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 |
|---|---|---|---|---|---|
harshaneelhg/scikit-learn | sklearn/metrics/tests/test_score_objects.py | 138 | 14048 | import pickle
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regexp
from sklearn.utils.testing import assert_true
from sklearn.utils.testing im... | bsd-3-clause |
mgraupe/acq4 | acq4/pyqtgraph/widgets/MatplotlibWidget.py | 30 | 1442 | from ..Qt import QtGui, QtCore, USE_PYSIDE, USE_PYQT5
import matplotlib
if not USE_PYQT5:
if USE_PYSIDE:
matplotlib.rcParams['backend.qt4']='PySide'
from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt4agg import NavigationToolbar2QTA... | mit |
aminert/scikit-learn | sklearn/linear_model/tests/test_randomized_l1.py | 214 | 4690 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.linear_model.randomized_l1 i... | bsd-3-clause |
0asa/scikit-learn | sklearn/linear_model/tests/test_ridge.py | 3 | 22131 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
lzamparo/SdA_reduce | utils/lle_neighbours_pipeline.py | 1 | 4247 | """
==========
ISOMAP neighbours parameter CV pipeline
==========
Use a pipeline to find the best neighbourhood size parameter for ISOMAP.
Adapted from:
http://scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html#example-decomposition-plot-kernel-pca-py
http://scikit-learn.org/stable/auto... | bsd-3-clause |
VigneshMohan1/spark-branch-2.3 | python/pyspark/sql/session.py | 21 | 25693 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | apache-2.0 |
AllenDowney/ThinkStats2 | code/chap13soln.py | 68 | 2961 | """This file contains code for use with "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function
import pandas
import numpy as np
import thinkplot
import thinkstats2
import sur... | gpl-3.0 |
printedheart/opennars | nars_gui/src/main/python/nef_minimal/nef_minimal.py | 1 | 10003 | # A Minimal Example of the Neural Engineering Framework
#
# The NEF is a method for building large-scale neural models using realistic
# neurons. It is a neural compiler: you specify the high-level computations
# the model needs to compute, and the properties of the neurons themselves,
# and the NEF determines the ... | agpl-3.0 |
vishank94/vishank94.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 |
yyjiang/scikit-learn | examples/ensemble/plot_voting_probas.py | 316 | 2824 | """
===========================================================
Plot class probabilities calculated by the VotingClassifier
===========================================================
Plot the class probabilities of the first sample in a toy dataset
predicted by three different classifiers and averaged by the
`VotingC... | bsd-3-clause |
rtrwalker/geotecha | examples/speccon/speccon1d_vr_mimic_terzaghi_with_pumping_at_mid_depth.py | 1 | 10319 | # speccon1d_vr example (if viewing this in docs, plots are at bottom of page)
# Mimic Terzaghi pervious top pervious bottom 1D vertical consolidation
# by specifying a time dependent pumping at mid-depth.
# A surcharge of 100kPa is applied. At mid depth a pumping vleocity is
# specified to keep the pore pressure zero... | gpl-3.0 |
Winand/pandas | doc/sphinxext/numpydoc/plot_directive.py | 89 | 20530 | """
A special directive for generating a matplotlib plot.
.. warning::
This is a hacked version of plot_directive.py from Matplotlib.
It's very much subject to change!
Usage
-----
Can be used like this::
.. plot:: examples/example.py
.. plot::
import matplotlib.pyplot as plt
plt.plot... | bsd-3-clause |
xzh86/scikit-learn | sklearn/feature_extraction/image.py | 263 | 17600 | """
The :mod:`sklearn.feature_extraction.image` submodule gathers utilities to
extract features from images.
"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Olivier Grisel
# Vlad Niculae
# License: BSD 3 clause
fro... | bsd-3-clause |
jeffmkw/DAT210x-Lab | Module4/assignment2_helper.py | 1 | 3825 | import math
import pandas as pd
from sklearn import preprocessing
# A Note on SKLearn .transform() calls:
#
# Any time you transform your data, you lose the column header names.
# This actually makes complete sense. There are essentially two types
# of transformations, those that change the scale of your features,
# ... | mit |
Myasuka/scikit-learn | examples/svm/plot_svm_regression.py | 249 | 1451 | """
===================================================================
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 |
mhue/scikit-learn | benchmarks/bench_glm.py | 297 | 1493 | """
A comparison of different methods in GLM
Data comes from a random square matrix.
"""
from datetime import datetime
import numpy as np
from sklearn import linear_model
from sklearn.utils.bench import total_seconds
if __name__ == '__main__':
import pylab as pl
n_iter = 40
time_ridge = np.empty(n_it... | bsd-3-clause |
winklerand/pandas | pandas/core/indexes/datetimes.py | 1 | 82224 | # pylint: disable=E1101
from __future__ import division
import operator
import warnings
from datetime import time, datetime, timedelta
import numpy as np
from pytz import utc
from pandas.core.base import _shared_docs
from pandas.core.dtypes.common import (
_NS_DTYPE, _INT64_DTYPE,
is_object_dtype, is_datetim... | bsd-3-clause |
btabibian/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 3 | 5427 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import itertools
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing impor... | bsd-3-clause |
abhishekkrthakur/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 |
ChanderG/scikit-learn | sklearn/tests/test_cross_validation.py | 70 | 41943 | """Test the cross_validation module"""
from __future__ import division
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from scipy import stats
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
from sklearn... | bsd-3-clause |
Mutos/SoC-Test-001 | utils/heatsim/heatsim.py | 20 | 7285 | #!/usr/bin/env python
#######################################################
#
# SIM CODE
#
#######################################################
# Imports
from frange import *
import math
import matplotlib.pyplot as plt
def clamp( a, b, x ):
return min( b, max( a, x ) )
class heatsim:
def __ini... | gpl-3.0 |
jangorecki/h2o-3 | h2o-py/tests/testdir_misc/pyunit_export_file.py | 6 | 1657 | from __future__ import print_function
from builtins import range
import sys
sys.path.insert(1,"../../../")
import h2o
from tests import pyunit_utils
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
import string
import random
import pandas as pd
from pandas.util.testing import assert_frame_equal
'''
Export... | apache-2.0 |
FNCS/ns-3.26 | src/core/examples/sample-rng-plot.py | 188 | 1246 | # -*- Mode:Python; -*-
# /*
# * This program is free software; you can redistribute it and/or modify
# * it under the terms of the GNU General Public License version 2 as
# * published by the Free Software Foundation
# *
# * This program is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRA... | gpl-2.0 |
balazssimon/ml-playground | udemy/lazyprogrammer/deep-reinforcement-learning-python/atari/dqn_theano.py | 1 | 10655 | # https://deeplearningcourses.com/c/deep-reinforcement-learning-in-python
# https://www.udemy.com/deep-reinforcement-learning-in-python
from __future__ import print_function, division
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future
import copy
import gym
im... | apache-2.0 |
alephu5/Soundbyte | environment/lib/python3.3/site-packages/matplotlib/backend_bases.py | 1 | 106921 | """
Abstract base classes define the primitives that renderers and
graphics contexts must implement to serve as a matplotlib backend
:class:`RendererBase`
An abstract base class to handle drawing/rendering operations.
:class:`FigureCanvasBase`
The abstraction layer that separates the
:class:`matplotlib.fi... | gpl-3.0 |
Sentient07/scikit-learn | examples/calibration/plot_calibration_curve.py | 113 | 5904 | """
==============================
Probability Calibration curves
==============================
When performing classification one often wants to predict not only the class
label, but also the associated probability. This probability gives some
kind of confidence on the prediction. This example demonstrates how to di... | bsd-3-clause |
MadsJensen/biomeg_class | classification.py | 1 | 1551 | import xgboost as xgb
import numpy as np
import mne
from sklearn.cross_validation import StratifiedShuffleSplit, cross_val_score
from sklearn.grid_search import GridSearchCV
from sklearn.ensemble import AdaBoostClassifier
from sklearn.externals import joblib
from my_settings import *
subject = 1
epochs = mne.read_e... | bsd-3-clause |
arvidfm/masters-thesis | src/utils.py | 1 | 23811 | # Copyright (C) 2016 Arvid Fahlström Myrman
#
# 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.
#
# This program is distr... | gpl-2.0 |
jor-/scipy | scipy/ndimage/fourier.py | 15 | 11266 | # Copyright (C) 2003-2005 Peter J. Verveer
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following d... | bsd-3-clause |
josephmfaulkner/stoqs | stoqs/loaders/CANON/realtime/Contour.py | 1 | 39846 | #!/usr/bin/env python
__author__ = 'D.Cline'
__version__ = '$Revision: $'.split()[1]
__date__ = '$Date: $'.split()[1]
__copyright__ = '2011'
__license__ = 'GPL v3'
__contact__ = 'dcline at mbari.org'
__doc__ = '''
Creates still and animated contour and dot plots plots from MBARI LRAUV data
D Cline
MBARI 25 Se... | gpl-3.0 |
bthirion/scikit-learn | examples/gaussian_process/plot_gpr_noisy.py | 104 | 3778 | """
=============================================================
Gaussian process regression (GPR) with noise-level estimation
=============================================================
This example illustrates that GPR with a sum-kernel including a WhiteKernel can
estimate the noise level of data. An illustration... | bsd-3-clause |
m4rx9/rna-pdb-tools | rna_tools/tools/clarna_play/ClaRNAlib/doublet-params.py | 2 | 14757 | #!/usr/bin/env python
#
import re
import sys
import itertools
import multiprocessing
import random
from optparse import OptionParser
import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
import scipy as scipy
import numpy as np
import scipy.cluster.hierarchy as sch
from utils import *
from distanc... | mit |
dpshelio/astropy-helpers | astropy_helpers/tests/test_setup_helpers.py | 1 | 13827 | import os
import sys
import stat
import shutil
import importlib
import contextlib
import pytest
from textwrap import dedent
from setuptools import Distribution
from ..setup_helpers import get_package_info, register_commands
from ..commands import build_ext
from . import reset_setup_helpers, reset_distutils_log # ... | bsd-3-clause |
justincely/rolodex | cos_monitoring/dark/plotting.py | 1 | 15970 | """ Make darkrate plots
"""
from __future__ import absolute_import, division
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.ticker import FormatStrFormatter
import scipy
from scipy.ndimage.filters import convolve
import numpy as np
import math
import os
from ..utils import r... | bsd-3-clause |
luchko/latticegraph_designer | latticegraph_designer/app/mpl_pane.py | 1 | 32631 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 9 18:13:18 2017
@author: Ivan Luchko (luchko.ivan@gmail.com)
This module contains the definition of the matplotlib manipulation pane.
class Arrow3D(FancyArrowPatch):
class Annotation3D(Annotation):
class GraphEdgesEditor(object):
... | mit |
siutanwong/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 |
psiq/gdsfactory | pp/pixelate.py | 1 | 4481 | import itertools as it
import numpy as np
from pp.geo_utils import polygon_grow
DEG2RAD = np.pi / 180
RAD2DEG = 1.0 / DEG2RAD
# from matplotlib import pyplot as plt
def pixelate_path(
pts, pixel_size=0.55, snap_res=0.05, middle_offset=0.5, theta_start=0, theta_end=90
):
"""
From a path, add one pixel per... | mit |
aspera1631/TweetScore | prob_weights.py | 1 | 1505 | __author__ = 'bdeutsch'
import numpy as np
import pandas as pd
import MySQLdb
def sql_to_df(database, table):
con = MySQLdb.connect(host='localhost', user='root', passwd='', db=database)
df = pd.read_sql_query("select * from %s" % table, con, index_col=None, coerce_float=True, params=None, parse_dates=None,... | mit |
dingocuster/scikit-learn | examples/svm/plot_rbf_parameters.py | 132 | 8096 | '''
==================
RBF SVM parameters
==================
This example illustrates the effect of the parameters ``gamma`` and ``C`` of
the Radial Basis Function (RBF) kernel SVM.
Intuitively, the ``gamma`` parameter defines how far the influence of a single
training example reaches, with low values meaning 'far' a... | bsd-3-clause |
etkirsch/scikit-learn | sklearn/utils/random.py | 234 | 10510 | # Author: Hamzeh Alsalhi <ha258@cornell.edu>
#
# License: BSD 3 clause
from __future__ import division
import numpy as np
import scipy.sparse as sp
import operator
import array
from sklearn.utils import check_random_state
from sklearn.utils.fixes import astype
from ._random import sample_without_replacement
__all__ =... | bsd-3-clause |
rahul003/mxnet | example/gluon/dcgan.py | 7 | 8812 | # 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 |
trnet4334/img_colorization | landscape_colorizer/colorization_concate_pretrained_conv_layers.py | 1 | 7895 | from keras.models import Sequential, model_from_json
from keras.layers.core import Dense, Dropout, Activation, Flatten, Reshape
from keras.layers import Merge
from keras.layers.convolutional import Convolution2D, MaxPooling2D,Conv2D
from keras.utils import np_utils
from keras.layers.normalization import BatchNormalizat... | mit |
mclaughlin6464/pylearn2 | pylearn2/optimization/test_batch_gradient_descent.py | 44 | 6402 | from __future__ import print_function
from pylearn2.optimization.batch_gradient_descent import BatchGradientDescent
import theano.tensor as T
from pylearn2.utils import sharedX
import numpy as np
from theano.compat.six.moves import xrange
from theano import config
from theano.printing import min_informative_str
def t... | bsd-3-clause |
samueljackson92/tsp-solver | results/ipython_log.py | 1 | 9972 | # IPython log file
get_ipython().magic(u'logstart')
get_ipython().magic(u'matplotlib inline')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tspsolver.tsp_generator import TSPGenerator
from tspsolver.ga.simulator import Simulator
from tspsolver.tuning import GeneticAlgorithmParameterEstim... | mit |
perryjohnson/biplaneblade | biplane_blade_lib/layer_plane_angles_stn18.py | 1 | 9219 | """Determine the layer plane angle of all the elements in a grid.
Author: Perry Roth-Johnson
Last modified: April 30, 2014
Usage:
1. Look through the mesh_stnXX.abq file and find all the element set names.
(Find all the lines that start with "*ELSET".)
2. Enter each of the element set names in one of the f... | gpl-3.0 |
JPalmerio/GRB_population_code | grbpop/basic_example.py | 1 | 1760 | from cosmology import init_cosmology
from GRB_population import create_GRB_population_from
import io_grb_pop as io
import numpy as np
import logging
import sys
log = logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format='%(asctime)s.%(msecs)03d [%(levelname)s... | gpl-3.0 |
facom/AstrodynTools | tides/animation-test.py | 1 | 1276 | """
See this interesting tutorial:
http://jakevdp.github.io/blog/2012/08/18/matplotlib-animation-tutorial
More examples at:
http://matplotlib.org/1.3.1/examples/animation/index.html
"""
from util import *
import numpy as np
import matplotlib.animation as animation
from matplotlib.pyplot import *
#####################... | gpl-2.0 |
q1ang/scikit-learn | sklearn/utils/tests/test_sparsefuncs.py | 157 | 13799 | 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 |
RomainBrault/scikit-learn | examples/linear_model/plot_logistic.py | 73 | 1568 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic function
=========================================================
Shown in the plot is how the logistic regression would, in this
synthetic dataset, classify values as either 0 or 1,
i.e. class one or tw... | bsd-3-clause |
antiface/mne-python | mne/io/edf/tests/test_edf.py | 6 | 10037 | """Data Equivalence Tests"""
from __future__ import print_function
# Authors: Teon Brooks <teon.brooks@gmail.com>
# Martin Billinger <martin.billinger@tugraz.at>
# Alan Leggitt <alan.leggitt@ucsf.edu>
# Alexandre Barachant <alexandre.barachant@gmail.com>
#
# License: BSD (3-clause)
import o... | bsd-3-clause |
pnedunuri/scikit-learn | examples/mixture/plot_gmm_selection.py | 248 | 3223 | """
=================================
Gaussian Mixture Model Selection
=================================
This example shows that model selection can be performed with
Gaussian Mixture Models using information-theoretic criteria (BIC).
Model selection concerns both the covariance type
and the number of components in th... | bsd-3-clause |
qiu997018209/MachineLearning | CSDN唐宇迪培训项目实战/机器学习之泰坦尼克号获救预测项目实战/机器学习之泰坦尼克号获救预测项目实战.py | 1 | 8971 |
# coding: utf-8
# In[5]:
import pandas
titanic = pandas.read_csv("D:\\test\\titanic_train.csv")
#进行简单的统计学分析
print titanic.describe()#std代表方差，Age中存在缺失值
# In[6]:
#以下操作为对数据进行预处理
#算法大多是矩阵运算，不能存在缺失值，用均值来填充缺失值
titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median())
print titanic.describe()#std代表方差，Age中存在缺失值
#... | apache-2.0 |
dettanym/Complex-Networks-Node-Removal | ERgraph_random_removal.py | 1 | 3095 | #!/usr/bin/python
import igraph, random, bisect
import matplotlib.pyplot as plt
trials = 100
N = 1000
p = 0.01 # ( as p >= 1/(n-1), the graph will have a giant component initially)
division=100 # No. of divisions of the occupation probability scale
# Limits to vary the occupation probability within
upper_limit=divis... | mit |
mattjj/pyhawkes | examples/inference/svi_demo.py | 2 | 8788 | import numpy as np
import os
import cPickle
import gzip
# np.seterr(all='raise')
import matplotlib.pyplot as plt
from sklearn.metrics import adjusted_mutual_info_score, \
adjusted_rand_score, roc_auc_score
from pyhawkes.models import \
DiscreteTimeNetworkHawkesModelGammaMixture, \
DiscreteTimeStandardHaw... | mit |
neeraj-kumar/nkpylib | nkplotutils.py | 1 | 4169 | """Lots of useful matplotlib utilities, written by Neeraj Kumar.
Licensed under the 3-clause BSD License:
Copyright (c) 2010, Neeraj Kumar (neerajkumar.org)
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are m... | bsd-3-clause |
crichardson17/starburst_atlas | Low_resolution_sims/Dusty_LowRes/Padova_cont/padova_cont_5/fullgrid/UV1.py | 31 | 9315 | import csv
import matplotlib.pyplot as plt
from numpy import *
import scipy.interpolate
import math
from pylab import *
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import matplotlib.patches as patches
from matplotlib.path import Path
import os
# --------------------------------------------------... | gpl-2.0 |
kayarre/Tools | womersley/womersley/components.py | 1 | 39174 | import os
import numpy as np
import womersley.utils
import scipy.special as special
import matplotlib.pyplot as plt
#import womersley.components
class waveform_info(object):
def __init__(self, dir_path, name, radius, period, kind="profile", mu=0.0035, rho=1050.0):
"""create instance of converted coeffic... | bsd-2-clause |
18padx08/PPTex | PPTexEnv_x86_64/lib/python2.7/site-packages/matplotlib/hatch.py | 10 | 7132 | """
Contains a classes for generating hatch patterns.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import xrange
import numpy as np
from matplotlib.path import Path
class HatchPatternBase:
"""
The base class for a... | mit |
nrhine1/scikit-learn | sklearn/feature_extraction/text.py | 110 | 50157 | # -*- coding: utf-8 -*-
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Robert Layton <robertlayton@gmail.com>
# Jochen Wersdörfer <jochen@wersdoerfer.de>
# Roman Sinayev <roman.sinayev@gma... | bsd-3-clause |
greytip/data-science-utils | datascienceutils/clusteringModels.py | 1 | 3347 | from bokeh.layouts import gridplot
from sklearn import cluster
from sklearn.neighbors import kneighbors_graph
import numpy as np
import pandas as pd
import time
# Custom utils
from . import sklearnUtils as sku
from . import plotter
from . import utils
#TODO: add a way of weakening the discovered cluster structure and... | gpl-3.0 |
laurent-george/bokeh | bokeh/charts/builder/tests/test_timeseries_builder.py | 33 | 2825 | """ 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 |
bokeh/bokeh | tests/unit/bokeh/core/property/test_container.py | 1 | 9429 | #-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2021, Anaconda, Inc., and Bokeh Contributors.
# All rights reserved.
#
# The full license is in the file LICENSE.txt, distributed with this software.
#-------------------------------------------------------------------... | bsd-3-clause |
AnasGhrab/scikit-learn | sklearn/metrics/tests/test_common.py | 83 | 41144 | from __future__ import division, print_function
from functools import partial
from itertools import product
import numpy as np
import scipy.sparse as sp
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils.multiclass import type_of_target
fro... | bsd-3-clause |
spbguru/repo1 | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_fltkagg.py | 69 | 20839 | """
A backend for FLTK
Copyright: Gregory Lielens, Free Field Technologies SA and
John D. Hunter 2004
This code is released under the matplotlib license
"""
from __future__ import division
import os, sys, math
import fltk as Fltk
from backend_agg import FigureCanvasAgg
import os.path
import matplotli... | gpl-3.0 |
xuewei4d/scikit-learn | examples/datasets/plot_random_multilabel_dataset.py | 23 | 3379 | """
==============================================
Plot randomly generated multilabel dataset
==============================================
This illustrates the :func:`~sklearn.datasets.make_multilabel_classification`
dataset generator. Each sample consists of counts of two features (up to 50 in
total), which are dif... | bsd-3-clause |
mne-tools/mne-python | tutorials/stats-sensor-space/50_cluster_between_time_freq.py | 18 | 4878 | """
=========================================================================
Non-parametric between conditions cluster statistic on single trial power
=========================================================================
This script shows how to compare clusters in time-frequency
power estimates between condition... | bsd-3-clause |
SGenheden/Scripts | Projects/Gpcr/gpcr_plot_resjointcontacts.py | 1 | 3408 | # Author: Samuel Genheden samuel.genheden@gmail.com
"""
Program to plot residue joint contact probability
Examples
--------
gpcr_plot_rescontacts.py -f r1_md3_en_fit_joint.npz -m ohburr -l oh --mol b2
"""
import os
import argparse
import sys
import numpy as np
import matplotlib
if not "DISPLAY" in os.environ or os.... | mit |
crickert1234/ParamAP | ParamAP.py | 1 | 51951 | #!/usr/bin/env python3
'''
ParamAP.py (parametrization of sinoatrial myocyte action potentials)
Copyright (C) 2018 Christian Rickert <christian.rickert@ucdenver.edu>
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 ... | gpl-2.0 |
Ecotrust/cogs-priorities | util/test_scenarios.py | 3 | 6555 | from django.core.management import setup_environ
import os
import sys
sys.path.append(os.path.dirname(os.path.join('..','priorities',__file__)))
import settings
setup_environ(settings)
#==================================#
from seak.models import Scenario, ConservationFeature, PlanningUnit, Cost, PuVsCf, PuVsCost
from ... | bsd-3-clause |
kyleabeauchamp/HMCNotes | code/correctness/old/compare_acceptance_with_shift.py | 1 | 1130 | import lb_loader
import pandas as pd
import simtk.openmm.app as app
import numpy as np
import simtk.openmm as mm
from simtk import unit as u
from openmmtools import hmc_integrators, testsystems
actual_timestep = 1.5 * u.femtoseconds
data = []
for sysname in ["ljbox", "switchedljbox", "shiftedljbox", "shortwater", "sh... | gpl-2.0 |
nitish-tripathi/Simplery | ANN/Tensorflow_Tutorial/mnist.py | 1 | 1354 |
import pylab
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# read data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# show images
"""
img_ = mnist.train.images[1].reshape(28,28)
print np.argmax(mnist.train.labe... | mit |
ishank08/scikit-learn | sklearn/utils/tests/test_fixes.py | 28 | 3156 | # Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Justin Vincent
# Lars Buitinck
# License: BSD 3 clause
import pickle
import numpy as np
import math
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_true
... | bsd-3-clause |
vermouthmjl/scikit-learn | benchmarks/bench_plot_approximate_neighbors.py | 244 | 6011 | """
Benchmark for approximate nearest neighbor search using
locality sensitive hashing forest.
There are two types of benchmarks.
First, accuracy of LSHForest queries are measured for various
hyper-parameters and index sizes.
Second, speed up of LSHForest queries compared to brute force
method in exact nearest neigh... | bsd-3-clause |
neilslater/nn_practice | 01_basic_mlp/mlp_pytorch.py | 1 | 5143 | import numpy as np
import sklearn
import sklearn.datasets
import matplotlib.pyplot as plt
import math
import torch
from torch import nn
def get_data():
X, y = sklearn.datasets.make_moons(600, noise=0.30)
y = y.reshape([600,1])
X_train = X[:400]; y_train = y[:400]
X_cv = X[400:500]; y_cv = y[400:500]
... | mit |
slinderman/pyhawkes | data/gifs/gif_demo.py | 1 | 5035 | import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_auc_score
import pyhawkes.models
import imp
imp.reload(pyhawkes.models)
from pyhawkes.models import DiscreteTimeNetworkHawkesModelSpikeAndSlab
from pyhawkes.internals.network import ErdosRenyiFixedSparsity
from pyhawkes.plotting.plotti... | mit |
tbtraltaa/medianshape | medianshape/experiment/median/test.py | 1 | 11430 | # encoding: utf-8
'''
2D Median surface embedded in 3D
--------------------------------
'''
from __future__ import absolute_import
import importlib
import os
import numpy as np
from scipy.spatial import Delaunay
from medianshape.simplicial import pointgen3d, mesh
from medianshape.simplicial.meshgen import meshgen2d, ... | gpl-3.0 |
wronk/mne-python | mne/viz/epochs.py | 1 | 62947 | """Functions to plot epochs data
"""
# 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>
# Jaakko Leppakangas <jaeilepp@student.jyu.f... | bsd-3-clause |
hsuantien/scikit-learn | sklearn/metrics/ranking.py | 75 | 25426 | """Metrics to assess performance on classification task given scores
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.... | bsd-3-clause |
RPGOne/scikit-learn | sklearn/tests/test_discriminant_analysis.py | 4 | 13141 | import sys
import numpy as np
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.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing im... | bsd-3-clause |
sinhrks/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 |
vincent-noel/SigNetSim | signetsim/settings/Settings.py | 2 | 1240 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2014-2017 Vincent Noel (vincent.noel@butantan.gov.br)
#
# This file is part of libSigNetSim.
#
# libSigNetSim 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 Fou... | agpl-3.0 |
zhakui/QMarkdowner | setup.py | 4 | 14173 | # -*- coding: utf-8 -*-
"""
利用Python脚本从svn获取最新版本信息，然后利用py2exe进行打包，最新进行相应的清理操作
"""
import os
import time
import glob
import shutil
import subprocess
import sys
import stat
import zipfile
import json
from distribution import Distribution
from Cheetah.Template import Template
def change_package_from... | mit |
arju88nair/projectCulminate | venv/lib/python3.5/site-packages/nltk/sentiment/util.py | 7 | 31020 | # coding: utf-8
#
# Natural Language Toolkit: Sentiment Analyzer
#
# Copyright (C) 2001-2017 NLTK Project
# Author: Pierpaolo Pantone <24alsecondo@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
"""
Utility methods for Sentiment Analysis.
"""
from __future__ import division
from copy i... | apache-2.0 |
DrigerG/IIITB-ML | experiments/image-analysis/mp-assignment-2/image_classification.py | 1 | 5100 | #!/usr/bin/env python
"""image_classification.py: Classify images to horses, bikes"""
import argparse
import os
import cv2
import numpy as np
from scipy.cluster import vq
from sklearn.preprocessing import StandardScaler
from sklearn.svm import LinearSVC
from sklearn.neighbors import KNeighborsClassifier
# Helper f... | apache-2.0 |
ebaste/word_cloud | doc/sphinxext/gen_rst.py | 17 | 33207 | """
Example generation for the python wordcloud project. Stolen from scikit-learn with modifications from PyStruct.
Generate the rst files for the examples by iterating over the python
example files.
Hacked to plot every example (not only those that start with 'plot').
"""
from time import time
import os
import shuti... | mit |
jseabold/scikit-learn | sklearn/_build_utils/cythonize.py | 19 | 5126 | #!/usr/bin/env python
""" cythonize
Cythonize pyx files into C files as needed.
Usage: cythonize [root_dir]
Default [root_dir] is 'sklearn'.
Checks pyx files to see if they have been changed relative to their
corresponding C files. If they have, then runs cython on these files to
recreate the C files.
The script ... | bsd-3-clause |
blink1073/scikit-image | doc/examples/features_detection/plot_orb.py | 33 | 1807 | """
==========================================
ORB feature detector and binary descriptor
==========================================
This example demonstrates the ORB feature detection and binary description
algorithm. It uses an oriented FAST detection method and the rotated BRIEF
descriptors.
Unlike BRIEF, ORB is c... | bsd-3-clause |
potash/drain | drain/exploration.py | 1 | 10781 | from tempfile import NamedTemporaryFile
from pprint import pformat
from itertools import product
from sklearn import tree
import pandas as pd
from collections import Counter
from six import StringIO
from drain import util, step
def explore(steps, reload=False):
return StepFrame(index=step.load(steps, reload=rel... | mit |
zfrenchee/pandas | pandas/tests/indexing/interval/test_interval_new.py | 1 | 7320 | import pytest
import numpy as np
import pandas as pd
from pandas import Series, IntervalIndex, Interval
import pandas.util.testing as tm
pytestmark = pytest.mark.skip(reason="new indexing tests for issue 16316")
class TestIntervalIndex(object):
def setup_method(self, method):
self.s = Series(np.arange... | bsd-3-clause |
buguen/pylayers | pylayers/simul/examples/ex_simulem_fur.py | 3 | 1157 | from pylayers.simul.simulem import *
from pylayers.signal.bsignal import *
from pylayers.measures.mesuwb import *
import matplotlib.pyplot as plt
from pylayers.gis.layout import *
#M=UWBMesure(173)
M=UWBMesure(13)
#M=UWBMesure(1)
cir=TUsignal()
cirf=TUsignal()
#cir.readcir("where2cir-tx001-rx145.mat","Tx001")
#cir... | lgpl-3.0 |
AyoubBelhadji/random_matrix_factorization | package/fast_svd.py | 1 | 2031 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 12 11:45:41 2017
@author: ayoubbelhadji1
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.pyplot
from scipy.stats import chi2
import pylab as mp
### Parameters
N = 1000 # Number of points
d = 2 # Dimension
s_n = 10 # Nu... | mit |
evanl/vesa_tough_comparison | vesa/vesa_v02_13/vesa_writing_functions.py | 2 | 13731 | #Author - Evan Leister
import eclipse_cells as ec
import numpy as np
import matplotlib.pyplot as plt
class Injector(object):
def __init__(self, index, x, y, ratio, layer_id, end_days, mass_rate):
self.index = index
self.x = x
self.y = y
self.ratio = ratio
self.layer_id = lay... | mit |
idlead/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_scalability.py | 17 | 5726 | """
============================================
Scalability of Approximate Nearest Neighbors
============================================
This example studies the scalability profile of approximate 10-neighbors
queries using the LSHForest with ``n_estimators=20`` and ``n_candidates=200``
when varying the number of sa... | bsd-3-clause |
r-mart/scikit-learn | sklearn/decomposition/tests/test_incremental_pca.py | 297 | 8265 | """Tests for Incremental PCA."""
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn import datasets
from sklearn.decomposition import PCA, IncrementalPCA
iris = datasets.load... | bsd-3-clause |
justincassidy/scikit-learn | sklearn/linear_model/least_angle.py | 57 | 49338 | """
Least Angle Regression algorithm. See the documentation on the
Generalized Linear Model for a complete discussion.
"""
from __future__ import print_function
# Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux
#
# License: BSD 3 ... | bsd-3-clause |
rosswhitfield/mantid | qt/python/mantidqt/plotting/test/test_figuretype.py | 3 | 2627 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
# T... | gpl-3.0 |
choupi/NDHUDLWorkshop | mnist/xgboost/xg.py | 1 | 2143 | '''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''
from __future__ import print_function
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.datasets im... | mit |
sahat/bokeh | bokeh/protocol.py | 1 | 3541 | import json
import logging
import time
import datetime as dt
import numpy as np
from six.moves import cPickle as pickle
from .utils import get_ref
try:
import pandas as pd
is_pandas = True
except ImportError:
is_pandas = False
try:
from dateutil.relativedelta import relativedelta
is_dateutil = Tr... | bsd-3-clause |
neutrons/FastGR | addie/processing/mantid/master_table/table_plot_handler.py | 1 | 6631 | from __future__ import (absolute_import, division, print_function)
from qtpy.QtCore import Qt
import os
import numpy as np
import glob
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cm
from addie.processing.idl.sample_environment_handler import SampleEnvironmentHandler
cla... | mit |
rajat1994/scikit-learn | sklearn/linear_model/coordinate_descent.py | 43 | 75144 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Gael Varoquaux <gael.varoquaux@inria.fr>
#
# License: BSD 3 clause
import sys
import warnings
from abc import ABCMeta, abstractmethod
import n... | bsd-3-clause |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.