repo_name stringlengths 9 55 | path stringlengths 7 120 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 169k | license stringclasses 12
values |
|---|---|---|---|---|---|
miaecle/deepchem | contrib/mpnn/donkey.py | 7 | 3029 | # 2017 DeepCrystal Technologies - Patrick Hop
#
# Data loading a splitting file
#
# MIT License - have fun!!
# ===========================================================
import os
import random
from collections import OrderedDict
import deepchem as dc
from deepchem.utils import ScaffoldGenerator
from deepchem.utils.... | mit |
RecipeML/Recipe | recipe/classifiers/randomLogistic.py | 1 | 2397 | # -*- coding: utf-8 -*-
"""
Copyright 2016 Walter José and Alex de Sá
This file is part of the RECIPE Algorithm.
The RECIPE 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 (a... | gpl-3.0 |
CompPhysics/MachineLearning | doc/src/LectureNotes/_build/jupyter_execute/chapter8.py | 1 | 48694 | # Dimensionality Reduction
## Reducing the number of degrees of freedom, overarching view
Many Machine Learning problems involve thousands or even millions of
features for each training instance. Not only does this make training
extremely slow, it can also make it much harder to find a good
solution, as we will see.... | cc0-1.0 |
weixuanfu/tpot | tpot/config/regressor_sparse.py | 1 | 3127 | # -*- coding: utf-8 -*-
"""This file is part of the TPOT library.
TPOT was primarily developed at the University of Pennsylvania by:
- Randal S. Olson (rso@randalolson.com)
- Weixuan Fu (weixuanf@upenn.edu)
- Daniel Angell (dpa34@drexel.edu)
- and many more generous open source contributors
TPOT is f... | lgpl-3.0 |
Nyker510/scikit-learn | sklearn/covariance/robust_covariance.py | 198 | 29735 | """
Robust location and covariance estimators.
Here are implemented estimators that are resistant to outliers.
"""
# Author: Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
from scipy import linalg
from scipy.stats import chi2
from . import empir... | bsd-3-clause |
briehl/narrative | src/biokbase/narrative/tests/test_viewers.py | 1 | 5395 | import unittest
import biokbase.auth
from . import util
"""
Tests for the viewer module
"""
__author__ = "James Jeffryes <jjeffryes@mcs.anl.gov>"
@unittest.skip("Skipping clustergrammer-based tests")
class ViewersTestCase(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.attribute_set_ref = ... | mit |
danuker/trading-with-python | nautilus/nautilus.py | 77 | 5403 | '''
Created on 26 dec. 2011
Copyright: Jev Kuznetsov
License: BSD
'''
from PyQt4.QtCore import *
from PyQt4.QtGui import *
from ib.ext.Contract import Contract
from ib.opt import ibConnection
from ib.ext.Order import Order
import tradingWithPython.lib.logger as logger
from tradingWithPython.lib.eve... | bsd-3-clause |
mfjb/scikit-learn | examples/linear_model/plot_polynomial_interpolation.py | 251 | 1895 | #!/usr/bin/env python
"""
========================
Polynomial interpolation
========================
This example demonstrates how to approximate a function with a polynomial of
degree n_degree by using ridge regression. Concretely, from n_samples 1d
points, it suffices to build the Vandermonde matrix, which is n_samp... | bsd-3-clause |
bartslinger/paparazzi | sw/misc/attitude_reference/test_att_ref.py | 49 | 3485 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2014 Antoine Drouin
#
# This file is part of paparazzi.
#
# paparazzi 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, or (at y... | gpl-2.0 |
vlukes/sfepy | examples/large_deformation/compare_elastic_materials.py | 5 | 6888 | #!/usr/bin/env python
"""
Compare various elastic materials w.r.t. uniaxial tension/compression test.
Requires Matplotlib.
"""
from __future__ import absolute_import
from argparse import ArgumentParser, RawDescriptionHelpFormatter
import sys
import six
sys.path.append('.')
import numpy as nm
def define():
"""Def... | bsd-3-clause |
bgris/ODL_bgris | lib/python3.5/site-packages/matplotlib/backends/backend_webagg.py | 10 | 12190 | """
Displays Agg images in the browser, with interactivity
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
# The WebAgg backend is divided into two modules:
#
# - `backend_webagg_core.py` contains code necessary to embed a WebAgg
# plot inside of a web... | gpl-3.0 |
nrhine1/scikit-learn | sklearn/utils/tests/test_random.py | 230 | 7344 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from scipy.misc import comb as combinations
from numpy.testing import assert_array_almost_equal
from sklearn.utils.random import sample_without_replacement
from sklearn.utils.random import random_choice_csc
from sklearn.utils.testing import ... | bsd-3-clause |
shikhardb/scikit-learn | examples/svm/plot_iris.py | 62 | 3251 | """
==================================================
Plot different SVM classifiers in the iris dataset
==================================================
Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:
- Sepal length
- Se... | bsd-3-clause |
0asa/scikit-learn | sklearn/ensemble/partial_dependence.py | 36 | 14909 | """Partial dependence plots for tree ensembles. """
# Authors: Peter Prettenhofer
# License: BSD 3 clause
from itertools import count
import numbers
import numpy as np
from scipy.stats.mstats import mquantiles
from ..utils.extmath import cartesian
from ..externals.joblib import Parallel, delayed
from ..externals im... | bsd-3-clause |
karstenw/nodebox-pyobjc | examples/Extended Application/matplotlib/examples/mplot3d/contour3d_3.py | 1 | 1072 | '''
========================================
Projecting contour profiles onto a graph
========================================
Demonstrates displaying a 3D surface while also projecting contour 'profiles'
onto the 'walls' of the graph.
See contourf3d_demo2 for the filled version.
'''
from mpl_toolkits.mplot3d import... | mit |
waterponey/scikit-learn | examples/applications/plot_out_of_core_classification.py | 51 | 13651 | """
======================================================
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 |
endolith/scikit-image | skimage/io/manage_plugins.py | 17 | 10353 | """Handle image reading, writing and plotting plugins.
To improve performance, plugins are only loaded as needed. As a result, there
can be multiple states for a given plugin:
available: Defined in an *ini file located in `skimage.io._plugins`.
See also `skimage.io.available_plugins`.
partial definiti... | bsd-3-clause |
moutai/scikit-learn | sklearn/datasets/tests/test_kddcup99.py | 59 | 1336 | """Test kddcup99 loader. Only 'percent10' mode is tested, as the full data
is too big to use in unit-testing.
The test is skipped if the data wasn't previously fetched and saved to
scikit-learn data folder.
"""
import errno
from sklearn.datasets import fetch_kddcup99
from sklearn.utils.testing import assert_equal, S... | bsd-3-clause |
shenzebang/scikit-learn | doc/datasets/mldata_fixture.py | 367 | 1183 | """Fixture module to skip the datasets loading when offline
Mock urllib2 access to mldata.org and create a temporary data folder.
"""
from os import makedirs
from os.path import join
import numpy as np
import tempfile
import shutil
from sklearn import datasets
from sklearn.utils.testing import install_mldata_mock
fr... | bsd-3-clause |
weaver-viii/h2o-3 | h2o-py/tests/testdir_algos/glm/pyunit_link_functions_binomialGLM.py | 3 | 1383 | import sys
sys.path.insert(1, "../../../")
import h2o
import pandas as pd
import zipfile
import statsmodels.api as sm
def link_functions_binomial(ip,port):
print("Read in prostate data.")
h2o_data = h2o.import_file(path=h2o.locate("smalldata/prostate/prostate_complete.csv.zip"))
h2o_data.head()
sm_dat... | apache-2.0 |
jaidevd/scikit-learn | sklearn/gaussian_process/tests/test_gaussian_process.py | 46 | 7057 | """
Testing for Gaussian Process module (sklearn.gaussian_process)
"""
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# License: BSD 3 clause
import numpy as np
from sklearn.gaussian_process import GaussianProcess
from sklearn.gaussian_process import regression_models as regression
from sklearn.gaussian_proce... | bsd-3-clause |
ibis-project/ibis | ibis/backends/parquet/tests/test_parquet.py | 1 | 2747 | import sys
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from pandas.util import testing as tm
import ibis
from ibis.backends.base.file import FileDatabase
from ibis.backends.parquet import ParquetTable
pytestmark = pytest.mark.skipif(
sys.platform == 'win32', reason='See ibis issue #1698'
)
... | apache-2.0 |
zonemercy/Kaggle | quora/solution/keras_oof.py | 1 | 10725 | from __future__ import division
import pandas as pd
import numpy as np
import random, os, gc
import config
from scipy import sparse as ssp
from sklearn.utils import resample,shuffle
from sklearn.metrics import log_loss, roc_auc_score
from sklearn.cross_validation import train_test_split
from sklearn.feature_selection i... | mit |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/pandas/tests/io/parser/common.py | 4 | 60970 | # -*- coding: utf-8 -*-
import csv
import os
import platform
import codecs
import re
import sys
from datetime import datetime
import pytest
import numpy as np
from pandas._libs.lib import Timestamp
import pandas as pd
import pandas.util.testing as tm
from pandas import DataFrame, Series, Index, MultiIndex
from pand... | mit |
loli/sklearn-ensembletrees | examples/decomposition/plot_ica_vs_pca.py | 43 | 3343 | """
==========================
FastICA on 2D point clouds
==========================
This example illustrates visually in the feature space a comparison by
results using two different component analysis techniques.
:ref:`ICA` vs :ref:`PCA`.
Representing ICA in the feature space gives the view of 'geometric ICA':
ICA... | bsd-3-clause |
q1ang/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_01_language_train_model.py | 254 | 2005 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
apbard/scipy | scipy/special/basic.py | 1 | 61138 | #
# Author: Travis Oliphant, 2002
#
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
import math
from scipy._lib.six import xrange
from numpy import (pi, asarray, floor, isscalar, iscomplex, real,
imag, sqrt, where, mgrid, sin, place, issubdtype,... | bsd-3-clause |
passiweinberger/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/bezier.py | 70 | 14387 | """
A module providing some utility functions regarding bezier path manipulation.
"""
import numpy as np
from math import sqrt
from matplotlib.path import Path
from operator import xor
# some functions
def get_intersection(cx1, cy1, cos_t1, sin_t1,
cx2, cy2, cos_t2, sin_t2):
""" return a... | agpl-3.0 |
djgagne/scikit-learn | sklearn/neighbors/tests/test_nearest_centroid.py | 305 | 4121 | """
Testing for the nearest centroid module.
"""
import numpy as np
from scipy import sparse as sp
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from sklearn.neighbors import NearestCentroid
from sklearn import datasets
from sklearn.metrics.pairwise import pairwise_distances
# t... | bsd-3-clause |
seokjunbing/cs75 | src/data_processing/compare_data_processing.py | 1 | 2664 | from Bio import SeqIO
from os import listdir
import pandas as pd
from data_processing.read_dicts import construct_dicts
"""
This file should be used for reading the data files from other papers we are comparing the results to.
This is required since their formats are somewhat different
"""
# FOLDERS
DATA_FOLDER = '..... | gpl-3.0 |
maximus009/kaggle-galaxies | predict_augmented_npy_maxout2048.py | 8 | 9452 | """
Load an analysis file and redo the predictions on the validation set / test set,
this time with augmented data and averaging. Store them as numpy files.
"""
import numpy as np
# import pandas as pd
import theano
import theano.tensor as T
import layers
import cc_layers
import custom
import load_data
import realtime... | bsd-3-clause |
pelson/cartopy | lib/cartopy/examples/wmts_time.py | 3 | 1957 | """
Web Map Tile Service time dimension demonstration
-------------------------------------------------
This example further demonstrates WMTS support within cartopy. Optional
keyword arguments can be supplied to the OGC WMTS 'gettile' method. This
allows for the specification of the 'time' dimension for a WMTS layer
... | lgpl-3.0 |
keirl/bigdata | code/plot_gas_per_capita.py | 1 | 1522 | #Import the numpy and pandas libraries
import pandas as pd
import plotly.plotly as plotly
import os
#Get the OS folder/directory path
data_path = os.path.join(os.path.dirname(__file__),os.pardir,'rawdata')
#Read the csv file with the state statistic results
df = pd.read_csv(data_path+'/STATE_SUMMARY.CSV');
#Define a... | mit |
marcsans/cnn-physics-perception | phy/lib/python2.7/site-packages/sklearn/neural_network/tests/test_rbm.py | 225 | 6278 | import sys
import re
import numpy as np
from scipy.sparse import csc_matrix, csr_matrix, lil_matrix
from sklearn.utils.testing import (assert_almost_equal, assert_array_equal,
assert_true)
from sklearn.datasets import load_digits
from sklearn.externals.six.moves import cStringIO as ... | mit |
jeffery-do/Vizdoombot | doom/lib/python3.5/site-packages/matplotlib/backends/qt_editor/formlayout.py | 4 | 20138 | # -*- coding: utf-8 -*-
"""
formlayout
==========
Module creating Qt form dialogs/layouts to edit various type of parameters
formlayout License Agreement (MIT License)
------------------------------------------
Copyright (c) 2009 Pierre Raybaut
Permission is hereby granted, free of charge, to any person
obtaining ... | mit |
RLPAgroScience/ROIseries | ROIseries/sub_routines/sub_routines.py | 1 | 1059 | import os
import pandas as pd
import numpy as np
def file_search(top_dir, extension):
result = []
for dir_path,dir_names,files in os.walk(top_dir):
for name in files:
if name.lower().endswith(extension):
result.append(os.path.join(dir_path, name))
return result
def so... | agpl-3.0 |
scienceguyrob/KnownSourceMatcher | KnownSourceMatcher/dist/Interactive.py | 2 | 29344 | """
This file is part of the KnownSourceMatcher.
KnownSourceMatcher 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.
KnownSourceMatcher is d... | gpl-2.0 |
zhoujh30/folium | folium/element.py | 1 | 15488 | # -*- coding: utf-8 -*-
"""
Elements
------
A generic class for creating Elements.
"""
from uuid import uuid4
from jinja2 import Environment, PackageLoader, Template
ENV = Environment(loader=PackageLoader('folium', 'templates'))
from collections import OrderedDict
import json
from .six import urlopen
from .utilities... | mit |
nhejazi/scikit-learn | sklearn/feature_selection/tests/test_from_model.py | 7 | 7314 | import numpy as np
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 import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal
from ... | bsd-3-clause |
lukeshingles/artistools | artistools/misc.py | 1 | 43415 | #!/usr/bin/env python3
import argparse
from functools import lru_cache
import gzip
import hashlib
# import inspect
import lzma
import math
import multiprocessing
import os.path
import pickle
import sys
import time
import xattr
from collections import namedtuple
from itertools import chain
from functools import wraps
#... | mit |
Nikea/VisTrails | vistrails/tests/runtestsuite.py | 2 | 20120 | #!/usr/bin/env python
# pragma: no testimport
###############################################################################
##
## Copyright (C) 2011-2014, NYU-Poly.
## Copyright (C) 2006-2011, University of Utah.
## All rights reserved.
## Contact: contact@vistrails.org
##
## This file is part of VisTrails.
##
## "R... | bsd-3-clause |
johnbachman/belpy | indra/sources/trrust/processor.py | 4 | 2252 | from copy import deepcopy
from indra.databases import hgnc_client
from indra.statements import Agent, IncreaseAmount, DecreaseAmount, Evidence
class TrrustProcessor(object):
"""Processor to extract INDRA Statements from Trrust data frame.
Attributes
----------
df : pandas.DataFrame
The Trrust... | mit |
rubikloud/scikit-learn | sklearn/metrics/cluster/bicluster.py | 359 | 2797 | from __future__ import division
import numpy as np
from sklearn.utils.linear_assignment_ import linear_assignment
from sklearn.utils.validation import check_consistent_length, check_array
__all__ = ["consensus_score"]
def _check_rows_and_columns(a, b):
"""Unpacks the row and column arrays and checks their shap... | bsd-3-clause |
alexrudy/AstroObject | Examples/spectra.py | 1 | 2361 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# spectra.py
# AstroObject
#
# Created by Alexander Rudy on 2012-04-17.
# Copyright 2012 Alexander Rudy. All rights reserved.
#
import numpy as np
import matplotlib.pyplot as plt
from AstroObject.loggers import *
from AstroObject.anaspec import InterpolatedSpec... | gpl-3.0 |
blakeboswell/valence | build/lib/pyvalence/build/agilentgcms.py | 1 | 20264 | """ Read GCMS files produced by Agilent
"""
import re
import csv
import os
import struct
import numpy as np
import pandas as pd
import scipy.sparse
class AgilentGcmsTableBase(object):
""" Base class for Agilent GCMS builders. This class should not be
instantiated directly.
"""
@classmethod
def ... | bsd-3-clause |
jefflyn/buddha | src/mlia/Ch05/EXTRAS/plot2D.py | 4 | 1233 | '''
Created on Oct 6, 2010
@author: Peter
'''
from numpy import *
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import logRegres
dataMat,labelMat=logRegres.loadDataSet()
dataArr = array(dataMat)
weights = logRegres.stocGradAscent0(dataArr,labelMat)
n = shape(dataArr)[0] #... | artistic-2.0 |
mne-tools/mne-tools.github.io | 0.20/_downloads/df37da09f8cc04b503dd281d8471168a/plot_dics.py | 2 | 12493 | # -*- coding: utf-8 -*-
"""
DICS for power mapping
======================
In this tutorial, we'll simulate two signals originating from two
locations on the cortex. These signals will be sinusoids, so we'll be looking
at oscillatory activity (as opposed to evoked activity).
We'll use dynamic imaging of coherent sourc... | bsd-3-clause |
udibr/fast-rcnn | lib/roi_data_layer/minibatch.py | 44 | 7337 | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Compute minibatch blobs for training a Fast R-CNN network."""
impor... | mit |
icdishb/scikit-learn | sklearn/linear_model/tests/test_base.py | 120 | 10082 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.linear_model.... | bsd-3-clause |
mne-tools/mne-python | tutorials/preprocessing/25_background_filtering.py | 3 | 48286 | # -*- coding: utf-8 -*-
r"""
.. _disc-filtering:
===================================
Background information on filtering
===================================
Here we give some background information on filtering in general, and
how it is done in MNE-Python in particular.
Recommended reading for practical applications ... | bsd-3-clause |
mihail911/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_pdf.py | 69 | 71773 | # -*- coding: iso-8859-1 -*-
"""
A PDF matplotlib backend (not yet complete)
Author: Jouni K Seppänen <jks@iki.fi>
"""
from __future__ import division
import os
import re
import sys
import time
import warnings
import zlib
import numpy as npy
from cStringIO import StringIO
from datetime import datetime
from math impo... | gpl-3.0 |
Guneet-Dhillon/mxnet | example/kaggle-ndsb1/training_curves.py | 52 | 1879 | # 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 u... | apache-2.0 |
jplourenco/bokeh | bokeh/_legacy_charts/builder/tests/test_timeseries_builder.py | 6 | 2807 | """ 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 |
dimkal/mne-python | examples/visualization/plot_evoked_topomap.py | 18 | 1498 | """
========================================
Plotting topographic maps of evoked data
========================================
Load evoked data and plot topomaps for selected time points.
"""
# Authors: Christian Brodbeck <christianbrodbeck@nyu.edu>
# Tal Linzen <linzen@nyu.edu>
# Denis A. Engeman <... | bsd-3-clause |
jereze/scikit-learn | examples/linear_model/plot_theilsen.py | 232 | 3615 | """
====================
Theil-Sen Regression
====================
Computes a Theil-Sen Regression on a synthetic dataset.
See :ref:`theil_sen_regression` for more information on the regressor.
Compared to the OLS (ordinary least squares) estimator, the Theil-Sen
estimator is robust against outliers. It has a breakd... | bsd-3-clause |
flightgong/scikit-learn | benchmarks/bench_plot_lasso_path.py | 301 | 4003 | """Benchmarks of Lasso regularization path computation using Lars and CD
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
from collections import defaultdict
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model import lars_pat... | bsd-3-clause |
benjaminwilson/word2vec-norm-experiments | article_generate_cosine_similarity.py | 1 | 1368 | import numpy as np
import pandas as pd
import sys
from parameters import *
from functions import *
vectors_syn0_filename = sys.argv[1]
word = sys.argv[2] # e.g. 'the'
def row_normalise_dataframe(df):
matrix = df.as_matrix() * 1.
norms = np.sqrt((matrix ** 2).sum(axis=1))
normed_mat = matrix / norms[:, n... | apache-2.0 |
pgierz/MyPythonModules | basemap_wrappers/basemap_pacific.py | 2 | 1642 | import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
def map_pacif(coastlines=True, thisax=plt.gca(), fill_color='aqua'):
m = Basemap(projection='cyl',
llcrnrlat=-60, urcrnrlat=60,
llcrnrlon=-270, urcrnrlon=-60,
resolution='c', ax=thisax)
if c... | gpl-2.0 |
KelumPerera/Pandas | Create Dataframes_DateTime Formating.py | 1 | 3583 | # -*- coding: utf-8 -*-
"""
Created on Fri May 05 21:25:51 2017
@author: Kelum Perera
"""
import pandas as pd
# Take a 2D array as input to your DataFrame
import numpy as np
my_2darray = np.array([[1, 2, 3], [4, 5, 6]])
print(pd.DataFrame(my_2darray))
# Take a dictionary as input to your DataFr... | gpl-3.0 |
chuan9/chromium-crosswalk | chrome/test/nacl_test_injection/buildbot_chrome_nacl_stage.py | 39 | 11336 | #!/usr/bin/python
# Copyright (c) 2012 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Do all the steps required to build and test against nacl."""
import optparse
import os.path
import re
import shutil
import subproc... | bsd-3-clause |
jsilter/scipy | scipy/stats/_discrete_distns.py | 2 | 20515 | #
# Author: Travis Oliphant 2002-2011 with contributions from
# SciPy Developers 2004-2011
#
from __future__ import division, print_function, absolute_import
from scipy import special
from scipy.special import entr, gammaln as gamln
from numpy import floor, ceil, log, exp, sqrt, log1p, expm1, tanh, cosh, s... | bsd-3-clause |
anaderi/lhcb_trigger_ml | setup.py | 1 | 1694 | from setuptools import setup
import codecs
with codecs.open('README.rst', encoding='utf-8') as readme_file:
long_description = readme_file.read()
setup(
name="hep_ml",
version="0.1.5",
description="Machine Learning for High Energy Physics",
long_description=long_description,
url='https://git... | mit |
tachylyte/HydroGeoPy | EXAMPLES/exampleMonteCarloUni.py | 2 | 1242 | # Simple test of probabilistic modelling
# Calculates time to break through assuming plug flow
from simplehydro import *
from monte_carlo import *
from conversion import *
import matplotlib.pyplot as plt
x = 10 # Distance, x (m)
n = 0.3 # Effective porosity, n (-)
K = 1e-7 # Hydraulic conductivi... | bsd-2-clause |
diogo149/CauseEffectPairsPaper | configs/default_categorical_only.py | 1 | 7287 | import numpy as np
from scipy.stats import skew, kurtosis, shapiro, pearsonr, ansari, mood, levene, fligner, bartlett, mannwhitneyu
from scipy.spatial.distance import braycurtis, canberra, chebyshev, cityblock, correlation, cosine, euclidean, hamming, jaccard, kulsinski, matching, russellrao, sqeuclidean
from sklearn.p... | mit |
shikhardb/scikit-learn | sklearn/utils/graph.py | 50 | 6169 | """
Graph utilities and algorithms
Graphs are represented with their adjacency matrices, preferably using
sparse matrices.
"""
# Authors: Aric Hagberg <hagberg@lanl.gov>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD 3 clause
impo... | bsd-3-clause |
sidnarayanan/BAdNet | train/images/utils.py | 1 | 3654 | import numpy as np
# import seaborn
from collections import namedtuple
from keras import backend as K
from keras.engine.topology import Layer
from scipy.interpolate import interp1d
## Loss functions
dice_smooth = 1.
def dice_coef(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
... | mit |
Chandra-MARX/marx-test | marxtest/plot_utils.py | 1 | 2865 | from matplotlib import scale as mscale
from matplotlib import transforms as mtransforms
from matplotlib.ticker import AutoLocator, ScalarFormatter
class PowerScale(mscale.ScaleBase):
"""
Scales values v with with v^pow.
"""
name = 'power'
def __init__(self, axis, **kwargs):
"""
po... | gpl-2.0 |
samzhang111/scikit-learn | sklearn/datasets/tests/test_svmlight_format.py | 228 | 11221 | from bz2 import BZ2File
import gzip
from io import BytesIO
import numpy as np
import os
import shutil
from tempfile import NamedTemporaryFile
from sklearn.externals.six import b
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert... | bsd-3-clause |
sapfo/medeas | src/old_scripts/main_simul_eigenvalues_distribution.py | 1 | 7741 | #!/usr/bin/env python
"""
Created Wed Oct 7 15:04:36 CEST 2015
@author: sapfo
"""
import matplotlib
#matplotlib.use('Agg')
import simul_ms
import python_cmdscale
#import python_pca
import exp
import sys
import numpy as np
import pylab as py
from scipy.stats import norm
'''
We want to pick n1, n2, D, T?
Simulate ... | gpl-3.0 |
Aasmi/scikit-learn | benchmarks/bench_plot_parallel_pairwise.py | 297 | 1247 | # Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import time
import pylab as pl
from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_kernels
def plot(func):
random_state = check_random_state(0)
... | bsd-3-clause |
fabioticconi/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | 69 | 3894 | import scipy.sparse as sp
import numpy as np
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.testing import assert_raises_regex, assert_true
from sklearn.utils.estimator_checks import check_estimator
from sklearn.utils.... | bsd-3-clause |
GGiecold/ECLAIR | src/ECLAIR/Statistical_performance/Robustness_analysis.py | 1 | 18145 | #!/usr/bin/env python
# ECLAIR/src/ECLAIR/Statistics/Robustness_analysis.py;
# Author: Gregory Giecold for the GC Yuan Lab
# Affiliation: Harvard University
# Contact: g.giecold@gmail.com; ggiecold@jimmy.harvard.edu
"""ECLAIR is a package for the robust and scalable
inference of cell lineages from gene expression... | mit |
pgaref/memcached_bench | Python_plots/plots/qjump_utils.py | 3 | 4079 | # Copyright (c) 2015, Malte Schwarzkopf
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and... | mit |
ChrisEberl/Python_DIC | functions/CpCorr.py | 1 | 14243 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on --/--/20--
@author: ---
Revised by Charlie Bourigault
@contact: bourigault.charlie@gmail.com
Please report issues and request on the GitHub project from ChrisEberl (Python_DIC)
More details regarding the project on the GitHub Wiki : https://github... | apache-2.0 |
yunfeilu/scikit-learn | examples/feature_selection/plot_rfe_with_cross_validation.py | 226 | 1384 | """
===================================================
Recursive feature elimination with cross-validation
===================================================
A recursive feature elimination example with automatic tuning of the
number of features selected with cross-validation.
"""
print(__doc__)
import matplotlib.p... | bsd-3-clause |
mattions/TimeScales | branch_dist/import_digitezed_data.py | 1 | 4254 | # Author Michele Mattioni
# Fri Oct 23 15:41:58 BST 2009
import pylab
import numpy as np
from numpy import sin, exp
import matplotlib.pyplot as plt
from helpers.loader import Loader
class FitHandler(object):
"""Fit the data with a polynomial"""
def fit(self, data, terms):
polycoeffs = np.po... | bsd-3-clause |
xzh86/scikit-learn | sklearn/metrics/cluster/tests/test_unsupervised.py | 230 | 2823 | import numpy as np
from scipy.sparse import csr_matrix
from sklearn import datasets
from sklearn.metrics.cluster.unsupervised import silhouette_score
from sklearn.metrics import pairwise_distances
from sklearn.utils.testing import assert_false, assert_almost_equal
from sklearn.utils.testing import assert_raises_regexp... | bsd-3-clause |
jrmyp/attelo | attelo/metrics/constituency.py | 3 | 11012 | """Metrics for constituency trees.
TODO
----
* [ ] factor out the report from the parseval function, see
`sklearn.metrics.classification.classification_report`
* [ ] refactor the selection functions that enable to break down
evaluations, to avoid almost duplicates (as currently)
"""
from __future__ import print_funct... | gpl-3.0 |
madjelan/scikit-learn | examples/feature_stacker.py | 246 | 1906 | """
=================================================
Concatenating multiple feature extraction methods
=================================================
In many real-world examples, there are many ways to extract features from a
dataset. Often it is beneficial to combine several methods to obtain good
performance. Th... | bsd-3-clause |
klusta-team/klustaviewa | klustaviewa/gui/mainwindow.py | 1 | 49325 | """Main window."""
# -----------------------------------------------------------------------------
# Imports
# -----------------------------------------------------------------------------
import pprint
import time
from StringIO import StringIO
import os
import sys
import inspect
import logging
from collections import... | bsd-3-clause |
tmhm/scikit-learn | examples/applications/plot_stock_market.py | 227 | 8284 | """
=======================================
Visualizing the stock market structure
=======================================
This example employs several unsupervised learning techniques to extract
the stock market structure from variations in historical quotes.
The quantity that we use is the daily variation in quote ... | bsd-3-clause |
quheng/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 |
mojoboss/scikit-learn | examples/ensemble/plot_gradient_boosting_quantile.py | 392 | 2114 | """
=====================================================
Prediction Intervals for Gradient Boosting Regression
=====================================================
This example shows how quantile regression can be used
to create prediction intervals.
"""
import numpy as np
import matplotlib.pyplot as plt
from skle... | bsd-3-clause |
JPFrancoia/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 |
avmarchenko/exatomic | exatomic/qchem/output.py | 2 | 2088 | # -*- coding: utf-8 -*-
# Copyright (c) 2015-2018, Exa Analytics Development Team
# Distributed under the terms of the Apache License 2.0
"""
Q-Chem Ouput Editor
#######################
Editor classes for simple Q-Chem output files
"""
import six
import numpy as np
import pandas as pd
from exa import TypedMeta
from ex... | apache-2.0 |
linan7788626/pandas_tutorial | exercises/pandas_wind_statistics/pandas_wind_statistics_solution.py | 4 | 5511 | # Copyright 2015 Enthought, Inc. All Rights Reserved
"""
Wind Statistics
----------------
This exercise is an alternative version of the Numpy exercise but this time we
will be using pandas for all tasks. The data have been modified to contain some
missing values, identified by NaN. Using pandas should make this exer... | mit |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/pandas/tests/series/test_sorting.py | 7 | 4805 | # coding=utf-8
import numpy as np
import random
from pandas import (DataFrame, Series, MultiIndex)
from pandas.util.testing import (assert_series_equal, assert_almost_equal)
import pandas.util.testing as tm
from .common import TestData
class TestSeriesSorting(TestData, tm.TestCase):
_multiprocess_can_split_ ... | mit |
pletisan/python-data-viz-cookbook | 3367OS_Code/3367OS_08_Code/ch08_rec06_textfont.py | 1 | 1382 | import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
# properties:
families = ['serif', 'sans-serif', 'cursive', 'fantasy', 'monospace']
sizes = ['xx-small', 'x-small', 'small', 'medium', 'large',
'x-large', 'xx-large']
styles = ['normal', 'italic', 'oblique']
weights = ['light... | mit |
Akshay0724/scikit-learn | examples/model_selection/plot_confusion_matrix.py | 63 | 3231 | """
================
Confusion matrix
================
Example of confusion matrix usage to evaluate the quality
of the output of a classifier on the iris data set. The
diagonal elements represent the number of points for which
the predicted label is equal to the true label, while
off-diagonal elements are those that ... | bsd-3-clause |
probcomp/cgpm | tests/test_factor_analysis.py | 1 | 8977 | # -*- coding: utf-8 -*-
# Copyright (c) 2015-2016 MIT Probabilistic Computing Project
# 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
# Unles... | apache-2.0 |
GenericMappingTools/gmt-python | examples/tutorials/plot.py | 1 | 3481 | """
Plotting data points
--------------------
GMT shines when it comes to plotting data on a map. We can use some sample data that is
packaged with GMT to try this out. PyGMT provides access to these datasets through the
:mod:`pygmt.datasets` package. If you don't have the data files already, they are
automatically do... | bsd-3-clause |
sergiohr/NeuroDB | test/test9.py | 1 | 1313 | '''
Created on Feb 25, 2015
@author: sergio
'''
import numpy as np
import ctypes
import numpy.ctypeslib as npct
import matplotlib.pyplot as plt
import psycopg2
import time
from math import e, pow
from scipy.optimize import leastsq
if __name__ == '__main__':
# username = 'postgres'
# password = 'postgres'
#... | gpl-3.0 |
fredhusser/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 |
ZENGXH/scikit-learn | sklearn/utils/tests/test_sparsefuncs.py | 57 | 13752 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from numpy.testing import assert_array_almost_equal, assert_array_equal
from sklearn.datasets import make_classification
from sklearn.utils.sparsefuncs import (mean_variance_axis,
inplace_column_scale,
... | bsd-3-clause |
annayqho/TheCannon | code/lamost/li_giants/plot_residual.py | 1 | 3418 | """ Create some model spectra vs. data """
import numpy as np
import matplotlib.pyplot as plt
from math import log10, floor
from matplotlib import rc
import matplotlib.gridspec as gridspec
from matplotlib.colors import LogNorm
plt.rc('text', usetex=True)
# rc('text.latex', preamble = ','.join('''\usepackage{txfonts}''... | mit |
Insight-book/data-science-from-scratch | first-edition-ko/code/ch20_natural_language_processing.py | 12 | 10007 | from __future__ import division
import math, random, re
from collections import defaultdict, Counter
from bs4 import BeautifulSoup
import requests
def plot_resumes(plt):
data = [ ("big data", 100, 15), ("Hadoop", 95, 25), ("Python", 75, 50),
("R", 50, 40), ("machine learning", 80, 20), ("statistics", 20, ... | unlicense |
lmarkely/enron_fraud | poi_id_modified.py | 1 | 28009 | #!/usr/bin/python
import sys
import pickle
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
from tester import dump_classifier_and_data
### Task 1: Select what features you'll use.
### features_list is a list of strings, each of which is a feature name.
### The first feature ... | mit |
researchstudio-sat/wonpreprocessing | python-processing/scripts/evaluation_algorithms.py | 1 | 20879 | import numpy as np
import sklearn.metrics as m
from tools.cosine_link_prediction import cosinus_link_prediciton
from tools.evaluation_utils import EvaluationReport, NeedEvaluationDetailDict, get_optimal_threshold, \
write_ROC_curve_file, write_precision_recall_curve_file
from tools.graph_utils import create_gexf_gr... | apache-2.0 |
Eric89GXL/scikit-learn | examples/gaussian_process/plot_gp_regression.py | 253 | 4054 | #!/usr/bin/python
# -*- coding: utf-8 -*-
r"""
=========================================================
Gaussian Processes regression: basic introductory example
=========================================================
A simple one-dimensional regression exercise computed in two different ways:
1. A noise-free cas... | bsd-3-clause |
ShownX/incubator-mxnet | example/rcnn/rcnn/pycocotools/coco.py | 41 | 19083 | # 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 u... | apache-2.0 |
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