repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
coolsgupta/machine_learning_nanodegree | Model_Evaluation_and_Validation/numpy_and_pandas_tutorial/eg_6.py | 1 | 1951 | from pandas import DataFrame, Series
import numpy
def avg_medal_count():
'''
Compute the average number of bronze medals earned by countries who
earned at least one gold medal.
Save this to a variable named avg_bronze_at_least_one_gold. You do not
need to call the function in your code when runni... | mit |
mne-tools/mne-python | mne/preprocessing/maxwell.py | 1 | 103928 | # -*- coding: utf-8 -*-
# Authors: Mark Wronkiewicz <wronk.mark@gmail.com>
# Eric Larson <larson.eric.d@gmail.com>
# Jussi Nurminen <jnu@iki.fi>
# License: BSD (3-clause)
from collections import Counter, OrderedDict
from functools import partial
from math import factorial
from os import path as op
... | bsd-3-clause |
ddenhartog/quantstart-backtester | backtester/portfolio.py | 1 | 4844 | #PYTHON
from abc import (
ABCMeta,
abstractmethod
)
from math import copysign
#PROJECT
from .events import (
SignalEvent,
OrderEvent
)
from .performance import (
create_sharpe_ratio,
create_drawdowns
)
class PortfolioMetaclass(metaclass=ABCMeta):
@abstractmethod
def update_signal(self... | mit |
GRAAL-Research/domain_adversarial_neural_network | experiments_amazon.py | 1 | 5516 | import numpy as np
from DANN import DANN
from mSDA import compute_msda_representation
from sklearn.datasets import load_svmlight_files
from sklearn import svm
def main():
data_folder = './data/' # where the datasets are
source_name = 'dvd' # source domain: books, dvd, kitchen, or electronics
t... | bsd-2-clause |
acorg/dark-matter | bin/alignment-panel-civ.py | 1 | 16440 | #!/usr/bin/env python
"""
Given a BLAST or DIAMOND JSON output files, the corresponding FASTA (or FASTQ)
sequence files, and filtering criteria, produce a summary of matched titles
and (optionally) an alignment panel.
Run with --help for help.
"""
from __future__ import print_function
import os
import sys
import ar... | mit |
PredictionIO/open-academy | KairatAshim/pio_assignment2/problem3/problem3.py | 1 | 3875 | # -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab
import math
import scipy.special as sps
import scipy.stats as stats
from scipy.stats import invgamma
pathToFile = "/Users/kairat/Desktop/pio_assignment/positiveSixMonthRevenue.csv"
bins_value = 500
#############... | apache-2.0 |
tomlof/scikit-learn | sklearn/feature_extraction/text.py | 19 | 52042 | # -*- coding: utf-8 -*-
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck
# Robert Layton <robertlayton@gmail.com>
# Jochen Wersdörfer <jochen@wersdoerfer.de>
# Roman Sinayev <roman.sinayev@gmail.com>
#
# License: B... | bsd-3-clause |
impactlab/eemeter | tests/modeling/test_split_modeled_energy_trace.py | 1 | 3467 | import tempfile
from datetime import datetime
import pandas as pd
import numpy as np
import pytest
import pytz
from eemeter.modeling.formatters import ModelDataFormatter
from eemeter.modeling.models.seasonal import SeasonalElasticNetCVModel
from eemeter.modeling.split import SplitModeledEnergyTrace
from eemeter.struc... | mit |
trungnt13/scikit-learn | sklearn/linear_model/tests/test_ransac.py | 16 | 12745 | import numpy as np
from numpy.testing import assert_equal, assert_raises
from numpy.testing import assert_array_almost_equal
from scipy import sparse
from sklearn.utils.testing import assert_less
from sklearn.linear_model import LinearRegression, RANSACRegressor
from sklearn.linear_model.ransac import _dynamic_max_tri... | bsd-3-clause |
joferkington/mplstereonet | examples/fault_slip_plot.py | 3 | 2875 | """
Illustrates two different methods of plotting fault slip data.
A fault-and-striae diagram is the traditional method. The tangent-lineation
diagram follows Twiss & Unruh, 1988 (this style was originally introduced by
Goldstein & Marshak, 1988 and also by Hoeppener, 1955, but both used the opposite
convention for a... | mit |
elijah513/scikit-learn | examples/text/mlcomp_sparse_document_classification.py | 292 | 4498 | """
========================================================
Classification of text documents: using a MLComp dataset
========================================================
This is an example showing how the scikit-learn can be used to classify
documents by topics using a bag-of-words approach. This example uses
a s... | bsd-3-clause |
fengzhyuan/scikit-learn | sklearn/tests/test_dummy.py | 129 | 17774 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from sklearn.base import clone
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.testing import assert_almost_eq... | bsd-3-clause |
jzt5132/scikit-learn | sklearn/svm/setup.py | 321 | 3157 | import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('svm', parent_package, top_path)
config.add_subpackage('tests')
# Section L... | bsd-3-clause |
binghongcha08/pyQMD | GWP/QTGB/resample/c.py | 28 | 1767 | ##!/usr/bin/python
import numpy as np
import pylab as plt
import seaborn as sns
sns.set_context('poster')
#with open("traj.dat") as f:
# data = f.read()
#
# data = data.split('\n')
#
# x = [row.split(' ')[0] for row in data]
# y = [row.split(' ')[1] for row in data]
#
# fig = plt.figure()
#
# ax1 ... | gpl-3.0 |
gdsglgf/tutorials | python/pillow/array_to_image.py | 1 | 1062 | from PIL import Image
import numpy as np
from matplotlib import pyplot as plt
def saveImage(data, mode, filename):
img = Image.fromarray(data, mode)
img.save(filename)
def createRGBImage():
h, w = 128, 128
data = np.zeros((h, w, 3), dtype=np.uint8)
data[64, 64] = [255, 0, 0]
return data
def createGreyscaleIma... | mit |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/scipy/interpolate/_bsplines.py | 15 | 29415 | from __future__ import division, print_function, absolute_import
import functools
import operator
import numpy as np
from scipy.linalg import (get_lapack_funcs, LinAlgError,
cholesky_banded, cho_solve_banded)
from . import _bspl
from . import _fitpack_impl
from . import _fitpack as _dierckx
... | mit |
lpenguin/pandas-qt | pandasqt/models/ColumnDtypeModel.py | 3 | 9494 | # -*- coding: utf-8 -*-
"""Easy integration of DataFrame into pyqt framework
@author: Matthias Ludwig - Datalyze Solutions
"""
from pandasqt.compat import Qt, QtCore, QtGui, Slot, Signal
import pandas
import numpy as np
from pandasqt.models.SupportedDtypes import SupportedDtypes
DTYPE_ROLE = Qt.UserRole + 1
DTYPE... | mit |
dsockwell/trading-with-python | lib/functions.py | 76 | 11627 | # -*- coding: utf-8 -*-
"""
twp support functions
@author: Jev Kuznetsov
Licence: GPL v2
"""
from scipy import polyfit, polyval
import datetime as dt
#from datetime import datetime, date
from pandas import DataFrame, Index, Series
import csv
import matplotlib.pyplot as plt
import numpy as np
import p... | bsd-3-clause |
HolgerPeters/scikit-learn | examples/plot_digits_pipe.py | 65 | 1652 | #!/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 |
karstenw/nodebox-pyobjc | examples/Extended Application/matplotlib/examples/api/custom_scale_example.py | 1 | 7424 | """
============
Custom scale
============
This example showcases how to create a custom scale, by implementing the
scaling use for latitude data in a Mercator Projection.
"""
from __future__ import unicode_literals
import numpy as np
from numpy import ma
from matplotlib import scale as mscale
from matplotlib impor... | mit |
mifox/html | d3test/netspider.py | 1 | 3539 | # -*- coding: utf-8 -*-
"""
Created on Wed Sep 28 09:06:01 2016
@author: dingchaoqun
"""
#encoding:UTF-8
import urllib.request
import pandas as pd
from bs4 import BeautifulSoup
def getcontrolgraghbyOnestockcode(stockcode,bx):
global url
print(stockcode)
url = "http://basic.10jqka.com.cn/16/%s/holder.html"... | gpl-3.0 |
cl4rke/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | 202 | 3757 | import scipy.sparse as sp
import numpy as np
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.testing import assert_raises_regex, assert_true
from sklearn.utils.estimator_checks import check_estimator
from sklearn.utils.... | bsd-3-clause |
huongttlan/mpld3 | mpld3/_display.py | 15 | 16996 | import warnings
import random
import json
import jinja2
import numpy
import re
import os
from ._server import serve
from .utils import deprecated, get_id, write_ipynb_local_js
from .mplexporter import Exporter
from .mpld3renderer import MPLD3Renderer
from . import urls
__all__ = ["fig_to_html", "fig_to_dict", "fig_to_... | bsd-3-clause |
lukeiwanski/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/data_feeder.py | 39 | 32726 | # 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 |
anirudhjayaraman/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 |
hmendozap/auto-sklearn | autosklearn/pipeline/components/regression/gradient_boosting.py | 1 | 6605 | import numpy as np
from HPOlibConfigSpace.configuration_space import ConfigurationSpace
from HPOlibConfigSpace.hyperparameters import UniformFloatHyperparameter, \
UniformIntegerHyperparameter, CategoricalHyperparameter, Constant, \
UnParametrizedHyperparameter
from HPOlibConfigSpace.conditions import InCondit... | bsd-3-clause |
StratsOn/zipline | tests/test_batchtransform.py | 2 | 9818 | #
# Copyright 2013 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 |
aiguofer/bokeh | bokeh/util/serialization.py | 2 | 8701 | """
Functions for helping with serialization and deserialization of
Bokeh objects.
Certain NunPy array dtypes can be serialized to a binary format for
performance and efficiency. The list of supported dtypes is:
%s
"""
from __future__ import absolute_import
import base64
from six import iterkeys
from .dependencie... | bsd-3-clause |
kennethdecker/MagnePlane | src/hyperloop/Python/mission/tests/test_straight_track.py | 3 | 6317 | from __future__ import division, print_function, absolute_import
import unittest
import numpy as np
try:
from openmdao.api import pyOptSparseDriver
except:
pyOptSparseDriver = None
from openmdao.api import ScipyOptimizer
from pointer.components import Problem, Trajectory, CollocationPhase
from hyperloop.Py... | apache-2.0 |
huongttlan/statsmodels | statsmodels/examples/ex_proportion.py | 33 | 1918 | # -*- coding: utf-8 -*-
"""
Created on Sun Apr 21 07:59:26 2013
Author: Josef Perktold
"""
from __future__ import print_function
from statsmodels.compat.python import lmap
import numpy as np
import statsmodels.stats.proportion as sms
import statsmodels.stats.weightstats as smw
from numpy.testing import assert_almos... | bsd-3-clause |
rbalda/neural_ocr | env/lib/python2.7/site-packages/matplotlib/axes/_subplots.py | 8 | 8357 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
from matplotlib.externals.six.moves import map
from matplotlib.gridspec import GridSpec, SubplotSpec
from matplotlib import docstring
import matplotlib.artist as martist
fr... | mit |
GrumpyNounours/PySeidon | setup.py | 2 | 1640 | #!/usr/bin/python2.7
# encoding: utf-8
from setuptools import setup, find_packages
from numpy.distutils.misc_util import Configuration
def readme():
with open('README.md') as f:
return f.read()
option = raw_input("Resolve dependencies (y) or (n): ")
option = option.lower()
if option=='n':
setup(name=... | agpl-3.0 |
wavelets/hyperopt-sklearn | hpsklearn/tests/test_estimator.py | 4 | 1604 |
try:
import unittest2 as unittest
except:
import unittest
import numpy as np
from hpsklearn.estimator import hyperopt_estimator
from hpsklearn import components
class TestIter(unittest.TestCase):
def setUp(self):
np.random.seed(123)
self.X = np.random.randn(1000, 2)
self.Y = (sel... | bsd-3-clause |
GuessWhoSamFoo/pandas | pandas/tests/frame/test_block_internals.py | 1 | 21539 | # -*- coding: utf-8 -*-
from __future__ import print_function
from datetime import datetime, timedelta
import itertools
import numpy as np
import pytest
from pandas.compat import StringIO
import pandas as pd
from pandas import (
Categorical, DataFrame, Series, Timestamp, compat, date_range,
option_context)... | bsd-3-clause |
ankurankan/scikit-learn | sklearn/neighbors/tests/test_ball_tree.py | 30 | 9727 | import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.neighbors.ball_tree import (BallTree, NeighborsHeap,
simultaneous_sort, kernel_norm,
nodeheap_sort, DTYPE, ITYPE)
from sklearn.neighbors.dist_metrics impo... | bsd-3-clause |
TomAugspurger/pandas | pandas/tests/dtypes/cast/test_downcast.py | 2 | 2790 | import decimal
import numpy as np
import pytest
from pandas.core.dtypes.cast import maybe_downcast_to_dtype
from pandas import DatetimeIndex, Series, Timestamp
import pandas._testing as tm
@pytest.mark.parametrize(
"arr,dtype,expected",
[
(
np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]... | bsd-3-clause |
alphaBenj/zipline | zipline/data/treasuries.py | 4 | 3414 | #
# Copyright 2013 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 |
aflaxman/scikit-learn | sklearn/model_selection/tests/test_validation.py | 6 | 53244 | """Test the validation module"""
from __future__ import division
import sys
import warnings
import tempfile
import os
from time import sleep
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.uti... | bsd-3-clause |
Haunter17/MIR_SU17 | exp3/exp3c/exp3c.py | 1 | 21749 | import numpy as np
import tensorflow as tf
import h5py
import time
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import sys
# Functions for initializing neural nets parameters
def init_weight_variable(shape, nameIn):
initial = tf.truncated_normal(shape, stddev=0.1, dtype=tf.float32)
retu... | mit |
krischer/mtspec | mtspec/multitaper.py | 1 | 31582 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Main functions of mtspec.
:copyright:
Lion Krischer (krischer@geophysik.uni-muenchen.de) and
Moritz Beyreuther, 2010-2016
:license:
GNU General Public License, Version 3
(http://www.gnu.org/copyleft/gpl.html)
"""
import ctypes as C
import numpy as np
f... | gpl-3.0 |
marqh/cartopy | lib/cartopy/tests/mpl/test_caching.py | 1 | 6536 | # (C) British Crown Copyright 2011 - 2012, Met Office
#
# This file is part of cartopy.
#
# cartopy 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, or
# (at your option)... | gpl-3.0 |
cogmission/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/pylab.py | 70 | 10245 | """
This is a procedural interface to the matplotlib object-oriented
plotting library.
The following plotting commands are provided; the majority have
Matlab(TM) analogs and similar argument.
_Plotting commands
acorr - plot the autocorrelation function
annotate - annotate something in the figure
arrow ... | agpl-3.0 |
tkaitchuck/nupic | external/darwin64/lib/python2.6/site-packages/matplotlib/backends/backend_gtkagg.py | 70 | 4184 | """
Render to gtk from agg
"""
from __future__ import division
import os
import matplotlib
from matplotlib.figure import Figure
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.backends.backend_gtk import gtk, FigureManagerGTK, FigureCanvasGTK,\
show, draw_if_interactive,\
error_ms... | gpl-3.0 |
clemkoa/scikit-learn | sklearn/tests/test_metaestimators.py | 30 | 5040 | """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.utils.validation import... | bsd-3-clause |
BarrelfishOS/barrelfish | tools/harness/tests/distopsbench.py | 1 | 12343 | import tests, debug
from common import TestCommon
from results import PassFailResult, RowResults
import sys, re, numpy, os, datetime
has_mpl = True
try:
import matplotlib.pyplot as plt
except:
has_mpl = False
OPERATIONHEADER = re.compile("^# Benchmarking ([A-Z 0-9]+): nodes=(\d+).*$")
DATAHEADER = re.compile... | mit |
massmutual/scikit-learn | examples/svm/plot_iris.py | 225 | 3252 | """
==================================================
Plot different SVM classifiers in the iris dataset
==================================================
Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:
- Sepal length
- Se... | bsd-3-clause |
homeslike/OpticalTweezer | scripts/data2/vCOMhistogramMass.py | 27 | 3006 | import math
import sys
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from subprocess import call
from scipy.stats import norm
# proc = call("ls *.dat",shell=True)
# datetime = "170123_2033_"
datetime = sys.argv[1]+"_"
gasTempDataIn = np.genfromtxt(datetime+"gasTempData.dat",usecols... | mit |
ykashou92/PyScraping | webscraper_2.py | 1 | 2246 | # Run if packages not yet installed. (Usually installed if using Anaconda distribution)
! pip install pandas
! pip install bs4
# Import necessary Libraries
import pandas as pd
import urllib.request
from bs4 import BeautifulSoup
# Specify target URL
# Wikipedia has list pages, for an exmaple we will access the list of... | mit |
g0v/sunshine.cy | parser/property/stock_symbol.py | 1 | 1163 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import re
import json
import pandas as pd
import common
import db_settings
def stock():
c.execute('''
SELECT id, name
FROM property_stock
''')
return c.fetchall()
def update_symbol(symbol, fullname, id):
c.execute('''
UPDATE prope... | cc0-1.0 |
zzw0929/deeplearning | tensorflow/neuralNet/mofan/addLayer.py | 1 | 2150 |
# coding:utf-8
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
def add_layer(inputs, in_size, out_size, activation_function=None):
with tf.name_scope('layer'):
with tf.name_scope('weights'):
Weights = tf.Variable(tf.random_normal([in_size, out_size]))
... | apache-2.0 |
simon-pepin/scikit-learn | sklearn/datasets/tests/test_base.py | 205 | 5878 | import os
import shutil
import tempfile
import warnings
import nose
import numpy
from pickle import loads
from pickle import dumps
from sklearn.datasets import get_data_home
from sklearn.datasets import clear_data_home
from sklearn.datasets import load_files
from sklearn.datasets import load_sample_images
from sklearn... | bsd-3-clause |
RethinkRobotics/intera_sdk | intera_interface/src/intera_joint_trajectory_action/minjerk.py | 1 | 10210 | #! /usr/bin/env python# Software License Agreement (BSD License)
#
# Copyright (c) 2016, Kei Okada
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must reta... | apache-2.0 |
sam81/eegutils | eegutils.py | 1 | 54478 | # -*- coding: utf-8 -*-
# Copyright (C) 2012-2015 Samuele Carcagno <sam.carcagno@gmail.com>
# This file is part of eegutils
# eegutils 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 versi... | gpl-3.0 |
Sentient07/scikit-learn | sklearn/utils/random.py | 46 | 10523 | # Author: Hamzeh Alsalhi <ha258@cornell.edu>
#
# License: BSD 3 clause
from __future__ import division
import numpy as np
import scipy.sparse as sp
import operator
import array
from sklearn.utils import check_random_state
from sklearn.utils.fixes import astype
from ._random import sample_without_replacement
__all__ =... | bsd-3-clause |
cbonnett/SkyNet_wrapper | src/test.py | 1 | 1208 | from sklearn.datasets import load_boston
from sklearn.datasets import load_iris
from sklearn.utils import shuffle
from SkyNet import SkyNetRegressor
from SkyNet import SkyNetClassifier
try:
import seaborn as sns
except:
pass
X,y = shuffle(load_boston().data,load_boston().target)
X_train = X[0:200]
y_train =... | gpl-3.0 |
jrleeman/MetPy | setup.py | 1 | 3270 | # Copyright (c) 2008,2010,2015,2016 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Setup script for installing MetPy."""
from __future__ import print_function
from setuptools import find_packages, setup
import versioneer
ver = versioneer.get_ve... | bsd-3-clause |
ashmanmode/TTSDNNRepo | src/a_addsamplefeats.py | 1 | 58162 |
import cPickle
import gzip
import os, sys, errno
import time
import math
import subprocess
import socket # only for socket.getfqdn()
# numpy & theano imports need to be done in this order (only for some numpy installations, not sure why)
import numpy
#import gnumpy as gnp
# we need to explicitly import this in some... | apache-2.0 |
sonnyhu/scikit-learn | sklearn/decomposition/truncated_svd.py | 13 | 7868 | """Truncated SVD for sparse matrices, aka latent semantic analysis (LSA).
"""
# Author: Lars Buitinck
# Olivier Grisel <olivier.grisel@ensta.org>
# Michael Becker <mike@beckerfuffle.com>
# License: 3-clause BSD.
import numpy as np
import scipy.sparse as sp
try:
from scipy.sparse.linalg import svd... | bsd-3-clause |
qifeigit/scikit-learn | examples/semi_supervised/plot_label_propagation_digits_active_learning.py | 294 | 3417 | """
========================================
Label Propagation digits active learning
========================================
Demonstrates an active learning technique to learn handwritten digits
using label propagation.
We start by training a label propagation model with only 10 labeled points,
then we select the t... | bsd-3-clause |
zihua/scikit-learn | sklearn/neural_network/tests/test_stochastic_optimizers.py | 146 | 4310 | import numpy as np
from sklearn.neural_network._stochastic_optimizers import (BaseOptimizer,
SGDOptimizer,
AdamOptimizer)
from sklearn.utils.testing import (assert_array_equal, assert_true,
... | bsd-3-clause |
JackIron/Q_Learning_Games | Second_Q_Learning_Game/Roulette.py | 1 | 1368 | import gym
#import roulette env
from gym.envs.toy_text import roulette
#import del tabular q agent
import tabular_q_agent_roulette
#libreria per generare grafici
import matplotlib.pyplot as plt
#lib to remove files
import os
wheel=roulette.RouletteEnv()#make the env
agent=tabular_q_agent_roulette.TabularQAgent(wheel... | mit |
yarikoptic/seaborn | seaborn/tests/test_linearmodels.py | 1 | 30323 | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import nose.tools as nt
import numpy.testing as npt
import pandas.util.testing as pdt
from numpy.testing.decorators import skipif
try:
import statsmodels.api as sm
_no_statsmodels = False
except ImportError:
_no_statsmodels = True
fro... | bsd-3-clause |
winklerand/pandas | pandas/tests/frame/test_quantile.py | 10 | 15893 | # -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
import numpy as np
from pandas import (DataFrame, Series, Timestamp, _np_version_under1p11)
import pandas as pd
from pandas.util.testing import assert_series_equal, assert_frame_equal
import pandas.util.testing as tm
from pandas.tests.fr... | bsd-3-clause |
manjunaths/tensorflow | tensorflow/contrib/learn/python/learn/grid_search_test.py | 18 | 2259 | # 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 |
schets/scikit-learn | examples/covariance/plot_robust_vs_empirical_covariance.py | 248 | 6359 | r"""
=======================================
Robust vs Empirical covariance estimate
=======================================
The usual covariance maximum likelihood estimate is very sensitive to the
presence of outliers in the data set. In such a case, it would be better to
use a robust estimator of covariance to guar... | bsd-3-clause |
mahak/spark | python/pyspark/pandas/config.py | 14 | 15723 | #
# 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 |
gplepage/lsqfit | examples/bayes.py | 1 | 3318 | # Copyright (c) 2017-20 G. Peter Lepage.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# any later version (see <http://www.gnu.org/licenses/>).
#
# This ... | gpl-3.0 |
seokjunbing/cs75 | src/ml/svm.py | 1 | 2386 | from sklearn import svm
from sklearn.model_selection import cross_val_score
import sys
sys.path.append('/Users/imac/Documents/CS75/liblinear/python')
sys.path.append('../')
from liblinearutil import *
from data_processing.read_data import read_preprocessed_data
INPUT_FILE = '../../data/plants/label_scores.txt'
FEATURE... | gpl-3.0 |
MartinSavc/scikit-learn | examples/model_selection/randomized_search.py | 201 | 3214 | """
=========================================================================
Comparing randomized search and grid search for hyperparameter estimation
=========================================================================
Compare randomized search and grid search for optimizing hyperparameters of a
random forest.
... | bsd-3-clause |
IndraVikas/scikit-learn | sklearn/calibration.py | 137 | 18876 | """Calibration of predicted probabilities."""
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Balazs Kegl <balazs.kegl@gmail.com>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Mathieu Blondel <mathieu@mblondel.org>
#
# License: BSD 3 clause
from __future__ impo... | bsd-3-clause |
Caranarq/01_Dmine | 01_Agua/P0109/P0109.py | 1 | 2757 | # -*- coding: utf-8 -*-
"""
Started on wed, may 9th, 2018
@author: carlos.arana
"""
# Librerias utilizadas
import pandas as pd
import sys
module_path = r'D:\PCCS\01_Dmine\Scripts'
if module_path not in sys.path:
sys.path.append(module_path)
from VarInt.VarInt import VarInt
from classes.Meta import Meta
from Compi... | gpl-3.0 |
NWine/trading-with-python | lib/qtpandas.py | 77 | 7937 | '''
Easy integration of DataFrame into pyqt framework
Copyright: Jev Kuznetsov
Licence: BSD
'''
from PyQt4.QtCore import (QAbstractTableModel,Qt,QVariant,QModelIndex,SIGNAL)
from PyQt4.QtGui import (QApplication,QDialog,QVBoxLayout, QHBoxLayout, QTableView, QPushButton,
QWidget,QTabl... | bsd-3-clause |
lzamparo/SdA_reduce | utils/sample_h5_SdA_csv.py | 1 | 5280 | """ Read the given .h5 files containing SdA reduced data (for a given dimension), and sample a number of labeled points and pack into a data frame. """
### N.B: this is meant to run in python 3!
import sys, re, os
import numpy as np
import pandas as pd
from collections import OrderedDict
from tables import *
from s... | bsd-3-clause |
sumspr/scikit-learn | sklearn/manifold/tests/test_mds.py | 324 | 1862 | import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_raises
from sklearn.manifold import mds
def test_smacof():
# test metric smacof using the data of "Modern Multidimensional Scaling",
# Borg & Groenen, p 154
sim = np.array([[0, 5, 3, 4],
... | bsd-3-clause |
yask123/scikit-learn | sklearn/utils/tests/test_murmurhash.py | 261 | 2836 | # Author: Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import numpy as np
from sklearn.externals.six import b, u
from sklearn.utils.murmurhash import murmurhash3_32
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from nose.tools import assert_equa... | bsd-3-clause |
luo66/scikit-learn | examples/model_selection/plot_underfitting_overfitting.py | 230 | 2649 | """
============================
Underfitting vs. Overfitting
============================
This example demonstrates the problems of underfitting and overfitting and
how we can use linear regression with polynomial features to approximate
nonlinear functions. The plot shows the function that we want to approximate,
wh... | bsd-3-clause |
glennq/scikit-learn | sklearn/ensemble/tests/test_forest.py | 22 | 41796 | """
Testing for the forest module (sklearn.ensemble.forest).
"""
# Authors: Gilles Louppe,
# Brian Holt,
# Andreas Mueller,
# Arnaud Joly
# License: BSD 3 clause
import pickle
from collections import defaultdict
from itertools import combinations
from itertools import product
import numpy ... | bsd-3-clause |
Clyde-fare/scikit-learn | sklearn/decomposition/tests/test_factor_analysis.py | 222 | 3055 | # Author: Christian Osendorfer <osendorf@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Licence: BSD3
import numpy as np
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing im... | bsd-3-clause |
aaltay/beam | sdks/python/apache_beam/runners/interactive/recording_manager.py | 1 | 15752 | #
# 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 |
dongjoon-hyun/spark | python/pyspark/pandas/tests/test_dataframe.py | 14 | 222596 | #
# 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 |
cleinias/Homeo | src/Helpers/StatsAnalyzer.py | 1 | 11517 | '''
Created on Jan 4, 2015
Functions that read the logbook produced by a DEAP GA simulation
and provide info, stats, and charts about the GA run
@author: stefano
'''
from deap import tools
import pickle
import os
import matplotlib.pyplot as plt
from Helpers.ExceptionAndDebugClasses import hDebug
from Helpers.GenomeDe... | gpl-3.0 |
luo66/scikit-learn | sklearn/tests/test_dummy.py | 186 | 17778 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from sklearn.base import clone
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.testing import assert_almost_eq... | bsd-3-clause |
soft-matter/mr | vb_suite/benchmarks.py | 5 | 3199 | from vbench.api import Benchmark, BenchmarkRunner
from datetime import datetime
common_setup = """
import mr
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
def random_walk(N):
return np.cumsum(np.random.randn(N))
"""
setup = common_setup + """
def draw_gaussian_spot(image, pos, r):
... | gpl-3.0 |
tonnrueter/pymca_devel | cx_setup.py | 1 | 16231 | # A cx_freeze setup script to create PyMca executables
#
# Use "python cx_setup.py install"
#
# It expects a properly configured compiler.
#
# Under windows you may need to set MINGW = True (untested) if you are
# not using VS2003 (python 2.5) or VS2008 (python > 2.5)
#
# If everything works well one should find a dire... | gpl-2.0 |
marionleborgne/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/table.py | 69 | 16757 | """
Place a table below the x-axis at location loc.
The table consists of a grid of cells.
The grid need not be rectangular and can have holes.
Cells are added by specifying their row and column.
For the purposes of positioning the cell at (0, 0) is
assumed to be at the top left and the cell at (max_row, max_col)
i... | agpl-3.0 |
jvivian/rnaseq-lib | src/rnaseq_lib/dim_red/__init__.py | 1 | 1376 | import numpy as np
import pandas as pd
from sklearn.manifold import TSNE
from trimap import trimap
def run_trimap(df, num_dims=2, kin=50, kout=5, krand=5, eta=10000.0):
"""
Runs t-ETE dimensionality reduction
:param pd.DataFrame df: Dataframe or numpy array. Features need to be columns.
:param int n... | mit |
simonvh/fluff | fluff/color.py | 1 | 3291 | # Define colors and color utilities for plots
#
# Copyright (c) 2012-2013 Simon van Heeringen <s.vanheeringen@ncmls.ru.nl>
#
# This script is free software. You can redistribute it and/or modify it under
# the terms of the MIT License
# use pallettable to use colorbrewer
from functools import reduce
from palettable i... | mit |
garaud/ezhc | ezhc/samples/build_samples.py | 2 | 50033 |
import numpy as np
import pandas as pd
import json
def df_one_idx_several_col():
dic = {'John': [5, 3, 4, 7, 2],
'Jane': [2, 2, 3, 2, 1],
'Joe': [3, 4, 4, 2, 5]}
df = pd.DataFrame.from_dict(dic)
df.index = ['Apples', 'Oranges', 'Pears', 'Grapes', 'Bananas']
df.index.name = 'F... | mit |
AWNystrom/SparseInteraction | paper/Final/code/simple_plots.py | 1 | 6590 | from scipy.sparse import random, vstack
from sparse_polynomial_features import SparsePolynomialFeatures
from dense_polynomial_features import DensePolynomialFeatures as PolynomialFeatures
from time import time
import numpy as np
import matplotlib.pyplot as plt
from code import interact
import cPickle
from sys import ar... | apache-2.0 |
jdavidrcamacho/Tests_GP | 03 - RV tests/RV_function.py | 1 | 3896 | # -*- coding: utf-8 -*-
"""
Created on Fri Feb 3 11:36:58 2017
@author: camacho
"""
import numpy as np
import matplotlib.pyplot as pl
pl.close("all")
##### RV FUNCTION 1 - circular orbit
def RV_circular(P=365,K=0.1,T=0,gamma=0,time=100,space=20):
#parameters
#P = period in days
#K = semi-amplitude of t... | mit |
ningchi/scikit-learn | examples/linear_model/plot_ols.py | 45 | 1985 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Linear Regression Example
=========================================================
This example uses the only the first feature of the `diabetes` dataset, in
order to illustrate a two-dimensional plot of this regre... | bsd-3-clause |
wzbozon/scikit-learn | sklearn/linear_model/logistic.py | 57 | 65098 | """
Logistic Regression
"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Fabian Pedregosa <f@bianp.net>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Manoj Kumar <manojkumarsivaraj334@gmail.com>
# Lars Buitinck
# Simon Wu <s8wu@uwaterloo.ca>
imp... | bsd-3-clause |
jayflo/scikit-learn | sklearn/neighbors/base.py | 115 | 29783 | """Base and mixin classes for nearest neighbors"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
# Multi-output... | bsd-3-clause |
DonBeo/scikit-learn | sklearn/datasets/tests/test_mldata.py | 384 | 5221 | """Test functionality of mldata fetching utilities."""
import os
import shutil
import tempfile
import scipy as sp
from sklearn import datasets
from sklearn.datasets import mldata_filename, fetch_mldata
from sklearn.utils.testing import assert_in
from sklearn.utils.testing import assert_not_in
from sklearn.utils.test... | bsd-3-clause |
Sentient07/scikit-learn | examples/missing_values.py | 71 | 3055 | """
======================================================
Imputing missing values before building an estimator
======================================================
This example shows that imputing the missing values can give better results
than discarding the samples containing any missing value.
Imputing does not ... | bsd-3-clause |
jakevdp/scipy | doc/source/tutorial/stats/plots/kde_plot4.py | 142 | 1457 | from functools import partial
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
def my_kde_bandwidth(obj, fac=1./5):
"""We use Scott's Rule, multiplied by a constant factor."""
return np.power(obj.n, -1./(obj.d+4)) * fac
loc1, scale1, size1 = (-2, 1, 175)
loc2, scale2, size2 = (2, ... | bsd-3-clause |
VoigtLab/dnaplotlib | gallery/sbol_interactive/sbol_viewer.py | 1 | 3526 | #!/usr/bin/env python
"""
Recombinase NOT-gate
"""
PROMOTER = "http://purl.obolibrary.org/obo/SO_0000167"
RBS = "http://purl.obolibrary.org/obo/SO_0000552"
CDS = "http://purl.obolibrary.org/obo/SO_0000316"
TERMINATOR = "http://purl.obolibrary.org/obo/SO_0000139"
import sbol
import assembly
import math
import dnaplo... | mit |
pratapvardhan/pandas | pandas/core/indexes/multi.py | 1 | 103821 |
# pylint: disable=E1101,E1103,W0232
import datetime
import warnings
from sys import getsizeof
import numpy as np
from pandas._libs import algos as libalgos, index as libindex, lib, Timestamp
from pandas.compat import range, zip, lrange, lzip, map
from pandas.compat.numpy import function as nv
from pandas import comp... | bsd-3-clause |
laserson/vdj | analysis.py | 2 | 34178 | # Copyright 2014 Uri Laserson
#
# 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,... | apache-2.0 |
hsuantien/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 355 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluate... | bsd-3-clause |
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