repo_name stringlengths 7 84 | path stringlengths 5 184 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 978 477k | license stringclasses 15
values |
|---|---|---|---|---|---|
yonglehou/scikit-learn | examples/neighbors/plot_classification.py | 287 | 1790 | """
================================
Nearest Neighbors Classification
================================
Sample usage of Nearest Neighbors classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColorm... | bsd-3-clause |
Bismarrck/tensorflow | tensorflow/contrib/factorization/python/ops/kmeans_test.py | 16 | 21836 | # 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 |
samuel1208/scikit-learn | examples/covariance/plot_sparse_cov.py | 300 | 5078 | """
======================================
Sparse inverse covariance estimation
======================================
Using the GraphLasso estimator to learn a covariance and sparse precision
from a small number of samples.
To estimate a probabilistic model (e.g. a Gaussian model), estimating the
precision matrix, t... | bsd-3-clause |
kernc/scikit-learn | examples/covariance/plot_robust_vs_empirical_covariance.py | 73 | 6451 | r"""
=======================================
Robust vs Empirical covariance estimate
=======================================
The usual covariance maximum likelihood estimate is very sensitive to the
presence of outliers in the data set. In such a case, it would be better to
use a robust estimator of covariance to guar... | bsd-3-clause |
tomlof/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | 24 | 14430 | import numpy as np
from scipy.linalg import block_diag
from scipy.sparse import csr_matrix
from scipy.special import psi
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition._online_lda import (_dirichlet_expectation_1d,
_dirichlet_expect... | bsd-3-clause |
kchodorow/tensorflow | tensorflow/examples/learn/iris_run_config.py | 86 | 2087 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | apache-2.0 |
DailyActie/Surrogate-Model | 01-codes/scikit-learn-master/sklearn/ensemble/tests/test_iforest.py | 1 | 6658 | """
Testing for Isolation Forest algorithm (sklearn.ensemble.iforest).
"""
# Authors: Nicolas Goix <nicolas.goix@telecom-paristech.fr>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
import numpy as np
from scipy.sparse import csc_matrix, csr_matrix
from sklearn.cross_v... | mit |
shenzebang/scikit-learn | examples/classification/plot_classifier_comparison.py | 181 | 4699 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=====================
Classifier comparison
=====================
A comparison of a several classifiers in scikit-learn on synthetic datasets.
The point of this example is to illustrate the nature of decision boundaries
of different classifiers.
This should be taken with ... | bsd-3-clause |
scipy/scipy | scipy/signal/wavelets.py | 16 | 14046 | import numpy as np
from scipy.linalg import eig
from scipy.special import comb
from scipy.signal import convolve
__all__ = ['daub', 'qmf', 'cascade', 'morlet', 'ricker', 'morlet2', 'cwt']
def daub(p):
"""
The coefficients for the FIR low-pass filter producing Daubechies wavelets.
p>=1 gives the order of... | bsd-3-clause |
clemkoa/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 |
mrkowalski/kaggle_santander | scikit/src/commons.py | 1 | 7185 | import pandas as pd
import numpy as np
from sklearn.externals import joblib
from sklearn.preprocessing import LabelEncoder
from functools import partial
import re
num_months = 4
chunk_size = 1000000
indicators = ['ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1',
... | mit |
kevinyu98/spark | dev/sparktestsupport/modules.py | 3 | 16591 | #
# 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 |
ldirer/scikit-learn | sklearn/utils/multiclass.py | 15 | 15056 | # Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi
#
# License: BSD 3 clause
"""
Multi-class / multi-label utility function
==========================================
"""
from __future__ import division
from collections import Sequence
from itertools import chain
from scipy.sparse import issparse
from scipy.sparse.b... | bsd-3-clause |
rvanderheyde/SoftwareSystems | hw04/wave3/generate_sine.py | 23 | 2124 | """This file contains code used in "Think DSP",
by Allen B. Downey, available from greenteapress.com
Copyright 2013 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
import thinkdsp
import thinkplot
import matplotlib.pyplot as pyplot
def print_reverse_tables():
print 'int reverse1[] =... | gpl-3.0 |
harterj/moose | modules/combined/examples/geochem-porous_flow/geotes_weber_tensleep/scaling.py | 9 | 1503 | #!/usr/bin/env python3
#* This file is part of the MOOSE framework
#* https://www.mooseframework.org
#*
#* All rights reserved, see COPYRIGHT for full restrictions
#* https://github.com/idaholab/moose/blob/master/COPYRIGHT
#*
#* Licensed under LGPL 2.1, please see LICENSE for details
#* https://www.gnu.org/licenses/lgp... | lgpl-2.1 |
tgsmith61591/skutil | skutil/h2o/util.py | 1 | 13251 | from __future__ import print_function, division, absolute_import
import numpy as np
import h2o
import pandas as pd
import warnings
from collections import Counter
from pkg_resources import parse_version
from ..utils import (validate_is_pd, human_bytes, corr_plot,
load_breast_cancer_df, load_iris_d... | bsd-3-clause |
fbuitron/FBMusic_ML_be | INTERACTIVE/MachineLearning/Classification.py | 1 | 4629 | import numpy as np
import pandas as pd
from sklearn import neighbors, tree
from sklearn import cross_validation
from . import Preprocessing
# import Preprocessing as Preprocessing
def excKNN(k, train_data, train_labels, test_data, test_labels):
errorCount = 0.0
knnclf = neighbors.KNeighborsClassifier(k, weight... | apache-2.0 |
bwc126/MLND-Subvocal | prepare_EMG.py | 1 | 2434 | # TODO: Separate EMG data into 50ms windows, run FFT + preprocessing.
from pandas import DataFrame
from scipy.fftpack import rfft, rfftfreq
import time
import numpy as np
class EMG_preparer():
""" An EMG_preparer prepares EMG data for training a subvocal recognition classification system. Scipy's cwt algorithm is... | mit |
jusjusjus/Motiftoolbox | Tools/network3N.py | 1 | 5648 | #!/usr/bin/env python
import sys
sys.path.insert(0, '../Tools')
import window as win
import numpy as np
import pylab as pl
import matplotlib.patches as mpatches
win_width, win_height, margin = 700, 600, 10
text2coupling = {}
text2coupling[0] = 2
text2coupling[1] = 0
text2coupling[2] = 5
text2coupling[3] = 3
text2c... | gpl-2.0 |
phipleg/pymlp | plt_pixels.py | 1 | 1131 | import time
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
def draw_pixels(fig, ax, pixel_sequences, inner, outer):
m = np.zeros((inner[1] * outer[1], inner[0] * outer[0]))
for k, pixel_seq in enumerate(pixel_sequences):
oy = k / outer[0]
ox = ... | apache-2.0 |
pythonvietnam/scikit-learn | sklearn/decomposition/nmf.py | 100 | 19059 | """ Non-negative matrix factorization
"""
# Author: Vlad Niculae
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Author: Chih-Jen Lin, National Taiwan University (original projected gradient
# NMF implementation)
# Author: Anthony Di Franco (original Python and NumPy port)
# License: BSD 3 clause
from __future__ ... | bsd-3-clause |
datapythonista/pandas | pandas/tests/frame/methods/test_rank.py | 1 | 15673 | from datetime import (
datetime,
timedelta,
)
import numpy as np
import pytest
from pandas._libs.algos import (
Infinity,
NegInfinity,
)
import pandas.util._test_decorators as td
from pandas import (
DataFrame,
Series,
)
import pandas._testing as tm
class TestRank:
s = Series([1, 3, 4, ... | bsd-3-clause |
beepee14/scikit-learn | examples/decomposition/plot_pca_vs_fa_model_selection.py | 142 | 4467 | """
===============================================================
Model selection with Probabilistic PCA and Factor Analysis (FA)
===============================================================
Probabilistic PCA and Factor Analysis are probabilistic models.
The consequence is that the likelihood of new data can be u... | bsd-3-clause |
josl/ThinkStats2 | code/regression.py | 62 | 9652 | """This file contains code used in "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2010 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function, division
import math
import pandas
import random
import numpy as np
import statsmode... | gpl-3.0 |
Adai0808/scikit-learn | sklearn/ensemble/partial_dependence.py | 251 | 15097 | """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 |
pradyu1993/scikit-learn | sklearn/utils/tests/test_shortest_path.py | 11 | 2828 | from collections import defaultdict
import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.utils.graph import (graph_shortest_path,
single_source_shortest_path_length)
def floyd_warshall_slow(graph, directed=False):
N = graph.shape[0]
#set nonzer... | bsd-3-clause |
uhjish/seaborn | seaborn/tests/test_rcmod.py | 11 | 7231 | import numpy as np
import matplotlib as mpl
from distutils.version import LooseVersion
import nose
import matplotlib.pyplot as plt
import nose.tools as nt
import numpy.testing as npt
from .. import rcmod
class RCParamTester(object):
def flatten_list(self, orig_list):
iter_list = map(np.atleast_1d, orig... | bsd-3-clause |
neelravi/vasp | bandplotting-orb-resolved-bug-removed.py | 1 | 5098 | #!/usr/bin/env python
# -*- coding=utf-8 -*-
# A Python code for plotting orbital-resolved bandstructure.
# Written by : Internet
# catalyst : Ravindra
# under the eagle eyes of : Rinkle
import sys
import os
import numpy as np
from numpy import array as npa
import matplotlib as... | gpl-3.0 |
apache/incubator-superset | superset/datasets/commands/importers/v1/utils.py | 1 | 4283 | # 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 |
Novasoft-India/OperERP-AM-Motors | openerp/addons/resource/faces/timescale.py | 170 | 3902 | ############################################################################
# Copyright (C) 2005 by Reithinger GmbH
# mreithinger@web.de
#
# This file is part of faces.
#
# faces is free software; you can redistribute it and/or modify
# ... | agpl-3.0 |
sniemi/SamPy | sandbox/src2/src/SplineFitting.py | 2 | 2887 | '''
Created on Nov 26, 2009
@author: Sami-Matias Niemi
'''
import numpy as N
import scipy.signal as SS
import scipy.interpolate as I
import scipy.optimize as O
import pylab as P
class SplineFitting:
def __init__(self, xnodes, spline_order = 3):
'''
'''
self.xnodes = xnodes
se... | bsd-2-clause |
chenjun0210/tensorflow | tensorflow/python/estimator/inputs/queues/feeding_queue_runner_test.py | 116 | 5164 | # 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 |
KristoferHellman/gimli | doc/examples/modelling/dev/multi/ert.py | 1 | 10724 | #!/usr/bin/env python
"""
Test multi
"""
import sys
import time
import matplotlib.pyplot as plt
import numpy as np
import pygimli as pg
from pygimli.viewer import *
from pygimli.solver import *
from pygimli.meshtools import *
import pybert as pb
import pybert.dataview
def createCacheName(base, mesh=None):
nc... | gpl-3.0 |
yl565/statsmodels | statsmodels/stats/sandwich_covariance.py | 3 | 28418 | # -*- coding: utf-8 -*-
"""Sandwich covariance estimators
Created on Sun Nov 27 14:10:57 2011
Author: Josef Perktold
Author: Skipper Seabold for HCxxx in linear_model.RegressionResults
License: BSD-3
Notes
-----
for calculating it, we have two versions
version 1: use pinv
pinv(x) scale pinv(x) used currently in... | bsd-3-clause |
rexshihaoren/scikit-learn | examples/ensemble/plot_ensemble_oob.py | 259 | 3265 | """
=============================
OOB Errors for Random Forests
=============================
The ``RandomForestClassifier`` is trained using *bootstrap aggregation*, where
each new tree is fit from a bootstrap sample of the training observations
:math:`z_i = (x_i, y_i)`. The *out-of-bag* (OOB) error is the average er... | bsd-3-clause |
yavalvas/yav_com | build/matplotlib/examples/pylab_examples/fill_betweenx_demo.py | 12 | 1576 | import matplotlib.mlab as mlab
from matplotlib.pyplot import figure, show
import numpy as np
## Copy of fill_between.py but using fill_betweenx() instead.
x = np.arange(0.0, 2, 0.01)
y1 = np.sin(2*np.pi*x)
y2 = 1.2*np.sin(4*np.pi*x)
fig = figure()
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312, sharex=ax1)
ax3... | mit |
michaelmanhart/pathman | Plot_RBM_output.py | 1 | 13564 | ################################################################################
# Plotting Script for Landscape and Properties of the RBM
# Copyright (c) 2015 Michael Manhart and Willow Kion-Crosby
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Gene... | gpl-3.0 |
shenzebang/scikit-learn | examples/applications/topics_extraction_with_nmf_lda.py | 133 | 3517 | """
========================================================================================
Topics extraction with Non-Negative Matrix Factorization And Latent Dirichlet Allocation
========================================================================================
This is an example of applying Non Negative Matr... | bsd-3-clause |
MatthewDaggitt/PathVision | modules/pathDisplayModule.py | 1 | 3864 | import tkinter
from itertools import groupby
from collections import defaultdict
import matplotlib.pyplot as plt
from matplotlib.colors import to_hex
import networkx as nx
import settings
from modules.shared.graphFrame import GraphFrame
from modules.shared.graphInteraction import DrawData
################
## Control... | mit |
JsNoNo/scikit-learn | examples/cluster/plot_kmeans_assumptions.py | 270 | 2040 | """
====================================
Demonstration of k-means assumptions
====================================
This example is meant to illustrate situations where k-means will produce
unintuitive and possibly unexpected clusters. In the first three plots, the
input data does not conform to some implicit assumptio... | bsd-3-clause |
srio/shadow3-scripts | test_binormal_sampling.py | 1 | 1618 | import numpy as np
import matplotlib.pyplot as plt
# inputs (mean is zero)
mean = [0,0]
sig1 = 1.0
sig2 = 2.0
rho = -0.75
Npoints = 5000
#covariance matrix
cov = np.array( [[sig1*sig1,rho*sig1*sig2],[rho*sig1*sig2,sig2*sig2]] )
print("\n\n input covariance matrix: ",cov)
#
# method 1: using np routine
#
x,y = np.... | mit |
cwhanse/pvlib-python | docs/examples/plot_interval_transposition_error.py | 2 | 6815 | """
Modeling with interval averages
===============================
Transposing interval-averaged irradiance data
"""
# %%
# This example shows how failing to account for the difference between
# instantaneous and interval-averaged time series data can introduce
# error in the modeling process. An instantaneous time ... | bsd-3-clause |
AlexRobson/scikit-learn | examples/ensemble/plot_adaboost_twoclass.py | 347 | 3268 | """
==================
Two-class AdaBoost
==================
This example fits an AdaBoosted decision stump on a non-linearly separable
classification dataset composed of two "Gaussian quantiles" clusters
(see :func:`sklearn.datasets.make_gaussian_quantiles`) and plots the decision
boundary and decision scores. The di... | bsd-3-clause |
lucasosouza/graph-competition | pagerank2.py | 1 | 3831 | import os
import sys
import math
import numpy
import pandas
import pickle
# Generalized matrix operations:
def __extractNodes(matrix):
nodes = set()
for colKey in matrix:
nodes.add(colKey)
for rowKey in matrix.T:
nodes.add(rowKey)
return nodes
def __makeSquare(matrix, keys, default=0... | mit |
eljost/pysisyphus | tests_staging/hcn_iso/hcn_iso.py | 1 | 1938 | #!/usr/bin/env python3
import matplotlib.pyplot as plt
import numpy as np
from calculators.ORCA import ORCA
from calculators.IDPP import idpp_interpolate
from cos.NEB import NEB
from cos.SimpleZTS import SimpleZTS
from Geometry import Geometry
from optimizers.SteepestDescent import SteepestDescent
from optimizers.FIR... | gpl-3.0 |
davidgbe/scikit-learn | sklearn/feature_selection/variance_threshold.py | 238 | 2594 | # Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: 3-clause BSD
import numpy as np
from ..base import BaseEstimator
from .base import SelectorMixin
from ..utils import check_array
from ..utils.sparsefuncs import mean_variance_axis
from ..utils.validation import check_is_fitted
class VarianceThreshold(BaseEstim... | bsd-3-clause |
mick-d/nipype | nipype/utils/config.py | 1 | 11215 | # -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
'''
Created on 20 Apr 2010
logging options : INFO, DEBUG
hash_method : content, timestamp
@author: Chris Filo Gorgolewski
'''
from __future__ import print_function, division, unico... | bsd-3-clause |
witcxc/scipy | scipy/signal/spectral.py | 3 | 13830 | """Tools for spectral analysis.
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from scipy import fftpack
from . import signaltools
from .windows import get_window
from ._spectral import lombscargle
import warnings
from scipy._lib.six import string_types
__all__ = ['periodogr... | bsd-3-clause |
bhargav/scikit-learn | sklearn/datasets/tests/test_lfw.py | 55 | 7877 | """This test for the LFW require medium-size data downloading and processing
If the data has not been already downloaded by running the examples,
the tests won't run (skipped).
If the test are run, the first execution will be long (typically a bit
more than a couple of minutes) but as the dataset loader is leveraging... | bsd-3-clause |
jeammimi/deepnano5bases | src/data/get_optimal_gamma.py | 1 | 15855 | if __name__ == "__main__":
import argparse
import json
from git import Repo
import os
from multiprocessing import Pool
import numpy as np
from ..data.dataset import Dataset, NotAllign
from ..features.helpers import scale_simple, scale_named, scale_named2, scale_named4, scale_named4s
... | mit |
dyoung418/tensorflow | tensorflow/contrib/learn/python/learn/estimators/_sklearn.py | 153 | 6723 | # 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 |
thilbern/scikit-learn | sklearn/linear_model/stochastic_gradient.py | 3 | 49778 | # Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)
# Mathieu Blondel (partial_fit support)
#
# License: BSD 3 clause
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import scipy.sparse as sp
from abc import ABCMeta, abstractmethod
from ... | bsd-3-clause |
datapythonista/pandas | pandas/tests/tseries/holiday/test_calendar.py | 4 | 3531 | from datetime import datetime
import pytest
from pandas import (
DatetimeIndex,
offsets,
to_datetime,
)
import pandas._testing as tm
from pandas.tseries.holiday import (
AbstractHolidayCalendar,
Holiday,
Timestamp,
USFederalHolidayCalendar,
USLaborDay,
USThanksgivingDay,
get_c... | bsd-3-clause |
yunfeilu/scikit-learn | examples/covariance/plot_sparse_cov.py | 300 | 5078 | """
======================================
Sparse inverse covariance estimation
======================================
Using the GraphLasso estimator to learn a covariance and sparse precision
from a small number of samples.
To estimate a probabilistic model (e.g. a Gaussian model), estimating the
precision matrix, t... | bsd-3-clause |
snurk/meta-strains | final_algo/read_files.py | 1 | 4683 | import pandas as pd
from collections import Counter
from itertools import permutations
import networkx as nx
from graph_functions import *
def read_graph(dataset_name="example"):
G = nx.DiGraph()
df_cov = pd.read_csv("data/{}/edge_profiles_0.txt".format(dataset_name),
sep=' ', index_... | mit |
eg-zhang/scikit-learn | sklearn/tree/tree.py | 59 | 34839 | """
This module gathers tree-based methods, including decision, regression and
randomized trees. Single and multi-output problems are both handled.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Da... | bsd-3-clause |
hsk81/rpc.js | server/py/plot.py | 1 | 3297 | #!/usr/bin/env python
###############################################################################
import argparse, os, sys
from datetime import datetime
from matplotlib import pyplot
from matplotlib import pylab
###############################################################################
#####################... | gpl-3.0 |
sapfo/medeas | processing/freqs.py | 1 | 2054 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 20 11:31:18 2017
@author: ivan
"""
import numpy as np
import sys
import matplotlib.pyplot as plt
from typing import Tuple
from collections import defaultdict
import pickle
from options import TESTING
freqs = defaultdict(list)
bars = defaultdict(l... | gpl-3.0 |
RapidApplicationDevelopment/tensorflow | tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py | 75 | 29377 | # 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 |
Vimos/scikit-learn | benchmarks/bench_covertype.py | 57 | 7378 | """
===========================
Covertype dataset benchmark
===========================
Benchmark stochastic gradient descent (SGD), Liblinear, and Naive Bayes, CART
(decision tree), RandomForest and Extra-Trees on the forest covertype dataset
of Blackard, Jock, and Dean [1]. The dataset comprises 581,012 samples. It ... | bsd-3-clause |
ldirer/scikit-learn | benchmarks/bench_plot_incremental_pca.py | 374 | 6430 | """
========================
IncrementalPCA benchmark
========================
Benchmarks for IncrementalPCA
"""
import numpy as np
import gc
from time import time
from collections import defaultdict
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_lfw_people
from sklearn.decomposition import Incre... | bsd-3-clause |
hlin117/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 28 | 3792 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_alm... | bsd-3-clause |
lancezlin/ml_template_py | lib/python2.7/site-packages/matplotlib/tests/test_rcparams.py | 6 | 15775 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
import io
import os
import sys
import warnings
from cycler import cycler, Cycler
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.tests import ass... | mit |
UCSC-iGEM-2016/taris_controller | taris_controller/taris_calibrate.py | 1 | 4385 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Button
import time
class ButtonSet:
def __init__(self, px, mx, kx):
self.buttonP = Button(px, '+')
self.buttonM = Button(mx, '-')
self.stVal = kx
self.buttonP.on_clicked(self.button_handler... | gpl-3.0 |
vigilv/scikit-learn | sklearn/ensemble/tests/test_base.py | 284 | 1328 | """
Testing for the base module (sklearn.ensemble.base).
"""
# Authors: Gilles Louppe
# License: BSD 3 clause
from numpy.testing import assert_equal
from nose.tools import assert_true
from sklearn.utils.testing import assert_raise_message
from sklearn.datasets import load_iris
from sklearn.ensemble import BaggingCla... | bsd-3-clause |
orbitfold/tardis | tardis/plasma/properties/level_population.py | 1 | 4820 | import logging
import pandas as pd
import numpy as np
from tardis.plasma.properties.base import ProcessingPlasmaProperty
logger = logging.getLogger(__name__)
__all__ = ['LevelNumberDensity', 'LevelNumberDensityHeNLTE']
class LevelNumberDensity(ProcessingPlasmaProperty):
"""
Attributes:
level_number_dens... | bsd-3-clause |
bthirion/scikit-learn | sklearn/utils/multiclass.py | 2 | 14743 |
# Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi
#
# License: BSD 3 clause
"""
Multi-class / multi-label utility function
==========================================
"""
from __future__ import division
from collections import Sequence
from itertools import chain
from scipy.sparse import issparse
from scipy.sparse.... | bsd-3-clause |
belltailjp/scikit-learn | examples/svm/plot_svm_nonlinear.py | 61 | 1089 | """
==============
Non-linear SVM
==============
Perform binary classification using non-linear SVC
with RBF kernel. The target to predict is a XOR of the
inputs.
The color map illustrates the decision function learn by the SVC.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn impor... | bsd-3-clause |
yunque/sms-tools | software/models_interface/sineModel_function.py | 21 | 2749 | # function to call the main analysis/synthesis functions in software/models/sineModel.py
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import get_window
import os, sys
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../models/'))
import utilFunctions as UF
import sineM... | agpl-3.0 |
trachelr/mne-python | examples/visualization/plot_topo_compare_conditions.py | 7 | 2375 | """
=================================================
Compare evoked responses for different conditions
=================================================
In this example, an Epochs object for visual and
auditory responses is created. Both conditions
are then accessed by their respective names to
create a sensor layout... | bsd-3-clause |
jeffery-do/Vizdoombot | doom/lib/python3.5/site-packages/scipy/integrate/quadrature.py | 33 | 28087 | from __future__ import division, print_function, absolute_import
import numpy as np
import math
import warnings
# trapz is a public function for scipy.integrate,
# even though it's actually a numpy function.
from numpy import trapz
from scipy.special.orthogonal import p_roots
from scipy.special import gammaln
from sc... | mit |
paladin74/neural-network-animation | matplotlib/tests/test_png.py | 10 | 1259 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import glob
import os
import numpy as np
from matplotlib.testing.decorators import image_comparison
from matplotlib import pyplot as plt
import matplotlib.cm as cm
@image_comparison(baseline_ima... | mit |
NNPDF/reportengine | example/flowers/actions.py | 1 | 2872 | """
actions.py
Basic tools to study the IRIS dataset.
"""
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, hamming_loss
from sklearn.model_selection import train_test_split
from reportengine import collect
from reportengine.figure import figure
from reporte... | gpl-2.0 |
Balandat/cont_no_regret | NIPS2_CNR_hollowbox.py | 1 | 3958 | '''
Comparison of Continuous No-Regret Algorithms for the 2nd NIPS paper
@author: Maximilian Balandat
@date: May 22, 2015
'''
# Set up infrastructure and basic problem parameters
import matplotlib as mpl
mpl.use('Agg') # this is needed when running on a linux server over terminal
import multiprocessing as mp
import n... | mit |
adamtiger/tensorflow | tensorflow/contrib/learn/python/learn/estimators/__init__.py | 7 | 12756 | # 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 |
ningchi/scikit-learn | sklearn/linear_model/__init__.py | 270 | 3096 | """
The :mod:`sklearn.linear_model` module implements generalized linear models. It
includes Ridge regression, Bayesian Regression, Lasso and Elastic Net
estimators computed with Least Angle Regression and coordinate descent. It also
implements Stochastic Gradient Descent related algorithms.
"""
# See http://scikit-le... | bsd-3-clause |
epfl-lts2/pygsp | pygsp/graphs/nngraphs/nngraph.py | 1 | 7452 | # -*- coding: utf-8 -*-
import traceback
import numpy as np
from scipy import sparse, spatial
from pygsp import utils
from pygsp.graphs import Graph # prevent circular import in Python < 3.5
_logger = utils.build_logger(__name__)
def _import_pfl():
try:
import pyflann as pfl
except Exception as e... | bsd-3-clause |
HHammond/kcbo | setup.py | 1 | 1281 | import os
import sys
import setuptools
from setuptools import setup, find_packages
# Utility function to read the README file.
# Used for the long_description. It's nice, because now 1) we have a top level
# README file and 2) it's easier to type in the README file than to put a raw
# string in below ...
def read(f... | mit |
digitalghost/pycv-gameRobot | cv.py | 1 | 2233 | import sys
import cv2
import numpy as np
from matplotlib import pyplot as plt
def mse(imageA, imageB):
# the 'Mean Squared Error' between the two images is the
# sum of the squared difference between the two images;
# NOTE: the two images must have the same dimension
err = np.sum((imageA.astype("float") -... | gpl-3.0 |
DmitryOdinoky/sms-tools | lectures/08-Sound-transformations/plots-code/stftMorph-frame.py | 21 | 2700 | import numpy as np
import time, os, sys
import matplotlib.pyplot as plt
from scipy.signal import hamming, resample
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import dftModel as DFT
import utilFunctions as UF
import math
(fs, x1) = UF.wavread('../../../sounds... | agpl-3.0 |
dfm/celerite | paper/figures/sho.py | 3 | 2042 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as plt
from celerite.plot_setup import setup, get_figsize
np.random.seed(42)
setup(auto=True)
def sho_psd(Q, x):
x2 = x*x
return 1.0 / ((x2 - 1)**2 + x2 / Q**2)
def sho_... | mit |
rsmailach/MultiServerSRPT | ClassBased_Multi_RR_Scaled.py | 1 | 45830 | #----------------------------------------------------------------------#
# ApproxSRPTE_Multi_RR.py
#
# This application simulates multiple server with Poisson arrivals
# and processing times of a general distribution. There are errors in
# time estimates within a range. Arrivals are assigned to SRPT classes
# using the... | mit |
nikitasingh981/scikit-learn | examples/decomposition/plot_incremental_pca.py | 175 | 1974 | """
===============
Incremental PCA
===============
Incremental principal component analysis (IPCA) is typically used as a
replacement for principal component analysis (PCA) when the dataset to be
decomposed is too large to fit in memory. IPCA builds a low-rank approximation
for the input data using an amount of memo... | bsd-3-clause |
jgillis/topaf | pathfollowing/pathfollowing.py | 2 | 27834 | # TOPAF -- Time optimal path following for differentially flat systems
# Copyright (C) 2013 Wannes Van Loock, KU Leuven. All rights reserved.
#
# TOPAF is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License as published by the Free Software Foundation; ei... | lgpl-3.0 |
ian-r-rose/SHTOOLS | examples/python/LocalizedSpectralAnalysis/SHWindowsBiasOther.py | 2 | 2535 | #!/usr/bin/env python
"""
This script tests other routines related to localized spectral analyses
"""
# standard imports:
import os
import sys
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
# import shtools:
sys.path.append(os.path.join(os.path.dirname(__file__), "../../.."))
import pysht... | bsd-3-clause |
ShapeNet/JointEmbedding | src/utilities_caffe.py | 1 | 5047 | #!/usr/bin/python
# -*- coding: utf-8 -*-
import os
import sys
import math
import shutil
import datetime
import numpy as np
from multiprocessing import Pool
from google.protobuf import text_format
#https://github.com/BVLC/caffe/issues/861#issuecomment-70124809
import matplotlib
matplotlib.use('Agg')
def _array4d_id... | bsd-3-clause |
quiltdata/quilt | lambdas/es/indexer/test/constants.py | 2 | 3726 | """
constants for use in testing
"""
NORMAL_EXTRACT = """%matplotlib inline
import keras
from keras.layers import Dense
from keras.models import Model
from keras.models import Sequential
from keras.utils.np_utils import to_categorical
from collections import Counter
import numpy ... | apache-2.0 |
jhamman/xarray | xarray/plot/dataset_plot.py | 2 | 14664 | import functools
import numpy as np
import pandas as pd
from ..core.alignment import broadcast
from .facetgrid import _easy_facetgrid
from .utils import (
_add_colorbar,
_is_numeric,
_process_cmap_cbar_kwargs,
get_axis,
label_from_attrs,
)
# copied from seaborn
_MARKERSIZE_RANGE = np.array([18.0,... | apache-2.0 |
info-370/python-intro | ed-cost/analysis.py | 1 | 2409 | #If a question is asked of you, output the answer to the STDOUT (google-able
# term)
# There are multiple equally valid ways to accomplish many of these tasks
# import pandas and plotly. You may want to comment out the plotly import until
# you get to that part because the code runs much slower with it
import pandas
#... | mit |
mgarg1/ecg | ecg_visualizer_ble_PC/galry/test/test.py | 7 | 3947 | """Galry unit tests.
Every test shows a GalryWidget with a white square (non filled) and a black
background. Every test uses a different technique to show the same picture
on the screen. Then, the output image is automatically saved as a PNG file and
it is then compared to the ground truth.
"""
import unittest
import... | agpl-3.0 |
IshitaTakeshi/PCANet | ensemble.py | 1 | 2702 | from multiprocessing import cpu_count
from itertools import repeat
from sklearn.svm import SVC
from multiprocessing import Pool
from numpy.random import randint
from pcanet import PCANet
import numpy as np
def most_frequent_label(v):
values, counts = np.unique(v, return_counts=True)
return values[np.argmax(c... | mit |
pymir3/pymir3 | scripts/dcase2016/resultados/bands_graph.py | 2 | 3754 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors
import six
def plot_bands(band_features, filename):
filename = filename.split(".")[0]
report = open(filename + "_REPORT.txt", "w")
print filename
report.write(filename + "\n")
feature_colors = {
'Energy' : ... | mit |
kazemakase/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 |
rousseab/pymatgen | pymatgen/io/abinitio/flows.py | 1 | 92896 | # coding: utf-8
"""
A Flow is a container for Works, and works consist of tasks.
Flows are the final objects that can be dumped directly to a pickle file on disk
Flows are executed using abirun (abipy).
"""
from __future__ import unicode_literals, division, print_function
import os
import sys
import time
import collec... | mit |
chenyyx/scikit-learn-doc-zh | examples/zh/gaussian_process/plot_gpc_iris.py | 100 | 2269 | """
=====================================================
Gaussian process classification (GPC) on iris dataset
=====================================================
This example illustrates the predicted probability of GPC for an isotropic
and anisotropic RBF kernel on a two-dimensional version for the iris-dataset.
... | gpl-3.0 |
saiwing-yeung/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 |
valexandersaulys/prudential_insurance_kaggle | venv/lib/python2.7/site-packages/pandas/tseries/tests/test_timeseries.py | 9 | 183904 | # pylint: disable-msg=E1101,W0612
import calendar
from datetime import datetime, time, timedelta
import sys
import operator
import warnings
import nose
import numpy as np
randn = np.random.randn
from pandas import (Index, Series, DataFrame,
isnull, date_range, Timestamp, Period, DatetimeIndex,
... | gpl-2.0 |
landlab/landlab | tests/ca/cts_model.py | 3 | 5412 | #!/usr/env/python
import time
from matplotlib.pyplot import axis
from numpy import random
from landlab.ca.celllab_cts import CAPlotter, Transition
from landlab.io.native_landlab import save_grid
_DEBUG = False
class CTSModel(object):
"""
Implement a generic CellLab-CTS model.
This is the base class fr... | mit |
brev/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/pylab.py | 70 | 10245 | """
This is a procedural interface to the matplotlib object-oriented
plotting library.
The following plotting commands are provided; the majority have
Matlab(TM) analogs and similar argument.
_Plotting commands
acorr - plot the autocorrelation function
annotate - annotate something in the figure
arrow ... | agpl-3.0 |
duguyue100/telaugesa | scripts/cifar10_stacked_improve_deconvae_test.py | 1 | 8454 | """Stacked fixed noise dCOnvAE test"""
import sys;
sys.path.append("..");
import numpy as np;
import matplotlib.pyplot as plt;
import cPickle as pickle;
import theano;
import theano.tensor as T;
import telaugesa.datasets as ds;
from telaugesa.fflayers import ReLULayer;
from telaugesa.fflayers import SoftmaxLayer;
f... | mit |
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