repo_name stringlengths 7 92 | path stringlengths 5 149 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 911 693k | license stringclasses 15
values |
|---|---|---|---|---|---|
fxia22/pointGAN | show_gan_rnn.py | 1 | 2043 | from __future__ import print_function
from show3d_balls import *
import argparse
import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torc... | mit |
shikhardb/scikit-learn | examples/linear_model/plot_iris_logistic.py | 283 | 1678 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic Regression 3-class Classifier
=========================================================
Show below is a logistic-regression classifiers decision boundaries on the
`iris <http://en.wikipedia.org/wiki/Iris_f... | bsd-3-clause |
CartoDB/crankshaft | release/python/0.8.2/crankshaft/crankshaft/segmentation/segmentation.py | 1 | 8893 | """
Segmentation creation and prediction
"""
import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn import metrics
from sklearn.cross_validation import train_test_split
from crankshaft.analysis_data_provider import AnalysisDataProvider
# NOTE: added optional param here
class Segmenta... | bsd-3-clause |
claesenm/HPOlib | HPOlib/Plotting/plotTrace_perExp.py | 5 | 6055 | #!/usr/bin/env python
##
# wrapping: A program making it easy to use hyperparameter
# optimization software.
# Copyright (C) 2013 Katharina Eggensperger and Matthias Feurer
#
# 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
# ... | gpl-3.0 |
GarrettSmith/Nearness | graphchi/conf/adminhtml/plots/plotter.py | 3 | 1235 | #!/usr/bin/python
import sys
import os
import matplotlib
import numpy
import matplotlib
matplotlib.use('AGG')
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
from matplotlib.ticker import FormatStrFormatter
def getArg(param, default=""):
if (sys.argv.count(param) == 0): return default
... | gpl-3.0 |
sukritranjan/ranjansasselov2016b | compute_UV_doses.py | 1 | 29816 | # -*- coding: iso-8859-1 -*-
"""
This code is used to weigh the UV radiances we compute by biological action spectra.
"""
########################
###Import useful libraries
########################
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import pdb
from matplotlib.pyp... | mit |
dtkav/naclports | ports/ipython-ppapi/kernel.py | 7 | 12026 | # Copyright (c) 2014 Google Inc. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""A simple shell that uses the IPython messaging system."""
# Override platform information.
import platform
platform.system = lambda: "pnacl"
platform.release =... | bsd-3-clause |
aquar25/losslessh264 | plot_prior_misses.py | 40 | 1124 | # Run h264dec on a single file compiled with PRIOR_STATS and then run this script
# Outputs timeseries plot at /tmp/misses.pdf
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import os
def temporal_misses(key):
values = data[key]
numbins = 100
binsize = len(values) // n... | bsd-2-clause |
omerwe/LEAP | leapUtils.py | 1 | 10998 | import numpy as np
from optparse import OptionParser
import scipy.linalg as la
import scipy.stats as stats
import scipy.linalg.blas as blas
import pandas as pd
import csv
import time
import fastlmm.util.VertexCut as vc
from pysnptools.snpreader.bed import Bed
import pysnptools.util as pstutil
import pysnptools.util.phe... | apache-2.0 |
puruckertom/ubertool | ubertool/varroapop/varroapop_functions.py | 1 | 11627 | from __future__ import division #brings in Python 3.0 mixed type calculation rules
import logging
import json
import requests
import math
import pandas as pd
import os
rest_url_varroapop = os.environ.get('OPENCPU_REST_SERVER')
#rest_url_varroapop = 'http://localhost'
if not os.environ.get('OPENCPU_REST_SERVER'):
... | unlicense |
lobnek/pyutil | test/test_mongo/test_engine/test_strategy.py | 1 | 3434 | from pyutil.mongo.engine.strategy import Strategy, strategies, configuration
from pyutil.mongo.engine.symbol import Symbol, Group
from pyutil.performance.drawdown import drawdown
from pyutil.performance.month import monthlytable
from pyutil.performance.return_series import from_nav
from pyutil.portfolio.portfolio impor... | mit |
carlvlewis/bokeh | bokeh/charts/builder/tests/test_line_builder.py | 33 | 2376 | """ This is the Bokeh charts testing interface.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with thi... | bsd-3-clause |
lamotriz/sistemas-de-aterramento | src/agilent_u2531a.py | 1 | 14700 | # -*- coding: utf-8 -*-
# Comunicacao com a placa agilent U2531A
#
# UFC - Universidade de Federal do Ceará
#
# Responsáveis:
# Felipe Bandeira da Silva
# Francisco Alexander
#
from __future__ import division
import platform
#if platform.system() == 'Windows':
# import visa
#else:
# import visa_linux_emulatio... | apache-2.0 |
xyguo/scikit-learn | examples/model_selection/plot_underfitting_overfitting.py | 53 | 2668 | """
============================
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 |
cgarrard/osgeopy-code | Chapter13/listing13_4.py | 1 | 1939 | # Script to draw world countries as patches.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.path import Path
import matplotlib.patches as patches
from osgeo import ogr
def order_coords(coords, clockwise):
"""Orders coordinates."""
total = 0
x1, y1 = coords[0]
for x, y in coords[1:... | mit |
tillahoffmann/tensorflow | tensorflow/contrib/metrics/python/kernel_tests/histogram_ops_test.py | 130 | 9577 | # 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 |
arthurmensch/modl | benchmarks/log.py | 1 | 2179 | import time
import numpy as np
from lightning.impl.primal_cd import CDClassifier
from lightning.impl.sag import SAGAClassifier
from sklearn.datasets import fetch_20newsgroups_vectorized
from lightning.classification import SAGClassifier
from sklearn.linear_model import LogisticRegression
bunch = fetch_20newsgroups_v... | bsd-2-clause |
phev8/ward-metrics | wardmetrics/visualisations.py | 1 | 16641 | import matplotlib.pyplot as plt
def plot_events_with_segment_scores(segment_results, ground_truth_events, detected_events, use_datetime_x=False, show=True):
"""
Test
:param segment_results:
:param ground_truth_events:
:param detected_events:
:param use_datetime_x:
:param show:
:return:... | mit |
cloud9ers/gurumate | environment/share/doc/ipython/examples/parallel/options/mcpricer.py | 2 | 3552 | # <nbformat>2</nbformat>
# <markdowncell>
# # Parallel Monto-Carlo options pricing
# <markdowncell>
# ## Problem setup
# <codecell>
from __future__ import print_function
import sys
import time
from IPython.parallel import Client
import numpy as np
from mckernel import price_options
from matplotlib import pyplot a... | lgpl-3.0 |
mattilyra/scikit-learn | examples/linear_model/plot_logistic.py | 312 | 1426 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logit function
=========================================================
Show in the plot is how the logistic regression would, in this
synthetic dataset, classify values as either 0 or 1,
i.e. class one or two, u... | bsd-3-clause |
cactusbin/nyt | matplotlib/lib/matplotlib/tests/test_text.py | 2 | 6893 | from __future__ import print_function
import numpy as np
import matplotlib
from matplotlib.testing.decorators import image_comparison, knownfailureif, cleanup
import matplotlib.pyplot as plt
import warnings
from nose.tools import with_setup
@image_comparison(baseline_images=['font_styles'])
def test_font_styles():
... | unlicense |
spectralDNS/shenfun | docs/paper/CG/CGpaper_dirichlet.py | 1 | 8842 | """
This script has been used to compute the Dirichlet results of the paper
Efficient spectral-Galerkin methods for second-order equations using different Chebyshev bases
The results have been computed using Python 3.9 and Shenfun 3.1.1.
The generalized Chebyshev-Tau results are computed with dedalus,
and are as... | bsd-2-clause |
jpautom/scikit-learn | sklearn/linear_model/tests/test_perceptron.py | 378 | 1815 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_raises
from sklearn.utils import check_random_state
from sklearn.datasets import load_iris
from sklearn.linear_model import Pe... | bsd-3-clause |
kashif/scikit-learn | sklearn/model_selection/tests/test_search.py | 23 | 30837 | """Test the search module"""
from collections import Iterable, Sized
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.externals.six.moves import xrange
from itertools import chain, product
import pickle
import sys
import numpy as np
import scipy.sparse as sp
from sklearn.utils.fixes import ... | bsd-3-clause |
tracierenea/gnuradio | gr-filter/examples/channelize.py | 58 | 7003 | #!/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 |
robwarm/gpaw-symm | tools/niflheim-agts.py | 1 | 5426 | import os
import sys
import glob
import shutil
import subprocess
def cmd(c):
x = os.system(c)
assert x == 0, c
def fail(subject, email=None, filename='/dev/null', mailer='mail'):
assert mailer in ['mailx', 'mail', 'mutt']
import os
if email is not None:
if filename == '/dev/null':
... | gpl-3.0 |
rahul-c1/scikit-learn | examples/linear_model/lasso_dense_vs_sparse_data.py | 348 | 1862 | """
==============================
Lasso on dense and sparse data
==============================
We show that linear_model.Lasso provides the same results for dense and sparse
data and that in the case of sparse data the speed is improved.
"""
print(__doc__)
from time import time
from scipy import sparse
from scipy ... | bsd-3-clause |
micahcochran/geopandas | geopandas/tools/tests/test_sjoin.py | 1 | 10287 | from __future__ import absolute_import
from distutils.version import LooseVersion
import numpy as np
import pandas as pd
from shapely.geometry import Point, Polygon
import geopandas
from geopandas import GeoDataFrame, GeoSeries, read_file, base
from geopandas import sjoin
import pytest
from pandas.util.testing impo... | bsd-3-clause |
r-mart/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 |
markelg/xray | xray/test/test_formatting.py | 6 | 3865 | import numpy as np
import pandas as pd
from xray.core import formatting
from xray.core.pycompat import PY3
from . import TestCase
class TestFormatting(TestCase):
def test_get_indexer_at_least_n_items(self):
cases = [
((20,), (slice(10),)),
((3, 20,), (0, slice(10))),
... | apache-2.0 |
krikru/tensorflow-opencl | tensorflow/examples/learn/wide_n_deep_tutorial.py | 24 | 8941 | # 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 |
hmendozap/auto-sklearn | test/util/test_common.py | 1 | 1264 | # -*- encoding: utf-8 -*-
from __future__ import print_function
from functools import partial
import os
import unittest
from autosklearn.util import set_auto_seed, get_auto_seed, del_auto_seed, \
check_pid
class TestUtilsCommon(unittest.TestCase):
_multiprocess_can_split_ = True
def setUp(self):
... | bsd-3-clause |
duncanmmacleod/gwpy | gwpy/plot/axes.py | 1 | 21895 | # -*- coding: utf-8 -*-
# Copyright (C) Duncan Macleod (2018-2020)
#
# This file is part of GWpy.
#
# GWpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option)... | gpl-3.0 |
phronesis-mnemosyne/census-schema-alignment | wit/wit/dev/authorship-embedding.py | 1 | 4685 | import pandas as pd
import urllib2
from pprint import pprint
from matplotlib import pyplot as plt
from bs4 import BeautifulSoup
from hashlib import md5
import sys
sys.path.append('/Users/BenJohnson/projects/what-is-this/wit/')
from wit import *
pd.set_option('display.max_rows', 50)
pd.set_option('display.max_column... | apache-2.0 |
shikhardb/scikit-learn | sklearn/feature_selection/tests/test_chi2.py | 221 | 2398 | """
Tests for chi2, currently the only feature selection function designed
specifically to work with sparse matrices.
"""
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix
import scipy.stats
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.feature_selection.univariate_selection im... | bsd-3-clause |
xubenben/scikit-learn | sklearn/manifold/locally_linear.py | 206 | 25061 | """Locally Linear Embedding"""
# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
# Jake Vanderplas -- <vanderplas@astro.washington.edu>
# License: BSD 3 clause (C) INRIA 2011
import numpy as np
from scipy.linalg import eigh, svd, qr, solve
from scipy.sparse import eye, csr_matrix
from ..base import B... | bsd-3-clause |
vbraga/irismatch | src/iris_detection.py | 2 | 1707 | #!/usr/bin/env python2.7
# Imported from https://gitorious.org/hough-circular-transform
# License: GPLv3
# Date: Fri, Mar 7 2014
import matplotlib.pyplot as plt
import matplotlib.patches as plt_patches
import houghcirculartransform as hct
import numpy as np
def detect_iris(filename):
"""
Example function call... | gpl-2.0 |
nathanielvarona/airflow | tests/providers/apache/pinot/hooks/test_pinot.py | 1 | 9827 | #
# 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... | apache-2.0 |
rubikloud/scikit-learn | benchmarks/bench_plot_lasso_path.py | 301 | 4003 | """Benchmarks of Lasso regularization path computation using Lars and CD
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
from collections import defaultdict
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model import lars_pat... | bsd-3-clause |
hugobowne/scikit-learn | sklearn/externals/joblib/parallel.py | 31 | 35665 | """
Helpers for embarrassingly parallel code.
"""
# Author: Gael Varoquaux < gael dot varoquaux at normalesup dot org >
# Copyright: 2010, Gael Varoquaux
# License: BSD 3 clause
from __future__ import division
import os
import sys
import gc
import warnings
from math import sqrt
import functools
import time
import thr... | bsd-3-clause |
nickgentoo/scikit-learn-graph | skgraph/datasets/load_graph_datasets.py | 1 | 27349 | # -*- coding: utf-8 -*-
"""
Created on Fri Mar 13 13:02:41 2015
Copyright 2015 Nicolo' Navarin
This file is part of scikit-learn-graph.
scikit-learn-graph 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 ... | gpl-3.0 |
SamProtas/PALiquor | geocode_fixes.py | 1 | 2733 | import os
import pandas as pd
import numpy as np
import sqlite3
import requests
import time
def fix_location(lid, new_address):
pd.set_option('display.mpl_style', 'default')
PROJECT_ROOT = os.path.dirname(os.path.realpath(__file__))
DATABASE1 = os.path.join(PROJECT_ROOT, 'dbs', 'licensees.db')
... | gpl-2.0 |
liyi193328/seq2seq | seq2seq/contrib/learn/tests/dataframe/arithmetic_transform_test.py | 62 | 2343 | # 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 |
jeffzheng1/tensorflow | tensorflow/contrib/learn/python/learn/experiment.py | 4 | 15233 | # 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 |
bendalab/thunderfish | thunderfish/pulseplots.py | 3 | 38137 | """
Plot and save key steps in pulses.py for visualizing the alorithm.
"""
import glob
import numpy as np
from scipy import stats
from matplotlib import rcParams, gridspec, ticker
import matplotlib.pyplot as plt
try:
from matplotlib.colors import colorConverter as cc
except ImportError:
import matp... | gpl-3.0 |
jerjorg/BZI | BZI/convergence.py | 1 | 6793 | import numpy as np
import matplotlib.pyplot as plt
import time
from BZI.symmetry import make_ptvecs
from BZI.sampling import make_grid
from BZI.pseudopots import Al_PP
from BZI.integration import monte_carlo
from BZI.plots import PlotMesh
class Convergence(object):
""" Compare integrations of pseudo-potentials by... | gpl-3.0 |
chrsrds/scikit-learn | examples/manifold/plot_swissroll.py | 72 | 1295 | """
===================================
Swiss Roll reduction with LLE
===================================
An illustration of Swiss Roll reduction
with locally linear embedding
"""
# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
# License: BSD 3 clause (C) INRIA 2011
print(__doc__)
import matplotlib.pyplot... | bsd-3-clause |
perrette/pyglacier | pyglacier/plotting.py | 1 | 1106 | import matplotlib.pyplot as plt
#
# plotting
#
def plot_elevation(ds, ax=None):
if ax is None:
ax = plt.gca()
ds['hs'].plot(ax=ax,label="surface")
ds['hb'].plot(ax=ax,label="bottom")
# add horizontal line to indicate sea level
ax.hlines(0, ds.x[0], ds.x[-1], linestyle='dashed', color='blac... | mit |
samzhang111/wikipedia-jargon | all-subjects/make_tf_differences.py | 1 | 2815 | from __future__ import print_function
import msgpack
import sys
import os
from collections import defaultdict
from helpers import text_dict_to_term_dict
from WikiExtractor import clean, compact
import pandas as pd
def remove_wikipedia_markup(text):
return compact(clean(text.decode('utf8')))
def print_help_and_exi... | gpl-3.0 |
ofgulban/scikit-image | doc/examples/filters/plot_rank_mean.py | 7 | 1525 | """
============
Mean filters
============
This example compares the following mean filters of the rank filter package:
* **local mean**: all pixels belonging to the structuring element to compute
average gray level.
* **percentile mean**: only use values between percentiles p0 and p1
(here 10% and 90%).
* **bila... | bsd-3-clause |
CrazyGuo/vincent | examples/map_examples.py | 11 | 6721 | # -*- coding: utf-8 -*-
"""
Vincent Map Examples
"""
#Build a map from scratch
from vincent import *
world_topo = r'world-countries.topo.json'
state_topo = r'us_states.topo.json'
lake_topo = r'lakes_50m.topo.json'
county_geo = r'us_counties.geo.json'
county_topo = r'us_counties.topo.json'
or_topo = r'or_counties.t... | mit |
sinhrks/scikit-learn | examples/svm/plot_custom_kernel.py | 171 | 1546 | """
======================
SVM with custom kernel
======================
Simple usage of Support Vector Machines to classify a sample. It will
plot the decision surface and the support vectors.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
# import some data... | bsd-3-clause |
cadrev/Titanic-Prediction | data-munging.py | 1 | 3412 | #
# Title : Data munging(AKA cleaning) the Titanic Data
# Author : Felan Carlo Garcia
#
# Notes:
# -- Code is based on the Kaggle Python Tutorial
# -- data cleaning prior to implementing a machine learning algorithm.
import numpy as np
import pandas as pd
def processdata(filename, outputname):
df = pd.read... | mit |
IsCoolEntertainment/debpkg_python-pyzmq | examples/bench/plot_latency.py | 12 | 2229 | """Plot latency data from messaging benchmarks.
To generate the data for each library, I started the server and then did
the following for each client::
from xmlrpc_client import client
for i in range(9):
s = '0'*10**i
print s
%timeit client.echo(s)
"""
from matplotlib.pylab import *
... | lgpl-3.0 |
toobaz/pandas | pandas/tests/indexes/datetimes/test_ops.py | 2 | 18288 | from datetime import datetime
import warnings
import numpy as np
import pytest
from pandas.core.dtypes.generic import ABCDateOffset
import pandas as pd
from pandas import (
DatetimeIndex,
Index,
PeriodIndex,
Series,
Timestamp,
bdate_range,
date_range,
)
from pandas.tests.test_base import ... | bsd-3-clause |
piyueh/PetIBM | examples/api_examples/oscillatingcylinder2dRe100_GPU/scripts/plotDragCoefficient.py | 2 | 2057 | """
Plot the drag coefficient over 4 oscillation cycles.
"""
import pathlib
import numpy
from matplotlib import pyplot
from scipy import signal
# Read the drag force from file.
simu_dir = pathlib.Path(__file__).parents[1]
data_dir = simu_dir / 'output'
filepath = data_dir / 'forces-0.txt'
with open(filepath, 'r') as... | bsd-3-clause |
lammy/artisan | setup-mac3.py | 9 | 8968 | """
This is a setup.py script generated by py2applet
Usage:
python3 setup-mac3.py py2app
"""
# manually remove sample-data mpl subdirectory from Python installation:
# sudo rm -rf /Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/matplotlib/mpl-data/sample_data
from distutils import s... | gpl-3.0 |
arjoly/scikit-learn | sklearn/ensemble/tests/test_base.py | 284 | 1328 | """
Testing for the base module (sklearn.ensemble.base).
"""
# Authors: Gilles Louppe
# License: BSD 3 clause
from numpy.testing import assert_equal
from nose.tools import assert_true
from sklearn.utils.testing import assert_raise_message
from sklearn.datasets import load_iris
from sklearn.ensemble import BaggingCla... | bsd-3-clause |
matthew-tucker/mne-python | examples/time_frequency/plot_source_power_spectrum.py | 19 | 1929 | """
=========================================================
Compute power spectrum densities of the sources with dSPM
=========================================================
Returns an STC file containing the PSD (in dB) of each of the sources.
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-pariste... | bsd-3-clause |
RomainBrault/scikit-learn | sklearn/utils/deprecation.py | 36 | 2418 | import warnings
__all__ = ["deprecated", ]
class deprecated(object):
"""Decorator to mark a function or class as deprecated.
Issue a warning when the function is called/the class is instantiated and
adds a warning to the docstring.
The optional extra argument will be appended to the deprecation mes... | bsd-3-clause |
haisland0909/Denoising-Dirty-Documents | script/prediction.py | 1 | 5057 | # coding: UTF8
from sklearn.pipeline import FeatureUnion
from sklearn.grid_search import GridSearchCV
from sklearn import preprocessing
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
import sklearn.linear_model
import img_to_pickle as i_p
import features as f
import classify
import prepr... | apache-2.0 |
skudriashev/incubator-airflow | setup.py | 2 | 9592 | # -*- 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 |
fbagirov/scikit-learn | benchmarks/bench_glm.py | 297 | 1493 | """
A comparison of different methods in GLM
Data comes from a random square matrix.
"""
from datetime import datetime
import numpy as np
from sklearn import linear_model
from sklearn.utils.bench import total_seconds
if __name__ == '__main__':
import pylab as pl
n_iter = 40
time_ridge = np.empty(n_it... | bsd-3-clause |
sjsrey/pysal | pysal/lib/common.py | 4 | 3385 | import copy
import sys
import time
# external imports
import numpy as np
import numpy.linalg as la
import scipy as sp
import scipy.stats as stats
from libpysal.cg.kdtree import KDTree
from scipy.spatial.distance import pdist, cdist
import pandas
try:
from patsy import PatsyError
except ImportError:
PatsyErr... | bsd-3-clause |
JosmanPS/scikit-learn | sklearn/semi_supervised/label_propagation.py | 128 | 15312 | # coding=utf8
"""
Label propagation in the context of this module refers to a set of
semisupervised classification algorithms. In the high level, these algorithms
work by forming a fully-connected graph between all points given and solving
for the steady-state distribution of labels at each point.
These algorithms per... | bsd-3-clause |
kedz/cuttsum | old/python/cuttsum/readers.py | 1 | 2494 | import codecs
import numpy as np
from sklearn.feature_extraction import DictVectorizer
def gold_reader(bow_file, l_file, sim_idx, vector=u'latent'):
op = codecs.open
sims = []
vectors = []
labels = []
unicodes = []
last_hour = None
with op(bow_file, u'r', u'utf-8') as bf, op(l_file, u'r', u... | apache-2.0 |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/jupyter_core/tests/dotipython/profile_default/ipython_console_config.py | 24 | 21691 | # Configuration file for ipython-console.
c = get_config()
#------------------------------------------------------------------------------
# ZMQTerminalIPythonApp configuration
#------------------------------------------------------------------------------
# ZMQTerminalIPythonApp will inherit config from: TerminalIP... | mit |
wdurhamh/statsmodels | statsmodels/tsa/arima_model.py | 9 | 77514 | # Note: The information criteria add 1 to the number of parameters
# whenever the model has an AR or MA term since, in principle,
# the variance could be treated as a free parameter and restricted
# This code does not allow this, but it adds consistency with other
# packages such as gretl and X1... | bsd-3-clause |
waynenilsen/statsmodels | statsmodels/tsa/statespace/tools.py | 19 | 12762 | """
Statespace Tools
Author: Chad Fulton
License: Simplified-BSD
"""
from __future__ import division, absolute_import, print_function
import numpy as np
from statsmodels.tools.data import _is_using_pandas
from . import _statespace
try:
from scipy.linalg.blas import find_best_blas_type
except ImportError: # prag... | bsd-3-clause |
codingpoets/tigl | misc/math-scripts/ms_componentSegmentGeom.py | 2 | 19336 | #
# Copyright (C) 2007-2013 German Aerospace Center (DLR/SC)
#
# Created: 2012-12-17 Martin Siggel <Martin.Siggel@dlr.de>
#
# 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.ap... | apache-2.0 |
bertdecoensel/noysim | noysim/viewer.py | 1 | 28081 | # Noysim -- Noise simulation tools for Aimsun.
# Copyright (c) 2010-2011 by Bert De Coensel, Ghent University & Griffith University.
#
# Classes for sending and viewing noise levels in real-time
import os
import sys
import socket
import threading
import time
import random
import msvcrt
if not hasattr(sys... | mit |
craigcitro/pydatalab | tests/bigquery/schema_tests.py | 6 | 4284 | # Copyright 2015 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | apache-2.0 |
claesenm/optunity-benchmark | optimizers/tpe/hyperopt_august2013_mod_src/hyperopt/mongoexp.py | 2 | 60046 | """
Mongo-based Experiment driver and worker client
===============================================
Components involved:
- mongo
e.g. mongod ...
- driver
e.g. hyperopt-mongo-search mongo://address bandit_json bandit_algo_json
- worker
e.g. hyperopt-mongo-worker --loop mongo://address
Mongo
=====
Mong... | gpl-3.0 |
sellberg/SACLA2016B8055 | scripts/03_plot_h5.py | 2 | 1294 | #!/home/doniach/dermen/epd731/bin/python
import numpy as np
import h5py
import matplotlib
import matplotlib.pyplot as plt
import argparse
import time
import pandas as pd
import sys
# -- default parameters
run = 448539
file_folder = '/UserData/fperakis/2016_6/01_test/' # h5 files folder
src_folder = '/home/fperakis... | bsd-2-clause |
pkruskal/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 |
berkeley-stat159/project-epsilon | code/utils/scripts/t_test_plot_script.py | 1 | 3981 | """
Purpose:
-----------------------------------------------------------
This script creates graphs for t-test for 4 conditions
For each subject each run each condition, plot the t statistics
-----------------------------------------------------------
"""
import sys, os
sys.path.append(os.path.join(os.path.dirname(_... | bsd-3-clause |
arvinsahni/ml4 | flask/app/vizarvin.py | 2 | 9900 | from __future__ import division
from flask import render_template, request, Response, jsonify,redirect,url_for,flash
from werkzeug.utils import secure_filename
from app import app
import pandas
from pandas.util import hash_pandas_object
import json
import psycopg2
import psycopg2.extras
import os
import pandas as p... | mit |
MatthieuBizien/scikit-learn | examples/exercises/plot_cv_digits.py | 135 | 1223 | """
=============================================
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 |
benitesf/Skin-Lesion-Analysis-Towards-Melanoma-Detection | main.py | 1 | 9476 | # Import methods of features extraction
from features_extraction.feature_extraction import FeatureExtraction
# Import methods of learning
from learning.learning import neural_network
# Import methods of classification
from classification.classification import classify, confusion_matrix, total_error, local_error
#
fr... | mit |
gprMax/gprMax | setup.py | 1 | 7984 | # Copyright (C) 2015-2020: The University of Edinburgh
# Authors: Craig Warren and Antonis Giannopoulos
#
# This file is part of gprMax.
#
# gprMax 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 Foundatio... | gpl-3.0 |
adamrvfisher/TechnicalAnalysisLibrary | Blankrunningslate.py | 1 | 1167 | # -*- coding: utf-8 -*-
"""
Created on Wed Apr 5 20:13:42 2017
@author: AmatVictoriaCuramIII
"""
firsttime = '07/01/1983'
secondtime = '01/01/1995'
thirdtime = '01/01/2006'
fourthtime = '01/01/2010'
lasttime = '01/01/2050'
ticker = '^GSPC'
import pandas as pd
from ChaikinAggMaker import ChaikinAggMaker
... | apache-2.0 |
aflaxman/scikit-learn | sklearn/__check_build/__init__.py | 13 | 1679 | """ 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 |
jstoxrocky/statsmodels | statsmodels/graphics/functional.py | 31 | 14477 | """Module for functional boxplots."""
from statsmodels.compat.python import combinations, range
import numpy as np
from scipy import stats
from scipy.misc import factorial
from . import utils
__all__ = ['fboxplot', 'rainbowplot', 'banddepth']
def fboxplot(data, xdata=None, labels=None, depth=None, method='MBD',
... | bsd-3-clause |
necozay/tulip-control | tulip/transys/export/graph2dot.py | 1 | 17106 | # Copyright (c) 2013-2014 by California Institute of Technology
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice,... | bsd-3-clause |
jakobworldpeace/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_02_sentiment.py | 104 | 3139 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a ... | bsd-3-clause |
chungjjang80/FRETBursts | fretbursts/utils/examples/matplotlib_figure_mod_toolbar.py | 2 | 1276 | """
Example on how to add widgets the toolbar of a Matplotlib figure using the
QT backend.
No QT application is created, only the toolbar of the native MPL figure is
modified.
"""
from PySide import QtGui, QtCore
import matplotlib
def test():
plot([1,2,3], lw=2)
q = qt4_interface(gcf())
return q # WA... | gpl-2.0 |
astroclark/bhextractor | bin/bhex_scalemassdemo.py | 1 | 4019 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (C) 2014-2015 James Clark <james.clark@ligo.org>
#
# 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 2 of the License, or
... | gpl-2.0 |
justrypython/EAST | svm_model_v2.py | 1 | 2801 | #encoding:UTF-8
import os
import numpy as np
import sys
import cv2
import matplotlib.pyplot as plt
from sklearn.svm import NuSVC, SVC
import datetime
import pickle
#calculate the area
def area(p):
p = p.reshape((-1, 2))
return 0.5 * abs(sum(x0*y1 - x1*y0
for ((x0, y0), (x1, y1)) in segme... | gpl-3.0 |
mojoboss/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_scalability.py | 225 | 5719 | """
============================================
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 |
michaelld/gnuradio | gnuradio-runtime/apps/evaluation_random_numbers.py | 7 | 5284 | #!/usr/bin/env python
#
# Copyright 2015 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# ... | gpl-3.0 |
jzt5132/scikit-learn | examples/ensemble/plot_adaboost_twoclass.py | 347 | 3268 | """
==================
Two-class AdaBoost
==================
This example fits an AdaBoosted decision stump on a non-linearly separable
classification dataset composed of two "Gaussian quantiles" clusters
(see :func:`sklearn.datasets.make_gaussian_quantiles`) and plots the decision
boundary and decision scores. The di... | bsd-3-clause |
iamgp/pyCa | pyCa/Graph.py | 1 | 2559 | from . import *
# Graphics Stuff
import matplotlib.pyplot as plt
class Graph(object):
"""docstring for Graph"""
def __init__(self, Experiment):
self.Experiment = Experiment
self.numberOfStimulantsAdded = 0
self.nameToUse = 0
def plot(self):
print ''
log(self.Exp... | gpl-3.0 |
neuropoly/spinalcordtoolbox | spinalcordtoolbox/scripts/sct_maths.py | 1 | 20433 | #!/usr/bin/env python
#########################################################################################
#
# Perform mathematical operations on images
#
# ---------------------------------------------------------------------------------------
# Copyright (c) 2015 Polytechnique Montreal <www.neuro.polymtl.ca>
# A... | mit |
rafaelmds/fatiando | gallery/gridder/cutting.py | 6 | 1326 | """
Cutting a section from spacial data
-----------------------------------
The :func:`fatiando.gridder.cut` function extracts points from spatially
distributed data that are inside a given area. It doesn't matter whether or
not the points are on a regular grid.
"""
from fatiando import gridder
import matplotlib.pypl... | bsd-3-clause |
danielhkl/matplotlib2tikz | matplotlib2tikz/color.py | 1 | 2761 | # -*- coding: utf-8 -*-
#
import matplotlib as mpl
import numpy
def mpl_color2xcolor(data, matplotlib_color):
'''Translates a matplotlib color specification into a proper LaTeX xcolor.
'''
# Convert it to RGBA.
my_col = numpy.array(mpl.colors.ColorConverter().to_rgba(matplotlib_color))
# If the a... | mit |
tdhopper/scikit-learn | sklearn/decomposition/tests/test_dict_learning.py | 69 | 8605 | 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 |
smblance/ggplot | ggplot/tests/test_chart_components.py | 12 | 1664 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
import pandas as pd
from nose.tools import assert_raises, assert_equal, assert_is_none
from ggplot import *
from ggplot.utils.exceptions import GgplotError
def test_chart_components():
... | bsd-2-clause |
noahbenson/neuropythy | neuropythy/graphics/__init__.py | 1 | 1109 | ####################################################################################################
# neuropythy/graphics/__init__.py
# Simple tools for making matplotlib/pyplot graphics with neuropythy.
# By Noah C. Benson
'''
The neuropythy.graphics package contains definitions of the various tools for making plots... | agpl-3.0 |
google/autocjk | src/model.py | 1 | 14838 | # Copyright 2021 Google LLC
#
# 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 |
nikitasingh981/scikit-learn | examples/preprocessing/plot_scaling_importance.py | 45 | 5269 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Importance of Feature Scaling
=========================================================
Feature scaling though standardization (or Z-score normalization)
can be an important preprocessing step for many machine learn... | bsd-3-clause |
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