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 |
|---|---|---|---|---|---|
DoWhatILove/turtle | ai/natural language understanding/conceptnet/distinguish_attributes.py | 1 | 4801 | '''
the paper: Luminoso at SemEval-2018 Task 10: distinguishing attributes using text corpora and relational knowledge.
this refers the blog: https://blog.conceptnet.io/posts/2018/distinguishing-attributes-using-conceptnet/
'''
from sklearn.metrics import f1_score
import numpy as np
import pandas as pd
def text_to_ur... | mit |
andaag/scikit-learn | sklearn/gaussian_process/tests/test_gaussian_process.py | 267 | 6813 | """
Testing for Gaussian Process module (sklearn.gaussian_process)
"""
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# Licence: BSD 3 clause
from nose.tools import raises
from nose.tools import assert_true
import numpy as np
from sklearn.gaussian_process import GaussianProcess
from sklearn.gaussian_process ... | bsd-3-clause |
nicjhan/MOM6-examples | tools/analysis/MOM6_annual_analysis.py | 6 | 4961 | # Script to plot sub-surface ocean temperature drift.
# Analysis: using newer python 2.7.3
"""
module purge
module use -a /home/fms/local/modulefiles
module load gcc
module load netcdf/4.2
module load python/2.7.3
"""
import os
import math
import numpy as np
from numpy import ma
from netCDF4 import Dataset, MFDatase... | gpl-3.0 |
magne-max/zipline-ja | zipline/utils/calendars/us_holidays.py | 6 | 4015 | from pandas import (
Timestamp,
DateOffset,
date_range,
)
from pandas.tseries.holiday import (
Holiday,
sunday_to_monday,
nearest_workday,
)
from dateutil.relativedelta import (
MO,
TH
)
from pandas.tseries.offsets import Day
from zipline.utils.calendars.trading_calendar import (
... | apache-2.0 |
yeasy/lazyctrl | others/lc_sim/src/topo.py | 1 | 5742 | #!/usr/bin/python
'''The Topology module for the HC project.
@version: 1.0
@author: U{Baohua Yang<mailto:yangbaohua@gmail.com>}
@created: Oct 12, 2011
@last update: Oct 22, 2011
@see: U{<https://github.com/yeasy/lazyctrl>}
@TODO: nothing
'''
import networkx as nx
import os.path, sys
from matplotlib import pyplot as plt... | apache-2.0 |
BitTiger-MP/DS502-AI-Engineer | DS502-1702/Homework/week2_homework_solution.py | 1 | 2319 | # coding=utf-8
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
#
# 构造训练数据
x = np.arange(0., 10., 0.2)
m = len(x) # 训练数据点数目
x0 = np.full(m, 1.0)
input_data = np.vstack([x0, x]).T # 将偏置b作为权向量的第一个分量
target_data = 2 * x + 5 + np.random.randn(m)
# 定义batch size
batch_size = 10
# 两种终止条件
loop_max... | apache-2.0 |
chrsrds/scikit-learn | benchmarks/bench_text_vectorizers.py | 15 | 2047 | """
To run this benchmark, you will need,
* scikit-learn
* pandas
* memory_profiler
* psutil (optional, but recommended)
"""
import timeit
import itertools
import numpy as np
import pandas as pd
from memory_profiler import memory_usage
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extrac... | bsd-3-clause |
ricket1978/ggplot | ggplot/geoms/geom_line.py | 12 | 1405 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import sys
from .geom import geom
class geom_line(geom):
DEFAULT_AES = {'color': 'black', 'alpha': None, 'linetype': 'solid', 'size': 1.0}
REQUIRED_AES = {'x', 'y'}
DEFAULT_PARAMS = {'stat': 'ident... | bsd-2-clause |
vikhyat/dask | dask/dataframe/tests/test_dataframe.py | 1 | 90686 | from itertools import product
from datetime import datetime
from operator import getitem
from distutils.version import LooseVersion
import pandas as pd
import pandas.util.testing as tm
import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal
import dask
from dask.async import get_sync
from... | bsd-3-clause |
johannesmik/neurons | MISC/plots/eta.py | 2 | 1132 | """
Show plots of the eta function for different
"""
import numpy as np
import matplotlib.pyplot as plt
def eta(s, eta_reset, t_membran):
ret = np.zeros(s.size)
ret = - eta_reset * np.exp(-s/t_membran)
ret[s < 0] = 0
ret[s == 0] = 0.9 # Spike
return ret
def plot_eta(ax, eta_reset, t_membran):... | bsd-2-clause |
leesavide/pythonista-docs | Documentation/matplotlib/examples/misc/contour_manual.py | 12 | 1630 | """
Example of displaying your own contour lines and polygons using ContourSet.
"""
import matplotlib.pyplot as plt
from matplotlib.contour import ContourSet
import matplotlib.cm as cm
# Contour lines for each level are a list/tuple of polygons.
lines0 = [ [[0,0],[0,4]] ]
lines1 = [ [[2,0],[1,2],[1,3]] ]
lines2 = [ [[... | apache-2.0 |
iismd17/scikit-learn | sklearn/utils/estimator_checks.py | 21 | 51976 | from __future__ import print_function
import types
import warnings
import sys
import traceback
import inspect
import pickle
from copy import deepcopy
import numpy as np
from scipy import sparse
import struct
from sklearn.externals.six.moves import zip
from sklearn.externals.joblib import hash, Memory
from sklearn.ut... | bsd-3-clause |
sanketloke/scikit-learn | examples/applications/plot_stock_market.py | 76 | 8522 | """
=======================================
Visualizing the stock market structure
=======================================
This example employs several unsupervised learning techniques to extract
the stock market structure from variations in historical quotes.
The quantity that we use is the daily variation in quote ... | bsd-3-clause |
kylerbrown/scikit-learn | sklearn/tests/test_calibration.py | 213 | 12219 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_greater, assert_almost_equal,
... | bsd-3-clause |
dbrowneup/PacificBlue | src/PacificBlue.py | 1 | 3661 | #!/usr/bin/env python
#PacificBlue Genome Scaffolding Tool for PacBio Long Reads
#
#Written by Dan Browne
#
#Devarenne Lab
#Department of Biochemistry & Biophysics
#Texas A&M University
#College Station, TX
#
#Contact: dbrowne.up@gmail.com
#Load modules
import sys
import argparse
from datetime import datetime
from m... | gpl-3.0 |
evidation-health/pymc3 | pymc3/tests/test_plots.py | 13 | 1721 | import matplotlib
matplotlib.use('Agg', warn=False)
import numpy as np
from .checks import close_to
import pymc3.plots
from pymc3.plots import *
from pymc3 import Slice, Metropolis, find_hessian, sample
def test_plots():
# Test single trace
from pymc3.examples import arbitrary_stochastic as asmod
with... | apache-2.0 |
simpeg/discretize | discretize/utils/code_utils.py | 1 | 7209 | import numpy as np
import warnings
SCALARTYPES = (complex, float, int, np.number)
def is_scalar(f):
"""Determine if the input argument is a scalar.
The function **is_scalar** returns *True* if the input is an integer,
float or complex number. The function returns *False* otherwise.
Parameters
-... | mit |
amolkahat/pandas | pandas/tests/util/test_testing.py | 1 | 35103 | # -*- coding: utf-8 -*-
import textwrap
import os
import pandas as pd
import pytest
import numpy as np
import sys
from pandas import Series, DataFrame
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas.util.testing import (assert_almost_equal, raise_with_traceback,
... | bsd-3-clause |
rvraghav93/scikit-learn | sklearn/ensemble/gradient_boosting.py | 4 | 76278 | """Gradient Boosted Regression Trees
This module contains methods for fitting gradient boosted regression trees for
both classification and regression.
The module structure is the following:
- The ``BaseGradientBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regressio... | bsd-3-clause |
rishikksh20/scikit-learn | examples/svm/plot_svm_margin.py | 88 | 2540 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
SVM Margins Example
=========================================================
The plots below illustrate the effect the parameter `C` has
on the separation line. A large value of `C` basically tells
our model that w... | bsd-3-clause |
nbeaver/numpy | doc/example.py | 81 | 3581 | """This is the docstring for the example.py module. Modules names should
have short, all-lowercase names. The module name may have underscores if
this improves readability.
Every module should have a docstring at the very top of the file. The
module's docstring may extend over multiple lines. If your docstring doe... | bsd-3-clause |
f2nd/yandex-tank | yandextank/plugins/Console/screen.py | 1 | 40733 | # -*- coding: utf-8 -*-
""" Classes to build full console screen """
import fcntl
import logging
import os
import struct
import termios
import time
import bisect
from collections import defaultdict
import pandas as pd
from ...common import util
def get_terminal_size():
"""
Gets width and height of terminal v... | lgpl-2.1 |
DonBeo/scikit-learn | examples/exercises/plot_cv_digits.py | 232 | 1206 | """
=============================================
Cross-validation on Digits Dataset Exercise
=============================================
A tutorial exercise using Cross-validation with an SVM on the Digits dataset.
This exercise is used in the :ref:`cv_generators_tut` part of the
:ref:`model_selection_tut` section... | bsd-3-clause |
zmlabe/IceVarFigs | Scripts/SeaIce/plot_sit_PIOMAS_monthly_v2.py | 1 | 8177 | """
Author : Zachary M. Labe
Date : 23 August 2016
"""
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
import datetime
import calendar as cal
import matplotlib.colors as c
import cmocean
### Define constants
### Directory and time
directo... | mit |
lenovor/scikit-learn | sklearn/datasets/samples_generator.py | 45 | 56433 | """
Generate samples of synthetic data sets.
"""
# Authors: B. Thirion, G. Varoquaux, A. Gramfort, V. Michel, O. Grisel,
# G. Louppe, J. Nothman
# License: BSD 3 clause
import numbers
import warnings
import array
import numpy as np
from scipy import linalg
import scipy.sparse as sp
from ..preprocessing impo... | bsd-3-clause |
lail3344/sms-tools | lectures/04-STFT/plots-code/windows-2.py | 24 | 1026 | import matplotlib.pyplot as plt
import numpy as np
import time, os, sys
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import dftModel as DF
import utilFunctions as UF
import math
(fs, x) = UF.wavread('../../../sounds/violin-B3.wav')
N = 1024
pin = 5000
w = np... | agpl-3.0 |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/sklearn/linear_model/__init__.py | 34 | 3161 | """
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... | mit |
metaml/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/pyplot.py | 69 | 77521 | import sys
import matplotlib
from matplotlib import _pylab_helpers, interactive
from matplotlib.cbook import dedent, silent_list, is_string_like, is_numlike
from matplotlib.figure import Figure, figaspect
from matplotlib.backend_bases import FigureCanvasBase
from matplotlib.image import imread as _imread
from matplotl... | agpl-3.0 |
karthikvadla16/spark-tk | regression-tests/sparktkregtests/testcases/dicom/dicom_filter_test.py | 13 | 10428 | # vim: set encoding=utf-8
# Copyright (c) 2016 Intel Corporation
#
# 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 require... | apache-2.0 |
larsmans/numpy | numpy/linalg/linalg.py | 2 | 73993 | """Lite version of scipy.linalg.
Notes
-----
This module is a lite version of the linalg.py module in SciPy which
contains high-level Python interface to the LAPACK library. The lite
version only accesses the following LAPACK functions: dgesv, zgesv,
dgeev, zgeev, dgesdd, zgesdd, dgelsd, zgelsd, dsyevd, zheevd, dgetr... | bsd-3-clause |
southpaw94/MachineLearning | DimensionalityReduction/pca.py | 1 | 2233 | # This script uses the 'primary component analysis' technique to
# determine the k dimensions with the most variance of the
# original set of d features.
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from n... | gpl-2.0 |
APMonitor/applications | scheduling_and_control/3products_beginning_application/apm.py | 4 | 26852 | # Import
import csv
import math
import os
import random
import string
import time
import webbrowser
from contextlib import closing
import sys
# Get Python version
ver = sys.version_info[0]
#print('Version: '+str(ver))
if ver==2: # Python 2
import urllib
else: # Python 3+
import urll... | apache-2.0 |
nrhine1/scikit-learn | sklearn/learning_curve.py | 27 | 13650 | """Utilities to evaluate models with respect to a variable
"""
# Author: Alexander Fabisch <afabisch@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import warnings
import numpy as np
from .base import is_classifier, clone
from .cross_validation import check_cv
from .externals.joblib import Parallel, delayed
fro... | bsd-3-clause |
vermouthmjl/scikit-learn | sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py | 65 | 5529 | """
Testing for the gradient boosting loss functions and initial estimators.
"""
import numpy as np
from numpy.testing import assert_array_equal
from numpy.testing import assert_almost_equal
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.utils import check_random_state
from ... | bsd-3-clause |
rabrahm/ceres | vbt/vbtutils.py | 1 | 11062 | import sys
import matplotlib
matplotlib.use("Agg")
base = '../'
sys.path.append(base+"utils/GLOBALutils")
import GLOBALutils
import numpy as np
import scipy
from astropy.io import fits as pyfits
import os
import glob
import scipy.signal
from scipy.signal import medfilt
from scipy import interpolate
import copy
from... | mit |
doncat99/StockRecommendSystem | Source/FetchData/Fetch_Data_Stock_US_Short.py | 1 | 3659 | import os, requests, time, datetime, configparser, warnings
from bs4 import BeautifulSoup
import pandas as pd
from Fetch_Data_Stock_US_Daily import getStocksList
import concurrent.futures
from tqdm import tqdm
def getSignleStockShortInfo(stock):
df = pd.DataFrame()
url = "http://shortsqueeze.com/?symbol=" + st... | mit |
olakiril/pipeline | python/pipeline/utils/quality.py | 5 | 4942 | import numpy as np
from sklearn.linear_model import TheilSenRegressor
from scipy import signal
def compute_quantal_size(scan):
""" Estimate the unit change in calcium response corresponding to a unit change in
pixel intensity (dubbed quantal size, lower is better).
Assumes images are stationary from one ... | lgpl-3.0 |
pkainz/pylearn2 | pylearn2/packaged_dependencies/theano_linear/unshared_conv/test_localdot.py | 44 | 5013 | from __future__ import print_function
import nose
import unittest
import numpy as np
from theano.compat.six.moves import xrange
import theano
from .localdot import LocalDot
from ..test_matrixmul import SymbolicSelfTestMixin
class TestLocalDot32x32(unittest.TestCase, SymbolicSelfTestMixin):
channels = 3
bs... | bsd-3-clause |
NelisVerhoef/scikit-learn | sklearn/tests/test_multiclass.py | 136 | 23649 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing ... | bsd-3-clause |
APPIAN-PET/APPIAN | src/utils.py | 1 | 8440 | import os
import re
import gzip
import shutil
import gzip
import subprocess
import nibabel as nib
import ntpath
import pandas as pd
import numpy as np
import tempfile
import nibabel as nib
from nipype.interfaces.base import (TraitedSpec, File, traits, InputMultiPath, CommandLine, CommandLineInputSpec,
BaseInt... | mit |
tracierenea/gnuradio | gr-filter/examples/chirp_channelize.py | 58 | 7169 | #!/usr/bin/env python
#
# Copyright 2009,2012,2013 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 ... | gpl-3.0 |
DailyActie/Surrogate-Model | 01-codes/scikit-learn-master/examples/calibration/plot_compare_calibration.py | 1 | 5011 | """
========================================
Comparison of Calibration of Classifiers
========================================
Well calibrated classifiers are probabilistic classifiers for which the output
of the predict_proba method can be directly interpreted as a confidence level.
For instance a well calibrated (bi... | mit |
hsiaoyi0504/scikit-learn | examples/plot_multilabel.py | 87 | 4279 | # Authors: Vlad Niculae, Mathieu Blondel
# License: BSD 3 clause
"""
=========================
Multilabel classification
=========================
This example simulates a multi-label document classification problem. The
dataset is generated randomly based on the following process:
- pick the number of labels: n ... | bsd-3-clause |
xiaoxiamii/scikit-learn | sklearn/feature_selection/variance_threshold.py | 238 | 2594 | # Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: 3-clause BSD
import numpy as np
from ..base import BaseEstimator
from .base import SelectorMixin
from ..utils import check_array
from ..utils.sparsefuncs import mean_variance_axis
from ..utils.validation import check_is_fitted
class VarianceThreshold(BaseEstim... | bsd-3-clause |
fxb22/BioGUI | plugins/Views/GEViewPlugins/HeatMap.py | 1 | 4003 | import wx
import plotter as mpl
import numpy as np
import matplotlib.pyplot as plt
class Plugin():
def OnSize(self):
self.bPSize = self.coverPanel.GetSize()
self.plotter.Show(False)
self.plotter.SetSize((self.bPSize[1], self.bPSize[1]))
self.plotter.SetPosition(((self.bPSize[0]-self... | gpl-2.0 |
cajohnst/Optimized_FX_Portfolio | fxstreet_google_sheet.py | 1 | 3172 | import gspread
import pandas as pd
from oauth2client.service_account import ServiceAccountCredentials
import datetime
from datetime import date, timedelta
import os
import fxstreet_scraper
import StringIO
import csv
import settings as sv
on_heroku = False
if 'DYNO' in os.environ:
on_heroku = True
def main():
... | mit |
nealchenzhang/Py4Invst | Backtest_Futures/data.py | 1 | 7656 | # -*- coding: utf-8 -*-
# data.py
from abc import ABCMeta, abstractmethod
import datetime
import os
import numpy as np
import pandas as pd
from Data.Futures_Data.MongoDB_Futures import df_fromMongoDB
from Backtest_Futures.event import MarketEvent
class DataHandler(object):
"""
DataHandler is an abstract b... | mit |
hainm/dask | dask/array/tests/test_percentiles.py | 8 | 1486 | import pytest
pytest.importorskip('numpy')
from dask.utils import skip
import dask.array as da
from dask.array.percentile import _percentile
import dask
import numpy as np
def eq(a, b):
if isinstance(a, da.Array):
a = a.compute(get=dask.get)
if isinstance(b, da.Array):
b = b.compute(get=dask.... | bsd-3-clause |
rs2/pandas | pandas/tests/extension/test_integer.py | 2 | 7327 | """
This file contains a minimal set of tests for compliance with the extension
array interface test suite, and should contain no other tests.
The test suite for the full functionality of the array is located in
`pandas/tests/arrays/`.
The tests in this file are inherited from the BaseExtensionTests, and only
minimal ... | bsd-3-clause |
acuzzio/GridQuantumPropagator | Scripts/multiGraphEneDipole.py | 1 | 7791 | '''
This scripts collects energies and transition dipole matrices from several h5 files and
makes graphs.
It is a 1D module
'''
from argparse import ArgumentParser
from collections import namedtuple
from itertools import repeat
import glob
import multiprocessing as mp
import numpy as np
from quantumpropagator import ... | gpl-3.0 |
huanzhang12/lightgbm-gpu | tests/python_package_test/test_sklearn.py | 3 | 6123 | # coding: utf-8
# pylint: skip-file
import unittest
import lightgbm as lgb
import numpy as np
from sklearn.base import clone
from sklearn.datasets import (load_boston, load_breast_cancer, load_digits,
load_svmlight_file)
from sklearn.externals import joblib
from sklearn.metrics import log... | mit |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/examples/animation/strip_chart_demo.py | 6 | 1514 | """
Emulate an oscilloscope. Requires the animation API introduced in
matplotlib 1.0 SVN.
"""
import matplotlib
import numpy as np
from matplotlib.lines import Line2D
import matplotlib.pyplot as plt
import matplotlib.animation as animation
class Scope:
def __init__(self, ax, maxt=2, dt=0.02):
self.ax = ax... | mit |
arnabgho/sklearn-theano | sklearn_theano/utils/ports.py | 9 | 5242 | import warnings
from sklearn.cross_validation import ShuffleSplit
from itertools import chain
from sklearn.utils import safe_indexing
import numpy as np
import scipy.sparse as sp
# A port of sklearn 0.16 utilities
# to avoid validation issues in older sklearn
def check_consistent_length(*arrays):
"""Check that al... | bsd-3-clause |
ctralie/TUMTopoTimeSeries2016 | Synthetic1DPeriodTests.py | 1 | 3277 | from VideoTools import *
from TDA import *
import os
import numpy as np
import scipy.io as sio
import scipy.interpolate as interp
from sklearn.decomposition import PCA
def getSlidingWindow(x, dim, Tau, dT):
NWindows = int(np.floor((N-dim*Tau)/dT))
X = np.zeros((NWindows, dim))
idx = np.arange(len(x))
f... | apache-2.0 |
lemonade512/BluebonnetsPointsApp | docs/conf.py | 1 | 8764 | # -*- coding: utf-8 -*-
#
# 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.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys
# ... | gpl-3.0 |
tbabej/astropy | astropy/visualization/units.py | 2 | 2941 | # -*- coding: utf-8 -*-
# Licensed under a 3-clause BSD style license - see LICENSE.rst
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
__doctest_skip__ = ['quantity_support']
def quantity_support(format='latex_inline'):
"""
En... | bsd-3-clause |
HolgerPeters/scikit-learn | sklearn/cluster/tests/test_k_means.py | 26 | 32656 | """Testing for K-means"""
import sys
import numpy as np
from scipy import sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing i... | bsd-3-clause |
ishanic/scikit-learn | sklearn/neural_network/tests/test_rbm.py | 142 | 6276 | import sys
import re
import numpy as np
from scipy.sparse import csc_matrix, csr_matrix, lil_matrix
from sklearn.utils.testing import (assert_almost_equal, assert_array_equal,
assert_true)
from sklearn.datasets import load_digits
from sklearn.externals.six.moves import cStringIO as ... | bsd-3-clause |
mjgrav2001/scikit-learn | sklearn/setup.py | 225 | 2856 | import os
from os.path import join
import warnings
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info, BlasNotFoundError
import numpy
libraries = []
if os.name == 'posix':
libraries.appe... | bsd-3-clause |
lukeiwanski/tensorflow-opencl | tensorflow/contrib/learn/python/learn/estimators/estimator.py | 3 | 53280 | # 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 |
Ziqi-Li/bknqgis | pandas/pandas/tests/io/msgpack/test_case.py | 13 | 2740 | # coding: utf-8
from pandas.io.msgpack import packb, unpackb
def check(length, obj):
v = packb(obj)
assert len(v) == length, \
"%r length should be %r but get %r" % (obj, length, len(v))
assert unpackb(v, use_list=0) == obj
def test_1():
for o in [None, True, False, 0, 1, (1 << 6), (1 << 7)... | gpl-2.0 |
themrmax/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_scalability.py | 85 | 5728 | """
============================================
Scalability of Approximate Nearest Neighbors
============================================
This example studies the scalability profile of approximate 10-neighbors
queries using the LSHForest with ``n_estimators=20`` and ``n_candidates=200``
when varying the number of sa... | bsd-3-clause |
rexshihaoren/scikit-learn | examples/linear_model/plot_sparse_recovery.py | 243 | 7461 | """
============================================================
Sparse recovery: feature selection for sparse linear models
============================================================
Given a small number of observations, we want to recover which features
of X are relevant to explain y. For this :ref:`sparse linear ... | bsd-3-clause |
nilbody/h2o-3 | h2o-py/tests/testdir_algos/gbm/pyunit_DEPRECATED_bernoulli_synthetic_data_mediumGBM.py | 1 | 2622 | from builtins import zip
import sys, os
sys.path.insert(1, os.path.join("..","..",".."))
import h2o
from tests import pyunit_utils
from h2o import H2OFrame
import numpy as np
import scipy.stats
from sklearn import ensemble
from sklearn.metrics import roc_auc_score
def bernoulli_synthetic_data_gbm_medium():
# Gener... | apache-2.0 |
jzt5132/scikit-learn | sklearn/__check_build/__init__.py | 345 | 1671 | """ Module to give helpful messages to the user that did not
compile the scikit properly.
"""
import os
INPLACE_MSG = """
It appears that you are importing a local scikit-learn source tree. For
this, you need to have an inplace install. Maybe you are in the source
directory and you need to try from another location.""... | bsd-3-clause |
harisbal/pandas | pandas/core/algorithms.py | 3 | 60557 | """
Generic data algorithms. This module is experimental at the moment and not
intended for public consumption
"""
from __future__ import division
from warnings import warn, catch_warnings, simplefilter
from textwrap import dedent
import numpy as np
from pandas.core.dtypes.cast import (
maybe_promote, construct_1... | bsd-3-clause |
emmaggie/hmmlearn | hmmlearn/tests/test_hmm.py | 2 | 21345 | from __future__ import print_function
from unittest import TestCase
import numpy as np
from nose import SkipTest
from numpy.testing import assert_array_equal, assert_array_almost_equal
from sklearn.datasets.samples_generator import make_spd_matrix
from sklearn import mixture
from sklearn.utils import check_random_sta... | bsd-3-clause |
leggitta/mne-python | examples/time_frequency/plot_source_space_time_frequency.py | 19 | 2314 | """
===================================================
Compute induced power in the source space with dSPM
===================================================
Returns STC files ie source estimates of induced power
for different bands in the source space. The inverse method
is linear based on dSPM inverse operator.
"... | bsd-3-clause |
RPGOne/Skynet | pactools-master/pactools/comodulogram.py | 1 | 34259 | import warnings
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d, interp2d
from .dar_model.base_dar import BaseDAR
from .dar_model.dar import DAR
from .dar_model.preprocess import multiple_extract_driver
from .utils.progress_bar import ProgressBar
from .utils... | bsd-3-clause |
buguen/pylayers | pylayers/simul/examples/ex_simulem_fur.py | 3 | 1157 | from pylayers.simul.simulem import *
from pylayers.signal.bsignal import *
from pylayers.measures.mesuwb import *
import matplotlib.pyplot as plt
from pylayers.gis.layout import *
#M=UWBMesure(173)
M=UWBMesure(13)
#M=UWBMesure(1)
cir=TUsignal()
cirf=TUsignal()
#cir.readcir("where2cir-tx001-rx145.mat","Tx001")
#cir... | lgpl-3.0 |
arnold-jr/sem-classify | semclassify/plots.py | 1 | 4028 | import matplotlib
import seaborn as sns
matplotlib.rcParams['savefig.dpi'] = 2 * matplotlib.rcParams['savefig.dpi']
matplotlib.rc('text', usetex=True)
from matplotlib import cm
import matplotlib.pyplot as plt
from pylab import imshow, show
import pandas as pd
pd.set_option('expand_frame_repr', False)
import numpy as np... | mit |
ClimbsRocks/scikit-learn | sklearn/tests/test_multioutput.py | 39 | 6609 | import numpy as np
import scipy.sparse as sp
from sklearn.utils import shuffle
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raises_regex
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing impor... | bsd-3-clause |
mprhode/malware-prediction-rnn | main.py | 1 | 1306 | import numpy as np
import pandas as pd
from copy import deepcopy
import gc
from col_headers import Header
from experiments import Experiments, Configs
from experiments.useful import timestamped_to_vector, unison_shuffled_copies, extract_neg
# Load data
headers = Header()
c = headers.classification_col
v = headers.vec... | apache-2.0 |
CKehl/pylearn2 | pylearn2/scripts/datasets/step_through_small_norb.py | 49 | 3123 | #! /usr/bin/env python
"""
A script for sequentially stepping through SmallNORB, viewing each image and
its label.
Intended as a demonstration of how to iterate through NORB images,
and as a way of testing SmallNORB's StereoViewConverter.
If you just want an image viewer, consider
pylearn2/scripts/show_binocular_gra... | bsd-3-clause |
rwcarlsen/cyclus.github.com | source/numpydoc/docscrape_sphinx.py | 41 | 9437 | from __future__ import division, absolute_import, print_function
import sys, re, inspect, textwrap, pydoc
import sphinx
import collections
from .docscrape import NumpyDocString, FunctionDoc, ClassDoc
if sys.version_info[0] >= 3:
sixu = lambda s: s
else:
sixu = lambda s: unicode(s, 'unicode_escape')
class Sp... | bsd-3-clause |
dsavoiu/kafe2 | kafe2/fit/xy/ensemble.py | 1 | 29429 | try:
import typing # help IDEs with type-hinting inside docstrings
except ImportError:
pass
import numpy as np
import scipy.stats
import six
from .._base import FitEnsembleBase, FitEnsembleException
from ..tools.ensemble import EnsembleVariable, EnsembleVariablePlotter
from .cost import XYCostFunction_Chi2
fr... | gpl-3.0 |
BU-PyCon/Meeting-1 | Programs/basic_plotting.py | 1 | 1412 | #BiMonBUPyCon - First meeting - 3/22/2015
selection = input('Input plot #: ')
print(type(selection))
#Basic plotting
if selection == '1':
#Example 1: Basic importing and plotting procedures
#-------------------------------------------------------------------------------------
import matplotlib
import matplotli... | mit |
GuessWhoSamFoo/pandas | pandas/tests/test_lib.py | 2 | 7875 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
from pandas._libs import lib, writers as libwriters
from pandas import Index
import pandas.util.testing as tm
class TestMisc(object):
def test_max_len_string_array(self):
arr = a = np.array(['foo', 'b', np.nan], dtype='object')
assert l... | bsd-3-clause |
bafnalab/CLEAR | CLEAR.py | 1 | 13998 | '''
Copyleft Oct 27, 2016 Arya Iranmehr, PhD Student, Bafna Lab, UC San Diego, Email: airanmehr@gmail.com
'''
import numpy as np;
np.set_printoptions(linewidth=200, precision=5, suppress=True)
import pandas as pd;
pd.options.display.max_rows = 20;
pd.options.display.expand_frame_repr = False
import pylab as plt
import... | mit |
avuan/PyMPA37 | main.pympa.dir/pympa.py | 1 | 33384 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# 2016/08/23 Version 34 - parameters24 input file needed
# 2017/10/27 Version 39 - Reformatted PEP8 Code
# 2017/11/05 Version 40 - Corrections to tdifmin, tstda calculations
# 2019/10/15 Version pympa - xcorr substitued with correlate_template from obspy
# First Version Au... | gpl-3.0 |
pierre-chaville/automlk | automlk/graphs.py | 1 | 21305 | import logging
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pylab as plt
import seaborn.apionly as sns
import itertools
import pickle
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import confusion_matrix
from .config import METRIC_NULL
from .con... | mit |
mne-tools/mne-tools.github.io | dev/_downloads/ceb76325480611dc7a2e973a3b7a782c/20_dipole_fit.py | 5 | 5301 | # -*- coding: utf-8 -*-
"""
============================================================
Source localization with equivalent current dipole (ECD) fit
============================================================
This shows how to fit a dipole :footcite:`Sarvas1987` using mne-python.
For a comparison of fits between MN... | bsd-3-clause |
sangwook236/SWDT | sw_dev/python/ext/test/high_performance_computing/spark/pyspark_descriptive_statistics.py | 2 | 4298 | #!/usr/bin/env python
from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession, SQLContext
import pyspark.sql.types as types
import matplotlib.pyplot as plt
from bokeh.plotting import figure, show, output_file
from bokeh.io import output_notebook
import traceback, sys
def describe_statistics()... | gpl-3.0 |
stargaser/astropy | astropy/visualization/wcsaxes/patches.py | 4 | 3408 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
from matplotlib.patches import Polygon
from astropy import units as u
from astropy.coordinates.representation import UnitSphericalRepresentation
from astropy.coordinates.matrix_utilities import rotation_matrix, matrix_product
__all_... | bsd-3-clause |
SU-ECE-17-7/hotspotter | hstest/warp_parallel.py | 2 | 6147 | '''
There is an issue with cv2.warpAffine on macs.
This is a test to further investigate the issue.
python -c "import cv2; help(cv2.warpAffine)"
'''
from __future__ import division, print_function
#import matplotlib
#matplotlib.use('Qt4Agg')
import os
import sys
from os.path import dirname, join, expanduser, exists
fro... | apache-2.0 |
JanetMatsen/Machine_Learning_CSE_546 | HW3/code/not_updated/ridge_regression.py | 2 | 12001 | import numpy as np
import pandas as pd
import scipy.sparse as sp
import scipy.sparse.linalg as splin
import time;
from classification_base import ClassificationBase
class RidgeMulti(ClassificationBase):
"""
Train multiple ridge models.
"""
def __init__(self, X, y, lam, W=None, verbose=False, sparse=... | mit |
robin-lai/scikit-learn | examples/ensemble/plot_forest_importances.py | 241 | 1761 | """
=========================================
Feature importances with forests of trees
=========================================
This examples shows the use of forests of trees to evaluate the importance of
features on an artificial classification task. The red bars are the feature
importances of the forest, along wi... | bsd-3-clause |
Nehoroshiy/urnn | examples/lasagne_rnn.py | 1 | 8511 | import theano
import lasagne
import numpy as np
import theano.tensor as T
from numpy import random as rnd, linalg as la
from layers import UnitaryLayer, UnitaryKronLayer, RecurrentUnitaryLayer, ComplexLayer, WTTLayer, ModRelu
from matplotlib import pyplot as plt
from utils.optimizations import nesterov_momentum, cust... | mit |
NeoBoy/STSP_IIUI-Spring2016 | Task2/nnet.py | 1 | 11003 | # -*- coding: utf-8 -*-
"""
The goal of this file is to design a class for Neural Networks
@author: Sharjeel Abid Butt
@References
1. http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm
2. https://grantbeyleveld.wordpress.com/2015/10/09/implementing-a-artificial-neural-network-in-python/
3. ... | bsd-2-clause |
marcsans/cnn-physics-perception | phy/lib/python2.7/site-packages/matplotlib/ticker.py | 4 | 63240 | """
Tick locating and formatting
============================
This module contains classes to support completely configurable tick locating
and formatting. Although the locators know nothing about major or minor
ticks, they are used by the Axis class to support major and minor tick
locating and formatting. Generic t... | mit |
mc-hammertimeseries/cs207project | procs/_corr.py | 1 | 2825 | import numpy.fft as nfft
import numpy as np
import timeseries as ts
from scipy.stats import norm
from .fft import fft
def tsmaker(m, s, j):
meta={}
meta['order'] = int(np.random.choice([-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5]))
meta['blarg'] = int(np.random.choice([1, 2]))
t = np.arange(0.0, 1.0, 0.01)
... | mit |
Myasuka/scikit-learn | sklearn/preprocessing/data.py | 113 | 56747 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Eric Martin <eric@ericmart.in>
# License: BSD 3 clause
from itertools import chain, combina... | bsd-3-clause |
alan-unravel/bokeh | bokeh/charts/builder/donut_builder.py | 31 | 8206 | """This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the Donut class which lets you build your Donut charts just passing
the arguments to the Chart class and calling the proper functions.
It also add a new chained stacked method.
"""
#---------------------... | bsd-3-clause |
gnavvy/JellyFish | app.py | 1 | 2566 | __author__ = 'ywang'
from gevent import monkey
monkey.patch_all()
from flask import Flask, render_template
from flask_socketio import SocketIO, emit
# from flask.ext import assets
app = Flask(__name__)
socketio = SocketIO(app)
# import os
# env = assets.Environment(app)
# env.load_path = [
# os.path.join(os.pat... | mit |
shusenl/scikit-learn | examples/manifold/plot_manifold_sphere.py | 258 | 5101 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=============================================
Manifold Learning methods on a severed sphere
=============================================
An application of the different :ref:`manifold` techniques
on a spherical data-set. Here one can see the use of
dimensionality reducti... | bsd-3-clause |
aabadie/scikit-learn | examples/ensemble/plot_gradient_boosting_oob.py | 82 | 4768 | """
======================================
Gradient Boosting Out-of-Bag estimates
======================================
Out-of-bag (OOB) estimates can be a useful heuristic to estimate
the "optimal" number of boosting iterations.
OOB estimates are almost identical to cross-validation estimates but
they can be compute... | bsd-3-clause |
jbrundle/earthquake-forecasts | plot_Forecast_EPS_Region_Circle.py | 1 | 1373 | #!/opt/local/bin python
##############################################################################
# Code plots Gutenberg-Richter relation for cumulative frequency-magnitude
# Usage: python plot_ANSS_seismicity.py NELat NELng SWLat SWLng MagLo
#
# Where: Latitude in degrees
# ... | mit |
RPGOne/Skynet | scikit-learn-c604ac39ad0e5b066d964df3e8f31ba7ebda1e0e/sklearn/tree/tree.py | 9 | 29885 | """
This module gathers tree-based methods, including decision, regression and
randomized trees. Single and multi-output problems are both handled.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Da... | bsd-3-clause |
robcarver17/pysystemtrade | syscore/dateutils.py | 1 | 20758 | """
Various routines to do with dates
"""
from enum import Enum
import datetime
import time
import calendar
import numpy as np
import pandas as pd
from syscore.genutils import sign
from syscore.objects import missing_data
"""
First some constants
"""
CALENDAR_DAYS_IN_YEAR = 365.25
BUSINESS_DAYS_IN_YEAR = 256.0
ROO... | gpl-3.0 |
jeffery-do/Vizdoombot | doom/lib/python3.5/site-packages/matplotlib/tests/test_bbox_tight.py | 4 | 3861 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from distutils.version import LooseVersion
from matplotlib.externals import six
from matplotlib.externals.six.moves import xrange
import numpy as np
from matplotlib import rcParams
from matplotlib.testing.dec... | mit |
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