repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
mlyundin/scikit-learn | sklearn/datasets/mlcomp.py | 289 | 3855 | # Copyright (c) 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
"""Glue code to load http://mlcomp.org data as a scikit.learn dataset"""
import os
import numbers
from sklearn.datasets.base import load_files
def _load_document_classification(dataset_path, metadata, set_=None, **kwargs):
if ... | bsd-3-clause |
andaag/scikit-learn | examples/feature_selection/plot_rfe_with_cross_validation.py | 226 | 1384 | """
===================================================
Recursive feature elimination with cross-validation
===================================================
A recursive feature elimination example with automatic tuning of the
number of features selected with cross-validation.
"""
print(__doc__)
import matplotlib.p... | bsd-3-clause |
B3AU/waveTree | examples/bicluster/plot_spectral_biclustering.py | 7 | 2010 | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and
bicluster it using the Spectral Biclustering algorithm.
The data is generated with the ``make_checkerboard`... | bsd-3-clause |
liangz0707/scikit-learn | benchmarks/bench_tree.py | 297 | 3617 | """
To run this, you'll need to have installed.
* scikit-learn
Does two benchmarks
First, we fix a training set, increase the number of
samples to classify and plot number of classified samples as a
function of time.
In the second benchmark, we increase the number of dimensions of the
training set, classify a sam... | bsd-3-clause |
mac389/at-risk-agents | analyze-abm.py | 1 | 8030 | import os, json,re
import numpy as np
import Graphics as artist
import matplotlib.pyplot as plt
plt.switch_backend('Agg')
from matplotlib import rcParams
from optparse import OptionParser
from scipy.stats import percentileofscore,scoreatpercentile
rcParams['text.usetex'] = True
parser = OptionParser(usage="usage:... | mit |
rseubert/scikit-learn | sklearn/neighbors/approximate.py | 3 | 21294 | """Approximate nearest neighbor search"""
# Author: Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
# Joel Nothman <joel.nothman@gmail.com>
import numpy as np
import warnings
from scipy import sparse
from .base import KNeighborsMixin, RadiusNeighborsMixin
from ..base import BaseEstimator
from ..utils.va... | bsd-3-clause |
thientu/scikit-learn | sklearn/utils/arpack.py | 265 | 64837 | """
This contains a copy of the future version of
scipy.sparse.linalg.eigen.arpack.eigsh
It's an upgraded wrapper of the ARPACK library which
allows the use of shift-invert mode for symmetric matrices.
Find a few eigenvectors and eigenvalues of a matrix.
Uses ARPACK: http://www.caam.rice.edu/software/ARPACK/
"""
#... | bsd-3-clause |
scollis/EGU16 | cluster/profile_mpi0/ipython_console_config.py | 4 | 21693 | # Configuration file for ipython-console.
c = get_config()
#------------------------------------------------------------------------------
# ZMQTerminalIPythonApp configuration
#------------------------------------------------------------------------------
# ZMQTerminalIPythonApp will inherit config from: TerminalIP... | bsd-3-clause |
plotly/python-api | packages/python/plotly/plotly/matplotlylib/mplexporter/utils.py | 1 | 11907 | """
Utility Routines for Working with Matplotlib Objects
====================================================
"""
import itertools
import io
import base64
import numpy as np
import warnings
import matplotlib
from matplotlib.colors import colorConverter
from matplotlib.path import Path
from matplotlib.markers import ... | mit |
morrisonwudi/zipline | tests/test_algorithm_gen.py | 18 | 7339 | #
# Copyright 2014 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
pratapvardhan/scikit-learn | sklearn/ensemble/voting_classifier.py | 11 | 8661 | """
Soft Voting/Majority Rule classifier.
This module contains a Soft Voting/Majority Rule classifier for
classification estimators.
"""
# Authors: Sebastian Raschka <se.raschka@gmail.com>,
# Gilles Louppe <g.louppe@gmail.com>
#
# Licence: BSD 3 clause
import numpy as np
from ..base import BaseEstimator
f... | bsd-3-clause |
to266/hyperspy | hyperspy/drawing/_widgets/scalebar.py | 2 | 5335 | # -*- coding: utf-8 -*-
# Copyright 2007-2016 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at... | gpl-3.0 |
bthirion/scikit-learn | sklearn/metrics/cluster/tests/test_bicluster.py | 394 | 1770 | """Testing for bicluster metrics module"""
import numpy as np
from sklearn.utils.testing import assert_equal, assert_almost_equal
from sklearn.metrics.cluster.bicluster import _jaccard
from sklearn.metrics import consensus_score
def test_jaccard():
a1 = np.array([True, True, False, False])
a2 = np.array([T... | bsd-3-clause |
ndingwall/scikit-learn | sklearn/cluster/tests/test_k_means.py | 1 | 44177 | """Testing for K-means"""
import re
import sys
import numpy as np
from scipy import sparse as sp
from threadpoolctl import threadpool_limits
import pytest
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.utils._testing import assert_allcl... | bsd-3-clause |
miaecle/deepchem | deepchem/models/tests/test_reload.py | 1 | 1303 | """
Test reload for trained models.
"""
__author__ = "Bharath Ramsundar"
__copyright__ = "Copyright 2016, Stanford University"
__license__ = "MIT"
import unittest
import tempfile
import numpy as np
import deepchem as dc
import tensorflow as tf
from sklearn.ensemble import RandomForestClassifier
class TestReload(unit... | mit |
arthurmensch/modl | modl/decomposition/dict_fact.py | 1 | 28827 | import atexit
from concurrent.futures import ThreadPoolExecutor
from math import log, ceil
from tempfile import TemporaryFile
import numpy as np
import scipy
import time
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import check_array, check_random_state, gen_batches
from sklearn.utils.va... | bsd-2-clause |
stiebels/letor_to_pandas_converter | Converter.py | 1 | 2061 | import pandas as pd
class Letor_Converter(object):
'''
Class Converter implements parsing from original letor txt files to
pandas data frame representation.
'''
def __init__(self, path):
'''
Arguments:
path: path to letor txt file
'''
s... | mit |
paulperry/quant | vti_agg_7030.py | 1 | 2066 | '''
A Basic Markowitz portfolio of Stocks and Bonds.
Change it from 50/50 to 60/40 or 70/30.
'''
from __future__ import division
import datetime
import pytz
import pandas as pd
from zipline.api import order_target_percent
def initialize(context):
set_long_only()
set_symbol_lookup_date('2005-01-01')
c... | mit |
zaxtax/scikit-learn | sklearn/cluster/__init__.py | 364 | 1228 | """
The :mod:`sklearn.cluster` module gathers popular unsupervised clustering
algorithms.
"""
from .spectral import spectral_clustering, SpectralClustering
from .mean_shift_ import (mean_shift, MeanShift,
estimate_bandwidth, get_bin_seeds)
from .affinity_propagation_ import affinity_propagati... | bsd-3-clause |
jmschrei/pomegranate | tests/test_markov_network.py | 1 | 18046 | # test_bayes_net.py
# Authors: Jacob Schreiber <jmschreiber91@gmail.com>
'''
These are unit tests for the Markov network model of pomegranate.
'''
from __future__ import division
from pomegranate import JointProbabilityTable
from pomegranate import MarkovNetwork
from pomegranate.io import DataGenerator
from pomegran... | mit |
breznak/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_ps.py | 69 | 50262 | """
A PostScript backend, which can produce both PostScript .ps and .eps
"""
from __future__ import division
import glob, math, os, shutil, sys, time
def _fn_name(): return sys._getframe(1).f_code.co_name
try:
from hashlib import md5
except ImportError:
from md5 import md5 #Deprecated in 2.5
from tempfile im... | agpl-3.0 |
nbfigueroa/daft | examples/galex.py | 7 | 2540 | """
The GALEX Photon Catalog
========================
This is the Hogg \& Schiminovich model for how photons turn into
counts in the GALEX satellite data stream. Note the use of relative
positioning.
"""
from matplotlib import rc
rc("font", family="serif", size=12)
rc("text", usetex=True)
import daft
pgm = daft.PGM... | mit |
Wittlich/DAT210x-Python | Module2/assignment4.py | 1 | 1354 | import pandas as pd
# TODO: Load up the table, and extract the dataset
# out of it. If you're having issues with this, look
# carefully at the sample code provided in the reading
df = pd.read_html('http://www.espn.com/nhl/statistics/player/_/stat/points/sort/points/year/2015/seasontype/2')[0]
# TODO: Rename the colum... | mit |
Tong-Chen/scikit-learn | sklearn/metrics/cluster/__init__.py | 312 | 1322 | """
The :mod:`sklearn.metrics.cluster` submodule contains evaluation metrics for
cluster analysis results. There are two forms of evaluation:
- supervised, which uses a ground truth class values for each sample.
- unsupervised, which does not and measures the 'quality' of the model itself.
"""
from .supervised import ... | bsd-3-clause |
nixingyang/Kaggle-Face-Verification | Digit Recognizer/solution.py | 1 | 3642 | import numpy as np
np.random.seed(666666)
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers.advanced_activations import PReLU
from keras.layers.normalization import BatchNormaliz... | mit |
spbguru/repo1 | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_qt4.py | 69 | 20664 | from __future__ import division
import math
import os
import sys
import matplotlib
from matplotlib import verbose
from matplotlib.cbook import is_string_like, onetrue
from matplotlib.backend_bases import RendererBase, GraphicsContextBase, \
FigureManagerBase, FigureCanvasBase, NavigationToolbar2, IdleEvent, curso... | gpl-3.0 |
Winand/pandas | pandas/core/dtypes/cast.py | 2 | 36135 | """ routings for casting """
from datetime import datetime, timedelta
import numpy as np
import warnings
from pandas._libs import tslib, lib
from pandas._libs.tslib import iNaT
from pandas.compat import string_types, text_type, PY3
from .common import (_ensure_object, is_bool, is_integer, is_float,
... | bsd-3-clause |
saullocastro/pyNastran | pyNastran/op2/tables/oes_stressStrain/complex/oes_rods.py | 1 | 11149 | from __future__ import (nested_scopes, generators, division, absolute_import,
print_function, unicode_literals)
from six import iteritems
from six.moves import range
import numpy as np
from numpy import zeros, array_equal, allclose
from pyNastran.op2.tables.oes_stressStrain.real.oes_objects imp... | lgpl-3.0 |
toobaz/pandas | pandas/tests/groupby/test_nth.py | 2 | 16800 | import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, isna
from pandas.util.testing import assert_frame_equal, assert_series_equal
def test_first_last_nth(df):
# tests for first / last / nth
grouped = df.groupby("A")
first = grouped.first... | bsd-3-clause |
abhitopia/tensorflow | tensorflow/python/estimator/inputs/queues/feeding_functions_test.py | 58 | 9375 | # Copyright 2017 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 |
fyfcauc/android_external_chromium-org | chrome/test/nacl_test_injection/buildbot_chrome_nacl_stage.py | 26 | 11131 | #!/usr/bin/python
# Copyright (c) 2012 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""Do all the steps required to build and test against nacl."""
import optparse
import os.path
import re
import shutil
import subproc... | bsd-3-clause |
DeveloperJose/Vision-Rat-Brain | feature_matching_v1/graph.py | 2 | 2648 | # -*- coding: utf-8 -*-
import pylab as plt
import numpy as np
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg
from PyQt5.QtWidgets import QSizePolicy
class Graph(FigureCanvasQTAgg):
def __init__(self, parent=None, width=10, height=10, dpi=100):
figure = plt.Figure(figsize=(width, height)... | mit |
kcyu2014/ml_project2 | project2/utils/images2patches.py | 1 | 5530 | """ This module processes the dataset Massachusetts obained from https://www.cs.toronto.edu/~vmnih/data/.
The original dataset contains sattelite images of Massachusetts and its surroundings. The images are
ofsize 1500x1500, 3 channels, TIFF format. The dataset also contains separate labels for roads and
buildings. Th... | mit |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/zz_old/TensorFlow/SkFlow_DEPRECATED/text_classification_character_cnn.py | 6 | 3495 | # Copyright 2015-present Scikit Flow 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... | apache-2.0 |
FRESNA/PyPSA | pypsa/stats.py | 1 | 8034 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Post-solving statistics of network. This module contains functions to anaylize
an optimized network. Basic information of network can be summarized as well as
constraint gaps can be double-checked.
"""
from .descriptors import (expand_series, get_switchable_as_dense a... | gpl-3.0 |
facom/Sinfin | db/test.py | 1 | 1814 | #-*-coding:utf-8-*-
from sinfin import *
from matplotlib import use,font_manager as fm
use('Agg')
from matplotlib import colors,ticker,patches,pylab as plt
from matplotlib.pyplot import cm
from matplotlib.font_manager import FontProperties
from matplotlib.transforms import offset_copy
from numpy import *
# CONNECT TO ... | gpl-3.0 |
toobaz/pandas | pandas/tests/io/formats/test_to_latex.py | 2 | 19273 | import codecs
from datetime import datetime
import pytest
import pandas as pd
from pandas import DataFrame, Series
from pandas.util import testing as tm
class TestToLatex:
def test_to_latex_filename(self, float_frame):
with tm.ensure_clean("test.tex") as path:
float_frame.to_latex(path)
... | bsd-3-clause |
mjirik/lisa | experiments/precise_liver_statistics.py | 1 | 8637 | #! /usr/bin/python
# -*- coding: utf-8 -*-
"""
Experiment s \"barevným\" modelem jater.
Pro spuštění zkuste help:
python experiments/20130919_liver_statistics.py --help
Měřené vlastnosti se přidávají do get_features().
Pro přidání dalších dat, editujte příslušný yaml soubor.
"""
# import funkcí z jiného adresáře
i... | bsd-3-clause |
JT5D/scikit-learn | sklearn/preprocessing/tests/test_imputation.py | 2 | 11013 | import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_not_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_false
from sklearn.utils.testing im... | bsd-3-clause |
sthenc/nc_packer | visual/mfcc_visualize.py | 1 | 2726 | #!/usr/bin/python
import numpy as np
import matplotlib as ml
import matplotlib.pyplot as plt
import features
import scipy.io.wavfile as wav
from wav2mfcc import wav2mfcc
#if __name__ == "__main__":
datadir = '/mnt/data/Fer/diplomski/nc_packer/test_data/PCCdata16kHz/devel/isolated/'
cleandir = 'clean/'
lilnois... | mit |
dsm054/pandas | pandas/plotting/_style.py | 2 | 5527 | # being a bit too dynamic
# pylint: disable=E1101
from __future__ import division
import warnings
from contextlib import contextmanager
import numpy as np
from pandas.core.dtypes.common import is_list_like
from pandas.compat import lrange, lmap
import pandas.compat as compat
def _get_standard_colors(num_colors=Non... | bsd-3-clause |
doublsky/MLProfile | benchmark/bench_bayes.py | 1 | 1231 | """
Benchmark Naive Bayes
"""
from util import *
import numpy as np
import argparse
from time import time
from sklearn import naive_bayes
import sys
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Benchmark naive bayes.")
parser.add_argument('-ns', default=1000000, type=int,
... | mit |
ychfan/tensorflow | tensorflow/python/estimator/canned/linear_testing_utils.py | 20 | 67865 | # Copyright 2017 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 |
alvarofierroclavero/scikit-learn | examples/model_selection/plot_train_error_vs_test_error.py | 349 | 2577 | """
=========================
Train error vs Test error
=========================
Illustration of how the performance of an estimator on unseen data (test data)
is not the same as the performance on training data. As the regularization
increases the performance on train decreases while the performance on test
is optim... | bsd-3-clause |
iamshang1/Projects | Papers/HiSAN/tf_hisan_multigpu.py | 1 | 14655 | import os
import numpy as np
import tensorflow as tf
from tensorflow.python.client import device_lib
import sys
import time
from sklearn.metrics import f1_score
import random
class hisan_multigpu(object):
def __init__(self,embedding_matrix,num_classes,max_sents,max_words,attention_heads=8,
attent... | mit |
ycopin/spectrogrism | spectrogrism/snifs.py | 1 | 4440 | # -*- coding: utf-8 -*-
# Time-stamp: <2016-03-23 00:19 ycopin@lyonovae03.in2p3.fr>
"""
snifs
-----
SNIFS optical configuration and utilities.
"""
from __future__ import division, print_function, absolute_import
__author__ = "Yannick Copin <y.copin@ipnl.in2p3.fr>"
import warnings
import numpy as N
if __name__ ==... | lgpl-3.0 |
ashiklom/studyGroup | lessons/RISE/demoUtilities.py | 2 | 3183 | import numpy as np
import matplotlib as mp
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def plotSetup(xmin = -3.0, xmax = 3.0, ymin = -3.0, ymax = 3.0, size=(6,6)):
"""
refactored version of ut.plotSetup to hide as much as possible when showing code
basics of 2D plot setup
d... | apache-2.0 |
mojoboss/scikit-learn | examples/linear_model/plot_ols_ridge_variance.py | 387 | 2060 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Ordinary Least Squares and Ridge Regression Variance
=========================================================
Due to the few points in each dimension and the straight
line that linear regression uses to follow thes... | bsd-3-clause |
koningrobot/sparse-coding-theano | example.py | 2 | 1895 | import random
import math
import numpy as np
import scipy.io
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import logging
logging.basicConfig(level=logging.WARNING)
import sparse_coding as sc
def onto_unit(x):
a = np.min(x)
b = np.max(x)
return (x - a) / (b - a)
def visualize_patches(B):
... | mit |
astrolitterbox/SAMI | utils.py | 1 | 6843 | from __future__ import division
import numpy as np
from astropy.coordinates.distances import Distance
import matplotlib.pyplot as plt
import pyfits
import db
import pyfits
from string import *
from astroML.plotting import hist
from geom import getIncl
def simple_plot(x, y, vel, filename):
fig = plt.figure(figsize=(1... | gpl-2.0 |
ishank08/scikit-learn | examples/svm/plot_rbf_parameters.py | 26 | 8016 | '''
==================
RBF SVM parameters
==================
This example illustrates the effect of the parameters ``gamma`` and ``C`` of
the Radial Basis Function (RBF) kernel SVM.
Intuitively, the ``gamma`` parameter defines how far the influence of a single
training example reaches, with low values meaning 'far' a... | bsd-3-clause |
josh314/squinty | train-agglo.py | 1 | 2487 | ################################################################
# Cluster reduction of pixel data
#
# Usage: python2 train-agglo.py <training data> [options]
#
# Builds the a data transformer (FeatureAgglomeration clusting object) which
# reduces problem dimension
#Parse command line options and arguments
#Do this be... | mit |
john5223/airflow | airflow/hooks/base_hook.py | 9 | 1407 | from builtins import object
import logging
import random
from airflow import settings
from airflow.models import Connection
from airflow.utils import AirflowException
class BaseHook(object):
"""
Abstract base class for hooks, hooks are meant as an interface to
interact with external systems. MySqlHook, H... | apache-2.0 |
CloverHealth/airflow | tests/hooks/test_hive_hook.py | 2 | 16206 | # -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
#... | apache-2.0 |
rbdavid/Distance_matrix | Test_Case1/plotting_functions.py | 4 | 13432 | #!/Library/Frameworks/Python.framework/Versions/2.7/bin/python
# USAGE:
# from fn_plotting.py import *
# PREAMBLE:
import numpy as np
import sys
import os
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib as mpl
from matplotlib.ticker import NullFormatter
stdev = np.std
sqrt = np.... | gpl-3.0 |
scollis/AGU_2016 | scripts/tmatrix_par.py | 1 | 5013 | import pyart
import netCDF4
import numpy as np
import platform
from matplotlib import pyplot as plt
from glob import glob
import os
from datetime import datetime
from scipy import interpolate
import fnmatch
import matplotlib.dates as mdates
from pytmatrix.tmatrix import TMatrix, Scatterer
from pytmatrix.tmatrix_psd imp... | bsd-3-clause |
geodynamics/burnman | misc/benchmarks/benchmark.py | 3 | 29420 | from __future__ import absolute_import
# This file is part of BurnMan - a thermoelastic and thermodynamic toolkit for the Earth and Planetary Sciences
# Copyright (C) 2012 - 2015 by the BurnMan team, released under the GNU
# GPL v2 or later.
import os.path
import sys
sys.path.insert(1, os.path.abspath('../..'))
impor... | gpl-2.0 |
nikitasingh981/scikit-learn | sklearn/neighbors/tests/test_approximate.py | 30 | 19128 | """
Testing for the approximate neighbor search using
Locality Sensitive Hashing Forest module
(sklearn.neighbors.LSHForest).
"""
# Author: Maheshakya Wijewardena, Joel Nothman
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
chrisburr/scikit-learn | examples/plot_multioutput_face_completion.py | 330 | 3019 | """
==============================================
Face completion with a multi-output estimators
==============================================
This example shows the use of multi-output estimator to complete images.
The goal is to predict the lower half of a face given its upper half.
The first column of images sho... | bsd-3-clause |
sclc/NAEF | exp_scripts/worker_exp_160606_isc16.py | 1 | 33697 | """
Experiment Diary 2016-06-06 for ISC 2016
"""
import sys
import math
import matplotlib.pyplot as plt
from scipy import io
import numpy as np
from scipy.sparse.linalg import *
from scipy.sparse import *
sys.path.append("../src/")
from worker import Worker
from native_conjugate_gradient import NativeConjugateGradien... | gpl-3.0 |
rvraghav93/scikit-learn | sklearn/gaussian_process/gaussian_process.py | 17 | 34869 | # -*- coding: utf-8 -*-
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# (mostly translation, see implementation details)
# License: BSD 3 clause
from __future__ import print_function
import numpy as np
from scipy import linalg, optimize
from ..base import BaseEstimator, RegressorMixin
from ..metrics... | bsd-3-clause |
loretoparisi/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/interpolate.py | 73 | 7068 | import numpy as np
from matplotlib._delaunay import compute_planes, linear_interpolate_grid, nn_interpolate_grid
from matplotlib._delaunay import nn_interpolate_unstructured
__all__ = ['LinearInterpolator', 'NNInterpolator']
def slice2gridspec(key):
"""Convert a 2-tuple of slices to start,stop,steps for x and y.... | agpl-3.0 |
xyguo/scikit-learn | examples/model_selection/plot_train_error_vs_test_error.py | 349 | 2577 | """
=========================
Train error vs Test error
=========================
Illustration of how the performance of an estimator on unseen data (test data)
is not the same as the performance on training data. As the regularization
increases the performance on train decreases while the performance on test
is optim... | bsd-3-clause |
duncanwp/iris | lib/iris/tests/test_quickplot.py | 1 | 7759 | # (C) British Crown Copyright 2010 - 2018, Met Office
#
# This file is part of Iris.
#
# Iris 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) any l... | lgpl-3.0 |
doubaoatthu/UWRoutingSystem | dt.py | 1 | 1669 | from sklearn import tree
from sklearn.externals.six import StringIO
from IPython.display import Image
import os
import pydot
import numpy
preference = []
label = []
with open("../data/survey.txt") as f:
contents = f.readlines()
for content in contents:
content = content[1:-2]
content = content.replace("\"","")
... | mit |
lbishal/scikit-learn | sklearn/linear_model/coordinate_descent.py | 8 | 76416 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Gael Varoquaux <gael.varoquaux@inria.fr>
#
# License: BSD 3 clause
import sys
import warnings
from abc import ABCMeta, abstractmethod
import n... | bsd-3-clause |
pulkitag/caffe-python-layers | streetview_data_group_rots.py | 1 | 13622 | import caffe
import numpy as np
import argparse, pprint
from multiprocessing import Pool
import scipy.misc as scm
from os import path as osp
import my_pycaffe_io as mpio
import my_pycaffe as mp
from easydict import EasyDict as edict
from transforms3d.transforms3d import euler as t3eu
import street_label_utils as slu
i... | bsd-3-clause |
zblz/naima | src/naima/model_fitter.py | 1 | 10519 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import astropy.units as u
import numpy as np
from .core import _prefit, lnprobmodel
from .extern.validator import validate_array
from .plot import _plot_data_to_ax, color_cycle
from .utils import sed_conversion, validate_data_table
__all__ = ["Interactiv... | bsd-3-clause |
ssorgatem/qiime | scripts/plot_taxa_summary.py | 15 | 12355 | #!/usr/bin/env python
# File created on 19 Jan 2011
from __future__ import division
__author__ = "Jesse Stombaugh"
__copyright__ = "Copyright 2011, The QIIME project"
__credits__ = ["Jesse Stombaugh", "Julia Goodrich", "Justin Kuczynski",
"John Chase", "Jose Antonio Navas Molina"]
__license__ = "GPL"
__... | gpl-2.0 |
pivotalsoftware/pymadlib | setup.py | 1 | 4378 | from setuptools import setup, find_packages
from distutils.util import convert_path
import os,sys
from fnmatch import fnmatchcase
# Provided as an attribute, so you can append to these instead
# of replicating them:
standard_exclude = ('*.py', '*.pyc', '*$py.class', '*~', '.*', '*.bak')
standard_exclude_directories = ... | bsd-2-clause |
FNCS/ns-3.22 | src/flow-monitor/examples/wifi-olsr-flowmon.py | 108 | 7439 | # -*- Mode: Python; -*-
# Copyright (c) 2009 INESC Porto
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 2 as
# published by the Free Software Foundation;
#
# This program is distributed in the hope that it will be useful,
#... | gpl-2.0 |
Xevaquor/the-art-of-ai | ann/ml_plot.py | 1 | 1759 | # -*- coding: utf-8 -*-
"""
The art of an Artificial Intelligence
http://art-of-ai.com
https://github.com/Xevaquor/the-art-of-ai
Module in this version does not handle w = [0,0,0] or w = [_, _, 0] properly
"""
__author__ = 'xevaquor'
__license__ = 'MIT'
import numpy as np
import matplotlib.pyplot as plt
def init_c... | gpl-3.0 |
Nyker510/scikit-learn | examples/cross_decomposition/plot_compare_cross_decomposition.py | 142 | 4761 | """
===================================
Compare cross decomposition methods
===================================
Simple usage of various cross decomposition algorithms:
- PLSCanonical
- PLSRegression, with multivariate response, a.k.a. PLS2
- PLSRegression, with univariate response, a.k.a. PLS1
- CCA
Given 2 multivari... | bsd-3-clause |
SKA-ScienceDataProcessor/algorithm-reference-library | deprecated_code/workflows/mpi/plot-results.py | 1 | 2132 | #!/usr/bim/python
# Script to plot the performance/scaling results
import os
import sys
sys.path.append(os.path.join('..', '..'))
results_dir = './results/timing-csd3/dask'
#from matplotlib import pylab
#pylab.rcParams['figure.figsize'] = (12.0, 12.0)
#pylab.rcParams['image.cmap'] = 'rainbow'
import numpy
from ... | apache-2.0 |
hunter-cameron/Bioinformatics | job_scripts/plate_scrapes/checkm/compare_checkm.py | 1 | 1571 |
import argparse
import sys
import pandas as pd
def read_data_table(data_f):
"""
Converts raw CheckM data into a data frame.
Should have an option to accept a .csv file from CheckM
"""
# return a df with the first column as an index and the first row as headers
return pd.read_csv(data_f, sep="... | mit |
joernhees/scikit-learn | sklearn/tests/test_multioutput.py | 23 | 12429 | from __future__ import division
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_false
from sklearn.utils.testing import assert_raises_regex
from s... | bsd-3-clause |
pompiduskus/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_02_sentiment.py | 256 | 2406 | """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 |
judaba13/GenrePredictor | dtCV.py | 1 | 4618 | '''
CS4780 Final Project
Post Pruning and Cross Validation
@author: Kelsey Duncan ked83
'''
from matplotlib import pyplot as plt
from dt import *
import random
#get data
trainData, testData = getData()
depths = range(2, 33)
testAccuracies = []
testPrecisions = []
trainAccuracies = []
trainPrecisions = []
#train a tr... | apache-2.0 |
smrjan/seldon-server | python/seldon/pipeline/tests/test_util.py | 2 | 2599 | import unittest
from .. import util as sutils
from .. import basic_transforms as bt
import pandas as pd
import json
import os.path
from sklearn.pipeline import Pipeline
from sklearn.externals import joblib
import logging
class Test_PipelineWrapper(unittest.TestCase):
def _create_test_json(self,fname,as_list=False... | apache-2.0 |
davidgbe/scikit-learn | sklearn/linear_model/stochastic_gradient.py | 130 | 50966 | # Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)
# Mathieu Blondel (partial_fit support)
#
# License: BSD 3 clause
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import scipy.sparse as sp
from abc import ABCMeta, abstractmethod
from ... | bsd-3-clause |
danche354/Sequence-Labeling | ner/senna-hash-2-pos-chunk-gazetteer-128-64.py | 1 | 7761 | from keras.models import Model
from keras.layers import Input, Masking, Dense, LSTM
from keras.layers import Dropout, TimeDistributed, Bidirectional, merge
from keras.layers.embeddings import Embedding
from keras.utils import np_utils
import numpy as np
import pandas as pd
import sys
import math
import os
from dateti... | mit |
sahg/PyTOPKAPI | pytopkapi/results_analysis/plot_Qsim_Qobs_Rain.py | 2 | 3658 | import datetime as dt
from configparser import SafeConfigParser
import h5py
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.dates import date2num
import pytopkapi.utils as ut
def run(ini_file='plot_Qsim_Qobs_Rain.ini'):
config = SafeConfigParser()
config.read(ini_file)
print('Read the ... | bsd-3-clause |
neilhan/python_cv_learning | 05-segment_grabcut/run_me.py | 1 | 7745 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This code testing the segmenting images using GrabCut method.
This code testing the segmenting images using GrabCut method.
Author: Neil Han
! This sample code is a copy from https://github.com/Itseez/opencv/blob/master/samples/python2/grabcut.py
@ 2013-11-29
The orig... | bsd-3-clause |
pratapvardhan/scikit-learn | sklearn/gaussian_process/gpc.py | 42 | 31571 | """Gaussian processes classification."""
# Authors: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import warnings
from operator import itemgetter
import numpy as np
from scipy.linalg import cholesky, cho_solve, solve
from scipy.optimize import fmin_l_bfgs_b
from scipy.special import erf... | bsd-3-clause |
xuewei4d/scikit-learn | sklearn/datasets/tests/test_openml.py | 5 | 51973 | """Test the openml loader.
"""
import gzip
import json
import numpy as np
import os
import re
import scipy.sparse
import sklearn
import pytest
from sklearn import config_context
from sklearn.datasets import fetch_openml
from sklearn.datasets._openml import (_open_openml_url,
_arff... | bsd-3-clause |
actlea/TopicalCrawler | TopicalCrawl/TopicalCrawl/classifier/increment_classifier.py | 1 | 2474 | #!/usr/bin/env python
# encoding: utf-8
"""
@version: ??
@author: phpergao
@license: Apache Licence
@file: increment_classifier.py
@time: 16-4-15 上午10:18
"""
from base import *
from sklearn.externals import joblib
import os
import numpy as np
import re
from collections import Counter
from collections import Counter... | gpl-3.0 |
danielhrisca/asammdf | asammdf/signal.py | 1 | 50266 | # -*- coding: utf-8 -*-
""" asammdf *Signal* class module for time correct signal processing """
import logging
from textwrap import fill
import numpy as np
from numpy.core.defchararray import encode
from .blocks import v2_v3_blocks as v3b
from .blocks import v4_blocks as v4b
from .blocks.conversion_utils import fro... | lgpl-3.0 |
nmayorov/scikit-learn | examples/svm/plot_weighted_samples.py | 95 | 1943 | """
=====================
SVM: Weighted samples
=====================
Plot decision function of a weighted dataset, where the size of points
is proportional to its weight.
The sample weighting rescales the C parameter, which means that the classifier
puts more emphasis on getting these points right. The effect might ... | bsd-3-clause |
steveryb/pyprocrustes | visualise.py | 1 | 1106 | import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
import Procrustes
def load_curve_data(filename):
with open(filename) as f:
line = f.readline()
data = line.split(",")
version = data[0]
num_points = data[1]
dat... | bsd-2-clause |
tuanavu/deep-learning-a-z | DeepLearningA-Z/02-supervised-deep-learning/01-Artificial-Neural-Networks-ANN/Artificial_Neural_Networks/ann.py | 5 | 2306 | # Artificial Neural Network
# Installing Theano
# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
# Installing Tensorflow
# pip install tensorflow
# Installing Keras
# pip install --upgrade keras
# Part 1 - Data Preprocessing
# Importing the libraries
import numpy as np
import matplotlib.pyp... | mit |
iosonofabio/singlet | setup.py | 1 | 2579 | #!/usr/bin/env python
# vim: fdm=indent
'''
author: Fabio Zanini
date: 08/08/17
content: Setup script for singlet
'''
import sys
import os
from distutils.log import INFO as logINFO
if ((sys.version_info[0] < 3) or
(sys.version_info[0] == 3 and sys.version_info[1] < 4)):
sys.stderr.write("Error in s... | mit |
Akshay0724/scikit-learn | examples/calibration/plot_calibration_multiclass.py | 95 | 6971 | """
==================================================
Probability Calibration for 3-class classification
==================================================
This example illustrates how sigmoid calibration changes predicted
probabilities for a 3-class classification problem. Illustrated is the
standard 2-simplex, wher... | bsd-3-clause |
astraw/pairs2groups | pairs2groups/__init__.py | 1 | 13702 | """find homogeneous groups of items based on pairwise information
A motivating example
--------------------
Suppose you have performed several experiments from four treatments
(treatments 1,2,3, and 4). From each treatment, you have collected
many independent samples. The question is into what groups of
'statisticall... | mit |
saimn/astropy | astropy/visualization/wcsaxes/transforms.py | 8 | 5762 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
# Note: This file incldues code dervived from pywcsgrid2
#
# This file contains Matplotlib transformation objects (e.g. from pixel to world
# coordinates, but also world-to-world).
import abc
import numpy as np
from matplotlib.path import Path
from ma... | bsd-3-clause |
Bihaqo/exp-machines | src/objectives/logistic.py | 1 | 2008 | """
Utils related to the logistic regression.
"""
import numpy as np
from sklearn.linear_model import LogisticRegression
def log1pexp(x):
"""log(1 + exp(x))"""
return np.logaddexp(0, x)
def sigmoid(x):
return 1.0 / log1pexp(-x)
def binary_logistic_loss(linear_o, y):
"""Returns a vector of logistic l... | mit |
andrescv/BehavioralCloning | utils.py | 1 | 2382 | '''
_ _ _ _ _
| | | | |_(_) |___
| |_| | _| | (_-<
\___/ \__|_|_/__/
Behavioral Cloning Project
Utilities
'''
import os
import h5py
import numpy as np
from scipy.misc import imresize
from sklearn.utils import shuffle
try:
from sklearn.model_selection import train_test_split
except ImportEr... | mit |
ChanderG/scikit-learn | sklearn/datasets/tests/test_20news.py | 280 | 3045 | """Test the 20news downloader, if the data is available."""
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import SkipTest
from sklearn import datasets
def test_20news():
try:
data = dat... | bsd-3-clause |
abhisg/scikit-learn | examples/ensemble/plot_forest_importances.py | 168 | 1793 | """
=========================================
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 |
rpmunoz/DECam | data_reduction/pipeline_python/decam_pipeline.py | 1 | 1303 | #! /usr/bin/env python
import warnings
warnings.filterwarnings("ignore")
import sys,os
import os.path
import time
import subprocess
import numpy as np
import pyfits
import multiprocessing, Queue
import ctypes
import matplotlib.pyplot as plt
import scipy.interpolate
from astropy.convolution import convolve, convolve_f... | apache-2.0 |
harisbal/pandas | pandas/tests/extension/json/array.py | 2 | 6299 | """Test extension array for storing nested data in a pandas container.
The JSONArray stores lists of dictionaries. The storage mechanism is a list,
not an ndarray.
Note:
We currently store lists of UserDicts (Py3 only). Pandas has a few places
internally that specifically check for dicts, and does non-scalar things
... | bsd-3-clause |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.