repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
alexandreday/fast_density_clustering | build/lib/fdc/plotting.py | 2 | 16580 | '''
Created on Jan 16, 2017
@author: Alexandre Day
'''
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.patheffects as PathEffects
from .mycolors import COLOR_PALETTE
from .fdc import FDC
import math
def set_nice_font(size = 18, usetex=False):
font = {'family' : 'serif', 'size' : size}... | bsd-3-clause |
linebp/pandas | pandas/tests/sparse/test_libsparse.py | 14 | 22152 | from pandas import Series
import pytest
import numpy as np
import operator
import pandas.util.testing as tm
from pandas import compat
from pandas.core.sparse.array import IntIndex, BlockIndex, _make_index
import pandas._libs.sparse as splib
TEST_LENGTH = 20
plain_case = dict(xloc=[0, 7, 15], xlen=[3, 5, 5], yloc=[... | bsd-3-clause |
hazelnusse/sympy-old | examples/intermediate/sample.py | 11 | 3354 | """
Utility functions for plotting sympy functions.
See examples\mplot2d.py and examples\mplot3d.py for usable 2d and 3d
graphing functions using matplotlib.
"""
from numpy import repeat, arange, empty, ndarray, array
from sympy import Symbol, Basic, Real, Rational, I, sympify
def sample2d(f, x_args):
"""
Sa... | bsd-3-clause |
alvarofierroclavero/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 |
BoltzmannBrain/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/mathtext.py | 69 | 101723 | r"""
:mod:`~matplotlib.mathtext` is a module for parsing a subset of the
TeX math syntax and drawing them to a matplotlib backend.
For a tutorial of its usage see :ref:`mathtext-tutorial`. This
document is primarily concerned with implementation details.
The module uses pyparsing_ to parse the TeX expression.
.. _p... | agpl-3.0 |
hncg/jieba | test/extract_topic.py | 65 | 1463 | import sys
sys.path.append("../")
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn import decomposition
import jieba
import time
import glob
import sys
import os
import random
if len(sys.argv)<2:
print("usage: extract_topic.py di... | mit |
kjung/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 355 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluate... | bsd-3-clause |
sbussmann/Bussmann2015 | Code/fluxplot.py | 2 | 2222 | """
2014 July 16
Shane Bussmann
Plot the distribution of fluxdensities for the ALMA sample. Compare total
observed flux (what a single-dish telescope with 20" FWHM resolution would see)
with the individual observed flux (accounting for blending) and with the
individual intrinsic flux (accounting for lensing).
"""
... | mit |
lin-credible/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 |
v0devil/jltom | datagenerator_py3.py | 1 | 38469 | from collections import OrderedDict
import json
import logging
#from pylab import *
import numpy as np
import pandas as pd
import sys
import re
import os
import zipfile
import sqlalchemy
import shutil
import time
import datetime
import argparse
from xml.etree.ElementTree import ElementTree
from os.path import basename
... | mit |
quimaguirre/diana | scripts/compare_profiles.py | 1 | 35855 | import argparse
import configparser
import copy
import ntpath
import numpy as np
import pandas as pd
import time
import sys, os, re
from context import diana
import diana.classes.drug as diana_drug
import diana.classes.comparison as comparison
import diana.classes.network_analysis as network_analysis
import diana.clas... | mit |
martin-hunt/foobar | docs/conf.py | 1 | 8160 | # -*- coding: utf-8 -*-
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys
imp... | mit |
deepakantony/sms-tools | lectures/05-Sinusoidal-model/plots-code/sineModelAnal-bendir.py | 24 | 1245 | import numpy as np
import matplotlib.pyplot as plt
import sys, os, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import stft as STFT
import sineModel as SM
import utilFunctions as UF
(fs, x) = UF.wavread(os.path.join(os.path.dirname(os.path.realpath(__fi... | agpl-3.0 |
gfyoung/pandas | pandas/tests/series/methods/test_to_csv.py | 3 | 6229 | from datetime import datetime
from io import StringIO
import numpy as np
import pytest
import pandas as pd
from pandas import Series
import pandas._testing as tm
from pandas.io.common import get_handle
class TestSeriesToCSV:
def read_csv(self, path, **kwargs):
params = {"squeeze": True, "index_col": 0,... | bsd-3-clause |
zanton123/HaSAPPy | program/DesignGeneInsertion.py | 1 | 9186 | # -*- coding: utf-8 -*-
"""
Created on Tue May 24 08:20:07 2016
@author: GDM
"""
#### Importing modules ####
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import HTSeq
import cPickle as pickle
import os
import re
mpl.interactive(False)
####
#### Class definition ####
... | mit |
sunshineDrizzle/FreeROI | froi/algorithm/unused/spectralmapper.py | 6 | 3516 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""Mapper for spectral clustering.
Date: 2012.05.29
"""
__docformat__ = 'restructuredtext'
import numpy as np
import scipy.sparse as sp
from mvpa2.base import warning
from mvpa2.base.doc... | bsd-3-clause |
alvations/oque | que.py | 1 | 8287 |
import io, sys
import numpy as np
from scipy.stats import uniform as sp_rand
from itertools import combinations
from sklearn.linear_model import BayesianRidge
from sklearn.grid_search import RandomizedSearchCV
from sklearn.metrics import mean_squared_error, mean_absolute_error
from sklearn.svm import SVR
from sklear... | mit |
glennq/scikit-learn | examples/svm/plot_svm_regression.py | 120 | 1520 | """
===================================================================
Support Vector Regression (SVR) using linear and non-linear kernels
===================================================================
Toy example of 1D regression using linear, polynomial and RBF kernels.
"""
print(__doc__)
import numpy as np
... | bsd-3-clause |
abhisg/scikit-learn | sklearn/tests/test_metaestimators.py | 226 | 4954 | """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 |
inkenbrandt/WellApplication | wellapplication/hydropy.py | 1 | 5866 | """
Hydropy package
@author: Stijn Van Hoey
from: https://github.com/stijnvanhoey/hydropy/tree/master/hydropy
for a better and more up to date copy of this script go to the original repo.
"""
from __future__ import absolute_import, division, print_function, unicode_literals
import pandas as pd
import numpy as np
from s... | mit |
Vimos/scikit-learn | examples/cluster/plot_kmeans_digits.py | 42 | 4491 | """
===========================================================
A demo of K-Means clustering on the handwritten digits data
===========================================================
In this example we compare the various initialization strategies for
K-means in terms of runtime and quality of the results.
As the gr... | bsd-3-clause |
xiaoxiamii/scikit-learn | sklearn/metrics/setup.py | 299 | 1024 | import os
import os.path
import numpy
from numpy.distutils.misc_util import Configuration
from sklearn._build_utils import get_blas_info
def configuration(parent_package="", top_path=None):
config = Configuration("metrics", parent_package, top_path)
cblas_libs, blas_info = get_blas_info()
if os.name ==... | bsd-3-clause |
krez13/scikit-learn | sklearn/metrics/classification.py | 8 | 68395 | """Metrics to assess performance on classification task given classe prediction
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.gram... | bsd-3-clause |
gfyoung/pandas | pandas/tests/arrays/categorical/test_constructors.py | 2 | 28825 | from datetime import date, datetime
import numpy as np
import pytest
from pandas.compat import IS64, is_platform_windows
from pandas.core.dtypes.common import is_float_dtype, is_integer_dtype
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
Categor... | bsd-3-clause |
NixaSoftware/CVis | venv/lib/python2.7/site-packages/pandas/tests/indexing/common.py | 7 | 9615 | """ common utilities """
import itertools
from warnings import catch_warnings
import numpy as np
from pandas.compat import lrange
from pandas.core.dtypes.common import is_scalar
from pandas import Series, DataFrame, Panel, date_range, UInt64Index
from pandas.util import testing as tm
from pandas.io.formats.printing i... | apache-2.0 |
RoboticsClubatUCF/RoboSub | ucf_sub_catkin_ros/src/sub_utils/src/color.py | 1 | 2950 | from matplotlib import pyplot as plt
import numpy as np
import argparse
import cv2
from imutils import paths
import imutils
import os
from sklearn.externals import joblib
#Argument Parsing
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--positive", required=True, help="path to positive images directory")
ap.a... | mit |
bdh1011/wau | venv/lib/python2.7/site-packages/pandas/stats/tests/test_math.py | 15 | 1927 | import nose
from datetime import datetime
from numpy.random import randn
import numpy as np
from pandas.core.api import Series, DataFrame, date_range
from pandas.util.testing import assert_almost_equal
import pandas.core.datetools as datetools
import pandas.stats.moments as mom
import pandas.util.testing as tm
import... | mit |
chrisdamba/mining | mining/controllers/data/__init__.py | 4 | 5907 | # -*- coding: utf-8 -*-
from gevent import monkey
monkey.patch_all()
import json
import gc
from bottle import Bottle, request, response
from bottle.ext.mongo import MongoPlugin
from pandas import DataFrame
from mining.settings import PROJECT_PATH
from mining.utils import conf, __from__
from mining.utils._pandas imp... | mit |
fbagirov/scikit-learn | sklearn/utils/estimator_checks.py | 33 | 48331 | from __future__ import print_function
import types
import warnings
import sys
import traceback
import inspect
import pickle
from copy import deepcopy
import numpy as np
from scipy import sparse
import struct
from sklearn.externals.six.moves import zip
from sklearn.externals.joblib import hash, Memory
from sklearn.ut... | bsd-3-clause |
Weihonghao/ECM | Vpy34/lib/python3.5/site-packages/pandas/io/sas/sas_xport.py | 14 | 14805 | """
Read a SAS XPort format file into a Pandas DataFrame.
Based on code from Jack Cushman (github.com/jcushman/xport).
The file format is defined here:
https://support.sas.com/techsup/technote/ts140.pdf
"""
from datetime import datetime
import pandas as pd
from pandas.io.common import get_filepath_or_buffer, BaseIt... | agpl-3.0 |
mikekestemont/beckett | code/diachron.py | 1 | 7046 | import matplotlib
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import seaborn as sb
sb.set_style("dark")
import os
import string
import codecs
import glob
from operator import itemgetter
from collections import named... | mit |
rajat1994/scikit-learn | examples/cluster/plot_kmeans_digits.py | 230 | 4524 | """
===========================================================
A demo of K-Means clustering on the handwritten digits data
===========================================================
In this example we compare the various initialization strategies for
K-means in terms of runtime and quality of the results.
As the gr... | bsd-3-clause |
ZENGXH/scikit-learn | sklearn/ensemble/tests/test_weight_boosting.py | 40 | 16837 | """Testing for the boost module (sklearn.ensemble.boost)."""
import numpy as np
from sklearn.utils.testing import assert_array_equal, assert_array_less
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal, assert_true
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
strands-project/strands_qsr_lib | qsr_lib/dbg/dbg_cardinal_directions.py | 8 | 3697 | #!/usr/bin/python
import math
from matplotlib import pyplot as plt
from matplotlib.patches import Rectangle
class Dbg(object):
def __init__(self):
pass
def return_bounding_box_2d(self, x, y, xsize, ysize):
"""Return the bounding box
:param x: x center
:param y: y center
... | mit |
ArcherSys/ArcherSys | eclipse/plugins/org.python.pydev_4.5.5.201603221110/pysrc/pydev_ipython/qt_for_kernel.py | 67 | 2337 | """ Import Qt in a manner suitable for an IPython kernel.
This is the import used for the `gui=qt` or `matplotlib=qt` initialization.
Import Priority:
if Qt4 has been imported anywhere else:
use that
if matplotlib has been imported and doesn't support v2 (<= 1.0.1):
use PyQt4 @v1
Next, ask ETS' QT_API env v... | mit |
louispotok/pandas | pandas/tests/frame/test_timezones.py | 7 | 5632 | # -*- coding: utf-8 -*-
"""
Tests for DataFrame timezone-related methods
"""
from datetime import datetime
import pytest
import pytz
import numpy as np
import pandas.util.testing as tm
from pandas.compat import lrange
from pandas.core.indexes.datetimes import date_range
from pandas.core.dtypes.dtypes import DatetimeT... | bsd-3-clause |
viiru-/pytrainer | pytrainer/lib/graphdata.py | 2 | 4969 | # -*- coding: iso-8859-1 -*-
#Copyright (C) Fiz Vazquez vud1@sindominio.net
#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 2
#of the License, or (at your option) any later version... | gpl-2.0 |
prasetiyohadi/learn-computations | monte-carlo/hitnmiss3.py | 1 | 2969 | from collections import OrderedDict as od
from matplotlib import pyplot as plt
import csv
import operator
import random
import time
# function
def myfunc(x, y, z):
return x*y**2*z+x**3*y*z**2-x*y*z**3
# analitically integrated function
def myintfunc(xa, ya, za, xb, yb, zb):
return (xb**2-xa**2)*(yb**3-ya**3... | mit |
da1z/intellij-community | python/helpers/pydev/pydevd.py | 1 | 69071 | '''
Entry point module (keep at root):
This module starts the debugger.
'''
import sys
if sys.version_info[:2] < (2, 6):
raise RuntimeError('The PyDev.Debugger requires Python 2.6 onwards to be run. If you need to use an older Python version, use an older version of the debugger.')
import atexit
import os
import... | apache-2.0 |
dpshelio/sunpy | examples/plotting/Finding_Local_Peaks_in_Solar_Data.py | 2 | 3152 | """
=================================
Finding Local Peaks in Solar Data
=================================
Detecting intensity peaks in solar images can be useful, for example as
a simple flare identification mechanism. This example illustrates detection
of areas where there is a spike in solar intensity.
We use the `~... | bsd-2-clause |
jniediek/mne-python | mne/viz/tests/test_3d.py | 5 | 9195 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Mainak Jas <mainak@neuro.hut.fi>
# Mark Wronkiewicz <wronk.mark@gmail.c... | bsd-3-clause |
GGiecold/pyRMT | pyRMT.py | 1 | 26076 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
r"""Python for Random Matrix Theory. This package implements several
cleaning schemes for noisy correlation matrices, including
the optimal shrinkage, rotationally-invariant estimator
to an underlying correlation matrix (as proposed by Joel Bun,
Jean-Philippe Bouchaud,... | mit |
zrhans/pythonanywhere | .virtualenvs/django19/lib/python3.4/site-packages/numpy/doc/creation.py | 118 | 5507 | """
==============
Array Creation
==============
Introduction
============
There are 5 general mechanisms for creating arrays:
1) Conversion from other Python structures (e.g., lists, tuples)
2) Intrinsic numpy array array creation objects (e.g., arange, ones, zeros,
etc.)
3) Reading arrays from disk, either from... | apache-2.0 |
costypetrisor/scikit-learn | sklearn/learning_curve.py | 13 | 13351 | """Utilities to evaluate models with respect to a variable
"""
# Author: Alexander Fabisch <afabisch@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import warnings
import numpy as np
from .base import is_classifier, clone
from .cross_validation import _check_cv
from .externals.joblib import Parallel, delayed
fr... | bsd-3-clause |
justincassidy/scikit-learn | sklearn/linear_model/tests/test_logistic.py | 105 | 26588 | import numpy as np
import scipy.sparse as sp
from scipy import linalg, optimize, sparse
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.util... | bsd-3-clause |
andyraib/data-storage | python_scripts/env/lib/python3.6/site-packages/pandas/tests/indexes/test_datetimelike.py | 7 | 52802 | # -*- coding: utf-8 -*-
from datetime import datetime, timedelta, time
import numpy as np
from pandas import (DatetimeIndex, Float64Index, Index, Int64Index,
NaT, Period, PeriodIndex, Series, Timedelta,
TimedeltaIndex, date_range, period_range,
timedelta_ra... | apache-2.0 |
shahankhatch/scikit-learn | examples/svm/plot_oneclass.py | 249 | 2302 | """
==========================================
One-class SVM with non-linear kernel (RBF)
==========================================
An example using a one-class SVM for novelty detection.
:ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
algorithm that learns a decision function for novelty detection:
... | bsd-3-clause |
bnaul/scikit-learn | sklearn/linear_model/tests/test_huber.py | 12 | 7600 | # Authors: Manoj Kumar mks542@nyu.edu
# License: BSD 3 clause
import numpy as np
from scipy import optimize, sparse
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.datasets import ma... | bsd-3-clause |
calum-chamberlain/EQcorrscan | eqcorrscan/utils/mag_calc.py | 1 | 49349 | """
Functions to aid magnitude estimation.
:copyright:
EQcorrscan developers.
:license:
GNU Lesser General Public License, Version 3
(https://www.gnu.org/copyleft/lesser.html)
"""
import numpy as np
import logging
import eqcorrscan # Used to get version number
import os
import glob
import matplotlib.pypl... | gpl-3.0 |
michaelbramwell/sms-tools | lectures/08-Sound-transformations/plots-code/stftMorph-orchestra.py | 18 | 2053 | import numpy as np
import time, os, sys
from scipy.signal import hamming, resample
import matplotlib.pyplot as plt
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/transfo... | agpl-3.0 |
Akshay0724/scikit-learn | sklearn/tests/test_multioutput.py | 23 | 12429 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from sklearn.utils import shuffle
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_raises_regex
from s... | bsd-3-clause |
ageron/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimators_test.py | 46 | 6682 | # 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 |
MohammedWasim/Data-Science-45min-Intros | support-vector-machines-101/svm-example.py | 26 | 2219 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
__author__="Josh Montague"
__license__="MIT License"
import sys
import pandas as pd
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.svm import SVC
import matplotlib.pyplot as plt
try:
import seaborn as sns
except ImportError as e:
sys.stde... | unlicense |
hannwoei/paparazzi | sw/tools/calibration/calibration_utils.py | 19 | 9087 |
# Copyright (C) 2010 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 your option)
# any later version.
#
# Paparazzi ... | gpl-2.0 |
loli/sklearn-ensembletrees | 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 |
CVML/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_02_sentiment.py | 256 | 2406 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a ... | bsd-3-clause |
seckcoder/lang-learn | python/sklearn/sklearn/ensemble/gradient_boosting.py | 1 | 40371 | """Gradient Boosted Regression Trees
This module contains methods for fitting gradient boosted regression trees for
both classification and regression.
The module structure is the following:
- The ``BaseGradientBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regressio... | unlicense |
mikebenfield/scikit-learn | examples/cluster/plot_ward_structured_vs_unstructured.py | 320 | 3369 | """
===========================================================
Hierarchical clustering: structured vs unstructured ward
===========================================================
Example builds a swiss roll dataset and runs
hierarchical clustering on their position.
For more information, see :ref:`hierarchical_clus... | bsd-3-clause |
MJuddBooth/pandas | pandas/tests/extension/base/interface.py | 3 | 2284 | import numpy as np
from pandas.core.dtypes.common import is_extension_array_dtype
from pandas.core.dtypes.dtypes import ExtensionDtype
import pandas as pd
import pandas.util.testing as tm
from .base import BaseExtensionTests
class BaseInterfaceTests(BaseExtensionTests):
"""Tests that the basic interface is sat... | bsd-3-clause |
soravux/pms | pms.py | 1 | 7603 | #!/usr/bin/env python
import argparse
import json
import pickle
import numpy as np
from scipy.misc import imread
from scipy import sparse
from scipy import optimize
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import mesh
def getImage(filename):
"""Open image file in greyscale m... | mit |
stopfer/opengm | src/interfaces/python/opengm/benchmark/__init__.py | 14 | 2396 | import opengm
import os
import numpy
try:
import matplotlib.pyplot as plt
from matplotlib import pyplot
from matplotlib import pylab
except:
pass
class ModelResult(object):
def __init__(self):
print opengm.configuration
def filenamesFromDir(path,ending='.h5'):
return [path+f for f in os.listdir(path) i... | mit |
JPFrancoia/scikit-learn | benchmarks/bench_20newsgroups.py | 377 | 3555 | from __future__ import print_function, division
from time import time
import argparse
import numpy as np
from sklearn.dummy import DummyClassifier
from sklearn.datasets import fetch_20newsgroups_vectorized
from sklearn.metrics import accuracy_score
from sklearn.utils.validation import check_array
from sklearn.ensemb... | bsd-3-clause |
jseabold/statsmodels | statsmodels/emplike/tests/test_regression.py | 5 | 5787 | from numpy.testing import assert_almost_equal
import pytest
from statsmodels.regression.linear_model import OLS
from statsmodels.tools import add_constant
from .results.el_results import RegressionResults
from statsmodels.datasets import stackloss
class GenRes(object):
"""
Loads data and creates class insta... | bsd-3-clause |
lewislone/mStocks | packets-analysis/lib/XlsxWriter-0.7.3/examples/pandas_chart.py | 9 | 1049 | ##############################################################################
#
# An example of converting a Pandas dataframe to an xlsx file with a chart
# using Pandas and XlsxWriter.
#
# Copyright 2013-2015, John McNamara, jmcnamara@cpan.org
#
import pandas as pd
# Create a Pandas dataframe from some data.
df = ... | mit |
vanatteveldt/semafor | src/main/python/semafor/framenet/pmi.py | 5 | 1525 | from itertools import chain, combinations, product
import codecs
import json
from math import log
import networkx as nx
import matplotlib as plt
from nltk import FreqDist
from semafor.framenet.frames import FrameHierarchy
THRESHOLD = 4
def draw_graph(graph):
pos = nx.graphviz_layout(graph, prog='dot')
nx.d... | gpl-3.0 |
q1ang/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 |
filipkilibarda/Ants-on-a-Polygon | simulation.py | 1 | 3153 | import matplotlib.pyplot as plt
import matplotlib.animation as animation
from math import pi,cos,sin,sqrt
import numpy as np
import ants
def calcAnalyticalSolution():
ngon = ants.Ngon(NUMBER_OF_ANTS)
phi = ngon.getInteriorAngle()
intialDistanceAnts = 2*INITIAL_DISTANCE_ORIGIN*sin(2*pi/NUMBER_OF_ANTS/2)
... | mit |
galactics/beyond | tests/propagators/test_keplernum.py | 2 | 15660 | import numpy as np
from contextlib import contextmanager
from pytest import fixture, raises, mark
from unittest.mock import patch
import beyond.io.ccsds as ccsds
from beyond.dates import Date, timedelta
from beyond.io.tle import Tle
from beyond.propagators.keplernum import KeplerNum, SOIPropagator
from beyond.env.sol... | mit |
trankmichael/scikit-learn | examples/model_selection/plot_precision_recall.py | 249 | 6150 | """
================
Precision-Recall
================
Example of Precision-Recall metric to evaluate classifier output quality.
In information retrieval, precision is a measure of result relevancy, while
recall is a measure of how many truly relevant results are returned. A high
area under the curve represents both ... | bsd-3-clause |
glorizen/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/path.py | 69 | 20263 | """
Contains a class for managing paths (polylines).
"""
import math
from weakref import WeakValueDictionary
import numpy as np
from numpy import ma
from matplotlib._path import point_in_path, get_path_extents, \
point_in_path_collection, get_path_collection_extents, \
path_in_path, path_intersects_path, con... | agpl-3.0 |
kastnerkyle/COCORA2012 | gui.py | 1 | 17016 | #!/usr/bin/python
import sys
from PyQt4 import QtGui as qtg
from PyQt4 import QtCore as qtc
from numpy import arange, sin, pi
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from matplotlib.patches import Rectangle
from matplotlib.ticker import Fun... | bsd-3-clause |
jzt5132/scikit-learn | examples/decomposition/plot_faces_decomposition.py | 204 | 4452 | """
============================
Faces dataset decompositions
============================
This example applies to :ref:`olivetti_faces` different unsupervised
matrix decomposition (dimension reduction) methods from the module
:py:mod:`sklearn.decomposition` (see the documentation chapter
:ref:`decompositions`) .
"""... | bsd-3-clause |
pratapvardhan/pandas | pandas/tests/scalar/timestamp/test_comparisons.py | 7 | 6112 | # -*- coding: utf-8 -*-
from datetime import datetime
import operator
import pytest
import numpy as np
from dateutil.tz import tzutc
from pytz import utc
from pandas.compat import long, PY2
from pandas import Timestamp
class TestTimestampComparison(object):
def test_comparison_object_array(self):
# GH#... | bsd-3-clause |
Silmathoron/NNGT | nngt/__init__.py | 1 | 10076 | #!/usr/bin/env python
#-*- coding:utf-8 -*-
#
# This file is part of the NNGT project to generate and analyze
# neuronal networks and their activity.
# Copyright (C) 2015-2019 Tanguy Fardet
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License a... | gpl-3.0 |
intel-analytics/analytics-zoo | pyzoo/test/zoo/orca/learn/ray/tf/test_tf_ray_estimator.py | 1 | 21559 | #
# Copyright 2018 Analytics Zoo Authors.
#
# 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 |
massmutual/scikit-learn | sklearn/externals/joblib/__init__.py | 72 | 4795 | """ Joblib is a set of tools to provide **lightweight pipelining in
Python**. In particular, joblib offers:
1. transparent disk-caching of the output values and lazy re-evaluation
(memoize pattern)
2. easy simple parallel computing
3. logging and tracing of the execution
Joblib is optimized to be **fast*... | bsd-3-clause |
dariox2/CADL | session-5/s5p3-latent_space_arithmetic.py | 1 | 18992 |
#
# Session 5, part 3
#
print("Begin import...")
import sys
import os
import numpy as np
import matplotlib.pyplot as plt
from skimage.transform import resize
#from skimage import data # ERROR: Cannot load libmkl_def.so
from scipy.misc import imresize
from scipy.ndimage.filters import gaussian_filter
print("Loading ... | apache-2.0 |
justincassidy/scikit-learn | sklearn/tests/test_metaestimators.py | 226 | 4954 | """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 |
seanpquinn/augerta | web_monitor/unhex_and_sort_mar2017_v4_catchup.py | 1 | 24486 | # Copyright (c) Case Western Reserve University 2017
# This software is distributed under Apache License 2.0
# Consult the file LICENSE.txt
# Author: Sean Quinn spq@case.edu
# Mar 23 2017
import binascii
import bz2
import struct
import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import n... | apache-2.0 |
PanDAWMS/panda-bigmon-core-old | core/common/models.py | 1 | 139296 | # Create your models here.
# This is an auto-generated Django model module.
# You'll have to do the following manually to clean this up:
# * Rearrange models' order
# * Make sure each model has one field with primary_key=True
# Feel free to rename the models, but don't rename db_table values or field names.
#
#... | apache-2.0 |
danielfrg/datasciencebox | datasciencebox/salt/_states/conda.py | 1 | 5256 | import os
__virtualname__ = 'conda'
def __virtual__():
"""
Only load if the conda module is available in __salt__
"""
if 'pip.list' in __salt__:
return __virtualname__
return False
def managed(name, packages=None, requirements=None, saltenv='base', user=None):
"""
Create and ins... | apache-2.0 |
OpenGenus/cosmos | code/artificial_intelligence/src/artificial_neural_network/ann.py | 3 | 1384 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv("dataset.csv")
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])... | gpl-3.0 |
pbashivan/EEGLearn | eeglearn/eeg_cnn_lib.py | 1 | 24878 | from __future__ import print_function
import time
import numpy as np
np.random.seed(1234)
from functools import reduce
import math as m
import scipy.io
import theano
import theano.tensor as T
from scipy.interpolate import griddata
from sklearn.preprocessing import scale
from utils import augment_EEG, c... | gpl-2.0 |
NickC1/skedm | build/lib/skedm/utilities.py | 1 | 9845 | """
Metrics for scoring predictions and also some more specialized
math needed for skedm
"""
import numpy as np
from scipy import stats as stats
from numba import jit
def weighted_mean(X, distances ):
"""
Calculates the weighted mean given a set of values and their corresponding
distances. Only 1/distan... | mit |
exepulveda/swfc | python/spatial_correction_kmean_2d.py | 1 | 2530 | import sys
import random
import logging
import collections
import math
import sys
import json
sys.path += ['..']
import numpy as np
import scipy.stats
from graph_labeling import graph_cut, make_neighbourhood
from scipy.spatial import cKDTree
from sklearn.preprocessing import StandardScaler
from sklearn.cluster imp... | gpl-3.0 |
kshedstrom/pyroms | examples/cobalt-preproc/Boundary_bio/remap_bdry_bio.py | 1 | 6606 | import numpy as np
import os
try:
import netCDF4 as netCDF
except:
import netCDF3 as netCDF
import matplotlib.pyplot as plt
import time
from datetime import datetime
from matplotlib.dates import date2num, num2date
import pyroms
import pyroms_toolbox
import _remapping
class nctime(object):
pass
def remap_bdry... | bsd-3-clause |
sara-02/fabric8-analytics-stack-analysis | analytics_platform/kronos/pgm/src/offline_training.py | 1 | 6130 | """Functions to perform offline training for Kronos PGM."""
import sys
import time
import os
from analytics_platform.kronos.src import config
import analytics_platform.kronos.pgm.src.pgm_constants as pgm_constants
from analytics_platform.kronos.pgm.src.pgm_pomegranate import PGMPomegranate
from util.analytics_platform... | gpl-3.0 |
DerThorsten/seglib | seglibpython/seglib/clustering/ce_multicut.py | 1 | 7168 | from seglib import cgp2d
from seglib.preprocessing import norm01
import opengm
import numpy
import vigra
from sklearn.cluster import Ward,WardAgglomeration
class CgpClustering(object):
def __init__(self,cgp):
self.cgp = cgp
self.labels = numpy.zeros(self.cgp.numCells(2),dtype=numpy.uint64)
class Hierarchica... | mit |
alephu5/Soundbyte | environment/lib/python3.3/site-packages/pandas/tests/test_format.py | 1 | 81984 | from __future__ import print_function
# -*- coding: utf-8 -*-
from pandas.compat import range, zip, lrange, StringIO, PY3, lzip, u
import pandas.compat as compat
import itertools
import os
import sys
from textwrap import dedent
import warnings
from numpy import nan
from numpy.random import randn
import numpy as np
f... | gpl-3.0 |
mmottahedi/neuralnilm_prototype | scripts/experiment035.py | 2 | 10172 | from __future__ import division, print_function
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from datetime import timedelta
from numpy.random import rand
from time import time
from nilmtk import TimeFrame, DataSet, MeterGroup
"""
INPUT: quantized mains fdiff, all-hot
OUTPUT: appliance fdiff
... | mit |
radjkarl/imgProcessor | imgProcessor/interpolate/interpolateCircular2dStructuredIDW.py | 1 | 6467 | from __future__ import division
import numpy as np
from numba import jit
from math import atan2
@jit(nopython=True)
def interpolateCircular2dStructuredIDW(grid, mask, kernel=15, power=2,
fr=1, fphi=1, cx=0, cy=0):
'''
same as interpolate2dStructuredIDW
but calcul... | gpl-3.0 |
herilalaina/scikit-learn | examples/cluster/plot_cluster_iris.py | 56 | 2815 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
K-means Clustering
=========================================================
The plots display firstly what a K-means algorithm would yield
using three clusters. It is then shown what the effect of a bad
initializa... | bsd-3-clause |
dmnfarrell/mirnaseq | smallrnaseq/plotting.py | 2 | 7525 | #!/usr/bin/env python
# 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.
#
# This program is distributed in the hope that ... | gpl-3.0 |
alshedivat/tensorflow | tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py | 30 | 40476 | # 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 |
tangyouze/tushare | tushare/datayes/future.py | 17 | 1740 | # -*- coding:utf-8 -*-
"""
通联数据
Created on 2015/08/24
@author: Jimmy Liu
@group : waditu
@contact: jimmysoa@sina.cn
"""
from pandas.compat import StringIO
import pandas as pd
from tushare.util import vars as vs
from tushare.util.common import Client
from tushare.util import upass as up
class Future():
def _... | bsd-3-clause |
ChanderG/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 |
vishwa91/OptSys | examples/objective.py | 1 | 1478 | #!/usr/bin/env python3
import os, sys
sys.path.append('../modules')
import numpy as np
import matplotlib.pyplot as plt
import raytracing as rt
import visualize as vis
import ray_utilities
if __name__ == '__main__':
# Create a relay lens system
components = []
rays = []
image_plane = -300
nrays =... | mit |
coreyabshire/stacko | src/competition_utilities.py | 1 | 5336 | from __future__ import division
from collections import Counter
import csv
import dateutil
from datetime import datetime
from dateutil.relativedelta import relativedelta
import numpy as np
import os
import pandas as pd
import pymongo
data_path = "C:/Projects/ML/stacko/data2"
submissions_path = data_path
if not data_pa... | bsd-2-clause |
madjelan/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 |
akshayka/edxclassify | edxclassify/classifiers/clf_util.py | 1 | 3557 | import numpy as np
from sklearn.cross_validation import StratifiedKFold
from sklearn import metrics
from sklearn.externals import joblib
import skll
def load_clf(pkl_file):
"""Load a joblib-dumped data_cleaner and trained classifier"""
data_cleaner, clf = joblib.load(pkl_file)
return data_cleaner, clf
d... | gpl-2.0 |
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