repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
rhyswhitley/spatial_plots | src/grid_spatial_plot.py | 1 | 4357 | #!/usr/bin/env python2.7
import os
import pickle
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.basemap import Basemap
from matplotlib.cm import get_cmap
from matplotlib.patches import PathPatch
from matplotlib.colors import SymLogNorm #PowerNorm
# -------... | cc0-1.0 |
CVML/scikit-learn | sklearn/tree/tree.py | 113 | 34767 | """
This module gathers tree-based methods, including decision, regression and
randomized trees. Single and multi-output problems are both handled.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Da... | bsd-3-clause |
jreback/pandas | pandas/tests/indexing/multiindex/test_multiindex.py | 2 | 2956 | import numpy as np
import pandas._libs.index as _index
from pandas.errors import PerformanceWarning
import pandas as pd
from pandas import DataFrame, Index, MultiIndex, Series
import pandas._testing as tm
class TestMultiIndexBasic:
def test_multiindex_perf_warn(self):
df = DataFrame(
{
... | bsd-3-clause |
rvilalta/OFC_TAPI_SC | tapi_app/tapi_app.py | 1 | 2858 | #!/usr/bin/python
# -*- coding: utf-8 -*-
import requests
from requests.auth import HTTPBasicAuth
import json
import matplotlib.pyplot as plt
import networkx as nx
import random
IP = '127.0.0.1'
PORT = '8080'
def retrieveTopologies(ip, port, user='', password=''):
print ("Reading network-topology")
topologi... | apache-2.0 |
phdowling/scikit-learn | examples/covariance/plot_outlier_detection.py | 235 | 3891 | """
==========================================
Outlier detection with several methods.
==========================================
When the amount of contamination is known, this example illustrates two
different ways of performing :ref:`outlier_detection`:
- based on a robust estimator of covariance, which is assumin... | bsd-3-clause |
maigimenez/jon-siamese | src/test.py | 1 | 11410 | import tensorflow as tf
from os.path import join, abspath, curdir
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import argparse
from utils import read_flags, load_binarize_data, input_pipeline_test, best_score
from siamese import Siamese
from double_siamese import DoubleSiamese
def get_arguments():
pa... | gpl-3.0 |
MartinIsoz/CFD_oF | 03_texturedPlate/02_transTexture/00_Scripts/caseConstructorV3.py | 2 | 25437 | #!/usr/bin/python
#FILE DESCRIPTION=======================================================
#~ Python script used for foam case construction (as automatic, as
#~ possible)
#~
#~ NOTES:
#~ - mesh grading in z direction
#~ USAGE:
#~ - modify and run the script
#~ TO DO:
#LICENSE=======================... | gpl-2.0 |
hmendozap/auto-sklearn | autosklearn/pipeline/components/data_preprocessing/imputation.py | 1 | 2104 | from HPOlibConfigSpace.configuration_space import ConfigurationSpace
from HPOlibConfigSpace.hyperparameters import CategoricalHyperparameter
from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
from autosklearn.pipeline.constants import *
class Imputation(AutoSklearnPreprocessingAlgorit... | bsd-3-clause |
pySTEPS/pysteps | examples/plot_ensemble_verification.py | 1 | 5819 | #!/bin/env python
"""
Ensemble verification
=====================
In this tutorial we perform a verification of a probabilistic extrapolation nowcast
using MeteoSwiss radar data.
"""
from datetime import datetime
import matplotlib.pyplot as plt
import numpy as np
from pprint import pprint
from pysteps import io, no... | bsd-3-clause |
mwv/scikit-learn | sklearn/neighbors/tests/test_neighbors.py | 22 | 45265 | from itertools import product
import pickle
import numpy as np
from scipy.sparse import (bsr_matrix, coo_matrix, csc_matrix, csr_matrix,
dok_matrix, lil_matrix)
from sklearn import metrics
from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.utils.testing impor... | bsd-3-clause |
jdanbrown/pydatalab | google/datalab/stackdriver/monitoring/_query_metadata.py | 10 | 3318 | # Copyright 2016 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | apache-2.0 |
aashish24/seaborn | seaborn/tests/test_linearmodels.py | 1 | 33161 | import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import nose.tools as nt
import numpy.testing as npt
import pandas.util.testing as pdt
from numpy.testing.decorators import skipif
from nose import SkipTest
try:
import statsmodels.regression.linear_model as smlm
_n... | bsd-3-clause |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/doc/mpl_examples/pylab_examples/contour_label_demo.py | 3 | 2238 | #!/usr/bin/env python
"""
Illustrate some of the more advanced things that one can do with
contour labels.
See also contour_demo.py.
"""
import matplotlib
import numpy as np
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.ticker as ticker
import matplotlib.pyplot as plt
matplotlib.rcParams... | gpl-2.0 |
cauchycui/scikit-learn | sklearn/linear_model/tests/test_sgd.py | 129 | 43401 | import pickle
import unittest
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing ... | bsd-3-clause |
mojoboss/scikit-learn | sklearn/utils/metaestimators.py | 283 | 2353 | """Utilities for meta-estimators"""
# Author: Joel Nothman
# Andreas Mueller
# Licence: BSD
from operator import attrgetter
from functools import update_wrapper
__all__ = ['if_delegate_has_method']
class _IffHasAttrDescriptor(object):
"""Implements a conditional property using the descriptor protocol.
... | bsd-3-clause |
Sentient07/scikit-learn | examples/linear_model/plot_logistic.py | 73 | 1568 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic function
=========================================================
Shown in the plot is how the logistic regression would, in this
synthetic dataset, classify values as either 0 or 1,
i.e. class one or tw... | bsd-3-clause |
jonyroda97/redbot-amigosprovaveis | lib/matplotlib/backend_bases.py | 2 | 113596 | """
Abstract base classes define the primitives that renderers and
graphics contexts must implement to serve as a matplotlib backend
:class:`RendererBase`
An abstract base class to handle drawing/rendering operations.
:class:`FigureCanvasBase`
The abstraction layer that separates the
:class:`matplotlib.fi... | gpl-3.0 |
makmac213/Tsupytero | tsupytero/core.py | 1 | 3997 | import datetime
import requests
import time
from matplotlib import pyplot
from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY
from matplotlib.finance import candlestick_ohlc
from pylab import *
#from .exceptions import SymbolNotFoundException
# inspired by https://github.com/edgedalmacio/p... | unlicense |
CDSFinance/zipline | zipline/sources/data_frame_source.py | 26 | 5253 | #
# Copyright 2015 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 |
tjhei/burnman_old | main.py | 1 | 11849 | #system libs:
import numpy
import scipy.optimize as opt
import math
import matplotlib.pyplot as pyplot
#own libs:
import geotherm
import prem
from tools import *
from eos_from_ian import birch_murnaghan
import seismic
# TODO: add up weight percent and check <100 and tell them how much
molar_mass = {'Fe':55.845, 'Mg'... | gpl-2.0 |
larsmans/scikit-learn | benchmarks/bench_lasso.py | 297 | 3305 | """
Benchmarks of Lasso vs LassoLars
First, we fix a training set and increase the number of
samples. Then we plot the computation time as function of
the number of samples.
In the second benchmark, we increase the number of dimensions of the
training set. Then we plot the computation time as function of
the number o... | bsd-3-clause |
vortex-ape/scikit-learn | sklearn/tests/test_discriminant_analysis.py | 4 | 13934 | 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_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert... | bsd-3-clause |
akionakamura/scikit-learn | examples/applications/plot_tomography_l1_reconstruction.py | 204 | 5442 | """
======================================================================
Compressive sensing: tomography reconstruction with L1 prior (Lasso)
======================================================================
This example shows the reconstruction of an image from a set of parallel
projections, acquired along dif... | bsd-3-clause |
sergiohr/NeuroDB | test/test_cluster_assig.py | 1 | 4742 | '''
Created on Mar 16, 2015
@author: sergio
'''
import numpy as np
import ctypes
import numpy.ctypeslib as npct
import matplotlib.pyplot as plt
import psycopg2
import time
import neurodb.neodb.core
from math import e, pow
from scipy.optimize import leastsq
from mpl_toolkits.mplot3d import Axes3D
from neurodb.cfsfdp im... | gpl-3.0 |
robbymeals/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_01_language_train_model.py | 254 | 2253 | """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 |
Weihonghao/ECM | Vpy34/lib/python3.5/site-packages/pandas/core/frame.py | 3 | 219601 | """
DataFrame
---------
An efficient 2D container for potentially mixed-type time series or other
labeled data series.
Similar to its R counterpart, data.frame, except providing automatic data
alignment and a host of useful data manipulation methods having to do with the
labeling information
"""
from __future__ import... | agpl-3.0 |
bikong2/scikit-learn | sklearn/linear_model/ridge.py | 60 | 44642 | """
Ridge regression
"""
# Author: Mathieu Blondel <mathieu@mblondel.org>
# Reuben Fletcher-Costin <reuben.fletchercostin@gmail.com>
# Fabian Pedregosa <fabian@fseoane.net>
# Michael Eickenberg <michael.eickenberg@nsup.org>
# License: BSD 3 clause
from abc import ABCMeta, abstractmethod
impor... | bsd-3-clause |
Titan-C/scikit-learn | sklearn/neighbors/tests/test_nearest_centroid.py | 12 | 4111 | """
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 numpy.testing import assert_raises
from sklearn.neighbors import NearestCentroid
from sklearn import datasets
# toy sample
X = [[... | bsd-3-clause |
chrsrds/scikit-learn | examples/cluster/plot_digits_linkage.py | 21 | 3092 | """
=============================================================================
Various Agglomerative Clustering on a 2D embedding of digits
=============================================================================
An illustration of various linkage option for agglomerative clustering on
a 2D embedding of the di... | bsd-3-clause |
selective-inference/selective-inference | doc/learning_examples/keras/keras_example.py | 3 | 3325 | import functools
import numpy as np
from scipy.stats import norm as ndist
import regreg.api as rr
from selection.tests.instance import gaussian_instance
from selection.learning.utils import (full_model_inference,
pivot_plot,
liu_inference)
from... | bsd-3-clause |
eg-zhang/scikit-learn | sklearn/decomposition/tests/test_online_lda.py | 49 | 13124 | import numpy as np
from scipy.linalg import block_diag
from scipy.sparse import csr_matrix
from scipy.special import psi
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition._online_lda import (_dirichlet_expectation_1d,
_dirichlet_expect... | bsd-3-clause |
jcrudy/py-earth | doc/xkcdify.py | 3 | 8298 | """
XKCD plot generator
-------------------
Author: Jake Vanderplas
This is a script that will take any matplotlib line diagram, and convert it
to an XKCD-style plot. It will work for plots with line & text elements,
including axes labels and titles (but not axes tick labels).
The idea for this comes from work by Da... | bsd-3-clause |
mcstrother/dicom-sr-qi | inquiries/modality_usage.py | 2 | 6552 | from srqi.core import inquiry, my_utils
def get_period_sum(period, val_func, event_types = ()):
"""
Parameters:
period : a list of Events
val_func : a function that takes an Event and returns the value of
the event to be summed. For example `lambda x:x.Dose_RP` would be
... | bsd-2-clause |
timcera/tsgettoolbox | tsgettoolbox/functions/terraclimate.py | 1 | 10519 | # -*- coding: utf-8 -*-
"""Download data from terraclimate."""
# http://thredds.northwestknowledge.net:8080/thredds/terraclimate_aggregated.html
from datetime import date, datetime
import mando
import numpy as np
import pandas as pd
try:
from mando.rst_text_formatter import RSTHelpFormatter as HelpFormatter
exc... | bsd-3-clause |
wasade/qiime | scripts/make_bootstrapped_tree.py | 1 | 2269 | #!/usr/bin/env python
# File created on 09 Feb 2010
from __future__ import division
__author__ = "Justin Kuczynski"
__copyright__ = "Copyright 2011, The QIIME Project"
__credits__ = [
"Justin Kuczynski",
"Jesse Stombaugh",
"Jose Antonio Navas Molina"]
__license__ = "GPL"
__version__ = "1.8.0-dev"
__maintai... | gpl-2.0 |
michael-hoffman/titanic-revisited | titanic_preprocessing.py | 1 | 5755 | #
# Set of functions used for preprocessing Titanic data.
#
# Author: Charlie Bonfield
# Last Modified: July 2017
# Import statements
import fancyimpute
import numpy as np
import pandas as pd
#from sklearn import preprocessing
class Useful_Preprocessing(object):
def one_hot_encode(self, x, n_classes):
... | gpl-3.0 |
ZENGXH/scikit-learn | sklearn/linear_model/coordinate_descent.py | 42 | 73973 | # 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 |
nhuntwalker/astroML | book_figures/chapter9/fig_rrlyrae_kernelsvm.py | 3 | 4615 | """
Kernel SVM Classification of photometry
---------------------------------------
Figure 9.11
Kernel SVM applied to the RR Lyrae data (see caption of figure 9.3 for
details). This example uses a Gaussian kernel with gamma = 20. With all four
colors, kernel SVM achieves a completeness of 1.0 and a contamination of 0.... | bsd-2-clause |
SelinaChe/Complex-Object-Detection-StackGAN | stackGAN-demo/demo.py | 1 | 9679 | from __future__ import division
from __future__ import print_function
import sys
sys.path.append(".")
import matplotlib.pyplot as plt
import prettytensor as pt
import tensorflow as tf
import numpy as np
import scipy.misc
import os
import argparse
import torchfile
from PIL import Image, ImageDraw, ImageFont
import re... | mit |
wanggang3333/scikit-learn | examples/ensemble/plot_ensemble_oob.py | 259 | 3265 | """
=============================
OOB Errors for Random Forests
=============================
The ``RandomForestClassifier`` is trained using *bootstrap aggregation*, where
each new tree is fit from a bootstrap sample of the training observations
:math:`z_i = (x_i, y_i)`. The *out-of-bag* (OOB) error is the average er... | bsd-3-clause |
sonnyhu/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 |
EmreAtes/spack | var/spack/repos/builtin/packages/julia/package.py | 3 | 10019 | ##############################################################################
# Copyright (c) 2013-2018, Lawrence Livermore National Security, LLC.
# Produced at the Lawrence Livermore National Laboratory.
#
# This file is part of Spack.
# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved.
# LLNL-CODE-64... | lgpl-2.1 |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_decoding_csp_space.py | 9 | 3982 | """
====================================================================
Decoding in sensor space data using the Common Spatial Pattern (CSP)
====================================================================
Decoding applied to MEG data in sensor space decomposed using CSP.
Here the classifier is applied to feature... | bsd-3-clause |
imaculate/scikit-learn | sklearn/feature_selection/rfe.py | 16 | 16420 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Vincent Michel <vincent.michel@inria.fr>
# Gilles Louppe <g.louppe@gmail.com>
#
# License: BSD 3 clause
"""Recursive feature elimination for feature ranking"""
import numpy as np
from ..utils import check_X_y, safe_sqr
from ..utils.metaes... | bsd-3-clause |
ray-project/ray | python/setup.py | 1 | 17074 | import argparse
import errno
import glob
import io
import logging
import os
import re
import shutil
import subprocess
import sys
import tarfile
import tempfile
import zipfile
from itertools import chain
from itertools import takewhile
import urllib.error
import urllib.parse
import urllib.request
logger = logging.get... | apache-2.0 |
lazywei/scikit-learn | examples/cluster/plot_dict_face_patches.py | 337 | 2747 | """
Online learning of a dictionary of parts of faces
==================================================
This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.
From the programming standpoint, it is interesting because it shows how
to use the online API of the sciki... | bsd-3-clause |
amontefusco/gnuradio-amontefusco | gr-msdd6000/src/python-examples/msdd_spectrum_waterfall.py | 8 | 11670 | #!/usr/bin/env python
#
# Copyright 2008 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 |
mdeger/nest-simulator | extras/ConnPlotter/colormaps.py | 21 | 6941 | # -*- coding: utf-8 -*-
#
# colormaps.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 |
DreamLiMu/ML_Python | tools/Ch07/adaboost.py | 4 | 5423 | '''
Created on Nov 28, 2010
Adaboost is short for Adaptive Boosting
@author: Peter
'''
from numpy import *
def loadSimpData():
datMat = matrix([[ 1. , 2.1],
[ 2. , 1.1],
[ 1.3, 1. ],
[ 1. , 1. ],
[ 2. , 1. ]])
classLabels = [1.0, 1.0, -1.0, -1.0, 1.0]
return datMat,clas... | gpl-2.0 |
dpaiton/OpenPV | pv-core/analysis/python/plot_inh_roc.py | 1 | 40370 | """
Plot the highest activity of four different bar positionings
"""
import sys
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib.cm as cm
import PVReadSparse as rs
import PVReadWeights as rw
import PVConversions as conv
import scipy.cluster.vq as sp
import math
def ... | epl-1.0 |
thorwhalen/ut | daf/plot.py | 1 | 1671 | __author__ = 'thor'
import numpy as np
import ut.pplot.hist
import pandas as pd
import matplotlib.pylab as plt
from ut.util.utime import utc_ms_to_utc_datetime
def count_hist(sr, sort_by='value', reverse=True, horizontal=None, ratio=False, **kwargs):
horizontal = horizontal or isinstance(sr.iloc[0], str)
ut.... | mit |
Habasari/sms-tools | lectures/07-Sinusoidal-plus-residual-model/plots-code/LPC.py | 24 | 1191 | import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import hamming, hanning, triang, blackmanharris, resample
import math
import sys, os, time
from scipy.fftpack import fft, ifft
import essentia.standard as ess
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../softwar... | agpl-3.0 |
ioam/holoviews | holoviews/tests/operation/testtimeseriesoperations.py | 2 | 3148 | from unittest import SkipTest, skipIf
try:
import pandas as pd
except:
raise SkipTest('Pandas not available')
try:
import scipy # noqa
except:
scipy = None
scipy_skip = skipIf(scipy is None, "SciPy is not available.")
import numpy as np
from holoviews import Curve, Scatter
from holoviews.element.com... | bsd-3-clause |
chengsoonong/acton | acton/plot.py | 1 | 2501 | """Script to plot a dump of predictions."""
import itertools
import sys
from typing import Iterable
from typing.io import BinaryIO
import acton.proto.io
from acton.proto.acton_pb2 import Predictions
import acton.proto.wrappers
import click
import matplotlib.pyplot as plt
import sklearn.metrics
def plot(predictions:... | bsd-3-clause |
friedrichromstedt/matplotlayers | matplotlayers/backends/PIL/figure_canvas.py | 1 | 2080 | # Copyright (c) 2010 Friedrich Romstedt <friedrichromstedt@gmail.com>
# See also <www.friedrichromstedt.org> (if e-mail has changed)
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without... | mit |
SaganBolliger/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/projections/geo.py | 69 | 19738 | import math
import numpy as np
import numpy.ma as ma
import matplotlib
rcParams = matplotlib.rcParams
from matplotlib.artist import kwdocd
from matplotlib.axes import Axes
from matplotlib import cbook
from matplotlib.patches import Circle
from matplotlib.path import Path
from matplotlib.ticker import Formatter, Locat... | agpl-3.0 |
vybstat/scikit-learn | sklearn/discriminant_analysis.py | 19 | 26162 | """
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 |
carrillo/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 |
romil93/SentimentAnalysis-CSCI544-Fall2016 | romil/flask-app/main.py | 1 | 2125 | from flask import Flask, render_template, redirect, request
from sklearn.externals import joblib
import urllib, os
import pandas as pd
import numpy as np
import re, nltk
from sklearn.feature_extraction.text import CountVectorizer
from nltk.stem.porter import PorterStemmer
from sklearn.linear_model import LogisticRegres... | apache-2.0 |
f3r/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 |
vivekmishra1991/scikit-learn | sklearn/neighbors/tests/test_neighbors.py | 76 | 45197 | from itertools import product
import pickle
import numpy as np
from scipy.sparse import (bsr_matrix, coo_matrix, csc_matrix, csr_matrix,
dok_matrix, lil_matrix)
from sklearn import metrics
from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.utils.testing impor... | bsd-3-clause |
pompiduskus/scikit-learn | sklearn/mixture/gmm.py | 128 | 31069 | """
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 |
okfn-brasil/gastos_abertos | utils/build_search_index.py | 2 | 1896 | # -*- coding: utf-8 -*-
''' Build the search indexes.
Usage:
./build_search_index [RESOURCE]
./build_search_index (-h | --help)
Options:
-h --help Show this message.
'''
import os
import pandas as pd
from concurrent import futures
from gastosabertos.contratos.models import Contrato
from utils import... | agpl-3.0 |
kadubarbosa/hydra1 | only_plot.py | 1 | 7042 | # -*- coding: utf-8 -*-
"""
Created on Mon Mar 31 01:09:38 2014
@author: kadu
Make plot of examples of pPFX fitting. Equivalent of only_plot_dat in Lodo's
IDL program, but much slower.
"""
import os
import pickle
import numpy as np
import pyfits as pf
import matplotlib.pyplot as plt
from matplotlib.backends.backend... | gpl-2.0 |
huzq/scikit-learn | sklearn/ensemble/_hist_gradient_boosting/tests/test_binning.py | 9 | 11588 | import numpy as np
from numpy.testing import assert_array_equal, assert_allclose
import pytest
from sklearn.ensemble._hist_gradient_boosting.binning import (
_BinMapper,
_find_binning_thresholds as _find_binning_thresholds_orig,
_map_to_bins
)
from sklearn.ensemble._hist_gradient_boosting.common import X_D... | bsd-3-clause |
rs2/pandas | pandas/tests/extension/conftest.py | 8 | 3785 | import operator
import pytest
from pandas import Series
@pytest.fixture
def dtype():
"""A fixture providing the ExtensionDtype to validate."""
raise NotImplementedError
@pytest.fixture
def data():
"""
Length-100 array for this type.
* data[0] and data[1] should both be non missing
* data[... | bsd-3-clause |
h2oai/h2o | py/testdir_single_jvm/test_KMeans_hastie_shuffle_fvec.py | 9 | 5295 | # Dataset created from this:
# Elements of Statistical Learning 2nd Ed.; Hastie, Tibshirani, Friedman; Feb 2011
# example 10.2 page 357
# Ten features, standard independent Gaussian. Target y is:
# y[i] = 1 if sum(X[i]) > .34 else -1
# 9.34 is the median of a chi-squared random variable with 10 degrees of freedom
# ... | apache-2.0 |
fzalkow/scikit-learn | examples/cluster/plot_kmeans_digits.py | 230 | 4524 | """
===========================================================
A demo of K-Means clustering on the handwritten digits data
===========================================================
In this example we compare the various initialization strategies for
K-means in terms of runtime and quality of the results.
As the gr... | bsd-3-clause |
mayavanand/RMMAFinalProject | build/lib/azimuth/models/DNN.py | 3 | 3068 | import numpy as np
import scipy as sp
import sklearn
def DNN_on_fold(feature_sets, train, test, y, y_all, X, dim, dimsum, learn_options):
import theanets
from sklearn.metrics import accuracy_score
y = np.array(y_all[learn_options['DNN target variable']].values, dtype=float)
y_train, X_train = y[train][... | bsd-3-clause |
asoliveira/NumShip | scripts/plot/r-cg-plt.py | 1 | 1980 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#É adimensional?
adi = False
#É para salvar as figuras(True|False)?
save = True
#Caso seja para salvar, qual é o formato desejado?
formato = 'jpg'
#Caso seja para salvar, qual é o diretório que devo salvar?
dircg = 'fig-sen'
#Caso seja para salvar, qual é o nome do arquivo... | gpl-3.0 |
jyt109/BDA_py_demos | demos_ch2/demo2_3.py | 19 | 1931 | """Bayesian Data Analysis, 3rd ed
Chapter 2, demo 3
Simulate samples from Beta(438,544), draw a histogram with quantiles, and do
the same for a transformed variable.
"""
import numpy as np
from scipy.stats import beta
import matplotlib.pyplot as plt
# Edit default plot settings (colours from colorbrewer2.org)
plt... | gpl-3.0 |
ofgulban/scikit-image | doc/examples/features_detection/plot_gabor.py | 21 | 4450 | """
=============================================
Gabor filter banks for texture classification
=============================================
In this example, we will see how to classify textures based on Gabor filter
banks. Frequency and orientation representations of the Gabor filter are
similar to those of the huma... | bsd-3-clause |
chenyyx/scikit-learn-doc-zh | examples/zh/exercises/plot_iris_exercise.py | 1 | 2434 | # -*- coding:UTF-8 -*-
"""
================================
SVM 练习
================================
使用不同的 SVM kernels 的教程练习。
这个教程应用于 :ref:`supervised_learning_tut` 章节的 :ref:`stat_learn_tut_index` 的 :ref:`using_kernels_tut` 这一部分。
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn impo... | gpl-3.0 |
mne-tools/mne-tools.github.io | 0.21/_downloads/d3272d21ad495ecb95dfdfa6465d8d29/plot_decoding_unsupervised_spatial_filter.py | 29 | 2496 | """
==================================================================
Analysis of evoked response using ICA and PCA reduction techniques
==================================================================
This example computes PCA and ICA of evoked or epochs data. Then the
PCA / ICA components, a.k.a. spatial filters,... | bsd-3-clause |
NixaSoftware/CVis | venv/lib/python2.7/site-packages/pandas/core/sparse/series.py | 2 | 29566 | """
Data structures for sparse float data. Life is made simpler by dealing only
with float64 data
"""
# pylint: disable=E1101,E1103,W0231
import numpy as np
import warnings
from pandas.core.dtypes.missing import isna, notna
from pandas.core.dtypes.common import is_scalar
from pandas.core.common import _values_from_o... | apache-2.0 |
clarka34/exploringShipLogbooks | exploringShipLogbooks/tests/test_fuzz_replacement.py | 2 | 2899 | """ Unit tests for basic_utils.py """
import exploringShipLogbooks
import pep8
import unittest
import numpy as np
import pandas as pd
import exploringShipLogbooks.fuzz_replacement as fr
class TestFuzzReplacement(unittest.TestCase):
def setUp(self):
self.all_log_values = [
'dutch',
... | mit |
ldirer/scikit-learn | examples/linear_model/plot_ols_3d.py | 350 | 2040 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Sparsity Example: Fitting only features 1 and 2
=========================================================
Features 1 and 2 of the diabetes-dataset are fitted and
plotted below. It illustrates that although feature... | bsd-3-clause |
pico12/trading-with-python | nautilus/nautilus.py | 77 | 5403 | '''
Created on 26 dec. 2011
Copyright: Jev Kuznetsov
License: BSD
'''
from PyQt4.QtCore import *
from PyQt4.QtGui import *
from ib.ext.Contract import Contract
from ib.opt import ibConnection
from ib.ext.Order import Order
import tradingWithPython.lib.logger as logger
from tradingWithPython.lib.eve... | bsd-3-clause |
rileymcdowell/genomic-neuralnet | genomic_neuralnet/config/data_definitions.py | 1 | 3378 | from __future__ import print_function
import os
import pandas as pd
_data_dir = os.path.join(os.path.dirname(__file__), '..', 'data')
class DataDefinition(object):
def __init__(self, trait_name, *args, **kwargs):
"""
Defines a datatype to predict.
Parameters:
trait_name: The... | mit |
manipopopo/tensorflow | tensorflow/examples/tutorials/input_fn/boston.py | 76 | 2920 | # 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 |
nhejazi/scikit-learn | sklearn/neural_network/tests/test_mlp.py | 20 | 22194 | """
Testing for Multi-layer Perceptron module (sklearn.neural_network)
"""
# Author: Issam H. Laradji
# License: BSD 3 clause
import sys
import warnings
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_equal
from sklearn.datasets import load_digits, load_boston, load_iris
from sklearn... | bsd-3-clause |
robbymeals/scikit-learn | sklearn/cluster/__init__.py | 364 | 1228 | """
The :mod:`sklearn.cluster` module gathers popular unsupervised clustering
algorithms.
"""
from .spectral import spectral_clustering, SpectralClustering
from .mean_shift_ import (mean_shift, MeanShift,
estimate_bandwidth, get_bin_seeds)
from .affinity_propagation_ import affinity_propagati... | bsd-3-clause |
ChanderG/scikit-learn | sklearn/metrics/tests/test_common.py | 27 | 44210 | from __future__ import division, print_function
from functools import partial
from itertools import product
import numpy as np
import scipy.sparse as sp
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import LabelBinarizer, MultiLabelBinarizer
from sklearn.utils.multiclass impo... | bsd-3-clause |
google/graph_distillation | classification/run.py | 1 | 8507 | # Copyright 2018 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 |
mahak/spark | python/pyspark/pandas/tests/test_stats.py | 9 | 18900 | #
# 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 us... | apache-2.0 |
yavalvas/yav_com | build/matplotlib/lib/mpl_examples/animation/unchained.py | 3 | 1875 | """
Comparative path demonstration of frequency from a fake signal of a pulsar.
(mostly known because of the cover for Joy Division's Unknown Pleasures)
Author: Nicolas P. Rougier
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
# Create new Figure with black background
... | mit |
mrahim/adni_fdg_pet_analysis | learn_voxels_norm_baseline_fdg_pet_adni.py | 1 | 4688 | """
A script that :
- computes a Masker from FDG PET (baseline uniform)
- cross-validates a linear SVM classifier
- computes a ROC curve and AUC
"""
import os, glob
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import nibabel as nib
from sklearn import svm
from sklearn import cross_validation
... | gpl-2.0 |
noelevans/sandpit | kaggle/cross-device-conns/analyse_home_rolled.py | 1 | 1941 | import pandas as pd
from collections import Counter
class Homogeneity(object):
SAME = 's' # eg "b b b b"
MIX = 'm' # eg "a b a a"
DIFF = 'd' # eg "a b c d"
@classmethod
def categorise(cls, ul):
if len(ul) == len(set(ul)):
return cls.DIFF
if len(set(ul)) == ... | mit |
icdishb/scikit-learn | examples/linear_model/plot_logistic.py | 312 | 1426 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logit function
=========================================================
Show in the plot is how the logistic regression would, in this
synthetic dataset, classify values as either 0 or 1,
i.e. class one or two, u... | bsd-3-clause |
rahuldhote/scikit-learn | sklearn/cluster/setup.py | 263 | 1449 | # 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 |
meduz/NeuroTools | examples/matlab_vs_python/smallnet.py | 3 | 3997 | # Created by Eugene M. Izhikevich, 2003 Modified by S. Fusi 2007
# Ported to Python by Eilif Muller, 2008.
#
# Notes:
#
# Requires matplotlib,ipython,numpy>=1.0.3
# On a debian/ubuntu based system:
# $ apt-get install python-matplotlib python-numpy ipython
#
# Start ipython with threaded plotting support:
# $ ipython -... | gpl-2.0 |
OshynSong/scikit-learn | examples/linear_model/plot_ransac.py | 250 | 1673 | """
===========================================
Robust linear model estimation using RANSAC
===========================================
In this example we see how to robustly fit a linear model to faulty data using
the RANSAC algorithm.
"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn import ... | bsd-3-clause |
cxhernandez/mdentropy | mdentropy/utils.py | 1 | 2859 | from __future__ import print_function
import time
from numpy import dtype, finfo, float32, nan_to_num, random, unique, void
from scipy.spatial import cKDTree
from scipy.special import digamma
__all__ = ['floor_threshold', 'shuffle', 'Timing', 'unique_row_count',
'avgdigamma']
EPS = finfo(float32).eps
... | gpl-3.0 |
jereze/scikit-learn | sklearn/pipeline.py | 61 | 21271 | """
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
# Licence: BSD
from collections import defaultdict... | bsd-3-clause |
sideshownick/Snaking_Networks | MyPython/Snaking/plot_bifdiag1.py | 2 | 1420 | # -*- coding: utf-8 -*-
"""
Created on Thu Dec 17 09:52:41 2015
@author: nm268
"""
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plt.rcParams.update({'font.size': 16, 'figure.autolayout': True})
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111)
df = pd.read_table('b.workfile', heade... | gpl-2.0 |
wzbozon/scikit-learn | benchmarks/bench_plot_ward.py | 290 | 1260 | """
Benchmark scikit-learn's Ward implement compared to SciPy's
"""
import time
import numpy as np
from scipy.cluster import hierarchy
import pylab as pl
from sklearn.cluster import AgglomerativeClustering
ward = AgglomerativeClustering(n_clusters=3, linkage='ward')
n_samples = np.logspace(.5, 3, 9)
n_features = n... | bsd-3-clause |
mugizico/scikit-learn | sklearn/mixture/gmm.py | 128 | 31069 | """
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 |
ajlongart/Tesis-UIP | AnalisisFourier.py | 1 | 3552 | #!/usr/bin/env python
'''
Modulo 2 Toolbox
Analisis Cuantitativo de la Imagen
Tesis Underwater Image Pre-processing
Armando Longart 10-10844
ajzlongart@gmail.com
#-----Analisis Cuantitativo de la Imagen------------------------------------------------
Se realiza analisis de Entropia de las imagenes originales y resulta... | gpl-3.0 |
ElDeveloper/scikit-learn | sklearn/neural_network/rbm.py | 11 | 12298 | """Restricted Boltzmann Machine
"""
# Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca>
# Vlad Niculae
# Gabriel Synnaeve
# Lars Buitinck
# License: BSD 3 clause
import time
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator
from ..base import TransformerMixi... | bsd-3-clause |
vrieni/orange | Orange/clustering/kmeans.py | 6 | 21404 | """
*******************************
K-means clustering (``kmeans``)
*******************************
.. index::
single: clustering, kmeans
.. index:: agglomerative clustering
.. autoclass:: Orange.clustering.kmeans.Clustering(data=None, centroids=3, maxiters=None, minscorechange=None, stopchanges=0, nstart=1, init... | gpl-3.0 |
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