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 |
|---|---|---|---|---|---|
ligovirgo/gwdetchar | gwdetchar/omega/tests/test_plot.py | 1 | 2702 | # -*- coding: utf-8 -*-
# Copyright (C) Alex Urban (2019)
#
# This file is part of the GW DetChar python package.
#
# GW DetChar 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,... | gpl-3.0 |
n7jti/machine_learning | project/tune-rain.py | 1 | 2065 | #!/usr/bin/python
import argparse
import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.svm import SVC
def loadData (subdir, prefix):
# Load a csv of floats:
data = np.genfromtxt(subdir +... | apache-2.0 |
mtat76/atm-py | build/lib/atmPy/for_removal/POPS/housekeeping.py | 6 | 4514 | # -*- coding: utf-8 -*-
"""
@author: Hagen Telg
"""
import datetime
import pandas as pd
# import os
# import pylab as plt
# from atmPy.tools import conversion_tools as ct
from atmPy.atmos import atmosphere_standards as atm_std, timeseries
def _read_housekeeping(fname):
"""Reads housekeeping file (f... | mit |
JackKelly/neuralnilm_prototype | scripts/e347.py | 2 | 6285 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource,
BLSTMLayer, DimshuffleLayer,
Bidirectio... | mit |
xwolf12/scikit-learn | sklearn/decomposition/truncated_svd.py | 199 | 7744 | """Truncated SVD for sparse matrices, aka latent semantic analysis (LSA).
"""
# Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# Olivier Grisel <olivier.grisel@ensta.org>
# Michael Becker <mike@beckerfuffle.com>
# License: 3-clause BSD.
import numpy as np
import scipy.sparse as sp
try:
from scipy.sp... | bsd-3-clause |
batxes/4c2vhic | src/prepare_data_binary.py | 2 | 2726 | #!/usr/bin/python
import sys, re, os
import collections
import matplotlib.pyplot as plt
#plt.style.use('ggplot')
#############################################################################################################
# This script takes the raw 4C-seq data. Then for each file it creates another one with some ch... | gpl-3.0 |
tmthyjames/Achoo | data/data/treatment_tracker.py | 1 | 1396 | # treatment_tracker.py
import time
import pandas as pd
import RPi.GPIO as GPIO
import munging.utils as utils
def main():
engine = utils.get_db_engine()
inhaler_btn = 18
breathing_treatment_btn = 23
GPIO.setmode(GPIO.BCM)
GPIO.setup(inhaler_btn, GPIO.IN, pull_up_down=GPIO.PUD_UP)
GPIO.setu... | mit |
Zhang-O/small | tensor__cpu/Numba/numba_example.py | 1 | 1425 | # -*- coding: utf-8 -*-
# http://numba.pydata.org/numba-doc/latest/user/examples.html
from __future__ import print_function, division, absolute_import
from timeit import default_timer as timer
from matplotlib.pylab import imshow, jet, show, ion
import numpy as np
from numba import jit, int32, int8, float64
@jit(i... | mit |
letsgoexploring/fredpy-package | build/lib/fredpy/__init__.py | 2 | 47252 | import requests
import dateutil
import datetime
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import warnings
import statsmodels.api as sm
import time
tsa = sm.tsa
# Read recession data data
cycle_data = pd.read_csv('https://raw.githubusercontent.com/letsgoexploring/fredpy-package/gh... | mit |
henridwyer/scikit-learn | sklearn/ensemble/tests/test_bagging.py | 127 | 25365 | """
Testing for the bagging ensemble module (sklearn.ensemble.bagging).
"""
# Author: Gilles Louppe
# License: BSD 3 clause
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.te... | bsd-3-clause |
CallaJun/hackprince | indico/skimage/filters/_gabor.py | 4 | 6929 | import numpy as np
from scipy import ndimage
from .._shared.utils import assert_nD
__all__ = ['gabor_kernel', 'gabor_filter']
def _sigma_prefactor(bandwidth):
b = bandwidth
# See http://www.cs.rug.nl/~imaging/simplecell.html
return 1.0 / np.pi * np.sqrt(np.log(2) / 2.0) * \
(2.0 ** b + 1) / (2.0... | lgpl-3.0 |
multipath-tcp/mptcp-analysis-scripts | scripts_graph/bursts_conn_duration.py | 1 | 4804 | #! /usr/bin/python
# -*- coding: utf-8 -*-
#
# Copyright 2015 Quentin De Coninck
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any... | gpl-3.0 |
cjermain/numpy | doc/source/conf.py | 63 | 9811 | # -*- coding: utf-8 -*-
from __future__ import division, absolute_import, print_function
import sys, os, re
# Check Sphinx version
import sphinx
if sphinx.__version__ < "1.0.1":
raise RuntimeError("Sphinx 1.0.1 or newer required")
needs_sphinx = '1.0'
# ----------------------------------------------------------... | bsd-3-clause |
shangwuhencc/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | 176 | 2169 | from nose.tools import assert_equal
import numpy as np
from sklearn.preprocessing import FunctionTransformer
def _make_func(args_store, kwargs_store, func=lambda X, *a, **k: X):
def _func(X, *args, **kwargs):
args_store.append(X)
args_store.extend(args)
kwargs_store.update(kwargs)
... | bsd-3-clause |
dcherian/tools | ROMS/pmacc/tools/post_tools/rompy/tags/rompy-0.1.6/test.py | 4 | 8114 | #!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from rompy import rompy, plot_utils, utils
map1 = False
map2 = False
map3 = False
map4 = False
map5 = False
map6 = False
map7 = Fa... | mit |
CVML/scikit-learn | sklearn/covariance/__init__.py | 389 | 1157 | """
The :mod:`sklearn.covariance` module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.
"""
from ... | bsd-3-clause |
treycausey/scikit-learn | examples/ensemble/plot_random_forest_embedding.py | 286 | 3531 | """
=========================================================
Hashing feature transformation using Totally Random Trees
=========================================================
RandomTreesEmbedding provides a way to map data to a
very high-dimensional, sparse representation, which might
be beneficial for classificati... | bsd-3-clause |
spyder-ide/conda-manager | scripts/convertainitodic.py | 2 | 31378 | # -*- coding: utf-8 -*-
"""
Created on Sun Sep 13 10:51:43 2015
@author: goanpeca
"""
a = """[_license]
description=Interactive prompt objects for printing the license text, a list of contributors and the copyright notice
home=
pypi=
docs=
dev=
[_windows]
description=
home=
pypi=
docs=
dev=
[abstract-rendering]
d... | mit |
phobson/seaborn | seaborn/tests/test_relational.py | 3 | 57698 | from __future__ import division
from itertools import product
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import pytest
from .. import relational as rel
from ..palettes import color_palette
from ..utils import categorical_order, sort_df
class TestRelationalPlotter(o... | bsd-3-clause |
snowicecat/umich-eecs445-f16 | lecture07_naive-bayes/Lec07.py | 2 | 5343 | # plotting
from matplotlib import pyplot as plt;
from matplotlib import colors
import matplotlib as mpl;
from mpl_toolkits.mplot3d import Axes3D
if "bmh" in plt.style.available: plt.style.use("bmh");
# matplotlib objects
from matplotlib import mlab;
from matplotlib import gridspec;
# scientific
import numpy as np;
im... | mit |
ClimbsRocks/scikit-learn | sklearn/utils/estimator_checks.py | 1 | 56576 | from __future__ import print_function
import types
import warnings
import sys
import traceback
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.utils.testing imp... | bsd-3-clause |
mr-cloud/deep-learning-udacity | download.py | 1 | 2153 | # These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy import ndimage
from sklearn.line... | mit |
brodoll/sms-tools | lectures/05-Sinusoidal-model/plots-code/sine-analysis-synthesis.py | 22 | 1543 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, triang, blackmanharris
import sys, os, functools, time
from scipy.fftpack import fft, ifft, fftshift
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import dftModel as DFT
import ... | agpl-3.0 |
daStrauss/sparseConv | src/convNet.py | 1 | 5746 | '''
Created on Dec 26, 2012
@author: dstrauss
Copyright 2013 David Strauss
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 |
atsoroka/cs207project | src/group5code/correlation.py | 5 | 8042 | import os, sys
curr_dir = os.getcwd().split('/')
sys.path.append('/'.join(curr_dir[:-1]))
ts_dir = curr_dir[:-1]
ts_dir.append('timeseries')
sys.path.append('/'.join(ts_dir))
import numpy.fft as nfft
import numpy as np
from timeseries.timeseries import TimeSeries
from scipy.stats import norm
class cor... | mit |
glennq/scikit-learn | sklearn/neighbors/tests/test_kd_tree.py | 159 | 7852 | import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.neighbors.kd_tree import (KDTree, NeighborsHeap,
simultaneous_sort, kernel_norm,
nodeheap_sort, DTYPE, ITYPE)
from sklearn.neighbors.dist_metrics import Dista... | bsd-3-clause |
evertrol/healpy | healpy/projaxes.py | 2 | 39008 | #
# This file is part of Healpy.
#
# Healpy 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
# (at your option) any later version.
#
# Healpy is distributed in the h... | gpl-2.0 |
dilawar/moose-full | moose-examples/traub_2005/py/testutils.py | 2 | 11988 | # test_utils.py ---
#
# Filename: test_utils.py
# Description:
# Author:
# Maintainer:
# Created: Sat May 26 10:41:37 2012 (+0530)
# Version:
# Last-Updated: Fri Dec 7 16:27:24 2012 (+0530)
# By: subha
# Update #: 400
# URL:
# Keywords:
# Compatibility:
#
#
# Commentary:
#
#
#
#
# Chang... | gpl-2.0 |
aman-iitj/scipy | scipy/interpolate/ndgriddata.py | 45 | 7161 | """
Convenience interface to N-D interpolation
.. versionadded:: 0.9
"""
from __future__ import division, print_function, absolute_import
import numpy as np
from .interpnd import LinearNDInterpolator, NDInterpolatorBase, \
CloughTocher2DInterpolator, _ndim_coords_from_arrays
from scipy.spatial import cKDTree
_... | bsd-3-clause |
manashmndl/scikit-learn | sklearn/metrics/tests/test_regression.py | 272 | 6066 | from __future__ import division, print_function
import numpy as np
from itertools import product
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.... | bsd-3-clause |
wlamond/scikit-learn | benchmarks/bench_plot_parallel_pairwise.py | 127 | 1270 | # Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD 3 clause
import time
import matplotlib.pyplot as plt
from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_kernels
def plot(func):
random_state = check_rand... | bsd-3-clause |
Featuretools/featuretools | featuretools/tests/primitive_tests/test_make_agg_primitives.py | 1 | 2901 | import numpy as np
import pandas as pd
import featuretools as ft
from featuretools.primitives.base.aggregation_primitive_base import (
make_agg_primitive
)
from featuretools.variable_types import Datetime, Numeric
# Check the custom agg primitives description
def test_description_make_agg_primitive():
def ma... | bsd-3-clause |
adrienpacifico/openfisca-france-data | openfisca_france_data/input_data_builders/build_openfisca_survey_data/step_04_famille.py | 2 | 26656 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
# OpenFisca -- A versatile microsimulation software
# By: OpenFisca Team <contact@openfisca.fr>
#
# Copyright (C) 2011, 2012, 2013, 2014, 2015 OpenFisca Team
# https://github.com/openfisca
#
# This file is part of OpenFisca.
#
# OpenFisca is free software; you can redist... | agpl-3.0 |
drphilmarshall/SpaceWarps | analysis/make_offline_reports.py | 2 | 17829 | #!/usr/bin/env python
# ======================================================================
import sys, getopt, numpy as np
import matplotlib
# Force matplotlib to not use any Xwindows backend:
matplotlib.use('Agg')
# Fonts, latex:
matplotlib.rc('font', **{'family':'serif', 'serif':['TimesNewRoman']})
matplotlib.... | mit |
abimannans/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_01_language_train_model.py | 254 | 2005 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
VINScodeReviewGroup/VINS_CodeReviewRep | VINS_ThirdPartyLib/ceres-solver/examples/slam/pose_graph_2d/plot_results.py | 8 | 1537 | #!/usr/bin/python
#
# Plots the results from the 2D pose graph optimization. It will draw a line
# between consecutive vertices. The commandline expects two optional filenames:
#
# ./plot_results.py --initial_poses optional --optimized_poses optional
#
# The files have the following format:
# ID x y yaw_radians
i... | gpl-3.0 |
tomsilver/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... | gpl-3.0 |
spatchcock/models | foraminifera/foraminiferal_test_accumulation_time_evolution.py | 1 | 6938 | # -*- coding: utf-8 -*-
"""
Created on Tue Apr 15 22:52:11 2014
@author: spatchcock
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
# Advection - diffusion - Decay - production
#
# Differential equation
#
# dC/dt = D(d^2C/dx^2) - w(dC/dx) - uC + Ra(x)
#
# Difference ... | unlicense |
nest/nest-simulator | pynest/examples/gif_population.py | 8 | 5045 | # -*- coding: utf-8 -*-
#
# gif_population.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST 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,... | gpl-2.0 |
dsanno/chainer-cifar | src/train.py | 1 | 11021 | import argparse
import cPickle as pickle
import numpy as np
import os
import matplotlib.pyplot as plt
import chainer
from chainer import optimizers
from chainer import serializers
import net
import trainer
import time
class CifarDataset(chainer.datasets.TupleDataset):
def __init__(self, x, y, augment=None):
... | mit |
hsaputra/tensorflow | tensorflow/contrib/learn/python/learn/estimators/multioutput_test.py | 136 | 1696 | # 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 |
acompa/leptoid | tests/test_graphite.py | 1 | 1437 | """ Unit test for calls to Graphite's /render API. """
import leptoid.graphite as g
import leptoid.namespaces as ns
from leptoid.scaler import LeptoidScaler
from unittest import TestCase
from numpy import arange
from time import ctime
from pandas import TimeSeries
class TestGraphite(TestCase):
""" Graphite Test Cas... | apache-2.0 |
mr3bn/DAT210x | Module6/assignment1.py | 1 | 5224 | import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import time
#
# INFO: Your Parameters.
# You can adjust them after completing the lab
C = 1
kernel = 'linear'
iterations = 5000 # TODO: Change to 200000 once you get to Question#2
#
# INFO: You can set this to false ... | mit |
vigilv/scikit-learn | benchmarks/bench_sgd_regression.py | 283 | 5569 | """
Benchmark for SGD regression
Compares SGD regression against coordinate descent and Ridge
on synthetic data.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
# License: BSD 3 clause
import numpy as np
import pylab as pl
import gc
from time import time
from sklearn.linear_model i... | bsd-3-clause |
stevenzhang18/Indeed-Flask | lib/pandas/io/pickle.py | 15 | 1656 | from pandas.compat import cPickle as pkl, pickle_compat as pc, PY3
def to_pickle(obj, path):
"""
Pickle (serialize) object to input file path
Parameters
----------
obj : any object
path : string
File path
"""
with open(path, 'wb') as f:
pkl.dump(obj, f, protocol=pkl.HIG... | apache-2.0 |
siutanwong/scikit-learn | examples/neural_networks/plot_rbm_logistic_classification.py | 258 | 4609 | """
==============================================================
Restricted Boltzmann Machine features for digit classification
==============================================================
For greyscale image data where pixel values can be interpreted as degrees of
blackness on a white background, like handwritten... | bsd-3-clause |
cwu2011/scikit-learn | sklearn/qda.py | 140 | 7682 | """
Quadratic Discriminant Analysis
"""
# Author: Matthieu Perrot <matthieu.perrot@gmail.com>
#
# License: BSD 3 clause
import warnings
import numpy as np
from .base import BaseEstimator, ClassifierMixin
from .externals.six.moves import xrange
from .utils import check_array, check_X_y
from .utils.validation import ... | bsd-3-clause |
ZENGXH/scikit-learn | sklearn/mixture/tests/test_dpgmm.py | 261 | 4490 | import unittest
import sys
import numpy as np
from sklearn.mixture import DPGMM, VBGMM
from sklearn.mixture.dpgmm import log_normalize
from sklearn.datasets import make_blobs
from sklearn.utils.testing import assert_array_less, assert_equal
from sklearn.mixture.tests.test_gmm import GMMTester
from sklearn.externals.s... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.17/_downloads/0919bcb81dbc886011b0f529b4baf6c3/plot_epochs_to_data_frame.py | 8 | 8847 | """
=================================
Export epochs to Pandas DataFrame
=================================
In this example the pandas exporter will be used to produce a DataFrame
object. After exploring some basic features a split-apply-combine
work flow will be conducted to examine the latencies of the response
maxima... | bsd-3-clause |
jhnnsnk/nest-simulator | pynest/examples/Potjans_2014/helpers.py | 14 | 13629 | # -*- coding: utf-8 -*-
#
# helpers.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST 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 |
neurodata/ndgrutedb | MR-OCP/MROCPdjango/computation/plotting/plotHelpers.py | 2 | 7503 |
# Copyright 2014 Open Connectome Project (http://openconnecto.me)
#
# 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 ap... | apache-2.0 |
hainm/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 |
huobaowangxi/scikit-learn | sklearn/kernel_approximation.py | 258 | 17973 | """
The :mod:`sklearn.kernel_approximation` module implements several
approximate kernel feature maps base on Fourier transforms.
"""
# Author: Andreas Mueller <amueller@ais.uni-bonn.de>
#
# License: BSD 3 clause
import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import svd
from .base im... | bsd-3-clause |
roryhr/yelp_kaggle | old_scripts/resnet_graph.py | 1 | 14485 | import cPickle as pickle
import glob
import numpy as np # 1.10.1
import pandas as pd
import random
import time
from keras.callbacks import EarlyStopping, TensorBoard, LearningRateScheduler
from keras.regularizers import l2
from keras.layers.normalization import BatchNormalization
from keras.models import Graph
fr... | gpl-3.0 |
Juanlu001/pfc-uc3m | code/plot_combined_ei_numeric.py | 1 | 3027 | import os
from datetime import datetime
import numpy as np
from numpy.linalg import norm
from matplotlib import rc
import matplotlib.pyplot as plt
from astropy import units as u
from poliastro.bodies import Earth
from poliastro.twobody import Orbit
from poliastro.twobody.propagation import cowell
from poliastro.tw... | mit |
poojavade/Genomics_Docker | Dockerfiles/gedlab-khmer-filter-abund/pymodules/python2.7/lib/python/statsmodels-0.5.0-py2.7-linux-x86_64.egg/statsmodels/examples/ex_kernel_regression_sigtest.py | 3 | 3113 | # -*- coding: utf-8 -*-
"""Kernel Regression and Significance Test
Warning: SLOW, 11 minutes on my computer
Created on Thu Jan 03 20:20:47 2013
Author: Josef Perktold
results - this version
----------------------
>>> execfile('ex_kernel_regression_censored1.py')
bw
[ 0.3987821 0.50933458]
[0.39878209999999997, 0... | apache-2.0 |
mc-suchecki/MSc | scripts/analyze_stars_and_views.py | 1 | 5989 | """Displays a histogram for photos metadata - number of stars and views."""
import datetime
from math import log
from matplotlib import pylab
import numpy
import pyprind
import sys
# settings
PHOTOS_LIST_LOCATION = '/media/p307k07/hdd/MSc/data/list.txt'
NUMBER_OF_BINS = 100
VIEWS_THRESHOLD = 0
DESIRED_WIDTH = 240
DESI... | gpl-3.0 |
ZenDevelopmentSystems/scikit-learn | benchmarks/bench_covertype.py | 120 | 7381 | """
===========================
Covertype dataset benchmark
===========================
Benchmark stochastic gradient descent (SGD), Liblinear, and Naive Bayes, CART
(decision tree), RandomForest and Extra-Trees on the forest covertype dataset
of Blackard, Jock, and Dean [1]. The dataset comprises 581,012 samples. It ... | bsd-3-clause |
kerimlcr/ab2017-dpyo | ornek/imageio/imageio-2.1.2/debian/python-imageio/usr/lib/python2.7/dist-packages/imageio/plugins/_tifffile.py | 2 | 219652 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# tifffile.py
# styletest: skip
# Copyright (c) 2008-2016, Christoph Gohlke
# Copyright (c) 2008-2016, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
# Redistribution and use in source and binary ... | gpl-3.0 |
ishanic/scikit-learn | examples/semi_supervised/plot_label_propagation_structure.py | 247 | 2432 | """
==============================================
Label Propagation learning a complex structure
==============================================
Example of LabelPropagation learning a complex internal structure
to demonstrate "manifold learning". The outer circle should be
labeled "red" and the inner circle "blue". Be... | bsd-3-clause |
julien6387/supvisors | supvisors/plot.py | 2 | 4007 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# ======================================================================
# Copyright 2016 Julien LE CLEACH
#
# 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 Lice... | apache-2.0 |
openego/dingo | ding0/grid/mv_grid/solvers/base.py | 1 | 6408 | """This file is part of DING0, the DIstribution Network GeneratOr.
DING0 is a tool to generate synthetic medium and low voltage power
distribution grids based on open data.
It is developed in the project open_eGo: https://openegoproject.wordpress.com
DING0 lives at github: https://github.com/openego/ding0/
The docume... | agpl-3.0 |
compops/gpo-joe2015 | para/ml_helpers.py | 2 | 9739 | ##############################################################################
##############################################################################
# Default settings and helpers for
# Maximum-likelihood inference
#
# Copyright (c) 2016 Johan Dahlin
# liu (at) johandahlin.com
#
###############################... | mit |
harisbal/pandas | pandas/tests/arrays/test_datetimelike.py | 1 | 7878 | # -*- coding: utf-8 -*-
import numpy as np
import pytest
import pandas as pd
from pandas.core.arrays import (
DatetimeArrayMixin, PeriodArray, TimedeltaArrayMixin)
import pandas.util.testing as tm
# TODO: more freq variants
@pytest.fixture(params=['D', 'B', 'W', 'M', 'Q', 'Y'])
def period_index(request):
"""... | bsd-3-clause |
tapomayukh/projects_in_python | classification/Classification_with_kNN/Single_Contact_Classification/Final/best_kNN_PCA/4-categories/96/test11_cross_validate_categories_96_no_motion_1200ms.py | 1 | 4743 |
# Principal Component Analysis Code :
from numpy import mean,cov,double,cumsum,dot,linalg,array,rank,size,flipud
from pylab import *
import numpy as np
import matplotlib.pyplot as pp
#from enthought.mayavi import mlab
import scipy.ndimage as ni
import roslib; roslib.load_manifest('sandbox_tapo_darpa_m3')
import ro... | mit |
Nyker510/scikit-learn | sklearn/utils/tests/test_fixes.py | 281 | 1829 | # Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Justin Vincent
# Lars Buitinck
# License: BSD 3 clause
import numpy as np
from nose.tools import assert_equal
from nose.tools import assert_false
from nose.tools import assert_true
from numpy.testing import (assert_almost_equal,
... | bsd-3-clause |
daneschi/berkeleytutorial | tutorial/tuliplib/tulipBin/test/pyTests/01_TUTORIAL/04_TUTORIAL_RVMRegression.py | 1 | 1314 | # Imports
import sys
sys.path.insert(0, '../../py')
import tulipUQ as uq
import numpy as np
import matplotlib.pyplot as plt
# =============
# MAIN FUNCTION
# =============
if __name__ == "__main__":
# Construct samples
samples = uq.uqSamples()
samples.addVariable('Var1',uq.kSAMPLEUniform,-1.0,1.0)
samples.ad... | mit |
cybernet14/scikit-learn | benchmarks/bench_sparsify.py | 323 | 3372 | """
Benchmark SGD prediction time with dense/sparse coefficients.
Invoke with
-----------
$ kernprof.py -l sparsity_benchmark.py
$ python -m line_profiler sparsity_benchmark.py.lprof
Typical output
--------------
input data sparsity: 0.050000
true coef sparsity: 0.000100
test data sparsity: 0.027400
model sparsity:... | bsd-3-clause |
xray/xray | xarray/tests/test_accessor_str.py | 1 | 25563 | # Tests for the `str` accessor are derived from the original
# pandas string accessor tests.
# For reference, here is a copy of the pandas copyright notice:
# (c) 2011-2012, Lambda Foundry, Inc. and PyData Development Team
# All rights reserved.
# Copyright (c) 2008-2011 AQR Capital Management, LLC
# All rights rese... | apache-2.0 |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/sklearn/gaussian_process/tests/test_gpr.py | 36 | 11813 | """Testing for Gaussian process regression """
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD 3 clause
import numpy as np
from scipy.optimize import approx_fprime
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels \
import RBF, Constan... | mit |
ChanderG/scikit-learn | examples/ensemble/plot_gradient_boosting_oob.py | 230 | 4762 | """
======================================
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 |
samzhang111/scikit-learn | examples/missing_values.py | 233 | 3056 | """
======================================================
Imputing missing values before building an estimator
======================================================
This example shows that imputing the missing values can give better results
than discarding the samples containing any missing value.
Imputing does not ... | bsd-3-clause |
yaukwankiu/armor | patternMatching/mark3.py | 1 | 9506 | """
mark3.py
switched to wepsFolder - the folder containing all forecasts made at different startTimes
fixed wrfPathList problem
ALGORITHM:
moment-normalised correlation
USE:
cd [.. FILL IN YOUR ROOT DIRECTORY HERE ..]/ARMOR/python/
python
from armor.patternMatching import mark3
x=mark3.main(verbose=Tr... | cc0-1.0 |
pandas-ml/pandas-ml | pandas_ml/skaccessors/test/test_preprocessing.py | 2 | 18331 | #!/usr/bin/env python
import pytest
import numpy as np
import pandas as pd
import sklearn.datasets as datasets
import sklearn.preprocessing as pp
import pandas_ml as pdml
import pandas_ml.util.testing as tm
class TestPreprocessing(tm.TestCase):
def test_objectmapper(self):
df = pdml.Mode... | bsd-3-clause |
HaydenFaulkner/phd | keras_code/rnns/sentence/train.py | 1 | 19333 | import os
import sys
dir_path = os.path.dirname(os.path.realpath(__file__))
dir_path = dir_path[:dir_path.find('/phd')+4]
if not dir_path in sys.path:
sys.path.append(dir_path)
print(sys.path)
from keras import backend as K
import numpy as np
import random
import matplotlib.pyplot as plt
import time
import da... | mit |
zymsys/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 |
sealhuang/brainDecodingToolbox | braincode/prf/quantitative_prf.py | 2 | 4569 | # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
import os
import numpy as np
import pandas as pd
import seaborn as sns
def load_roi_prf(roi_dir):
"""Load all pRF data for specific ROI.
Usage:
orig_data = load_roi_prf(roi_dir)
""" ... | bsd-3-clause |
paladin74/neural-network-animation | matplotlib/mpl.py | 11 | 1069 | """
.. note:: Deprecated in 1.3
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import warnings
from matplotlib import cbook
cbook.warn_deprecated(
'1.3', name='matplotlib.mpl', alternative='`import matplotlib as mpl`',
obj_type='module')
from ma... | mit |
Chandlercjy/OnePy | OnePy/custom_module/analysis.py | 1 | 15556 | import json
from collections import defaultdict
import arrow
import numpy as np
import pandas as pd
from OnePy.constants import ActionType, OrderType
from OnePy.sys_module.metabase_env import OnePyEnvBase
from OnePy.utils.memo_for_cache import memo
# mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体
# mpl.rcPar... | mit |
adrn/Biff | biff/scf/tests/test_computecoeff_discrete.py | 1 | 1314 | # coding: utf-8
from __future__ import division, print_function
import os
# Third-party
import numpy as np
from astropy.utils.data import get_pkg_data_filename
from astropy.constants import G
import gala.potential as gp
from gala.units import galactic
_G = G.decompose(galactic).value
# Project
from ..core import c... | mit |
GaZ3ll3/scikit-image | doc/examples/plot_marching_cubes.py | 32 | 2051 | """
==============
Marching Cubes
==============
Marching cubes is an algorithm to extract a 2D surface mesh from a 3D volume.
This can be conceptualized as a 3D generalization of isolines on topographical
or weather maps. It works by iterating across the volume, looking for regions
which cross the level of interest. ... | bsd-3-clause |
themrmax/scikit-learn | examples/applications/plot_model_complexity_influence.py | 323 | 6372 | """
==========================
Model Complexity Influence
==========================
Demonstrate how model complexity influences both prediction accuracy and
computational performance.
The dataset is the Boston Housing dataset (resp. 20 Newsgroups) for
regression (resp. classification).
For each class of models we m... | bsd-3-clause |
rl-institut/reegis_hp | reegis_hp/de21/scenario_tools.py | 3 | 22415 | # -*- coding: utf-8 -*-
import pandas as pd
import os
import os.path as path
import logging
from oemof import network
from oemof.solph import EnergySystem
from oemof.solph.options import BinaryFlow, Investment
from oemof.solph.plumbing import sequence
from oemof.solph.network import (Bus, Source, Sink, Flow, LinearTra... | gpl-3.0 |
MHarland/cthyb_vs_wick | g2plot.py | 1 | 3675 | import numpy as np, matplotlib, itertools as itt
from pytriqs.gf.local import Block2Gf, GfImFreqTv4
class G2ConstiwPlot:
def __init__(self, g2, bosonic_frequency_to_plot = 0):
self.g2 = g2
self.n = dict()
for s, b in g2:
g2mesh = np.array([w.imag for w in g2[s].mesh.components[... | gpl-3.0 |
CINPLA/exana | exana/waveform/tools.py | 1 | 6026 | import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.vq import kmeans, vq
def calculate_waveform_features(sptrs, calc_all_spikes=False):
"""Calculates waveform features for spiketrains; full-width half-maximum
(half width) and minimum-to-maximum peak width (peak-to-peak width) for
mean sp... | gpl-3.0 |
Akshay0724/scikit-learn | examples/neighbors/plot_kde_1d.py | 60 | 5120 | """
===================================
Simple 1D Kernel Density Estimation
===================================
This example uses the :class:`sklearn.neighbors.KernelDensity` class to
demonstrate the principles of Kernel Density Estimation in one dimension.
The first plot shows one of the problems with using histogram... | bsd-3-clause |
johanvdw/niche_vlaanderen | tests/test_niche.py | 1 | 21967 | from __future__ import division
from unittest import TestCase
import pytest
import niche_vlaanderen
from niche_vlaanderen.exception import NicheException
from rasterio.errors import RasterioIOError
import numpy as np
import pandas as pd
import tempfile
import shutil
import os
import sys
import distutils.spawn
import... | mit |
boland1992/seissuite_iran | build/lib/seissuite/spacing/dataless_map.py | 8 | 4579 | # -*- coding: utf-8 -*-
"""
Created on Wed May 20 14:12:37 2015
@author: boland
"""
from pysismo import pscrosscorr, pserrors, psstation
import os
import sys
sys.path.append('/home/boland/Anaconda/lib/python2.7/site-packages')
import warnings
import datetime as dt
import itertools as it
import pickle
import obspy.sign... | gpl-3.0 |
dereneaton/ipyrad | ipyrad/analysis/structure.py | 1 | 40544 | #!/usr/bin/env python
"convenience wrappers for running structure in a jupyter notebook"
# py2/3 compat
from __future__ import print_function
from builtins import range
# standard lib
import os
import re
import sys
import glob
import time
import subprocess as sps
# third party
import numpy as np
import pandas as pd... | gpl-3.0 |
Garrett-R/scikit-learn | sklearn/neighbors/tests/test_nearest_centroid.py | 1 | 3401 | """
Testing for the nearest centroid module.
"""
import numpy as np
from scipy import sparse as sp
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from sklearn.neighbors import NearestCentroid
from sklearn import datasets
from sklearn.metrics.pairwise import pairwise_distances
# t... | bsd-3-clause |
nelson-liu/scikit-learn | sklearn/linear_model/coordinate_descent.py | 4 | 81531 | # 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 |
jorik041/scikit-learn | sklearn/metrics/ranking.py | 75 | 25426 | """Metrics to assess performance on classification task given scores
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.... | bsd-3-clause |
yaroslavvb/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py | 18 | 13185 | # 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 |
xwolf12/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 |
russel1237/scikit-learn | examples/cluster/plot_affinity_propagation.py | 349 | 2304 | """
=================================================
Demo of affinity propagation clustering algorithm
=================================================
Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
"""
print(__doc__)
from sklearn.cluster impor... | bsd-3-clause |
toastedcornflakes/scikit-learn | examples/model_selection/randomized_search.py | 9 | 3278 | """
=========================================================================
Comparing randomized search and grid search for hyperparameter estimation
=========================================================================
Compare randomized search and grid search for optimizing hyperparameters of a
random forest.
... | bsd-3-clause |
loli/sklearn-ensembletrees | examples/neighbors/plot_digits_kde_sampling.py | 251 | 2022 | """
=========================
Kernel Density Estimation
=========================
This example shows how kernel density estimation (KDE), a powerful
non-parametric density estimation technique, can be used to learn
a generative model for a dataset. With this generative model in place,
new samples can be drawn. These... | bsd-3-clause |
treverhines/RBF | docs/scripts/gproc.c.py | 1 | 1709 | '''
This script describes how to use the *outliers* method to detect and
remove outliers prior to conditioning a *GaussinaProcess*.
'''
import numpy as np
import matplotlib.pyplot as plt
import logging
from rbf.gproc import gpiso, gppoly
logging.basicConfig(level=logging.DEBUG)
np.random.seed(1)
y = np.linspace(-7.5... | mit |
hlin117/scikit-learn | sklearn/discriminant_analysis.py | 27 | 26804 | """
Linear Discriminant Analysis and Quadratic Discriminant Analysis
"""
# Authors: Clemens Brunner
# Martin Billinger
# Matthieu Perrot
# Mathieu Blondel
# License: BSD 3-Clause
from __future__ import print_function
import warnings
import numpy as np
from scipy import linalg
from .extern... | bsd-3-clause |
glennhickey/teHmm | bin/compareBedStates.py | 1 | 35043 | #!/usr/bin/env python
#Copyright (C) 2013 by Glenn Hickey
#
#Released under the MIT license, see LICENSE.txt
import unittest
import sys
import os
import argparse
import logging
import numpy as np
import copy
import ast
import itertools
from collections import defaultdict
from teHmm.trackIO import readBedIntervals
fro... | mit |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.