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 |
|---|---|---|---|---|---|
dsquareindia/scikit-learn | examples/model_selection/grid_search_digits.py | 33 | 2764 | """
============================================================
Parameter estimation using grid search with cross-validation
============================================================
This examples shows how a classifier is optimized by cross-validation,
which is done using the :class:`sklearn.model_selection.GridS... | bsd-3-clause |
kernc/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 |
fzalkow/scikit-learn | examples/mixture/plot_gmm_classifier.py | 250 | 3918 | """
==================
GMM classification
==================
Demonstration of Gaussian mixture models for classification.
See :ref:`gmm` for more information on the estimator.
Plots predicted labels on both training and held out test data using a
variety of GMM classifiers on the iris dataset.
Compares GMMs with sp... | bsd-3-clause |
bendudson/BOUT | tools/pylib/boututils/plotdata.py | 4 | 2488 | # Plot a data set
try:
import numpy as np
import matplotlib
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
except ImportError:
print "ERROR: plotdata needs numpy and matplotlib to work"
raise
matplotlib.rcParams['xtick.direction'] = 'out'
matplotl... | gpl-3.0 |
endolith/scipy | scipy/stats/_qmc.py | 5 | 47162 | """Quasi-Monte Carlo engines and helpers."""
from __future__ import annotations
import os
import copy
import numbers
from abc import ABC, abstractmethod
import math
from typing import (
ClassVar,
List,
Optional,
overload,
TYPE_CHECKING,
)
import warnings
import numpy as np
if TYPE_CHECKING:
i... | bsd-3-clause |
ryfeus/lambda-packs | Keras_tensorflow_nightly/source2.7/numpy/lib/twodim_base.py | 10 | 25817 | """ Basic functions for manipulating 2d arrays
"""
from __future__ import division, absolute_import, print_function
from numpy.core.numeric import (
absolute, asanyarray, arange, zeros, greater_equal, multiply, ones,
asarray, where, int8, int16, int32, int64, empty, promote_types, diagonal,
nonzero
)
... | mit |
cauchycui/scikit-learn | examples/classification/plot_classifier_comparison.py | 181 | 4699 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=====================
Classifier comparison
=====================
A comparison of a several classifiers in scikit-learn on synthetic datasets.
The point of this example is to illustrate the nature of decision boundaries
of different classifiers.
This should be taken with ... | bsd-3-clause |
luo66/scikit-learn | sklearn/tests/test_grid_search.py | 83 | 28713 | """
Testing for grid search module (sklearn.grid_search)
"""
from collections import Iterable, Sized
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.externals.six.moves import xrange
from itertools import chain, product
import pickle
import sys
import numpy as np
import scipy.sparse as sp
... | bsd-3-clause |
ultimateprogramer/formhub | odk_viewer/tests/test_exports.py | 4 | 88316 | from sys import stdout
import os
import datetime
import json
import StringIO
import csv
import tempfile
import zipfile
import shutil
from openpyxl import load_workbook
from time import sleep
from pyxform.builder import create_survey_from_xls
from django.conf import settings
from main.tests.test_base import MainTestCase... | bsd-2-clause |
argriffing/scipy | scipy/signal/wavelets.py | 67 | 10523 | from __future__ import division, print_function, absolute_import
import numpy as np
from numpy.dual import eig
from scipy.special import comb
from scipy import linspace, pi, exp
from scipy.signal import convolve
__all__ = ['daub', 'qmf', 'cascade', 'morlet', 'ricker', 'cwt']
def daub(p):
"""
The coefficient... | bsd-3-clause |
CivilNet/Gemfield | src/python/caffe2/resnet50_gemfield.py | 1 | 11628 | # Author: Gemfield
import argparse
import numpy as np
import time
import os
import sys
import cv2
from matplotlib import pyplot
from caffe2.python import core,workspace,utils,net_drawer,cnn,optimizer,model_helper,brew,visualize
from caffe2.proto import caffe2_pb2
from caffe2.python.modeling.initializers import Initial... | gpl-3.0 |
kanhua/pypvcell | tests/test_filters.py | 1 | 3099 | __author__ = 'kanhua'
import unittest
from illumination import BpFilter, qe_filter, illumination
import numpy as np
from photocurrent import gen_square_qe_array
import matplotlib.pyplot as plt
from units_system import UnitsSystem
us = UnitsSystem()
# TODO Need to do some work here. Apprently this test class does not... | apache-2.0 |
victor-prado/broker-manager | environment/lib/python3.5/site-packages/pandas/tseries/common.py | 7 | 7836 | """
datetimelike delegation
"""
import numpy as np
from pandas.types.common import (_NS_DTYPE, _TD_DTYPE,
is_period_arraylike,
is_datetime_arraylike, is_integer_dtype,
is_datetime64_dtype, is_datetime64tz_dtype,
... | mit |
pnedunuri/scikit-learn | examples/model_selection/plot_roc_crossval.py | 247 | 3253 | """
=============================================================
Receiver Operating Characteristic (ROC) with cross validation
=============================================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality using cross-validation.
ROC curv... | bsd-3-clause |
vshtanko/scikit-learn | examples/model_selection/plot_train_error_vs_test_error.py | 349 | 2577 | """
=========================
Train error vs Test error
=========================
Illustration of how the performance of an estimator on unseen data (test data)
is not the same as the performance on training data. As the regularization
increases the performance on train decreases while the performance on test
is optim... | bsd-3-clause |
dgies/incubator-airflow | airflow/contrib/hooks/salesforce_hook.py | 30 | 12110 | # -*- coding: utf-8 -*-
#
# 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 writing, software
... | apache-2.0 |
jonycgn/scipy | scipy/stats/morestats.py | 1 | 82932 | # Author: Travis Oliphant, 2002
#
# Further updates and enhancements by many SciPy developers.
#
from __future__ import division, print_function, absolute_import
import math
import warnings
from collections import namedtuple
import numpy as np
from numpy import (isscalar, r_, log, sum, around, unique, asarray,
... | bsd-3-clause |
josephbakarji/Gluvn | gluvn_python/run2hands.py | 1 | 1290 | from __init__ import keyboard_portname, EXPDIR, testDir, settingsDir, figDir, portL, portR, baud
from run_glove import RunGlove
from data_analysis import Analyze, Stats, Analyze2Hands
from learning import Learn
import numpy as np
import matplotlib.pyplot as plt
import sys
import copy
class play2hands:
def __init_... | mit |
TeamHG-Memex/eli5 | eli5/formatters/as_dataframe.py | 1 | 6571 | from itertools import chain
from typing import Any, Dict, List, Optional
import warnings
import pandas as pd
import eli5
from eli5.base import (
Explanation, FeatureImportances, TargetExplanation,
TransitionFeatureWeights,
)
from eli5.base_utils import singledispatch
def explain_weights_df(estimator, **kwar... | mit |
mayblue9/bokeh | bokeh/charts/builder/tests/test_area_builder.py | 33 | 3666 | """ This is the Bokeh charts testing interface.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with thi... | bsd-3-clause |
bgroveben/python3_machine_learning_projects | oreilly_GANs_for_beginners/oreilly_GANs_for_beginners/probabilistic_programming_from_scratch/main.py | 3 | 8309 | import itertools
import random
import matplotlib.pyplot as plt
### Probabilistic Programming From Scratch ###
## A simple algorithm for Bayesian inference ##
# Let's take a specific data analysis problem: a simple A/B test for a website.
# Suppose our site has two layouts.
# During our test, 4% of visitors to layout... | mit |
raincoatrun/basemap | examples/plotmap_oo.py | 4 | 2650 | # make plot of etopo bathymetry/topography data on
# lambert conformal conic map projection, drawing coastlines, state and
# country boundaries, and parallels/meridians.
# the data is interpolated to the native projection grid.
##################################
# pyplot/pylab-free version of plotmap.py
#############... | gpl-2.0 |
blokeley/forcelib | setup.py | 1 | 3321 | """Setup commands for the forcelib package.
See:
https://packaging.python.org/en/latest/distributing.html
https://github.com/pypa/sampleproject
"""
# Always prefer setuptools over distutils
from setuptools import setup
# To use a consistent encoding
from codecs import open
import io
from os import path
import re
he... | mit |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/sklearn/linear_model/stochastic_gradient.py | 3 | 55745 | # Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)
# Mathieu Blondel (partial_fit support)
#
# License: BSD 3 clause
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import warnings
from abc import ABCMeta, abstractmethod
from ..external... | mit |
GaryLv/GaryLv.github.io | codes/k-nearest neighbours/knn1.py | 1 | 2049 | # -*- coding: utf-8 -*-
"""
knn分类iris的前两个属性，并可视化分类结果
"""
from sklearn.datasets import load_iris
from collections import Counter
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
iris = load_iris()
# X_train, X_test, y_train, y_test = cross_validation.train_test_split(iris.d... | apache-2.0 |
silicon-beach/news-in-short | summarizer/train2.7.py | 1 | 11554 | import os, random, sys, h5py
import cPickle as pickle
from sklearn.model_selection import train_test_split
import numpy as np
from keras.preprocessing import sequence
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Activation, Dropout, RepeatVector, Lambda
from ... | mit |
linebp/pandas | asv_bench/benchmarks/packers.py | 6 | 9228 | from .pandas_vb_common import *
from numpy.random import randint
import pandas as pd
from collections import OrderedDict
from pandas.compat import BytesIO
import sqlite3
import os
from sqlalchemy import create_engine
import numpy as np
from random import randrange
class _Packers(object):
goal_time = 0.2
def _... | bsd-3-clause |
joshamilton/Hamilton_acI_2017 | code/auxotrophies/01auxotrophyCOGs.py | 1 | 4852 | ###############################################################################
# auxotrophyCOGs.py
# Copyright (c) 2017, Joshua J Hamilton and Katherine D McMahon
# Affiliation: Department of Bacteriology
# University of Wisconsin-Madison, Madison, Wisconsin, USA
# URL: http://http://mcmahonlab.wisc.edu/
... | mit |
imprm/nummet_I | esenummet.py | 1 | 7807 | import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import scipy.optimize as scop
import scipy.interpolate as scip
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
# ================
# NONLINER SESSION
# ===============... | mit |
mbonsma/phageParser | parserscripts/filterByExpect.py | 3 | 2149 | # filterByExpect_all_v2
# for each xml output file, use NCBIXML to extract the important things
# Note: this requires blast output in xml format (set 'outfmt = 5')
"""
USAGE:
python filterByExpect.py <indir> <outdir>
"""
import os
import sys
import pandas as pd
def parse_blast(resultfile):
"""takes in the BLA... | mit |
p0cisk/Quantum-GIS | python/plugins/processing/algs/qgis/BarPlot.py | 7 | 3278 | # -*- coding: utf-8 -*-
"""
***************************************************************************
BarPlot.py
---------------------
Date : January 2013
Copyright : (C) 2013 by Victor Olaya
Email : volayaf at gmail dot com
******************************... | gpl-2.0 |
moinulkuet/machine-learning | Part 3 - Classification/Section 16 - Support Vector Machine (SVM)/classification_template.py | 37 | 2538 | # Classification template
# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Importing the dataset
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, 4].values
# Splitting the dataset into the Training set and Test se... | gpl-3.0 |
pradyu1993/scikit-learn | examples/manifold/plot_mds.py | 3 | 2421 | """
=========================
Multi-dimensional scaling
=========================
An illustration of the metric and non-metric MDS on generated noisy data.
The reconstructed points using the metric MDS and non metric MDS are slightly
shifted to avoid overlapping.
"""
# Author: Nelle Varoquaux <nelle.varoquaux@gmail.... | bsd-3-clause |
bkj/wit | wit/dep/forum-testing.py | 2 | 3544 | import pandas as pd
import urllib2
from pprint import pprint
from matplotlib import pyplot as plt
from keras.layers.convolutional import Convolution1D, MaxPooling1D
import sys
sys.path.append('/Users/BenJohnson/projects/what-is-this/wit/')
from wit import *
# --
# Config + Init
num_features = 500 # Character
ma... | apache-2.0 |
rajul/mne-python | mne/viz/tests/test_topomap.py | 3 | 8994 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
#
# License: Simplified BSD
import os.path as op
import warnings
import numpy as np
from ... | bsd-3-clause |
JDTimlin/QSO_Clustering | highz_clustering/classification/SpIESHighzQuasarsS82all_JTmultiproc_lz.py | 1 | 6238 | from astropy.table import Table
import numpy as np
import matplotlib.pyplot as plt
from time import time
# Read in training file
#data = Table.read('GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean.fits')
#My path to the Training file
#data = Table.read('../Training_set/GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_star... | mit |
tknomanzr/scripts | tint2/executors/bitcoin/show_bitcoin_detail.py | 1 | 1333 | #! /usr/bin/python
# A simple utility that will calculate bitcoin balance from an electrum wallet.
# Requires: gdax, pandas, matplotlib
# Assumes: you currently have bitcoin stored in an electrum wallet.
# Author: William Bradley
# BunsenLabs Forum Handle: tknomanzr
# License: GPL3.0.
import gdax
import pandas as pd
i... | gpl-3.0 |
shyamalschandra/scikit-learn | examples/model_selection/plot_confusion_matrix.py | 47 | 2495 | """
================
Confusion matrix
================
Example of confusion matrix usage to evaluate the quality
of the output of a classifier on the iris data set. The
diagonal elements represent the number of points for which
the predicted label is equal to the true label, while
off-diagonal elements are those that ... | bsd-3-clause |
padraic-padraic/bad-boids | boids/tests/test_boids.py | 1 | 4651 | import matplotlib
matplotlib.use('Agg')
from boids.flock import Flock
from mock import Mock, patch
from nose.tools import assert_equal, assert_almost_equal
import os
import yaml
def test_bad_boids_regression():
regression_data=yaml.load(open(os.path.join(os.path.dirname(__file__),
... | gpl-2.0 |
zaxtax/scikit-learn | examples/ensemble/plot_gradient_boosting_regression.py | 87 | 2510 | """
============================
Gradient Boosting regression
============================
Demonstrate Gradient Boosting on the Boston housing dataset.
This example fits a Gradient Boosting model with least squares loss and
500 regression trees of depth 4.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prett... | bsd-3-clause |
amueller/astro_hackweek | plots/plot_2d_separator.py | 41 | 1513 | import numpy as np
import matplotlib.pyplot as plt
def plot_2d_separator(classifier, X, fill=False, ax=None, eps=None):
if eps is None:
eps = X.std() / 2.
x_min, x_max = X[:, 0].min() - eps, X[:, 0].max() + eps
y_min, y_max = X[:, 1].min() - eps, X[:, 1].max() + eps
xx = np.linspace(x_min, x_m... | bsd-2-clause |
carlgogo/vip_exoplanets | vip_hci/stats/im_stats.py | 2 | 3777 | #! /usr/bin/env python
"""
Module for image statistics.
"""
__author__ = 'Carlos Alberto Gomez Gonzalez'
__all__ = ['frame_histo_stats',
'frame_average_radprofile']
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from ..var import frame_center
from ..conf.utils_conf import chec... | bsd-3-clause |
TomAugspurger/pandas | pandas/core/computation/eval.py | 1 | 13106 | """
Top level ``eval`` module.
"""
import tokenize
from typing import Optional
import warnings
from pandas._libs.lib import no_default
from pandas.util._validators import validate_bool_kwarg
from pandas.core.computation.engines import _engines
from pandas.core.computation.expr import Expr, _parsers
from pandas.core.... | bsd-3-clause |
MaryanMorel/utopical-brotherhood | actors/Learner.py | 1 | 1576 | #! /usr/bin/python2
# -*- coding: utf8 -*-
import pykka
import time
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
from sklearn.pipeline import Pipeline
from sklearn.cluster import SpectralClustering, AffinityPropagation
from datetime import datetime
class Learner(pykka.ThreadingActor):... | mit |
jungla/ICOM-fluidity-toolbox | Detectors/plot_Tracer_v.py | 1 | 1660 | import os, sys
import fio
import numpy as np
import matplotlib as mpl
mpl.use('ps')
import matplotlib.pyplot as plt
import myfun
#label = 'm_25_2_512'
label = 'm_25_1_particles'
dayi = 0 #10*24*2
dayf = 600 #10*24*4
days = 1
#label = sys.argv[1]
#basename = sys.argv[2]
#dayi = int(sys.argv[3])
#dayf = int(sys.a... | gpl-2.0 |
cs60050/TeamGabru | helpers/PlotDataAndResults.py | 1 | 1089 | import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import numpy as np
def plot_data_points(first_dimension, second_dimension, result_labels): #result_labels = 0 or 1
figure = plt.figure(figsize=(8,8))
cm = plt.cm.RdBu
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
plt.... | mit |
blachlylab/mucor | build/scripts-2.7/mucor.py | 1 | 33732 | #!/home/OSUMC.EDU/blac96/source/venv/test_mucor_pip/bin/python
# -*- coding: utf8
# Copyright 2013-2015 James S Blachly, MD and The Ohio State University
#
# This file is part of Mucor.
#
# Mucor is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License a... | gpl-3.0 |
tdhopper/scikit-learn | examples/decomposition/plot_ica_vs_pca.py | 306 | 3329 | """
==========================
FastICA on 2D point clouds
==========================
This example illustrates visually in the feature space a comparison by
results using two different component analysis techniques.
:ref:`ICA` vs :ref:`PCA`.
Representing ICA in the feature space gives the view of 'geometric ICA':
ICA... | bsd-3-clause |
Vvucinic/Wander | venv_2_7/lib/python2.7/site-packages/pandas/stats/math.py | 25 | 3253 | # pylint: disable-msg=E1103
# pylint: disable-msg=W0212
from __future__ import division
from pandas.compat import range
import numpy as np
import numpy.linalg as linalg
def rank(X, cond=1.0e-12):
"""
Return the rank of a matrix X based on its generalized inverse,
not the SVD.
"""
X = np.asarray(... | artistic-2.0 |
johnbachman/indra | indra/assemblers/indranet/net.py | 3 | 14200 | import json
import logging
from os import path
import numpy as np
import pandas as pd
import networkx as nx
from decimal import Decimal
import indra
from indra.belief import SimpleScorer
from indra.statements import Evidence
from indra.statements import Statement
logger = logging.getLogger(__name__)
simple_scorer = ... | bsd-2-clause |
GeoffEvans/aol_model | aol_model/aod_model_tune.py | 1 | 2450 | # take the expt data, fit model trnasducer using least squares
# use smoothing splene to join up points
# check second order
import numpy as np
from scipy.constants import pi
from aol_model.ray import Ray
from aol_model.acoustics import Acoustics
import expt_data as data
import aol_model.set_up_utils as setup
import sc... | gpl-3.0 |
jonyroda97/redbot-amigosprovaveis | lib/mpl_toolkits/axes_grid1/inset_locator.py | 2 | 18678 | """
A collection of functions and objects for creating or placing inset axes.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib import docstring
import six
from matplotlib.offsetbox import AnchoredOffsetbox
from matplotlib.patches import Pa... | gpl-3.0 |
shahankhatch/scikit-learn | examples/neural_networks/plot_rbm_logistic_classification.py | 258 | 4609 | """
==============================================================
Restricted Boltzmann Machine features for digit classification
==============================================================
For greyscale image data where pixel values can be interpreted as degrees of
blackness on a white background, like handwritten... | bsd-3-clause |
asnorkin/sentiment_analysis | site/lib/python2.7/site-packages/sklearn/utils/tests/test_metaestimators.py | 86 | 2304 | from sklearn.utils.testing import assert_true, assert_false
from sklearn.utils.metaestimators import if_delegate_has_method
class Prefix(object):
def func(self):
pass
class MockMetaEstimator(object):
"""This is a mock meta estimator"""
a_prefix = Prefix()
@if_delegate_has_method(delegate="a... | mit |
mengyun1993/RNN-binary | rnn07.py | 1 | 27067 | """ Vanilla RNN
@author Graham Taylor
"""
import numpy as np
import theano
import theano.tensor as T
from sklearn.base import BaseEstimator
import logging
import time
import os
import datetime
import pickle as pickle
import math
import matplotlib.pyplot as plt
plt.ion()
mode = theano.Mode(linker='cvm')
#mode = '... | bsd-3-clause |
chiotlune/ext | gnuradio-3.7.0.1/gr-filter/examples/interpolate.py | 58 | 8816 | #!/usr/bin/env python
#
# Copyright 2009,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your ... | gpl-2.0 |
nwillemse/misc-scripts | adj-split-div/plot_ohlc.py | 1 | 1355 | #!/usr/bin/env python2
"""
plot_ohlc.py
Created on Sun Jul 17 19:53:29 2016
@author: nwillemse
"""
import matplotlib
matplotlib.use('Qt4Agg')
import click
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from bokeh.plotting import figure, output_file, show
from os import path
@click.comman... | mit |
dsullivan7/scikit-learn | examples/covariance/plot_sparse_cov.py | 300 | 5078 | """
======================================
Sparse inverse covariance estimation
======================================
Using the GraphLasso estimator to learn a covariance and sparse precision
from a small number of samples.
To estimate a probabilistic model (e.g. a Gaussian model), estimating the
precision matrix, t... | bsd-3-clause |
lucidfrontier45/scikit-learn | sklearn/decomposition/tests/test_dict_learning.py | 2 | 6643 | import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from ... | bsd-3-clause |
bloyl/mne-python | tutorials/evoked/30_eeg_erp.py | 3 | 18032 | """
.. _tut-erp:
EEG processing and Event Related Potentials (ERPs)
==================================================
This tutorial shows how to perform standard ERP analyses in MNE-Python. Most of
the material here is covered in other tutorials too, but for convenience the
functions and methods most useful for ERP ... | bsd-3-clause |
RyanChinSang/LeagueLatency | History/Raw/v2.0a Stable/LoLPing.py | 1 | 5603 | import sys
import subprocess
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib import style
from datetime import datetime
from matplotlib.widgets import RadioButtons
CREATE_NO_WINDOW = 0x08000000
style.use('seaborn-darkgrid')
ltimes = []
lpings = []
avg_lis = []... | gpl-3.0 |
bitemyapp/ggplot | ggplot/components/loess.py | 13 | 1602 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
"""
loess(formula, data, weights, subset, na.action, model = FALSE,
span = 0.75, enp.target, degree = 2,
parametric = FALSE, drop.square = FALSE, normalize = TRUE,
family = c("gaussian", "symme... | bsd-2-clause |
pv/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 |
DSLituiev/scikit-learn | examples/svm/plot_svm_anova.py | 85 | 2024 | """
=================================================
SVM-Anova: SVM with univariate feature selection
=================================================
This example shows how to perform univariate feature selection before running a
SVC (support vector classifier) to improve the classification scores.
"""
print(__doc_... | bsd-3-clause |
eblur/AstroHackWeek2015 | day3-machine-learning/plots/plot_interactive_forest.py | 40 | 1279 | import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.ensemble import RandomForestClassifier
X, y = make_blobs(centers=[[0, 0], [1, 1]], random_state=61526, n_samples=50)
def plot_forest(max_depth=1):
plt.figure()
ax = plt.gca()
h = 0.02
x_min, x_m... | gpl-2.0 |
dhruv13J/scikit-learn | sklearn/ensemble/tests/test_bagging.py | 127 | 25365 | """
Testing for the bagging ensemble module (sklearn.ensemble.bagging).
"""
# Author: Gilles Louppe
# License: BSD 3 clause
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.te... | bsd-3-clause |
feilchenfeldt/enrichme | setup.py | 1 | 1752 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from setuptools import setup, find_packages # Always prefer setuptools over distutils
from codecs import open # To use a consistent encoding
import sys, os
import enrichme
def publish():
"""Publish to PyPi"""
os.system("python setup.py bdist_wheel sdist upload"... | mit |
jkarnows/scikit-learn | sklearn/tests/test_dummy.py | 129 | 17774 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from sklearn.base import clone
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_eq... | bsd-3-clause |
LiaoPan/scikit-learn | examples/linear_model/plot_sgd_weighted_samples.py | 344 | 1458 | """
=====================
SGD: Weighted samples
=====================
Plot decision function of a weighted dataset, where the size of points
is proportional to its weight.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
# we create 20 points
np.random.seed(0)
X ... | bsd-3-clause |
massmutual/scikit-learn | sklearn/linear_model/tests/test_sgd.py | 1 | 44284 | 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 |
unsiloai/syntaxnet-ops-hack | tensorflow/python/estimator/inputs/queues/feeding_queue_runner_test.py | 116 | 5164 | # Copyright 2017 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 |
stylianos-kampakis/scikit-learn | examples/model_selection/plot_precision_recall.py | 249 | 6150 | """
================
Precision-Recall
================
Example of Precision-Recall metric to evaluate classifier output quality.
In information retrieval, precision is a measure of result relevancy, while
recall is a measure of how many truly relevant results are returned. A high
area under the curve represents both ... | bsd-3-clause |
terkkila/scikit-learn | examples/linear_model/plot_polynomial_interpolation.py | 251 | 1895 | #!/usr/bin/env python
"""
========================
Polynomial interpolation
========================
This example demonstrates how to approximate a function with a polynomial of
degree n_degree by using ridge regression. Concretely, from n_samples 1d
points, it suffices to build the Vandermonde matrix, which is n_samp... | bsd-3-clause |
justincely/classwork | UMD/AST630/HW2/hw2.py | 1 | 4890 | """Functions and script for problems in HW2
For problem 3:
--------------
class oblate:
provides the mechanics for calulating the desired orbital values from an
initilized object of a given mass, radius, and moments.
function problem_3():
function to output results using the oblate class on given scenarios
... | bsd-3-clause |
wateraccounting/wa | Collect/MOD17/DataAccessGPP.py | 1 | 15230 | # -*- coding: utf-8 -*-
"""
Authors: Tim Hessels
UNESCO-IHE 2016
Contact: t.hessels@unesco-ihe.org
Repository: https://github.com/wateraccounting/wa
Module: Collect/MOD17
"""
# import general python modules
import os
import numpy as np
import pandas as pd
import gdal
import urllib
import urllib2
from bs4 impo... | apache-2.0 |
nguyentu1602/statsmodels | statsmodels/examples/ex_univar_kde.py | 34 | 5127 | """
This example tests the nonparametric estimator
for several popular univariate distributions with the different
bandwidth selction methods - CV-ML; CV-LS; Scott's rule of thumb.
Produces six different plots for each distribution
1) Beta
2) f
3) Pareto
4) Laplace
5) Weibull
6) Poisson
"""
from __future__ import p... | bsd-3-clause |
richford/AFQ-viz | afqbrowser/tests/test_browser.py | 3 | 1236 | import os.path as op
import afqbrowser as afqb
import tempfile
import json
import pandas as pd
import numpy.testing as npt
def test_assemble():
data_path = op.join(afqb.__path__[0], 'site')
tdir = tempfile.mkdtemp()
afqb.assemble(op.join(data_path, 'client', 'data', 'afq.mat'),
target=td... | bsd-3-clause |
mjudsp/Tsallis | sklearn/datasets/base.py | 11 | 23497 | """
Base IO code for all datasets
"""
# Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
# 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
import os
import csv
import sys
import shutil
from os import environ... | bsd-3-clause |
Lawrence-Liu/scikit-learn | examples/decomposition/plot_incremental_pca.py | 244 | 1878 | """
===============
Incremental PCA
===============
Incremental principal component analysis (IPCA) is typically used as a
replacement for principal component analysis (PCA) when the dataset to be
decomposed is too large to fit in memory. IPCA builds a low-rank approximation
for the input data using an amount of memo... | bsd-3-clause |
trdean/grEME | gr-filter/examples/fir_filter_fff.py | 6 | 4018 | #!/usr/bin/env python
#
# Copyright 2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# ... | gpl-3.0 |
ssaeger/scikit-learn | examples/covariance/plot_covariance_estimation.py | 99 | 5074 | """
=======================================================================
Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood
=======================================================================
When working with covariance estimation, the usual approach is to use
a maximum likelihood estimator,... | bsd-3-clause |
dingocuster/scikit-learn | sklearn/utils/tests/test_linear_assignment.py | 421 | 1349 | # Author: Brian M. Clapper, G Varoquaux
# License: BSD
import numpy as np
# XXX we should be testing the public API here
from sklearn.utils.linear_assignment_ import _hungarian
def test_hungarian():
matrices = [
# Square
([[400, 150, 400],
[400, 450, 600],
[300, 225, 300]],
... | bsd-3-clause |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/examples/pylab_examples/barchart_demo.py | 6 | 1134 |
#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
N = 5
menMeans = (20, 35, 30, 35, 27)
menStd = (2, 3, 4, 1, 2)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars
plt.subplot(111)
rects1 = plt.bar(ind, menMeans, width,
color... | mit |
lewisodriscoll/sasview | src/sas/sasgui/plottools/SimpleFont.py | 3 | 4024 |
"""
This software was developed by Institut Laue-Langevin as part of
Distributed Data Analysis of Neutron Scattering Experiments (DANSE).
Copyright 2012 Institut Laue-Langevin
"""
# this is a dead simple dialog for getting font family, size,style and weight
import wx
from matplotlib.font_manager import FontPropert... | bsd-3-clause |
qiime2-plugins/feature-table | q2_feature_table/_summarize/_vega_spec.py | 1 | 12366 | # ----------------------------------------------------------------------------
# Copyright (c) 2016-2020, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | bsd-3-clause |
arielmakestuff/loadlimit | test/unit/stat/test_timeseries.py | 1 | 6054 | # -*- coding: utf-8 -*-
# test/unit/stat/test_timeseries.py
# Copyright (C) 2016 authors and contributors (see AUTHORS file)
#
# This module is released under the MIT License.
"""Test timeseries()"""
# ============================================================================
# Imports
# ===========================... | mit |
Rossonero/bmlswp | ch06/04_sent.py | 22 | 10125 | # This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
#
# This script trains tries to tweak hyperparameters to improve P/R AUC
#
import time
start_time = ti... | mit |
rs2/pandas | pandas/tests/reshape/test_melt.py | 1 | 37168 | import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, lreshape, melt, wide_to_long
import pandas._testing as tm
class TestMelt:
def setup_method(self, method):
self.df = tm.makeTimeDataFrame()[:10]
self.df["id1"] = (self.df["A"] > 0).astype(np.int64)
self.df["... | bsd-3-clause |
all-umass/metric-learn | setup.py | 1 | 1748 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from setuptools import setup
import os
import io
version = {}
with io.open(os.path.join('metric_learn', '_version.py')) as fp:
exec(fp.read(), version)
# Get the long description from README.md
with io.open('README.rst', encoding='utf-8') as f:
long_description = f.re... | mit |
WangWenjun559/Weiss | summary/sumy/sklearn/tests/test_multiclass.py | 72 | 24581 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_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_false
from sklearn.utils.testing ... | apache-2.0 |
siutanwong/scikit-learn | examples/model_selection/plot_learning_curve.py | 250 | 4171 | """
========================
Plotting Learning Curves
========================
On the left side the learning curve of a naive Bayes classifier is shown for
the digits dataset. Note that the training score and the cross-validation score
are both not very good at the end. However, the shape of the curve can be found
in ... | bsd-3-clause |
tuulos/luigi | examples/pyspark_wc.py | 56 | 3361 | # -*- coding: utf-8 -*-
#
# Copyright 2012-2015 Spotify AB
#
# 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... | apache-2.0 |
alexsavio/scikit-learn | examples/gaussian_process/plot_gpr_prior_posterior.py | 104 | 2878 | """
==========================================================================
Illustration of prior and posterior Gaussian process for different kernels
==========================================================================
This example illustrates the prior and posterior of a GPR with different
kernels. Mean, st... | bsd-3-clause |
fxia22/pointGAN | show_gan_rnn2.py | 1 | 1289 | from __future__ import print_function
from show3d_balls import *
import argparse
import os
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torc... | mit |
rishikksh20/scikit-learn | examples/applications/plot_outlier_detection_housing.py | 110 | 5681 | """
====================================
Outlier detection on a real data set
====================================
This example illustrates the need for robust covariance estimation
on a real data set. It is useful both for outlier detection and for
a better understanding of the data structure.
We selected two sets o... | bsd-3-clause |
gxyang/hstore | scripts/anticache/plotter.py | 9 | 2968 | #!/usr/bin/env python
import os
import sys
import csv
import logging
import matplotlib.pyplot as plot
import pylab
OPT_GRAPH_WIDTH = 1200
OPT_GRAPH_HEIGHT = 600
OPT_GRAPH_DPI = 100
## ==============================================
## main
## ==============================================
if __name__ == '__main__':
... | gpl-3.0 |
kyleabeauchamp/HMCNotes | code/optimize/old/test_optimize_hyper.py | 2 | 3950 | import sklearn.grid_search
import scipy.stats.distributions
import lb_loader
import simtk.openmm.app as app
import numpy as np
import pandas as pd
import simtk.openmm as mm
from simtk import unit as u
from openmmtools import hmc_integrators, testsystems
pd.set_option('display.width', 1000)
n_steps = 1500
temperature =... | gpl-2.0 |
zfrenchee/pandas | pandas/core/dtypes/api.py | 16 | 2399 | # flake8: noqa
import sys
from .common import (pandas_dtype,
is_dtype_equal,
is_extension_type,
# categorical
is_categorical,
is_categorical_dtype,
# interval
is_interva... | bsd-3-clause |
m-takeuchi/ilislife_wxp | test/test3.py | 2 | 3375 | #!/usr/bin/env python
"""
An example of how to use wx or wxagg in an application with a custom
toolbar
"""
# matplotlib requires wxPython 2.8+
# set the wxPython version in lib\site-packages\wx.pth file
# or if you have wxversion installed un-comment the lines below
#import wxversion
#wxversion.ensureMinimal('2.8')
f... | mit |
ecohealthalliance/eidr-connect | .scripts/gen_region_to_countries.py | 1 | 3213 | import json
import pandas as pd
import requests
from io import StringIO
import re
continent_names = {
"AF": "Africa",
"AS": "Asia",
"EU": "Europe",
"NA": "North America",
"OC": "Oceania",
"SA": "South America",
"AN": "Antarctica",
}
continent_geonameids = {
"AF": "6255146",
"AS": "6... | apache-2.0 |
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