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 |
|---|---|---|---|---|---|
takuya1981/sms-tools | lectures/08-Sound-transformations/plots-code/sineModelTimeScale-functions.py | 24 | 2725 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, hanning, triang, blackmanharris, resample
from scipy.fftpack import fft, ifft, fftshift
import sys, os, functools, time, math
from scipy.interpolate import interp1d
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__... | agpl-3.0 |
akaszynski/vtkInterface | pyvista/plotting/colors.py | 1 | 10079 | """Color module supporting plotting module.
Used code from matplotlib.colors. Thanks for your work!
SUPPORTED COLORS
aliceblue
antiquewhite
aqua
aquamarine
azure
beige
bisque
black
blanchedalmond
blue
blueviolet
brown
burlywood
cadetblue
chartreuse
chocolate
coral
cornflowerblue
cornsilk
crimson
cyan
darkblue
darkc... | mit |
rohanp/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 |
macks22/scikit-learn | sklearn/ensemble/forest.py | 176 | 62555 | """Forest of trees-based ensemble methods
Those methods include random forests and extremely randomized trees.
The module structure is the following:
- The ``BaseForest`` base class implements a common ``fit`` method for all
the estimators in the module. The ``fit`` method of the base ``Forest``
class calls the ... | bsd-3-clause |
abhitopia/tensorflow | tensorflow/examples/learn/text_classification.py | 39 | 5106 | # 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 appl... | apache-2.0 |
gmsanchez/mpc_comparison_rpic2017 | vdp_comparison_ltv.py | 1 | 8028 | # Control of the Van der Pol
# oscillator using pure CasADi.
import casadi
import casadi.tools as ctools
import numpy as np
import matplotlib.pyplot as plt
import time
import scipy.linalg
# Set to True if you want to create a QP solver (qpOASES) and
# to False if you want to use a NLP solver (IPOPT).
isQP = True
# ... | gpl-3.0 |
RomainBrault/scikit-learn | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
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 |
cancan101/tensorflow | tensorflow/examples/learn/boston.py | 13 | 1945 | # 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 appl... | apache-2.0 |
sameersingh/onebusaway | ml/oba_ml/ridge_paths.py | 1 | 1512 | from __future__ import division
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
from common import *
def main():
np.set_printoptions(threshold=np.nan)
x_train, y_train, = get_data("training.dat")
n_alphas = 100
alphas = np.logspace(-5, 5, n_alp... | apache-2.0 |
mtat76/atm-py | build/lib/atmPy/for_removal/SMPS/SMPS.py | 6 | 2502 | # -*- coding: utf-8 -*-
"""
Created on Fri Jan 23 16:39:10 2015
@author: htelg
"""
import sys
sys.path.append('/Users/htelg/projecte/POPS/prog/')
from POPS_lib import sizedistribution
import pandas as pd
import numpy as np
def bincenters2BinStuff(bincenters):
if type(bincenters) != np.ndarray:
rai... | mit |
espenhgn/LFPy | examples/nsg_example/nsg_example.py | 1 | 8889 | #!/usr/bin/env python
'''
################################################################################
#
# This is an example scripts using LFPy with a passive cell model adapted from
# Mainen and Sejnowski, Nature 1996, for the original files, see
# http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=2488
#
#... | gpl-3.0 |
akaszynski/vtkInterface | pyvista/plotting/plotting.py | 1 | 148771 | """Pyvista plotting module."""
import collections
import logging
import os
import time
import warnings
from functools import wraps
from threading import Thread
import imageio
import numpy as np
import vtk
from vtk.util import numpy_support as VN
from vtk.util.numpy_support import numpy_to_vtk, vtk_to_numpy
import py... | mit |
anparser/anparser | anparser/plugins/other_plugins/yara_parser.py | 1 | 3312 | # -*- coding: utf-8 -*-
"""
anparser - an Open Source Android Artifact Parser
Copyright (C) 2015 Preston Miller
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
(a... | gpl-3.0 |
omarocegueda/dipy | dipy/core/optimize.py | 12 | 15237 | """ A unified interface for performing and debugging optimization problems.
Only L-BFGS-B and Powell is supported in this class for versions of
Scipy < 0.12. All optimizers are available for scipy >= 0.12.
"""
import abc
from distutils.version import LooseVersion
import numpy as np
import scipy
import scipy.sparse as ... | bsd-3-clause |
breznak/nupic | src/nupic/research/monitor_mixin/plot.py | 20 | 5229 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2014-2015, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This p... | agpl-3.0 |
memo/tensorflow | tensorflow/examples/learn/iris_with_pipeline.py | 62 | 1824 | # 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 appl... | apache-2.0 |
anorfleet/turntable | turntable/press.py | 3 | 11422 | '''The press module is used to create Record Collections.
'''
import shutil
import sys
import os
import pandas as pd
import turntable.utils
import traceback
import turntable
class RecordPress(object):
'''This class auto-seralizes any attributes assigned to an instance and clears them from memmory
when an a... | mit |
licco/zipline | tests/test_algorithm.py | 1 | 30499 | #
# 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 |
liyu1990/sklearn | 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 |
JeroenZegers/Nabu-MSSS | nabu/hyperparameteroptimization/estimators.py | 1 | 9692 | import warnings
import numpy as np
import copy
from skopt.space import space as skopt_space
from skopt.learning import GaussianProcessRegressor
from scipy.linalg import cho_solve
from sklearn.utils.validation import check_array
from utils import check_parameter_count
class BoundedGaussianProcessRegressor(GaussianP... | mit |
sonnyhu/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 |
sebalander/sebaPhD | runExperiment/readArduSerial.py | 1 | 1235 | # -*- coding: utf-8 -*-
"""
Created on Mon May 16 19:00:12 2016
reading arduino uno output
@author: sebalander
"""
# %% IMPORTS
import serial
import numpy as np
import matplotlib.pyplot as plt
import datetime
import time
# %% DECLARATIONS
ser = serial.Serial('/dev/ttyUSB0', 9600)
N = 50 # number of data points to... | bsd-3-clause |
jakevdp/scipy | scipy/stats/morestats.py | 6 | 95788 | from __future__ import division, print_function, absolute_import
import math
import warnings
from collections import namedtuple
import numpy as np
from numpy import (isscalar, r_, log, around, unique, asarray,
zeros, arange, sort, amin, amax, any, atleast_1d,
sqrt, ceil, floor, a... | bsd-3-clause |
brentp/crystal | crystal/tests/test_models.py | 2 | 3828 | import pandas as pd
import numpy as np
import crystal
np.random.seed(42)
covs = pd.DataFrame({'gender': ['F'] * 10 + ['M'] * 10,
'age': np.random.uniform(10, 25, size=20) })
methylation = np.random.normal(-1, 1, size=(5, covs.shape[0]))
cluster = [crystal.Feature('chr1', i* 10, m) for i, m in ... | mit |
michalkurka/h2o-3 | h2o-py/tests/testdir_algos/glm/pyunit_pubdev_8194_ordinal_fail.py | 2 | 1927 | from builtins import range
import sys
sys.path.insert(1,"../../../")
import h2o
from tests import pyunit_utils
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
import pandas as pd
# test taken from Ben Epstein. Thank you.
# PUBDEV-8197: ordinal prediction returns the wrong class even though other classes ... | apache-2.0 |
imh/gnss-analysis | gnss_analysis/analysis_io.py | 1 | 2804 | #!/usr/bin/env python
# Copyright (C) 2015 Swift Navigation Inc.
# Contact: Bhaskar Mookerji <mookerji@swiftnav.com>
#
# This source is subject to the license found in the file 'LICENSE' which must
# be be distributed together with this source. All other rights reserved.
#
# THIS CODE AND INFORMATION IS PROVIDED "AS IS... | lgpl-3.0 |
0asa/scikit-learn | benchmarks/bench_plot_approximate_neighbors.py | 85 | 6377 | """
Benchmark for approximate nearest neighbor search using
locality sensitive hashing forest.
There are two types of benchmarks.
First, accuracy of LSHForest queries are measured for various
hyper-parameters and index sizes.
Second, speed up of LSHForest queries compared to brute force
method in exact nearest neigh... | bsd-3-clause |
Gillu13/scipy | scipy/stats/kde.py | 17 | 17717 | #-------------------------------------------------------------------------------
#
# Define classes for (uni/multi)-variate kernel density estimation.
#
# Currently, only Gaussian kernels are implemented.
#
# Written by: Robert Kern
#
# Date: 2004-08-09
#
# Modified: 2005-02-10 by Robert Kern.
# Contr... | bsd-3-clause |
phobson/bokeh | examples/charts/file/heatmap.py | 2 | 2091 | import pandas as pd
from bokeh.charts import HeatMap, bins, output_file, show
from bokeh.layouts import column, gridplot
from bokeh.palettes import RdYlGn6, RdYlGn9
from bokeh.sampledata.autompg import autompg
from bokeh.sampledata.unemployment1948 import data
# setup data sources
del data['Annual']
data['Year'] = da... | bsd-3-clause |
jiaphuan/models | research/autoencoder/VariationalAutoencoderRunner.py | 8 | 1705 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import sklearn.preprocessing as prep
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from autoencoder_models.VariationalAutoencoder import VariationalAutoe... | apache-2.0 |
dimdat/dimdat | raw_data/us_states_and_territories/build.py | 1 | 1313 | import json
import os
import pandas as pd
cols = [
'iso_3166',
'ansi_alphabetic_code',
'ansi_numeric_code',
'usps_code',
'uscg_code',
'gpo_abbrev',
'ap_abbrev',
'capital',
'established_date',
'total_square_miles',
'total_square_kilometers',
'land_square_miles',
'land... | mit |
zqhuang/COOP | mapio/pyscripts/plot_real_data_6plots.py | 1 | 4505 | #!/usr/bin/env python
#!/usr/bin/env python
import numpy as np
import healpy as hp
from newsetup_matplotlib import *
from planckcolors import planck_parchment_cmap, planck_grey_cmap,colombi1_cmap
from matplotlib import cm
from plot import *
import idlsave
import pyfits as py
nside=512
width=18.0
cmap ... | gpl-3.0 |
chrisburr/scikit-learn | sklearn/preprocessing/tests/test_label.py | 156 | 17626 | import numpy as np
from scipy.sparse import issparse
from scipy.sparse import coo_matrix
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import dok_matrix
from scipy.sparse import lil_matrix
from sklearn.utils.multiclass import type_of_target
from sklearn.utils.testing impor... | bsd-3-clause |
weissercn/learningml | learningml/GoF/optimisation_and_evaluation/automatisation_sin/optimisation_1000/nn/classifier_eval_wrapper.py | 1 | 1572 | import os
import signal
import numpy as np
import math
import sys
sys.path.insert(0,os.environ["learningml"]+"/GoF")
import os
import classifier_eval
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.svm import SVC
from keras.wrappers.scikit_learn im... | mit |
Myasuka/scikit-learn | sklearn/tests/test_common.py | 127 | 7665 | """
General tests for all estimators in sklearn.
"""
# Authors: Andreas Mueller <amueller@ais.uni-bonn.de>
# Gael Varoquaux gael.varoquaux@normalesup.org
# License: BSD 3 clause
from __future__ import print_function
import os
import warnings
import sys
import pkgutil
from sklearn.externals.six import PY3
fr... | bsd-3-clause |
losonczylab/Zaremba_NatNeurosci_2017 | scripts/FigS1_performance_by_mouse.py | 1 | 3298 | """Figure S1 - Task performance by mouse"""
FIG_FORMAT = 'svg'
import matplotlib as mpl
if FIG_FORMAT == 'svg':
mpl.use('agg')
elif FIG_FORMAT == 'pdf':
mpl.use('pdf')
elif FIG_FORMAT == 'interactive':
mpl.use('TkAgg')
import matplotlib.pyplot as plt
import seaborn.apionly as sns
import lab.analysis.rewa... | mit |
fraricci/pymatgen | pymatgen/analysis/interface.py | 4 | 46759 | # coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
This module provides classes to store, generate, and manipulate material interfaces.
"""
from pymatgen.core.surface import SlabGenerator
from pymatgen import Lattice, Structure
from pymatgen.core.surface i... | mit |
PrashntS/scikit-learn | examples/covariance/plot_sparse_cov.py | 300 | 5078 | """
======================================
Sparse inverse covariance estimation
======================================
Using the GraphLasso estimator to learn a covariance and sparse precision
from a small number of samples.
To estimate a probabilistic model (e.g. a Gaussian model), estimating the
precision matrix, t... | bsd-3-clause |
lcdb/lcdblib | lcdblib/parse/picard.py | 1 | 1930 | from io import StringIO
import pandas as pd
def parse_picardCollect_summary(sample, file):
"""Parser for picard collectRNAMetrics summary.
Parameters
----------
sample: str
Sample name which will be added as row index.
file: str
Path to the fastqc zip file.
"""
with open(f... | mit |
wbengine/SPMILM | egs/1-billion/run_trf.py | 1 | 4738 | import os
import sys
import numpy as np
import matplotlib.pyplot as plt
sys.path.insert(0, os.getcwd() + '/../../tools/')
import wb
import trf
# revise this function to config the dataset used to train different model
def data(tskdir):
train = tskdir + 'data/train.txt'
valid = tskdir + 'data/valid.txt'
te... | apache-2.0 |
mhdella/scikit-learn | benchmarks/bench_plot_omp_lars.py | 266 | 4447 | """Benchmarks of orthogonal matching pursuit (:ref:`OMP`) versus least angle
regression (:ref:`least_angle_regression`)
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model impo... | bsd-3-clause |
osh/gnuradio | gr-filter/examples/fir_filter_fff.py | 47 | 4014 | #!/usr/bin/env python
#
# Copyright 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 option)
# ... | gpl-3.0 |
rhyolight/nupic.research | projects/neural_correlations/EXP5-Bar/barMovieDemo.py | 10 | 1554 | #!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2016, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions ... | gpl-3.0 |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/pandas/tests/indexes/timedeltas/test_timedelta.py | 7 | 22169 | import pytest
import numpy as np
from datetime import timedelta
import pandas as pd
import pandas.util.testing as tm
from pandas import (timedelta_range, date_range, Series, Timedelta,
DatetimeIndex, TimedeltaIndex, Index, DataFrame,
Int64Index, _np_version_under1p8)
from panda... | mit |
moutai/scikit-learn | examples/manifold/plot_manifold_sphere.py | 16 | 5103 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=============================================
Manifold Learning methods on a severed sphere
=============================================
An application of the different :ref:`manifold` techniques
on a spherical data-set. Here one can see the use of
dimensionality reducti... | bsd-3-clause |
QuantCrimAtLeeds/PredictCode | open_cp/gui/load_network_model.py | 1 | 6359 | """
load_network_model
~~~~~~~~~~~~~~~~~~
"""
import open_cp.gui.predictors.geo_clip as geo_clip
import open_cp.network
import enum as enum
import logging
import open_cp.gui.projectors as projectors
try:
import geopandas as gpd
except:
gpd = None
_logger = logging.getLogger(__name__)
class NetworkModel():
... | artistic-2.0 |
kushalbhola/MyStuff | Practice/PythonApplication/env/Lib/site-packages/pandas/tests/arrays/test_integer.py | 2 | 25131 | import numpy as np
import pytest
from pandas.core.dtypes.generic import ABCIndexClass
import pandas as pd
from pandas.api.types import is_float, is_float_dtype, is_integer, is_scalar
from pandas.core.arrays import IntegerArray, integer_array
from pandas.core.arrays.integer import (
Int8Dtype,
Int16Dtype,
... | apache-2.0 |
Clyde-fare/scikit-learn | examples/model_selection/plot_confusion_matrix.py | 244 | 2496 | """
================
Confusion matrix
================
Example of confusion matrix usage to evaluate the quality
of the output of a classifier on the iris data set. The
diagonal elements represent the number of points for which
the predicted label is equal to the true label, while
off-diagonal elements are those that ... | bsd-3-clause |
jeremyfix/pylearn2 | pylearn2/cross_validation/tests/test_train_cv_extensions.py | 49 | 1681 | """
Tests for TrainCV extensions.
"""
import os
import tempfile
from pylearn2.config import yaml_parse
from pylearn2.testing.skip import skip_if_no_sklearn
def test_monitor_based_save_best_cv():
"""Test MonitorBasedSaveBestCV."""
handle, filename = tempfile.mkstemp()
skip_if_no_sklearn()
trainer = ya... | bsd-3-clause |
adykstra/mne-python | mne/decoding/tests/test_transformer.py | 5 | 9446 | # Author: Mainak Jas <mainak@neuro.hut.fi>
# Romain Trachel <trachelr@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import pytest
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_allclose, assert_equal)
from mne impor... | bsd-3-clause |
ywcui1990/htmresearch | htmresearch/support/sequence_learning_utils.py | 10 | 4876 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This progra... | agpl-3.0 |
Snazz2001/BDA_py_demos | demos_ch2/demo2_2.py | 19 | 3023 | """Bayesian data analysis, 3rd ed
Chapter 2, demo 2
Illustrate the effect of a prior. Comparison of posterior distributions with
different parameter values for Beta prior distribution.
"""
import numpy as np
from scipy.stats import beta
import matplotlib.pyplot as plt
# Edit default plot settings (colours from co... | gpl-3.0 |
kazemakase/scikit-learn | sklearn/metrics/scorer.py | 211 | 13141 | """
The :mod:`sklearn.metrics.scorer` submodule implements a flexible
interface for model selection and evaluation using
arbitrary score functions.
A scorer object is a callable that can be passed to
:class:`sklearn.grid_search.GridSearchCV` or
:func:`sklearn.cross_validation.cross_val_score` as the ``scoring`` parame... | bsd-3-clause |
ClimbsRocks/scikit-learn | sklearn/mixture/tests/test_gmm.py | 4 | 20668 | # These tests are those of the deprecated GMM class
import unittest
import copy
import sys
from nose.tools import assert_true
import numpy as np
from numpy.testing import (assert_array_equal, assert_array_almost_equal,
assert_raises)
from scipy import stats
from sklearn import mixture
from ... | bsd-3-clause |
cloudera/hue | desktop/core/ext-py/openpyxl-2.6.4/openpyxl/compat/numbers.py | 2 | 1879 | from __future__ import absolute_import
# Copyright (c) 2010-2019 openpyxl
try:
# Python 2
long = long
except NameError:
# Python 3
long = int
from decimal import Decimal
NUMERIC_TYPES = (int, float, long, Decimal)
try:
import numpy
NUMPY = True
except ImportError:
NUMPY = False
if NUM... | apache-2.0 |
BorisJeremic/Real-ESSI-Examples | education_examples/_Chapter_Modeling_and_Simulation_Examples_Dynamic_Examples/upU/coupled_contact_upU_Sequential/plot.py | 3 | 3443 |
###########################################################################################################################
# #
# Wet Contact Modelling in Real ESSI ... | cc0-1.0 |
LohithBlaze/scikit-learn | examples/svm/plot_svm_margin.py | 318 | 2328 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
SVM Margins Example
=========================================================
The plots below illustrate the effect the parameter `C` has
on the separation line. A large value of `C` basically tells
our model that w... | bsd-3-clause |
sauloal/cnidaria | scripts/venv/lib/python2.7/site-packages/cogent/draw/distribution_plots.py | 1 | 26035 | #!/usr/bin/env python
__author__ = "Jai Ram Rideout"
__copyright__ = "Copyright 2007-2012, The Cogent Project"
__credits__ = ["Jai Ram Rideout"]
__license__ = "GPL"
__version__ = "1.5.3"
__maintainer__ = "Jai Ram Rideout"
__email__ = "jai.rideout@gmail.com"
__status__ = "Production"
"""This module contains functions ... | mit |
markneville/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/artist.py | 69 | 33042 | from __future__ import division
import re, warnings
import matplotlib
import matplotlib.cbook as cbook
from transforms import Bbox, IdentityTransform, TransformedBbox, TransformedPath
from path import Path
## Note, matplotlib artists use the doc strings for set and get
# methods to enable the introspection methods of ... | agpl-3.0 |
tawsifkhan/scikit-learn | examples/svm/plot_svm_margin.py | 318 | 2328 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
SVM Margins Example
=========================================================
The plots below illustrate the effect the parameter `C` has
on the separation line. A large value of `C` basically tells
our model that w... | bsd-3-clause |
potash/scikit-learn | sklearn/utils/graph.py | 289 | 6239 | """
Graph utilities and algorithms
Graphs are represented with their adjacency matrices, preferably using
sparse matrices.
"""
# Authors: Aric Hagberg <hagberg@lanl.gov>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD 3 clause
impo... | bsd-3-clause |
WangWenjun559/Weiss | summary/sumy/sklearn/externals/joblib/parallel.py | 29 | 28665 | """
Helpers for embarrassingly parallel code.
"""
# Author: Gael Varoquaux < gael dot varoquaux at normalesup dot org >
# Copyright: 2010, Gael Varoquaux
# License: BSD 3 clause
import os
import sys
import gc
import warnings
from collections import Sized
from math import sqrt
import functools
import time
import thread... | apache-2.0 |
dariomangoni/chrono | src/demos/python/irrlicht/demo_IRR_crank_plot.py | 4 | 5790 | #------------------------------------------------------------------------------
# Name: pychrono example
# Purpose:
#
# Author: Alessandro Tasora
#
# Created: 1/01/2019
# Copyright: (c) ProjectChrono 2019
#------------------------------------------------------------------------------
import pychrono... | bsd-3-clause |
Oscarlight/PiNN_Caffe2 | transiNXOR_modeling/transixor_predictor.py | 1 | 3492 | import sys, os
sys.path.append('../')
import numpy as np
from itertools import product
from pinn_api import predict_ids_grads, predict_ids
import matplotlib.pyplot as plt
import glob
## ------------ Input ---------------
VDS = None
VTG = 0.1
VBG = 0.1
## ------------ True data ---------------
ids_file = glob.glob(... | mit |
B3AU/waveTree | examples/linear_model/plot_lasso_lars.py | 8 | 1059 | #!/usr/bin/env python
"""
=====================
Lasso path using LARS
=====================
Computes Lasso Path along the regularization parameter using the LARS
algorithm on the diabetes dataset. Each color represents a different
feature of the coefficient vector, and this is displayed as a function
of the regulariza... | bsd-3-clause |
Islast/BrainNetworksInPython | scona/classes.py | 1 | 24678 | import numpy as np
import networkx as nx
import pandas as pd
from scona.make_graphs import assign_node_names, \
assign_node_centroids, anatomical_copy, threshold_graph, \
weighted_graph_from_matrix, anatomical_node_attributes, \
anatomical_graph_attributes, get_random_graphs, is_nodal_match, \
is_anatom... | mit |
AlessandroCorsi/fibermodes | plots/neff.py | 2 | 1737 |
from fibermodes import Wavelength, Mode, constants
from fibermodes.material import Silica, SiO2GeO2, Fixed
from fibermodes.simulator import PSimulator as Simulator
import numpy
from matplotlib import pyplot
wl = numpy.linspace(800e-9, 1800e-9, 200)
print(wl[1] - wl[0])
sim = Simulator(delta=1e-4, epsilon=1e-12)
sim... | gpl-3.0 |
wtmmac/airflow | airflow/contrib/plugins/metastore_browser/main.py | 42 | 5126 | from datetime import datetime
import json
from flask import Blueprint, request
from flask.ext.admin import BaseView, expose
import pandas as pd
from airflow.hooks import HiveMetastoreHook, MySqlHook, PrestoHook, HiveCliHook
from airflow.plugins_manager import AirflowPlugin
from airflow.www import utils as wwwutils
M... | apache-2.0 |
CallaJun/hackprince | indico/matplotlib/fontconfig_pattern.py | 11 | 6601 | """
A module for parsing and generating fontconfig patterns.
See the `fontconfig pattern specification
<http://www.fontconfig.org/fontconfig-user.html>`_ for more
information.
"""
# Author : Michael Droettboom <mdroe@stsci.edu>
# License : matplotlib license (PSF compatible)
# This class is defined here because it m... | lgpl-3.0 |
hmendozap/auto-sklearn | autosklearn/evaluation/util.py | 1 | 2220 | import os
import lockfile
import numpy as np
from autosklearn.constants import *
from autosklearn.metrics import sanitize_array, \
regression_metrics, classification_metrics, create_multiclass_solution
__all__ = [
'calculate_score',
'get_new_run_num'
]
def calculate_score(solution, prediction, task_ty... | bsd-3-clause |
SmokinCaterpillar/pypet | examples/example_13_post_processing/main.py | 2 | 6575 | __author__ = 'robert'
import numpy as np
import pandas as pd
import logging
import os # For path names working under Linux and Windows
from pypet import Environment, cartesian_product
def run_neuron(traj):
"""Runs a simulation of a model neuron.
:param traj:
Container with all parameters.
:re... | bsd-3-clause |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/pandas/tests/io/parser/usecols.py | 11 | 18059 | # -*- coding: utf-8 -*-
"""
Tests the usecols functionality during parsing
for all of the parsers defined in parsers.py
"""
import pytest
import numpy as np
import pandas.util.testing as tm
from pandas import DataFrame, Index
from pandas._libs.lib import Timestamp
from pandas.compat import StringIO
class UsecolsT... | mit |
chrissly31415/amimanera | competition_scripts/otto.py | 1 | 27139 | #!/usr/bin/python
# coding: utf-8
"""
Otto product classification
"""
from qsprLib import *
import pandas as pd
from sklearn import preprocessing
from sklearn.lda import LDA
from sklearn.qda import QDA
from pandas.tools.plotting import scatter_matrix
from xgboost_sklearn import *
import xgboost as xgb
#from OneH... | lgpl-3.0 |
Jorge-C/bipy | doc/sphinxext/numpydoc/numpydoc/plot_directive.py | 89 | 20530 | """
A special directive for generating a matplotlib plot.
.. warning::
This is a hacked version of plot_directive.py from Matplotlib.
It's very much subject to change!
Usage
-----
Can be used like this::
.. plot:: examples/example.py
.. plot::
import matplotlib.pyplot as plt
plt.plot... | bsd-3-clause |
mehdidc/scikit-learn | sklearn/covariance/graph_lasso_.py | 11 | 23920 | """GraphLasso: sparse inverse covariance estimation with an l1-penalized
estimator.
"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
# Copyright: INRIA
import warnings
import operator
import sys
import time
import numpy as np
from scipy import linalg
from .empirical_covariance_ im... | bsd-3-clause |
lfairchild/PmagPy | programs/magic_gui.py | 1 | 23955 | #!/usr/bin/env pythonw
"""
doc string
"""
# pylint: disable=C0103,E402
print('-I- Importing MagIC GUI dependencies')
import matplotlib
if not matplotlib.get_backend() == 'WXAgg':
matplotlib.use('WXAgg')
import wx
import wx.lib.buttons as buttons
import sys
import os
import pmagpy
from pmagpy import data_model3
fro... | bsd-3-clause |
MatthieuBizien/scikit-learn | sklearn/cross_decomposition/pls_.py | 35 | 30767 | """
The :mod:`sklearn.pls` module implements Partial Least Squares (PLS).
"""
# Author: Edouard Duchesnay <edouard.duchesnay@cea.fr>
# License: BSD 3 clause
from distutils.version import LooseVersion
from sklearn.utils.extmath import svd_flip
from ..base import BaseEstimator, RegressorMixin, TransformerMixin
from ..u... | bsd-3-clause |
vikhyat/dask | dask/bag/tests/test_bag.py | 1 | 21272 | # coding=utf-8
from __future__ import absolute_import, division, print_function
from sys import getdefaultencoding
import pytest
from toolz import (merge, join, pipe, filter, identity, merge_with, take,
partial, valmap)
import math
from dask.bag.core import (Bag, lazify, lazify_task, fuse, map, collect,
... | bsd-3-clause |
vybstat/scikit-learn | sklearn/neighbors/regression.py | 100 | 11017 | """Nearest Neighbor Regression"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck <L.J.Buitinck@uva.nl>
# Multi-output support by Arna... | bsd-3-clause |
cschlosberg/me-class | methylation_interpolation.py | 1 | 91637 | ### Custom container Class definitions
class Window:
def __init__(self,low,high,bins,bp_window_size=None):
self.low = low
self.high = high
self.len = self.high-self.low
self.bins = bins
# print "Window Bins: ",self.bins
self.x = list()
self.x_raw_meth = list()... | gpl-3.0 |
ShashShukla/SIFT | Application/image_transformation/pano.py | 1 | 2277 | import cv2
import numpy as np
import math
#from matplotlib import pyplot as plt
img = cv2.imread('cat.jpg',0)
def resize(image, width):
r = float(width) / image.shape[1]
dim = (int(image.shape[0] * r),width)
image = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
return image
img = resize(img,40... | mit |
schreiberx/sweet | benchmarks_sphere/paper_jrn_nla_rexi_linear/sph_rexi_linear_paper_gaussian_ts_comparison_earth_scale_cheyenne_performance/postprocessing_output_h_err_vs_dt.py | 1 | 3189 | #! /usr/bin/env python3
import sys
import matplotlib.pyplot as plt
import re
from matplotlib.lines import Line2D
#
# First, use
# ./postprocessing.py > postprocessing_output.txt
# to generate the .txt file
#
fig, ax = plt.subplots(figsize=(10,7))
ax.set_xscale("log", nonposx='clip')
ax.set_yscale("log", nonposy=... | mit |
bdmckean/MachineLearning | fall_2017/hw3/CNN3.py | 1 | 4904 |
import argparse
import pickle
import gzip
from collections import Counter, defaultdict
import keras
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import Dense
from keras.layers import MaxPool2D
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.c... | mit |
paragguruji/fintechontwitter | fintechontwitter/core.py | 1 | 3476 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 07 04:00:28 2017
@author: Parag
"""
from collections import Counter
from itertools import chain
from fintechontwitter.preprocess import load_frame
from matplotlib import pyplot
import pandas as pd
import logging
import mpld3
logger = logging.getLogger('fintechontwitter'... | gpl-3.0 |
luturonunca/LAGOmaps | SitiosAlturas/plotalturas2.py | 1 | 3768 | #pylab inline
from pandas import read_csv
from matplotlib.pyplot import *
import sys,os
##########################################################################
ignore=[0,0,0]
for j in range(0,len(sys.argv)):
if sys.argv[j]=='-on':
ignore[0]=1
if sys.argv[j]=='-soon':
ignore[1]=1
if sys.argv[j]=='-uc'... | cc0-1.0 |
samuel1208/scikit-learn | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
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 |
timcera/tsgettoolbox | tsgettoolbox/functions/modis.py | 1 | 30089 | # -*- coding: utf-8 -*-
import datetime
import mando
try:
from mando.rst_text_formatter import RSTHelpFormatter as HelpFormatter
except ImportError:
from argparse import RawTextHelpFormatter as HelpFormatter
import numpy as np
import pandas as pd
from requests import Session
from tstoolbox import tsutils
fro... | bsd-3-clause |
btrzecia/AliPhysics | PWGPP/FieldParam/fitsol.py | 39 | 8343 | #!/usr/bin/env python
debug = True # enable trace
def trace(x):
global debug
if debug: print(x)
trace("loading...")
from itertools import combinations, combinations_with_replacement
from glob import glob
from math import *
import operator
from os.path import basename
import matplotlib.pyplot as plt
import numpy as... | bsd-3-clause |
JamesWo/cs194-16-data_manatees | precision_recall_split.py | 2 | 2630 | import matplotlib.pyplot as plt
import numpy as np
import sklearn
from sklearn import svm, datasets
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import average_precision_score
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.m... | apache-2.0 |
icdishb/scikit-learn | sklearn/mixture/gmm.py | 9 | 27514 | """
Gaussian Mixture Models.
This implementation corresponds to frequentist (non-Bayesian) formulation
of Gaussian Mixture Models.
"""
# Author: Ron Weiss <ronweiss@gmail.com>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Bertrand Thirion <bertrand.thirion@inria.fr>
import warnings
import numpy as... | bsd-3-clause |
bigaidream-projects/drmad | cpu_ver/hypergrad/omniglot.py | 1 | 7055 | import scipy.io
import numpy as np
import pickle
import os
import numpy.random as npr
from hypergrad.util import dictslice, RandomState
NUM_CHARS = 55
NUM_ALPHABETS = 50
NUM_EXAMPLES = 15
CURATED_ALPHABETS = [6, 10, 23, 38, 39, 8, 9, 21, 22, 41]
ROTATED_ALPHABETS = [6, 10, 23, 38, 39]
FLIPPED_ALPHABETS = [6, 10, 23, 38... | mit |
tdhopper/scikit-learn | examples/calibration/plot_calibration_curve.py | 225 | 5903 | """
==============================
Probability Calibration curves
==============================
When performing classification one often wants to predict not only the class
label, but also the associated probability. This probability gives some
kind of confidence on the prediction. This example demonstrates how to di... | bsd-3-clause |
nhejazi/scikit-learn | examples/cluster/plot_segmentation_toy.py | 33 | 3442 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solve... | bsd-3-clause |
allanino/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/artist.py | 69 | 33042 | from __future__ import division
import re, warnings
import matplotlib
import matplotlib.cbook as cbook
from transforms import Bbox, IdentityTransform, TransformedBbox, TransformedPath
from path import Path
## Note, matplotlib artists use the doc strings for set and get
# methods to enable the introspection methods of ... | agpl-3.0 |
stevenzhang18/Indeed-Flask | lib/pandas/io/ga.py | 9 | 16202 | """
1. Goto https://code.google.com/apis/console
2. Create new project
3. Goto APIs and register for OAuth2.0 for installed applications
4. Download JSON secret file and move into same directory as this file
"""
from datetime import datetime
import re
from pandas import compat
import numpy as np
from pandas import Data... | apache-2.0 |
simon-pepin/scikit-learn | examples/bicluster/plot_spectral_coclustering.py | 276 | 1736 | """
==============================================
A demo of the Spectral Co-Clustering algorithm
==============================================
This example demonstrates how to generate a dataset and bicluster it
using the the Spectral Co-Clustering algorithm.
The dataset is generated using the ``make_biclusters`` f... | bsd-3-clause |
zak-k/cartopy | lib/cartopy/io/img_tiles.py | 1 | 16018 | # (C) British Crown Copyright 2011 - 2016, Met Office
#
# This file is part of cartopy.
#
# cartopy is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option)... | lgpl-3.0 |
abhishekgahlot/scikit-learn | sklearn/linear_model/ransac.py | 16 | 13870 | # coding: utf-8
# Author: Johannes Schönberger
#
# License: BSD 3 clause
import numpy as np
from ..base import BaseEstimator, MetaEstimatorMixin, RegressorMixin, clone
from ..utils import check_random_state, check_array, check_consistent_length
from ..utils.random import sample_without_replacement
from .base import ... | bsd-3-clause |
DailyActie/Surrogate-Model | 01-codes/scipy-master/scipy/interpolate/_fitpack_impl.py | 1 | 46657 | #!/usr/bin/env python
"""
fitpack (dierckx in netlib) --- A Python-C wrapper to FITPACK (by P. Dierckx).
FITPACK is a collection of FORTRAN programs for curve and surface
fitting with splines and tensor product splines.
See
http://www.cs.kuleuven.ac.be/cwis/research/nalag/research/topics/fitpack.html
... | mit |
theoryno3/scikit-learn | sklearn/decomposition/tests/test_kernel_pca.py | 14 | 8137 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import (assert_array_almost_equal, assert_less,
assert_equal, assert_not_equal,
assert_raises)
from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import mak... | bsd-3-clause |
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