3/demo3_2.py | 19 | 6319 | """Bayesian Data Analysis, 3rd ed
Chapter 3, demo 2
Visualise factored sampling and the corresponding marginal and conditional densities.
"""
from __future__ import division
import os
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
# import from util... | gpl-3.0 |
cmoutard/mne-python | logo/generate_mne_logos.py | 12 | 6091 | # -*- coding: utf-8 -*-
"""
===============================================================================
Script 'mne logo'
===============================================================================
This script makes the logo for MNE.
"""
# @author: drmccloy
# Created on Mon Jul 20 11:28:16 2015
# License: BSD ... | bsd-3-clause |
giorgiop/scikit-learn | sklearn/metrics/tests/test_pairwise.py | 13 | 26241 | import numpy as np
from numpy import linalg
from scipy.sparse import dok_matrix, csr_matrix, issparse
from scipy.spatial.distance import cosine, cityblock, minkowski, wminkowski
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing impo... | bsd-3-clause |
rosswhitfield/mantid | Framework/PythonInterface/mantid/plots/resampling_image/samplingimage.py | 3 | 10843 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2020 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
impo... | gpl-3.0 |
kit-cel/wt | nt2/modulation_pulsformung/Spektren_digitale_Modulation.py | 1 | 2940 | # -*- coding: utf-8 -*-
"""
Created on Wed Aug 13 10:31:13 2014
NTII Demo - Quellencodierung - Auswirkungen auf Spektrum des Sendesignals
Systemmodell: Quelle --> QPSK --> Pulsformung
@author: Michael Schwall
"""
from __future__ import division
import numpy as np
import matplotlib.pylab as plt
import scipy.signal a... | gpl-2.0 |
zardav/FaceDetection | FaceDetection/temp.py | 1 | 3849 | import numpy as np
from scipy import ndimage, misc
from matplotlib import pyplot as plt
import glob
from MyViola import MyViolaClassifier
from Svm import Svm
import funcs
def find_face(img, shape, mv):
res_i = (0, 0)
res_j = (0, 0)
res_scl = 1
max_ = 0
scales = np.arange(.2, .35, .025)
m, n = ... | gpl-3.0 |
xuerenlv/PaperWork | my_study/pandas_study/test1.py | 1 | 1044 | # -*- coding: utf-8 -*-
'''
Created on Oct 16, 2015
@author: nlp
'''
import sys
import traceback
from store_model import Single_weibo_store
import datetime
from datetime import timedelta
import pprint
import jieba
reload(sys)
sys.setdefaultencoding('utf8')
from sklearn import svm
X = [[0, 0], [1, 1]]
y = [... | apache-2.0 |
mwaskom/seaborn | doc/tools/generate_logos.py | 2 | 6982 | import numpy as np
import seaborn as sns
from matplotlib import patches
import matplotlib.pyplot as plt
from scipy.signal import gaussian
from scipy.spatial import distance
XY_CACHE = {}
STATIC_DIR = "_static"
plt.rcParams["savefig.dpi"] = 300
def poisson_disc_sample(array_radius, pad_radius, candidates=100, d=2, ... | bsd-3-clause |
omnirom/android_kernel_htc_flounder | scripts/tracing/dma-api/plotting.py | 96 | 4043 | """Ugly graph drawing tools"""
import matplotlib.pyplot as plt
import matplotlib.cm as cmap
#import numpy as np
from matplotlib import cbook
# http://stackoverflow.com/questions/4652439/is-there-a-matplotlib-equivalent-of-matlabs-datacursormode
class DataCursor(object):
"""A simple data cursor widget that displays... | gpl-2.0 |
Didou09/tofu | tofu/mag/regression_test.py | 2 | 11158 | # -*- coding: utf-8 -*-
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
# Also if needed: retab
'''
Regression test
'''
from __future__ import (unicode_literals, absolute_import, \
print_function, division)
import argparse
import matplotlib.pyplot as plt
import numpy as np
import os
i... | mit |
lancezlin/ml_template_py | lib/python2.7/site-packages/sklearn/tests/test_kernel_approximation.py | 78 | 7586 | import numpy as np
from scipy.sparse import csr_matrix
from sklearn.utils.testing import assert_array_equal, assert_equal, assert_true
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import assert_array_almost_equal, assert_raises
from sklearn.utils.testing import assert_less_equal
from ... | mit |
Alexsaphir/TP_EDP_Python | TP2_error.py | 1 | 1912 | # -*- coding: utf-8 -*-
from numpy import * # importation du module numpy
from numpy.linalg import * # importation du module numpy.linalg
from numpy.random import *
from matplotlib.pyplot import *
from mpl_toolkits.mplot3d import Axes3D
#Calcul l'erreur en faisant varier Ns
def Ud(x):
y = sin(2*pi*x)*sinh(2*pi)
... | lgpl-3.0 |
vortex-ape/scikit-learn | examples/model_selection/plot_nested_cross_validation_iris.py | 9 | 4415 | """
=========================================
Nested versus non-nested cross-validation
=========================================
This example compares non-nested and nested cross-validation strategies on a
classifier of the iris data set. Nested cross-validation (CV) is often used to
train a model in which hyperparam... | bsd-3-clause |
abergeron/pylearn2 | pylearn2/utils/image.py | 39 | 18841 | """
Utility functions for working with images.
"""
import logging
import numpy as np
plt = None
axes = None
from theano.compat.six.moves import xrange
from theano.compat.six import string_types
import warnings
try:
import matplotlib.pyplot as plt
import matplotlib.axes
except (RuntimeError, ImportError, TypeErr... | bsd-3-clause |
yavalvas/yav_com | build/matplotlib/doc/mpl_examples/axes_grid/demo_edge_colorbar.py | 11 | 2597 | import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
def get_demo_image():
import numpy as np
from matplotlib.cbook import get_sample_data
f = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)
z = np.load(f)
# z is a numpy array of 15x15
return z, (-3,4,... | mit |
suyashdb/hcp2bids | setup.py | 1 | 2182 | from setuptools import setup
import os, glob, shutil
import re, json, numpy
import nibabel as ni
here = os.path.abspath(os.path.dirname(__file__))
setup(
name="hcp2bids",
# Versions should comply with PEP440. For a discussion on single-sourcing
# the version across setup.py and the project code, see
... | mit |
andyraib/data-storage | python_scripts/env/lib/python3.6/site-packages/pandas/tseries/plotting.py | 7 | 9969 | """
Period formatters and locators adapted from scikits.timeseries by
Pierre GF Gerard-Marchant & Matt Knox
"""
# TODO: Use the fact that axis can have units to simplify the process
import numpy as np
from matplotlib import pylab
from pandas.tseries.period import Period
from pandas.tseries.offsets import DateOffset
... | apache-2.0 |
aringh/odl | odl/contrib/solvers/spdhg/misc.py | 1 | 22813 | # Copyright 2014-2018 The ODL contributors
#
# This file is part of ODL.
#
# 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/.
"""Functions for folders and files."""
from... | mpl-2.0 |
valexandersaulys/airbnb_kaggle_contest | venv/lib/python3.4/site-packages/pandas/tseries/tests/test_base.py | 9 | 82416 | from __future__ import print_function
import re
from datetime import datetime, timedelta
import numpy as np
import pandas as pd
from pandas.tseries.base import DatetimeIndexOpsMixin
from pandas.util.testing import assertRaisesRegexp, assertIsInstance
from pandas.tseries.common import is_datetimelike
from pandas import ... | gpl-2.0 |
maistrovas/My-Courses-Solutions | Coursera Algorithmic Thinking (Part 1)/Module 2/Application/Application2.py | 1 | 7494 | """
Provided code for Application portion of Module 2
Answers 4/6
Application Grade is 13 out of 15
Text Answers
-Question 2:
All three graphs are resilient in this case.
Question5:
-UPA and ER graphs are steel resilient
(UPA is very close to overcoming 25% roughnes)
in this type of attack.
"""
# general imports... | mit |
PatrickOReilly/scikit-learn | examples/linear_model/plot_ridge_path.py | 55 | 2138 | """
===========================================================
Plot Ridge coefficients as a function of the regularization
===========================================================
Shows the effect of collinearity in the coefficients of an estimator.
.. currentmodule:: sklearn.linear_model
:class:`Ridge` Regressi... | bsd-3-clause |
franchenstein/master_project | main.py | 1 | 18773 | #!/usr/bin
import probabilisticgraph as pg
import graphgenerator as gg
import dmarkov as dm
import sequenceanalyzer as sa
import yaml
import matplotlib.pyplot as plt
import synchwordfinder as swf
def main(config_file, fsw=False, terminate=False, dmark=False, generate=False, gen_seq=False, an_seq=False, plot=False,
... | mit |
zaxliu/deepnap | experiments/kdd-exps/experiment_message_2016-6-11_BUF2_G5_FR100_legacy.py | 1 | 4371 | # System built-in modules
import time
from datetime import datetime
import sys
import os
from multiprocessing import Pool
# Project dependency modules
import pandas as pd
pd.set_option('mode.chained_assignment', None) # block warnings due to DataFrame value assignment
import lasagne
# Project modules
sys.path.append('... | bsd-3-clause |
LaboratoireMecaniqueLille/Ximea | old/ximea_display_REC.py | 1 | 8856 | import numpy as np
import cv2
import time
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import rcParams
import SimpleITK as sitk
from multiprocessing import Process, Pipe, Value
from matplotlib.widgets import Slider, Button
rcParams['font.family'] = 'serif'
#ps aux | grep pyth... | gpl-2.0 |
rileymcdowell/genomic-neuralnet | genomic_neuralnet/methods/generic_keras_net.py | 1 | 6998 | from __future__ import print_function
import os
import time
import json
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Lambda
from keras.optimizers import Nadam as Trainer
#from keras.optimizers import Adam as Trainer
from keras.regularizers import WeightRe... | mit |
tntnatbry/tensorflow | tensorflow/examples/learn/iris_custom_model.py | 50 | 2613 | # 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 appl... | apache-2.0 |
matplotlib/viscm | tests.py | 1 | 4429 | from viscm.gui import *
from viscm.bezierbuilder import *
import numpy as np
import matplotlib as mpl
from matplotlib.backends.qt_compat import QtGui, QtCore
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
cms = {"viscm/examples/sample_linear.jscm",
"viscm/examples/sample_dive... | mit |
sanjayankur31/nest-simulator | pynest/examples/balancedneuron.py | 8 | 7344 | # -*- coding: utf-8 -*-
#
# balancedneuron.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST 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 2 of the License,... | gpl-2.0 |
djgagne/scikit-learn | sklearn/covariance/__init__.py | 389 | 1157 | """
The :mod:`sklearn.covariance` module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.
"""
from ... | bsd-3-clause |
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