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 |
|---|---|---|---|---|---|
crazy-cat/incubator-mxnet | example/autoencoder/data.py | 27 | 1272 | # 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 |
ssaeger/scikit-learn | examples/neural_networks/plot_mnist_filters.py | 57 | 2195 | """
=====================================
Visualization of MLP weights on MNIST
=====================================
Sometimes looking at the learned coefficients of a neural network can provide
insight into the learning behavior. For example if weights look unstructured,
maybe some were not used at all, or if very l... | bsd-3-clause |
flightgong/scikit-learn | sklearn/gaussian_process/tests/test_gaussian_process.py | 3 | 5073 | """
Testing for Gaussian Process module (sklearn.gaussian_process)
"""
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# Licence: BSD 3 clause
from nose.tools import raises
from nose.tools import assert_true
import numpy as np
from sklearn.gaussian_process import GaussianProcess
from sklearn.gaussian_process ... | bsd-3-clause |
siou83/trading-with-python | spreadApp/makeDist.py | 77 | 1720 | from distutils.core import setup
import py2exe
manifest_template = '''
<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<assembly xmlns="urn:schemas-microsoft-com:asm.v1" manifestVersion="1.0">
<assemblyIdentity
version="5.0.0.0"
processorArchitecture="x86"
name="%(prog)s"
type="win32"... | bsd-3-clause |
ChristophSchranz/ecg_via_raspberrypi | basic-ecg.py | 1 | 3093 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import time # import the module called "time"
# import the grafic-module as plt
import matplotlib.pyplot as plt
import RPi.GPIO as GPIO # import GPIO to use the Pins
# set the sample rate (measurements per seconds)
# and its duration
sample_rate = 200
duration = 3
... | gpl-3.0 |
tammoippen/nest-simulator | topology/doc/user_manual_scripts/connections.py | 8 | 18562 | # -*- coding: utf-8 -*-
#
# connections.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 |
murali-munna/scikit-learn | examples/plot_kernel_ridge_regression.py | 230 | 6222 | """
=============================================
Comparison of kernel ridge regression and SVR
=============================================
Both kernel ridge regression (KRR) and SVR learn a non-linear function by
employing the kernel trick, i.e., they learn a linear function in the space
induced by the respective k... | bsd-3-clause |
strongh/GPy | GPy/core/parameterization/priors.py | 2 | 26352 | # Copyright (c) 2012 - 2014, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
from scipy.special import gammaln, digamma
from ...util.linalg import pdinv
from domains import _REAL, _POSITIVE
import warnings
import weakref
class Prior(object):
domain =... | bsd-3-clause |
basilfx/RIOT | tests/pkg_tensorflow-lite/mnist/generate_digit.py | 19 | 1164 | #!/usr/bin/env python3
"""Generate a binary file from a sample image of the MNIST dataset.
Pixel of the sample are stored as float32, images have size 28x28.
"""
import os
import argparse
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.datasets import mnist
SCRIPT_DIR = os.path.dirname(os.... | lgpl-2.1 |
clemkoa/scikit-learn | sklearn/ensemble/tests/test_voting_classifier.py | 15 | 17696 | """Testing for the VotingClassifier"""
import numpy as np
from sklearn.utils.testing import assert_almost_equal, assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal, assert_true, assert_false
from sklearn.utils.testing import assert_raise_messag... | bsd-3-clause |
pratapvardhan/scikit-learn | examples/cluster/plot_mean_shift.py | 351 | 1793 | """
=============================================
A demo of the mean-shift clustering algorithm
=============================================
Reference:
Dorin Comaniciu and Peter Meer, "Mean Shift: A robust approach toward
feature space analysis". IEEE Transactions on Pattern Analysis and
Machine Intelligence. 2002. ... | bsd-3-clause |
ecrc/girih | scripts/sisc/paper_plot_thread_scaling_naive_out_cache.py | 2 | 6303 | #!/usr/bin/env python
def main():
import sys
raw_data = load_csv(sys.argv[1])
k_l = set()
for k in raw_data:
k_l.add(get_stencil_num(k))
k_l = list(k_l)
# for ts in ['Naive', 'Dynamic-Intra-Diamond']
for k in k_l:
for is_dp in [1]:
plot_lines(raw_data, k, is_d... | bsd-3-clause |
annayqho/TheCannon | code/lamost/tutorial/run_tutorial.py | 1 | 4224 | import numpy as np
from astropy.table import Table
from TheCannon.lamost import load_spectra
import matplotlib.pyplot as plt
from TheCannon import dataset
from TheCannon import model
labdir = "/Users/annaho/Github_Repositories/TheCannon/docs/source"
data = Table.read("%s/lamost_labels.fits" %labdir)
print(data.colname... | mit |
fzalkow/scikit-learn | examples/tree/plot_iris.py | 271 | 2186 | """
================================================================
Plot the decision surface of a decision tree on the iris dataset
================================================================
Plot the decision surface of a decision tree trained on pairs
of features of the iris dataset.
See :ref:`decision tree ... | bsd-3-clause |
blueburningcoder/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 |
snurk/meta-strains | scripts/accuracy.py | 1 | 5070 | import pandas as pd
import numpy as np
from collections import Counter
from itertools import permutations
import networkx as nx
def print_edges(edges):
print(','.join([str(e) for e in edges]))
# Считываем граф
# Oставляем только большую компоненту связности
G = nx.DiGraph()
with open("assembly/K127/prelim_gra... | mit |
larsmans/scikit-learn | examples/decomposition/plot_pca_vs_lda.py | 182 | 1743 | """
=======================================================
Comparison of LDA and PCA 2D projection of Iris dataset
=======================================================
The Iris dataset represents 3 kind of Iris flowers (Setosa, Versicolour
and Virginica) with 4 attributes: sepal length, sepal width, petal length
a... | bsd-3-clause |
eg-zhang/scikit-learn | benchmarks/bench_plot_lasso_path.py | 301 | 4003 | """Benchmarks of Lasso regularization path computation using Lars and CD
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
from collections import defaultdict
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model import lars_pat... | bsd-3-clause |
arabenjamin/scikit-learn | sklearn/feature_extraction/tests/test_text.py | 110 | 34127 | 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 |
pratapvardhan/scikit-learn | examples/applications/wikipedia_principal_eigenvector.py | 16 | 7821 | """
===============================
Wikipedia principal eigenvector
===============================
A classical way to assert the relative importance of vertices in a
graph is to compute the principal eigenvector of the adjacency matrix
so as to assign to each vertex the values of the components of the first
eigenvect... | bsd-3-clause |
SpaceKatt/CSPLN | apps/scaffolding/mac/web2py/web2py.app/Contents/Resources/lib/python2.7/matplotlib/axis.py | 2 | 72371 | """
Classes for the ticks and x and y axis
"""
from __future__ import division
from matplotlib import rcParams
import matplotlib.artist as artist
from matplotlib.artist import allow_rasterization
import matplotlib.cbook as cbook
import matplotlib.font_manager as font_manager
import matplotlib.lines as mlines
import m... | gpl-3.0 |
bollu/sandhi | modules/gr36/gr-filter/examples/interpolate.py | 13 | 8584 | #!/usr/bin/env python
#
# Copyright 2009,2012 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 optio... | gpl-3.0 |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/sklearn/learning_curve.py | 35 | 15441 | """Utilities to evaluate models with respect to a variable
"""
# Author: Alexander Fabisch <afabisch@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import warnings
import numpy as np
from .base import is_classifier, clone
from .cross_validation import check_cv
from .externals.joblib import Parallel, delayed
fro... | mit |
CalebBell/thermo | tests/test_datasheet.py | 1 | 8705 | # -*- coding: utf-8 -*-
'''Chemical Engineering Design Library (ChEDL). Utilities for process modeling.
Copyright (C) 2016, Caleb Bell <Caleb.Andrew.Bell@gmail.com>
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal... | mit |
readevalprint/zipline | tests/finance/test_slippage.py | 3 | 16080 | #
# 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 law or agreed to in wr... | apache-2.0 |
MPIBGC-TEE/CompartmentalSystems | src/CompartmentalSystems/bins/TsMassFieldsPerTimeStep.py | 1 | 3427 | # vim: set ff=unix expandtab ts=4 sw=4:
import numpy as np
from sympy import latex
from .FieldsPerTimeStep import FieldsPerTimeStep
from matplotlib import cm
import matplotlib.pyplot as plt
class TsMassFieldsPerTimeStep(FieldsPerTimeStep):
@property
def max_number_of_Ts_entries(self):
return(max([v.num... | mit |
anntzer/scikit-learn | asv_benchmarks/benchmarks/model_selection.py | 12 | 2511 | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV, cross_val_score
from .common import Benchmark, Estimator, Predictor
from .datasets import _synth_classification_dataset
from .utils import make_gen_classif_scorers
class CrossValidationBenchmark(Benchmark):
"""
... | bsd-3-clause |
jorik041/scikit-learn | examples/mixture/plot_gmm_sin.py | 248 | 2747 | """
=================================
Gaussian Mixture Model Sine Curve
=================================
This example highlights the advantages of the Dirichlet Process:
complexity control and dealing with sparse data. The dataset is formed
by 100 points loosely spaced following a noisy sine curve. The fit by
the GMM... | bsd-3-clause |
costypetrisor/scikit-learn | sklearn/cluster/spectral.py | 18 | 18027 | # -*- coding: utf-8 -*-
"""Algorithms for spectral clustering"""
# Author: Gael Varoquaux gael.varoquaux@normalesup.org
# Brian Cheung
# Wei LI <kuantkid@gmail.com>
# License: BSD 3 clause
import warnings
import numpy as np
from ..base import BaseEstimator, ClusterMixin
from ..utils import check_rand... | bsd-3-clause |
johnbachman/ras_model | gxp_exchange.py | 6 | 2587 | from rasmodel.scenarios.default import model
import numpy as np
from matplotlib import pyplot as plt
from pysb.integrate import Solver
from pysb import *
from tbidbaxlipo.util import fitting
# Zero out all initial conditions
for ic in model.initial_conditions:
ic[1].value = 0
KRAS = model.monomers['KRAS']
GDP =... | mit |
bsipocz/ginga | ginga/mplw/MplHelp.py | 2 | 2894 | #
# MplHelp.py -- help classes for Matplotlib drawing
#
# Eric Jeschke (eric@naoj.org)
#
# Copyright (c) Eric R. Jeschke. All rights reserved.
# This is open-source software licensed under a BSD license.
# Please see the file LICENSE.txt for details.
from ginga import colors
import matplotlib.textpath as textpath
... | bsd-3-clause |
tornadoalert/Retinet | models/basic_model.py | 1 | 17510 | import os
import numpy as np
import pandas as p
import theano.tensor as T
import lasagne as nn
from lasagne.layers import dnn
from lasagne.nonlinearities import LeakyRectify
from layers import ApplyNonlinearity
from utils import (oversample_set,
get_img_ids_from_dir,
softmax,
... | mit |
ytsvetko/adjective_supersense_classifier | src/classify.py | 1 | 9656 | #!/usr/bin/env python2.7
from __future__ import division
from itertools import izip_longest
import sys
import codecs
import argparse
import json
from sklearn import svm
from sklearn import cross_validation
from sklearn import ensemble
from collections import Counter
import numpy as np
parser = argparse.ArgumentParser... | gpl-2.0 |
AkademieOlympia/sympy | sympy/interactive/session.py | 43 | 15119 | """Tools for setting up interactive sessions. """
from __future__ import print_function, division
from distutils.version import LooseVersion as V
from sympy.core.compatibility import range
from sympy.external import import_module
from sympy.interactive.printing import init_printing
preexec_source = """\
from __futu... | bsd-3-clause |
egorburlakov/CrisisModelingPython | Main.py | 1 | 11779 | import pandas as pd
import numpy as np
from os import listdir
from os.path import isfile, join
import matplotlib.pyplot as plt
################
#Pars
################
dat_types = {"SigCaught" : np.float32, "NSigs" : np.uint8, "NMissedSigs" : np.uint8, "NDecMade" : np.uint8, "CrT" : np.uint16, "NTp" : np.uint8, "NBr" :... | gpl-2.0 |
lux-jwang/goodoos | drawnetwork.py | 1 | 2393 | #! /usr/bin/env python
# We probably will need some things from several places
import sys
# We need to import the graph_tool module itself
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from graph_tool.all import *
sys.path.append("./src")
from dataset import get_friends_data_index
def draw_edge... | mit |
koobonil/Boss2D | Boss2D/addon/tensorflow-1.2.1_for_boss/tensorflow/contrib/learn/python/learn/estimators/estimators_test.py | 37 | 5114 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | mit |
JohnKendrick/PDielec | PDielec/GUI/AnalysisTab.py | 1 | 21560 | import math
import numpy as np
import PDielec.Calculator as Calculator
from PyQt5.QtWidgets import QWidget, QApplication
from PyQt5.QtWidgets import QComboBox, QLabel, QLineEdit
from PyQt5.QtWidgets import QVBoxLayout, QHBoxLayout, QFormLayout
from PyQt5.QtWidgets import QSpinBox, QDoubleSpinBox
from PyQt5.QtWi... | mit |
rexshihaoren/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 |
yyjiang/scikit-learn | sklearn/decomposition/tests/test_truncated_svd.py | 240 | 6055 | """Test truncated SVD transformer."""
import numpy as np
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from sklearn.utils import check_random_state
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_raises, assert_greater,
... | bsd-3-clause |
daviddesancho/BestMSM | bestmsm/visual_lib.py | 1 | 3173 | import sys
import math
import itertools
import numpy as np
import networkx as nx
import matplotlib.pyplot as plt
# Much indebted to Randal Olson (http://www.randalolson.com/)
# These are the "Tableau 20" colors as RGB.
tableau20 = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
(44... | gpl-2.0 |
eqcorrscan/ci.testing | eqcorrscan/utils/despike.py | 1 | 7758 | """
Functions for despiking seismic data.
.. warning:: Despike functions are in beta, they do not work that well.
.. todo:: Deconvolve spike to remove it, find peaks in the f-domain.
:copyright:
EQcorrscan developers.
:license:
GNU Lesser General Public License, Version 3
(https://www.gnu.org/copyleft/l... | lgpl-3.0 |
0todd0000/spm1d | spm1d/rft1d/examples/paper/fig11_val_cluster.py | 1 | 2030 |
import numpy as np
from scipy import stats
from matplotlib import pyplot
from spm1d import rft1d
eps = np.finfo(float).eps #smallest float
### EPS production preliminaries:
fig_width_mm = 100
fig_height_mm = 80
mm2in = 1/25.4
fig_width = fig_width_mm*mm2in # width in inches
fig_height = fig_height_m... | gpl-3.0 |
poryfly/scikit-learn | benchmarks/bench_plot_svd.py | 325 | 2899 | """Benchmarks of Singular Value Decomposition (Exact and Approximate)
The data is mostly low rank but is a fat infinite tail.
"""
import gc
from time import time
import numpy as np
from collections import defaultdict
from scipy.linalg import svd
from sklearn.utils.extmath import randomized_svd
from sklearn.datasets.s... | bsd-3-clause |
bmcfee/pescador | docs/conf.py | 1 | 10180 | # -*- coding: utf-8 -*-
#
# pescador documentation build configuration file, created by
# sphinx-quickstart on Fri Apr 3 10:03:34 2015.
#
# 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.
#
# ... | isc |
annapowellsmith/openpresc | openprescribing/frontend/tests/commands/test_import_ppu_savings.py | 1 | 5252 | from datetime import date
import os
from gcutils.bigquery import Client
from django.test import TestCase
import pandas as pd
from mock import patch
from frontend import bq_schemas
from frontend.management.commands import import_ppu_savings
from frontend.models import PPUSaving, PCT, Practice, Chemical
class Bigque... | mit |
jundongl/scikit-feast | skfeature/function/sparse_learning_based/MCFS.py | 3 | 2089 | import scipy
import numpy as np
from sklearn import linear_model
from skfeature.utility.construct_W import construct_W
def mcfs(X, n_selected_features, **kwargs):
"""
This function implements unsupervised feature selection for multi-cluster data.
Input
-----
X: {numpy array}, shape (n_samples, n_... | gpl-2.0 |
Zhenxingzhang/kaggle-cdiscount-classification | src/inference/inference_inception_v3_ft.py | 1 | 6742 | import os
import numpy as np
import pandas as pd
import tensorflow as tf
import csv
from src.common import consts
from src.data_preparation import dataset
from src.models import denseNN
from src.common import paths
import argparse
import yaml
from src.training.train_model import neural_model
from tensorflow.contrib.sl... | apache-2.0 |
vidartf/hyperspy | hyperspy/drawing/signal1d.py | 1 | 14204 | # -*- coding: utf-8 -*-
# Copyright 2007-2016 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at... | gpl-3.0 |
BNIA/tidyall | src/modules/tidydate.py | 2 | 2523 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""tidydate
This file declares and implements the TidyDate class for TidyAll.
TidyDate converts and formats valid date columns into ISO 8601 (yyyy-mm-dd).
"""
import sys
from dateutil import parser as date_parser
import numpy as np
import pandas as pd
from .settings im... | mit |
atantet/transferLorenz | plot/plotEigValRho.py | 1 | 6587 | import os
import numpy as np
import matplotlib.pyplot as plt
import pylibconfig2
import ergoPlot
configFile = '../cfg/Lorenz63.cfg'
compName1 = 'x'
compName2 = 'y'
compName3 = 'z'
#readSpec = ergoPlot.readSpectrum
readSpec = ergoPlot.readSpectrumCompressed
cfg = pylibconfig2.Config()
cfg.read_file(configFile)
L = cf... | gpl-3.0 |
nomadcube/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 213 | 3359 | # 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 |
jougs/nest-simulator | pynest/examples/BrodyHopfield.py | 7 | 4218 | # -*- coding: utf-8 -*-
#
# BrodyHopfield.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, ... | gpl-2.0 |
blaisb/cfdemUtilities | graph/contour_demo.py | 14 | 3478 | #!/usr/bin/env python
"""
Illustrate simple contour plotting, contours on an image with
a colorbar for the contours, and labelled contours.
See also contour_image.py.
"""
import matplotlib
import numpy as np
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
matplotlib.rcParams[... | lgpl-3.0 |
jgurtowski/ectools | lengthhist.py | 1 | 1997 | #!/usr/bin/env python
import sys
from operator import attrgetter, itemgetter
from itertools import imap
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import numpy as np
from nucio import lineRecordIterator, M4Record, M4RecordTypes, lineI... | bsd-3-clause |
blink1073/oct2py | setup.py | 1 | 2074 | """Setup script for oct2py package.
"""
import glob
DISTNAME = 'oct2py'
DESCRIPTION = 'Python to GNU Octave bridge --> run m-files from python.'
LONG_DESCRIPTION = open('README.rst', 'rb').read().decode('utf-8')
MAINTAINER = 'Steven Silvester'
MAINTAINER_EMAIL = 'steven.silvester@ieee.org'
URL = 'http://githu... | mit |
huzq/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 71 | 3578 | """
=============================
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
evalua... | bsd-3-clause |
tosolveit/scikit-learn | sklearn/metrics/tests/test_classification.py | 53 | 49781 | from __future__ import division, print_function
import numpy as np
from scipy import linalg
from functools import partial
from itertools import product
import warnings
from sklearn import datasets
from sklearn import svm
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import la... | bsd-3-clause |
hakyimlab/MetaXcan-Postprocess | source/ProcessMetaXcanResultsFolder.py | 1 | 3889 | #! /usr/bin/env python
__author__ = 'heroico'
import os
import re
import logging
import numpy
import Gencode
import Logging
import Utilities
import pandas
import rpy2.robjects as robjects
from rpy2.robjects.packages import importr
dplyr = importr('dplyr', on_conflict="warn")
#import rpy2.robjects.lib.dplyr as dplyr
r... | mit |
rneher/FitnessInference | flu/figure_scripts/flu_examples_fig4ab_polarizer.py | 1 | 3665 | #!/ebio/ag-neher/share/programs/bin/python2.7
#
#makes figure 3ab consisting of 2 trees of year 2007 colored with the prediction
#for the external nodes. It also produces a joint tree with the following season
#to illustrates the prediction. In the actual figure, the trees are rotated by 90degrees
#but without changing... | mit |
lail3344/sms-tools | lectures/08-Sound-transformations/plots-code/hps-morph.py | 24 | 2691 | # function for doing a morph between two sounds using the hpsModel
import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import get_window
import sys, os
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../models/'))
sys.path.append(os.path.join(os.path.dirname(os.path.re... | agpl-3.0 |
fmfn/BayesianOptimization | tests/test_util.py | 1 | 4503 | import pytest
import numpy as np
from bayes_opt import BayesianOptimization
from bayes_opt.util import UtilityFunction, Colours
from bayes_opt.util import acq_max, load_logs, ensure_rng
from sklearn.gaussian_process.kernels import Matern
from sklearn.gaussian_process import GaussianProcessRegressor
def get_globals(... | mit |
ElDeveloper/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 |
ndingwall/scikit-learn | sklearn/linear_model/tests/test_huber.py | 12 | 7600 | # Authors: Manoj Kumar mks542@nyu.edu
# License: BSD 3 clause
import numpy as np
from scipy import optimize, sparse
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.utils._testing import assert_array_almost_equal
from sklearn.datasets import ma... | bsd-3-clause |
rs2/pandas | pandas/tests/frame/conftest.py | 2 | 8855 | from itertools import product
import numpy as np
import pytest
from pandas import DataFrame, NaT, date_range
import pandas._testing as tm
@pytest.fixture(params=product([True, False], [True, False]))
def close_open_fixture(request):
return request.param
@pytest.fixture
def float_frame_with_na():
"""
F... | bsd-3-clause |
ajribeiro/rtapp | data/ml.py | 1 | 5670 | import cPickle
import mysql.connector
import dbUtils
import os
import hashlib
from sklearn.feature_extraction import FeatureHasher, DictVectorizer
def classify(title):
with open('data/perceptron.pkl', 'rb') as fid:
clf = cPickle.load(fid)
(user,pas) = dbUtils.get_user_pass()
mydb = dbUtils.db_acc... | mit |
ilayn/scipy | scipy/optimize/_lsq/least_squares.py | 12 | 39190 | """Generic interface for least-squares minimization."""
from warnings import warn
import numpy as np
from numpy.linalg import norm
from scipy.sparse import issparse, csr_matrix
from scipy.sparse.linalg import LinearOperator
from scipy.optimize import _minpack, OptimizeResult
from scipy.optimize._numdiff import approx... | bsd-3-clause |
florian-f/sklearn | examples/feature_stacker.py | 8 | 1941 | """
=================================================
Concatenating multiple feature extraction methods
=================================================
In many real-world examples, there are many ways to extract features from a
dataset. Often it is benefitial to combine several methods to obtain good
performance. Th... | bsd-3-clause |
booya-at/paraBEM | examples/openglider/shape_cl_cd.py | 2 | 4070 | from copy import deepcopy
import numpy as np
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
# from openglider.glider.parametric import ParametricGlider
from openglider.jsonify import dump, load
from openglider.glider.in_out.export_3d import parabem_Panels
from openglider.utils.distributio... | gpl-3.0 |
dingocuster/scikit-learn | examples/decomposition/plot_ica_blind_source_separation.py | 349 | 2228 | """
=====================================
Blind source separation using FastICA
=====================================
An example of estimating sources from noisy data.
:ref:`ICA` is used to estimate sources given noisy measurements.
Imagine 3 instruments playing simultaneously and 3 microphones
recording the mixed si... | bsd-3-clause |
rmcdermo/macfp-db | Extinction/FM_Burner/Computational_Results/2021/UWuppertal/UWuppertal_FM_Burner_plot_results.py | 1 | 2510 | #!/usr/bin/env python3
# McDermott
# Feb 2021
# first, make sure the macfp module directory is in your path
# if not, uncomment the lines below and replace <path to macfp-db>
# with the path (absolute or relative) to your macfp-db repository
#===========================================================================... | mit |
cybernet14/scikit-learn | sklearn/decomposition/tests/test_pca.py | 199 | 10949 | import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_rai... | bsd-3-clause |
mahajrod/MACE | scripts/windows_boxplots.py | 1 | 12032 | #!/usr/bin/env python
__author__ = 'Sergei F. Kliver'
import os
import argparse
import pandas as pd
import matplotlib.pyplot as plt
from _collections import OrderedDict
from RouToolPa.Collections.General import SynDict, IdList
from MACE.Routines import Visualization, StatsVCF
parser = argparse.ArgumentParser()
parse... | apache-2.0 |
ali-abdullah/nlp | bag-of-words.py | 1 | 3849 | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sklearn as sk
import re
BOW_df = pd.DataFrame(columns=['1','2','3','4','5'])
df = pd.read_csv("Reviews.csv")
words_set = {'.'}
#print(BOW_df.head())
def prepare_data(df):
# This function should ... | mit |
yuanagain/seniorthesis | venv/lib/python2.7/site-packages/matplotlib/testing/noseclasses.py | 8 | 2186 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
import os
from nose.plugins.errorclass import ErrorClass, ErrorClassPlugin
from matplotlib.testing.exceptions import (KnownFailureTest,
... | mit |
bjornsturmberg/EMUstack | docs/source/conf.py | 2 | 8976 | # -*- coding: utf-8 -*-
#
# EMUstack documentation build configuration file, created by
# sphinx-quickstart on Sat Jun 14 14:17:22 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.
#
# ... | gpl-3.0 |
makelove/OpenCV-Python-Tutorial | ch46-机器学习-K近邻/2-英文字母的OCR.py | 1 | 1291 | # -*- coding: utf-8 -*-
# @Time : 2017/7/13 下午7:35
# @Author : play4fun
# @File : 2-英文字母的OCR.py
# @Software: PyCharm
"""
2-英文字母的OCR.py:
"""
import cv2
import numpy as np
import matplotlib.pyplot as plt
# Load the data, converters convert the letter to a number
data = np.loadtxt('../data/letter-recognition.dat... | mit |
mjudsp/Tsallis | examples/neighbors/plot_classification.py | 58 | 1790 | """
================================
Nearest Neighbors Classification
================================
Sample usage of Nearest Neighbors classification.
It will plot the decision boundaries for each class.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColorm... | bsd-3-clause |
kingsBSD/MobileMinerPlugin | ckanext-mobileminer/ckanext/mobileminer/st_cluster.py | 1 | 5541 | # Licensed under the Apache License Version 2.0: http://www.apache.org/licenses/LICENSE-2.0.txt
__author__ = 'Giles Richard Greenway'
import dateutil.parser
import time
#import numpy as np
from itertools import tee, izip
#from sklearn import preprocessing
#from sklearn.cluster import KMeans
import requests
def vecto... | apache-2.0 |
schets/scikit-learn | examples/cluster/plot_kmeans_silhouette_analysis.py | 242 | 5885 | """
===============================================================================
Selecting the number of clusters with silhouette analysis on KMeans clustering
===============================================================================
Silhouette analysis can be used to study the separation distance between the... | bsd-3-clause |
jkarnows/scikit-learn | benchmarks/bench_multilabel_metrics.py | 86 | 7286 | #!/usr/bin/env python
"""
A comparison of multilabel target formats and metrics over them
"""
from __future__ import division
from __future__ import print_function
from timeit import timeit
from functools import partial
import itertools
import argparse
import sys
import matplotlib.pyplot as plt
import scipy.sparse as... | bsd-3-clause |
arkadoel/AprendiendoPython | Udacity_tutorial/p5.py | 1 | 2164 | from pandas import DataFrame, Series
#################
# Syntax Reminder:
#
# The following code would create a two-column pandas DataFrame
# named df with columns labeled 'name' and 'age':
#
# people = ['Sarah', 'Mike', 'Chrisna']
# ages = [28, 32, 25]
# df = DataFrame({'name' : Series(people),
# 'a... | gpl-3.0 |
sgenoud/scikit-learn | sklearn/naive_bayes.py | 1 | 15049 | # -*- coding: utf-8 -*-
"""
The :mod:`sklearn.naive_bayes` module implements Naive Bayes algorithms. These
are supervised learning methods based on applying Bayes' theorem with strong
(naive) feature independence assumptions.
"""
# Author: Vincent Michel <vincent.michel@inria.fr>
# Minor fixes by Fabian Pedre... | bsd-3-clause |
Obus/scikit-learn | sklearn/metrics/cluster/tests/test_bicluster.py | 394 | 1770 | """Testing for bicluster metrics module"""
import numpy as np
from sklearn.utils.testing import assert_equal, assert_almost_equal
from sklearn.metrics.cluster.bicluster import _jaccard
from sklearn.metrics import consensus_score
def test_jaccard():
a1 = np.array([True, True, False, False])
a2 = np.array([T... | bsd-3-clause |
rbaravalle/imfractal | tests/testcomparison.py | 1 | 8144 | """
Copyright (c) 2013 Rodrigo Baravalle
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following... | bsd-3-clause |
RachitKansal/scikit-learn | sklearn/metrics/cluster/supervised.py | 207 | 27395 | """Utilities to evaluate the clustering performance of models
Functions named as *_score return a scalar value to maximize: the higher the
better.
"""
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Wei LI <kuantkid@gmail.com>
# Diego Molla <dmolla-aliod@gmail.com>
# License: BSD 3 clause
fr... | bsd-3-clause |
jakobworldpeace/scikit-learn | examples/tree/plot_tree_regression.py | 95 | 1516 | """
===================================================================
Decision Tree Regression
===================================================================
A 1D regression with decision tree.
The :ref:`decision trees <tree>` is
used to fit a sine curve with addition noisy observation. As a result, it
learns ... | bsd-3-clause |
vascotenner/holoviews | holoviews/core/data/pandas.py | 1 | 8030 | from __future__ import absolute_import
from distutils.version import LooseVersion
try:
import itertools.izip as zip
except ImportError:
pass
import numpy as np
import pandas as pd
from .interface import Interface
from ..dimension import Dimension
from ..element import Element, NdElement
from ..dimension imp... | bsd-3-clause |
naturali/tensorflow | tensorflow/contrib/learn/python/learn/tests/dataframe/feeding_functions_test.py | 30 | 4777 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
MatthieuBizien/scikit-learn | sklearn/decomposition/tests/test_factor_analysis.py | 112 | 3203 | # Author: Christian Osendorfer <osendorf@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD3
import numpy as np
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing im... | bsd-3-clause |
ElDeveloper/scikit-learn | sklearn/tests/test_pipeline.py | 29 | 15239 | """
Test the pipeline module.
"""
import numpy as np
from scipy import sparse
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_raises, assert_raises_regex, assert_raise_message
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_false
from sklearn... | bsd-3-clause |
AIML/scikit-learn | sklearn/ensemble/tests/test_forest.py | 57 | 35265 | """
Testing for the forest module (sklearn.ensemble.forest).
"""
# Authors: Gilles Louppe,
# Brian Holt,
# Andreas Mueller,
# Arnaud Joly
# License: BSD 3 clause
import pickle
from collections import defaultdict
from itertools import product
import numpy as np
from scipy.sparse import csr_... | bsd-3-clause |
rrohan/scikit-learn | sklearn/metrics/cluster/tests/test_bicluster.py | 394 | 1770 | """Testing for bicluster metrics module"""
import numpy as np
from sklearn.utils.testing import assert_equal, assert_almost_equal
from sklearn.metrics.cluster.bicluster import _jaccard
from sklearn.metrics import consensus_score
def test_jaccard():
a1 = np.array([True, True, False, False])
a2 = np.array([T... | bsd-3-clause |
linebp/pandas | pandas/tests/io/formats/test_to_latex.py | 1 | 12983 | from datetime import datetime
import pytest
import pandas as pd
from pandas import DataFrame, compat, Series
from pandas.util import testing as tm
from pandas.compat import u
import codecs
@pytest.fixture
def frame():
return DataFrame(tm.getSeriesData())
class TestToLatex(object):
def test_to_latex_filen... | bsd-3-clause |
EtienneCmb/tensorpac | examples/misc/plot_frequency_vectors.py | 1 | 1758 | """
========================
Define frequency vectors
========================
In Tensorpac, you can define your phase and amplitude vectors in sevral ways :
* Manually define one band (ex : [2, 4])
* Define multiple bands using a list/tuple/array (ex : [[2, 4], [5, 7]])
* Using a (start, stop width step) definition.... | bsd-3-clause |
emotrix/Emotrix | emotrix/emotrix.py | 1 | 10735 | # -*- coding: utf-8 -*-
# **********************************************************************************************************************
# Archivo: emotrix.py
# Proposito: Este archivo es el encargado de gestionar todas las llamadas al API, para trabajar con EMOTRIX
# Autor: Henzer Garcia 12538... | bsd-2-clause |
linksuccess/linksuccess | parsingframework/HypTrails.py | 1 | 6551 | # further implementations can be found:
# Python: https://github.com/psinger/hyptrails
# Java: https://bitbucket.org/florian_lemmerich/hyptrails4j
# Apache spark: http://dmir.org/sparktrails/
# also see: http://www.philippsinger.info/hyptrails/
from __future__ import division
import itertools
from scipy.sparse import... | mit |
hitszxp/scikit-learn | sklearn/linear_model/tests/test_base.py | 13 | 10412 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from scipy import sparse
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.linear_model.... | bsd-3-clause |
AtsushiSakai/PythonRobotics | PathPlanning/WavefrontCPP/wavefront_coverage_path_planner.py | 1 | 6924 | """
Distance/Path Transform Wavefront Coverage Path Planner
author: Todd Tang
paper: Planning paths of complete coverage of an unstructured environment
by a mobile robot - Zelinsky et.al.
link: http://pinkwink.kr/attachment/cfile3.uf@1354654A4E8945BD13FE77.pdf
"""
import os
import sys
import matplotlib.pypl... | mit |
fabioticconi/scikit-learn | sklearn/datasets/tests/test_20news.py | 280 | 3045 | """Test the 20news downloader, if the data is available."""
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import SkipTest
from sklearn import datasets
def test_20news():
try:
data = dat... | bsd-3-clause |
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