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 |
|---|---|---|---|---|---|
mkukielka/oddt | oddt/scoring/__init__.py | 1 | 15324 | from os.path import dirname, join as path_join
import gzip
from itertools import chain
from functools import partial
import six
from six.moves import cPickle as pickle
import numpy as np
from scipy.sparse import vstack as sparse_vstack
import pandas as pd
from joblib import Parallel, delayed
from sklearn.model_selec... | bsd-3-clause |
mrshu/scikit-learn | sklearn/linear_model/tests/test_bayes.py | 8 | 1627 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD Style.
import numpy as np
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import SkipTest
from sklearn.linear_model.bayes import BayesianRidge, ARDRegressi... | bsd-3-clause |
njuwangchen/TravelRec | backend/hack.py | 1 | 15425 |
# coding: utf-8
# In[1]:
import requests, json
from math import radians, cos, sin, asin, sqrt
from datetime import datetime
import random
import time
import cPickle as pickle
# import xgboost as xgb
# import pandas as pd
# In[2]:
# REQUEST POINT OF INTEREST
def requestPoints(city_name):
search_type = "points-... | apache-2.0 |
Chemcy/vnpy | vn.trader/ctaStrategy/strategy/strategyDualThrust.py | 4 | 9481 | # encoding: UTF-8
"""
DualThrust交易策略
"""
from datetime import time
from ctaBase import *
from ctaTemplate import CtaTemplate
########################################################################
class DualThrustStrategy(CtaTemplate):
"""DualThrust交易策略"""
className = 'DualThrustStrategy'
author = u'用... | mit |
3manuek/scikit-learn | examples/neighbors/plot_kde_1d.py | 347 | 5100 | """
===================================
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 |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_visualize_epochs.py | 3 | 4239 | """
.. _tut_viz_epochs:
Visualize Epochs data
=====================
"""
import os.path as op
import mne
data_path = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample')
raw = mne.io.read_raw_fif(op.join(data_path, 'sample_audvis_raw.fif'))
raw.set_eeg_reference() # set EEG average reference
event_id = {'audit... | bsd-3-clause |
treycausey/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 31 | 3340 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Virgile Fritsch <virgile.fritsch@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_alm... | bsd-3-clause |
trankmichael/scipy | scipy/spatial/_plotutils.py | 53 | 4034 | from __future__ import division, print_function, absolute_import
import numpy as np
from scipy._lib.decorator import decorator as _decorator
__all__ = ['delaunay_plot_2d', 'convex_hull_plot_2d', 'voronoi_plot_2d']
@_decorator
def _held_figure(func, obj, ax=None, **kw):
import matplotlib.pyplot as plt
if ax... | bsd-3-clause |
astrofrog/glue-3d-viewer | glue_vispy_viewers/common/vispy_widget.py | 2 | 6339 | from __future__ import absolute_import, division, print_function
import sys
import numpy as np
from ..extern.vispy import scene
from .axes import AxesVisual3D
from ..utils import NestedSTTransform
from matplotlib.colors import ColorConverter
from glue.config import settings
rgb = ColorConverter().to_rgb
LIMITS_P... | bsd-2-clause |
dialounke/pylayers | pylayers/simul/link.py | 1 | 74985 | #
# -*- coding: utf-8 -*-
#
from __future__ import print_function
r"""
.. currentmodule:: pylayers.simul.link
.. autosummary::
:members:
"""
try:
from tvtk.api import tvtk
from mayavi.sources.vtk_data_source import VTKDataSource
from mayavi import mlab
except:
print('Layout:Mayavi is not instal... | mit |
dllsf/odootest | addons/resource/faces/timescale.py | 170 | 3902 | ############################################################################
# Copyright (C) 2005 by Reithinger GmbH
# mreithinger@web.de
#
# This file is part of faces.
#
# faces is free software; you can redistribute it and/or modify
# ... | agpl-3.0 |
shicai/cuda-convnet2 | convdata.py | 174 | 14675 | # Copyright 2014 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | apache-2.0 |
Evidlo/imgurt | setup.py | 2 | 1130 | from setuptools import setup
from redrum import version
import os
import shutil
module_path = os.path.dirname(os.path.realpath(__file__)) + '/redrum'
config_file = os.path.expanduser('~/.config/redrum.ini')
shutil.copyfile(module_path + '/redrum.ini', config_file)
setup(
name='redrum',
version=version.__vers... | mit |
xuanyuanking/spark | python/docs/source/conf.py | 12 | 13269 | # -*- coding: utf-8 -*-
#
# pyspark documentation build configuration file, created by
# sphinx-quickstart on Thu Aug 28 15:17:47 2014.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# A... | apache-2.0 |
marionleborgne/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/font_manager.py | 69 | 42655 | """
A module for finding, managing, and using fonts across platforms.
This module provides a single :class:`FontManager` instance that can
be shared across backends and platforms. The :func:`findfont`
function returns the best TrueType (TTF) font file in the local or
system font path that matches the specified :class... | agpl-3.0 |
jmetzen/scikit-learn | sklearn/feature_extraction/text.py | 7 | 50272 | # -*- coding: utf-8 -*-
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Robert Layton <robertlayton@gmail.com>
# Jochen Wersdörfer <jochen@wersdoerfer.de>
# Roman Sinayev <roman.sinayev@gma... | bsd-3-clause |
grollins/calm | examples/denoising/denoise_traces.py | 1 | 5410 | # standard library imports
from os import mkdir
from os.path import join, exists, basename
from glob import glob
from collections import defaultdict, Counter
# non-standard library imports
from numpy import array, float64
from pandas import DataFrame, Series
# local imports
from plot_fcn import plot_traces
# calm im... | bsd-2-clause |
simonsfoundation/CaImAn | use_cases/NWB/demo_pipeline_NWB.py | 2 | 11632 | #!/usr/bin/env python
"""
This script follows closely the demo_pipeline.py script but uses the
Neurodata Without Borders (NWB) file format for loading the input and saving
the output. It is meant as an example on how to use NWB files with CaImAn.
authors: @agiovann and @epnev
"""
import cv2
import glob
import logging... | gpl-2.0 |
jlegendary/opencog | scripts/make_benchmark_graphs.py | 56 | 3139 | #!/usr/bin/env python
# Requires matplotlib for graphing
# reads *_benchmark.csv files as output by atomspace_bm and turns them into
# graphs.
import csv
import numpy as np
import matplotlib.colors as colors
#import matplotlib.finance as finance
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
i... | agpl-3.0 |
public-ink/public-ink | server/appengine/lib/matplotlib/patches.py | 4 | 151301 | # -*- coding: utf-8 -*-
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import map, zip
import warnings
import math
import matplotlib as mpl
import numpy as np
import matplotlib.cbook as cbook
import matplotlib.artist as artist
f... | gpl-3.0 |
SanPen/GridCal | src/GridCal/Gui/GridEditorWidget/generator_graphics.py | 1 | 7722 | # This file is part of GridCal.
#
# GridCal 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 later version.
#
# GridCal is distributed in the hope that... | gpl-3.0 |
zehpunktbarron/iOSMAnalyzer | scripts/c6_shop.py | 1 | 3201 | # -*- coding: utf-8 -*-
#!/usr/bin/python2.7
#description :This file creates a plot: Calculates the development of all objects with a "shop"-tag
#author :Christopher Barron @ http://giscience.uni-hd.de/
#date :19.01.2013
#version :0.1
#usage :python pyscript.py
#=============... | gpl-3.0 |
ominux/scikit-learn | examples/neighbors/plot_regression.py | 1 | 1368 | """
============================
Nearest Neighbors regression
============================
Demonstrate the resolution of a regression problem
using a k-Nearest Neighbor and the interpolation of the
target using both barycenter and constant weights.
"""
print __doc__
# Author: Alexandre Gramfort <alexandre.gramfort@i... | bsd-3-clause |
jseabold/scikit-learn | sklearn/utils/validation.py | 16 | 26075 | """Utilities for input validation"""
# Authors: Olivier Grisel
# Gael Varoquaux
# Andreas Mueller
# Lars Buitinck
# Alexandre Gramfort
# Nicolas Tresegnie
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from ..externals i... | bsd-3-clause |
zihua/scikit-learn | sklearn/model_selection/_split.py | 7 | 61646 | """
The :mod:`sklearn.model_selection._split` module includes classes and
functions to split the data based on a preset strategy.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>,
# Olivier Grisel <olivier.grisel@ensta.org>
# Ragha... | bsd-3-clause |
richstoner/proletariat | ipython_profile/profile_girderconfig/ipython_notebook_config.py | 1 | 24158 | # Configuration file for ipython-notebook.
c = get_config()
#------------------------------------------------------------------------------
# NotebookApp configuration
#------------------------------------------------------------------------------
# NotebookApp will inherit config from: BaseIPythonApplication, Appli... | apache-2.0 |
aflaxman/scikit-learn | examples/cluster/plot_mini_batch_kmeans.py | 53 | 4096 | """
====================================================================
Comparison of the K-Means and MiniBatchKMeans clustering algorithms
====================================================================
We want to compare the performance of the MiniBatchKMeans and KMeans:
the MiniBatchKMeans is faster, but give... | bsd-3-clause |
jaeilepp/mne-python | examples/time_frequency/plot_time_frequency_simulated.py | 1 | 8415 | """
======================================================================
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell)
======================================================================
This example demonstrates the different time-frequency estimation methods
on simulated data. It shows ... | bsd-3-clause |
totalgood/nlpia | src/nlpia/book/examples/ch04_catdog_lsa_3x6x16.py | 1 | 16654 | from nlpia.data.loaders import get_data
from nltk.tokenize import casual_tokenize
import pandas as pd
import numpy as np
import os
from sklearn.feature_extraction.text import TfidfVectorizer
# from nltk.stem import PorterStemmer
from sklearn.decomposition import PCA
from nlpia.constants import DATA_PATH
NUM_TOPICS = ... | mit |
seckcoder/lang-learn | python/sklearn/examples/linear_model/plot_logistic_l1_l2_sparsity.py | 2 | 2566 | """
==============================================
L1 Penalty and Sparsity in Logistic Regression
==============================================
Comparison of the sparsity (percentage of zero coefficients) of solutions when
L1 and L2 penalty are used for different values of C. We can see that large
values of C give mo... | unlicense |
0asa/scikit-learn | examples/text/mlcomp_sparse_document_classification.py | 292 | 4498 | """
========================================================
Classification of text documents: using a MLComp dataset
========================================================
This is an example showing how the scikit-learn can be used to classify
documents by topics using a bag-of-words approach. This example uses
a s... | bsd-3-clause |
wk8910/bio_tools | 01.dadi_fsc/02.two_pop_model/10.changePop2First/01.model.py | 1 | 2358 | #! /usr/bin/env python
import os,sys,re
# import matplotlib
# matplotlib.use('Agg')
import numpy
import sys
from numpy import array
# import pylab
import dadi
import custom_model
spectrum_file = sys.argv[1]
data = dadi.Spectrum.from_file(spectrum_file)
data = data.fold()
ns = data.sample_sizes
pts_l = [40,50,60]
# n... | mpl-2.0 |
danielru/pySDC | playgrounds/deprecated/acoustic_2d_imex/HookClass.py | 1 | 1847 | from __future__ import division
from pySDC.core.Hooks import hooks
from pySDC.core.Stats import stats
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatte... | bsd-2-clause |
KTH-dESA/PyOnSSET | onsset/onsset.py | 1 | 198624 | # Author: KTH dESA Last modified by Andreas Sahlberg
# Date: 05 June 2019
# Python version: 3.5
import os
import logging
import pandas as pd
from math import ceil, pi, exp, log, sqrt, radians, cos, sin, asin
# from pyproj import Proj
import numpy as np
from collections import defaultdict
# from IPython.display import... | mit |
saleemayman/scalapack_svd | smuc/postprocess/plotCase3Results.py | 1 | 2323 | import glob, os, sys
import numpy as np
from random import*
import matplotlib.pyplot as plt
import matplotlib.cm as cmx
import matplotlib.colors as colors
def get_rand_color(val):
h,s,v = random()*6, 0.5, 243.2
colors = []
for i in range(val):
h += 3.75#3.708
tmp = ((v, v-v*s*abs(1-h%2), v-... | apache-2.0 |
K-Phoen/runner | runner/fusion.py | 1 | 1312 | #!/usr/bin/env python
import pandas, time, math
class Fusion:
def merge_activities(self, main_activity, cardio_activity):
complete_cardio = self._interpolate_cardio(main_activity, cardio_activity)
# and merge it back into the main activity
for trackpoint in main_activity.trackpoints:
... | mit |
mihalybaci/keras | mnist_deep_tutorial.py | 1 | 4145 | # -*- coding: utf-8 -*-
"""
Created on Wed Jun 28 21:47:59 2017
@author: michael
"""
#
# Begin code from TensorFlow Deep MNIST Tutorial
#
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#import data
mnist = input_data.read_data_sets('MNIST_d... | gpl-3.0 |
jmccormac/pySceneNetRGBD | calculate_optical_flow.py | 1 | 10949 | from PIL import Image
import math
import matplotlib
import numpy as np
import os
import pathlib
import random
import scenenet_pb2 as sn
import sys
import scipy.misc
def normalize(v):
return v/np.linalg.norm(v)
def load_depth_map_in_m(file_name):
image = Image.open(file_name)
pixel = np.array(image)
re... | gpl-3.0 |
RayMick/scikit-learn | sklearn/cross_decomposition/pls_.py | 187 | 28507 | """
The :mod:`sklearn.pls` module implements Partial Least Squares (PLS).
"""
# Author: Edouard Duchesnay <edouard.duchesnay@cea.fr>
# License: BSD 3 clause
from ..base import BaseEstimator, RegressorMixin, TransformerMixin
from ..utils import check_array, check_consistent_length
from ..externals import six
import w... | bsd-3-clause |
JohanComparat/pySU | spm/bin_SMF/create_table_completeness.py | 1 | 9615 | import astropy.io.fits as fits
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as p
import numpy as n
import os
import sys
from scipy.stats import scoreatpercentile as sc
survey = sys.argv[1]
z_min, z_max = 0., 1.6
imfs = ["Chabrier_ELODIE_", "Chabrier_MILES_", "Chabrier_STELIB_", "Kroupa_ELODIE_", ... | cc0-1.0 |
mjgrav2001/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_scalability.py | 225 | 5719 | """
============================================
Scalability of Approximate Nearest Neighbors
============================================
This example studies the scalability profile of approximate 10-neighbors
queries using the LSHForest with ``n_estimators=20`` and ``n_candidates=200``
when varying the number of sa... | bsd-3-clause |
UASLab/ImageAnalysis | scripts/99-plot-matches.py | 1 | 3445 | #!/usr/bin/env python3
# do pairwise triangulation to estimate local surface height, but try
# to also build up a tracking structure so we could use and refine
# this as we proceed through the matching process.
import argparse
from matplotlib import pyplot as plt
from matplotlib import collections as mc
import numpy ... | mit |
nrweir/pyto_segmenter | PytoSegment.py | 1 | 2847 | '''Classes for merging segmented cells with subcellular structures'''
import pandas as pd
import pickle
class PytoSegmentObj:
'''A metaclass to merge together a CellSegmentObj with child objects
segmented in other channels.
'''
def __init__(self, cell_obj, daughter_objs = {}):
'''Generate a P... | gpl-3.0 |
xubenben/scikit-learn | examples/ensemble/plot_gradient_boosting_quantile.py | 392 | 2114 | """
=====================================================
Prediction Intervals for Gradient Boosting Regression
=====================================================
This example shows how quantile regression can be used
to create prediction intervals.
"""
import numpy as np
import matplotlib.pyplot as plt
from skle... | bsd-3-clause |
Neurita/bamboo | setup.py | 1 | 2824 | #!/usr/bin/env python
"""
Bamboo
-----
bambo is a set of tools for pandas DataFrames.
"""
from __future__ import print_function
import os.path as op
import io
import sys
from setuptools import Command, setup, find_packages
from setuptools.command.test import test as TestCommand
from pip.req import parse_requirement... | bsd-3-clause |
maksyuki/vim | python/numerical_calculation.py | 2 | 6539 | #!/usr/bin/python
#filename :numerical_calculation.py
#author :Yuchi Miao
#created time :2016/4/18 19:44:10
#last modified :2016/9/22 11:28:18
#python_version :2.7.6
#version :1.0
#description :use the numerical integration method(Runge_Kutta Method) to
# solve the first order... | gpl-3.0 |
mikelum/pyspeckit | pyspeckit/spectrum/plotters.py | 1 | 31130 | """
=======
Plotter
=======
.. moduleauthor:: Adam Ginsburg <adam.g.ginsburg@gmail.com>
"""
from __future__ import print_function
import matplotlib
import matplotlib.pyplot
import matplotlib.figure
import numpy as np
import astropy.units as u
import copy
import inspect
try:
from matplotlib.cbook import BoundMetho... | mit |
sanginnwoo/longtailedtit | doc/tests/radialinjection.py | 5 | 4190 | #!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
#
# The two phase radial injection problem has a similarity solution (r^2/t)
#
# Read MOOSE simulation data for constant time (tdata) and constant
# radial distance (rdata)
tdata = np.genfromtxt('../../tests/dirackernels/theis3_line_0016.csv', d... | lgpl-2.1 |
nomadcube/scikit-learn | sklearn/tree/tests/test_export.py | 76 | 9318 | """
Testing for export functions of decision trees (sklearn.tree.export).
"""
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.tree import export_graphviz
from sklearn.externals.six import StringIO
# toy sa... | bsd-3-clause |
russel1237/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 |
navjeet0211/phd | misc/rama-regions-on-plot.py | 1 | 1599 | import numpy as np
import matplotlib.pyplot as plt
#f = np.loadtxt('rama-bg.dat')
f = np.array([[-156.5, -70.4, -54.7, -54.7, -136.9, -136.9, -156.5, -156.5,
-180. , -140.8, -86. , -74.3, -74.3, -44.3, -44.3, -46.9,
-180. , -180. , -164.3, -133. , -109.4, -106.9, -44.3, -44.3,
... | gpl-2.0 |
ky822/scikit-learn | examples/linear_model/plot_ard.py | 248 | 2622 | """
==================================================
Automatic Relevance Determination Regression (ARD)
==================================================
Fit regression model with Bayesian Ridge Regression.
See :ref:`bayesian_ridge_regression` for more information on the regressor.
Compared to the OLS (ordinary l... | bsd-3-clause |
mcallaghan/tmv | BasicBrowser/tmv_app/management/commands/compare_topics.py | 1 | 2280 | from django.core.management.base import BaseCommand, CommandError
from tmv_app.models import *
import numpy as np
from sklearn.decomposition import NMF
from scipy.sparse import csr_matrix, find
from functools import partial
from multiprocess import Pool
from utils.db import *
from utils.utils import *
from scoping.mode... | gpl-3.0 |
beepee14/scikit-learn | sklearn/feature_extraction/tests/test_text.py | 41 | 35602 | from __future__ import unicode_literals
import warnings
from sklearn.feature_extraction.text import strip_tags
from sklearn.feature_extraction.text import strip_accents_unicode
from sklearn.feature_extraction.text import strip_accents_ascii
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.fe... | bsd-3-clause |
miloharper/neural-network-animation | matplotlib/docstring.py | 23 | 3995 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from matplotlib import cbook
import sys
import types
class Substitution(object):
"""
A decorator to take a function's docstring and perform string
substitution on it.
This decorat... | mit |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/matplotlib/axes/_axes.py | 1 | 275832 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
from matplotlib.externals.six.moves import reduce, xrange, zip, zip_longest
import math
import warnings
import numpy as np
from numpy import ma
import matplotlib
from mat... | mit |
agrawalabhishek/NAOS | python/plotSpringMass.py | 1 | 3901 | '''
Copyright (c) 2016 Abhishek Agrawal (abhishek.agrawal@protonmail.com)
Distributed under the MIT License.
See accompanying file LICENSE.md or copy at http://opensource.org/licenses/MIT
'''
# Set up modules and packages
# I/O
import csv
from pprint import pprint
# Numerical
import numpy as np
import pandas as pd
fr... | mit |
aleaf/pest_tools | pest_tools/load_jco.py | 2 | 2515 | import numpy as np
import pandas as pd
import struct
def load_jco(file_name, return_par = True, return_obs = True):
'''Read PEST Jacobian matrix file (binary) into Pandas data frame
Parameters
----------
file_name : string
File name for .jco (binary) produced by PEST
return_par : {Tru... | mit |
CVML/scikit-learn | examples/linear_model/plot_theilsen.py | 232 | 3615 | """
====================
Theil-Sen Regression
====================
Computes a Theil-Sen Regression on a synthetic dataset.
See :ref:`theil_sen_regression` for more information on the regressor.
Compared to the OLS (ordinary least squares) estimator, the Theil-Sen
estimator is robust against outliers. It has a breakd... | bsd-3-clause |
andrewnc/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 355 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluate... | bsd-3-clause |
joostvanzwieten/nutils | examples/laplace.py | 1 | 7208 | #! /usr/bin/env python3
#
# In this script we solve the Laplace equation :math:`u_{,kk} = 0` on a unit
# square domain :math:`Ω` with boundary :math:`Γ`, subject to boundary
# conditions:
#
# .. math:: u &= 0 && Γ_{\rm left}
#
# ∂_n u &= 0 && Γ_{\rm bottom}
#
# ∂_n u ... | mit |
abimannans/scikit-learn | sklearn/tests/test_qda.py | 155 | 3481 | import numpy as np
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import ignore_war... | bsd-3-clause |
jakob223/ipython_sound_animator | sound_animator.py | 1 | 3042 | from matplotlib import animation
from ipywidgets import HTML
import IPython.display as ipd
import random
from tempfile import NamedTemporaryFile
import math
import matplotlib.pyplot as plt
VIDEO_TAG = """<video controls id="video_{1}">
<source src="data:video/x-m4v;base64,{0}" type="video/mp4">
Your browser does not ... | mit |
precice/elastictube1d | postproc/plot-fluid.py | 1 | 1894 | #!/usr/bin/python
import vtk
import numpy as np
import os
import sys
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
T = 100 # number of timesteps performed
arrayname = sys.argv[1] # Which dataset should be plotted?
data_path = sys.argv[2] # Where is the data?
file_name_generator = lambda... | gpl-3.0 |
billy-inn/scikit-learn | examples/svm/plot_svm_nonlinear.py | 268 | 1091 | """
==============
Non-linear SVM
==============
Perform binary classification using non-linear SVC
with RBF kernel. The target to predict is a XOR of the
inputs.
The color map illustrates the decision function learned by the SVC.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn imp... | bsd-3-clause |
semio/zipline | zipline/examples/pairtrade.py | 16 | 5197 | #!/usr/bin/env python
#
# Copyright 2013 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 ... | apache-2.0 |
ilo10/scikit-learn | examples/decomposition/plot_pca_vs_fa_model_selection.py | 78 | 4510 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
===============================================================
Model selection with Probabilistic PCA and Factor Analysis (FA)
===============================================================
Probabilistic PCA and Factor Analysis are probabilistic models.
The consequence ... | bsd-3-clause |
jm-begon/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 |
averagehat/bio_pieces | tests/test_amos2fastq.py | 2 | 4832 | try:
import unittest2 as unittest
except ImportError:
import unittest
from operator import itemgetter
from pandas.util.testing import assert_frame_equal
import pandas as pd
import mock
from bio_pieces import amos2fastq_main
from bio_pieces import amos2fastq as a2f
import os
import random
from functools impor... | gpl-2.0 |
MichielCottaar/pymc3 | pymc3/tests/test_plots.py | 13 | 1721 | import matplotlib
matplotlib.use('Agg', warn=False)
import numpy as np
from .checks import close_to
import pymc3.plots
from pymc3.plots import *
from pymc3 import Slice, Metropolis, find_hessian, sample
def test_plots():
# Test single trace
from pymc3.examples import arbitrary_stochastic as asmod
with... | apache-2.0 |
rosswhitfield/javelin | javelin/io.py | 1 | 9604 | """
==
io
==
"""
def read_mantid_MDHisto(filename):
"""Read the saved MDHisto from from Mantid and returns an xarray.DataArray object"""
import h5py
import numpy as np
import xarray as xr
from javelin.unitcell import UnitCell
with h5py.File(filename, "r") as f:
if ('SaveMDVersion' not ... | mit |
yask123/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 |
architecture-building-systems/CEAforArcGIS | cea/utilities/doc_schemas.py | 1 | 9354 | """
Create a schemas.yml-compatible entry given a locator method by reading the file from the current scenario.
NOTE: This is meant to help _write_ the schemas.yml file, not to CREATE it - you'll have to edit constraints and types
by hand too!
"""
import os
import yaml
import json
import pandas as pd
from p... | mit |
IshankGulati/scikit-learn | sklearn/utils/setup.py | 77 | 2993 | import os
from os.path import join
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
import numpy
from numpy.distutils.misc_util import Configuration
config = Configuration('utils', parent_package, top_path)
config.add_subpackage('sparsetools')
... | bsd-3-clause |
meduz/scikit-learn | sklearn/feature_selection/tests/test_base.py | 98 | 3681 | import numpy as np
from scipy import sparse as sp
from numpy.testing import assert_array_equal
from sklearn.base import BaseEstimator
from sklearn.feature_selection.base import SelectorMixin
from sklearn.utils import check_array
from sklearn.utils.testing import assert_raises, assert_equal
class StepSelector(Select... | bsd-3-clause |
trungnt13/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 |
numenta/htmresearch | htmresearch/frameworks/poirazi_neuron_model/neuron_model.py | 2 | 11852 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2017, 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 |
cbrunet/fibermodes | scripts/tof/tofanalyse.py | 2 | 1186 | import pickle
import numpy
from matplotlib import pyplot
from tofcommon import r1, r2, diam, RCF, X
def errng(ng, target):
if len(ng) < len(target) + 1:
return float("nan")
ng.sort()
e1 = (ng[2] - ng[1] - target[1] + target[0])**2
e2 = (ng[3] - ng[2] - target[2] + target[1])**2
if len(t... | gpl-3.0 |
QuantEcon/QuantEcon.notebooks | dependencies/lq_markov.py | 1 | 8130 | """
Author: Sebastian Graves
Provides a class called LQ_Markov for analyzing Markov jump LQ problems.
Provides a function to map first type of Barro model into LQ Markov problem.
Provides a function to map second type of Barro model (with restructuring) into LQ Markov problem.
"""
import numpy as np
from collection... | bsd-3-clause |
NINAnor/sentinel4nature | Tree canopy cover/regression/GBRT_Dovre1_manual_FCLS.py | 1 | 8831 | # GBRT for Dovre1 case study site
# Training data: manually digitized training areas, including water pixels
# Predictors: results of FCLS spectral unmixing
# Authors: Stefan Blumentrath
import numpy as np
import matplotlib.pyplot as plt
from sklearn import ensemble
from sklearn import datasets
from sklearn.utils imp... | gpl-2.0 |
AlexanderFabisch/scikit-learn | doc/sphinxext/gen_rst.py | 106 | 40198 | """
Example generation for the scikit learn
Generate the rst files for the examples by iterating over the python
example files.
Files that generate images should start with 'plot'
"""
from __future__ import division, print_function
from time import time
import ast
import os
import re
import shutil
import traceback
i... | bsd-3-clause |
brupoon/nextTwilight | iso_theoretical.py | 1 | 1193 | # -*- coding: utf-8 -*-
"""
Created on Tue Apr 22 09:23:19 2014
@author: B Poon (demure)
"""
import numpy as np
from matplotlib import pyplot as plt
#from math import log10
#definitions
first = r"C:\pyf\ast\iso100Mfixed.txt"
second = r"C:\pyf\ast\iso1G.txt"
third = r"C:\pyf\ast\iso10G.txt"
def iso_theo(filepath, col... | mit |
shirishr/My-Progress-at-Machine-Learning | Udacity_Machine_Learning/customer_segments/visuals.py | 21 | 6047 | ###########################################
# Suppress matplotlib user warnings
# Necessary for newer version of matplotlib
import warnings
warnings.filterwarnings("ignore", category = UserWarning, module = "matplotlib")
#
# Display inline matplotlib plots with IPython
from IPython import get_ipython
get_ipython().run_... | mit |
cpcloud/arrow | python/pyarrow/tests/test_parquet.py | 1 | 68774 | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | apache-2.0 |
piyush0609/scipy | scipy/stats/stats.py | 1 | 161773 | # Copyright (c) Gary Strangman. All rights reserved
#
# Disclaimer
#
# This software is provided "as-is". There are no expressed or implied
# warranties of any kind, including, but not limited to, the warranties
# of merchantability and fitness for a given application. In no event
# shall Gary Strangman be liable fo... | bsd-3-clause |
ggodreau/c3t2 | concatenate.py | 1 | 2987 | #!/usr/bin/python
"""
To use, please type in:
python concatenate.py
The script will only look at files that are within
folders that are one level below the directory supplied
to the script. For example, if no argument is given to
the script, script will parse all folders within the
current working directory as ... | mit |
IshankGulati/scikit-learn | sklearn/pipeline.py | 13 | 30670 | """
The :mod:`sklearn.pipeline` module implements utilities to build a composite
estimator, as a chain of transforms and estimators.
"""
# Author: Edouard Duchesnay
# Gael Varoquaux
# Virgile Fritsch
# Alexandre Gramfort
# Lars Buitinck
# License: BSD
from collections import defaultdict... | bsd-3-clause |
elkingtonmcb/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 355 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluate... | bsd-3-clause |
tawsifkhan/scikit-learn | examples/applications/topics_extraction_with_nmf_lda.py | 133 | 3517 | """
========================================================================================
Topics extraction with Non-Negative Matrix Factorization And Latent Dirichlet Allocation
========================================================================================
This is an example of applying Non Negative Matr... | bsd-3-clause |
mrshu/scikit-learn | sklearn/datasets/tests/test_20news.py | 11 | 2398 | """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 |
larsmans/scikit-learn | examples/linear_model/plot_multi_task_lasso_support.py | 249 | 2211 | #!/usr/bin/env python
"""
=============================================
Joint feature selection with multi-task Lasso
=============================================
The multi-task lasso allows to fit multiple regression problems
jointly enforcing the selected features to be the same across
tasks. This example simulates... | bsd-3-clause |
davidgbe/scikit-learn | sklearn/linear_model/tests/test_ridge.py | 130 | 22974 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
aminert/scikit-learn | sklearn/datasets/tests/test_lfw.py | 230 | 7880 | """This test for the LFW require medium-size data dowloading and processing
If the data has not been already downloaded by running the examples,
the tests won't run (skipped).
If the test are run, the first execution will be long (typically a bit
more than a couple of minutes) but as the dataset loader is leveraging
... | bsd-3-clause |
cdegroc/scikit-learn | sklearn/gaussian_process/gaussian_process.py | 1 | 32730 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# (mostly translation, see implementation details)
# License: BSD style
import numpy as np
from scipy import linalg, optimize, rand
from ..base import BaseEstimator, RegressorMixin
from ..metrics.pairwise import m... | bsd-3-clause |
joelgrus/data-science-from-scratch | scratch/logistic_regression.py | 3 | 7642 |
tuples = [(0.7,48000,1),(1.9,48000,0),(2.5,60000,1),(4.2,63000,0),(6,76000,0),(6.5,69000,0),(7.5,76000,0),(8.1,88000,0),(8.7,83000,1),(10,83000,1),(0.8,43000,0),(1.8,60000,0),(10,79000,1),(6.1,76000,0),(1.4,50000,0),(9.1,92000,0),(5.8,75000,0),(5.2,69000,0),(1,56000,0),(6,67000,0),(4.9,74000,0),(6.4,63000,1),(6.2,8200... | mit |
tawsifkhan/scikit-learn | benchmarks/bench_plot_neighbors.py | 287 | 6433 | """
Plot the scaling of the nearest neighbors algorithms with k, D, and N
"""
from time import time
import numpy as np
import pylab as pl
from matplotlib import ticker
from sklearn import neighbors, datasets
def get_data(N, D, dataset='dense'):
if dataset == 'dense':
np.random.seed(0)
return np.... | bsd-3-clause |
asteca/ASteCA | packages/out/mp_centers.py | 1 | 3202 |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.offsetbox as offsetbox
def pl_full_frame(
N, gs, fig, x_name, y_name, coord, x_min, x_max, y_min, y_max, asp_ratio,
x, y, st_sizes_arr, mag_range, main_mag):
"""
x,y finding chart of stars in frame within 'mag_range'
"""
... | gpl-3.0 |
alexsavio/scikit-learn | sklearn/cluster/setup.py | 24 | 1690 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
cblas_libs, blas_info = ... | bsd-3-clause |
jorik041/scikit-learn | examples/cluster/plot_color_quantization.py | 297 | 3443 | # -*- coding: utf-8 -*-
"""
==================================
Color Quantization using K-Means
==================================
Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace
(China), reducing the number of colors required to show the image from 96,615
unique colors to 64, while pre... | bsd-3-clause |
vsmolyakov/kaggle | toxic/toxic_kernel2.py | 1 | 8062 | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
import re
import csv
import math
import codecs
import keras
from keras import optimizers
from keras import backend as K
from keras import regularizers
from keras.models import Sequential
from keras.layers import Den... | mit |
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