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 |
|---|---|---|---|---|---|
detrout/debian-statsmodels | statsmodels/sandbox/examples/example_crossval.py | 33 | 2232 |
import numpy as np
from statsmodels.sandbox.tools import cross_val
if __name__ == '__main__':
#A: josef-pktd
import statsmodels.api as sm
from statsmodels.api import OLS
#from statsmodels.datasets.longley import load
from statsmodels.datasets.stackloss import load
from statsmodels.iolib.tab... | bsd-3-clause |
bdh1011/wau | venv/lib/python2.7/site-packages/pandas/core/index.py | 1 | 201512 | # pylint: disable=E1101,E1103,W0232
import datetime
import warnings
import operator
from functools import partial
from pandas.compat import range, zip, lrange, lzip, u, reduce, filter, map
from pandas import compat
import numpy as np
from sys import getsizeof
import pandas.tslib as tslib
import pandas.lib as lib
impo... | mit |
flightgong/scikit-learn | sklearn/utils/graph.py | 50 | 6169 | """
Graph utilities and algorithms
Graphs are represented with their adjacency matrices, preferably using
sparse matrices.
"""
# Authors: Aric Hagberg <hagberg@lanl.gov>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD 3 clause
impo... | bsd-3-clause |
hamishcunningham/fishy-wifi | wegrow-web/wegrow_data_graphing/data_loader.py | 1 | 4573 | import os
import datetime
import json
import pandas as pd
from config import DATA_DIR, OUTPUT_DIR
"""Functions to easily return flat-file Elf data as pandas dataframes for visualisation.
Plotting can be done with the `seaborn` package.
Writing this has made me questioning why we're not using a data... | agpl-3.0 |
widdowquinn/Teaching-Dundee-BS32010 | workshop_2/bs32010/ex06.py | 2 | 8315 | # ex08.py
#
# Functions and data useful in exercise 8 (finding RBBH) of
# the BS32010 course at the University of Dundee
from matplotlib.colors import LogNorm
from Bio import SeqIO
import matplotlib # to get version
import matplotlib.pyplot as plt
import pandas as pd
import os
# GLOBALS
datadir = "rbbh_data/rbbh... | mit |
harshaneelhg/scikit-learn | sklearn/neighbors/unsupervised.py | 106 | 4461 | """Unsupervised nearest neighbors learner"""
from .base import NeighborsBase
from .base import KNeighborsMixin
from .base import RadiusNeighborsMixin
from .base import UnsupervisedMixin
class NearestNeighbors(NeighborsBase, KNeighborsMixin,
RadiusNeighborsMixin, UnsupervisedMixin):
"""Unsu... | bsd-3-clause |
HoliestCow/ece692_deeplearning | project4/word_rnn.py | 1 | 7885 | import tensorflow as tf
import numpy as np
from tensorflow.contrib.rnn import LSTMCell, GRUCell
import sys
import time
import gensim
import string
import re
import collections
import logging
from matplotlib import pyplot as plt
from itertools import islice
def window(seq, n=2):
"Returns a sliding window (of widt... | mit |
McIntyre-Lab/mcscript | mclib_Python/plotting.py | 4 | 1107 | """ Various plotting function """
import numpy as np
def blandAltman(s1, s2, ax=None):
""" Generate a Bland-Altman plot.
Arguments:
:type s1: numpy.array
:param s1: An array of sample1 data.
:type s2: numpy.array
:param s2: An array of sample2 data.
:param ax:
... | gpl-3.0 |
jmontgom10/PRISM_pyPol | 04_buildMasks.py | 2 | 18023 | # -*- coding: utf-8 -*-
"""
Launches a GUI mask builder for each file associated with the targets specidied
by the 'targets' variable.
When the script is launched, a three pane plot of the science images will be
displayed. The center pane in this triptych is the active pane for which you are
build a mask. The followi... | mit |
tapomayukh/projects_in_python | classification/Classification_with_HMM/Single_Contact_Classification/Variable_Stiffness_Variable_Velocity/HMM/with 2s/hmm_crossvalidation_force_motion_10_states_scaled_wrt_all_data.py | 1 | 39761 | # 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 |
zihua/scikit-learn | sklearn/neighbors/tests/test_ball_tree.py | 159 | 10196 | import pickle
import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.neighbors.ball_tree import (BallTree, NeighborsHeap,
simultaneous_sort, kernel_norm,
nodeheap_sort, DTYPE, ITYPE)
from sklearn.neighbors.dis... | bsd-3-clause |
Weihonghao/ECM | Vpy34/lib/python3.5/site-packages/pandas/tests/plotting/test_misc.py | 6 | 13131 | # coding: utf-8
""" Test cases for misc plot functions """
import pytest
from pandas import Series, DataFrame
from pandas.compat import lmap
import pandas.util.testing as tm
from pandas.util.testing import slow
import numpy as np
from numpy import random
from numpy.random import randn
import pandas.plotting as plo... | agpl-3.0 |
kastnerkyle/crikey | chirp_tests/frequency_modulation_rnn.py | 1 | 6631 | # Author: Kyle Kastner
# License: BSD 3-clause
# THEANO_FLAGS="optimizer=None,compute_test_value=raise" python tanh_rnn.py
import numpy as np
import theano
import theano.tensor as T
from scipy import linalg
class sgd(object):
# Only here for API conformity with other optimizers
def __init__(self, params):
... | bsd-3-clause |
codein/poc | github_poller.py | 1 | 2466 | import os
import requests
import json
import urllib
from urllib import urlencode
import pandas as pd
import argparse
import numpy as np
import datetime
import settings
import utils
base_url = 'https://api.github.com'
issue_attributes = ['title', 'html_url']
def extract_issue_attributes(raw_issue):
try:
i... | mit |
jmargeta/scikit-learn | benchmarks/bench_plot_parallel_pairwise.py | 10 | 1181 | # Author: Mathieu Blondel <mathieu@mblondel.org>
# License: BSD Style.
import time
import pylab as pl
from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.metrics.pairwise import pairwise_kernels
def plot(func):
random_state = check_random_state(0)
... | bsd-3-clause |
seckcoder/lang-learn | python/sklearn/examples/linear_model/plot_polynomial_interpolation.py | 7 | 1647 | #!/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... | unlicense |
jhanley634/testing-tools | problem/conda_env_yml/env_report.py | 1 | 2585 | #! /usr/bin/env python
# Copyright 2019 John Hanley.
#
# 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, me... | mit |
PyPSA/PyPSA | examples/opf-storage-hvdc/export_opf-storage_network.py | 1 | 5451 |
#This script builds a network manually using network.add, so that it
#has two 3-bus disconnected AC networks, connected by HVDC
#links. Storage and generation (wind and gas) is then optimised.
#The network is then written as CSV files to csv_folder_name.
import pypsa
import datetime
import pandas as pd
import nump... | gpl-3.0 |
tfiedor/perun | perun/fuzz/interpret.py | 1 | 7884 | """ Module contains a set of functions for fuzzing results interpretation."""
import demandimport
import os.path as path
import difflib
import scipy.stats.mstats as stats
with demandimport.enabled():
import matplotlib.pyplot as plt
import perun.utils.streams as streams
import perun.utils.log as log
import perun.f... | gpl-3.0 |
hsiaoyi0504/scikit-learn | examples/linear_model/plot_lasso_model_selection.py | 311 | 5431 | """
===================================================
Lasso model selection: Cross-Validation / AIC / BIC
===================================================
Use the Akaike information criterion (AIC), the Bayes Information
criterion (BIC) and cross-validation to select an optimal value
of the regularization paramet... | bsd-3-clause |
WojciechMigda/TCO-PCFStupskiPrize1 | src/lbp_tpl_features.py | 1 | 10225 | #!/opt/anaconda2/bin/python
# -*- coding: utf-8 -*-
"""
################################################################################
#
# Copyright (c) 2015 Wojciech Migda
# All rights reserved
# Distributed under the terms of the MIT license
#
####################################################################... | mit |
gbrammer/pygrism | hudf.py | 2 | 227635 | import os
import glob
import numpy as np
import matplotlib.pyplot as plt
#import pyfits
import astropy.io.fits as pyfits
import threedhst
import threedhst.catIO as catIO
import threedhst.eazyPy as eazy
#import threedhst.dq
import unicorn
#### nJy
bouwens = {'F435W':-1.7, 'F606W':-1.1, 'F775W':-1.4, 'F814W':-3.3, 'F... | mit |
jni/skan | skan/csr.py | 1 | 38144 | import numpy as np
import pandas as pd
from scipy import sparse, ndimage as ndi
from scipy.sparse import csgraph
from scipy import spatial
import numba
from .nputil import raveled_steps_to_neighbors
## NBGraph and Numba-based implementation
csr_spec = [
('indptr', numba.int32[:]),
('indices', numba.int32[:]... | bsd-3-clause |
wrichert/BuildingMachineLearningSystemsWithPython | ch05/PosTagFreqVectorizer.py | 27 | 9486 | # 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
import re
from operator import itemgetter
from collections import Mapping
import scipy.sparse as sp
f... | mit |
trankmichael/scikit-learn | sklearn/manifold/tests/test_locally_linear.py | 232 | 4761 | from itertools import product
from nose.tools import assert_true
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from scipy import linalg
from sklearn import neighbors, manifold
from sklearn.manifold.locally_linear import barycenter_kneighbors_graph
from sklearn.utils.testi... | bsd-3-clause |
trichter/yam | yam/tests/test_correlate.py | 1 | 16313 | # Copyright 2017-2019 Tom Eulenfeld, MIT license
import unittest
import sys
import numpy as np
from obspy import read, read_inventory, UTCDateTime as UTC
from obspy.signal.cross_correlation import correlate, xcorr_max
from scipy.signal import periodogram
from scipy.fftpack import next_fast_len
from yam.correlate impo... | mit |
michigraber/scikit-learn | sklearn/datasets/tests/test_mldata.py | 384 | 5221 | """Test functionality of mldata fetching utilities."""
import os
import shutil
import tempfile
import scipy as sp
from sklearn import datasets
from sklearn.datasets import mldata_filename, fetch_mldata
from sklearn.utils.testing import assert_in
from sklearn.utils.testing import assert_not_in
from sklearn.utils.test... | bsd-3-clause |
nicolaoun/NS3-AM-Proto-Simulation | src/flow-monitor/examples/wifi-olsr-flowmon.py | 59 | 7427 | # -*- Mode: Python; -*-
# Copyright (c) 2009 INESC Porto
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License version 2 as
# published by the Free Software Foundation;
#
# This program is distributed in the hope that it will be useful,
#... | gpl-2.0 |
barbagroup/cuIBM | examples/lidDrivenCavity/Re1000/scripts/plotCenterlineVelocities.py | 1 | 2897 | """
Plots the velocities along the centerlines of the 2D cavity at Reynolds number
1000 and compares with the numerical data reported in Ghia et al. (1982).
_References:_
* Ghia, U. K. N. G., Ghia, K. N., & Shin, C. T. (1982).
High-Re solutions for incompressible flow using the Navier-Stokes equations
and a multig... | mit |
PythonCharmers/bokeh | bokeh/charts/builder/scatter_builder.py | 43 | 7792 | """This is the Bokeh charts interface. It gives you a high level API
to build complex plot is a simple way.
This is the Scatter class which lets you build your Scatter charts
just passing the arguments to the Chart class and calling the proper
functions.
"""
#-----------------------------------------------------------... | bsd-3-clause |
kaichogami/scikit-learn | sklearn/neighbors/tests/test_approximate.py | 55 | 19053 | """
Testing for the approximate neighbor search using
Locality Sensitive Hashing Forest module
(sklearn.neighbors.LSHForest).
"""
# Author: Maheshakya Wijewardena, Joel Nothman
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
joelgrus/data-science-from-scratch | first-edition/code/linear_algebra.py | 12 | 3701 | # -*- coding: iso-8859-15 -*-
from __future__ import division # want 3 / 2 == 1.5
import re, math, random # regexes, math functions, random numbers
import matplotlib.pyplot as plt # pyplot
from collections import defaultdict, Counter
from functools import partial
#
# functions for working with vectors
#
def vector_... | mit |
18padx08/PPTex | PPTexEnv_x86_64/lib/python2.7/site-packages/matplotlib/tests/test_compare_images.py | 15 | 3854 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import os
import shutil
from nose.tools import assert_equal, assert_not_equal, assert_almost_equal
from matplotlib.testing.compare import compare_images
from matplotlib.testing.decorators import _... | mit |
kelle/astropy | astropy/nddata/utils.py | 3 | 32776 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This module includes helper functions for array operations.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from copy import deepcopy
import numpy as np
from .decorators import support_... | bsd-3-clause |
mhue/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 |
rubikloud/scikit-learn | sklearn/neighbors/nearest_centroid.py | 38 | 7356 | # -*- coding: utf-8 -*-
"""
Nearest Centroid Classification
"""
# Author: Robert Layton <robertlayton@gmail.com>
# Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import warnings
import numpy as np
from scipy import sparse as sp
from ..base import BaseEstimator, ClassifierMixin
from ..met... | bsd-3-clause |
pvlib/pvlib-python | pvlib/tests/iotools/test_crn.py | 2 | 3259 | import pandas as pd
import numpy as np
from numpy import dtype, nan
import pytest
from pvlib.iotools import crn
from ..conftest import DATA_DIR, assert_frame_equal
@pytest.fixture
def columns():
return [
'WBANNO', 'UTC_DATE', 'UTC_TIME', 'LST_DATE', 'LST_TIME', 'CRX_VN',
'longitude', 'latitude', '... | bsd-3-clause |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/numpydoc/plot_directive.py | 89 | 20530 | """
A special directive for generating a matplotlib plot.
.. warning::
This is a hacked version of plot_directive.py from Matplotlib.
It's very much subject to change!
Usage
-----
Can be used like this::
.. plot:: examples/example.py
.. plot::
import matplotlib.pyplot as plt
plt.plot... | gpl-3.0 |
hsiaoyi0504/scikit-learn | sklearn/linear_model/tests/test_least_angle.py | 57 | 16523 | 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.testing import assert_true
from sklearn.utils.testing import assert_... | bsd-3-clause |
hainm/scipy | scipy/stats/_multivariate.py | 35 | 69253 | #
# Author: Joris Vankerschaver 2013
#
from __future__ import division, print_function, absolute_import
import numpy as np
import scipy.linalg
from scipy.misc import doccer
from scipy.special import gammaln, psi, multigammaln
from scipy._lib._util import check_random_state
__all__ = ['multivariate_normal', 'dirichle... | bsd-3-clause |
MartinDelzant/scikit-learn | examples/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 |
synergetics/nest | pynest/examples/HillTononi/ht_poisson.py | 4 | 3406 | # -*- coding: utf-8 -*-
#
# ht_poisson.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
... | gpl-2.0 |
rajegannathan/grasp-lift-eeg-cat-dog-solution-updated | python-packages/pyRiemann-0.2.2/build/lib.linux-x86_64-2.7/pyriemann/clustering.py | 2 | 5060 | import numpy
from sklearn.base import BaseEstimator, ClassifierMixin, TransformerMixin, ClusterMixin
from sklearn.cluster.k_means_ import _init_centroids
from sklearn.externals.joblib import Parallel
from sklearn.externals.joblib import delayed
from .utils.mean import mean_covariance
from .utils.distance import distan... | bsd-3-clause |
abhishekgahlot/scikit-learn | examples/model_selection/grid_search_text_feature_extraction.py | 253 | 4158 | """
==========================================================
Sample pipeline for text feature extraction and evaluation
==========================================================
The dataset used in this example is the 20 newsgroups dataset which will be
automatically downloaded and then cached and reused for the do... | bsd-3-clause |
StevenLOL/aicyber_semeval_2016_ivector | System_2/0042_test_ivector_SemEval2016.py | 1 | 1492 | from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import BaggingClassifier, RandomForestClassifier
from sklearn.linear_model import SGDClassifier
from sklearn.preprocessing import StandardScaler, RobustScaler
from sklearn.metrics import classification_report, confusion_matrix
fr... | gpl-3.0 |
Hiyorimi/scikit-image | doc/examples/segmentation/plot_peak_local_max.py | 9 | 1460 | """
====================
Finding local maxima
====================
The ``peak_local_max`` function returns the coordinates of local peaks (maxima)
in an image. A maximum filter is used for finding local maxima. This operation
dilates the original image and merges neighboring local maxima closer than the
size of the di... | bsd-3-clause |
loli/sklearn-ensembletrees | sklearn/lda.py | 4 | 9599 | """
The :mod:`sklearn.lda` module implements Linear Discriminant Analysis (LDA).
"""
from __future__ import print_function
# Authors: Matthieu Perrot
# Mathieu Blondel
import warnings
import numpy as np
from scipy import linalg
from .base import BaseEstimator, ClassifierMixin, TransformerMixin
from .utils.e... | bsd-3-clause |
michalkurka/h2o-3 | h2o-py/h2o/model/dim_reduction.py | 2 | 4901 | # -*- encoding: utf-8 -*-
from __future__ import absolute_import, division, print_function, unicode_literals
# noinspection PyUnresolvedReferences
from h2o.utils.compatibility import * # NOQA
import h2o
from h2o.utils.ext_dependencies import get_matplotlib_pyplot
from h2o.utils.shared_utils import can_use_pandas
from... | apache-2.0 |
pv/scikit-learn | examples/text/document_classification_20newsgroups.py | 222 | 10500 | """
======================================================
Classification of text documents using sparse features
======================================================
This is an example showing how scikit-learn can be used to classify documents
by topics using a bag-of-words approach. This example uses a scipy.spars... | bsd-3-clause |
treycausey/scikit-learn | sklearn/covariance/tests/test_covariance.py | 28 | 10115 | # 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 |
theakholic/ThinkStats2 | code/hypothesis.py | 75 | 10162 | """This file contains code used in "Think Stats",
by Allen B. Downey, available from greenteapress.com
Copyright 2010 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function, division
import nsfg
import nsfg2
import first
import thinkstats2
import thinkplot
... | gpl-3.0 |
rs2/pandas | pandas/tests/series/test_block_internals.py | 2 | 1418 | import pandas as pd
# Segregated collection of methods that require the BlockManager internal data
# structure
class TestSeriesBlockInternals:
def test_setitem_invalidates_datetime_index_freq(self):
# GH#24096 altering a datetime64tz Series inplace invalidates the
# `freq` attribute on the under... | bsd-3-clause |
nwillemse/misc-scripts | ib-downloader/ib-downloader1.py | 1 | 6580 | #!/usr/bin/env python2
"""
ib-downloader.py
"""
import click
import time
import pandas as pd
from datetime import datetime
from ib.ext.Contract import Contract
from ib.opt import Connection
class Downloader:
def __init__(
self, tickers, barsize, start_date, end_date, ib_client_id, ib_port
):
... | mit |
edonyM/emthesis | code/ransac.py | 1 | 4301 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
r"""
# .---. .-----------
# / \ __ / ------
# / / \( )/ ----- (`-') _ _(`-') <-. (`-')_
# ////// '\/ ` --- ( OO).-/( (OO ).-> .-> \( OO) ) .->
# //// / // : : --- (,------... | mit |
ProjectsUCSC/NLP | User Modelling/rnn.py | 1 | 25404 | from lib import *
#from keras.layers.merge import Concatenate
from keras.layers import Merge
import copy
from collections import Counter
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
import math
word2topic = pickle.load(open("word2topic", "r"))
embedding = pickle.load(open("word2top... | mit |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/mpl_toolkits/exceltools.py | 10 | 3958 | """
Some io tools for excel -- requires xlwt
Example usage:
import matplotlib.mlab as mlab
import mpl_toolkits.exceltools as exceltools
r = mlab.csv2rec('somefile.csv', checkrows=0)
formatd = dict(
weight = mlab.FormatFloat(2),
change = mlab.FormatPercent(2),
cost = mlab.Fo... | mit |
xiwei-zhang/vigra | vigranumpy/examples/non_local_mean_2d_color.py | 10 | 1407 | import vigra
from vigra import numpy
from matplotlib import pylab
from time import time
import multiprocessing
path = "69015.jpg"
#path = "12074.jpg"
path = "100075.jpg"
path = "12003.jpg"
data = vigra.impex.readImage(path).astype(numpy.float32)
cpus = multiprocessing.cpu_count()
print "nCpus",cpus
t0 =time()
#fo... | mit |
tkaitchuck/nupic | external/linux64/lib/python2.6/site-packages/matplotlib/delaunay/testfuncs.py | 72 | 20890 | """Some test functions for bivariate interpolation.
Most of these have been yoinked from ACM TOMS 792.
http://netlib.org/toms/792
"""
import numpy as np
from triangulate import Triangulation
class TestData(dict):
def __init__(self, *args, **kwds):
dict.__init__(self, *args, **kwds)
self.__dict__ ... | gpl-3.0 |
ctada/napCAD | testOutputs/gui_test.py | 1 | 6294 | """
OpenCV portions copied from http://kieleth.blogspot.com/2014/05/webcam-with-opencv-and-tkinter.html
"""
import Tkinter as tk
import tkFileDialog, Tkconstants, tkMessageBox
import cv2
import numpy as np
from PIL import Image, ImageTk # sudo pip install Pillow, sudo apt-get install python-imaging-tk
import stl_test... | mit |
kashif/scikit-learn | sklearn/linear_model/logistic.py | 9 | 67760 |
"""
Logistic Regression
"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Fabian Pedregosa <f@bianp.net>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Manoj Kumar <manojkumarsivaraj334@gmail.com>
# Lars Buitinck
# Simon Wu <s8wu@uwaterloo.ca>
im... | bsd-3-clause |
hparik11/Deep-Learning-Nanodegree-Foundation-Repository | weight-initialization/helper.py | 153 | 3649 | import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
def hist_dist(title, distribution_tensor, hist_range=(-4, 4)):
"""
Display histogram of a TF distribution
"""
with tf.Session() as sess:
values = sess.run(distribution_tensor)
plt.title(title)
plt.hist(values, ... | mit |
YuepengGuo/zipline | zipline/examples/buyapple.py | 11 | 2079 | #!/usr/bin/env python
#
# 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 ... | apache-2.0 |
ctherien/pysptools | pysptools/skl/base.py | 1 | 4627 | #
#------------------------------------------------------------------------------
# Copyright (c) 2013-2017, Christian Therien
#
# 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://ww... | apache-2.0 |
DailyActie/Surrogate-Model | 01-codes/scikit-learn-master/sklearn/utils/tests/test_sparsefuncs.py | 1 | 17389 | import numpy as np
import scipy.sparse as sp
from numpy.testing import (assert_array_almost_equal,
assert_array_equal,
assert_equal)
from scipy import linalg
from sklearn.datasets import make_classification
from sklearn.utils.sparsefuncs import (mean_variance_axis,
... | mit |
massmutual/scikit-learn | sklearn/manifold/tests/test_locally_linear.py | 232 | 4761 | from itertools import product
from nose.tools import assert_true
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from scipy import linalg
from sklearn import neighbors, manifold
from sklearn.manifold.locally_linear import barycenter_kneighbors_graph
from sklearn.utils.testi... | bsd-3-clause |
loretoparisi/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 |
jyeatman/dipy | tools/make_examples.py | 6 | 4149 | #!/usr/bin/env python
"""Run the py->rst conversion and run all examples.
Steps are:
analyze example index file for example py filenames
check for any filenames in example directory not included
do py to rst conversion, writing into build directory
run
"""
#---------------------------------------------... | bsd-3-clause |
davidtwomey/greengraphs_cw | greengraph/command.py | 1 | 1207 | from matplotlib import pyplot as plt
from greengraph import Greengraph
from argparse import ArgumentParser
parser = ArgumentParser(description = "Evaluate green pixels between two locations")
parser.add_argument('--from', '-f', help = 'Start location, default: London', dest='startLoc', default='London')
parser.add_... | mit |
jzt5132/scikit-learn | examples/ensemble/plot_ensemble_oob.py | 259 | 3265 | """
=============================
OOB Errors for Random Forests
=============================
The ``RandomForestClassifier`` is trained using *bootstrap aggregation*, where
each new tree is fit from a bootstrap sample of the training observations
:math:`z_i = (x_i, y_i)`. The *out-of-bag* (OOB) error is the average er... | bsd-3-clause |
dridk/ion-wisecondor | wisecondor/test.py | 1 | 16686 | ##############################################################################
# #
# Test a maternal DNA sample for fetal Copy Number Aberrations. #
# Copyright(C) 2013 TU Delft & VU University Medical Center Amsterdam #
# ... | gpl-3.0 |
clemkoa/scikit-learn | examples/text/document_clustering.py | 21 | 8531 | """
=======================================
Clustering text documents using k-means
=======================================
This is an example showing how the scikit-learn can be used to cluster
documents by topics using a bag-of-words approach. This example uses
a scipy.sparse matrix to store the features instead of ... | bsd-3-clause |
NunoEdgarGub1/scikit-learn | sklearn/utils/tests/test_class_weight.py | 140 | 11909 | import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_blobs
from sklearn.utils.class_weight import compute_class_weight
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testin... | bsd-3-clause |
sargas/scipy | scipy/signal/ltisys.py | 1 | 29747 | """
ltisys -- a collection of classes and functions for modeling linear
time invariant systems.
"""
from __future__ import division, print_function, absolute_import
#
# Author: Travis Oliphant 2001
#
# Feb 2010: Warren Weckesser
# Rewrote lsim2 and added impulse2.
#
from .filter_design import tf2zpk, zpk2tf, normal... | bsd-3-clause |
astroML/sklearn_tutorial | doc/skeletons/exercise_01.py | 4 | 7513 | """
Astronomy Tutorial: exercise 1
Classification of photometric sources
usage: python exercise_01.py datadir
- datadir is $TUTORIAL_DIR/data/sdss_colors
This directory should contain the files:
- sdssdr6_colors_class_train.npy
- sdssdr6_colors_class.200000.npy
Description:
In the tutorial, we u... | bsd-3-clause |
lukovkin/ufcnn-keras | models/a3c/Trading.py | 1 | 9754 |
import json
import numpy as np
np.set_printoptions(threshold=np.inf)
import random
from constants import TRADING_FEE
from constants import SHOW_TRADES
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
class Trade:
'''
Encapsulate a trade
Statistics can be calculated from a list of ... | mit |
to266/hyperspy | hyperspy/drawing/_markers/horizontal_line_segment.py | 1 | 3267 | # -*- coding: utf-8 -*-
# Copyright 2007-2016 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at... | gpl-3.0 |
mvernacc/proptools | docs/source/examples/plots/exp_ratio_cf.py | 1 | 1237 | """Effect of expansion ratio on thrust coefficient."""
import numpy as np
from matplotlib import pyplot as plt
from proptools import nozzle
p_c = 10e6 # Chamber pressure [units: pascal]
p_a = 100e3 # Ambient pressure [units: pascal]
gamma = 1.2 # Exhaust heat capacity ratio [units: dimensionless]
p_e = np.lin... | mit |
AlexanderFabisch/scikit-learn | sklearn/linear_model/coordinate_descent.py | 8 | 76416 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Gael Varoquaux <gael.varoquaux@inria.fr>
#
# License: BSD 3 clause
import sys
import warnings
from abc import ABCMeta, abstractmethod
import n... | bsd-3-clause |
ElDeveloper/scikit-learn | examples/ensemble/plot_forest_importances_faces.py | 403 | 1519 | """
=================================================
Pixel importances with a parallel forest of trees
=================================================
This example shows the use of forests of trees to evaluate the importance
of the pixels in an image classification task (faces). The hotter the pixel,
the more impor... | bsd-3-clause |
hochthom/kaggle-taxi-ii | src/blend_submissions.py | 2 | 1148 |
import numpy as np
import pandas as pd
import cPickle as pickle
print('reading general model predictions ...')
df = pd.read_csv('../data/test_pp_RND.csv')
submissions = ['my_submission_N1.csv','my_submission_N2.csv',
'my_submission_N3.csv','my_submission_RND.csv']
idx = np.argsort(df['... | mit |
jigargandhi/UdemyMachineLearning | Machine Learning A-Z Template Folder/Part 3 - Classification/Section 19 - Decision Tree Classification/j_decision_tree_classification.py | 1 | 2750 | # 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... | mit |
3DGenomes/tadbit | test/test.py | 2 | 1393 | """
25 Oct 2012
manual test
"""
from pytadbit import tadbit, batch_tadbit
from sys import argv
from matplotlib import pyplot as plt
from numpy import log2, matrix
def get_matrix(f_name):
nums = []
for line in open(f_name):
values = line.split()
try:
nums.append([float(v) for v in... | gpl-3.0 |
OwaJawa/kaggle-galaxies | predict_augmented_npy_maxout2048_extradense_pysexgen1_dup.py | 7 | 9736 | """
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 |
fermiPy/fermipy | fermipy/jobs/target_collect.py | 1 | 12235 | #!/usr/bin/env python
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Collect information for simulated realizations of an analysis
"""
from __future__ import absolute_import, division, print_function
import os
import sys
import yaml
import numpy as np
from astropy.table import Table, Column, vs... | bsd-3-clause |
victor-prado/broker-manager | environment/lib/python3.5/site-packages/pandas/stats/ols.py | 7 | 40448 | """
Ordinary least squares regression
"""
# pylint: disable-msg=W0201
# flake8: noqa
from pandas.compat import zip, range, StringIO
from itertools import starmap
from pandas import compat
import numpy as np
from pandas.core.api import DataFrame, Series, isnull
from pandas.core.base import StringMixin
from pandas.ty... | mit |
yask123/scikit-learn | sklearn/preprocessing/tests/test_function_transformer.py | 176 | 2169 | from nose.tools import assert_equal
import numpy as np
from sklearn.preprocessing import FunctionTransformer
def _make_func(args_store, kwargs_store, func=lambda X, *a, **k: X):
def _func(X, *args, **kwargs):
args_store.append(X)
args_store.extend(args)
kwargs_store.update(kwargs)
... | bsd-3-clause |
detrout/debian-statsmodels | statsmodels/sandbox/examples/example_gam.py | 33 | 2343 | '''original example for checking how far GAM works
Note: uncomment plt.show() to display graphs
'''
example = 2 # 1,2 or 3
import numpy as np
import numpy.random as R
import matplotlib.pyplot as plt
from statsmodels.sandbox.gam import AdditiveModel
from statsmodels.sandbox.gam import Model as GAM #?
from statsmode... | bsd-3-clause |
markelg/xray | xray/core/coordinates.py | 5 | 7037 | from collections import Mapping
from contextlib import contextmanager
import pandas as pd
from .pycompat import iteritems, basestring, OrderedDict
from . import formatting
from . import utils
def _coord_merge_finalize(target, other, target_conflicts, other_conflicts,
promote_dims={}):
f... | apache-2.0 |
Pranav-Rastogi/barbell-lift | barbell_lift.py | 1 | 1832 | import pandas as pd
from sklearn import tree
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score, train_test_split
'''
Convert the csv file to a pandas DataFrame. The read_csv function might give
a DtypeWarning due to the l... | mit |
q1ang/scikit-learn | examples/covariance/plot_outlier_detection.py | 235 | 3891 | """
==========================================
Outlier detection with several methods.
==========================================
When the amount of contamination is known, this example illustrates two
different ways of performing :ref:`outlier_detection`:
- based on a robust estimator of covariance, which is assumin... | bsd-3-clause |
LohithBlaze/scikit-learn | sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py | 221 | 5517 | """
Testing for the gradient boosting loss functions and initial estimators.
"""
import numpy as np
from numpy.testing import assert_array_equal
from numpy.testing import assert_almost_equal
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.utils import check_random_state
from ... | bsd-3-clause |
shahankhatch/scikit-learn | examples/applications/svm_gui.py | 287 | 11161 | """
==========
Libsvm GUI
==========
A simple graphical frontend for Libsvm mainly intended for didactic
purposes. You can create data points by point and click and visualize
the decision region induced by different kernels and parameter settings.
To create positive examples click the left mouse button; to create
neg... | bsd-3-clause |
IndraVikas/scikit-learn | examples/plot_digits_pipe.py | 250 | 1809 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Pipelining: chaining a PCA and a logistic regression
=========================================================
The PCA does an unsupervised dimensionality reduction, while the logistic
regression does the predictio... | bsd-3-clause |
dimm0/tacc_stats | tacc_stats/site/machine/views.py | 1 | 15509 | from django.http import HttpResponse, HttpResponseRedirect
from django.shortcuts import render_to_response, render
from django.views.generic import DetailView, ListView
from django.db.models import Q
from django.core.cache import cache
import os,sys,pwd
import cPickle as pickle
import operator
from tacc_stats.analy... | lgpl-2.1 |
rahuldhote/scikit-learn | sklearn/metrics/__init__.py | 214 | 3440 | """
The :mod:`sklearn.metrics` module includes score functions, performance metrics
and pairwise metrics and distance computations.
"""
from .ranking import auc
from .ranking import average_precision_score
from .ranking import coverage_error
from .ranking import label_ranking_average_precision_score
from .ranking imp... | bsd-3-clause |
anirudhjayaraman/scikit-learn | sklearn/tests/test_naive_bayes.py | 70 | 17509 | import pickle
from io import BytesIO
import numpy as np
import scipy.sparse
from sklearn.datasets import load_digits, load_iris
from sklearn.cross_validation import cross_val_score, train_test_split
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.te... | bsd-3-clause |
bmcage/stickproject | stick/yarn2d/distribution_method.py | 1 | 11829 | #
# Copyright (C) 2010 B. Malengier
# Copyright (C) 2010 P.Li
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# Th... | gpl-2.0 |
daodaoliang/neural-network-animation | matplotlib/backends/backend_wx.py | 10 | 65412 | """
A wxPython backend for matplotlib, based (very heavily) on
backend_template.py and backend_gtk.py
Author: Jeremy O'Donoghue (jeremy@o-donoghue.com)
Derived from original copyright work by John Hunter
(jdhunter@ace.bsd.uchicago.edu)
Copyright (C) Jeremy O'Donoghue & John Hunter, 2003-4
License: This work ... | mit |
ProkopHapala/SimpleSimulationEngine | cpp/sketches_SDL/Molecular/python/eFF_KineticAndOverlap.py | 1 | 4811 | #!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
import scipy.special as spc
'''
To evaluate Change of Kinetic energy due to orthogonalization (Deltat E_kin ) which is Valence-Bond model for Pauli repulsion
see:
[1] eq.4 in http://aip.scitation.org/doi/10.1063/1.3272671
Jaramillo-Bo... | mit |
OshynSong/scikit-learn | sklearn/externals/joblib/parallel.py | 79 | 35628 | """
Helpers for embarrassingly parallel code.
"""
# Author: Gael Varoquaux < gael dot varoquaux at normalesup dot org >
# Copyright: 2010, Gael Varoquaux
# License: BSD 3 clause
from __future__ import division
import os
import sys
import gc
import warnings
from math import sqrt
import functools
import time
import thr... | bsd-3-clause |
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