repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
ishank08/scikit-learn | sklearn/linear_model/bayes.py | 14 | 19671 | """
Various bayesian regression
"""
from __future__ import print_function
# Authors: V. Michel, F. Pedregosa, A. Gramfort
# License: BSD 3 clause
from math import log
import numpy as np
from scipy import linalg
from .base import LinearModel
from ..base import RegressorMixin
from ..utils.extmath import fast_logdet, p... | bsd-3-clause |
glouppe/scikit-learn | sklearn/neighbors/base.py | 30 | 30586 | """Base and mixin classes for nearest neighbors"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
# Multi-output... | bsd-3-clause |
saurav111/keras | examples/kaggle_otto_nn.py | 70 | 3775 | from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import pandas as pd
np.random.seed(1337) # for reproducibility
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.normalization import BatchNormalization
from ke... | mit |
r0k3/trading-with-python | cookbook/getDataFromYahooFinance.py | 77 | 1391 | # -*- coding: utf-8 -*-
"""
Created on Sun Oct 16 18:37:23 2011
@author: jev
"""
from urllib import urlretrieve
from urllib2 import urlopen
from pandas import Index, DataFrame
from datetime import datetime
import matplotlib.pyplot as plt
sDate = (2005,1,1)
eDate = (2011,10,1)
symbol = 'SPY'
fNa... | bsd-3-clause |
bundgus/python-playground | matplotlib-playground/examples/animation/strip_chart_demo.py | 1 | 1512 | """
Emulate an oscilloscope. Requires the animation API introduced in
matplotlib 1.0 SVN.
"""
import numpy as np
from matplotlib.lines import Line2D
import matplotlib.pyplot as plt
import matplotlib.animation as animation
class Scope(object):
def __init__(self, ax, maxt=2, dt=0.02):
self.ax = ax
... | mit |
stevenjoelbrey/PMFutures | Python/plotParameterSpace.py | 1 | 5647 | #!/usr/bin/env python2
# plotEmissionSummary.py
###############################################################################
# ------------------------- Description ---------------------------------------
###############################################################################
# This script will be used to... | mit |
okadate/romspy | romspy/tplot/tplot_param.py | 1 | 4044 | # coding: utf-8
# (c) 2016-01-27 Teruhisa Okada
import netCDF4
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
from matplotlib.offsetbox import AnchoredText
import numpy as np
import pandas as pd
import glob
import romspy
def tplot_param(inifiles, vname, ax=plt.gca()):
for inifile in i... | mit |
frank-tancf/scikit-learn | sklearn/utils/metaestimators.py | 283 | 2353 | """Utilities for meta-estimators"""
# Author: Joel Nothman
# Andreas Mueller
# Licence: BSD
from operator import attrgetter
from functools import update_wrapper
__all__ = ['if_delegate_has_method']
class _IffHasAttrDescriptor(object):
"""Implements a conditional property using the descriptor protocol.
... | bsd-3-clause |
hsiaoyi0504/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 |
KarlTDebiec/Moldynplot | moldynplot/dataset/TimeSeriesDataset.py | 2 | 18697 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# moldynplot.dataset.TimeSeriesDataset.py
#
# Copyright (C) 2015-2017 Karl T Debiec
# All rights reserved.
#
# This software may be modified and distributed under the terms of the
# BSD license. See the LICENSE file for details.
"""
Represents timeseries data
.. todo... | bsd-3-clause |
MMKrell/pyspace | pySPACE/missions/nodes/feature_generation/correlation_features.py | 3 | 25671 | """ Extract statistical properties like moments or correlation coefficients
**Known issues**
No unit tests!
"""
import numpy
import scipy.stats
import copy
from matplotlib import mlab
import warnings
from pySPACE.missions.nodes.base_node import BaseNode
from pySPACE.resources.data_types.feature_vector import Feat... | gpl-3.0 |
kgsn1763/deep-learning-from-scratch | ch06/batch_norm_test.py | 1 | 2841 | #!/usr/bin/env python
# coding: utf-8
import sys, os
sys.path.append(os.pardir) # 親ディレクトリのファイルをインポートするための設定
import numpy as np
import matplotlib.pyplot as plt
from dataset.mnist import load_mnist
from common.multi_layer_net_extend import MultiLayerNetExtend
from common.optimizer import SGD
(x_train, t_train), (x_tes... | mit |
Roboticmechart22/sms-tools | lectures/09-Sound-description/plots-code/spectralFlux-onsetFunction.py | 25 | 1330 | import numpy as np
import matplotlib.pyplot as plt
import essentia.standard as ess
M = 1024
N = 1024
H = 512
fs = 44100
spectrum = ess.Spectrum(size=N)
window = ess.Windowing(size=M, type='hann')
flux = ess.Flux()
onsetDetection = ess.OnsetDetection(method='hfc')
x = ess.MonoLoader(filename = '../../../sounds/speech-m... | agpl-3.0 |
statsmodels/statsmodels.github.io | v0.10.0/plots/graphics_gofplots_qqplot.py | 6 | 1926 | # -*- coding: utf-8 -*-
"""
Created on Sun May 06 05:32:15 2012
Author: Josef Perktold
editted by: Paul Hobson (2012-08-19)
"""
from scipy import stats
from matplotlib import pyplot as plt
import statsmodels.api as sm
#example from docstring
data = sm.datasets.longley.load(as_pandas=False)
data.exog = sm.add_constant... | bsd-3-clause |
tosolveit/scikit-learn | examples/model_selection/plot_underfitting_overfitting.py | 230 | 2649 | """
============================
Underfitting vs. Overfitting
============================
This example demonstrates the problems of underfitting and overfitting and
how we can use linear regression with polynomial features to approximate
nonlinear functions. The plot shows the function that we want to approximate,
wh... | bsd-3-clause |
pkruskal/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 |
bthirion/nistats | examples/03_second_level_models/plot_oasis.py | 1 | 5248 | """Voxel-Based Morphometry on Oasis dataset
========================================
This example uses Voxel-Based Morphometry (VBM) to study the relationship
between aging, sex and gray matter density.
The data come from the `OASIS <http://www.oasis-brains.org/>`_ project.
If you use it, you need to agree with the d... | bsd-3-clause |
jmetzen/scikit-learn | sklearn/decomposition/pca.py | 20 | 23579 | """ Principal Component Analysis
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis A. Engemann <d.engemann@fz-juelich.de>
# Michael Eickenberg <michael.eickenberg@inria.fr>
#
# Lice... | bsd-3-clause |
jamesliu/mxnet | example/kaggle-ndsb1/training_curves.py | 52 | 1879 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | apache-2.0 |
SteveDiamond/cvxpy | examples/machine_learning/lasso_regression.py | 2 | 2270 | import cvxpy as cp
import numpy as np
import matplotlib.pyplot as plt
def loss_fn(X, Y, beta):
return cp.norm2(cp.matmul(X, beta) - Y)**2
def regularizer(beta):
return cp.norm1(beta)
def objective_fn(X, Y, beta, lambd):
return loss_fn(X, Y, beta) + lambd * regularizer(beta)
def mse(X, Y, beta):
... | gpl-3.0 |
sumspr/scikit-learn | examples/decomposition/plot_incremental_pca.py | 244 | 1878 | """
===============
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 |
xiaohan2012/cotrain | view_extraction/views.py | 1 | 4283 | import cPickle as pkl
import numpy as np
from collections import (Counter, defaultdict)
from scipy.sparse import (hstack, issparse, csr_matrix, csc_matrix)
from sklearn.feature_extraction import DictVectorizer
from mynlp.dependency.tree import NodeNotFoundError
from mynlp.string_util.multistring_matching import Multi... | mit |
cdegroc/scikit-learn | sklearn/check_build/__init__.py | 2 | 1625 | """ Module to give helpful messages to the user that did not
compile the scikit properly.
"""
import os
INPLACE_MSG = """
It appears that you are importing a local tree of the scikit-learn. For
this, you need to have an inplace install. Maybe you are in the source
directory and you need to try from another location.""... | bsd-3-clause |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/sklearn/datasets/__init__.py | 72 | 3807 | """
The :mod:`sklearn.datasets` module includes utilities to load datasets,
including methods to load and fetch popular reference datasets. It also
features some artificial data generators.
"""
from .base import load_diabetes
from .base import load_digits
from .base import load_files
from .base import load_iris
from .... | mit |
DavidQiuChao/CS231nHomeWorks | assignment2/FullyConnectedNets.py | 1 | 27403 |
# coding: utf-8
# # Fully-Connected Neural Nets
# In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer net... | mit |
pprett/scikit-learn | sklearn/feature_selection/tests/test_rfe.py | 56 | 11274 | """
Testing Recursive feature elimination
"""
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from scipy import sparse
from sklearn.feature_selection.rfe import RFE, RFECV
from sklearn.datasets import load_iris, make_friedman1
from sklearn.metrics import zero_one_loss
from sk... | bsd-3-clause |
nguyentu1602/statsmodels | statsmodels/stats/sandwich_covariance.py | 19 | 27944 | # -*- 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 |
rsivapr/scikit-learn | examples/covariance/plot_robust_vs_empirical_covariance.py | 8 | 6264 | """
=======================================
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 guara... | bsd-3-clause |
jseabold/scikit-learn | examples/model_selection/plot_roc.py | 49 | 5041 | """
=======================================
Receiver Operating Characteristic (ROC)
=======================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality.
ROC curves typically feature true positive rate on the Y axis, and false
positive rate on the X a... | bsd-3-clause |
Titan-C/selfspy | doc/conf.py | 1 | 5433 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# SelfSpy documentation build configuration file, created by
# sphinx-quickstart on Sun Apr 30 16:14:35 2017.
#
# 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
# au... | gpl-3.0 |
ArnaudBelcour/liasis | setup.py | 1 | 1297 | import os
from io import open
from setuptools import setup
with open(os.path.join(os.path.dirname(__file__), 'README.rst'), encoding='utf-8') as readme_file:
readme = readme_file.read()
setup(name='pbsea',
description='Singular Enrichment Analysis',
long_description=readme,
version... | gpl-3.0 |
eduardoftoliveira/oniomMacGyver | omg/asciiplot.py | 2 | 27736 | """
From https://github.com/mfouesneau/asciiplot
Package that allows you to plot simple graphs in ASCII, a la matplotlib.
This package is a inspired from Imri Goldberg's ASCII-Plotter 1.0
(https://pypi.python.org/pypi/ASCII-Plotter/1.0)
At a time I was enoyed by security not giving me direct access to my computer,
a... | gpl-3.0 |
Opendigitalradio/ODR-StaticPrecorrection | calc_lag.py | 1 | 1161 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import re
import sys
from tqdm import tqdm
from glob import glob
from natsort import natsorted
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import src.dab_util as du
... | mit |
madjelan/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 |
McDermott-Group/LabRAD | LabRAD/TestScripts/fpgaTest/pyle/pyle/dataking/diagnostics.py | 2 | 2195 | import numpy as np
import matplotlib.pyplot as plt
from pyle.plotting import dstools as ds
import time
def uwaveTraces(sample, channels = [1,2,3], name = '', muxServ = None, scopeServ = None,
scopeMuxChan = 1, plotData = True, spacing = 100, holdFig=False): # spacing in units of [mv], just for the pl... | gpl-2.0 |
voxlol/scikit-learn | examples/ensemble/plot_random_forest_embedding.py | 286 | 3531 | """
=========================================================
Hashing feature transformation using Totally Random Trees
=========================================================
RandomTreesEmbedding provides a way to map data to a
very high-dimensional, sparse representation, which might
be beneficial for classificati... | bsd-3-clause |
PedroTrujilloV/nest-simulator | extras/ConnPlotter/tcd_nest.py | 13 | 6838 | # -*- coding: utf-8 -*-
#
# tcd_nest.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST 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
# ... | gpl-2.0 |
bbreslauer/PySciPlot | src/WavePair.py | 1 | 11319 | # Copyright (C) 2010-2011 Ben Breslauer
#
# 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 distribu... | gpl-3.0 |
vermouthmjl/scikit-learn | sklearn/__check_build/__init__.py | 345 | 1671 | """ Module to give helpful messages to the user that did not
compile the scikit properly.
"""
import os
INPLACE_MSG = """
It appears that you are importing a local scikit-learn source tree. For
this, you need to have an inplace install. Maybe you are in the source
directory and you need to try from another location.""... | bsd-3-clause |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/dask/dataframe/groupby.py | 2 | 44826 | from __future__ import absolute_import, division, print_function
import collections
import itertools as it
import operator
import warnings
import numpy as np
import pandas as pd
from .core import (DataFrame, Series, aca, map_partitions, merge,
new_dd_object, no_default, split_out_on_index)
from .m... | gpl-3.0 |
shakamunyi/tensorflow | tensorflow/contrib/factorization/python/ops/kmeans.py | 19 | 17291 | # 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 |
jskDr/jamespy_py3 | krealdl.py | 1 | 7145 | # Sungjin Kim, 2016-5-7
# Python 3
from importlib import reload
import tensorflow as tf
import pandas as pd
# Import MINST data
import input_data
def multilayer_perceptron(_X, _weights, _biases):
#Hidden layer with RELU activation
layer_1 = tf.nn.relu(tf.add(tf.matmul(_X, _weights['h1']), _biases['b1']))
#Hidden... | mit |
phobson/statsmodels | statsmodels/imputation/tests/test_mice.py | 4 | 10458 | import numpy as np
import pandas as pd
from statsmodels.imputation import mice
import statsmodels.api as sm
from numpy.testing import assert_equal, assert_allclose, dec
try:
import matplotlib.pyplot as plt #makes plt available for test functions
have_matplotlib = True
except:
have_matplotlib = False
pdf_... | bsd-3-clause |
ryfeus/lambda-packs | Tensorflow_Pandas_Numpy/source3.6/pandas/core/api.py | 1 | 3109 |
# pylint: disable=W0614,W0401,W0611
# flake8: noqa
import numpy as np
from pandas.core.algorithms import factorize, unique, value_counts
from pandas.core.dtypes.missing import isna, isnull, notna, notnull
from pandas.core.arrays import Categorical
from pandas.core.groupby.groupby import Grouper
from pandas.io.format... | mit |
f3r/scikit-learn | examples/cluster/plot_dbscan.py | 346 | 2479 | # -*- coding: utf-8 -*-
"""
===================================
Demo of DBSCAN clustering algorithm
===================================
Finds core samples of high density and expands clusters from them.
"""
print(__doc__)
import numpy as np
from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn... | bsd-3-clause |
tosolveit/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 |
collbb/ThinkStats2 | code/chap12soln.py | 68 | 4459 | """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 statsmodels.formula.api as smf
import t... | gpl-3.0 |
louisLouL/pair_trading | hist_data/backtest/backtest.py | 1 | 3041 | import pandas as pd
import numpy as np
from collections import defaultdict
from config import backup_database, pair
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
class BackTest:
def __init__(self, pair_list):
self.pair_list = pair_list
@staticmethod
def signal(x):
... | mit |
ivano666/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator_test.py | 1 | 5210 | # Copyright 2015 Google Inc. 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 applicable law o... | apache-2.0 |
vibhorag/scikit-learn | sklearn/feature_extraction/tests/test_dict_vectorizer.py | 276 | 3790 | # Authors: Lars Buitinck <L.J.Buitinck@uva.nl>
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from random import Random
import numpy as np
import scipy.sparse as sp
from numpy.testing import assert_array_equal
from sklearn.utils.testing import (assert_equal, assert_in,
... | bsd-3-clause |
moorepants/DynamicistToolKit | dtk/bicycle.py | 1 | 30216 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# standard library
from math import sin, cos, tan, atan, pi
# external libraries
import numpy as np
from scipy.optimize import newton
from matplotlib.pyplot import figure, rcParams
# local libraries
from .inertia import y_rot
def benchmark_state_space_vs_speed(M, C1, K... | unlicense |
darionyaphet/spark | dev/sparktestsupport/modules.py | 4 | 16318 | #
# 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 |
heli522/scikit-learn | examples/cluster/plot_digits_agglomeration.py | 377 | 1694 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Feature agglomeration
=========================================================
These images how similar features are merged together using
feature agglomeration.
"""
print(__doc__)
# Code source: Gaël Varoquaux
#... | bsd-3-clause |
BillMills/AutoQC | util/benchmarks.py | 3 | 4332 | import util.combineTests as combinatorics
import matplotlib.pyplot as plt
import numpy as np
def compare_to_truth(combos, trueResult):
'''Given the results from all the possible combinations of tests (combos)
and a set of truth results (trueResult), the false positive rate and the
true positive rate ... | mit |
flennerhag/mlens | benchmarks/ensemble_comp.py | 1 | 2359 | """ML-ENSEMBLE
Comparison of ensemble performance across scale.
"""
import numpy as np
from mlens.ensemble import BlendEnsemble, SuperLearner, Subsemble
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoosti... | mit |
FCP-INDI/C-PAC | CPAC/qc/utils.py | 1 | 68748 | import os
import re
import math
import base64
import subprocess
import pkg_resources as p
import numpy as np
import nibabel as nb
import numpy.ma as ma
import numpy
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import matplotlib.cm as cm
from matplotlib import gridspec as mgs
from matp... | bsd-3-clause |
antoinecarme/pyaf | tests/neuralnet/test_ozone_rnn_only_LSTM.py | 1 | 1418 | import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
import logging
import logging.config
#logging.config.fileConfig('logging.conf')
logging.basicConfig(level=logging.INFO)
#get_ipython().magic('matplotlib inline')
b1 = tsds.load_ozone()
df = b1.mPastDa... | bsd-3-clause |
kecnry/autofig | autofig/cyclers.py | 2 | 6059 | from matplotlib import colors, markers, cm
import matplotlib.pyplot as plt
from . import common
_mplcolors = ['black', 'blue', 'red', 'green']
_mplcolors += [common.coloralias.map(c) for c in list(colors.ColorConverter.colors.keys()) + list(colors.cnames.keys()) if common.coloralias.map(c) not in _mplcolors and 'xkcd'... | gpl-3.0 |
AICreators/test_code | RL_01/reinforcement.py | 1 | 3434 | import gym
import numpy as np
import random
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam
from collections import deque
import matplotlib.pyplot as plt
class DQN:
def __init__(self, env):
self.env = env
self.memory = deque(maxlen=200... | gpl-3.0 |
ClimbsRocks/scikit-learn | examples/exercises/plot_iris_exercise.py | 323 | 1602 | """
================================
SVM Exercise
================================
A tutorial exercise for using different SVM kernels.
This exercise is used in the :ref:`using_kernels_tut` part of the
:ref:`supervised_learning_tut` section of the :ref:`stat_learn_tut_index`.
"""
print(__doc__)
import numpy as np
i... | bsd-3-clause |
mojoboss/scikit-learn | examples/calibration/plot_calibration.py | 225 | 4795 | """
======================================
Probability calibration of classifiers
======================================
When performing classification you often want to predict not only
the class label, but also the associated probability. This probability
gives you some kind of confidence on the prediction. However,... | bsd-3-clause |
pprett/scikit-learn | sklearn/linear_model/tests/test_randomized_l1.py | 7 | 5998 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
from tempfile import mkdtemp
import shutil
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... | bsd-3-clause |
anntzer/scikit-learn | benchmarks/bench_plot_fastkmeans.py | 12 | 4570 | from collections import defaultdict
from time import time
import numpy as np
from numpy import random as nr
from sklearn.cluster import KMeans, MiniBatchKMeans
def compute_bench(samples_range, features_range):
it = 0
results = defaultdict(lambda: [])
chunk = 100
max_it = len(samples_range) * len(f... | bsd-3-clause |
TheHonestGene/imputor | setup.py | 1 | 1812 | from setuptools import setup, find_packages # Always prefer setuptools over distutils
from codecs import open # To use a consistent encoding
from os import path
here = path.abspath(path.dirname(__file__))
# Get the long description from the relevant file
with open(path.join(here, 'README.rst'), encoding='utf-8') as... | mit |
suzlab/Autoware | ros/src/computing/perception/localization/packages/orb_localizer/src/analysis/orbndt.py | 1 | 36681 | from __future__ import division
import numpy as np
import datetime
import rosbag
import rospy
from copy import copy, deepcopy
from exceptions import KeyError, ValueError
from segway_rmp.msg import SegwayStatusStamped
from geometry_msgs.msg import PoseStamped
import matplotlib.pyplot as plt
import matplotlib.animation a... | bsd-3-clause |
Adai0808/scikit-learn | sklearn/decomposition/tests/test_dict_learning.py | 85 | 8565 | import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
talbrecht/pism_pik07 | site-packages/siple/opt/linesearchHZ.py | 2 | 11502 | ############################################################################
#
# This file is a part of siple.
#
# Copyright 2010, 2014 David Maxwell
#
# siple 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 Foundati... | gpl-3.0 |
abhitopia/tensorflow | tensorflow/examples/learn/iris_custom_model.py | 50 | 2613 | # 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 |
almarklein/bokeh | examples/charts/stacked_bar.py | 1 | 1124 | from collections import OrderedDict
import pandas as pd
# we throw the data into a pandas df
from bokeh.sampledata.olympics2014 import data
from bokeh.charts import Bar
from bokeh.plotting import output_file, show
df = pd.io.json.json_normalize(data['data'])
# we filter by countries with at least one medal and sort
... | bsd-3-clause |
theoryno3/scikit-learn | sklearn/datasets/mlcomp.py | 41 | 3803 | # Copyright (c) 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
"""Glue code to load http://mlcomp.org data as a scikit.learn dataset"""
import os
import numbers
from sklearn.datasets.base import load_files
def _load_document_classification(dataset_path, metadata, set_=None, **kwargs):
if ... | bsd-3-clause |
hammerlab/cohorts | test/test_df_loading.py | 1 | 1315 | # Copyright (c) 2016. Mount Sinai School of Medicine
#
# 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 o... | apache-2.0 |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/sklearn/utils/deprecation.py | 20 | 4075 | import sys
import warnings
import functools
__all__ = ["deprecated", "DeprecationDict"]
class deprecated(object):
"""Decorator to mark a function or class as deprecated.
Issue a warning when the function is called/the class is instantiated and
adds a warning to the docstring.
The optional extra arg... | mit |
TNick/pylearn2 | pylearn2/scripts/train.py | 34 | 8573 | #!/usr/bin/env python
"""
Script implementing the logic for training pylearn2 models.
This is a "driver" that we recommend using for all but the most unusual
training experiments.
Basic usage:
.. code-block:: none
train.py yaml_file.yaml
The YAML file should contain a pylearn2 YAML description of a
`pylearn2.t... | bsd-3-clause |
nilmtk/nilmtk | nilmtk/disaggregate/mean.py | 1 | 2165 | from warnings import warn
import pandas as pd
import numpy as np
import json
from nilmtk.disaggregate import Disaggregator
import os
class Mean(Disaggregator):
def __init__(self, params):
self.model = {}
self.MODEL_NAME = 'Mean' # Add the name for the algorithm
self.save_model_path = param... | apache-2.0 |
Crespo911/pyspace | pySPACE/missions/nodes/visualization/feature_vector_vis.py | 1 | 5372 | """ Visualize :class:`~pySPACE.resources.data_types.feature_vector.FeatureVector` elements"""
import itertools
import pylab
import numpy
try:
import mdp.nodes
except:
pass
from pySPACE.missions.nodes.base_node import BaseNode
class LLEVisNode(BaseNode):
""" Show a 2d scatter plot of all :class:`~pySPACE.r... | gpl-3.0 |
CaymanUnterborn/burnman | setup.py | 5 | 1333 | from __future__ import absolute_import
import re
versionstuff = dict(
re.findall("(.+) = '(.+)'\n", open('burnman/version.py').read()))
metadata = dict(name='burnman',
version=versionstuff['version'],
description='a thermoelastic and thermodynamic toolkit for Earth and planetary sc... | gpl-2.0 |
bmcfee/crema | training/chords/02-train.py | 1 | 11960 | #!/usr/bin/env python
'''CREMA structured chord model'''
import argparse
import os
import sys
from glob import glob
import pickle
import pandas as pd
import keras as K
from sklearn.model_selection import ShuffleSplit
import pescador
import pumpp
import librosa
import crema.utils
import crema.layers
from jams.util i... | bsd-2-clause |
richardwolny/sms-tools | lectures/07-Sinusoidal-plus-residual-model/plots-code/hprModelFrame.py | 22 | 2847 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, triang, blackmanharris
import math
from scipy.fftpack import fft, ifft, fftshift
import sys, os, functools, time
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import dftModel a... | agpl-3.0 |
alexsavio/scikit-learn | sklearn/tests/test_naive_bayes.py | 72 | 19944 | import pickle
from io import BytesIO
import numpy as np
import scipy.sparse
from sklearn.datasets import load_digits, load_iris
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert... | bsd-3-clause |
mblue9/tools-iuc | tools/vsnp/vsnp_build_tables.py | 2 | 17888 | #!/usr/bin/env python
import argparse
import multiprocessing
import os
import queue
import re
import pandas
import pandas.io.formats.excel
from Bio import SeqIO
INPUT_JSON_AVG_MQ_DIR = 'input_json_avg_mq_dir'
INPUT_JSON_DIR = 'input_json_dir'
INPUT_NEWICK_DIR = 'input_newick_dir'
# Maximum columns allowed in a Libre... | mit |
r-mart/scikit-learn | examples/linear_model/plot_robust_fit.py | 238 | 2414 | """
Robust linear estimator fitting
===============================
Here a sine function is fit with a polynomial of order 3, for values
close to zero.
Robust fitting is demoed in different situations:
- No measurement errors, only modelling errors (fitting a sine with a
polynomial)
- Measurement errors in X
- M... | bsd-3-clause |
mattilyra/scikit-learn | examples/svm/plot_svm_nonlinear.py | 268 | 1091 | """
==============
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 learned by the SVC.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn imp... | bsd-3-clause |
cojacoo/testcases_echoRD | gen_test2211.py | 1 | 4396 | import numpy as np
import pandas as pd
import scipy as sp
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os, sys
try:
import cPickle as pickle
except:
import pickle
#connect echoRD Tools
pathdir='../echoRD' #path to echoRD
lib_path = os.path.abspath(pathdir)
#sys.path.append(lib_pa... | gpl-3.0 |
manashmndl/scikit-learn | examples/plot_isotonic_regression.py | 303 | 1767 | """
===================
Isotonic Regression
===================
An illustration of the isotonic regression on generated data. The
isotonic regression finds a non-decreasing approximation of a function
while minimizing the mean squared error on the training data. The benefit
of such a model is that it does not assume a... | bsd-3-clause |
nelango/ViralityAnalysis | model/lib/sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py | 221 | 5517 | """
Testing for the gradient boosting loss functions and initial estimators.
"""
import numpy as np
from numpy.testing import assert_array_equal
from numpy.testing import assert_almost_equal
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.utils import check_random_state
from ... | mit |
Vastra-Gotalandsregionen/verifierad.nu | dependencies/readability/readability/__init__.py | 1 | 8765 | """Simple readability measures.
Usage: %(cmd)s [--lang=<x>] [FILE]
or: %(cmd)s [--lang=<x>] --csv FILES...
By default, input is read from standard input.
Text should be encoded with UTF-8,
one sentence per line, tokens space-separated.
Options:
-L, --lang=<x> Set language (available: %(lang)s).
--csv ... | mit |
zyoohv/zyoohv.github.io | code_repository/tencent_ad_contest/tencent_contest/arrange_dataset/vw2csv.py | 1 | 1582 | #! /usr/bin/python3
from tqdm import tqdm
import numpy as np
import pandas as pd
import os
root_path = '/home/zyoohv/Documents/tencent_dataset/preliminary_contest_data/'
input_path = root_path + 'userFeature.data'
output_path = root_path + 'userFeature.csv'
os.system('rm {}'.format(output_path))
output_file = []
d... | mit |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/scipy/spatial/_plotutils.py | 23 | 5505 | from __future__ import division, print_function, absolute_import
import numpy as np
from scipy._lib.decorator import decorator as _decorator
__all__ = ['delaunay_plot_2d', 'convex_hull_plot_2d', 'voronoi_plot_2d']
@_decorator
def _held_figure(func, obj, ax=None, **kw):
import matplotlib.pyplot as plt
if ax... | mit |
magne-max/zipline-ja | mk_bundle.py | 1 | 7175 | # -*- coding: utf-8 -*-
"""
zipline の data bundle として import するための前処理
* unit32 以上の volume は upper bound clip
Author: Kohei
"""
from logging import getLogger, Formatter, StreamHandler, DEBUG
import os
import re
from pathlib import Path
from sklearn.externals.joblib import Parallel, delayed
import tqdm
import pandas a... | apache-2.0 |
matty-jones/MorphCT | tests/assets/update_pickle/MCT2.0_pickle/obtainChromophores.py | 1 | 16164 | import numpy as np
import sys
import helperFunctions
import copy
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
try:
import mpl_toolkits.mplot3d.axes3d as p3
except ImportError:
print()
pass
class chromophore:
def __init__(self, chromoID, chromophoreCGSites, CGMorphologyDict, ... | gpl-3.0 |
BiaDarkia/scikit-learn | examples/ensemble/plot_adaboost_regression.py | 67 | 1530 | """
======================================
Decision Tree Regression with AdaBoost
======================================
A decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D
sinusoidal dataset with a small amount of Gaussian noise.
299 boosts (300 decision trees) is compared with a single decision t... | bsd-3-clause |
Patrick-Cole/pygmi | pygmi/mt/dataprep.py | 1 | 56454 | # -----------------------------------------------------------------------------
# Name: dataprep.py (part of PyGMI)
#
# Author: Patrick Cole
# E-Mail: pcole@geoscience.org.za
#
# Copyright: (c) 2019 Council for Geoscience
# Licence: GPL-3.0
#
# This file is part of PyGMI
#
# PyGMI is free softwar... | gpl-3.0 |
BorisJeremic/Real-ESSI-Examples | education_examples/_Chapter_Material_Behaviour_Examples/Interface_Models/Axial_Models/Bonded_Contact/plot.py | 1 | 1408 | #!/usr/bin/python
import h5py
import matplotlib.pylab as plt
import matplotlib as mpl
import sys
import numpy as np;
plt.rcParams.update({'font.size': 24})
# set tick width
mpl.rcParams['xtick.major.size'] = 10
mpl.rcParams['xtick.major.width'] = 5
mpl.rcParams['xtick.minor.size'] = 10
mpl.rcParams['xtick.minor.width... | cc0-1.0 |
ankur-gupta/numerical-software-examples | examples/casadi/ode_example.py | 1 | 1049 | import numpy as np
import casadi as ca
import matplotlib.pyplot as plt
# Simple Reaction System
# A -> B; k1
# B -> C; k2
# Symbolic rate constants
k = ca.MX.sym('k', 2, 1)
# States
x = ca.MX.sym('x', 3, 1)
# RHS of the ODE
# Works for version casadi v3.1.1. Check ca.__version__.
# For casadi v2.4.3, put all args w... | gpl-3.0 |
willhaines/scikit-rf | setup.py | 3 | 1100 | #!/usr/bin/env python
#import ez_setup
#ez_setup.use_setuptools()
from setuptools import setup, find_packages
from distutils.core import Extension
with open('skrf/__init__.py') as fid:
for line in fid:
if line.startswith('__version__'):
VERSION = line.strip().split()[-1][1:-1]
bre... | bsd-3-clause |
joequant/zipline | zipline/utils/security_list.py | 18 | 4472 | from datetime import datetime
from os import listdir
import os.path
import pandas as pd
import pytz
import zipline
from zipline.finance.trading import with_environment
DATE_FORMAT = "%Y%m%d"
zipline_dir = os.path.dirname(zipline.__file__)
SECURITY_LISTS_DIR = os.path.join(zipline_dir, 'resources', 'security_lists')
... | apache-2.0 |
apark263/tensorflow | tensorflow/contrib/metrics/python/ops/metric_ops.py | 6 | 177807 | # 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 |
erh3cq/hyperspy | hyperspy/defaults_parser.py | 2 | 10674 | # -*- coding: utf-8 -*-
# Copyright 2007-2020 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy 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... | gpl-3.0 |
jshoyer/plantcv | plantcv/analyze_color.py | 2 | 11048 | # Analyze Color of Object
import os
import cv2
import numpy as np
from . import print_image
from . import plot_image
from . import fatal_error
from . import plot_colorbar
def _pseudocolored_image(device, histogram, bins, img, mask, background, channel, filename, resolution,
analysis_images, ... | mit |
asnorkin/sentiment_analysis | site/lib/python2.7/site-packages/sklearn/semi_supervised/tests/test_label_propagation.py | 5 | 1998 | """ test the label propagation module """
import numpy as np
from sklearn.utils.testing import assert_equal
from sklearn.semi_supervised import label_propagation
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
ESTIMATORS = [
(label_propagation.LabelPropagation, {... | mit |
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