repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
Adai0808/scikit-learn | sklearn/grid_search.py | 32 | 36586 | """
The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters
of an estimator.
"""
from __future__ import print_function
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# ... | bsd-3-clause |
DistrictDataLabs/machine-learning | code/utils.py | 1 | 4163 | # utils
# Utility functions for handling data
#
# Author: Benjamin Bengfort <bbengfort@districtdatalabs.com>
# Created: Thu Feb 26 17:47:35 2015 -0500
#
# Copyright (C) 2015 District Data Labs
# For license information, see LICENSE.txt
#
# ID: utils.py [] benjamin@bengfort.com $
"""
Utility functions for handling d... | mit |
mugizico/scikit-learn | examples/neighbors/plot_species_kde.py | 282 | 4059 | """
================================================
Kernel Density Estimate of Species Distributions
================================================
This shows an example of a neighbors-based query (in particular a kernel
density estimate) on geospatial data, using a Ball Tree built upon the
Haversine distance metric... | bsd-3-clause |
JiaMingLin/de-identification | test/test_common.py | 1 | 3809 | import common.constant as c
from common.data_utilities import DataUtils
from common.base import Base
from django.test import TestCase
TESTING_FILE = c.TEST_ORIGIN_DATA_PATH
class DataUtilitiesTests(TestCase):
# TODO: The Data Coarse and Generalize step should seperate, to simulate a more real case.
def setUp(self)... | apache-2.0 |
fredhusser/scikit-learn | sklearn/covariance/__init__.py | 389 | 1157 | """
The :mod:`sklearn.covariance` module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.
"""
from ... | bsd-3-clause |
brentjm/Impurity-Predictions | server/package/desiccant.py | 1 | 4140 | """
Desiccant class
Brent Maranzano
2016-04-16
"""
import pandas as pd
class Desiccant(object):
"""
Define the desiccant inside the container (e.g. type, amount, water...etc).
Class Attributes
ID : string - unique identification number to lookup parameters
name : string - Desiccant material.
... | bsd-2-clause |
ryanpdwyer/myscipkg2 | setup.py | 1 | 1557 | # -*- coding: utf-8 -*-
import sys
import io
try:
from setuptools import setup, find_packages
except ImportError:
print("Please install or upgrade setuptools or pip")
sys.exit(1)
readme = io.open('README.rst', mode='r', encoding='utf-8').read()
doclink = """
Documentation
-------------
The full documenta... | mit |
bgris/ODL_bgris | lib/python3.5/site-packages/ipykernel/pylab/config.py | 10 | 4485 | """Configurable for configuring the IPython inline backend
This module does not import anything from matplotlib.
"""
#-----------------------------------------------------------------------------
# Copyright (C) 2011 The IPython Development Team
#
# Distributed under the terms of the BSD License. The full lice... | gpl-3.0 |
karoraw1/GLM_Wrapper | bin/CleanUpMetaboliteData.py | 1 | 1977 | # -*- coding: utf-8 -*-
"""
Created on Mon May 16 15:47:12 2016
@author: login
"""
import pandas as pd
import numpy as np
import os, sys
def strip(text):
try:
return text.strip()
except AttributeError:
return text
data_fn = "MysticLakeIons_Clean.csv"
data2_fn = "MysticLakeIons_Part2_Clean... | mit |
StratsOn/zipline | tests/test_rolling_panel.py | 20 | 7005 | #
# 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 |
madjelan/scikit-learn | sklearn/ensemble/tests/test_gradient_boosting.py | 127 | 37672 | """
Testing for the gradient boosting module (sklearn.ensemble.gradient_boosting).
"""
import warnings
import numpy as np
from sklearn import datasets
from sklearn.base import clone
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble.grad... | bsd-3-clause |
cython-testbed/pandas | pandas/core/indexes/timedeltas.py | 1 | 26186 | """ implement the TimedeltaIndex """
from datetime import datetime
import numpy as np
from pandas.core.dtypes.common import (
_TD_DTYPE,
is_integer,
is_float,
is_list_like,
is_scalar,
is_timedelta64_dtype,
is_timedelta64_ns_dtype,
pandas_dtype,
ensure_int64)
from pandas.core.dtypes.... | bsd-3-clause |
mifads/pyscripts | emxgeo/readKoeppenGeiger.py | 1 | 3213 | #!/usr/bin/env python3
from collections import OrderedDict as odict
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
home=os.environ['HOME']
ifile= home + '/Work/LANDUSE/KoeppenGeiger/Koeppen-Geiger-ASCII.txt'
# Mar 2019, not used (yet)
#KG_boreal = 1
#KG_temperate = 2
#KG_medit = 3
#KG_tropical... | gpl-3.0 |
hyperspy/hyperspy | hyperspy/tests/signals/test_2D_tools.py | 2 | 6999 | # Copyright 2007-2021 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 your option) any later ve... | gpl-3.0 |
miloharper/neural-network-animation | matplotlib/tests/test_text.py | 9 | 12004 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import warnings
import numpy as np
from numpy.testing import assert_almost_equal
from nose.tools import eq_
from matplotlib.transforms import Bbox
import matplotlib
import matplotlib.pyplot as plt
... | mit |
Scapogo/zipline | tests/finance/test_transaction.py | 5 | 1137 | #
# Copyright 2017 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 |
untom/scikit-learn | examples/cluster/plot_cluster_comparison.py | 246 | 4684 | """
=========================================================
Comparing different clustering algorithms on toy datasets
=========================================================
This example aims at showing characteristics of different
clustering algorithms on datasets that are "interesting"
but still in 2D. The last ... | bsd-3-clause |
gustable/Img-Recog | nl_dbn.py | 1 | 1729 |
import cPickle
import numpy as np
from sklearn.externals import joblib
from nolearn.dbn import DBN
def load(name):
with open(name, 'rb') as f:
return cPickle.load(f)
dataset1 = load('/home/gp/data/cifar-10-batches-py/data_batch_1')
dataset2 = load('/home/gp/data/cifar-10-batches-py/data_batch_2')
dataset... | mit |
YubinXie/Computational-Pathology | Sample_Preprocess.py | 1 | 5387 | import cv2
import numpy as np
from matplotlib import pyplot as plt
import matplotlib.patches as mpatches
from skimage.morphology import thin, closing, square
from skimage.util import invert
from skimage.color import rgb2gray, label2rgb
from skimage import data,img_as_ubyte
from skimage.filters import threshold_otsu
fro... | gpl-2.0 |
JPFrancoia/scikit-learn | sklearn/utils/tests/test_metaestimators.py | 86 | 2304 | from sklearn.utils.testing import assert_true, assert_false
from sklearn.utils.metaestimators import if_delegate_has_method
class Prefix(object):
def func(self):
pass
class MockMetaEstimator(object):
"""This is a mock meta estimator"""
a_prefix = Prefix()
@if_delegate_has_method(delegate="a... | bsd-3-clause |
Insight-book/data-science-from-scratch | first-edition-ko/code-python3/gradient_descent.py | 12 | 5816 | from collections import Counter
from linear_algebra import distance, vector_subtract, scalar_multiply
from functools import reduce
import math, random
def sum_of_squares(v):
"""computes the sum of squared elements in v"""
return sum(v_i ** 2 for v_i in v)
def difference_quotient(f, x, h):
return (f(x + h)... | unlicense |
antoinecarme/sklearn2sql_heroku | tests/databases/test_client_bad_model.py | 1 | 1084 |
import pickle, json, requests, base64
from sklearn import datasets
iris = datasets.load_iris()
X = iris.data
Y = iris.target
# print(iris.DESCR)
from sklearn.neural_network import MLPClassifier
clf = MLPClassifier()
clf.fit(X, Y)
def test_ws_sql_gen(pickle_data):
WS_URL="https://sklearn2sql.herokuapp.com/mo... | bsd-3-clause |
CVML/scikit-learn | examples/cluster/plot_segmentation_toy.py | 258 | 3336 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solve... | bsd-3-clause |
djgagne/scikit-learn | examples/cluster/plot_affinity_propagation.py | 349 | 2304 | """
=================================================
Demo of affinity propagation clustering algorithm
=================================================
Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
"""
print(__doc__)
from sklearn.cluster impor... | bsd-3-clause |
waterponey/scikit-learn | sklearn/manifold/setup.py | 43 | 1283 | import os
from os.path import join
import numpy
from numpy.distutils.misc_util import Configuration
from sklearn._build_utils import get_blas_info
def configuration(parent_package="", top_path=None):
config = Configuration("manifold", parent_package, top_path)
libraries = []
if os.name == 'posix':
... | bsd-3-clause |
carpyncho/feets | res/paper/reports/features_montecarlo.py | 1 | 1591 |
import sys
import time as tmod
import warnings
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import pandas as pd
warnings.simplefilter("ignore")
sys.path.insert(0, "../FATS/")
import FATS
iterations = 100000
lc_size = 1000
random = np.random.RandomState(42)
results = {
... | mit |
KshitijT/fundamentals_of_interferometry | data/scripts/plotUVcoverage.py | 4 | 3438 | #!/usr/bin/python
"""Plot UVW positions from an MS"""
import matplotlib
matplotlib.rc('xtick', labelsize=15)
matplotlib.rc('ytick', labelsize=15)
import numpy as np
import matplotlib.pyplot as plt
import sys
import pyrap.tables as tbls
cc = 299792458.
if __name__ == '__main__': ... | gpl-2.0 |
lmallin/coverage_test | python_venv/lib/python2.7/site-packages/pandas/errors/__init__.py | 6 | 1478 | # flake8: noqa
""" expose public exceptions & warnings """
from pandas._libs.tslib import OutOfBoundsDatetime
class PerformanceWarning(Warning):
"""
Warnings shown when there is a possible performance
impact.
"""
class UnsupportedFunctionCall(ValueError):
"""
If attempting to call a numpy f... | mit |
Djabbz/scikit-learn | doc/sphinxext/gen_rst.py | 106 | 40198 | """
Example generation for the scikit learn
Generate the rst files for the examples by iterating over the python
example files.
Files that generate images should start with 'plot'
"""
from __future__ import division, print_function
from time import time
import ast
import os
import re
import shutil
import traceback
i... | bsd-3-clause |
ishanic/scikit-learn | examples/linear_model/plot_sgd_separating_hyperplane.py | 260 | 1219 | """
=========================================
SGD: Maximum margin separating hyperplane
=========================================
Plot the maximum margin separating hyperplane within a two-class
separable dataset using a linear Support Vector Machines classifier
trained using SGD.
"""
print(__doc__)
import numpy as n... | bsd-3-clause |
hyperspy/hyperspy | hyperspy/tests/learn/test_cluster.py | 2 | 16658 | # -*- coding: utf-8 -*-
# Copyright 2007-2021 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 |
FluVigilanciaBR/seasonality | methods/data_filter/contingency_level.py | 1 | 8367 | # coding:utf8
__author__ = 'Marcelo Ferreira da Costa Gomes'
import pandas as pd
import numpy as np
from fludashboard.libs.flu_data import FluDB
db = FluDB()
def get_all_territories_and_years(filtertype: str='srag'):
table_suffix = {
'srag': '',
'sragnofever': '_sragnofever',
'hospdeath... | gpl-3.0 |
chrisdembia/agent-bicycle | randlov1998/analysis.py | 1 | 2255 | """Functions for plotting results, etc.
"""
import numpy as np
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import pylab as pl
from scipy import r_
from pybrain.tools.customxml.networkreader import NetworkReader
from pybrain.utilities import one_to_n
def plot_nfq_action_value_history(netw... | mit |
Evfro/polara | polara/datasets/yahoo.py | 1 | 1594 | import tarfile
import pandas as pd
def get_yahoo_music_data(path=None, fileid=0, include_test=True, read_attributes=False, read_genres=False):
res = []
if path:
data_folder = 'ydata-ymusic-user-song-ratings-meta-v1_0'
col_names = ['userid', 'songid', 'rating']
with tarfile.open(path, 'r... | mit |
xiaoxiamii/scikit-learn | examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py | 218 | 3893 | """
==============================================
Feature agglomeration vs. univariate selection
==============================================
This example compares 2 dimensionality reduction strategies:
- univariate feature selection with Anova
- feature agglomeration with Ward hierarchical clustering
Both metho... | bsd-3-clause |
MiguelAguilera/critical-learning | Mountain-Car/train.py | 1 | 7919 | #!/usr/bin/env python
from embodied_ising import ising
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# plt.switch_backend('agg')
import ising_correlations as ic
from copy import copy
import numpy.random as rnd
plt.rc('text', usetex=True)
font = {'family': 'serif', 'size': 15, 'serif': ['comput... | gpl-3.0 |
neskk/PokemonGo-Map | pogom/geofence.py | 14 | 5762 | #!/usr/bin/python
# -*- coding: utf-8 -*-
import sys
import timeit
import logging
from .utils import get_args
log = logging.getLogger(__name__)
args = get_args()
# Trying to import matplotlib, which is not compatible with all hardware.
# Matlplotlib is faster for big calculations.
try:
from matplotlib.path imp... | agpl-3.0 |
vortex-ape/scikit-learn | sklearn/tests/test_init.py | 4 | 2258 | # Basic unittests to test functioning of module's top-level
import subprocess
import pkgutil
import pytest
import sklearn
from sklearn.utils.testing import assert_equal
__author__ = 'Yaroslav Halchenko'
__license__ = 'BSD'
try:
from sklearn import * # noqa
_top_import_error = None
except Exception as e:... | bsd-3-clause |
Tomasuh/Tomasuh.github.io | files/cyclic/analyse.py | 1 | 1922 | import pandas
import sqlite3
import dbcommands
import numpy as np
import time
import matplotlib.pyplot as plt
db_obj = dbcommands.the_db()
posts = db_obj.fetch_posts()
df = pandas.DataFrame(data=posts, columns = ["key",\
"title",\
"user",\
"date",\
"size",\
... | mit |
cwhanse/pvlib-python | pvlib/atmosphere.py | 3 | 24256 | """
The ``atmosphere`` module contains methods to calculate relative and
absolute airmass and to determine pressure from altitude or vice versa.
"""
from warnings import warn
import numpy as np
import pandas as pd
APPARENT_ZENITH_MODELS = ('simple', 'kasten1966', 'kastenyoung1989',
'gueyma... | bsd-3-clause |
Ambuj-UF/ConCat-1.0 | src/Utils/Bio/Phylo/_utils.py | 1 | 20969 | # Copyright (C) 2009 by Eric Talevich (eric.talevich@gmail.com)
# This code is part of the Biopython distribution and governed by its
# license. Please see the LICENSE file that should have been included
# as part of this package.
import sys
# Add path to Bio
sys.path.append('../..')
"""Utilities for handling, displa... | gpl-2.0 |
jpautom/scikit-learn | benchmarks/bench_plot_omp_lars.py | 266 | 4447 | """Benchmarks of orthogonal matching pursuit (:ref:`OMP`) versus least angle
regression (:ref:`least_angle_regression`)
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model impo... | bsd-3-clause |
FourthCohortAwesome/NightThree | exercise_gd.py | 1 | 1821 | import pandas as pd
import csv
def length_3(data):
"""
This function reads a .csv file and to determines corrections
:param data: csv file
:return: write a new file without missing values
"""
df = pd.read_csv(data)
headings = ['One', 'Two', 'Three', 'Four', 'Five', 'Six', 'Seven']
row ... | mit |
rochefort-lab/fissa | fissa/neuropil.py | 1 | 9035 | """
Functions for removal of neuropil from calcium signals.
Authors:
- Sander W Keemink <swkeemink@scimail.eu>
- Scott C Lowe <scott.code.lowe@gmail.com>
Created:
2015-05-15
"""
import numpy as np
import numpy.random as rand
import sklearn.decomposition
def separate(
S,
sep_method="nmf",
n=N... | gpl-3.0 |
loli/sklearn-ensembletrees | benchmarks/bench_sgd_regression.py | 14 | 4594 | """
Benchmark for SGD regression
Compares SGD regression against coordinate descent and Ridge
on synthetic data.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# License: BSD 3 clause
import numpy as np
import pylab as pl
import gc
from time import time
from sklearn.linear_model i... | bsd-3-clause |
jbogaardt/chainladder-python | chainladder/tails/bondy.py | 1 | 5896 | # 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 https://mozilla.org/MPL/2.0/.
import numpy as np
import pandas as pd
from scipy.optimize import least_squares
from chainladder.tails import TailBase
f... | mit |
meduz/scikit-learn | examples/model_selection/grid_search_text_feature_extraction.py | 99 | 4163 |
"""
==========================================================
Sample pipeline for text feature extraction and evaluation
==========================================================
The dataset used in this example is the 20 newsgroups dataset which will be
automatically downloaded and then cached and reused for the d... | bsd-3-clause |
ElDeveloper/scikit-learn | examples/cluster/plot_agglomerative_clustering_metrics.py | 402 | 4492 | """
Agglomerative clustering with different metrics
===============================================
Demonstrates the effect of different metrics on the hierarchical clustering.
The example is engineered to show the effect of the choice of different
metrics. It is applied to waveforms, which can be seen as
high-dimens... | bsd-3-clause |
GoogleCloudPlatform/professional-services | tools/ml-auto-eda/ml_eda/analysis/quantitative_analyzer.py | 1 | 5502 | # Copyright 2019 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 or ... | apache-2.0 |
gfyoung/pandas | pandas/tests/test_sorting.py | 2 | 18315 | from collections import defaultdict
from datetime import datetime
from itertools import product
import numpy as np
import pytest
from pandas import DataFrame, MultiIndex, Series, array, concat, merge
import pandas._testing as tm
from pandas.core.algorithms import safe_sort
import pandas.core.common as com
from pandas... | bsd-3-clause |
karstenw/nodebox-pyobjc | examples/Extended Application/matplotlib/examples/misc/findobj_demo.py | 1 | 1660 | """
============
Findobj Demo
============
Recursively find all objects that match some criteria
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.text as text
# nodebox section
if __name__ == '__builtin__':
# were in nodebox
import os
import tempfile
W = 800
inset = 20
... | mit |
dyermd/legos | marketing/temp.py | 1 | 1914 | #generate all the plots for the market project
from optparse import OptionParser
import matplotlib.pyplot as plt
__author__ = 'mattdyer'
# add labels to a plot
# @param points The plot object
# @param axis The axis object
def autolabel(points, axis, data):
# attach some text labels
for i, point in enumerate... | gpl-2.0 |
itu-oss-project-team/oss-github-analysis-project | github_analysis_tool/analyzer/classification.py | 1 | 6943 | import collections
import numpy as np
import os.path
from sklearn import neighbors
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.model_selection import *
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
from github_analysis_tool.servic... | mit |
zooniverse/aggregation | experimental/penguins/clusterAnalysis/distance_.py | 2 | 3492 | #!/usr/bin/env python
__author__ = 'greghines'
import numpy as np
import os
import sys
import cPickle as pickle
import math
import matplotlib.pyplot as plt
import pymongo
import urllib
import matplotlib.cbook as cbook
if os.path.exists("/home/ggdhines"):
sys.path.append("/home/ggdhines/PycharmProjects/reduction/ex... | apache-2.0 |
sstoma/CellProfiler | cellprofiler/modules/colortogray.py | 2 | 21721 | '''
<b> Color to Gray</b> converts an image with three color channels to a set of individual
grayscale images.
<hr>
This module converts RGB (Red, Green, Blue) color images to grayscale. All channels
can be merged into one grayscale image (<i>Combine</i>), or each channel
can be extracted into a separate grayscale im... | gpl-2.0 |
xavierfav/freesound-python | manager.py | 1 | 75825 | """
Upgrade of the python client for freesound
Find the API documentation at http://www.freesound.org/docs/api/.
This lib provides method for managing files and data with a local data storage
"""
import sys
sys.path.append('/home/xavier/Documents/dev/freesound-python/')
import copy
import freesound
import os
import j... | mit |
eramirem/astroML | book_figures/chapter9/fig_svm_diagram.py | 3 | 2321 | """
SVM Diagram
-----------
Figure 9.9
Illustration of SVM. The region between the dashed lines is the margin, and
the points which the dashed lines touch are called the support vectors.
"""
# Author: Jake VanderPlas
# License: BSD
# The figure produced by this code is published in the textbook
# "Statistics, Data... | bsd-2-clause |
zejin/water | visualizer.py | 10 | 2005 | #!/usr/bin/env python
"""
Visualize shallow water simulation results.
NB: Requires a modern Matplotlib version; also needs
either FFMPeg (for MP4) or ImageMagick (for GIF)
"""
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import ma... | mit |
YinongLong/scikit-learn | sklearn/mixture/dpgmm.py | 5 | 35315 | """Bayesian Gaussian Mixture Models and
Dirichlet Process Gaussian Mixture Models"""
from __future__ import print_function
# Author: Alexandre Passos (alexandre.tp@gmail.com)
# Bertrand Thirion <bertrand.thirion@inria.fr>
#
# Based on mixture.py by:
# Ron Weiss <ronweiss@gmail.com>
# Fabian Ped... | bsd-3-clause |
gengliangwang/spark | python/pyspark/pandas/tests/plot/test_frame_plot_plotly.py | 1 | 10017 | #
# 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 |
fourpartswater/cuda-convnet2 | shownet.py | 180 | 18206 | # Copyright 2014 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 or... | apache-2.0 |
megahertz0/tusharedemo | kline_tushare.py | 1 | 3639 | # -*- coding: utf-8 -*-
"""
Created on Mon Aug 15 17:31:36 2016
@author: megahertz
"""
import matplotlib.pyplot as plt
import matplotlib as mpl
import tushare as ts
import datetime
import time
from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY,YEARLY
from matplotlib.finance import fetch_h... | lgpl-3.0 |
cmoutard/mne-python | examples/stats/plot_cluster_stats_evoked.py | 18 | 2991 | """
=======================================================
Permutation F-test on sensor data with 1D cluster level
=======================================================
One tests if the evoked response is significantly different
between conditions. Multiple comparison problem is addressed
with cluster level permuta... | bsd-3-clause |
nlhepler/idepi | idepi/feature_extraction/_pairwisesitevectorizer.py | 1 | 3511 |
from numpy import zeros
from sklearn.base import BaseEstimator, TransformerMixin
from idepi.filters import null_filter
from idepi.labeledmsa import LabeledMSA
__all__ = ['PairwiseSiteVectorizer']
class PairwiseSiteVectorizer(BaseEstimator, TransformerMixin):
def __init__(self, encoder, filter=null_filter, r... | gpl-3.0 |
mauzeh/formation-flight | validation/get_max_fuel_burn.py | 1 | 2818 | import math
import lib.sim
from lib.debug import print_dictionary
from lib.geo.util import get_fuel_burned_during_cruise
from lib.geo.util import formationburn
from lib.geo.util import get_weight_ratio
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams['font.size'] = 15.
matplotl... | mit |
dhruv13J/scikit-learn | sklearn/decomposition/tests/test_incremental_pca.py | 297 | 8265 | """Tests for Incremental PCA."""
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn import datasets
from sklearn.decomposition import PCA, IncrementalPCA
iris = datasets.load... | bsd-3-clause |
kernc/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 |
nikitasingh981/scikit-learn | doc/conf.py | 22 | 9789 | # -*- coding: utf-8 -*-
#
# scikit-learn documentation build configuration file, created by
# sphinx-quickstart on Fri Jan 8 09:13:42 2010.
#
# This file is execfile()d with the current directory set to its containing
# dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
... | bsd-3-clause |
alvason/probability-insighter | code/gaussian_random_distribution.py | 1 | 10467 |
# coding: utf-8
# # Probability-insighter
# https://github.com/alvason/probability-insighter
#
# Gaussian random distribution (standard normal distribution)
# In[1]:
'''
author: Alvason Zhenhua Li
date: 03/19/2015
'''
get_ipython().magic('matplotlib inline')
import numpy as np
import matplotlib.pyplot as plt
i... | gpl-2.0 |
jreback/pandas | pandas/tests/reshape/merge/test_merge_cross.py | 2 | 2794 | import pytest
from pandas import DataFrame
import pandas._testing as tm
from pandas.core.reshape.merge import MergeError, merge
@pytest.mark.parametrize(
("input_col", "output_cols"), [("b", ["a", "b"]), ("a", ["a_x", "a_y"])]
)
def test_merge_cross(input_col, output_cols):
# GH#5401
left = DataFrame({"a... | bsd-3-clause |
hrjn/scikit-learn | examples/cluster/plot_cluster_iris.py | 350 | 2593 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
K-means Clustering
=========================================================
The plots display firstly what a K-means algorithm would yield
using three clusters. It is then shown what the effect of a bad
initializa... | bsd-3-clause |
michigraber/scikit-learn | examples/neighbors/plot_kde_1d.py | 347 | 5100 | """
===================================
Simple 1D Kernel Density Estimation
===================================
This example uses the :class:`sklearn.neighbors.KernelDensity` class to
demonstrate the principles of Kernel Density Estimation in one dimension.
The first plot shows one of the problems with using histogram... | bsd-3-clause |
LIKAIMO/MissionPlanner | Lib/site-packages/scipy/signal/filter_design.py | 53 | 63381 | """Filter design.
"""
import types
import warnings
import numpy
from numpy import atleast_1d, poly, polyval, roots, real, asarray, allclose, \
resize, pi, absolute, logspace, r_, sqrt, tan, log10, arctan, arcsinh, \
cos, exp, cosh, arccosh, ceil, conjugate, zeros, sinh
from numpy import mintypecode
from scipy... | gpl-3.0 |
Eric89GXL/scikit-learn | examples/applications/plot_out_of_core_classification.py | 3 | 12406 | """
======================================================
Out-of-core classification of text documents
======================================================
This is an example showing how scikit-learn can be used for classification
using an out-of-core approach: learning from data that doesn't fit into main
memory. ... | bsd-3-clause |
JonnaStalring/AZOrange | ConfPred/conformal-master/cp/evaluation.py | 1 | 14351 | """Evaluation module contains methods for evaluation of conformal predictors.
Function :py:func:`run` produces Results of an appropriate type by using a Sampler on a given data set
to split it into a training and testing set.
Structure:
- Sampler (sampling methods)
- :py:class:`RandomSampler`
- :py:... | lgpl-3.0 |
brain-research/mirage-rl-qprop | convert-stdout-to-csv.py | 1 | 1440 | """
Copyright 2018 Google LLC
Use of this source code is governed by an MIT-style
license that can be found in the LICENSE file or at
https://opensource.org/licenses/MIT.
"""
import os
import sys
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
import a... | mit |
davidgbe/scikit-learn | examples/linear_model/lasso_dense_vs_sparse_data.py | 348 | 1862 | """
==============================
Lasso on dense and sparse data
==============================
We show that linear_model.Lasso provides the same results for dense and sparse
data and that in the case of sparse data the speed is improved.
"""
print(__doc__)
from time import time
from scipy import sparse
from scipy ... | bsd-3-clause |
lxybox1/MissionPlanner | Lib/site-packages/scipy/stats/distributions.py | 53 | 207806 | # Functions to implement several important functions for
# various Continous and Discrete Probability Distributions
#
# Author: Travis Oliphant 2002-2011 with contributions from
# SciPy Developers 2004-2011
#
import math
import warnings
from copy import copy
from scipy.misc import comb, derivative
from s... | gpl-3.0 |
IndraVikas/scikit-learn | examples/linear_model/plot_sgd_comparison.py | 167 | 1659 | """
==================================
Comparing various online solvers
==================================
An example showing how different online solvers perform
on the hand-written digits dataset.
"""
# Author: Rob Zinkov <rob at zinkov dot com>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot a... | bsd-3-clause |
Fireblend/scikit-learn | examples/linear_model/plot_multi_task_lasso_support.py | 249 | 2211 | #!/usr/bin/env python
"""
=============================================
Joint feature selection with multi-task Lasso
=============================================
The multi-task lasso allows to fit multiple regression problems
jointly enforcing the selected features to be the same across
tasks. This example simulates... | bsd-3-clause |
eladnoor/equilibrator-api | pathways_cmd.py | 1 | 1031 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 1 13:24:15 2017
@author: noore
"""
import argparse
import logging
from equilibrator_api import Pathway
from matplotlib.backends.backend_pdf import PdfPages
import pandas as pd
if __name__ == '__main__':
parser = argparse.ArgumentParser(
... | mit |
costypetrisor/scikit-learn | examples/applications/plot_species_distribution_modeling.py | 254 | 7434 | """
=============================
Species distribution modeling
=============================
Modeling species' geographic distributions is an important
problem in conservation biology. In this example we
model the geographic distribution of two south american
mammals given past observations and 14 environmental
varia... | bsd-3-clause |
yavalvas/yav_com | build/matplotlib/lib/mpl_examples/user_interfaces/embedding_in_wx5.py | 12 | 1586 | # Used to guarantee to use at least Wx2.8
import wxversion
wxversion.ensureMinimal('2.8')
import wx
import wx.aui
import matplotlib as mpl
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as Canvas
from matplotlib.backends.backend_wxagg import NavigationToolbar2Wx as Toolbar
class Plot(wx.Panel):
d... | mit |
mikebenfield/scikit-learn | sklearn/linear_model/tests/test_huber.py | 54 | 7619 | # Authors: Manoj Kumar mks542@nyu.edu
# License: BSD 3 clause
import numpy as np
from scipy import optimize, sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_a... | bsd-3-clause |
anntzer/scikit-learn | examples/calibration/plot_calibration_curve.py | 24 | 5902 | """
==============================
Probability Calibration curves
==============================
When performing classification one often wants to predict not only the class
label, but also the associated probability. This probability gives some
kind of confidence on the prediction. This example demonstrates how to di... | bsd-3-clause |
Winand/pandas | pandas/core/dtypes/api.py | 16 | 2399 | # flake8: noqa
import sys
from .common import (pandas_dtype,
is_dtype_equal,
is_extension_type,
# categorical
is_categorical,
is_categorical_dtype,
# interval
is_interva... | bsd-3-clause |
wangyum/mxnet | example/kaggle-ndsb1/submission_dsb.py | 52 | 5048 | # 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 |
markovg/nest-simulator | pynest/examples/intrinsic_currents_spiking.py | 13 | 5954 | # -*- coding: utf-8 -*-
#
# intrinsic_currents_spiking.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 ... | gpl-2.0 |
gomezstevena/x-wind | src/trajectory.py | 1 | 1976 | # import matplotlib
# matplotlib.use('Agg')
from numpy import *
from scipy.interpolate import PiecewisePolynomial
class Trajectory:
def __init__(self, t, u, ddt=None):
assert t.ndim == 1 and t.size == u.shape[0]
self.shape = u.shape[1:]
data = u.reshape([t.size, 1, -1])
if ddt is no... | gpl-3.0 |
louispotok/pandas | pandas/core/indexes/numeric.py | 4 | 14410 | import numpy as np
from pandas._libs import (index as libindex,
join as libjoin)
from pandas.core.dtypes.common import (
is_dtype_equal,
pandas_dtype,
needs_i8_conversion,
is_integer_dtype,
is_bool,
is_bool_dtype,
is_scalar)
from pandas import compat
from pandas.co... | bsd-3-clause |
mdhaber/scipy | scipy/signal/ltisys.py | 12 | 128865 | """
ltisys -- a collection of classes and functions for modeling linear
time invariant systems.
"""
#
# Author: Travis Oliphant 2001
#
# Feb 2010: Warren Weckesser
# Rewrote lsim2 and added impulse2.
# Apr 2011: Jeffrey Armstrong <jeff@approximatrix.com>
# Added dlsim, dstep, dimpulse, cont2discrete
# Aug 2013: Jua... | bsd-3-clause |
Cadair/ginga | ginga/web/pgw/Plot.py | 3 | 4306 | #
# Plot.py -- Plotting widget canvas wrapper.
#
# Copyright (c) Eric R. Jeschke. All rights reserved.
# This is open-source software licensed under a BSD license.
# Please see the file LICENSE.txt for details.
#
from io import BytesIO
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from ... | bsd-3-clause |
bharatsingh430/py-R-FCN-multiGPU | caffe/examples/web_demo/app.py | 41 | 7793 | import os
import time
import cPickle
import datetime
import logging
import flask
import werkzeug
import optparse
import tornado.wsgi
import tornado.httpserver
import numpy as np
import pandas as pd
from PIL import Image
import cStringIO as StringIO
import urllib
import exifutil
import caffe
REPO_DIRNAME = os.path.abs... | mit |
amolkahat/pandas | pandas/tests/indexes/datetimes/test_formats.py | 5 | 8703 | from datetime import datetime
from pandas import DatetimeIndex, Series
import numpy as np
import dateutil.tz
import pytz
import pytest
import pandas.util.testing as tm
import pandas as pd
def test_to_native_types():
index = DatetimeIndex(freq='1D', periods=3, start='2017-01-01')
# First, with no arguments.... | bsd-3-clause |
lenovor/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 |
draekko/androguard | elsim/elsim/elsim.py | 37 | 16175 | # This file is part of Elsim
#
# Copyright (C) 2012, Anthony Desnos <desnos at t0t0.fr>
# All rights reserved.
#
# Elsim 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, ... | apache-2.0 |
iismd17/scikit-learn | examples/semi_supervised/plot_label_propagation_versus_svm_iris.py | 286 | 2378 | """
=====================================================================
Decision boundary of label propagation versus SVM on the Iris dataset
=====================================================================
Comparison for decision boundary generated on iris dataset
between Label Propagation and SVM.
This demon... | bsd-3-clause |
krez13/scikit-learn | sklearn/neighbors/regression.py | 32 | 11019 | """Nearest Neighbor Regression"""
# 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 support by Arna... | bsd-3-clause |
lmallin/coverage_test | python_venv/lib/python2.7/site-packages/pandas/tests/tseries/test_offsets.py | 6 | 216497 | import os
from distutils.version import LooseVersion
from datetime import date, datetime, timedelta
from dateutil.relativedelta import relativedelta
import pytest
from pandas.compat import range, iteritems
from pandas import compat
import numpy as np
from pandas.compat.numpy import np_datetime64_compat
from pandas.... | mit |
eggplantbren/ExperimentalNS | TwoScalars/DNest/postprocess.py | 1 | 7100 | # Copyright (c) 2009, 2010, 2011, 2012 Brendon J. Brewer.
#
# This file is part of DNest3.
#
# DNest3 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 ... | gpl-3.0 |
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