repo_name stringlengths 7 92 | path stringlengths 5 149 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 911 693k | license stringclasses 15
values |
|---|---|---|---|---|---|
justincassidy/ThinkStats2 | code/hinc_soln.py | 67 | 4296 | """This file contains code used in "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function
import numpy as np
import pandas
import hinc
import thinkplot
import thinkstats2
""... | gpl-3.0 |
yhilpisch/dx | dx/plot.py | 1 | 5805 | #
# DX Analytics
# Helper Function for Plotting
# dx_plot.py
#
# DX Analytics is a financial analytics library, mainly for
# derviatives modeling and pricing by Monte Carlo simulation
#
# (c) Dr. Yves J. Hilpisch
# The Python Quants GmbH
#
# This program is free software: you can redistribute it and/or modify
# it unde... | agpl-3.0 |
samuel1208/scikit-learn | sklearn/manifold/tests/test_isomap.py | 226 | 3941 | from itertools import product
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from sklearn import datasets
from sklearn import manifold
from sklearn import neighbors
from sklearn import pipeline
from sklearn import preprocessing
from sklearn.utils.testing import assert_less
... | bsd-3-clause |
dwavesystems/dimod | dimod/sampleset.py | 1 | 63014 | # Copyright 2018 D-Wave Systems 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... | apache-2.0 |
cellular-nanoscience/pyotic | pyoti/modification/modification.py | 1 | 26859 | # -*- coding: utf-8 -*-
"""
Created on Fri Feb 12 14:22:31 2016
@author: Tobias Jachowski
"""
import collections
import matplotlib.pyplot as plt
import numpy as np
from abc import ABCMeta, abstractmethod
from .. import gui
from .. import helpers as hp
from .. import traces as tc
from ..evaluate import signal as sn
fr... | apache-2.0 |
omnirom/android_kernel_htc_flounder | scripts/tracing/dma-api/trace.py | 96 | 12420 | """Main program and stuff"""
#from pprint import pprint
from sys import stdin
import os.path
import re
from argparse import ArgumentParser
import cPickle as pickle
from collections import namedtuple
from plotting import plotseries, disp_pic
import smmu
class TracelineParser(object):
"""Parse the needed informatio... | gpl-2.0 |
RayMick/scikit-learn | sklearn/metrics/classification.py | 95 | 67713 | """Metrics to assess performance on classification task given classe prediction
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gram... | bsd-3-clause |
phgupta/Building-Analytics | building-analytics/TS_Util_Clean_Data.py | 1 | 15125 | # -*- coding: utf-8 -*-
"""
@author : Armando Casillas <armcasillas@ucdavis.edu>
@author : Marco Pritoni <marco.pritoni@gmail.com>
Created on Wed Jul 26 2017
Update Aug 08 2017
"""
from __future__ import division
import pandas as pd
import os
import sys
import requests as req
import json
import numpy as np
import dat... | mit |
MartinSavc/scikit-learn | sklearn/kernel_approximation.py | 258 | 17973 | """
The :mod:`sklearn.kernel_approximation` module implements several
approximate kernel feature maps base on Fourier transforms.
"""
# Author: Andreas Mueller <amueller@ais.uni-bonn.de>
#
# License: BSD 3 clause
import warnings
import numpy as np
import scipy.sparse as sp
from scipy.linalg import svd
from .base im... | bsd-3-clause |
badjr/pysal | pysal/contrib/spint/tests/test_gravity_stats.py | 8 | 12472 | """
Tests for statistics for gravity-style spatial interaction models
"""
__author__ = 'toshan'
import unittest
import numpy as np
import pandas as pd
import gravity as grav
import mle_stats as stats
class SingleParameter(unittest.TestCase):
"""Unit tests statistics when there is a single parameters estimated""... | bsd-3-clause |
allanino/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/pylab.py | 70 | 10245 | """
This is a procedural interface to the matplotlib object-oriented
plotting library.
The following plotting commands are provided; the majority have
Matlab(TM) analogs and similar argument.
_Plotting commands
acorr - plot the autocorrelation function
annotate - annotate something in the figure
arrow ... | agpl-3.0 |
fxia22/pointGAN | show_ae.py | 1 | 1680 | 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 |
petosegan/scikit-learn | sklearn/calibration.py | 137 | 18876 | """Calibration of predicted probabilities."""
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Balazs Kegl <balazs.kegl@gmail.com>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Mathieu Blondel <mathieu@mblondel.org>
#
# License: BSD 3 clause
from __future__ impo... | bsd-3-clause |
BU-PyCon/Meeting-2 | Programs/PyPlot.py | 1 | 18763 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.patches import *
import pdb
print("""
MatPlotLib Advanced Tutorial
----------------------------
This is a tutorial covering the features and usage of the matplotlib package
in more detail. In truth, no... | mit |
ivano666/tensorflow | tensorflow/examples/skflow/text_classification_character_rnn.py | 9 | 2530 | # Copyright 2015-present The Scikit Flow 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 require... | apache-2.0 |
edhuckle/statsmodels | statsmodels/sandbox/regression/tests/test_gmm_poisson.py | 31 | 13338 | '''
TestGMMMultTwostepDefault() has lower precision
'''
from statsmodels.compat.python import lmap
import numpy as np
from numpy.testing.decorators import skipif
import pandas
import scipy
from scipy import stats
from statsmodels.regression.linear_model import OLS
from statsmodels.sandbox.regression import gmm
fro... | bsd-3-clause |
cbertinato/pandas | pandas/tests/plotting/test_backend.py | 1 | 1151 | import pytest
import pandas
def test_matplotlib_backend_error():
msg = ('matplotlib is required for plotting when the default backend '
'"matplotlib" is selected.')
try:
import matplotlib # noqa
except ImportError:
with pytest.raises(ImportError, match=msg):
pandas... | bsd-3-clause |
leggitta/mne-python | examples/realtime/ftclient_rt_compute_psd.py | 17 | 2460 | """
==============================================================
Compute real-time power spectrum density with FieldTrip client
==============================================================
Please refer to `ftclient_rt_average.py` for instructions on
how to get the FieldTrip connector working in MNE-Python.
This e... | bsd-3-clause |
ChanderG/scikit-learn | sklearn/manifold/tests/test_isomap.py | 226 | 3941 | from itertools import product
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from sklearn import datasets
from sklearn import manifold
from sklearn import neighbors
from sklearn import pipeline
from sklearn import preprocessing
from sklearn.utils.testing import assert_less
... | bsd-3-clause |
ovpn-to/oVPN.to-Client-Software | else/python/hooks.py | 1 | 17289 | # -*- coding: utf-8 -*-
#
# Hooks module for py2exe.
# Inspired by cx_freeze's hooks.py, which is:
#
# Copyright © 2007-2013, Anthony Tuininga.
# Copyright © 2001-2006, Computronix (Canada) Ltd., Edmonton, Alberta, Canada.
# All rights reserved.
#
import os, sys
# Exclude modules that the standard library imp... | gpl-2.0 |
idlead/scikit-learn | examples/neural_networks/plot_mlp_training_curves.py | 56 | 3596 | """
========================================================
Compare Stochastic learning strategies for MLPClassifier
========================================================
This example visualizes some training loss curves for different stochastic
learning strategies, including SGD and Adam. Because of time-constrai... | bsd-3-clause |
musteryu/Data-Mining | assignment-黄煜-3120100937/question_4.py | 1 | 1258 | from mylib import *
import os,sys
import numpy as np
import matplotlib.pyplot as plt
import math
import random
from time import time
if __name__ == '__main__':
DIR_PATH = sys.path[0] + '\\'
# normal distribution vector file
nvctr_file1 = DIR_PATH + 'normal_500_1.txt'
nvctr_file2 = DIR_PATH + 'normal_500_2.txt'
#... | gpl-2.0 |
riddlezyc/geolab | src/structure/Z.py | 1 | 1474 | # -*- coding: utf-8 -*-
# from framesplit import trajectory
# too slow using this module
import matplotlib.pyplot as plt
dirName = r"F:\simulations\asphaltenes\na-mont\TMBO-oil\water\373-continue/"
xyzName = 'all.xyz'
hetero = 'O' # 'oh' 'N' 'sp' 'O' 'Np' 'sp'
with open(dirName + xyzName, 'r') as f... | gpl-3.0 |
rraadd88/dms2dfe | dms2dfe/lib/io_mut_class.py | 2 | 9503 | # Copyright 2017, Rohan Dandage <rraadd_8@hotmail.com,rohan@igib.in>
# This program is distributed under General Public License v. 3.
"""
================================
``io_mut_files``
================================
"""
from __future__ import division
import sys
from os.path import splitext,exists,basename,absp... | gpl-3.0 |
leejw51/BumblebeeNet | Test/AddLayer.py | 1 | 1320 | import numpy as np
import matplotlib.pylab as plt
from MulLayer import MulLayer
class AddLayer:
def __init__ (self):
pass
def forward(self, x, y):
out = x + y
return out
def backward(self, dout):
dx = dout * 1
... | mit |
stephenliu1989/HK_DataMiner | hkdataminer/cluster/faiss_dbscan_.py | 1 | 14197 | # -*- coding: utf-8 -*-
"""
DBSCAN Acclerated by Facebook AI Faiss
DBSCAN: Density-Based Spatial Clustering of Applications with Noise
"""
# Author: Robert Layton <robertlayton@gmail.com>
# Joel Nothman <joel.nothman@gmail.com>
# Lars Buitinck
#
# License: BSD 3 clause
import numpy as np
import time
f... | apache-2.0 |
joergsimon/gesture-analysis | analysis/feature_selection.py | 1 | 6744 | from analysis.preparation import labelMatrixToArray
from analysis.preparation import normalizeZeroClassArray
from visualise.trace_features import trace_feature_origin
from visualise.confusion_matrix import plot_confusion_matrix
import numpy as np
import sklearn
import sklearn.linear_model
import sklearn.preprocessing ... | apache-2.0 |
brodoll/sms-tools | lectures/09-Sound-description/plots-code/centroid.py | 23 | 1086 | import numpy as np
import matplotlib.pyplot as plt
import essentia.standard as ess
M = 1024
N = 1024
H = 512
fs = 44100
spectrum = ess.Spectrum(size=N)
window = ess.Windowing(size=M, type='hann')
centroid = ess.Centroid(range=fs/2.0)
x = ess.MonoLoader(filename = '../../../sounds/speech-male.wav', sampleRate = fs)()
c... | agpl-3.0 |
Rignak/Scripts-Python | DeepLearning/TagPrediction/TagPrediction.py | 1 | 10291 | import numpy as np
import matplotlib.pyplot as plt
import os
from os.path import join
import cv2
from skimage.transform import resize
from tqdm import tqdm
from datetime import datetime
import functools
os.environ['TF_CPP_MIN_VLOG_LEVEL'] = '3'
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from keras import optimizers
fro... | gpl-3.0 |
asnorkin/sentiment_analysis | site/lib/python2.7/site-packages/sklearn/feature_selection/variance_threshold.py | 123 | 2572 | # Author: Lars Buitinck
# License: 3-clause BSD
import numpy as np
from ..base import BaseEstimator
from .base import SelectorMixin
from ..utils import check_array
from ..utils.sparsefuncs import mean_variance_axis
from ..utils.validation import check_is_fitted
class VarianceThreshold(BaseEstimator, SelectorMixin):
... | mit |
cogeorg/BlackRhino | networkx/drawing/nx_pylab.py | 1 | 32861 | # Copyright (C) 2004-2016 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
#
# Author: Aric Hagberg (hagberg@lanl.gov)
"""
**********
Matplotlib
**********
Draw networks with matplotlib.
See Also
--------
m... | gpl-3.0 |
yavalvas/yav_com | build/matplotlib/lib/matplotlib/dviread.py | 11 | 33923 | """
An experimental module for reading dvi files output by TeX. Several
limitations make this not (currently) useful as a general-purpose dvi
preprocessor, but it is currently used by the pdf backend for
processing usetex text.
Interface::
dvi = Dvi(filename, 72)
# iterate over pages (but only one page is support... | mit |
l11x0m7/Paper | Modulation/code/signal_analysis.py | 1 | 7322 | # -*- encoding:utf-8 -*-
import os
import sys
import logging
from copy import deepcopy
from matplotlib import pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
reload(sys)
def drawModulation(dirpath, rownum=200):
"""信号文件绘图
:param filepath: 需要显示绘图的信号文件路径
:return: None
... | apache-2.0 |
marcusrehm/serenata-de-amor | research/src/fetch_cnpj_info.py | 2 | 12631 | from concurrent import futures
import json
import argparse
import time
import random
import itertools
import numpy as np
import os.path
import sys
import pandas as pd
import shutil
import requests
import requests.exceptions
import re
import logging
import json
from datetime import datetime, timedelta
LOGGER_NAME = 'fe... | mit |
decebel/librosa | librosa/filters.py | 2 | 24021 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Filters
=======
Filter bank construction
------------------------
.. autosummary::
:toctree: generated/
dct
mel
chroma
constant_q
Miscellaneous
-------------
.. autosummary::
:toctree: generated/
constant_q_lengths
cq_to_chroma
wi... | isc |
hyqneuron/pylearn2-maxsom | pylearn2/scripts/datasets/browse_norb.py | 44 | 15741 | #!/usr/bin/env python
"""
A browser for the NORB and small NORB datasets. Navigate the images by
choosing the values for the label vector. Note that for the 'big' NORB
dataset, you can only set the first 5 label dimensions. You can then cycle
through the 3-12 images that fit those labels.
"""
import sys
import os
imp... | bsd-3-clause |
jaidevd/scikit-learn | examples/cluster/plot_kmeans_silhouette_analysis.py | 83 | 5888 | """
===============================================================================
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 |
billy-inn/scikit-learn | examples/linear_model/plot_ransac.py | 250 | 1673 | """
===========================================
Robust linear model estimation using RANSAC
===========================================
In this example we see how to robustly fit a linear model to faulty data using
the RANSAC algorithm.
"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn import ... | bsd-3-clause |
alexsavio/scikit-learn | examples/gaussian_process/plot_gpc_iris.py | 81 | 2231 | """
=====================================================
Gaussian process classification (GPC) on iris dataset
=====================================================
This example illustrates the predicted probability of GPC for an isotropic
and anisotropic RBF kernel on a two-dimensional version for the iris-dataset.
... | bsd-3-clause |
bouhlelma/smt | smt/sampling_methods/tests/test_sampling_method_examples.py | 3 | 1403 | import unittest
import matplotlib
matplotlib.use("Agg")
class Test(unittest.TestCase):
def test_random(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.sampling_methods import Random
xlimits = np.array([[0.0, 4.0], [0.0, 3.0]])
sampling = Random(xlimit... | bsd-3-clause |
vortex-ape/scikit-learn | examples/datasets/plot_random_multilabel_dataset.py | 278 | 3402 | """
==============================================
Plot randomly generated multilabel dataset
==============================================
This illustrates the `datasets.make_multilabel_classification` dataset
generator. Each sample consists of counts of two features (up to 50 in
total), which are differently distri... | bsd-3-clause |
jefffohl/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/__init__.py | 72 | 2225 |
import matplotlib
import inspect
import warnings
# ipython relies on interactive_bk being defined here
from matplotlib.rcsetup import interactive_bk
__all__ = ['backend','show','draw_if_interactive',
'new_figure_manager', 'backend_version']
backend = matplotlib.get_backend() # validates, to match all_bac... | gpl-3.0 |
sohyongsheng/kaggle-carvana | plot_learning_curves.py | 1 | 2337 | import numpy
import matplotlib.pyplot
import pylab
import sys
def plot_learning_curves(experiment, epochs, train_losses, cross_validation_losses, dice_scores, x_limits = None, y_limits = None):
axes = matplotlib.pyplot.figure().gca()
x_axis = axes.get_xaxis()
x_axis.set_major_locator(pylab.MaxNLocator(... | gpl-3.0 |
JesseLivezey/plankton | pylearn2/packaged_dependencies/theano_linear/unshared_conv/localdot.py | 5 | 4839 | """
WRITEME
"""
import logging
from ..linear import LinearTransform
from .unshared_conv import FilterActs, ImgActs
from theano.compat.six.moves import xrange
from theano.sandbox import cuda
if cuda.cuda_available:
import gpu_unshared_conv # register optimizations
import numpy as np
try:
import matplotlib.pyp... | bsd-3-clause |
vivekmishra1991/scikit-learn | sklearn/decomposition/dict_learning.py | 104 | 44632 | """ Dictionary learning
"""
from __future__ import print_function
# Author: Vlad Niculae, Gael Varoquaux, Alexandre Gramfort
# License: BSD 3 clause
import time
import sys
import itertools
from math import sqrt, ceil
import numpy as np
from scipy import linalg
from numpy.lib.stride_tricks import as_strided
from ..b... | bsd-3-clause |
snario/geopandas | geopandas/plotting.py | 2 | 9645 | from __future__ import print_function
import numpy as np
from six import next
from six.moves import xrange
def plot_polygon(ax, poly, facecolor='red', edgecolor='black', alpha=0.5):
""" Plot a single Polygon geometry """
from descartes.patch import PolygonPatch
a = np.asarray(poly.exterior)
# without... | bsd-3-clause |
shirtsgroup/pygo | analysis/MBAR_foldingcurve_umbrella.py | 1 | 6397 | #!/usr/bin/python2.4
import sys
import numpy
import pymbar # for MBAR analysis
import timeseries # for timeseries analysis
import os
import os.path
import pdb # for debugging
from optparse import OptionParser
import MBAR_pmfQz
import wham
import MBAR_pmfQ
import cPickle
def parse_args():
parser=OptionParser()
... | gpl-2.0 |
rahul-c1/scikit-learn | benchmarks/bench_multilabel_metrics.py | 11 | 7258 | #!/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 |
plotly/python-api | packages/python/plotly/plotly/graph_objs/scatter/marker/_colorbar.py | 1 | 69628 | from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class ColorBar(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "scatter.marker"
_path_str = "scatter.marker.colorbar"
_valid_props = {
"bgcolor",
... | mit |
dominicmeroux/Reading-In-and-Analyzing-Calendar-Data-by-Interfacing-Between-MySQL-and-Python | Utilization-Report-MySQL.py | 1 | 18653 | from __future__ import print_function
from icalendar import *
from datetime import date, datetime, timedelta
import mysql.connector
from mysql.connector import errorcode
import pickle
import csv
import pandas
from pandas.io import sql
import matplotlib.pyplot as plt
import xlsxwriter
import numpy as np
import os
import... | mit |
gviejo/ThalamusPhysio | python/main_pop_pca.py | 1 | 15802 |
import numpy as np
import pandas as pd
# from matplotlib.pyplot import plot,show,draw
import scipy.io
from functions import *
import _pickle as cPickle
import time
import os, sys
import ipyparallel
import neuroseries as nts
data_directory = '/mnt/DataGuillaume/MergedData/'
datasets = np.loadtxt(data_directory+'datas... | gpl-3.0 |
RPGOne/Skynet | scikit-learn-c604ac39ad0e5b066d964df3e8f31ba7ebda1e0e/sklearn/utils/__init__.py | 2 | 12026 | """
The :mod:`sklearn.utils` module includes various utilities.
"""
from collections import Sequence
import numpy as np
from scipy.sparse import issparse
import warnings
from .murmurhash import murmurhash3_32
from .validation import (as_float_array,
assert_all_finite, warn_if_not_float,
... | bsd-3-clause |
AstroFloyd/LearningPython | Fitting/scipy.optimize.least_squares.py | 1 | 2724 | #!/bin/env python3
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.least_squares.html
"""Solve a curve fitting problem using robust loss function to take care of outliers in the data. Define the
model function as y = a + b * exp(c * t), where t is a predictor variable, y is an observation and a,... | gpl-3.0 |
sugartom/tensorflow-alien | tensorflow/contrib/learn/python/learn/tests/dataframe/in_memory_source_test.py | 62 | 3960 | # Copyright 2015 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 |
scienceopen/spectral_analysis | scripts/FilterDesign.py | 1 | 3846 | #!/usr/bin/env python
"""
Design FIR filter coefficients using Parks-McClellan or windowing algorithm
and plot filter transfer function.
Michael Hirsch, Ph.D.
example for PiRadar CW prototype,
writing filter coefficients for use by filters.f90:
./FilterDesign.py 9950 10050 100e3 -L 4096 -m firwin -o cwfir.asc
Refs:
h... | mit |
biocore/qiita | qiita_db/meta_util.py | 2 | 20723 | r"""
Util functions (:mod: `qiita_db.meta_util`)
===========================================
..currentmodule:: qiita_db.meta_util
This module provides utility functions that use the ORM objects. ORM objects
CANNOT import from this file.
Methods
-------
..autosummary::
:toctree: generated/
get_lat_longs
"""... | bsd-3-clause |
pratapvardhan/scikit-learn | examples/decomposition/plot_faces_decomposition.py | 103 | 4394 | """
============================
Faces dataset decompositions
============================
This example applies to :ref:`olivetti_faces` different unsupervised
matrix decomposition (dimension reduction) methods from the module
:py:mod:`sklearn.decomposition` (see the documentation chapter
:ref:`decompositions`) .
"""... | bsd-3-clause |
gunan/tensorflow | tensorflow/python/keras/engine/data_adapter_test.py | 1 | 43158 | # Copyright 2019 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 |
sandeepgupta2k4/tensorflow | tensorflow/examples/learn/iris_val_based_early_stopping.py | 62 | 2827 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | apache-2.0 |
NunoEdgarGub1/scikit-learn | examples/cross_decomposition/plot_compare_cross_decomposition.py | 142 | 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 |
duthchao/kaggle-galaxies | predict_augmented_npy_maxout2048_pysex.py | 7 | 9584 | """
Load an analysis file and redo the predictions on the validation set / test set,
this time with augmented data and averaging. Store them as numpy files.
"""
import numpy as np
# import pandas as pd
import theano
import theano.tensor as T
import layers
import cc_layers
import custom
import load_data
import realtime... | bsd-3-clause |
anhaidgroup/py_stringsimjoin | py_stringsimjoin/join/overlap_coefficient_join_py.py | 1 | 17453 | # overlap coefficient join
from joblib import delayed, Parallel
from six import iteritems
import pandas as pd
import pyprind
from py_stringsimjoin.filter.overlap_filter import OverlapFilter
from py_stringsimjoin.index.inverted_index import InvertedIndex
from py_stringsimjoin.utils.generic_helper import convert_datafra... | bsd-3-clause |
numairmansur/RoBO | examples/example_bagged_nets.py | 1 | 1284 | import sys
import logging
import numpy as np
import matplotlib.pyplot as plt
import robo.models.neural_network as robo_net
import robo.models.bagged_networks as bn
from robo.initial_design.init_random_uniform import init_random_uniform
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
def f(x):
return... | bsd-3-clause |
Adai0808/scikit-learn | sklearn/cluster/tests/test_affinity_propagation.py | 341 | 2620 | """
Testing for Clustering methods
"""
import numpy as np
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.cluster.affinity_propagation_ import AffinityPropagation
from sklearn.cluster.affinity_propagatio... | bsd-3-clause |
f3r/scikit-learn | sklearn/tests/test_metaestimators.py | 57 | 4958 | """Common tests for metaestimators"""
import functools
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.externals.six import iterkeys
from sklearn.datasets import make_classification
from sklearn.utils.testing import assert_true, assert_false, assert_raises
from sklearn.pipeline import Pipeline... | bsd-3-clause |
apaloczy/ap_tools | utils.py | 1 | 54151 | # Description: General-purpose functions for personal use.
# Author: André Palóczy
# E-mail: paloczy@gmail.com
__all__ = ['seasonal_avg',
'seasonal_std',
'deseason',
'blkavg',
'blkavgdir',
'blkavgt',
'blkapply',
'stripmsk',
... | mit |
miaecle/deepchem | examples/tox21/tox21_KernelSVM.py | 6 | 1174 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sun Jul 23 16:02:07 2017
@author: zqwu
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
import numpy as np
import deepchem as dc
import tempfile
from sklearn.svm import SVC
# Only for debug... | mit |
idlead/scikit-learn | sklearn/naive_bayes.py | 29 | 28917 | # -*- 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 |
MichaelAquilina/numpy | numpy/lib/npyio.py | 42 | 71218 | from __future__ import division, absolute_import, print_function
import sys
import os
import re
import itertools
import warnings
import weakref
from operator import itemgetter
import numpy as np
from . import format
from ._datasource import DataSource
from numpy.core.multiarray import packbits, unpackbits
from ._ioto... | bsd-3-clause |
0x0all/kaggle-galaxies | predict_augmented_npy_maxout2048.py | 8 | 9452 | """
Load an analysis file and redo the predictions on the validation set / test set,
this time with augmented data and averaging. Store them as numpy files.
"""
import numpy as np
# import pandas as pd
import theano
import theano.tensor as T
import layers
import cc_layers
import custom
import load_data
import realtime... | bsd-3-clause |
fivejjs/AD3 | python/example.py | 3 | 2817 | import matplotlib.pyplot as plt
import numpy as np
from ad3 import simple_grid, general_graph
def example_binary():
# generate trivial data
x = np.ones((10, 10))
x[:, 5:] = -1
x_noisy = x + np.random.normal(0, 0.8, size=x.shape)
x_thresh = x_noisy > .0
# create unaries
unaries = x_noisy
... | lgpl-3.0 |
zseder/hunmisc | hunmisc/utils/plotting/matplotlib_simple_xy.py | 1 | 1535 | """
Copyright 2011-13 Attila Zseder
Email: zseder@gmail.com
This file is part of hunmisc project
url: https://github.com/zseder/hunmisc
hunmisc 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
versi... | gpl-3.0 |
cs591B1-Project/Social-Media-Impact-on-Stock-Market-and-Price | data/07 exxon/dataAnalysis.py | 26 | 6163 | import numpy
from numpy import *
from operator import truediv
from ast import literal_eval
import matplotlib.pyplot as plt
import statsmodels.tsa.stattools as st
import scipy.stats as scit
def calCorrelation(s,v):
# STEP 1: Read all data files
p = [line.rstrip('\n') for line in open("positive.txt")]
n = [line.rs... | mit |
ZENGXH/scikit-learn | sklearn/neighbors/tests/test_kde.py | 208 | 5556 | import numpy as np
from sklearn.utils.testing import (assert_allclose, assert_raises,
assert_equal)
from sklearn.neighbors import KernelDensity, KDTree, NearestNeighbors
from sklearn.neighbors.ball_tree import kernel_norm
from sklearn.pipeline import make_pipeline
from sklearn.dataset... | bsd-3-clause |
nolanliou/tensorflow | tensorflow/examples/get_started/regression/imports85.py | 24 | 6638 | # 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 |
pratapvardhan/pandas | pandas/tests/indexes/multi/test_integrity.py | 3 | 9142 | # -*- coding: utf-8 -*-
import re
import numpy as np
import pandas as pd
import pandas.util.testing as tm
import pytest
from pandas import IntervalIndex, MultiIndex, RangeIndex
from pandas.compat import lrange, range
from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
def test_labels_dtypes(... | bsd-3-clause |
rohit21122012/DCASE2013 | runs/2016/dnn2016med_gd_50/task3_sound_event_detection_in_real_life_audio.py | 15 | 46699 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# DCASE 2016::Sound Event Detection in Real-life Audio / Baseline System
import argparse
import csv
import math
import numpy
import textwrap
import warnings
from sklearn import mixture
from src.dataset import *
from src.evaluation import *
from src.features import *
fr... | mit |
victor-prado/broker-manager | environment/lib/python3.5/site-packages/pandas/sparse/tests/test_indexing.py | 7 | 38977 | # pylint: disable-msg=E1101,W0612
import nose # noqa
import numpy as np
import pandas as pd
import pandas.util.testing as tm
class TestSparseSeriesIndexing(tm.TestCase):
_multiprocess_can_split_ = True
def setUp(self):
self.orig = pd.Series([1, np.nan, np.nan, 3, np.nan])
self.sparse = sel... | mit |
yarikoptic/NiPy-OLD | nipy/neurospin/viz/activation_maps.py | 1 | 25526 | #!/usr/bin/env python
"""
Functions to do automatic visualization of activation-like maps.
For 2D-only visualization, only matplotlib is required.
For 3D visualization, Mayavi, version 3.0 or greater, is required.
"""
# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# License: BSD
# Standard libr... | bsd-3-clause |
ElDeveloper/qiita | qiita_pet/handlers/rest/study_samples.py | 3 | 4239 | # -----------------------------------------------------------------------------
# Copyright (c) 2014--, The Qiita Development Team.
#
# Distributed under the terms of the BSD 3-clause License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | bsd-3-clause |
chongyangtao/gmmreg | Python/_plotting.py | 14 | 2435 | #!/usr/bin/env python
#coding=utf-8
##====================================================
## $Author$
## $Date$
## $Revision$
##====================================================
from pylab import *
from configobj import ConfigObj
import matplotlib.pyplot as plt
def display2Dpointset(A):
fig = plt.figure()
... | gpl-3.0 |
liang42hao/bokeh | bokeh/compat/mplexporter/renderers/base.py | 44 | 14355 | import warnings
import itertools
from contextlib import contextmanager
import numpy as np
from matplotlib import transforms
from .. import utils
from .. import _py3k_compat as py3k
class Renderer(object):
@staticmethod
def ax_zoomable(ax):
return bool(ax and ax.get_navigate())
@staticmethod
... | bsd-3-clause |
dongjoon-hyun/tensorflow | tensorflow/contrib/learn/python/learn/estimators/linear_test.py | 23 | 77821 | # 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 |
bovulpes/AliceO2 | Detectors/FIT/benchmark/process.py | 6 | 12238 | # load modules
import re
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator
# use classic plot style
plt.style.use('classic')
# read and save user input filenames
mem_filename = sys.argv[1]
cpu_filename = sys.argv[2]
# save the process id ... | gpl-3.0 |
mjgrav2001/scikit-learn | sklearn/linear_model/tests/test_bayes.py | 299 | 1770 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
#
# License: BSD 3 clause
import numpy as np
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import SkipTest
from sklearn.linear_model.bayes import BayesianRidge, ARDRegres... | bsd-3-clause |
drabastomek/practicalDataAnalysisCookbook | Codes/Chapter07/ts_detrendAndRemoveSeasonality.py | 1 | 2625 | import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
# change the font size
matplotlib.rc('xtick', labelsize=9)
matplotlib.rc('ytick', labelsize=9)
matplotlib.rc('font', size=14)
# time series tools
import statsmodels.api as sm
def period_mean(data, freq):
'''
Method t... | gpl-2.0 |
jlegendary/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/widgets.py | 69 | 40833 | """
GUI Neutral widgets
All of these widgets require you to predefine an Axes instance and
pass that as the first arg. matplotlib doesn't try to be too smart in
layout -- you have to figure out how wide and tall you want your Axes
to be to accommodate your widget.
"""
import numpy as np
from mlab import dist
from p... | gpl-3.0 |
murrayrm/python-control | examples/pvtol-nested.py | 2 | 4551 | # pvtol-nested.py - inner/outer design for vectored thrust aircraft
# RMM, 5 Sep 09
#
# This file works through a fairly complicated control design and
# analysis, corresponding to the planar vertical takeoff and landing
# (PVTOL) aircraft in Astrom and Murray, Chapter 11. It is intended
# to demonstrate the basic fun... | bsd-3-clause |
rc/sfepy | script/plot_mesh.py | 4 | 4164 | #!/usr/bin/env python
"""
Plot mesh connectivities, facet orientations, global and local DOF ids etc.
To switch off plotting some mesh entities, set the corresponding color to
`None`.
"""
from __future__ import absolute_import
import sys
sys.path.append('.')
from argparse import ArgumentParser
import matplotlib.pyplo... | bsd-3-clause |
adamginsburg/APEX_CMZ_H2CO | plot_codes/tmap_figure.py | 2 | 12670 | import pylab as pl
import numpy as np
import aplpy
import os
import copy
from astropy import log
from paths import h2copath, figurepath
import paths
import matplotlib
from scipy import stats as ss
from astropy.io import fits
matplotlib.rc_file(paths.pcpath('pubfiguresrc'))
pl.ioff()
# Close these figures so we can rem... | bsd-3-clause |
drammock/mne-python | mne/viz/backends/_abstract.py | 4 | 24939 | """ABCs."""
# Authors: Guillaume Favelier <guillaume.favelier@gmail.com
# Eric Larson <larson.eric.d@gmail.com>
#
# License: Simplified BSD
from abc import ABC, abstractmethod, abstractclassmethod
from contextlib import nullcontext
import warnings
from ..utils import tight_layout
class _AbstractRenderer(A... | bsd-3-clause |
RPGroup-PBoC/gist_pboc_2017 | code/inclass/phase_portrait_in_class.py | 1 | 1286 | # Duhhhh
import numpy as np
import matplotlib.pyplot as plt
import seaborn
plt.close('all')
# Define the parameters
r = 20 # the production rate
gamma = 1 / 30 # the degradation rate
k = 200 # in units of concentration
max_R = 1000 # maximum number of R1 and R2
R1 = np.linspace(0, max_R, 500)
R2 = np.linspace(0, m... | mit |
mhue/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | 215 | 11427 | import numpy as np
from sklearn.utils.testing import (assert_array_almost_equal,
assert_array_equal, assert_true, assert_raise_message)
from sklearn.datasets import load_linnerud
from sklearn.cross_decomposition import pls_
from nose.tools import assert_equal
def test_pls():
d =... | bsd-3-clause |
OshynSong/scikit-learn | examples/model_selection/grid_search_digits.py | 227 | 2665 | """
============================================================
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.grid_search.GridSearc... | bsd-3-clause |
glouppe/scikit-learn | benchmarks/bench_isotonic.py | 268 | 3046 | """
Benchmarks of isotonic regression performance.
We generate a synthetic dataset of size 10^n, for n in [min, max], and
examine the time taken to run isotonic regression over the dataset.
The timings are then output to stdout, or visualized on a log-log scale
with matplotlib.
This alows the scaling of the algorith... | bsd-3-clause |
nmartensen/pandas | scripts/file_sizes.py | 7 | 4949 | from __future__ import print_function
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from pandas import DataFrame
from pandas.util.testing import set_trace
from pandas import compat
dirs = []
names = []
lengths = []
if len(sys.argv) > 1:
loc = sys.argv[1]
else:
loc = '.'
walked = os... | bsd-3-clause |
jakobkolb/MayaSim | mayasim/model/ModelCore.py | 1 | 66303 | from __future__ import print_function
import datetime
import operator
import os
import sys
import traceback
import warnings
from itertools import compress
import networkx as nx
import numpy as np
import pandas
import pkg_resources
import scipy.ndimage as ndimage
import scipy.sparse as sparse
try:
import cPickle ... | gpl-3.0 |
tapomayukh/projects_in_python | rapid_categorization/haptic_map/outlier/hmm_crossvalidation_force.py | 1 | 19066 | # Hidden Markov Model Implementation
import pylab as pyl
import numpy as np
import matplotlib.pyplot as pp
#from enthought.mayavi import mlab
import scipy as scp
import scipy.ndimage as ni
import roslib; roslib.load_manifest('sandbox_tapo_darpa_m3')
import rospy
#import hrl_lib.mayavi2_util as mu
import hrl_lib.viz ... | mit |
dav-stott/phd-thesis | spectra_thesis_ais.py | 1 | 70177 | # -*- coding: utf-8 -*-
"""
Created on Fri Jul 25 08:48:28 2014
@author: david
"""
#*************** IMPORT DEPENDANCIES*******************************************
import numpy as np
#import spec_gdal4 as spg
from osgeo import gdal
import os
import csv
#import h5py
import datetime
import numpy.ma as ma
#from StringIO ... | mit |
omanor/MUSiCC | tests/test_musicc.py | 1 | 5038 | #!/usr/bin/env python
"""
This is the testing unit for MUSiCC
"""
# to comply with both Py2 and Py3
from __future__ import absolute_import, division, print_function
import unittest
import os
import pandas as pd
import musicc
from musicc.core import correct_and_normalize
class MUSiCCTestCase(unittest.TestCase):
... | bsd-3-clause |
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