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 |
|---|---|---|---|---|---|
AzamYahya/shogun | examples/undocumented/python_modular/graphical/interactive_kmm_demo.py | 16 | 12372 | #
# This program 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.
#
# Written (C) 2013 Cameron Lai, based on interactive_svm_demo by Chris... | gpl-3.0 |
TitasNandi/Summer_Project | yodaqa/data/ml/fbpath/test_classifier.py | 3 | 3215 | #!/usr/bin/python
#
# Usage: fbpath_train_logistic.py TRAIN.JSON VAL.JSON [print]
#
# Trains and validate classifier for branched paths. The optional print parameter tells whether
# to print question text, predicted paths and gold standard for branched paths or not.
# The last line of output contains information about ... | apache-2.0 |
vortex-ape/scikit-learn | sklearn/kernel_ridge.py | 12 | 7382 | """Module :mod:`sklearn.kernel_ridge` implements kernel ridge regression."""
# Authors: Mathieu Blondel <mathieu@mblondel.org>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD 3 clause
import numpy as np
from .base import BaseEstimator, RegressorMixin
from .metrics.pairwise import pairwise... | bsd-3-clause |
guorendong/iridium-browser-ubuntu | native_client/pnacl/driver/pnacl-ld.py | 2 | 23962 | #!/usr/bin/python
# Copyright (c) 2012 The Native Client Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from driver_tools import ArchMerge, DriverChain, GetArch, \
ParseArgs, ParseTriple, RunDriver, RunWithEnv, SetArch, \
SetE... | bsd-3-clause |
JoshDaly/scriptShed | separate_connected_components.py | 1 | 4413 | #!/usr/bin/env python
###############################################################################
#
# separate_connected_components
#
###############################################################################
# #
# This program is ... | gpl-2.0 |
jlegendary/scikit-learn | sklearn/linear_model/tests/test_ransac.py | 216 | 13290 | import numpy as np
from numpy.testing import assert_equal, assert_raises
from numpy.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises_regexp
from scipy import sparse
from sklearn.utils.testing import assert_less
from sklearn.linear_model import LinearRegression, RANSACRegressor
f... | bsd-3-clause |
massmutual/scikit-learn | examples/applications/plot_species_distribution_modeling.py | 254 | 7434 | """
=============================
Species distribution modeling
=============================
Modeling species' geographic distributions is an important
problem in conservation biology. In this example we
model the geographic distribution of two south american
mammals given past observations and 14 environmental
varia... | bsd-3-clause |
huaj1101/ML-PY | SCIKIT_LEARN/plot_lasso_lars.py | 363 | 1080 | #!/usr/bin/env python
"""
=====================
Lasso path using LARS
=====================
Computes Lasso Path along the regularization parameter using the LARS
algorithm on the diabetes dataset. Each color represents a different
feature of the coefficient vector, and this is displayed as a function
of the regulariza... | apache-2.0 |
schets/scikit-learn | examples/classification/plot_lda.py | 164 | 2224 | """
====================================================================
Normal and Shrinkage Linear Discriminant Analysis for classification
====================================================================
Shows how shrinkage improves classification.
"""
from __future__ import division
import numpy as np
import... | bsd-3-clause |
jzt5132/scikit-learn | examples/plot_multilabel.py | 236 | 4157 | # Authors: Vlad Niculae, Mathieu Blondel
# License: BSD 3 clause
"""
=========================
Multilabel classification
=========================
This example simulates a multi-label document classification problem. The
dataset is generated randomly based on the following process:
- pick the number of labels: n ... | bsd-3-clause |
fierval/retina | DiabeticRetinopathy/Learning/learn_boost.py | 1 | 1310 | import pandas as pd
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.cross_validation import train_test_split
from kobra.tr_utils import time_now_str
import numpy as np
import sklearn.preprocessing as prep
from sklearn import metrics
sample_file = '/kaggle/re... | mit |
toastedcornflakes/scikit-learn | examples/hetero_feature_union.py | 4 | 6236 | """
=============================================
Feature Union with Heterogeneous Data Sources
=============================================
Datasets can often contain components of that require different feature
extraction and processing pipelines. This scenario might occur when:
1. Your dataset consists of hetero... | bsd-3-clause |
elkingtonmcb/scikit-learn | sklearn/metrics/cluster/unsupervised.py | 230 | 8281 | """ Unsupervised evaluation metrics. """
# Authors: Robert Layton <robertlayton@gmail.com>
#
# License: BSD 3 clause
import numpy as np
from ...utils import check_random_state
from ..pairwise import pairwise_distances
def silhouette_score(X, labels, metric='euclidean', sample_size=None,
random... | bsd-3-clause |
vshtanko/scikit-learn | examples/linear_model/plot_sgd_penalties.py | 249 | 1563 | """
==============
SGD: Penalties
==============
Plot the contours of the three penalties.
All of the above are supported by
:class:`sklearn.linear_model.stochastic_gradient`.
"""
from __future__ import division
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
def l1(xs):
return np.array([np.... | bsd-3-clause |
jplourenco/bokeh | examples/interactions/us_marriages_divorces/us_marriages_divorces_interactive.py | 26 | 3437 | # coding: utf-8
# Plotting U.S. marriage and divorce statistics
#
# Example code by Randal S. Olson (http://www.randalolson.com)
from bokeh.plotting import figure, show, output_file, ColumnDataSource
from bokeh.models import HoverTool, NumeralTickFormatter
from bokeh.models import SingleIntervalTicker, LinearAxis
imp... | bsd-3-clause |
hitszxp/scikit-learn | examples/cluster/plot_color_quantization.py | 297 | 3443 | # -*- coding: utf-8 -*-
"""
==================================
Color Quantization using K-Means
==================================
Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace
(China), reducing the number of colors required to show the image from 96,615
unique colors to 64, while pre... | bsd-3-clause |
mike-seeber/Character | code/model_step2_run9.py | 1 | 5331 | # To run on ec2
import matplotlib
matplotlib.use('Agg')
from keras import backend as K
from keras.callbacks import EarlyStopping
from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPool2D
from keras.models import Sequential
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
import os
i... | mit |
maxlikely/scikit-learn | examples/feature_stacker.py | 8 | 1941 | """
=================================================
Concatenating multiple feature extraction methods
=================================================
In many real-world examples, there are many ways to extract features from a
dataset. Often it is benefitial to combine several methods to obtain good
performance. Th... | bsd-3-clause |
MatthieuBizien/scikit-learn | sklearn/feature_selection/tests/test_mutual_info.py | 56 | 6268 | from __future__ import division
import numpy as np
from numpy.testing import run_module_suite
from scipy.sparse import csr_matrix
from sklearn.utils.testing import (assert_array_equal, assert_almost_equal,
assert_false, assert_raises, assert_equal)
from sklearn.feature_selection.mut... | bsd-3-clause |
pkreissl/espresso | src/python/espressomd/visualization_opengl.py | 1 | 115343 | # Copyright (C) 2010-2019 The ESPResSo project
#
# This file is part of ESPResSo.
#
# ESPResSo 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 v... | gpl-3.0 |
lindsayberry/lindsayberry.github.io | markdown_generator/publications.py | 197 | 3887 |
# coding: utf-8
# # Publications markdown generator for academicpages
#
# Takes a TSV of publications with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook, with the core python code in publications.py. Run either from the `markdown_g... | mit |
frank-tancf/scikit-learn | sklearn/feature_selection/variance_threshold.py | 123 | 2572 | # Author: Lars Buitinck
# License: 3-clause BSD
import numpy as np
from ..base import BaseEstimator
from .base import SelectorMixin
from ..utils import check_array
from ..utils.sparsefuncs import mean_variance_axis
from ..utils.validation import check_is_fitted
class VarianceThreshold(BaseEstimator, SelectorMixin):
... | bsd-3-clause |
zrhans/pythonanywhere | .virtualenvs/django19/lib/python3.4/site-packages/matplotlib/stackplot.py | 7 | 4266 | """
Stacked area plot for 1D arrays inspired by Douglas Y'barbo's stackoverflow
answer:
http://stackoverflow.com/questions/2225995/how-can-i-create-stacked-line-graph-with-matplotlib
(http://stackoverflow.com/users/66549/doug)
"""
from __future__ import (absolute_import, division, print_function,
... | apache-2.0 |
rolandwz/pymisc | ustrader/voters/maVoter.py | 2 | 2157 | # -*- coding: utf-8 -*-
import datetime, time, csv, os
import numpy as np
import matplotlib.pyplot as plt
from utils.db import SqliteDB
from utils.rwlogging import log
from utils.rwlogging import strategyLogger as logs
from utils.rwlogging import balLogger as logb
from indicator import ma, macd, bolling, rsi, kdj
from ... | mit |
zooniverse/aggregation | experimental/milkway/dbscan.py | 2 | 4437 | #!/usr/bin/env python
import pymongo
from sklearn.cluster import DBSCAN
import matplotlib.pyplot as plt
from pylab import figure, show, rand
import numpy as np
from sklearn.datasets.samples_generator import make_blobs
from matplotlib.patches import Ellipse
from copy import deepcopy
__author__ = 'greghines'
client = py... | apache-2.0 |
Totoketchup/das | utils/postprocessing/reconstruction.py | 1 | 1631 | import numpy as np
from audio import istft_, create_spectrogram
from sklearn.cluster import KMeans
import config
# Compute the reconstruction of the signal from the filtered spectrogram
def reconstruct_signal(filtered_spec, orig_spec, fs=config.fs, fftsize=config.fftsize):
if orig_spec != None :
angle = np.angle(... | mit |
annarev/tensorflow | tensorflow/python/keras/preprocessing/image.py | 3 | 48694 | # 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 |
gotomypc/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 |
evgchz/scikit-learn | sklearn/linear_model/tests/test_passive_aggressive.py | 31 | 6147 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal, assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.base import ClassifierMixin
from skle... | bsd-3-clause |
terhorst/psmcpp | util/posterior_decoding.py | 2 | 10345 | #!/usr/bin/env python2.7
from __future__ import division, print_function
import numpy as np
import scipy.optimize
import scipy.ndimage
import pprint
import multiprocessing
import sys
import itertools
from collections import Counter
import sys
import argparse
import os, os.path
import logging
from stepfun import StepF... | gpl-3.0 |
samuel1208/scikit-learn | sklearn/utils/__init__.py | 132 | 14185 | """
The :mod:`sklearn.utils` module includes various utilities.
"""
from collections import Sequence
import numpy as np
from scipy.sparse import issparse
import warnings
from .murmurhash import murmurhash3_32
from .validation import (as_float_array,
assert_all_finite,
... | bsd-3-clause |
oliverlee/sympy | examples/advanced/autowrap_ufuncify.py | 45 | 2446 | #!/usr/bin/env python
"""
Setup ufuncs for the legendre polynomials
-----------------------------------------
This example demonstrates how you can use the ufuncify utility in SymPy
to create fast, customized universal functions for use with numpy
arrays. An autowrapped sympy expression can be significantly faster tha... | bsd-3-clause |
jmmease/pandas | pandas/tests/plotting/test_groupby.py | 7 | 2412 | # coding: utf-8
""" Test cases for GroupBy.plot """
from pandas import Series, DataFrame
import pandas.util.testing as tm
import numpy as np
from pandas.tests.plotting.common import TestPlotBase
tm._skip_if_no_mpl()
class TestDataFrameGroupByPlots(TestPlotBase):
def test_series_groupby_plotting_nominally_w... | bsd-3-clause |
zaxtax/scikit-learn | sklearn/tests/test_metaestimators.py | 57 | 4958 | """Common tests for metaestimators"""
import functools
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.externals.six import iterkeys
from sklearn.datasets import make_classification
from sklearn.utils.testing import assert_true, assert_false, assert_raises
from sklearn.pipeline import Pipeline... | bsd-3-clause |
nvoron23/scikit-learn | sklearn/decomposition/tests/test_dict_learning.py | 69 | 8605 | 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 |
loretoparisi/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/legend.py | 69 | 30705 | """
Place a legend on the axes at location loc. Labels are a
sequence of strings and loc can be a string or an integer
specifying the legend location
The location codes are
'best' : 0, (only implemented for axis legends)
'upper right' : 1,
'upper left' : 2,
'lower left' : 3,
'lower right' : 4... | agpl-3.0 |
chugunovyar/factoryForBuild | env/lib/python2.7/site-packages/mpl_toolkits/axes_grid1/mpl_axes.py | 10 | 5045 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
import matplotlib.axes as maxes
from matplotlib.artist import Artist
from matplotlib.axis import XAxis, YAxis
class SimpleChainedObjects(object):
def __init__(self, objects):
... | gpl-3.0 |
vibhorag/scikit-learn | examples/cluster/plot_color_quantization.py | 297 | 3443 | # -*- coding: utf-8 -*-
"""
==================================
Color Quantization using K-Means
==================================
Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace
(China), reducing the number of colors required to show the image from 96,615
unique colors to 64, while pre... | bsd-3-clause |
giorgiop/scikit-learn | benchmarks/bench_sample_without_replacement.py | 397 | 8008 | """
Benchmarks for sampling without replacement of integer.
"""
from __future__ import division
from __future__ import print_function
import gc
import sys
import optparse
from datetime import datetime
import operator
import matplotlib.pyplot as plt
import numpy as np
import random
from sklearn.externals.six.moves i... | bsd-3-clause |
ast0815/likelihood-machine | tests.py | 2 | 68409 | from __future__ import division
import sys
import unittest2 as unittest
import yaml
from remu.binning import *
from remu.migration import *
from remu.likelihood import *
from remu.plotting import *
from remu.matrix_utils import *
from remu.likelihood_utils import *
import numpy as np
from numpy import array, inf
import... | mit |
dmytroKarataiev/MachineLearning | learning/ud120-projects/choose_your_own/your_algorithm.py | 1 | 2647 | #!/usr/bin/python
import matplotlib.pyplot as plt
from prep_terrain_data import makeTerrainData
from class_vis import prettyPicture
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
features_train, labels_train, featu... | mit |
SEMAFORInformatik/femagtools | docs/conf.py | 1 | 11932 | # -*- coding: utf-8 -*-
#
# femagtools documentation build configuration file, created by
# sphinx-quickstart on Sun Dec 13 12:36:51 2015.
#
# 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.
#
... | bsd-2-clause |
elastic/examples | Exploring Public Datasets/nyc_restaurants/scripts/ingestRestaurantData.py | 3 | 5504 | # coding: utf-8
# In[ ]:
import pandas as pd
import elasticsearch
import json
import re
import certifi
# If you are using the Elastic cloud, or need https/ssl, toggle the below
# commented sections. Note that the Elastic cloud may be using port 9243
#
es = elasticsearch.Elasticsearch(
# ['host1'],
# http_... | apache-2.0 |
sodafree/backend | build/ipython/build/lib.linux-i686-2.7/IPython/frontend/qt/console/rich_ipython_widget.py | 3 | 13670 | #-----------------------------------------------------------------------------
# Copyright (c) 2010, IPython Development Team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file COPYING.txt, distributed with this software.
#--------------------------------------------------... | bsd-3-clause |
gkno/gkno_launcher | src/networkx/readwrite/gml.py | 32 | 11854 | """
Read graphs in GML format.
"GML, the G>raph Modelling Language, is our proposal for a portable
file format for graphs. GML's key features are portability, simple
syntax, extensibility and flexibility. A GML file consists of a
hierarchical key-value lists. Graphs can be annotated with arbitrary
data structures. The... | mit |
alexandrebarachant/mne-python | tutorials/plot_stats_cluster_methods.py | 6 | 8607 | # doc:slow-example
"""
.. _tut_stats_cluster_methods:
======================================================
Permutation t-test on toy data with spatial clustering
======================================================
Following the illustrative example of Ridgway et al. 2012,
this demonstrates some basic ideas behin... | bsd-3-clause |
dashmoment/facerecognition | py/facerec/svm.py | 1 | 2341 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) Philipp Wagner. All rights reserved.
# Licensed under the BSD license. See LICENSE file in the project root for full license information.
from facerec.classifier import SVM
from facerec.validation import KFoldCrossValidation
from facerec.model import Predi... | bsd-3-clause |
manahl/arctic | arctic/chunkstore/date_chunker.py | 1 | 5275 | import pandas as pd
from arctic.date import DateRange, to_pandas_closed_closed
from ._chunker import Chunker, START, END
class DateChunker(Chunker):
TYPE = 'date'
def to_chunks(self, df, chunk_size='D', func=None, **kwargs):
"""
chunks the dataframe/series by dates
Parameters
... | lgpl-2.1 |
anntzer/scipy | scipy/stats/_stats_mstats_common.py | 12 | 16438 | import numpy as np
import scipy.stats.stats
from . import distributions
from .._lib._bunch import _make_tuple_bunch
__all__ = ['_find_repeats', 'linregress', 'theilslopes', 'siegelslopes']
# This is not a namedtuple for backwards compatibility. See PR #12983
LinregressResult = _make_tuple_bunch('LinregressResult',
... | bsd-3-clause |
ThomasMiconi/nupic.research | projects/capybara/anomaly_detection/plot_results.py | 9 | 1755 | __author__ = 'mleborgne'
import matplotlib.pyplot as plt
import csv
import os
from settings import (METRICS,
SENSORS,
PATIENT_IDS,
ANOMALY_LIKELIHOOD_THRESHOLD,
MODEL_RESULTS_DIR,
PLOT_RESULTS_DIR)
for patie... | agpl-3.0 |
denimalpaca/293n | regression_berkeley.py | 1 | 3991 | import csv
import sys
from datetime import datetime
from sklearn import svm
from sklearn.ensemble import RandomForestRegressor
import numpy
numpy.set_printoptions(threshold=numpy.nan)
data = []
target = []
num_comments_max = 0
score_max = 0
title_max = 0
gilded_max = 0
# Domain Categories
school_list = {"alumni.berk... | gpl-3.0 |
mr3bn/DAT210x | Module4/assignment3.py | 1 | 3450 | import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import assignment2_helper as helper
from sklearn.decomposition import PCA
# Look pretty...
# matplotlib.style.use('ggplot')
plt.style.use('ggplot')
# Do * NOT * alter this line, until instructed!
scaleFeatures = True
# TODO: Load up the dataset ... | mit |
eg-zhang/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 |
CameronTEllis/brainiak | tests/funcalign/test_rsrm.py | 7 | 5042 | # Copyright 2016 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | apache-2.0 |
Reagankm/KnockKnock | venv/lib/python3.4/site-packages/nltk/parse/transitionparser.py | 5 | 31354 | # Natural Language Toolkit: Arc-Standard and Arc-eager Transition Based Parsers
#
# Author: Long Duong <longdt219@gmail.com>
#
# Copyright (C) 2001-2015 NLTK Project
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
from __future__ import absolute_import
from __future__ import division
from __future... | gpl-2.0 |
QuantScientist/JupyterGPUBidMach | jupyter_notebook_config.py | 1 | 22383 | # Configuration file for jupyter-notebook.
#------------------------------------------------------------------------------
# Configurable configuration
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
# Logg... | mit |
vermouthmjl/scikit-learn | benchmarks/bench_plot_svd.py | 325 | 2899 | """Benchmarks of Singular Value Decomposition (Exact and Approximate)
The data is mostly low rank but is a fat infinite tail.
"""
import gc
from time import time
import numpy as np
from collections import defaultdict
from scipy.linalg import svd
from sklearn.utils.extmath import randomized_svd
from sklearn.datasets.s... | bsd-3-clause |
Achuth17/scikit-learn | sklearn/manifold/isomap.py | 229 | 7169 | """Isomap for manifold learning"""
# Author: Jake Vanderplas -- <vanderplas@astro.washington.edu>
# License: BSD 3 clause (C) 2011
import numpy as np
from ..base import BaseEstimator, TransformerMixin
from ..neighbors import NearestNeighbors, kneighbors_graph
from ..utils import check_array
from ..utils.graph import... | bsd-3-clause |
webmasterraj/FogOrNot | flask/lib/python2.7/site-packages/pandas/tseries/tests/test_offsets.py | 2 | 143864 | import os
from datetime import date, datetime, timedelta
from dateutil.relativedelta import relativedelta
from pandas.compat import range, iteritems
from pandas import compat
import nose
from nose.tools import assert_raises
import numpy as np
from pandas.core.datetools import (
bday, BDay, CDay, BQuarterEnd, BMo... | gpl-2.0 |
keflavich/fil_finder | examples/paper_figures/ks_plots.py | 3 | 1672 | # Licensed under an MIT open source license - see LICENSE
'''
KS p-values for different properties.
'''
import numpy as np
from pandas import read_csv
import matplotlib.pyplot as p
import numpy as np
import seaborn as sn
sn.set_context('talk')
sn.set_style('ticks')
# sn.mpl.rc("figure", figsize=(7, 9))
# Widths
wid... | mit |
jenhantao/nuclearReceptorOverlap | plotThresholdSummary.py | 1 | 2503 | # given results from compareFilterThresholds.sh, produces a plot summarizing the number of peaks per factor at each threshold and the number of groups at each threshold; also accepts a mapping file to convert file names to factors
### imports ###
import sys
import math
import matplotlib
matplotlib.use('Agg')
import m... | mit |
MJuddBooth/pandas | pandas/tests/arrays/categorical/test_analytics.py | 1 | 11988 | # -*- coding: utf-8 -*-
import sys
import numpy as np
import pytest
from pandas.compat import PYPY
from pandas import Categorical, Index, Series
from pandas.api.types import is_scalar
import pandas.util.testing as tm
class TestCategoricalAnalytics(object):
def test_min_max(self):
# unordered cats ha... | bsd-3-clause |
ppizarror/Hero-of-Antair | bin/pympler/classtracker_stats.py | 1 | 27299 | """
Provide saving, loading and presenting gathered `ClassTracker` statistics.
"""
from copy import deepcopy
import os
import sys
from pympler.asizeof import Asized
from pympler.util.compat import pickle
from pympler.util.stringutils import trunc, pp, pp_timestamp
__all__ = ["Stats", "ConsoleStats", "HtmlStats"]
... | gpl-2.0 |
pradyu1993/scikit-learn | examples/plot_roc_crossval.py | 4 | 2035 | """
=============================================================
Receiver operating characteristic (ROC) with cross validation
=============================================================
Example of Receiver operating characteristic (ROC) metric to
evaluate the quality of the output of a classifier using
cross-valid... | bsd-3-clause |
jseabold/statsmodels | statsmodels/tsa/arima/estimators/yule_walker.py | 5 | 2517 | """
Yule-Walker method for estimating AR(p) model parameters.
Author: Chad Fulton
License: BSD-3
"""
from statsmodels.compat.pandas import deprecate_kwarg
from statsmodels.regression import linear_model
from statsmodels.tools.tools import Bunch
from statsmodels.tsa.arima.params import SARIMAXParams
from statsmodels.t... | bsd-3-clause |
fredhusser/scikit-learn | examples/cluster/plot_lena_ward_segmentation.py | 271 | 1998 | """
===============================================================
A demo of structured Ward hierarchical clustering on Lena image
===============================================================
Compute the segmentation of a 2D image with Ward hierarchical
clustering. The clustering is spatially constrained in order
... | bsd-3-clause |
farhaanbukhsh/sympy | sympy/plotting/plot.py | 55 | 64797 | """Plotting module for Sympy.
A plot is represented by the ``Plot`` class that contains a reference to the
backend and a list of the data series to be plotted. The data series are
instances of classes meant to simplify getting points and meshes from sympy
expressions. ``plot_backends`` is a dictionary with all the bac... | bsd-3-clause |
hardingnj/xpclr | xpclr/util.py | 1 | 5388 | import pandas as pd
import allel
import numpy as np
import logging
logger = logging.getLogger(__name__)
# FUNCTIONS
def load_hdf5_data(hdf5_fn, chrom, s1, s2, gdistkey=None):
import hdf5
samples1 = get_sample_ids(s1)
samples2 = get_sample_ids(s2)
samples_x = h5py.File(hdf5_fn)[chrom]["samples"][:]
... | mit |
Miiha/FilmAnalyzerKit | analyzer/shot_detection.py | 1 | 12581 | import glob
import math
import os
import shlex
import subprocess
from os.path import join
from pprint import pprint
from shutil import copy2
from statistics import mean
import cv2
import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import distance as dist
from tqdm import tqdm
from analyzer import p... | mit |
bavardage/statsmodels | statsmodels/graphics/plot_grids.py | 4 | 5667 | '''create scatterplot with confidence ellipsis
Author: Josef Perktold
License: BSD-3
TODO: update script to use sharex, sharey, and visible=False
see http://www.scipy.org/Cookbook/Matplotlib/Multiple_Subplots_with_One_Axis_Label
for sharex I need to have the ax of the last_row when editing the earlier
row... | bsd-3-clause |
sdh11/gnuradio | gr-fec/python/fec/polar/channel_construction_awgn.py | 7 | 8712 | #!/usr/bin/env python
#
# Copyright 2015 Free Software Foundation, Inc.
#
# GNU Radio 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, or (at your option)
# any later version.
#
# GNU Radio is... | gpl-3.0 |
jamesblunt/sympy | examples/intermediate/sample.py | 107 | 3494 | """
Utility functions for plotting sympy functions.
See examples\mplot2d.py and examples\mplot3d.py for usable 2d and 3d
graphing functions using matplotlib.
"""
from sympy.core.sympify import sympify, SympifyError
from sympy.external import import_module
np = import_module('numpy')
def sample2d(f, x_args):
"""
... | bsd-3-clause |
jmschrei/scikit-learn | sklearn/datasets/tests/test_samples_generator.py | 181 | 15664 | from __future__ import division
from collections import defaultdict
from functools import partial
import numpy as np
import scipy.sparse as sp
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing imp... | bsd-3-clause |
andaag/scikit-learn | sklearn/decomposition/tests/test_nmf.py | 130 | 6059 | import numpy as np
from scipy import linalg
from sklearn.decomposition import nmf
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import raises
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_gr... | bsd-3-clause |
evature/android | EvaSDK/evasdk/src/main/jni/webrtc/modules/audio_coding/audio_network_adaptor/parse_ana_dump.py | 9 | 4718 | #!/usr/bin/python2
# Copyright (c) 2017 The WebRTC project authors. All Rights Reserved.
#
# Use of this source code is governed by a BSD-style license
# that can be found in the LICENSE file in the root of the source
# tree. An additional intellectual property rights grant can be found
# in the file PATENTS. All... | mit |
mwv/scikit-learn | sklearn/feature_extraction/tests/test_text.py | 110 | 34127 | from __future__ import unicode_literals
import warnings
from sklearn.feature_extraction.text import strip_tags
from sklearn.feature_extraction.text import strip_accents_unicode
from sklearn.feature_extraction.text import strip_accents_ascii
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.fe... | bsd-3-clause |
ibis-project/ibis | ibis/backends/parquet/__init__.py | 1 | 3026 | from typing import Optional
import pyarrow as pa
import pyarrow.parquet as pq
import regex as re
from pkg_resources import parse_version
import ibis.expr.datatypes as dt
import ibis.expr.operations as ops
import ibis.expr.schema as sch
import ibis.expr.types as ir
from ibis.backends.base import BaseBackend
from ibis.... | apache-2.0 |
usnistgov/SimpleFactory | Analysis/SimpleFactoryHistogram.py | 1 | 5726 | # -*- coding: utf-8 -*-
#Created on Wed Jul 20 10:15:07 2016
#@author: nmc1
#*** Occasionally (<0.1%), client time subtracted shifts decimal places? (e.g. turns from 1469131829 to 14691); Only when buffer overflows & messages dump all at once
#Inputting a multiple of 7 uses suggested values
##########################... | mit |
wanggang3333/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 |
abhitopia/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimators_test.py | 37 | 5114 | # 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 |
jhayworth/config | .emacs.d/elpy/rpc-venv/lib/python2.7/site-packages/jedi/api/completion.py | 2 | 23715 | import re
from textwrap import dedent
from parso.python.token import PythonTokenTypes
from parso.python import tree
from parso.tree import search_ancestor, Leaf
from parso import split_lines
from jedi._compatibility import Parameter
from jedi import debug
from jedi import settings
from jedi.api import classes
from je... | gpl-3.0 |
kevin-kaixu/grass_pytorch | python2/dynamicplot.py | 1 | 1311 | from __future__ import absolute_import
import matplotlib.pyplot as plt
from itertools import izip
class DynamicPlot(object):
def __init__(self, title, xdata, ydata):
if len(xdata) == 0:
return
plt.ion()
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111)
... | apache-2.0 |
e-koch/TurbuStat | Examples/paper_plots/test_fBM_wavelet_normalization.py | 2 | 1754 |
'''
Make a plot of Wavelets with and without normalization
'''
# from turbustat.data_reduction import Mask_and_Moments
from turbustat.statistics import Wavelet
from turbustat.simulator import make_extended
import astropy.io.fits as fits
import matplotlib.pyplot as plt
import astropy.units as u
import seaborn as sb
... | mit |
sssllliang/edx-analytics-pipeline | edx/analytics/tasks/reports/tests/test_total_enrollments.py | 1 | 11337 | """Tests for Total Users and Enrollment report."""
import datetime
import textwrap
from StringIO import StringIO
import luigi
import luigi.hdfs
from mock import MagicMock
from numpy import isnan
import pandas
from edx.analytics.tasks.user_registrations import UserRegistrationsPerDay
from edx.analytics.tasks.reports.... | agpl-3.0 |
rohit21122012/DCASE2013 | runs/2016/dnn2016med_traps/traps2/src/evaluation.py | 56 | 43426 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import numpy
import sys
from sklearn import metrics
class DCASE2016_SceneClassification_Metrics():
"""DCASE 2016 scene classification metrics
Examples
--------
>>> dcase2016_scene_metric = DCASE2016_SceneClassification_Metrics(class_lis... | mit |
beepee14/scikit-learn | examples/cluster/plot_lena_ward_segmentation.py | 271 | 1998 | """
===============================================================
A demo of structured Ward hierarchical clustering on Lena image
===============================================================
Compute the segmentation of a 2D image with Ward hierarchical
clustering. The clustering is spatially constrained in order
... | bsd-3-clause |
jonwright/ImageD11 | sandbox/ev78/integrate_them.py | 1 | 14573 | #!/usr/bin/python
from __future__ import print_function
import sys
#sys.path.append('/users/wright/software/lib/python')
import os, time, fabio, numpy
import pyFAI
print(pyFAI.__file__)
SOLID_ANGLE = True
#print "PATH:", sys.path
from pyFAI.azimuthalIntegrator import AzimuthalIntegrator
class darkflood(object):
... | gpl-2.0 |
wenhuchen/ETHZ-Bootstrapped-Captioning | visual-concepts/eval.py | 1 | 11962 | from __future__ import division
from _init_paths import *
import os
import os.path as osp
import sg_utils as utils
import numpy as np
import skimage.io
import skimage.transform
import h5py
import pickle
import json
import math
import argparse
import time
import cv2
from collections import Counter
from json import encod... | bsd-3-clause |
siutanwong/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 |
aselle/tensorflow | tensorflow/contrib/timeseries/examples/predict.py | 69 | 5579 | # 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 |
akhilaananthram/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_wxagg.py | 70 | 9051 | from __future__ import division
"""
backend_wxagg.py
A wxPython backend for Agg. This uses the GUI widgets written by
Jeremy O'Donoghue (jeremy@o-donoghue.com) and the Agg backend by John
Hunter (jdhunter@ace.bsd.uchicago.edu)
Copyright (C) 2003-5 Jeremy O'Donoghue, John Hunter, Illinois Institute of
Technolo... | agpl-3.0 |
ZhuangER/hackerrank_solution | python/Laptop-Battery-Life/Laptop_Battery_Life.py | 1 | 1028 | # Enter your code here. Read input from STDIN. Print output to STDOUT
import sys
data = float(sys.stdin.readline())
# Enter your code here
import numpy as np
training_data = np.genfromtxt('trainingdata.txt', delimiter=',')
#data preprocessing
features = training_data[:,0]
targets = training_data[:,1]
maximum_battery... | mit |
fabianp/scikit-learn | benchmarks/bench_plot_svd.py | 325 | 2899 | """Benchmarks of Singular Value Decomposition (Exact and Approximate)
The data is mostly low rank but is a fat infinite tail.
"""
import gc
from time import time
import numpy as np
from collections import defaultdict
from scipy.linalg import svd
from sklearn.utils.extmath import randomized_svd
from sklearn.datasets.s... | bsd-3-clause |
KjongLehmann/m53 | libs/viz.py | 1 | 2586 | import matplotlib
matplotlib.use('AGG')
import matplotlib.pyplot as plt
import scipy as sp
import pdb
def plotBias(vals, fn_plot, myidx, logScale = False, refname = 'TCGA'):
iqr = ( (sp.percentile(vals[~myidx],75) - sp.percentile(vals[~myidx],25) ) * 1.5)
iqr2 = ( (sp.percentile(vals[myidx],75) - sp.pe... | mit |
dvornikita/blitznet | training.py | 1 | 14424 | #!/usr/bin/env python3
from config import get_logging_config, args, train_dir
from config import config as net_config
import time
import os
import sys
import socket
import logging
import logging.config
import subprocess
import tensorflow as tf
import numpy as np
import matplotlib
matplotlib.use('Agg')
from vgg imp... | mit |
losonczylab/Zaremba_NatNeurosci_2017 | losonczy_analysis_bundle/lab/analysis/calc_activity.py | 1 | 26015 | import numpy as np
import pandas as pd
from scipy.integrate import trapz
from itertools import count, izip
import cPickle as pkl
# import imaging_analysis as ia
from ..classes.interval import Interval, ImagingInterval
def calc_activity(
experiment, method, interval=None, dF='from_file', channel='Ch2',
... | mit |
huongttlan/seaborn | seaborn/tests/test_matrix.py | 4 | 32492 | import itertools
import tempfile
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
from scipy.spatial import distance
from scipy.cluster import hierarchy
import nose.tools as nt
import numpy.testing as npt
import pandas.util.testing as pdt
from numpy.testing.decorators im... | bsd-3-clause |
Joukahainen/trading-with-python | lib/cboe.py | 76 | 4433 | # -*- coding: utf-8 -*-
"""
toolset working with cboe data
@author: Jev Kuznetsov
Licence: BSD
"""
from datetime import datetime, date
import urllib2
from pandas import DataFrame, Index
from pandas.core import datetools
import numpy as np
import pandas as pd
def monthCode(month):
"""
perfo... | bsd-3-clause |
aetilley/scikit-learn | sklearn/decomposition/nmf.py | 30 | 19208 | """ Non-negative matrix factorization
"""
# Author: Vlad Niculae
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Author: Chih-Jen Lin, National Taiwan University (original projected gradient
# NMF implementation)
# Author: Anthony Di Franco (original Python and NumPy port)
# License: BSD 3 clause
from __future__ ... | bsd-3-clause |
andrespires/python-buildpack | cf_spec/fixtures/miniconda_simple_app_python_2/app.py | 12 | 2078 | from flask import Flask
import pytest
import os
import importlib
import sys
MODULE_NAMES = ['numpy', 'scipy', 'sklearn', 'pandas']
modules = {}
for m in MODULE_NAMES:
try:
modules[m] = importlib.import_module(m)
except ImportError:
modules[m] = None
app = Flask(__name__)
@app.route('/<modul... | mit |
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