repo_name stringlengths 7 84 | path stringlengths 5 184 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 978 477k | license stringclasses 15
values |
|---|---|---|---|---|---|
ryfeus/lambda-packs | Tensorflow_OpenCV_Nightly/source/tensorflow/python/estimator/inputs/queues/feeding_functions.py | 46 | 15782 | # 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... | mit |
ddf-project/DDF | python/ddf/util.py | 3 | 5533 | from __future__ import unicode_literals
import numpy as np
import json
import pandas as pd
from py4j import java_collections
"""
Mapping from DDF types to python types
"""
TYPE_MAPPING = {'integer': int,
'int': int,
'tinyint': int,
'smallint': int,
'big... | apache-2.0 |
Ledoux/ShareYourSystem | Pythonlogy/ShareYourSystem/Specials/Predicters/Predicter/draft/__init__ copy 2.py | 4 | 6766 | # -*- coding: utf-8 -*-
"""
<DefineSource>
@Date : Fri Nov 14 13:20:38 2014 \n
@Author : Erwan Ledoux \n\n
</DefineSource>
"""
#<DefineAugmentation>
import ShareYourSystem as SYS
import types
BaseModuleStr="ShareYourSystem.Standards.Controllers.Systemer"
DecorationModuleStr="ShareYourSystem.Standards.Classors.Clas... | mit |
luxinator/Pore-Network-Generator | tools/pb_size_gen.py | 1 | 1053 | #!/usr/bin/python
'''
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License version 2 as
* published by the Free Software Foundation.
'''
__author__ = 'Lucas van Oosterhout'
import matplotlib.pyplot as plt
import numpy as np
mu, sigma = np.log... | gpl-2.0 |
LiaoPan/scikit-learn | sklearn/svm/tests/test_bounds.py | 280 | 2541 | import nose
from nose.tools import assert_equal, assert_true
from sklearn.utils.testing import clean_warning_registry
import warnings
import numpy as np
from scipy import sparse as sp
from sklearn.svm.bounds import l1_min_c
from sklearn.svm import LinearSVC
from sklearn.linear_model.logistic import LogisticRegression... | bsd-3-clause |
wrobstory/seaborn | seaborn/categorical.py | 19 | 102299 | from __future__ import division
from textwrap import dedent
import colorsys
import numpy as np
from scipy import stats
import pandas as pd
from pandas.core.series import remove_na
import matplotlib as mpl
import matplotlib.pyplot as plt
import warnings
from .external.six import string_types
from .external.six.moves im... | bsd-3-clause |
jmetzen/scikit-learn | examples/preprocessing/plot_robust_scaling.py | 85 | 2698 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Robust Scaling on Toy Data
=========================================================
Making sure that each Feature has approximately the same scale can be a
crucial preprocessing step. However, when data contains o... | bsd-3-clause |
themrmax/scikit-learn | sklearn/linear_model/tests/test_least_angle.py | 27 | 25397 | import numpy as np
from scipy import linalg
from sklearn.model_selection import train_test_split
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.test... | bsd-3-clause |
CVML/scikit-learn | examples/decomposition/plot_kernel_pca.py | 353 | 2011 | """
==========
Kernel PCA
==========
This example shows that Kernel PCA is able to find a projection of the data
that makes data linearly separable.
"""
print(__doc__)
# Authors: Mathieu Blondel
# Andreas Mueller
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomp... | bsd-3-clause |
louisLouL/pair_trading | capstone_env/lib/python3.6/site-packages/pandas/tests/dtypes/test_common.py | 3 | 19448 | # -*- coding: utf-8 -*-
import pytest
import numpy as np
import pandas as pd
from pandas.core.dtypes.dtypes import (DatetimeTZDtype, PeriodDtype,
CategoricalDtype, IntervalDtype)
import pandas.core.dtypes.common as com
import pandas.util.testing as tm
class TestPandasDtype(ob... | mit |
gajduk/network-inference-from-short-time-series-gajduk | src/methods/symbolic.py | 1 | 1549 | from itertools import permutations
from numpy import argsort, matrix, zeros
from sklearn.metrics import normalized_mutual_info_score
def one_pair_at_a_time_symbol(method):
def inner(instance):
x = matrix(instance.x)
n_nodes, n_time_points = x.shape
if n_time_points > 12:
delta = 6
else:
delta = int(n_... | mit |
bykoianko/omim | tools/python/booking_hotels_quality.py | 20 | 2632 | #!/usr/bin/env python
# coding: utf8
from __future__ import print_function
from collections import namedtuple, defaultdict
from datetime import datetime
from sklearn import metrics
import argparse
import base64
import json
import logging
import matplotlib.pyplot as plt
import os
import pickle
import time
import urllib... | apache-2.0 |
pratapvardhan/pandas | pandas/tests/tseries/offsets/test_offsets.py | 1 | 130943 | from distutils.version import LooseVersion
from datetime import date, datetime, timedelta
import pytest
from pandas.compat import range
from pandas import compat
import numpy as np
from pandas.compat.numpy import np_datetime64_compat
from pandas.core.series import Series
from pandas._libs.tslibs import conversion
f... | bsd-3-clause |
ebernhardson/l2r | code/data_augment_es_docs.py | 1 | 1880 | import pandas as pd
import requests
import grequests
import json
import progressbar
import config
from utils import np_utils, table_utils
def exception_handler(req, e):
raise e
def main():
# returns an ndarray
page_ids = table_utils._read(config.CLICK_DATA)['hit_page_id'].unique()
url = config.ES_U... | mit |
gdementen/larray | larray/inout/hdf.py | 2 | 6860 | from __future__ import absolute_import, print_function
import warnings
import numpy as np
from pandas import HDFStore
from larray.core.array import Array
from larray.core.axis import Axis
from larray.core.constants import nan
from larray.core.group import Group, LGroup, _translate_group_key_hdf
from larray.core.meta... | gpl-3.0 |
EPFL-LCSB/pytfa | pytfa/analysis/manipulation.py | 1 | 2376 | from ..core.model import Solution
import pandas as pd
def apply_reaction_variability(tmodel, va, inplace = True):
"""
Applies the VA results as bounds for the reactions of a cobra_model
:param inplace:
:param tmodel:
:param va:
:return:
"""
if inplace:
_tmodel = tmodel
else:... | apache-2.0 |
jtwhite79/pyemu | pyemu/pst/pst_controldata.py | 1 | 18195 | """This module contains several class definitions for obseure parts of the
PEST control file: `ControlData` ('* control data'), `RegData` ('* regularization')
and `SvdData` ('* singular value decomposition'). These
classes are automatically created and appended to `Pst` instances;
users shouldn't need to deal with the... | bsd-3-clause |
zuku1985/scikit-learn | examples/linear_model/plot_sgd_loss_functions.py | 73 | 1232 | """
==========================
SGD: convex loss functions
==========================
A plot that compares the various convex loss functions supported by
:class:`sklearn.linear_model.SGDClassifier` .
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
def modified_huber_loss(y_true, y_pred):
z ... | bsd-3-clause |
tulip-control/tulip-control | examples/developer/fuel_tank/continuous_switched_test.py | 1 | 2637 | """test hybrid construction"""
from __future__ import print_function
import logging
logging.basicConfig(level=logging.INFO)
import time
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
from tulip import abstract, hybrid
from polytope import box2poly
input_bound = 0.4
uncertainty = 0.05
cont_state_space ... | bsd-3-clause |
qifeigit/scikit-learn | doc/sphinxext/gen_rst.py | 142 | 40026 | """
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 |
cfei18/incubator-airflow | setup.py | 1 | 11546 | # -*- coding: utf-8 -*-
#
# 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
#... | apache-2.0 |
wazeerzulfikar/scikit-learn | examples/linear_model/plot_logistic_path.py | 37 | 1195 | #!/usr/bin/env python
"""
=================================
Path with L1- Logistic Regression
=================================
Computes path on IRIS dataset.
"""
print(__doc__)
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
from datetime import datetime
import numpy as np
import... | bsd-3-clause |
mayblue9/scikit-learn | sklearn/ensemble/tests/test_voting_classifier.py | 140 | 6926 | """Testing for the boost module (sklearn.ensemble.boost)."""
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestCl... | bsd-3-clause |
DonBeo/statsmodels | statsmodels/nonparametric/_kernel_base.py | 29 | 18238 | """
Module containing the base object for multivariate kernel density and
regression, plus some utilities.
"""
from statsmodels.compat.python import range, string_types
import copy
import numpy as np
from scipy import optimize
from scipy.stats.mstats import mquantiles
try:
import joblib
has_joblib = True
exce... | bsd-3-clause |
ominux/scikit-learn | sklearn/neighbors/graph.py | 14 | 2839 | """Nearest Neighbors graph functions"""
# Author: Jake Vanderplas <vanderplas@astro.washington.edu>
#
# License: BSD, (C) INRIA, University of Amsterdam
from .base import KNeighborsMixin, RadiusNeighborsMixin
from .unsupervised import NearestNeighbors
def kneighbors_graph(X, n_neighbors, mode='connectivity'):
"... | bsd-3-clause |
trungnt13/scikit-learn | sklearn/linear_model/__init__.py | 270 | 3096 | """
The :mod:`sklearn.linear_model` module implements generalized linear models. It
includes Ridge regression, Bayesian Regression, Lasso and Elastic Net
estimators computed with Least Angle Regression and coordinate descent. It also
implements Stochastic Gradient Descent related algorithms.
"""
# See http://scikit-le... | bsd-3-clause |
architecture-building-systems/CEAforArcGIS | cea/technologies/heat_exchangers.py | 2 | 6169 | """
heat exchangers
"""
from math import log, ceil
import pandas as pd
import numpy as np
from cea.constants import HEAT_CAPACITY_OF_WATER_JPERKGK
from cea.technologies.constants import MAX_NODE_FLOW
from cea.analysis.costs.equations import calc_capex_annualized, calc_opex_annualized
__author__ = "Thuy-An Nguyen"
... | mit |
vantares/trading-with-python | lib/bats.py | 78 | 3458 | #-------------------------------------------------------------------------------
# Name: BATS
# Purpose: get data from BATS exchange
#
# Author: jev
#
# Created: 17/08/2013
# Copyright: (c) Jev Kuznetsov 2013
# Licence: BSD
#------------------------------------------------------------... | bsd-3-clause |
idaks/PW-explorer | PW_explorer/Input_Parsers/Clingo_Parser/clingo_parser.py | 1 | 6566 | from antlr4 import *
from .Antlr_Files.ClingoLexer import ClingoLexer
from .Antlr_Files.ClingoParser import ClingoParser
from .Antlr_Files.ClingoListener import ClingoListener
from ...helper import isfloat, PossibleWorld, Relation
import pandas as pd
import numpy as np
from antlr4.tree.Trees import Trees
def rearrang... | apache-2.0 |
prheenan/Research | Personal/EventDetection/Docs/ToyGraphs/dna_tables/main_dna_sequences.py | 1 | 2026 | # force floating point division. Can still use integer with //
from __future__ import division
# This file is used for importing the common utilities classes.
import numpy as np
import matplotlib.pyplot as plt
import sys
def read_seq(input_file):
with open(input_file) as f:
# first line is comments
... | gpl-3.0 |
lweasel/piquant | test/test_classifiers.py | 2 | 4224 | import pandas as pd
import piquant.classifiers as classifiers
def _get_test_classifier(
column_name="dummy", value_extractor=lambda x: x,
grouped_stats=True, distribution_plot_range=None):
return classifiers._Classifier(
column_name, value_extractor, grouped_stats, distribution_plot_range)... | mit |
xavierwu/scikit-learn | examples/exercises/plot_cv_diabetes.py | 231 | 2527 | """
===============================================
Cross-validation on diabetes Dataset Exercise
===============================================
A tutorial exercise which uses cross-validation with linear models.
This exercise is used in the :ref:`cv_estimators_tut` part of the
:ref:`model_selection_tut` section of ... | bsd-3-clause |
xapple/plumbing | testing/database/access_db/test_access_db.py | 1 | 1153 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Typically you would run this file from a command line like this:
ipython.exe -i -- /deploy/plumbing/tests/database/access_db/test_access_db.py
"""
# Built-in module #
import inspect, os
# Internal modules #
from plumbing.databases.access_database import Access... | mit |
felipemontefuscolo/bitme | common/quote.py | 1 | 1314 | from common import Symbol
import pandas as pd
class Quote:
def __init__(self,
symbol: Symbol,
timestamp: pd.Timestamp = None,
bid_size=None,
bid_price=None,
ask_size=None,
ask_price=None):
self.timestamp ... | mpl-2.0 |
ioam/param | tests/API1/testpandas.py | 2 | 7360 | """
Test Parameters based on pandas
"""
import unittest
import os
import param
from . import API1TestCase
try:
import pandas
except ImportError:
if os.getenv('PARAM_TEST_PANDAS','0') == '1':
raise ImportError("PARAM_TEST_PANDAS=1 but pandas not available.")
else:
raise unittest.SkipTest("p... | bsd-3-clause |
Frankkkkk/arctic | arctic/serialization/numpy_arrays.py | 1 | 6258 | import logging
import numpy as np
import numpy.ma as ma
import pandas as pd
from bson import Binary, SON
from .._compression import compress, decompress, compress_array
from ._serializer import Serializer
DATA = 'd'
MASK = 'm'
TYPE = 't'
DTYPE = 'dt'
COLUMNS = 'c'
INDEX = 'i'
METADATA = 'md'
LENGTHS = 'ln'
class ... | lgpl-2.1 |
CallaJun/hackprince | indico/skimage/io/manage_plugins.py | 7 | 10329 | """Handle image reading, writing and plotting plugins.
To improve performance, plugins are only loaded as needed. As a result, there
can be multiple states for a given plugin:
available: Defined in an *ini file located in `skimage.io._plugins`.
See also `skimage.io.available_plugins`.
partial definiti... | lgpl-3.0 |
bravelittlescientist/kdd-particle-physics-ml-fall13 | src/nearest_neighbors.py | 1 | 1162 | #!/usr/bin/python2
# This is a NN classifier based on the scikit-learn documentation.
#
# http://scikit-learn.org/stable/modules/neighbors.html
import sys
from imputation import load_data
from util import shuffle_split
from metrics import suite
from sklearn.neighbors import KNeighborsClassifier
def train(Xtrain, Y... | gpl-2.0 |
PalmDr/XRD-Data-Analysis-Toolkit | Beta1.4/DataAnalysisClass.py | 1 | 4047 | __author__ = 'j'
import os
from tkinter import *
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
from Builder import *
from TreeView import *
from ConvertCSV import *
from SQ_Pilatus import *
class DataAnalysis():
def __init__(self)... | apache-2.0 |
ryfeus/lambda-packs | Tensorflow_LightGBM_Scipy_nightly/source/scipy/integrate/quadrature.py | 20 | 28269 | from __future__ import division, print_function, absolute_import
import numpy as np
import math
import warnings
# trapz is a public function for scipy.integrate,
# even though it's actually a numpy function.
from numpy import trapz
from scipy.special import roots_legendre
from scipy.special import gammaln
from scipy.... | mit |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_find_eog_artifacts.py | 24 | 1228 | """
==================
Find EOG artifacts
==================
Locate peaks of EOG to spot blinks and general EOG artifacts.
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mn... | bsd-3-clause |
kapteyn-astro/kapteyn | doc/source/EXAMPLES/kmpfit_voigt.py | 1 | 4841 | #!/usr/bin/env python
#------------------------------------------------------------
# Script which demonstrates how to find the best-fit
# parameters of a Voigt line-shape model
#
# Vog, 26 Mar 2012
#------------------------------------------------------------
import numpy
from matplotlib.pyplot import figure, show, r... | bsd-3-clause |
hfutsuchao/Python2.6 | stocks/strategy_stock_tech_corr_bak_nonapart.py | 1 | 14626 | #coding:utf-8
from sqlalchemy import create_engine
import pandas as pd
import numpy as np
from sqlalchemy.orm import sessionmaker
import talib
import matplotlib.pyplot as plt
from sklearn import preprocessing
from multiprocessing import Pool
from multiprocessing.dummy import Pool as ThreadPool
import time
f... | gpl-2.0 |
kmike/scikit-learn | examples/covariance/plot_covariance_estimation.py | 4 | 4992 | """
=======================================================================
Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood
=======================================================================
The usual estimator for covariance is the maximum likelihood estimator,
:class:`sklearn.covariance.Em... | bsd-3-clause |
evanbiederstedt/RRBSfun | trees/chrom_scripts/normal_chr20.py | 1 | 25844 | import glob
import pandas as pd
import numpy as np
pd.set_option('display.max_columns', 50) # print all rows
import os
os.chdir("/gpfs/commons/home/biederstedte-934/evan_projects/correct_phylo_files")
normalB = glob.glob("binary_position_RRBS_normal_B_cell*")
mcell = glob.glob("binary_position_RRBS_NormalBCD19pCD27... | mit |
UNR-AERIAL/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 |
btabibian/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 |
kylerbrown/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 |
Erotemic/ibeis | ibeis/annots.py | 1 | 17911 | # -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
import utool as ut
import six
import itertools as it
from ibeis import _ibeis_object
from ibeis.control.controller_inject import make_ibs_register_decorator
(print, rrr, profile) = ut.inject2(__name__, '[annot]')
... | apache-2.0 |
janusnic/21v-python | unit_20/matplotlib/custom_cmap.py | 2 | 5759 | #!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
"""
Example: suppose you want red to increase from 0 to 1 over the bottom
half, green to do the same over the middle half, and blue over the top
half. Then you would use:
cdict = {'red': ... | mit |
EmmaIshta/QUANTAXIS | QUANTAXIS/QASU/update_tdx.py | 1 | 2553 | import datetime
from QUANTAXIS.QAFetch.QATdx import (QA_fetch_get_stock_day,
QA_fetch_get_stock_min,
QA_fetch_get_stock_transaction,
QA_fetch_get_stock_xdxr)
from QUANTAXIS.QAFetch.QATushare import QA_fetch_g... | mit |
herilalaina/scikit-learn | sklearn/cluster/birch.py | 18 | 23684 | # Authors: Manoj Kumar <manojkumarsivaraj334@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Joel Nothman <joel.nothman@gmail.com>
# License: BSD 3 clause
from __future__ import division
import warnings
import numpy as np
from scipy import sparse
from math import sqrt
fro... | bsd-3-clause |
xya/sms-tools | software/models_interface/dftModel_function.py | 21 | 2413 | # function to call the main analysis/synthesis functions in software/models/dftModel.py
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import get_window
import os, sys
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../models/'))
import utilFunctions as UF
import dftMod... | agpl-3.0 |
MadsJensen/CAA | time_decoding_sensor-grad_ent.py | 1 | 1847 | import sys
import mne
from mne.decoding import GeneralizationAcrossTime
from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from my_settings import (data_path, epochs_folder)
import matplot... | bsd-3-clause |
bert9bert/statsmodels | statsmodels/sandbox/distributions/examples/ex_mvelliptical.py | 34 | 5169 | # -*- coding: utf-8 -*-
"""examples for multivariate normal and t distributions
Created on Fri Jun 03 16:00:26 2011
@author: josef
for comparison I used R mvtnorm version 0.9-96
"""
from __future__ import print_function
import numpy as np
import statsmodels.sandbox.distributions.mv_normal as mvd
from numpy.testi... | bsd-3-clause |
ndingwall/scikit-learn | sklearn/gaussian_process/tests/test_kernels.py | 9 | 14133 | """Testing for kernels for Gaussian processes."""
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD 3 clause
import pytest
import numpy as np
from inspect import signature
from sklearn.gaussian_process.kernels import _approx_fprime
from sklearn.metrics.pairwise \
import PAIRWISE_KERNEL_... | bsd-3-clause |
jrdurrant/insect_analysis | vision/measurements/subspace_shape.py | 1 | 4784 | import numpy as np
from skimage.transform import SimilarityTransform, estimate_transform, matrix_transform
import matplotlib.pyplot as plt
import scipy
from skimage.filters import gaussian
def plot_closest_points(image_points, edge_points, closest_edge_points):
plt.plot(edge_points[:, 0], edge_points[:, 1], 'r+')... | gpl-2.0 |
h2educ/scikit-learn | examples/linear_model/plot_sgd_comparison.py | 77 | 1820 | """
==================================
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 |
PalNilsson/pilot2 | pilot/user/atlas/setup.py | 1 | 17850 | #!/usr/bin/env python
# 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
#
# Authors:
# - Paul Nilsson, paul.nilsson@cern.ch, 2017-2020
import os
impo... | apache-2.0 |
frank-tancf/scikit-learn | examples/plot_digits_pipe.py | 70 | 1813 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Pipelining: chaining a PCA and a logistic regression
=========================================================
The PCA does an unsupervised dimensionality reduction, while the logistic
regression does the predictio... | bsd-3-clause |
hrjn/scikit-learn | sklearn/utils/graph.py | 24 | 6326 | """
Graph utilities and algorithms
Graphs are represented with their adjacency matrices, preferably using
sparse matrices.
"""
# Authors: Aric Hagberg <hagberg@lanl.gov>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD 3 clause
impo... | bsd-3-clause |
jasdumas/jasdumas.github.io | post_data/kmeans-cluster-poker-hands.py | 1 | 7337 | # load libraries
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing
from sklearn.cluster import KMeans
import urllib.request
from pylab import rcParams
rcParams['figure.figsize... | mit |
winklerand/pandas | pandas/tests/io/msgpack/test_newspec.py | 22 | 2650 | # coding: utf-8
from pandas.io.msgpack import packb, unpackb, ExtType
def test_str8():
header = b'\xd9'
data = b'x' * 32
b = packb(data.decode(), use_bin_type=True)
assert len(b) == len(data) + 2
assert b[0:2] == header + b'\x20'
assert b[2:] == data
assert unpackb(b) == data
data = ... | bsd-3-clause |
dingocuster/scikit-learn | examples/ensemble/plot_adaboost_multiclass.py | 354 | 4124 | """
=====================================
Multi-class AdaBoosted Decision Trees
=====================================
This example reproduces Figure 1 of Zhu et al [1] and shows how boosting can
improve prediction accuracy on a multi-class problem. The classification
dataset is constructed by taking a ten-dimensional ... | bsd-3-clause |
bzero/statsmodels | examples/python/regression_plots.py | 33 | 9585 |
## Regression Plots
from __future__ import print_function
from statsmodels.compat import lzip
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.formula.api import ols
### Duncan's Prestige Dataset
#### Load the Data
# We can use a utility function... | bsd-3-clause |
cogmission/nupic.research | projects/vehicle-control/agent/run_q.py | 12 | 5498 | #!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2015, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions ... | agpl-3.0 |
IndraVikas/scikit-learn | sklearn/linear_model/tests/test_base.py | 120 | 10082 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.linear_model.... | bsd-3-clause |
neale/CS-program | 434-MachineLearning/final_project/linearClassifier/sklearn/linear_model/tests/test_logistic.py | 24 | 39507 | import numpy as np
import scipy.sparse as sp
from scipy import linalg, optimize, sparse
from sklearn.utils.testing import assert_almost_equal
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.util... | unlicense |
nsat/gnuradio | gr-digital/examples/berawgn.py | 32 | 4886 | #!/usr/bin/env python
#
# Copyright 2012,2013 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 optio... | gpl-3.0 |
hrjn/scikit-learn | sklearn/metrics/tests/test_common.py | 19 | 43631 | from __future__ import division, print_function
from functools import partial
from itertools import product
import numpy as np
import scipy.sparse as sp
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils.multiclass import type_of_target
fro... | bsd-3-clause |
wdm0006/sklearn-extensions | examples/kernel_regression/example.py | 1 | 1546 | """
Example from: https://raw.githubusercontent.com/jmetzen/kernel_regression/master/plot_kernel_regression.py
========================================================================
Comparison of kernel regression (KR) and support vector regression (SVR)
==============================================================... | bsd-3-clause |
srowen/spark | python/pyspark/pandas/tests/data_type_ops/test_binary_ops.py | 7 | 6774 | #
# 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 |
telefar/stockEye | coursera-compinvest1-master/coursera-compinvest1-master/homework/homework/homework5/HW5/hw5_bollinger.py | 3 | 1321 | ## Computational Investing I
## HW 5
##
## Author: alexcpsec
import pandas as pd
import pandas.stats.moments as pdsm
import numpy as np
import math
import copy
import QSTK.qstkutil.qsdateutil as du
import datetime as dt
import QSTK.qstkutil.DataAccess as da
import QSTK.qstkutil.tsutil as tsu
import QSTK.qstkstudy.Even... | bsd-3-clause |
costypetrisor/scikit-learn | sklearn/utils/tests/test_utils.py | 215 | 8100 | import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import pinv2
from itertools import chain
from sklearn.utils.testing import (assert_equal, assert_raises, assert_true,
assert_almost_equal, assert_array_equal,
SkipTest, ... | bsd-3-clause |
mfjb/scikit-learn | examples/linear_model/plot_ols_ridge_variance.py | 387 | 2060 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Ordinary Least Squares and Ridge Regression Variance
=========================================================
Due to the few points in each dimension and the straight
line that linear regression uses to follow thes... | bsd-3-clause |
CTJChen/ctc_astropylib | mlematch.py | 1 | 23653 | import pandas as pd
from ctc_observ import *
from ctc_arrays import *
from scipy.interpolate import pchip
from statsmodels.nonparametric.smoothers_lowess import lowess
# load HSC catalog first
# hsc = pd.read_csv('/cuc36/xxl/multiwavelength/HSC/wide.csv')
def pdf_sep_gen(sep_arcsec, xposerr, opterr, pdf='Rayleigh'):
... | apache-2.0 |
swapnilgt/tablaPercPatternsISMIR | rlcs/run.py | 2 | 14062 | import os
import sys
from src import impl as rlcs
import utils as ut
import analysis as anls
import matplotlib.pyplot as plt
import logging
import pickle as pkl
import time
config = ut.loadConfig('config')
sylbSimFolder=config['sylbSimFolder']
transFolder=config['transFolder']
lblDir=config['lblDir']
onsDir=config['o... | agpl-3.0 |
PythonProgramming/Support-Vector-Machines---Basics-and-Fundamental-Investing-Project | p26.py | 2 | 3329 | # back testing
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, preprocessing
import pandas as pd
from matplotlib import style
import statistics
from collections import Counter
style.use("ggplot")
how_much_better = 5
FEATURES = [
'DE Ratio',
'Trailing P/E',
'Price/Sales',
'Price/B... | mit |
adykstra/mne-python | mne/viz/tests/test_epochs.py | 1 | 8962 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Jaakko Leppakangas <jaeilepp@student.jyu.fi>
#
# License: Simplified BSD
import... | bsd-3-clause |
devincornell/semanticanlysis | dictionary.py | 1 | 5447 | import spacy
from .parallel import *
import re
import functools
#from .topicmodel import TopicModel
''' This class parses data according to the MFD format. Available are the empath, mft and mft2.0 dictionaries.
For further information about the format of the dictionary files, see Haidt et al.'s example:
http://www.m... | mit |
adykstra/mne-python | mne/channels/channels.py | 1 | 52814 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
# Denis Engemann <denis.engemann@gmail.com>
# Andrew Dykstra <andrew.r.dykstra@gmail.com>
# Teon Brooks <teon.brooks@gmail.com>
#
# License: BSD (3-clause)
import os
... | bsd-3-clause |
mfjb/scikit-learn | examples/cluster/plot_agglomerative_clustering.py | 343 | 2931 | """
Agglomerative clustering with and without structure
===================================================
This example shows the effect of imposing a connectivity graph to capture
local structure in the data. The graph is simply the graph of 20 nearest
neighbors.
Two consequences of imposing a connectivity can be s... | bsd-3-clause |
bikong2/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 |
wdurhamh/statsmodels | examples/python/tsa_dates.py | 29 | 1169 |
## Dates in timeseries models
from __future__ import print_function
import statsmodels.api as sm
import pandas as pd
# ## Getting started
data = sm.datasets.sunspots.load()
# Right now an annual date series must be datetimes at the end of the year.
dates = sm.tsa.datetools.dates_from_range('1700', length=len(da... | bsd-3-clause |
plissonf/scikit-learn | examples/model_selection/grid_search_digits.py | 227 | 2665 | """
============================================================
Parameter estimation using grid search with cross-validation
============================================================
This examples shows how a classifier is optimized by cross-validation,
which is done using the :class:`sklearn.grid_search.GridSearc... | bsd-3-clause |
anntzer/scikit-learn | examples/gaussian_process/plot_gpc_xor.py | 43 | 2170 | """
========================================================================
Illustration of Gaussian process classification (GPC) on the XOR dataset
========================================================================
This example illustrates GPC on XOR data. Compared are a stationary, isotropic
kernel (RBF) and ... | bsd-3-clause |
billy-inn/scikit-learn | sklearn/linear_model/tests/test_sparse_coordinate_descent.py | 244 | 9986 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_true
from sklearn.utils.t... | bsd-3-clause |
ronfung/incubator-airflow | airflow/contrib/hooks/bigquery_hook.py | 6 | 39980 | # -*- coding: utf-8 -*-
#
# 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 writing, software
... | apache-2.0 |
rouseguy/scipy2015_tutorial | check_env.py | 6 | 2002 | problems = 0
try:
import IPython
print('IPython', IPython.__version__)
assert(IPython.__version__ >= '3.0')
except ImportError:
print("IPython version 3 is not installed. Please install via pip or conda.")
problems += 1
try:
import numpy
print('NumPy', numpy.__version__)
assert(nu... | cc0-1.0 |
jkibele/OpticalRS | OpticalRS/Lyzenga2006.py | 1 | 23015 | # -*- coding: utf-8 -*-
"""
Lyzenga2006
===========
This module implements methods described in Lyzenga et al. 2006. The methods
implemented so far are mostly the image preprocessing steps.
This implementation is the work of the author of this code (Jared Kibele), not
the authors of the original paper. I tried to get... | bsd-3-clause |
saimn/astropy | docs/conf.py | 2 | 12625 | # -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
#
# Astropy documentation build configuration file.
#
# 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 file.
#
# All configurati... | bsd-3-clause |
oesteban/dipy | dipy/tests/test_scripts.py | 9 | 4292 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
""" Test scripts
Run scripts and check outputs
"""
from __future__ import division, print_function, absolute_import
import os
import shutil
from os.path import (dirname, join as pjoin, abspath)
from nos... | bsd-3-clause |
steinnp/Big-Data-Final | Classification/bayes_most_informative.py | 1 | 3013 | import nltk
import csv
import matplotlib.pyplot as plt
word_features = []
def get_words_in_tweets(tweets):
all_words = []
for (words, sentiment) in tweets:
all_words.extend(words)
return all_words
def get_word_features(wordlist):
wordlist = nltk.FreqDist(wordlist)
word_features = wordlist.... | mit |
justincassidy/scikit-learn | sklearn/metrics/cluster/__init__.py | 312 | 1322 | """
The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the 'quality' of the model itself.
"""
from .supervised import ... | bsd-3-clause |
glenflet/ZtoRGBpy | ZtoRGBpy/_core.py | 1 | 22787 | # -*- coding: utf-8 -*-
# =================================================================================
# Copyright 2019 Glen Fletcher <mail@glenfletcher.com>
#
# 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... | mit |
silky/sms-tools | lectures/07-Sinusoidal-plus-residual-model/plots-code/LPC.py | 24 | 1191 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, hanning, triang, blackmanharris, resample
import math
import sys, os, time
from scipy.fftpack import fft, ifft
import essentia.standard as ess
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../softwar... | agpl-3.0 |
witgo/spark | python/pyspark/sql/pandas/conversion.py | 4 | 21163 | #
# 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 |
demis001/biopandas | tests/testbiopandas.py | 2 | 4473 | import mock
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
import unittest
#from bioframes import bioframes as bf
from bioframes import sequenceframes
import sys
import pandas as pd
from pandas.util.testing import assert_series_equal, assert_frame_equal, assert_index_equal
from numpy.testing import assert_... | gpl-2.0 |
raghavrv/scikit-learn | sklearn/tests/test_cross_validation.py | 79 | 47914 | """Test the cross_validation module"""
from __future__ import division
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from scipy.sparse import csr_matrix
from scipy import stats
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.testing import assert_true
from sklearn.utils.t... | bsd-3-clause |
lsiemens/lsiemens.github.io | theory/fractional_calculus/code/generateanim2dfc.py | 1 | 2823 | import numpy
import fc2dpy
from scipy import special
from matplotlib import pyplot
from matplotlib.animation import FuncAnimation
resolution = 64 # 256
order = 64 # 128
PD = fc2dpy.polydisk(0, 0, 1, 1, resolution=resolution)
PD.set_parameterization(1.0 + 0.0j, 1.0 + 0.0j)
def encoding(a):
# return 1.0/fc2dpy.Gamm... | mit |
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