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 |
|---|---|---|---|---|---|
ycaihua/scikit-learn | sklearn/datasets/tests/test_samples_generator.py | 67 | 14842 | from __future__ import division
from collections import defaultdict
from functools import partial
import numpy as np
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
fr... | bsd-3-clause |
juanmirocks/LocText | scripts/visualize_svm_projected_features.py | 2 | 1385 | from __future__ import print_function
import random
from scipy import sparse
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.svm import SVC
from sklearn.decomposition impo... | apache-2.0 |
rsivapr/scikit-learn | sklearn/manifold/isomap.py | 12 | 7145 | """Isomap for manifold learning"""
# Author: Jake Vanderplas -- <vanderplas@astro.washington.edu>
# License: BSD 3 clause (C) 2011
import numpy as np
from ..base import BaseEstimator, TransformerMixin
from ..neighbors import NearestNeighbors, kneighbors_graph
from ..utils import check_arrays
from ..utils.graph impor... | bsd-3-clause |
costypetrisor/scikit-learn | sklearn/linear_model/tests/test_least_angle.py | 44 | 17033 | import tempfile
import shutil
import os.path as op
import warnings
from nose.tools import assert_equal
import numpy as np
from scipy import linalg
from sklearn.cross_validation import train_test_split
from sklearn.externals import joblib
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.t... | bsd-3-clause |
GeorgKunk/MachineLearning | Autoencoder_MNIST.py | 1 | 1852 | import matplotlib.pyplot as plt
from sklearn.datasets import fetch_mldata
import pickle
import os
mnist = fetch_mldata("MNIST original")
X, y = mnist.data / 255., mnist.target
X_train, X_test = X[:60000], X[60000:]
y_train, y_test = y[:60000], y[60000:]
def ae_store_name(hidden_units):
directory = "storedClassifi... | mit |
wronk/mne-python | examples/realtime/rt_feedback_server.py | 3 | 4954 | """
==============================================
Real-time feedback for decoding :: Server Side
==============================================
This example demonstrates how to setup a real-time feedback
mechanism using StimServer and StimClient.
The idea here is to display future stimuli for the class which
is pred... | bsd-3-clause |
IISH/nlgis2 | web/api/api.py | 1 | 29784 | # Copyright (C) 2014 International Institute of Social History.
# @author Vyacheslav Tykhonov
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License, version 3,
# as published by the Free Software Foundation.
#
# This program is distribute... | gpl-3.0 |
damaggu/SAMRI | samri/examples/registration_qc.py | 1 | 1190 | import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
from os import path
from samri.plotting.aggregate import registration_qc
from samri.typesetting import inline_anova
data_dir = path.join(path.dirname(path.realpath(__file__)),"../../example_data/")
df_path = path.join(data_dir,"f_re... | gpl-3.0 |
anaguma2261/scikit-learn-sample | ridge_regression.py | 1 | 2421 | #coding:utf-8
import numpy
import sys
import pickle
from sklearn.linear_model import Ridge
from sklearn.cross_validation import cross_val_score
DELIMITOR=","
def load_data(data_path="data/YearPredictionMSD.txt"):
'''
f[^ÌÇ
YearPredictionMSD
https://archive.ics.uci.edu/ml/datasets/YearPredicti... | mit |
JosmanPS/scikit-learn | examples/svm/plot_svm_scale_c.py | 223 | 5375 | """
==============================================
Scaling the regularization parameter for SVCs
==============================================
The following example illustrates the effect of scaling the
regularization parameter when using :ref:`svm` for
:ref:`classification <svm_classification>`.
For SVC classificati... | bsd-3-clause |
uglyboxer/linear_neuron | net-p3/lib/python3.5/site-packages/sklearn/neighbors/graph.py | 19 | 6904 | """Nearest Neighbors graph functions"""
# Author: Jake Vanderplas <vanderplas@astro.washington.edu>
#
# License: BSD 3 clause (C) INRIA, University of Amsterdam
import warnings
from .base import KNeighborsMixin, RadiusNeighborsMixin
from .unsupervised import NearestNeighbors
def _check_params(X, metric, p, metric_p... | mit |
mementum/backtrader | samples/tradingcalendar/tcal-intra.py | 1 | 5833 | #!/usr/bin/env python
# -*- coding: utf-8; py-indent-offset:4 -*-
###############################################################################
#
# Copyright (C) 2015-2020 Daniel Rodriguez
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License a... | gpl-3.0 |
ua-snap/downscale | old/bin/clt_ar5_model_data_preprocess.py | 2 | 12238 | #!/usr/bin/python2
# #
# pre-processing of raw downloaded CMIP5 data from the PCMDI portal to something that is standardized for
# use in later downscaling to ALFRESCO AK/Canada extent and resolution needs.
# # # # #
def group_input_filenames( prefix, root_dir ):
import fnmatch, functools, itertools, os, glob
imp... | mit |
lukeiwanski/tensorflow | tensorflow/contrib/timeseries/examples/known_anomaly.py | 14 | 7880 | # 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 |
DiamondLightSource/auto_tomo_calibration-experimental | measure_resolution/lmfit/ui/ipy_fitter.py | 7 | 10328 | import warnings
import numpy as np
from ..model import Model
from .basefitter import MPLFitter, _COMMON_DOC, _COMMON_EXAMPLES_DOC
# Note: If IPython is not available of the version is < 2,
# this module will not be imported, and a different Fitter.
import IPython
from IPython.display import display, clear_output
# ... | apache-2.0 |
Morijarti/dota-2-heatmap | heatmap_generator.py | 1 | 10141 | import json
import numpy
from heroes import HEROES
__author__ = 'yanbo'
import io
import os
from smoke.io.wrap import demo as io_wrap_demo
from smoke.replay import demo as replay_demo
from smoke.replay.const import Data
import pylab
import matplotlib
import matplotlib.image
import scipy.ndimage
import cPickle
from map... | mit |
yanlend/scikit-learn | examples/gaussian_process/plot_gp_regression.py | 253 | 4054 | #!/usr/bin/python
# -*- coding: utf-8 -*-
r"""
=========================================================
Gaussian Processes regression: basic introductory example
=========================================================
A simple one-dimensional regression exercise computed in two different ways:
1. A noise-free cas... | bsd-3-clause |
asnir/airflow | airflow/www/views.py | 3 | 95451 | # -*- 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 |
ibackus/custom_python_packages | isaac.py | 1 | 51159 | """
-----------------------------------------------
Some simple python code to be easily imported from python
-----------------------------------------------
"""
import pynbody
SimArray = pynbody.array.SimArray
pb = pynbody
import copy
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
imp... | mit |
olvhammar/GNURadio-FFTS | Software_HRC/Finalize.py | 1 | 9060 | #!/usr/bin/env python2
##################################################
# Finalize spectrums
##################################################
import ephem
import matplotlib.pyplot as plt
import numpy as np
import math
import os
import timeit
import time
import glob
import threading
import astropy
import sys
from as... | mit |
nikoonia/gem5v | util/stats/output.py | 90 | 7981 | # Copyright (c) 2005-2006 The Regents of The University of Michigan
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met: redistributions of source code must retain the above copyright
# notice, this ... | bsd-3-clause |
pxsdirac/tushare | tushare/util/dateu.py | 3 | 2480 | # -*- coding:utf-8 -*-
import datetime
import time
import pandas as pd
def year_qua(date):
mon = date[5:7]
mon = int(mon)
return[date[0:4], _quar(mon)]
def _quar(mon):
if mon in [1, 2, 3]:
return '1'
elif mon in [4, 5, 6]:
return '2'
elif mon in [7, 8, ... | bsd-3-clause |
adore-hrzz/nao-sound-classification | scripts/validator.py | 2 | 2179 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import csv
import itertools
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
def plot_confusion_matrix(cm, classes, data_type,... | gpl-3.0 |
erdc-cm/air-water-vv | 2d/numericalTanks/nonlinearWaves/postprocess_NLW_all.py | 1 | 6465 | import numpy as np
import collections as cll
import csv
import os
import matplotlib.pyplot as plt
from proteus import WaveTools as wt
from AnalysisTools import signalFilter,zeroCrossing,reflStat
#####################################################################################
folders = ['A1','A2','A3','A4... | mit |
martianstudio/stockanalysis | first_try.py | 1 | 4354 | import datetime
import numpy as np
import pandas as pd
import sklearn
from pandas.io.data import DataReader
from sklearn.linear_model import LogisticRegression
from sklearn.lda import LDA
from sklearn.qda import QDA
from sklearn.svm import SVC
"""
Feature X:
T = Today's Price
T - n = The n days before Today
[T - 5,... | apache-2.0 |
annahs/atmos_research | NC_size_distrs_with_variable_coating_bin_at_end_avg_flights.py | 1 | 17996 | from pymiecoated import Mie
import sys
import os
import numpy as np
from pprint import pprint
from datetime import datetime
import mysql.connector
import math
import matplotlib.pyplot as plt
import matplotlib.colors
import calendar
from scipy.optimize import curve_fit
cloud_droplet_conc = 0.5
min_coat = 0 #assumed m... | mit |
1kastner/analyse_weather_data | plot_weather_data/plot_precipitation_all_pws_stations.py | 1 | 4671 | """
You can limit this by
- passing the limit=n parameter to station_repository.load_all_stations()
- restricting the date range, e.g. plot_station("2016-01-01", "2016-01-31")
Depends on filter_weather_data.filters.preparation.average_husconet_temperature
-m plot_weather_data.plot_temperature_all_pws_stations
"""
i... | agpl-3.0 |
rhyolight/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/ticker.py | 69 | 37420 | """
Tick locating and formatting
============================
This module contains classes to support completely configurable tick
locating and formatting. Although the locators know nothing about
major or minor ticks, they are used by the Axis class to support major
and minor tick locating and formatting. Generic t... | agpl-3.0 |
laosiaudi/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimators_test.py | 15 | 4396 | # 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 |
valexandersaulys/prudential_insurance_kaggle | venv/lib/python2.7/site-packages/sklearn/feature_selection/__init__.py | 33 | 1159 | """
The :mod:`sklearn.feature_selection` module implements feature selection
algorithms. It currently includes univariate filter selection methods and the
recursive feature elimination algorithm.
"""
from .univariate_selection import chi2
from .univariate_selection import f_classif
from .univariate_selection import f_... | gpl-2.0 |
zhreshold/mxnet-ssd | evaluate/eval_metric.py | 1 | 11378 | import mxnet as mx
import numpy as np
import os
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
class MApMetric(mx.metric.EvalMetric):
"""
Calculate mean AP for object detection task
Parameters:
---------
ovp_thresh : float
overlap threshold for TP
use_difficul... | mit |
Odingod/mne-python | mne/surface.py | 4 | 49208 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
# Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import os
from os import path as op
import sys
from struct import pack
from glob import glob
import numpy as... | bsd-3-clause |
allisony/aplpy | aplpy/tests/test_convolve.py | 3 | 1201 | import os
import matplotlib
matplotlib.use('Agg')
import numpy as np
from astropy.tests.helper import pytest
from astropy.io import fits
from astropy.wcs import WCS as AstropyWCS
from .helpers import generate_file, generate_hdu, generate_wcs
from .. import FITSFigure
def test_convolve_default():
data = np.rand... | mit |
kambysese/mne-python | examples/datasets/spm_faces_dataset.py | 9 | 4727 | """
.. _ex-spm-faces:
==========================================
From raw data to dSPM on SPM Faces dataset
==========================================
Runs a full pipeline using MNE-Python:
- artifact removal
- averaging Epochs
- forward model computation
- source reconstruction using dSPM on the con... | bsd-3-clause |
nzufelt/lmlp_theano | solutions/cnn.py | 1 | 6290 | """
A convolutional neural network using theano.
This script was created by Nicholas Zufelt as a part of the London Machine Learning
Practice meetup.
Calling this script with an example (may not converge):
$ python cnn.py 5 5 3 2 25 100 20 1000 128 .01 .01
Parameters:
filter_height -- int, convolution layer filt... | mit |
joshloyal/scikit-learn | benchmarks/bench_isolation_forest.py | 46 | 3782 | """
==========================================
IsolationForest benchmark
==========================================
A test of IsolationForest on classical anomaly detection datasets.
"""
print(__doc__)
from time import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationFore... | bsd-3-clause |
allqoow/exerciseML | week05.py | 1 | 6505 | #!/usr/bin/python
# -*- coding: utf-8 -*-
#
# Author : allqoow
# Contact : allqoow@gmail.com
# Started on: 20161121(yyyymmdd)
# Project : exerciseML(Exercise for Machine Learning)
# H5.1.A
import matplotlib.pyplot
import math
import numpy.random
import numpy.linalg
def getNumberRows(mat):
return len(mat)
de... | mit |
modflowpy/flopydoc | docs/pysrc/tutorial1.py | 1 | 1598 | import os
import sys
import numpy as np
flopypth = os.path.join('..', '..', 'flopy3.git')
if flopypth not in sys.path:
sys.path.append(flopypth)
import flopy
#Assign name and create modflow model object
modelname = 'tutorial1'
mf = flopy.modflow.Modflow(modelname, exe_name='mf2005')
#model domain and grid definit... | bsd-3-clause |
argonnexraydetector/RoachFirmPy | Roach2DevelopmentTree/pyfiles/debug.py | 1 | 26132 | import itertools
from collections import OrderedDict
'''
execfile('debug.py')
execfile('sim928.py')
plotAllRes2File('/home/oxygen31/TMADDEN/ROACH2/datafiles/jul7_2016/res1-5bblb.pdf')
/home/oxygen31/TMADDEN/ROACH2/datafiles/jul5_2016
calcBBLOFromRFFreqs( arange(3000e6, 3200e6,20e6))
vlist = arange(10,0,-0.02)
... | gpl-2.0 |
nhejazi/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_02_sentiment.py | 104 | 3139 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a ... | bsd-3-clause |
mdepasca/miniature-adventure | util/IO.py | 1 | 12197 | import numpy as np
import pandas as pd
import subprocess
import os
import classes
def get_sn_from_file(pathToSN, magFlag=False):
"""Reads photometric data of SN from file formatted as in SNPhotCC
Keyword arguments:
pathToSN -- path to file from which extract data.
Returns:
sn -- object of class ... | unlicense |
nrhine1/scikit-learn | sklearn/svm/tests/test_bounds.py | 280 | 2541 | import nose
from nose.tools import assert_equal, assert_true
from sklearn.utils.testing import clean_warning_registry
import warnings
import numpy as np
from scipy import sparse as sp
from sklearn.svm.bounds import l1_min_c
from sklearn.svm import LinearSVC
from sklearn.linear_model.logistic import LogisticRegression... | bsd-3-clause |
podhrmic/paparazzi | sw/ground_segment/python/gvf/gvfframe.py | 4 | 13633 | import wx
import time
from scipy import linalg as la
from matplotlib.path import Path
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
import matplotlib.pyplot as pl
import matplotlib.patches as patches
import numpy as np
import sys
from os import path, getenv
PPRZ_SRC = getenv("PAPARAZ... | gpl-2.0 |
saimn/glue | setup.py | 1 | 4848 | #!/usr/bin/env python
from __future__ import print_function
from setuptools import setup, find_packages
from distutils.core import Command
import os
import re
import sys
import subprocess
# Generate version.py
with open('glue/version.py') as infile:
exec(infile.read())
# If the version is not stable, we can ad... | bsd-3-clause |
rosswhitfield/mantid | scripts/DGSPlanner/InstrumentSetupWidget.py | 3 | 16000 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
#pyl... | gpl-3.0 |
Obus/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 |
michelp/pywt | demo/dwt2_dwtn_image.py | 3 | 1732 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import pywt
import pywt.data
# Load image
original = pywt.data.aero()
# Wavelet transform of image, and plot approximation and details
titles = ['Approximation', ' Horizontal detail',
'Vertical detail', 'Diag... | mit |
gbtimmon/ase16GBT | code/8/NSGA.py | 1 | 7785 | #%matplotlib inline
# All the imports
from __future__ import print_function, division
import matplotlib.pyplot as plt
import os.path
from Model import *
from DTLZ import *
def refresh_objectives(problem, population):
for i, obj in enumerate(problem.objectives):
scores = [point.objectives[i] for point in p... | unlicense |
PrashntS/scikit-learn | examples/svm/plot_svm_nonlinear.py | 268 | 1091 | """
==============
Non-linear SVM
==============
Perform binary classification using non-linear SVC
with RBF kernel. The target to predict is a XOR of the
inputs.
The color map illustrates the decision function learned by the SVC.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn imp... | bsd-3-clause |
pkruskal/scikit-learn | examples/plot_multilabel.py | 236 | 4157 | # Authors: Vlad Niculae, Mathieu Blondel
# License: BSD 3 clause
"""
=========================
Multilabel classification
=========================
This example simulates a multi-label document classification problem. The
dataset is generated randomly based on the following process:
- pick the number of labels: n ... | bsd-3-clause |
jeremymcrae/denovoFilter | denovoFilter/missing_symbols.py | 1 | 5967 | """
Copyright (c) 2016 Genome Research Ltd.
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 restriction, including without limitation the rights to
use, copy, modify, merge, publish, distr... | mit |
spatialaudio/sweep | log_sweep_kaiser_window_bandlimited_script5/log_sweep_kaiser_window_bandlimited_script5.py | 2 | 2156 | #!/usr/bin/env python3
"""The influence of windowing of log. bandlimited sweep signals when using a
Kaiser Window by fixing beta (=2) and fade_out (=0).
fstart = 100 Hz
fstop = 5000 Hz
"""
import sys
sys.path.append('..')
import measurement_chain
import plotting
import calculation
import ir_imitation
impo... | mit |
dryadb11781/machine-learning-python | cross_decomposition/plot_compare_cross_decomposition.py | 128 | 4761 | """
===================================
Compare cross decomposition methods
===================================
Simple usage of various cross decomposition algorithms:
- PLSCanonical
- PLSRegression, with multivariate response, a.k.a. PLS2
- PLSRegression, with univariate response, a.k.a. PLS1
- CCA
Given 2 multivari... | bsd-3-clause |
IvarsKarpics/mxcube | gui/bricks/ESRF/ESRFCenteringBrick.py | 1 | 14511 | #
# Project: MXCuBE
# https://github.com/mxcube
#
# This file is part of MXCuBE software.
#
# MXCuBE is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your... | lgpl-3.0 |
boland1992/SeisSuite | seissuite/sort_later/inverse_clustering.py | 2 | 11741 | # -*- coding: utf-8 -*-
"""
Created on Mon Jun 20 12:28:32 2015
@author: boland
"""
import sys
sys.path.append('/home/boland/Anaconda/lib/python2.7/site-packages')
import pickle
import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.vq import kmeans
import multiprocessing as mp
import pyproj
import os
... | gpl-3.0 |
jlnh/SeizurePrediction | seizure/tasks.py | 1 | 23718 | from collections import namedtuple
import os.path
import numpy as np
import pylab as pl
import scipy.io
import common.time as time
from sklearn import cross_validation, preprocessing
from sklearn.metrics import roc_curve, auc
from scipy.signal import resample
from sklearn.linear_model import LogisticRegression as LR
fr... | mit |
eweill/ConwayGameOfLife | src/python/conway.py | 1 | 28326 | # Import necessary libraries
import matplotlib.pyplot as plt
from random import randint
from copy import deepcopy
import numpy as np
import unittest, sys
import math
from GoLquadtree import GoLNode, GoLQuadTree
#import pylab
# Conway Game of Life Grid Class
class ConwayGOLGrid():
"""
Represents a grid in the ... | mit |
Srisai85/scikit-learn | sklearn/tests/test_kernel_ridge.py | 342 | 3027 | import numpy as np
import scipy.sparse as sp
from sklearn.datasets import make_regression
from sklearn.linear_model import Ridge
from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics.pairwise import pairwise_kernels
from sklearn.utils.testing import ignore_warnings
from sklearn.utils.testing import assert... | bsd-3-clause |
GuessWhoSamFoo/pandas | pandas/tests/plotting/test_hist_method.py | 1 | 15812 | # coding: utf-8
""" Test cases for .hist method """
import numpy as np
from numpy.random import randn
import pytest
import pandas.util._test_decorators as td
from pandas import DataFrame, Series
from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
import pandas.util.testing as tm
from pandas.pl... | bsd-3-clause |
Tong-Chen/scikit-learn | examples/mixture/plot_gmm_selection.py | 8 | 3193 | """
=================================
Gaussian Mixture Model Selection
=================================
This example shows that model selection can be performed with
Gaussian Mixture Models using information-theoretic criteria (BIC).
Model selection concerns both the covariance type
and the number of components in th... | bsd-3-clause |
dhruvparamhans/zipline | zipline/history/history.py | 2 | 10716 | #
# Copyright 2014 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
yanlend/scikit-learn | sklearn/decomposition/tests/test_incremental_pca.py | 297 | 8265 | """Tests for Incremental PCA."""
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_raises
from sklearn import datasets
from sklearn.decomposition import PCA, IncrementalPCA
iris = datasets.load... | bsd-3-clause |
thilbern/scikit-learn | sklearn/grid_search.py | 12 | 30036 | """
The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters
of an estimator.
"""
from __future__ import print_function
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# ... | bsd-3-clause |
justincassidy/scikit-learn | examples/calibration/plot_calibration.py | 225 | 4795 | """
======================================
Probability calibration of classifiers
======================================
When performing classification you often want to predict not only
the class label, but also the associated probability. This probability
gives you some kind of confidence on the prediction. However,... | bsd-3-clause |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/scipy/ndimage/fourier.py | 15 | 11266 | # Copyright (C) 2003-2005 Peter J. Verveer
#
# 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 d... | gpl-3.0 |
DonBeo/statsmodels | statsmodels/tsa/x13.py | 7 | 23281 | """
Run x12/x13-arima specs in a subprocess from Python and curry results back
into python.
Notes
-----
Many of the functions are called x12. However, they are also intended to work
for x13. If this is not the case, it's a bug.
"""
from __future__ import print_function
import os
import subprocess
import tempfile
impor... | bsd-3-clause |
RachitKansal/scikit-learn | sklearn/linear_model/bayes.py | 220 | 15248 | """
Various bayesian regression
"""
from __future__ import print_function
# Authors: V. Michel, F. Pedregosa, A. Gramfort
# License: BSD 3 clause
from math import log
import numpy as np
from scipy import linalg
from .base import LinearModel
from ..base import RegressorMixin
from ..utils.extmath import fast_logdet, p... | bsd-3-clause |
BhallaLab/moose | moose-examples/neuroml2/run_cell.py | 2 | 2868 | # -*- coding: utf-8 -*-
# run_cell.py ---
#
# Filename: run_cell.py
# Description:
# Author:
# Maintainer: P Gleeson
# Version:
# URL:
# Keywords:
# Compatibility:
#
#
# Commentary:
#
#
#
#
# Change log:
# Sunday 16 September 2018 10:04:24 AM IST
# - Tweaked file to to make it compatible with moose.
#
#
#
# T... | gpl-3.0 |
yunfeilu/scikit-learn | sklearn/utils/estimator_checks.py | 21 | 51976 | from __future__ import print_function
import types
import warnings
import sys
import traceback
import inspect
import pickle
from copy import deepcopy
import numpy as np
from scipy import sparse
import struct
from sklearn.externals.six.moves import zip
from sklearn.externals.joblib import hash, Memory
from sklearn.ut... | bsd-3-clause |
cloudera/ibis | ibis/tests/sql/test_compiler.py | 1 | 69036 | import datetime
import unittest
import pytest
import ibis
import ibis.expr.api as api
import ibis.expr.operations as ops
from ibis.backends.base_sql.compiler import BaseDialect, build_ast, to_sql
from ibis.tests.expr.mocks import MockConnection
pytest.importorskip('sqlalchemy')
class TestASTBuilder(unittest.TestCa... | apache-2.0 |
mirceamironenco/GANs | basic_gan.py | 1 | 4869 | from datetime import datetime
import argparse
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from helpers.utils import load_mnist, print_flags
from helpers.initializers import he_xavier
BATCH_SIZE = 128
LEARNING_RATE = 1e-3
EPOCHS = 75
Z_DIM = 100
cl... | mit |
Obus/scikit-learn | sklearn/utils/validation.py | 66 | 23629 | """Utilities for input validation"""
# Authors: Olivier Grisel
# Gael Varoquaux
# Andreas Mueller
# Lars Buitinck
# Alexandre Gramfort
# Nicolas Tresegnie
# License: BSD 3 clause
import warnings
import numbers
import numpy as np
import scipy.sparse as sp
from ..externals i... | bsd-3-clause |
klocey/ScalingMicroBiodiversity | ExtraTests/lognormal/Prestons_a.py | 2 | 2188 | from __future__ import division
#from bigfloat import BigFloat, sqrt, exp, log, log2, erf, const_pi
import numpy as np
import math
from numpy import log, log2, exp, sqrt,log10
from scipy.optimize import fsolve
import scipy.optimize as opt
import matplotlib.pyplot as plt
from scipy.special import erf
import sys
pi = ma... | gpl-3.0 |
pompiduskus/scikit-learn | sklearn/datasets/mldata.py | 309 | 7838 | """Automatically download MLdata datasets."""
# Copyright (c) 2011 Pietro Berkes
# License: BSD 3 clause
import os
from os.path import join, exists
import re
import numbers
try:
# Python 2
from urllib2 import HTTPError
from urllib2 import quote
from urllib2 import urlopen
except ImportError:
# Pyt... | bsd-3-clause |
jmetzen/scikit-learn | examples/mixture/plot_gmm_classifier.py | 22 | 4015 | """
==================
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 |
rhattersley/cartopy | lib/cartopy/examples/geostationary.py | 4 | 2106 | """
Reprojecting images from a Geostationary projection
---------------------------------------------------
This example demonstrates Cartopy's ability to project images into the desired
projection on-the-fly. The image itself is retrieved from a URL and is loaded
directly into memory without storing it intermediately... | lgpl-3.0 |
uds-se/backstage | scripts/tag_lemma.py | 1 | 7801 | import argparse
import csv
import re
import numpy
import pandas
import webcolors
from gensim.models import Word2Vec
from nltk.corpus import stopwords, wordnet, words
from spacy.en import English
# data_dir = os.environ.get('SPACY_DATA', LOCAL_DATA_DIR)
class Filter(object):
model_file = 'data/GoogleNews-vector... | gpl-3.0 |
hainm/statsmodels | statsmodels/tsa/statespace/tests/test_mlemodel.py | 10 | 16681 | """
Tests for the generic MLEModel
Author: Chad Fulton
License: Simplified-BSD
"""
from __future__ import division, absolute_import, print_function
import numpy as np
import pandas as pd
import os
import re
import warnings
from statsmodels.tsa.statespace import sarimax, kalman_filter
from statsmodels.tsa.statespace.... | bsd-3-clause |
nsdf/nsdf | examples/datadump/test_parse_datadump_spikes.py | 1 | 10724 | import os
import h5py
import bisect
import numpy as np
import pandas as pd
import nsdf
def iter_loadtxt(filename, delimiter='\t', skiprows=0, dtype=float):
def iter_func():
with open(filename, 'r') as infile:
for _ in range(skiprows):
next(infile)
for line in infile:... | gpl-3.0 |
SGMAP-AGD/anonymisation | examples/Transparence Santé/import_insee.py | 1 | 2349 | import pandas as pd
# Nouvelle fonction pour expand la base insee, afin de pouvoir l'intégrer ensuite à la base transparence
# Prend en arguments :
# - les données INSEE sous forme de dataframe, groupées par département
# - annuaire de correspondances entre les professions INSEE et les professions Transparence Santé
... | gpl-3.0 |
datapythonista/pandas | pandas/tests/groupby/conftest.py | 3 | 3451 | import numpy as np
import pytest
from pandas import (
DataFrame,
MultiIndex,
)
import pandas._testing as tm
from pandas.core.groupby.base import (
reduction_kernels,
transformation_kernels,
)
@pytest.fixture
def mframe():
index = MultiIndex(
levels=[["foo", "bar", "baz", "qux"], ["one", "... | bsd-3-clause |
openhumanoids/exotica | exotations/solvers/exotica_ddp_solver/test/test_boxqp_configurations.py | 2 | 2944 | #!/usr/bin/env python
from __future__ import print_function, division
import matplotlib.pyplot as plt
import pyexotica as exo
from collections import OrderedDict
def test_solver(use_new_boxqp, use_polynomial_linesearch, use_cholesky):
config = '{exotica_examples}/resources/configs/dynamic_time_indexed/13_control... | bsd-3-clause |
akionakamura/scikit-learn | examples/covariance/plot_lw_vs_oas.py | 248 | 2903 | """
=============================
Ledoit-Wolf vs OAS estimation
=============================
The usual covariance maximum likelihood estimate can be regularized
using shrinkage. Ledoit and Wolf proposed a close formula to compute
the asymptotically optimal shrinkage parameter (minimizing a MSE
criterion), yielding th... | bsd-3-clause |
Musicophilia/nga_hacks | flask/src/cnn_processing.py | 1 | 3047 | import numpy as np
from preprocessing import *
from sklearn.cross_validation import train_test_split
degree_interval = 0.08
num_timesteps = 10
def fill_grid(grid, clean_dict, cardinals):
north, south, east, west = cardinals
grid_province_dict = {}
for province, rows in clean_dict.iteritems():
rows... | mit |
jayrambhia/SimpleCV2 | SimpleCV/LineScan.py | 2 | 35420 |
from SimpleCV.base import *
import scipy.signal as sps
import scipy.optimize as spo
import numpy as np
import copy, operator
class LineScan(list):
"""
**SUMMARY**
A line scan is a one dimensional signal pulled from the intensity
of a series of a pixels in an image. LineScan allows you to do a series... | bsd-3-clause |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/pandas/core/reshape/pivot.py | 6 | 20058 | # pylint: disable=E1103
from pandas.core.dtypes.common import is_list_like, is_scalar
from pandas.core.reshape.concat import concat
from pandas import Series, DataFrame, MultiIndex, Index
from pandas.core.groupby import Grouper
from pandas.core.reshape.util import cartesian_product
from pandas.compat import range, lr... | mit |
kstory8/egret | egret/psfmakers/despsfmaker.py | 2 | 7218 | #!/usr/bin/env python
import os
import sys
import numpy as np
import galsim
from .psfmaker import PSFMaker
from ..observation import Observation
class DESPSFMaker(PSFMaker):
"""
make a DES-like PSF
from esheldon's fork of the great3 code
blessed by Mike and Aaron to roughly match DES
M. R... | bsd-3-clause |
herilalaina/scikit-learn | examples/svm/plot_svm_nonlinear.py | 62 | 1119 | """
==============
Non-linear SVM
==============
Perform binary classification using non-linear SVC
with RBF kernel. The target to predict is a XOR of the
inputs.
The color map illustrates the decision function learned by the SVC.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn imp... | bsd-3-clause |
yarden-livnat/regulus | regulus/core/data.py | 1 | 1531 | import pandas as pd
from sklearn.preprocessing import StandardScaler
class Data(object):
def __init__(self, x, values):
self.x = pd.DataFrame(x)
self.values = pd.DataFrame(values)
self.scaler = None
@staticmethod
def read_csv(filename, ndims=None):
return Data.from_df(pd.r... | bsd-3-clause |
florentchandelier/keras | examples/addition_rnn.py | 50 | 5900 | # -*- coding: utf-8 -*-
from __future__ import print_function
from keras.models import Sequential, slice_X
from keras.layers.core import Activation, Dense, RepeatVector
from keras.layers import recurrent
from sklearn.utils import shuffle
import numpy as np
"""
An implementation of sequence to sequence learning for per... | mit |
Irsan88/SeqTools | DataProcessing/roda/branches/irna/bin/scripts/new_qc_v6.py | 6 | 35920 | #!/usr/bin/env python
# Script based on qc.R R-script by sdentro
# Daphne van Beek
import matplotlib
import sys
from pylab import *
from reportlab.lib import colors as ncolors
from reportlab.lib.pagesizes import letter, inch
from reportlab.platypus import SimpleDocTemplate, Table, TableStyle, Paragraph, Image, Spacer... | gpl-2.0 |
stefanseefeld/numba | numba/tests/test_extending.py | 1 | 12725 | from __future__ import print_function, division, absolute_import
from collections import namedtuple
import math
import sys
import numpy as np
from numba import unittest_support as unittest
from numba import jit, types, errors, typeof, numpy_support, cgutils
from numba.compiler import compile_isolated
from .support i... | bsd-2-clause |
lbishal/scikit-learn | examples/calibration/plot_compare_calibration.py | 241 | 5008 | """
========================================
Comparison of Calibration of Classifiers
========================================
Well calibrated classifiers are probabilistic classifiers for which the output
of the predict_proba method can be directly interpreted as a confidence level.
For instance a well calibrated (bi... | bsd-3-clause |
jayflo/scikit-learn | doc/datasets/mldata_fixture.py | 367 | 1183 | """Fixture module to skip the datasets loading when offline
Mock urllib2 access to mldata.org and create a temporary data folder.
"""
from os import makedirs
from os.path import join
import numpy as np
import tempfile
import shutil
from sklearn import datasets
from sklearn.utils.testing import install_mldata_mock
fr... | bsd-3-clause |
ChanderG/scikit-learn | sklearn/cluster/tests/test_hierarchical.py | 230 | 19795 | """
Several basic tests for hierarchical clustering procedures
"""
# Authors: Vincent Michel, 2010, Gael Varoquaux 2012,
# Matteo Visconti di Oleggio Castello 2014
# License: BSD 3 clause
from tempfile import mkdtemp
import shutil
from functools import partial
import numpy as np
from scipy import sparse
from... | bsd-3-clause |
cjayb/mne-python | mne/preprocessing/ica.py | 1 | 113755 | # -*- coding: utf-8 -*-
#
# Authors: Denis A. Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Juergen Dammers <j.dammers@fz-juelich.de>
#
# License: BSD (3-clause)
from inspect import isfunction
from collections import namedtuple
from copy import deepcopy
from... | bsd-3-clause |
IntelLabs/hpat | sdc/datatypes/hpat_pandas_rolling_types.py | 1 | 9782 | # *****************************************************************************
# Copyright (c) 2020, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of sou... | bsd-2-clause |
zrhans/pythonanywhere | .virtualenvs/django19/lib/python3.4/site-packages/pandas/core/panelnd.py | 10 | 3665 | """ Factory methods to create N-D panels """
from pandas.compat import zip
import pandas.compat as compat
def create_nd_panel_factory(klass_name, orders, slices, slicer, aliases=None,
stat_axis=2, info_axis=0, ns=None):
""" manufacture a n-d class:
Parameters
--------... | apache-2.0 |
thientu/scikit-learn | sklearn/cluster/tests/test_bicluster.py | 226 | 9457 | """Testing for Spectral Biclustering methods"""
import numpy as np
from scipy.sparse import csr_matrix, issparse
from sklearn.grid_search import ParameterGrid
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from... | bsd-3-clause |
ajrichards/bayesian-examples | visualization/mpl-event-picking.py | 3 | 2345 | import numpy as np
class PointBrowser(object):
"""
Click on a point to select and highlight it -- the data that
generated the point will be shown in the lower axes. Use the 'n'
and 'p' keys to browse through the next and previous points
"""
def __init__(self):
self.lastind = 0
... | bsd-3-clause |
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