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 |
|---|---|---|---|---|---|
PatrickOReilly/scikit-learn | sklearn/cluster/tests/test_k_means.py | 20 | 31445 | """Testing for K-means"""
import sys
import numpy as np
from scipy import sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing i... | bsd-3-clause |
radjkarl/fancyWidgets | fancywidgets/pyQtBased/_installed_modules.py | 1 | 3835 | #this file is auto generated by
#CodeEditor.py - do not delete it
l=['ahkab (0.18)', 'alabaster (0.7.9)', 'anaconda-clean (1.0)', 'anaconda-client (1.5.1)', 'anaconda-navigator (1.3.1)', 'appbase (0.3.5)', 'appdirs (1.4.0)', 'argcomplete (1.0.0)', 'astroid (1.4.7)', 'astropy (1.2.1)', 'babel (2.3.4)', 'backports.shutil... | gpl-3.0 |
jwkanggist/EveryBodyTensorFlow | run_tf_slim_example_lenet5_nmist.py | 1 | 17021 | #-*- coding: utf-8 -*-
#! /usr/bin/env python
'''
filename: run_tf_basic_lenet5_mnist.py
description: simple end-to-end LetNet5 implementation
- For the purpose of EverybodyTensorFlow tutorial
-
- training with Mnist data set from Yann's website.
- the benchmark test error r... | unlicense |
fracpete/python-weka-wrapper3 | python/weka/plot/__init__.py | 4 | 1786 | # 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 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# bu... | gpl-3.0 |
ameya30/IMaX_pole_data_scripts | my_scripts/noise_test.py | 1 | 1663 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Nov 20 14:55:38 2017
@author: prabhu
"""
from scipy.io import readsav
import numpy as np
#from matplotlib import pyplot as plt
from astropy.io import fits
unbin = readsav("/home/prabhu/sunrise_holly/movie_data/mk_magneto_tr2_reduc_rnr_300_21.sav")
b... | mit |
qifeigit/scikit-learn | benchmarks/bench_random_projections.py | 397 | 8900 | """
===========================
Random projection benchmark
===========================
Benchmarks for random projections.
"""
from __future__ import division
from __future__ import print_function
import gc
import sys
import optparse
from datetime import datetime
import collections
import numpy as np
import scipy.s... | bsd-3-clause |
stainbank/simulocloud | simulocloud/visualise.py | 1 | 5885 | """ visualise.py
See, plot and visually explore pointclouds
"""
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
import itertools
import simulocloud.exceptions
# Mapping of dimension to index in bounds
_IDIM = {'x': 0, 'y': 1, 'z': 2}
def scatter(pcs, dims, bounds=None, highlight=None,... | mit |
Sbalbp/DIRAC | Core/Utilities/Graphs/GraphUtilities.py | 10 | 14613 | ########################################################################
# $HeadURL$
########################################################################
""" GraphUtilities is a a collection of utility functions and classes used
in the DIRAC Graphs package.
The DIRAC Graphs package is derived from the... | gpl-3.0 |
zrhans/pythonanywhere | pyscripts/ply_CH4.py | 1 | 9135 | """
DATA,Chuva,Chuva_min,Chuva_max,VVE,VVE_min,VVE_max,DVE,DVE_min,DVE_max,
Temp.,Temp._min,Temp._max,Umidade,Umidade_min,Umidade_max,Rad.,Rad._min,Rad._max,
Pres.Atm.,Pres.Atm._min,Pres.Atm._max,
Temp.Int.,Temp.Int._min,Temp.Int._max,
CH4,CH4_min,CH4_max,HCnM,HCnM_min,HCnM_max,HCT,HCT_min,HCT_max,
SO2,SO2_min,SO2_max,... | apache-2.0 |
MatthieuBizien/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 |
hlin117/scikit-learn | benchmarks/bench_random_projections.py | 397 | 8900 | """
===========================
Random projection benchmark
===========================
Benchmarks for random projections.
"""
from __future__ import division
from __future__ import print_function
import gc
import sys
import optparse
from datetime import datetime
import collections
import numpy as np
import scipy.s... | bsd-3-clause |
Ziqi-Li/bknqgis | pandas/pandas/tests/dtypes/test_io.py | 14 | 4606 | # -*- coding: utf-8 -*-
import numpy as np
import pandas._libs.lib as lib
import pandas.util.testing as tm
from pandas.compat import long, u
class TestParseSQL(object):
def test_convert_sql_column_floats(self):
arr = np.array([1.5, None, 3, 4.2], dtype=object)
result = lib.convert_sql_column(ar... | gpl-2.0 |
shangwuhencc/scikit-learn | sklearn/gaussian_process/gaussian_process.py | 78 | 34552 | # -*- coding: utf-8 -*-
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# (mostly translation, see implementation details)
# Licence: BSD 3 clause
from __future__ import print_function
import numpy as np
from scipy import linalg, optimize
from ..base import BaseEstimator, RegressorMixin
from ..metrics... | bsd-3-clause |
partofthething/ace | ace/smoother.py | 1 | 12949 | """
Scatterplot smoother with a fixed span.
Takes x,y scattered data and returns a set of
(x,s) points that form a smoother curve fitting the data with moving least squares estimates.
Similar to a moving average, but with better characteristics. The fundamental issue
with this smoother is that the choice of span (wind... | mit |
louisLouL/pair_trading | capstone_env/lib/python3.6/site-packages/matplotlib/backends/wx_compat.py | 2 | 5736 | #!/usr/bin/env python
"""
A wx API adapter to hide differences between wxPython classic and phoenix.
It is assumed that the user code is selecting what version it wants to use,
here we just ensure that it meets the minimum required by matplotlib.
For an example see embedding_in_wx2.py
"""
from __future__ import (abso... | mit |
ashmanmode/TTSDNNRepo | src/work_in_progress/oliver/run_dnn_hourly_check.py | 3 | 53690 |
import cPickle
import gzip
import os, sys, errno
import time
import math
# numpy & theano imports need to be done in this order (only for some numpy installations, not sure why)
import numpy
# we need to explicitly import this in some cases, not sure why this doesn't get imported with numpy itself
import numpy.distu... | apache-2.0 |
hwroitzsch/BikersLifeSaver | lib/python3.5/site-packages/numpy/fft/fftpack.py | 72 | 45497 | """
Discrete Fourier Transforms
Routines in this module:
fft(a, n=None, axis=-1)
ifft(a, n=None, axis=-1)
rfft(a, n=None, axis=-1)
irfft(a, n=None, axis=-1)
hfft(a, n=None, axis=-1)
ihfft(a, n=None, axis=-1)
fftn(a, s=None, axes=None)
ifftn(a, s=None, axes=None)
rfftn(a, s=None, axes=None)
irfftn(a, s=None, axes=None... | mit |
chriscrosscutler/scikit-image | doc/examples/plot_template.py | 20 | 1663 | """
=================
Template Matching
=================
In this example, we use template matching to identify the occurrence of an
image patch (in this case, a sub-image centered on a single coin). Here, we
return a single match (the exact same coin), so the maximum value in the
``match_template`` result corresponds... | bsd-3-clause |
BhallaLab/moose | moose-examples/snippets/twocells.py | 2 | 5300 | # twocells.py ---
#
# Filename: twocells.py
# Description:
# Author:Subhasis Ray
# Maintainer:
# Created: Sat Aug 11 14:30:21 2012 (+0530)
# Version:
# Last-Updated: Sun Aug 12 15:45:38 2012 (+0530)
# By: subha
# Update #: 521
# URL:
# Keywords:
# Compatibility:
#
#
# Commentary:
#
#
#
# Change log:
#
#... | gpl-3.0 |
cainiaocome/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 |
tonylampada/octaveplay | andrew_exercises/mlex1/ex1.py | 1 | 3108 | import os
import numpy as np
from matplotlib.pyplot import *
from mpl_toolkits.mplot3d import axes3d, Axes3D
from matplotlib import cm
import itertools
FOLDER = os.path.dirname(os.path.realpath(__file__))
def load_data():
datafile = FOLDER + '/ex1data1.txt'
data = np.loadtxt(datafile, delimiter=',')
y = ... | mit |
jjx02230808/project0223 | sklearn/datasets/species_distributions.py | 64 | 7917 | """
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_... | bsd-3-clause |
guschmue/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/pandas_io_test.py | 111 | 7865 | # 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 |
DistributedSystemsGroup/YELP-DS | Scikit_Classification.py | 2 | 14584 | #!/usr/bin/env python
# encoding: utf-8
from time import gmtime, strftime
from sklearn import cross_validation
from sklearn.metrics import r2_score
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import mean_squared_error as mse
import numpy as np
import io, os
import json
def Result_Evalua... | apache-2.0 |
yuraic/koza4ok | skTMVA/sci_bdt_electron_BDT_structure.py | 1 | 4530 | from array import array
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import classification_report, roc_auc_score, roc_curve
from sklearn import tree
import cPickle
... | mit |
laosiaudi/Wiki-search | src/Heuristic_Bidirection.py | 1 | 7280 | #!/usr/bin/python
#-*-coding:utf-8-*-
import Queue
import networkx as nx
import matplotlib.pyplot as plt
import random
import math
import time
from sets import Set
class Heuristic_Bidirection:
def __init__(self, Source, Destination, Datalines, Outlines, Inlines):
self.Origin= Source
self.Destination... | mit |
lpsinger/astropy | astropy/utils/compat/optional_deps.py | 5 | 1183 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Checks for optional dependencies using lazy import from
`PEP 562 <https://www.python.org/dev/peps/pep-0562/>`_.
"""
import importlib
# First, the top-level packages:
# TODO: This list is a duplicate of the dependencies in setup.cfg "all", but
# some of... | bsd-3-clause |
anaviltripathi/pgmpy | pgmpy/estimators/MLE.py | 2 | 3230 | from pgmpy.estimators import BaseEstimator
from pgmpy.factors import TabularCPD
from pgmpy.models import BayesianModel
import numpy as np
class MaximumLikelihoodEstimator(BaseEstimator):
"""
Class used to compute parameters for a model using Maximum Likelihood Estimate.
Parameters
----------
mode... | mit |
SaganBolliger/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/fontconfig_pattern.py | 72 | 6429 | """
A module for parsing and generating fontconfig patterns.
See the `fontconfig pattern specification
<http://www.fontconfig.org/fontconfig-user.html>`_ for more
information.
"""
# Author : Michael Droettboom <mdroe@stsci.edu>
# License : matplotlib license (PSF compatible)
# This class is defined here because it m... | agpl-3.0 |
B3AU/waveTree | examples/cluster/plot_kmeans_stability_low_dim_dense.py | 8 | 4308 | """
============================================================
Empirical evaluation of the impact of k-means initialization
============================================================
Evaluate the ability of k-means initializations strategies to make
the algorithm convergence robust as measured by the relative stan... | bsd-3-clause |
CJ-Jewell/ThinkStats2 | code/timeseries.py | 66 | 18035 | """This file contains code for use with "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 pandas
import numpy as np
import statsmodels.formula.api as smf
import st... | gpl-3.0 |
drammock/mne-python | mne/decoding/mixin.py | 14 | 2851 |
class TransformerMixin(object):
"""Mixin class for all transformers in scikit-learn."""
def fit_transform(self, X, y=None, **fit_params):
"""Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params
and returns a transformed version of X.
... | bsd-3-clause |
ikaee/bfr-attendant | facerecognitionlibrary/jni-build/jni/include/tensorflow/examples/skflow/multioutput_regression.py | 9 | 2552 | # 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 |
meteoswiss-mdr/precipattractor | pymodules/io_tools_attractor.py | 1 | 60532 | #!/usr/bin/env python
'''
Module to perform various input/output operations on gif radar files and netCDF/CSV files containing the statistics of the attractor.
The module also provide functionality to generate filenames, etc
Documentation convention from https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT... | gpl-3.0 |
sgenoud/scikit-learn | examples/linear_model/plot_lasso_coordinate_descent_path.py | 3 | 2804 | """
=====================
Lasso and Elastic Net
=====================
Lasso and elastic net (L1 and L2 penalisation) implemented using a
coordinate descent.
The coefficients can be forced to be positive.
"""
print __doc__
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD Style.
import numpy ... | bsd-3-clause |
jseabold/statsmodels | statsmodels/examples/ex_kde_confint.py | 4 | 1941 | # -*- coding: utf-8 -*-
"""
Created on Mon Dec 16 11:02:59 2013
Author: Josef Perktold
"""
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.nonparametric.api as npar
from statsmodels.sandbox.nonparametric import kernels
from statsmodels.distributions.mixture_rvs import mi... | bsd-3-clause |
anne-urai/RT_RDK | graphicalModels/examples/exoplanets.py | 7 | 1616 | """
The Fergus model of exoplanet detection
=======================================
Besides being generally awesome, this example also demonstrates how you can
color the nodes and add arbitrary labels to the figure.
"""
from matplotlib import rc
rc("font", family="serif", size=12)
rc("text", usetex=True)
import daf... | mit |
KaelChen/numpy | numpy/doc/creation.py | 118 | 5507 | """
==============
Array Creation
==============
Introduction
============
There are 5 general mechanisms for creating arrays:
1) Conversion from other Python structures (e.g., lists, tuples)
2) Intrinsic numpy array array creation objects (e.g., arange, ones, zeros,
etc.)
3) Reading arrays from disk, either from... | bsd-3-clause |
bradmontgomery/ml | book/ch05/utils.py | 24 | 7111 | # 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 os
try:
import ujson as json # UltraJSON if available
except:
import json
from matplo... | mit |
jorge2703/scikit-learn | sklearn/gaussian_process/gaussian_process.py | 83 | 34544 | # -*- coding: utf-8 -*-
# Author: Vincent Dubourg <vincent.dubourg@gmail.com>
# (mostly translation, see implementation details)
# Licence: BSD 3 clause
from __future__ import print_function
import numpy as np
from scipy import linalg, optimize
from ..base import BaseEstimator, RegressorMixin
from ..metrics... | bsd-3-clause |
htygithub/bokeh | bokeh/compat/mplexporter/utils.py | 2 | 11621 | """
Utility Routines for Working with Matplotlib Objects
====================================================
"""
import itertools
import io
import base64
import numpy as np
import warnings
import matplotlib
from matplotlib.colors import colorConverter
from matplotlib.path import Path
from matplotlib.markers import ... | bsd-3-clause |
DGrady/pandas | pandas/tests/groupby/test_groupby.py | 1 | 150439 | # -*- coding: utf-8 -*-
from __future__ import print_function
import pytest
from warnings import catch_warnings
from string import ascii_lowercase
from datetime import datetime
from numpy import nan
from pandas import (date_range, bdate_range, Timestamp,
Index, MultiIndex, DataFrame, Series,
... | bsd-3-clause |
rkpatel33/xlwings-utilities | xlwings_utilities.py | 1 | 2350 | """
Wrapper functions for xlwings
"""
import pandas as pd
from xlwings import Workbook, Sheet, Range
# Utililty functions for dataframes
def clear(range_name, ws=None):
"""Clear a table started in a range cell, ex: clear('a1').
clear(range_name, ws=None)
"""
ws = Sheet.active() if ws is None else w... | mit |
skyuuka/fast-rcnn | tools/train_svms.py | 42 | 13247 | #!/usr/bin/env python
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""
Train post-hoc SVMs using the algorithm and ... | mit |
tscholak/smbkmeans | transactions_to_tfidf.py | 1 | 5822 | # -*- coding: utf-8 -*-
import os
import sys
import inspect
cmd_folder = os.path.realpath(
os.path.abspath(
os.path.split(
inspect.getfile(
inspect.currentframe()
)
)[0]
)
)
if cmd_folder not in sys.path:
sys.path.insert... | mit |
DSLituiev/scikit-learn | examples/neighbors/plot_kde_1d.py | 347 | 5100 | """
===================================
Simple 1D Kernel Density Estimation
===================================
This example uses the :class:`sklearn.neighbors.KernelDensity` class to
demonstrate the principles of Kernel Density Estimation in one dimension.
The first plot shows one of the problems with using histogram... | bsd-3-clause |
kkochubey1/docker-sikuli-novnc-off | noVNC/utils/json2graph.py | 46 | 6674 | #!/usr/bin/env python
'''
Use matplotlib to generate performance charts
Copyright 2011 Joel Martin
Licensed under MPL-2.0 (see docs/LICENSE.MPL-2.0)
'''
# a bar plot with errorbars
import sys, json, pprint
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
def usage... | apache-2.0 |
cmu-delphi/delphi-epidata | src/acquisition/covid_hosp/state_daily/database.py | 1 | 7326 | # first party
from delphi.epidata.acquisition.covid_hosp.common.database import Database as BaseDatabase
from delphi.epidata.acquisition.covid_hosp.common.utils import Utils
import pandas as pd
class Database(BaseDatabase):
# note we share a database with state_timeseries
TABLE_NAME = 'covid_hosp_state_timeseri... | mit |
ch3ll0v3k/scikit-learn | examples/svm/plot_iris.py | 225 | 3252 | """
==================================================
Plot different SVM classifiers in the iris dataset
==================================================
Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:
- Sepal length
- Se... | bsd-3-clause |
mrshu/scikit-learn | examples/plot_isotonic_regression.py | 7 | 1730 | """
===================
Isotonic Regression
===================
An illustration of the isotonic regression on generated data. The
isotonic regression finds a non-decreasing approximation of a function
while minimizing the mean squared error on the training data. The benefit
of such a model is that it does not assume a... | bsd-3-clause |
ben-heil/Data-Science-Bowl-2017 | CNN.py | 1 | 3320 | # file: CNN.py
# description: Implements a CNN that can be used to analyze DICOM images
# author: Ben Heil
# date: 4/27/17
import matplotlib.pyplot as plt
import argparse
import math
import random
import keras
import sys
import numpy
from utilityFunctions import imageCount, readScan, readValidationImages, readBatch, r... | mit |
stscieisenhamer/glue | glue/viewers/image/qt/standalone_image_viewer.py | 2 | 5898 | from __future__ import absolute_import, division, print_function
import numpy as np
from qtpy import QtCore, QtWidgets
from glue.config import colormaps
from glue.viewers.matplotlib.qt.toolbar import MatplotlibViewerToolbar
from glue.viewers.matplotlib.qt.widget import MplWidget
from glue.viewers.image.composite_arr... | bsd-3-clause |
MartinSavc/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 |
themrmax/scikit-learn | sklearn/cluster/bicluster.py | 11 | 20245 | """Spectral biclustering algorithms.
Authors : Kemal Eren
License: BSD 3 clause
"""
from abc import ABCMeta, abstractmethod
import numpy as np
from scipy.linalg import norm
from scipy.sparse import dia_matrix, issparse
from scipy.sparse.linalg import eigsh, svds
from . import KMeans, MiniBatchKMeans
from ..base im... | bsd-3-clause |
ikaee/bfr-attendant | facerecognitionlibrary/jni-build/jni/include/tensorflow/examples/learn/wide_n_deep_tutorial.py | 29 | 8985 | # 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 |
darcy0511/Dato-Core | src/unity/python/graphlab/data_structures/sarray.py | 13 | 91593 | """
This module defines the SArray class which provides the
ability to create, access and manipulate a remote scalable array object.
SArray acts similarly to pandas.Series but without indexing.
The data is immutable, homogeneous, and is stored on the GraphLab Server side.
"""
'''
Copyright (C) 2015 Dato, Inc.
All rig... | agpl-3.0 |
TakayukiSakai/tensorflow | tensorflow/examples/skflow/iris_with_pipeline.py | 9 | 1560 | # 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 |
tawsifkhan/scikit-learn | examples/datasets/plot_iris_dataset.py | 283 | 1928 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
The Iris Dataset
=========================================================
This data sets consists of 3 different types of irises'
(Setosa, Versicolour, and Virginica) petal and sepal
length, stored in a 150x4 numpy... | bsd-3-clause |
harpone/nonpy | nonpy/nonpy.py | 1 | 18805 | # Nonpy - nonparametric time series modeling with Python
# Copyright (C) 2014 Heikki Arponen (heikki.a.arponen@gmail.com)
#
# 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, eit... | gpl-3.0 |
numenta/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/delaunay/triangulate.py | 70 | 7732 | import warnings
try:
set
except NameError:
from sets import Set as set
import numpy as np
from matplotlib._delaunay import delaunay
from interpolate import LinearInterpolator, NNInterpolator
__all__ = ['Triangulation', 'DuplicatePointWarning']
class DuplicatePointWarning(RuntimeWarning):
"""Duplicate p... | agpl-3.0 |
olvhammar/GNURadio-FFTS | Software_CO-O3/Finalize.py | 1 | 6065 | #!/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 |
artwr/airflow | airflow/hooks/base_hook.py | 5 | 3203 | # -*- coding: utf-8 -*-
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
#... | apache-2.0 |
gclenaghan/scikit-learn | sklearn/covariance/tests/test_covariance.py | 34 | 11120 | # 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 |
darshanthaker/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_macosx.py | 69 | 15397 | from __future__ import division
import os
import numpy
from matplotlib._pylab_helpers import Gcf
from matplotlib.backend_bases import RendererBase, GraphicsContextBase,\
FigureManagerBase, FigureCanvasBase, NavigationToolbar2
from matplotlib.cbook import maxdict
from matplotlib.figure import Figure
from matplotl... | agpl-3.0 |
choderalab/open-forcefield-group | nmr/code/analyze_dcd.py | 2 | 2427 | import os
import itertools
import mdtraj
import pandas as pd
cos = np.cos
sin = np.sin
ave = lambda x: x.mean(0).mean(0)
phi0 = np.deg2rad(0.0)
amino_acids = ["A" , "C" , "D" , "E" , "F" , "G" , "H" , "I" , "K" , "L" , "M" , "N" , "Q" , "R" , "S" , "T" , "V" , "W" , "Y"]
labels = ["%s%s" % (a0,a1) for (a0, a1) in ite... | gpl-2.0 |
aravindhv10/CPP_Wrappers | NewData/SRC/MXNET_DCGAN/OldTrain.py | 1 | 5830 | #!/usr/bin/python3
from __future__ import print_function
import os
import matplotlib as mpl
import tarfile
import matplotlib.image as mpimg
from matplotlib import pyplot as plt
import mxnet as mx
from mxnet import gluon
from mxnet import ndarray as nd
from mxnet.gluon import nn, utils
from mxnet import autograd
impor... | gpl-2.0 |
mbayon/TFG-MachineLearning | venv/lib/python3.6/site-packages/pandas/tests/indexes/datetimes/test_datetimelike.py | 9 | 2526 | """ generic tests from the Datetimelike class """
import numpy as np
import pandas as pd
from pandas.util import testing as tm
from pandas import Series, Index, DatetimeIndex, date_range
from ..datetimelike import DatetimeLike
class TestDatetimeIndex(DatetimeLike):
_holder = DatetimeIndex
def setup_method(... | mit |
harmslab/epistasis | epistasis/pyplot/nonlinear.py | 2 | 3477 | import functools
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
def plot_scale(
model=None,
yadd=None,
yobs=None,
yerr=None,
function=None,
cmap=None,
color=None,
s=50,
alpha=1,
ax=None,
function_line=True,
line_color='r',
**kwargs):
... | unlicense |
WillArmentrout/galSims | plotting/ClusterNearby.py | 1 | 7183 | import matplotlib.pyplot as plt
import math
import csv
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("iterTotal", type=int,
help="Number of Clustering Iterations")
args = parser.parse_args()
iterTot = args.iterTotal # Prompt User for number of iterations of clustering
# Op... | gpl-2.0 |
stonneau/cwc_tests | src/plot_cond.py | 1 | 1959 | """
Created on march 6 2017
@author: stonneau
"""
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
def __gen_values(min_point, max_point, inc):
assert(max_point > min_point)
num_points = int(float(max_point - min_point) / float(in... | gpl-3.0 |
JosmanPS/scikit-learn | examples/decomposition/plot_sparse_coding.py | 247 | 3846 | """
===========================================
Sparse coding with a precomputed dictionary
===========================================
Transform a signal as a sparse combination of Ricker wavelets. This example
visually compares different sparse coding methods using the
:class:`sklearn.decomposition.SparseCoder` esti... | bsd-3-clause |
subramgo/Instacart | timeseries_features.py | 1 | 2800 | import numpy as np
import pandas as pd
import gc
print('loading prior')
priors = pd.read_csv('./data/order_products__prior.csv')
train = pd.read_csv('./data/train_new.csv')
train_eval = pd.read_csv('./data/train_eval.csv')
print('loading orders')
orders = pd.read_csv('./data/orders.csv')
###
# some memory meas... | apache-2.0 |
Becksteinlab/GromacsWrapper | gromacs/fileformats/xpm.py | 1 | 10360 | # GromacsWrapper: xpm.py
# Copyright (c) 2012 Oliver Beckstein <orbeckst@gmail.com>
# Copyright (c) 2010 Tsjerk Wassenaar <tsjerkw@gmail.com>
# Released under the GNU Public License 3 (or higher, your choice)
# See the file COPYING for details.
"""
Gromacs XPM file format
=======================
Gromacs stores matrix ... | gpl-3.0 |
victorbergelin/scikit-learn | sklearn/decomposition/tests/test_pca.py | 199 | 10949 | import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_rai... | bsd-3-clause |
imaculate/scikit-learn | examples/cluster/plot_adjusted_for_chance_measures.py | 105 | 4300 | """
==========================================================
Adjustment for chance in clustering performance evaluation
==========================================================
The following plots demonstrate the impact of the number of clusters and
number of samples on various clustering performance evaluation me... | bsd-3-clause |
fzalkow/scikit-learn | sklearn/cluster/tests/test_dbscan.py | 114 | 11393 | """
Tests for DBSCAN clustering algorithm
"""
import pickle
import numpy as np
from scipy.spatial import distance
from scipy import sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing im... | bsd-3-clause |
deepesch/scikit-learn | sklearn/externals/joblib/__init__.py | 86 | 4795 | """ Joblib is a set of tools to provide **lightweight pipelining in
Python**. In particular, joblib offers:
1. transparent disk-caching of the output values and lazy re-evaluation
(memoize pattern)
2. easy simple parallel computing
3. logging and tracing of the execution
Joblib is optimized to be **fast*... | bsd-3-clause |
roxyboy/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 |
RayMick/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 |
cowlicks/numpy | numpy/doc/creation.py | 118 | 5507 | """
==============
Array Creation
==============
Introduction
============
There are 5 general mechanisms for creating arrays:
1) Conversion from other Python structures (e.g., lists, tuples)
2) Intrinsic numpy array array creation objects (e.g., arange, ones, zeros,
etc.)
3) Reading arrays from disk, either from... | bsd-3-clause |
PatrickOReilly/scikit-learn | sklearn/semi_supervised/tests/test_label_propagation.py | 307 | 1974 | """ test the label propagation module """
import nose
import numpy as np
from sklearn.semi_supervised import label_propagation
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
ESTIMATORS = [
(label_propagation.LabelPropagation, {'kernel': 'rbf'}),
(label_propa... | bsd-3-clause |
jcornford/pyecog | pyecog/ndf/make_pdfs.py | 1 | 5148 | '''
This module is not currently being used
'''
from __future__ import print_function
import pickle
import os
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.font_manager as fm
dir = os.path.dirname(__file__)
filename = os.path.join(dir, '../HelveticaNeue-Light.otf')
prop = fm.FontProperties(fna... | mit |
lukeiwanski/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py | 46 | 13101 | # 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 |
widdowquinn/pyani | pyani/pyani_report.py | 1 | 7164 | # -*- coding: utf-8 -*-
# (c) The James Hutton Institute 2016-2019
# (c) University of Strathclyde 2019
# Author: Leighton Pritchard
#
# Contact:
# leighton.pritchard@strath.ac.uk
#
# Leighton Pritchard,
# Strathclyde Institute for Pharmacy and Biomedical Sciences,
# Cathedral Street,
# Glasgow,
# G1 1XQ
# Scotland,
# ... | mit |
adrn/streams | scripts/figures/paper1_figures.py | 1 | 26475 | # coding: utf-8
""" A script for making figures for our Streams Paper 1 """
from __future__ import division, print_function
__author__ = "adrn <adrn@astro.columbia.edu>"
# Standard library
import os, sys
import cPickle as pickle
import inspect
# Third-party
import astropy.units as u
from astropy.io.misc import fnp... | mit |
osblinnikov/ros-docker | noVNC/utils/json2graph.py | 46 | 6674 | #!/usr/bin/env python
'''
Use matplotlib to generate performance charts
Copyright 2011 Joel Martin
Licensed under MPL-2.0 (see docs/LICENSE.MPL-2.0)
'''
# a bar plot with errorbars
import sys, json, pprint
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
def usage... | unlicense |
cloudera/hue | desktop/core/ext-py/openpyxl-2.6.4/openpyxl/utils/dataframe.py | 2 | 2069 | from __future__ import absolute_import
# Copyright (c) 2010-2019 openpyxl
import operator
from openpyxl.compat import accumulate
def dataframe_to_rows(df, index=True, header=True):
"""
Convert a Pandas dataframe into something suitable for passing into a worksheet.
If index is True then the index will be... | apache-2.0 |
vdrhtc/Measurement-automation | lib/plotting.py | 1 | 7856 | import matplotlib.pyplot as plt
from matplotlib import colorbar
from numpy import meshgrid, unwrap, append
import numpy as np
from scipy import fftpack as fp
"""
Frequently used plotting functions
"""
def generate_mesh(X, Y):
step_X = X[1] - X[0]
step_Y = Y[1] - Y[0]
return meshgrid(append(X - step_X / 2... | gpl-3.0 |
ycaihua/scikit-learn | sklearn/covariance/graph_lasso_.py | 4 | 23542 | """GraphLasso: sparse inverse covariance estimation with an l1-penalized
estimator.
"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
# Copyright: INRIA
import warnings
import operator
import sys
import time
import numpy as np
from scipy import linalg
from .empirical_covariance_ im... | bsd-3-clause |
ufaks/addons-yelizariev | sugarcrm_migration/import_sugarcrm.py | 16 | 44410 | # -*- coding: utf-8 -*-
import logging
_logger = logging.getLogger(__name__)
try:
import MySQLdb
import MySQLdb.cursors
from pandas import merge, DataFrame
except ImportError:
pass
from openerp.addons.import_framework.import_base import import_base, create_childs
from openerp.addons.import_framework.... | lgpl-3.0 |
paladin74/neural-network-animation | matplotlib/blocking_input.py | 11 | 11636 | """
This provides several classes used for blocking interaction with figure
windows:
:class:`BlockingInput`
creates a callable object to retrieve events in a blocking way for
interactive sessions
:class:`BlockingKeyMouseInput`
creates a callable object to retrieve key or mouse clicks in a blocking
way... | mit |
caseyclements/bokeh | bokeh/charts/builder/tests/test_scatter_builder.py | 33 | 2895 | """ This is the Bokeh charts testing interface.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.txt, distributed with thi... | bsd-3-clause |
HeraclesHX/scikit-learn | sklearn/linear_model/logistic.py | 105 | 56686 | """
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>
imp... | bsd-3-clause |
PatrickOReilly/scikit-learn | sklearn/tests/test_dummy.py | 186 | 17778 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from sklearn.base import clone
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_eq... | bsd-3-clause |
michigraber/scikit-learn | examples/svm/plot_rbf_parameters.py | 57 | 8096 | '''
==================
RBF SVM parameters
==================
This example illustrates the effect of the parameters ``gamma`` and ``C`` of
the Radius Basis Function (RBF) kernel SVM.
Intuitively, the ``gamma`` parameter defines how far the influence of a single
training example reaches, with low values meaning 'far' a... | bsd-3-clause |
grantjenks/pyannote-core | setup.py | 1 | 2736 | #!/usr/bin/env python
# encoding: utf-8
# The MIT License (MIT)
# Copyright (c) 2014-2017 CNRS
# 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 limita... | mit |
glennlive/gnuradio-wg-grc | gr-fec/python/fec/polar/channel_construction_bec.py | 22 | 8068 | #!/usr/bin/env python
#
# Copyright 2015 Free Software Foundation, Inc.
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# GNU Radio is... | gpl-3.0 |
nesterione/scikit-learn | examples/decomposition/plot_ica_blind_source_separation.py | 349 | 2228 | """
=====================================
Blind source separation using FastICA
=====================================
An example of estimating sources from noisy data.
:ref:`ICA` is used to estimate sources given noisy measurements.
Imagine 3 instruments playing simultaneously and 3 microphones
recording the mixed si... | bsd-3-clause |
jpn--/larch | larch/data_services/h5/h5pod/generic.py | 1 | 31510 |
import os
from pathlib import Path
import tables as tb
import numpy
import pandas
import logging
from ....util import Dict
from ....util.aster import asterize
from ....util.text_manip import truncate_path_for_display
from ... import _reserved_names_
from ...pod import Pod
from ...general import _sqz_same, selector_len... | gpl-3.0 |
josenavas/qiime | scripts/plot_semivariogram.py | 9 | 15002 | #!/usr/bin/env python
# File created on 09 Feb 2010
from __future__ import division
__author__ = "Antonio Gonzalez Pena"
__copyright__ = "Copyright 2011, The QIIME Project"
__credits__ = ["Antonio Gonzalez Pena", "Kyle Patnode", "Yoshiki Vazquez-Baeza"]
__license__ = "GPL"
__version__ = "1.9.1-dev"
__maintainer__ = "A... | gpl-2.0 |
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