repo_name stringlengths 7 92 | path stringlengths 5 149 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 911 693k | license stringclasses 15
values |
|---|---|---|---|---|---|
plissonf/scikit-learn | sklearn/linear_model/coordinate_descent.py | 59 | 76336 | # 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 |
DonBeo/scikit-learn | sklearn/utils/tests/test_class_weight.py | 14 | 6559 | import numpy as np
from sklearn.utils.class_weight import compute_class_weight
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.uti... | bsd-3-clause |
Phil9l/cosmos | code/artificial_intelligence/src/naive_bayes/gaussian_naive_bayes.py | 3 | 1370 | # example using iris dataset
# Part of Cosmos by OpenGenus
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import classification_report, confusion_matrix
dataset = pd.read_csv("ir... | gpl-3.0 |
xyguo/scikit-learn | sklearn/decomposition/nmf.py | 6 | 46993 | """ Non-negative matrix factorization
"""
# Author: Vlad Niculae
# Lars Buitinck
# Mathieu Blondel <mathieu@mblondel.org>
# Tom Dupre la Tour
# Author: Chih-Jen Lin, National Taiwan University (original projected gradient
# NMF implementation)
# ... | bsd-3-clause |
embray/numpy | numpy/lib/npyio.py | 1 | 66490 | from __future__ import division, absolute_import, print_function
import sys
import os
import re
import itertools
import warnings
import weakref
from operator import itemgetter
import numpy as np
from . import format
from ._datasource import DataSource
from ._compiled_base import packbits, unpackbits
from ._iotools im... | bsd-3-clause |
Healthcast/RSV | python/all_year_predict/methods.py | 2 | 3879 | #!/usr/bin/pyhton
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets, neighbors, linear_model
from sklearn import svm
from sklearn import metrics
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
def apply_algorithm(paras, X, y):
... | gpl-2.0 |
nmayorov/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 |
rs2/pandas | pandas/tests/io/parser/test_na_values.py | 2 | 15082 | """
Tests that NA values are properly handled during
parsing for all of the parsers defined in parsers.py
"""
from io import StringIO
import numpy as np
import pytest
from pandas._libs.parsers import STR_NA_VALUES
from pandas import DataFrame, Index, MultiIndex
import pandas._testing as tm
def test_string_nas(all_... | bsd-3-clause |
kazemakase/scikit-learn | sklearn/feature_extraction/text.py | 24 | 50103 | # -*- 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 |
rjenc29/numerical | course/matplotlib/examples/fill_example.py | 1 | 2229 | """
Illustrate different ways of using the various fill functions.
"""
import numpy as np
import matplotlib.pyplot as plt
import example_utils
def main():
fig, axes = example_utils.setup_axes()
fill_example(axes[0])
fill_between_example(axes[1])
stackplot_example(axes[2])
example_utils.title(fig... | mit |
tjhunter/phd-thesis-tjhunter | python/kdd/plot_network.py | 1 | 1065 |
__author__ = 'tjhunter'
import build
import json
import pylab as pl
from matplotlib.collections import LineCollection
# Draws the network as a pdf and SVG file.
def draw_network(ax, fd, link_style):
def decode_line(l):
#print l
dct = json.loads(l)
lats = dct['lats']
lons = dct['lons']
return zi... | apache-2.0 |
kcompher/thunder | thunder/extraction/source.py | 6 | 31847 | from numpy import asarray, mean, sqrt, ndarray, amin, amax, concatenate, sum, zeros, maximum, \
argmin, newaxis, ones, delete, NaN, inf, isnan, clip, logical_or, unique, where, all
from thunder.utils.serializable import Serializable
from thunder.utils.common import checkParams, aslist
from thunder.rdds.images impo... | apache-2.0 |
amanzi/ats-dev | tools/utils/transect_data.py | 2 | 7741 | """Loads and/or plots 2D, topologlically structured data on quadrilaterals using matplotlib.
"""
import sys,os
import numpy as np
import h5py
import mesh
import colors
def fullname(varname):
fullname = varname
if not '.cell.' in fullname:
fullname = fullname+'.cell.0'
return fullname
def transec... | bsd-3-clause |
lbishal/scikit-learn | examples/gaussian_process/plot_gpc_isoprobability.py | 45 | 3025 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=================================================================
Iso-probability lines for Gaussian Processes classification (GPC)
=================================================================
A two-dimensional classification example showing iso-probability lines for... | bsd-3-clause |
montagnero/political-affiliation-prediction | newsreader.py | 2 | 11936 | # -*- coding: utf-8 -*-
from sklearn.decomposition import KernelPCA
from sklearn.metrics.pairwise import pairwise_distances
from scipy.stats.mstats import zscore
import glob
import json
import re
import datetime
import os
import cPickle
import codecs
import itertools
from sklearn.feature_extraction.text import TfidfVec... | mit |
bikong2/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 |
hsuantien/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_01_language_train_model.py | 254 | 2253 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
MostafaGazar/tensorflow | tensorflow/contrib/learn/python/learn/dataframe/dataframe.py | 85 | 4704 | # 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 |
mtconley/turntable | test/lib/python2.7/site-packages/scipy/stats/tests/test_morestats.py | 7 | 38719 | # Author: Travis Oliphant, 2002
#
# Further enhancements and tests added by numerous SciPy developers.
#
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from numpy.random import RandomState
from numpy.testing import (TestCase, run_module_suite, assert_array_equal,
... | mit |
mjgrav2001/scikit-learn | sklearn/decomposition/base.py | 313 | 5647 | """Principal Component Analysis Base Classes"""
# 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>
# Kyle Kastner <kastnerkyle@gmail.com>
#
# Licen... | bsd-3-clause |
markhamstra/spark | examples/src/main/python/sql/arrow.py | 13 | 3997 | #
# 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 |
elkingtonmcb/scikit-learn | sklearn/setup.py | 225 | 2856 | import os
from os.path import join
import warnings
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info, BlasNotFoundError
import numpy
libraries = []
if os.name == 'posix':
libraries.appe... | bsd-3-clause |
Vimos/scikit-learn | sklearn/utils/testing.py | 29 | 25405 | """Testing utilities."""
# Copyright (c) 2011, 2012
# Authors: Pietro Berkes,
# Andreas Muller
# Mathieu Blondel
# Olivier Grisel
# Arnaud Joly
# Denis Engemann
# Giorgio Patrini
# Thierry Guillemot
# License: BSD 3 clause
import os
import inspect
import p... | bsd-3-clause |
neale/CS-program | 434-MachineLearning/final_project/linearClassifier/sklearn/datasets/mlcomp.py | 289 | 3855 | # 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 ... | unlicense |
antoinecarme/pyaf | tests/perf/test_ozone_debug_perf.py | 1 | 1566 | import pandas as pd
import numpy as np
# from memory_profiler import profile
# from memprof import *
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
#get_ipython().magic('matplotlib inline')
# @memprof
def test_ozone_debug_perf():
b1 = tsds.load_ozone()
df = b1.mPastData
# df... | bsd-3-clause |
nkhuyu/seizure-prediction | ensemble.py | 2 | 9703 | #!/usr/bin/env python2.7
from multiprocessing import Pool
import sys
import numpy as np
from sklearn.metrics import roc_auc_score
from seizure_prediction.classifiers import make_svm, make_simple_lr, make_lr
from seizure_prediction.feature_selection import generate_feature_masks
from seizure_prediction.fft_bins impor... | mit |
keir-rex/zipline | zipline/utils/tradingcalendar_tse.py | 24 | 10413 | #
# Copyright 2014 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
Sentient07/scikit-learn | sklearn/tests/test_grid_search.py | 27 | 29492 | """
Testing for grid search module (sklearn.grid_search)
"""
from collections import Iterable, Sized
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.externals.six.moves import xrange
from itertools import chain, product
import pickle
import warnings
import sys
import numpy as np
import sci... | bsd-3-clause |
fdudatamining/framework | tests/draw/test_simple.py | 1 | 1233 | import numpy as np
import pandas as pd
from unittest import TestCase
from framework import draw
X = np.array([1, 2, 3, 4, 5])
class TestSimplePlots(TestCase):
def test_kinds(self):
self.assertIsNotNone(draw.draw_kinds)
def test_line(self):
draw.draw(clear=True, kind='line', x=X, y=X)
draw.draw(clear=... | gpl-2.0 |
stevertaylor/NX01 | newcmaps.py | 28 | 50518 | # New matplotlib colormaps by Nathaniel J. Smith, Stefan van der Walt,
# and (in the case of viridis) Eric Firing.
#
# This file and the colormaps in it are released under the CC0 license /
# public domain dedication. We would appreciate credit if you use or
# redistribute these colormaps, but do not impose any legal r... | mit |
anntzer/scikit-learn | sklearn/ensemble/__init__.py | 12 | 1655 | """
The :mod:`sklearn.ensemble` module includes ensemble-based methods for
classification, regression and anomaly detection.
"""
import typing
from ._base import BaseEnsemble
from ._forest import RandomForestClassifier
from ._forest import RandomForestRegressor
from ._forest import RandomTreesEmbedding
from ._forest i... | bsd-3-clause |
natj/bender | paper/figs/fig9.py | 1 | 4141 | import numpy as np
import math
from pylab import *
from palettable.wesanderson import Zissou_5 as wsZ
import matplotlib.ticker as mtick
from scipy.interpolate import interp1d
from scipy.interpolate import griddata
from scipy.signal import savgol_filter
def smooth(xx, yy):
yy = savgol_filter(yy, 7, 2)
np.cl... | mit |
pianomania/scikit-learn | sklearn/linear_model/stochastic_gradient.py | 16 | 50617 | # 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
from abc import ABCMeta, abstractmethod
from ..externals.joblib import ... | bsd-3-clause |
mhue/scikit-learn | examples/text/document_classification_20newsgroups.py | 222 | 10500 | """
======================================================
Classification of text documents using sparse features
======================================================
This is an example showing how scikit-learn can be used to classify documents
by topics using a bag-of-words approach. This example uses a scipy.spars... | bsd-3-clause |
vermouthmjl/scikit-learn | sklearn/gaussian_process/tests/test_gpr.py | 23 | 11915 | """Testing for Gaussian process regression """
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Licence: BSD 3 clause
import numpy as np
from scipy.optimize import approx_fprime
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels \
import RBF, Constan... | bsd-3-clause |
kushalbhola/MyStuff | Practice/PythonApplication/env/Lib/site-packages/pandas/tseries/frequencies.py | 2 | 15559 | from datetime import timedelta
import re
from typing import Dict
import numpy as np
from pytz import AmbiguousTimeError
from pandas._libs.algos import unique_deltas
from pandas._libs.tslibs import Timedelta, Timestamp
from pandas._libs.tslibs.ccalendar import MONTH_ALIASES, int_to_weekday
from pandas._libs.tslibs.fie... | apache-2.0 |
jmargeta/scikit-learn | sklearn/tests/test_pls.py | 6 | 9383 | import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.datasets import load_linnerud
from sklearn import pls
def test_pls():
d = load_linnerud()
X = d.data
Y = d.target
# 1) Canonical (symetric) PLS (PLS 2 blocks canonical mode A)
# ============================... | bsd-3-clause |
Shatki/PyIMU | test/magnetosphere.py | 1 | 1580 | from mpl_toolkits.mplot3d import axes3d
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from socket import *
import time
# Объявляем все глобальные переменные
HOST = '192.168.0.76'
PORT = 21566
BUFSIZ = 512
ADDR = (HOST, PORT)
bad_packet = 0
good_packet = 0
# fig, ax... | gpl-3.0 |
DamCB/tyssue | tyssue/draw/ipv_draw.py | 2 | 8114 | """3D visualisation inside the notebook.
"""
import warnings
import numpy as np
import pandas as pd
from matplotlib import cm
from ipywidgets import interact
from ..config.draw import sheet_spec
from ..utils.utils import spec_updater, get_sub_eptm
try:
import ipyvolume as ipv
except ImportError:
print(
... | gpl-3.0 |
LiaoPan/scikit-learn | sklearn/cluster/tests/test_dbscan.py | 114 | 11393 | """
Tests for DBSCAN clustering algorithm
"""
import pickle
import numpy as np
from scipy.spatial import distance
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.utils.testing im... | bsd-3-clause |
ephes/scikit-learn | examples/decomposition/plot_faces_decomposition.py | 204 | 4452 | """
============================
Faces dataset decompositions
============================
This example applies to :ref:`olivetti_faces` different unsupervised
matrix decomposition (dimension reduction) methods from the module
:py:mod:`sklearn.decomposition` (see the documentation chapter
:ref:`decompositions`) .
"""... | bsd-3-clause |
devanshdalal/scikit-learn | examples/ensemble/plot_isolation_forest.py | 39 | 2361 | """
==========================================
IsolationForest example
==========================================
An example using IsolationForest for anomaly detection.
The IsolationForest 'isolates' observations by randomly selecting a feature
and then randomly selecting a split value between the maximum and minimu... | bsd-3-clause |
thp44/delphin_6_automation | data_process/2d_1d/archieve/moisture_content_comparison.py | 1 | 18274 | __author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import pandas as pd
import matplotlib.pyplot as plt
# RiBuild Modules
# -----------------------------------------------... | mit |
miaecle/deepchem | devtools/archive/jenkins/generate_graph.py | 2 | 5220 | import csv
import os
import numpy as np
import matplotlib.pyplot as plt
import time
plt.switch_backend('agg')
TODO = {
('tox21', 'random'): [
'weave', 'graphconv', 'tf', 'tf_robust', 'irv', 'xgb', 'logreg',
'textcnn'
],
('clintox', 'random'): [
'weave', 'graphconv', 'tf', 'tf_robust... | mit |
xiongzhenggang/xiongzhenggang.github.io | AI/data/deeplearning24054/planar_utils.py | 2 | 2271 | import matplotlib.pyplot as plt
import numpy as np
import sklearn
import sklearn.datasets
import sklearn.linear_model
def plot_decision_boundary(model, X, Y):
# Set min and max values and give it some padding
x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
y_min, y_max = X[1, :].min() - 1, X[1, :].max(... | gpl-3.0 |
airanmehr/bio | Scripts/TimeSeriesPaper/Plot/topSNPs.py | 1 | 1589 | '''
Copyleft Oct 14, 2016 Arya Iranmehr, PhD Student, Bafna Lab, UC San Diego, Email: airanmehr@gmail.com
'''
import numpy as np;
np.set_printoptions(linewidth=200, precision=5, suppress=True)
import pandas as pd;
pd.options.display.max_rows = 20;
pd.options.display.expand_frame_repr = False
import seaborn as sns
im... | mit |
sighingnow/sighingnow.github.io | resource/k_nearest_neighbors/dating.py | 1 | 3622 | #! /usr/bin/env python
# -*- coding: utf-8
'''
Name: dating.py(KNN algorithm)
Training and test dataset: dating.txt
Created on Feb 8, 2015
@author: Tao He
'''
__author__ = 'Tao He'
from numpy import array as nmarray
from matplotlib import pyplot as plt
LABEL_MAP = {
'didntLike': 1,
'sma... | mit |
ZenDevelopmentSystems/scikit-learn | sklearn/cluster/mean_shift_.py | 96 | 15434 | """Mean shift clustering algorithm.
Mean shift clustering aims to discover *blobs* in a smooth density of
samples. It is a centroid based algorithm, which works by updating candidates
for centroids to be the mean of the points within a given region. These
candidates are then filtered in a post-processing stage to elim... | bsd-3-clause |
Obus/scikit-learn | benchmarks/bench_plot_approximate_neighbors.py | 85 | 6377 | """
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 |
nightjean/Deep-Learning | tensorflow/examples/learn/text_classification_character_rnn.py | 61 | 3350 | # 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 |
cswiercz/sympy | sympy/physics/quantum/state.py | 58 | 29186 | """Dirac notation for states."""
from __future__ import print_function, division
from sympy import (cacheit, conjugate, Expr, Function, integrate, oo, sqrt,
Tuple)
from sympy.core.compatibility import u, range
from sympy.printing.pretty.stringpict import stringPict
from sympy.physics.quantum.qexpr ... | bsd-3-clause |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/doc/mpl_examples/pylab_examples/demo_bboximage.py | 12 | 1805 | import matplotlib.pyplot as plt
import numpy as np
from matplotlib.image import BboxImage
from matplotlib.transforms import Bbox, TransformedBbox
if __name__ == "__main__":
fig = plt.figure(1)
ax = plt.subplot(121)
txt = ax.text(0.5, 0.5, "test", size=30, ha="center", color="w")
kwargs = dict()
... | gpl-2.0 |
bikong2/scikit-learn | sklearn/tests/test_discriminant_analysis.py | 19 | 11711 | try:
# Python 2 compat
reload
except NameError:
# Regular Python 3+ import
from importlib import reload
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.t... | bsd-3-clause |
kgullikson88/General | Analyze_CCF.py | 1 | 9048 | """
This is a module to read in an HDF5 file with CCFs.
Use this to determine the best parameters, and plot the best CCF for each star/date
"""
from collections import defaultdict
import logging
import h5py
import numpy as np
import pandas as pd
from scipy.interpolate import InterpolatedUnivariateSpline as spline
cl... | gpl-3.0 |
Obus/scikit-learn | sklearn/setup.py | 225 | 2856 | import os
from os.path import join
import warnings
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info, BlasNotFoundError
import numpy
libraries = []
if os.name == 'posix':
libraries.appe... | bsd-3-clause |
cle1109/scot | doc/sphinxext/inheritance_diagram.py | 4 | 13650 | """
Defines a docutils directive for inserting inheritance diagrams.
Provide the directive with one or more classes or modules (separated
by whitespace). For modules, all of the classes in that module will
be used.
Example::
Given the following classes:
class A: pass
class B(A): pass
class C(A): pass
... | mit |
great-expectations/great_expectations | tests/dataset/test_sparkdfdataset.py | 1 | 14191 | import importlib.util
import json
from unittest import mock
import pandas as pd
import pytest
from great_expectations.dataset.sparkdf_dataset import SparkDFDataset
from great_expectations.util import is_library_loadable
def test_sparkdfdataset_persist(spark_session):
df = pd.DataFrame({"a": [1, 2, 3]})
sdf ... | apache-2.0 |
hdmetor/scikit-learn | examples/text/hashing_vs_dict_vectorizer.py | 284 | 3265 | """
===========================================
FeatureHasher and DictVectorizer Comparison
===========================================
Compares FeatureHasher and DictVectorizer by using both to vectorize
text documents.
The example demonstrates syntax and speed only; it doesn't actually do
anything useful with the e... | bsd-3-clause |
davidtrem/ThunderStorm | thunderstorm/lightning/utils.py | 1 | 5027 | # -*- coding: utf-8 -*-
# Copyright (C) 2010-2013 Trémouilles David
#This file is part of Thunderstorm.
#
#ThunderStrom 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, either version 3 of the License, o... | gpl-3.0 |
klahnakoski/ActiveData | vendor/mo_testing/fuzzytestcase.py | 1 | 9712 | # encoding: utf-8
#
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this file,
# You can obtain one at http://mozilla.org/MPL/2.0/.
#
# Contact: Kyle Lahnakoski (kyle@lahnakoski.com)
#
from __future__ import unicode_literals
impor... | mpl-2.0 |
tbullmann/heuhaufen | publication/generators_and_depth/aggregate.py | 1 | 5320 | import os
import pandas
import numpy as np
from bokeh.palettes import Viridis4 as palette
from bokeh.layouts import layout, column, row
from bokeh.plotting import figure, output_file, show, ColumnDataSource
from bokeh.models import HoverTool, Div, DataTable, TableColumn, NumberFormatter, LinearAxis, Select, CustomJS, ... | mit |
attia42/twitter_word2vec | kmeans/experimentm.py | 1 | 3559 | import csv
import nltk
from nltk.tokenize import word_tokenize
import string
from nltk import pos_tag
from gensim.models.word2vec import Word2Vec
from gensim import matutils
from numpy import array, float32 as REAL
from sklearn.cluster import MiniBatchKMeans, KMeans
from multiprocessing import Pool
from collections imp... | mit |
mtconley/turntable | test/lib/python2.7/site-packages/scipy/interpolate/fitpack2.py | 7 | 57978 | """
fitpack --- curve and surface fitting with splines
fitpack is based on a collection of Fortran routines DIERCKX
by P. Dierckx (see http://www.netlib.org/dierckx/) transformed
to double routines by Pearu Peterson.
"""
# Created by Pearu Peterson, June,August 2003
from __future__ import division, print_function, abs... | mit |
nilbody/h2o-3 | h2o-py/tests/testdir_golden/pyunit_svd_1_golden.py | 1 | 2402 | from __future__ import print_function
from builtins import zip
import sys
sys.path.insert(1,"../../")
import h2o
from tests import pyunit_utils
def svd_1_golden():
print("Importing USArrests.csv data...")
arrestsH2O = h2o.upload_file(pyunit_utils.locate("smalldata/pca_test/USArrests.csv"))
print("Co... | apache-2.0 |
UKPLab/emnlp2017-claim-identification | src/main/python/process_data_se_WithDevel.py | 1 | 4976 | import cPickle
import numpy as np
import pandas as pd
import re
import sys
from collections import defaultdict
def build_data_cv(data_folder, cv=10, clean_string=True):
"""
Loads data.
"""
revs = []
pos_file = data_folder[0] # train file
neg_file = data_folder[1] # test file
devel_file = d... | apache-2.0 |
imatge-upc/saliency | shallow/train.py | 2 | 3064 | # add to kfkd.py
from lasagne import layers
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet,BatchIterator
import os
import numpy as np
from sklearn.utils import shuffle
import cPickle as pickle
import matplotlib.pyplot as plt
import Image
import ImageOps
from scipy import misc
import... | mit |
bromjiri/Presto | predictor/predictor_new.py | 1 | 8137 | import settings
import pandas as pd
import numpy as np
import os
from datetime import datetime
from datetime import timedelta
import predictor.predictor_classifier as cls
import predictor.predictor_statistic as stat
import random
import nltk
class Stock:
def __init__(self, subject):
input_file = settings... | mit |
dingmingliu/quanttrade | bt/core.py | 1 | 37660 | """
Contains the core building blocks of the framework.
"""
import math
from copy import deepcopy
import pandas as pd
import numpy as np
import cython as cy
class Node(object):
"""
The Node is the main building block in bt's tree structure design.
Both StrategyBase and SecurityBase inherit Node. It cont... | apache-2.0 |
MikeLing/shogun | examples/undocumented/python/graphical/interactive_svm_demo.py | 6 | 12586 | """
Shogun demo, based on PyQT Demo by Eli Bendersky
Christian Widmer
Soeren Sonnenburg
License: GPLv3
"""
import numpy
import sys, os, csv
from PyQt4.QtCore import *
from PyQt4.QtGui import *
import matplotlib
from matplotlib.colorbar import make_axes, Colorbar
from matplotlib.backends.backend_qt4agg import FigureCa... | gpl-3.0 |
jlcarmic/producthunt_simulator | venv/lib/python2.7/site-packages/scipy/integrate/odepack.py | 62 | 9420 | # Author: Travis Oliphant
from __future__ import division, print_function, absolute_import
__all__ = ['odeint']
from . import _odepack
from copy import copy
import warnings
class ODEintWarning(Warning):
pass
_msgs = {2: "Integration successful.",
1: "Nothing was done; the integration time was 0.",
... | mit |
blab/antibody-response-pulse | bcell-array/code/Virus_Bcell_IgM_IgG_Infection_OAS_new.py | 1 | 13195 |
# coding: utf-8
# # Antibody Response Pulse
# https://github.com/blab/antibody-response-pulse
#
# ### B-cells evolution --- cross-reactive antibody response after influenza virus infection or vaccination
# ### Adaptive immune response for repeated infection
# In[3]:
'''
author: Alvason Zhenhua Li
date: 04/09/201... | gpl-2.0 |
adhix11/pmtk3 | python/demos/linregDemo1.py | 26 | 1104 | #!/usr/bin/python2.4
import numpy
import scipy.stats
import matplotlib.pyplot as plt
def main():
# true parameters
w = 2
w0 = 3
sigma = 2
# make data
numpy.random.seed(1)
Ntrain = 20
xtrain = numpy.linspace(0,10,Ntrain)
ytrain = w*xtrain + w0 + numpy.random.random(Ntrain)*sigma
... | mit |
marcusmueller/gnuradio | gr-filter/examples/fft_filter_ccc.py | 7 | 4367 | #!/usr/bin/env python
#
# Copyright 2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio 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, or (at your option)
# ... | gpl-3.0 |
devs1991/test_edx_docmode | venv/lib/python2.7/site-packages/sklearn/lda.py | 3 | 9301 | """
The :mod:`sklearn.lda` module implements Linear Discriminant Analysis (LDA).
"""
# Authors: Matthieu Perrot
# Mathieu Blondel
import warnings
import numpy as np
from scipy import linalg
from .base import BaseEstimator, ClassifierMixin, TransformerMixin
from .utils.extmath import logsumexp
from .utils.fi... | agpl-3.0 |
grimfang/panda3d | samples/carousel/main.py | 25 | 9571 | #!/usr/bin/env python
# Author: Shao Zhang, Phil Saltzman, and Eddie Canaan
# Last Updated: 2015-03-13
#
# This tutorial will demonstrate some uses for intervals in Panda
# to move objects in your panda world.
# Intervals are tools that change a value of something, like position,
# rotation or anything else, linearly,... | bsd-3-clause |
harisbal/pandas | pandas/core/tools/datetimes.py | 4 | 30680 | from functools import partial
from datetime import datetime, time
from collections import MutableMapping
import numpy as np
from pandas._libs import tslib, tslibs
from pandas._libs.tslibs.strptime import array_strptime
from pandas._libs.tslibs import parsing, conversion, Timestamp
from pandas._libs.tslibs.parsing imp... | bsd-3-clause |
ttthy1/2017sejongAI | week14/Mnist.py | 1 | 2273 | # Lab 7 Learning rate and Evaluation
import tensorflow as tf
import random
import matplotlib.pyplot as plt
tf.set_random_seed(777) # for reproducibility
from tensorflow.examples.tutorials.mnist import input_data
# Check out https://www.tensorflow.org/get_started/mnist/beginners for
# more information about the mnist d... | gpl-3.0 |
jaidevd/scikit-learn | sklearn/externals/joblib/testing.py | 23 | 3042 | """
Helper for testing.
"""
import sys
import warnings
import os.path
import re
import subprocess
import threading
from sklearn.externals.joblib._compat import PY3_OR_LATER
def warnings_to_stdout():
""" Redirect all warnings to stdout.
"""
showwarning_orig = warnings.showwarning
def showwarning(msg... | bsd-3-clause |
trungnt13/scikit-learn | sklearn/feature_selection/__init__.py | 244 | 1088 | """
The :mod:`sklearn.feature_selection` module implements feature selection
algorithms. It currently includes univariate filter selection methods and the
recursive feature elimination algorithm.
"""
from .univariate_selection import chi2
from .univariate_selection import f_classif
from .univariate_selection import f_... | bsd-3-clause |
Micket/CCBuilder | make_cc.py | 1 | 8680 | #!/usr/bin/env python3
from __future__ import print_function
from __future__ import division
import argparse
import pickle
import time
import CCBuilder as ccb
import CCBuilder_c as ccb_c
import numpy as np
import scipy.special
def uniform_dist(x):
""" Returns uniform distributions of given range """
return la... | gpl-3.0 |
andersgs/dingo | dingo/random_forest.py | 1 | 2551 | '''
Some functions to fit a random forest
'''
import sklearn.ensemble
import pandas
import progressbar
bar = progressbar.ProgressBar()
def test_max_features(max_features):
if (max_features not in ['sqrt', 'auto', 'log2', None]):
try:
max_features = int(max_features)
except ValueError:... | bsd-3-clause |
BlueBrain/NEST | testsuite/manualtests/cross_check_test_mip_corrdet.py | 13 | 2594 | # -*- coding: utf-8 -*-
#
# cross_check_test_mip_corrdet.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 o... | gpl-2.0 |
NicovincX2/Python-3.5 | Algèbre/Opération/scalar_product.py | 1 | 1933 | # -*- coding: utf-8 -*-
import os
import seaborn
seaborn.set()
colors = seaborn.color_palette()
import utils
# For 3D plotting we need to import some extra stuff
from mpl_toolkits.mplot3d import Axes3D
# First create two random vectors in 3 dimensional space
v1 = rand(3, 1)
v2 = rand(3, 1)
# And scale them to uni... | gpl-3.0 |
jcasner/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/path.py | 69 | 20263 | """
Contains a class for managing paths (polylines).
"""
import math
from weakref import WeakValueDictionary
import numpy as np
from numpy import ma
from matplotlib._path import point_in_path, get_path_extents, \
point_in_path_collection, get_path_collection_extents, \
path_in_path, path_intersects_path, con... | agpl-3.0 |
giorgiop/scikit-learn | sklearn/linear_model/__init__.py | 83 | 3139 | """
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 |
nik-hil/fastai | deeplearning2/rossman_exp.py | 10 | 5451 | train_ratio=0.9
use_dict=True
use_scaler=False
init_emb=False
split_contins=True
samp_size = 100000
#samp_size = 0
import math, keras, datetime, pandas as pd, numpy as np, keras.backend as K
import matplotlib.pyplot as plt, xgboost, operator, random, pickle, os
from sklearn_pandas import DataFrameMapper
from sklearn.p... | apache-2.0 |
adiIspas/Machine-Learning_A-Z | Machine Learning A-Z/Part 7 - Natural Language Processing/Section 36 - Natural Language Processing/natural_language_processing.py | 3 | 1452 | # Natural Language Processing
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\t', quoting = 3)
# Cleaning the texts
import re
import nltk
nltk.download('stopwords')
from nltk.corpus ... | mit |
hsuantien/scikit-learn | sklearn/metrics/regression.py | 23 | 16771 | """Metrics to assess performance on regression task
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.fr>
# Ma... | bsd-3-clause |
yunfeilu/scikit-learn | sklearn/decomposition/tests/test_sparse_pca.py | 160 | 6028 | # Author: Vlad Niculae
# License: BSD 3 clause
import sys
import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import ass... | bsd-3-clause |
winklerand/pandas | pandas/tests/reshape/test_merge_ordered.py | 2 | 2966 | import pandas as pd
from pandas import DataFrame, merge_ordered
from pandas.util import testing as tm
from pandas.util.testing import assert_frame_equal
from numpy import nan
class TestMergeOrdered(object):
def setup_method(self, method):
self.left = DataFrame({'key': ['a', 'c', 'e'],
... | bsd-3-clause |
nomadcube/scikit-learn | examples/bicluster/plot_spectral_coclustering.py | 276 | 1736 | """
==============================================
A demo of the Spectral Co-Clustering algorithm
==============================================
This example demonstrates how to generate a dataset and bicluster it
using the the Spectral Co-Clustering algorithm.
The dataset is generated using the ``make_biclusters`` f... | bsd-3-clause |
tayebzaidi/snova_analysis | Miscellaneous/typ1a_features.py | 1 | 2252 | import matplotlib.pyplot as plt
import scipy.interpolate as scinterp
import numpy as np
import peakfinding
import peak_original
import smoothing
import plotter
import random
import readin
import sys
import os
if __name__== '__main__':
Mbdata = []
delM15data = []
path = "/Users/zaidi/Documents/REU/restframe... | gpl-3.0 |
billbrod/spatial-frequency-preferences | sfp/image_computable.py | 1 | 6815 | #!/usr/bin/python
"""code to help run the image-computable version of the model
we're using this primarily to check the effect of vignetting, but this does make our project
image-computable (though it's a linear model and so will fail in some trivial cases)
"""
import itertools
import argparse
import numpy as np
impo... | mit |
IshankGulati/scikit-learn | sklearn/feature_selection/variance_threshold.py | 123 | 2572 | # Author: Lars Buitinck
# 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(BaseEstimator, SelectorMixin):
... | bsd-3-clause |
Winand/pandas | pandas/tests/io/formats/test_eng_formatting.py | 22 | 8085 | import numpy as np
import pandas as pd
from pandas import DataFrame
from pandas.compat import u
import pandas.io.formats.format as fmt
from pandas.util import testing as tm
class TestEngFormatter(object):
def test_eng_float_formatter(self):
df = DataFrame({'A': [1.41, 141., 14100, 1410000.]})
fm... | bsd-3-clause |
gmatteo/pymatgen | pymatgen/io/gaussian.py | 2 | 59623 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module implements input and output processing from Gaussian.
"""
import re
import warnings
import numpy as np
import scipy.constants as cst
from monty.io import zopen
from pymatgen.core.composition ... | mit |
CalvinNeo/EasyMLPlatform | py/graphic/tree.py | 1 | 4067 | #coding:utf8
import numpy as np
import math
import pylab as pl
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import json
class GraphTree:
def __init__(self):
self.jsonobj = {}
self.leafNode = dict(boxstyle = 'roun... | apache-2.0 |
khiner/aubio | python/demos/demo_waveform_plot.py | 10 | 2099 | #! /usr/bin/env python
import sys
from aubio import pvoc, source
from numpy import zeros, hstack
def get_waveform_plot(filename, samplerate = 0, block_size = 4096, ax = None, downsample = 2**4):
import matplotlib.pyplot as plt
if not ax:
fig = plt.figure()
ax = fig.add_subplot(111)
hop_s =... | gpl-3.0 |
Ziqi-Li/bknqgis | pandas/pandas/tests/io/test_packers.py | 7 | 31902 | import pytest
from warnings import catch_warnings
import os
import datetime
import numpy as np
import sys
from distutils.version import LooseVersion
from pandas import compat
from pandas.compat import u, PY3
from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
date_range, period_... | gpl-2.0 |
jeffzheng1/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py | 14 | 3569 | # 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 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.