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 |
|---|---|---|---|---|---|
astocko/statsmodels | examples/python/formulas.py | 33 | 4968 |
## Formulas: Fitting models using R-style formulas
# Since version 0.5.0, ``statsmodels`` allows users to fit statistical models using R-style formulas. Internally, ``statsmodels`` uses the [patsy](http://patsy.readthedocs.org/) package to convert formulas and data to the matrices that are used in model fitting. The ... | bsd-3-clause |
ewels/MultiQC | multiqc/plots/linegraph.py | 1 | 24486 | #!/usr/bin/env python
""" MultiQC functions to plot a linegraph """
from __future__ import print_function, division
from collections import OrderedDict
import base64
import inspect
import io
import logging
import os
import random
import re
import sys
from multiqc.utils import config, report, util_functions
logger =... | gpl-3.0 |
CKPalk/MachineLearning | FinalProject/MachineLearning/KNN/knn.py | 1 | 1351 | ''' Work of Cameron Palk '''
import sys
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
def main( argv ):
try:
training_filename = argv[ 1 ]
testing_filename = argv[ 2 ]
output_filename = argv[ 3 ]
except IndexError:
print( "Error, usage: \"python3 {} <t... | mit |
juergenhamel/cuon | cuon_client/cuon/Charts/standardChart.py | 5 | 4207 | # coding=utf-8
##Copyright (C) [2011] [Jürgen Hamel, D-32584 Löhne]
##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.
##Thi... | gpl-3.0 |
thejevans/pointSourceAnalysis | DetermineBinSize/plots.py | 1 | 1813 | import pandas as pd
import seaborn as sns
import likelihood
from itertools import product
def RateVsBin(mc, rates, bins, rateType = 'value', outfile = 'out.csv', **kwargs):
likelihoods = {}
kwargs2 = {}
for rate, binDiameter in product(rates, bins):
kwargs2 = {'value': {'rate_value': rate, 'bin... | gpl-3.0 |
ethorne/set-card-game-AI | cardtracker.py | 1 | 5209 | import cv2
import numpy as np
import imutils
import matplotlib.pyplot as plt
class card:
MIN_HEIGHT = 120
MIN_WIDTH = 180
MIN_PERIMETER = 400
MAX_PERIMETER = 1400
def __init__(self):
self.x = 0;
self.y = 0;
self.w = 0;
self.h = 0;
self.peri... | mit |
keir-rex/zipline | zipline/data/treasuries.py | 29 | 4671 | #
# Copyright 2013 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
dataculture/pysemantic | pysemantic/tests/test_base.py | 2 | 14118 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2015 jaidev <jaidev@newton>
#
# Distributed under terms of the BSD 3-clause license.
"""Base classes and functions for tests."""
import os
import unittest
import tempfile
import shutil
import os.path as op
from copy import deepcopy
from C... | bsd-3-clause |
DailyActie/Surrogate-Model | 01-codes/scikit-learn-master/examples/linear_model/plot_ridge_coeffs.py | 157 | 2785 | """
==============================================================
Plot Ridge coefficients as a function of the L2 regularization
==============================================================
.. currentmodule:: sklearn.linear_model
:class:`Ridge` Regression is the estimator used in this example.
Each color in the le... | mit |
HUGG/NGWM2016-modelling-course | Lessons/04-Basic-fluid-mechanics/scripts/solutions/1D-asthenospheric-counterflow.py | 1 | 1890 | # -*- coding: utf-8 -*-
"""
1D-asthenospheric-counterflow.py
A script for plotting velocity magnitudes for 1D counterflow in the
asthenosphere.
dwhipp 01.16
"""
#--- User-defined input variables
hl = 100.0 # Thickness of lithosphere [km]
h = 200.0 ... | mit |
lin-credible/scikit-learn | examples/semi_supervised/plot_label_propagation_digits.py | 268 | 2723 | """
===================================================
Label Propagation digits: Demonstrating performance
===================================================
This example demonstrates the power of semisupervised learning by
training a Label Spreading model to classify handwritten digits
with sets of very few labels.... | bsd-3-clause |
georgetown-analytics/envirohealth | CapstoneSEER/LoadSeer.py | 1 | 5394 | #SEER database
# SEER data should be loaded into the Data sub-directory of this project. Uses SQlite3
#
# .\Data
# \incidence
# read.seer.research.nov14.sas <- Data Dictionary
# *.txt <- Data files in fixed width text format
# \populations
#
# regex to read data di... | mit |
pravsripad/mne-python | mne/io/fiff/tests/test_raw_fiff.py | 3 | 71072 | # -*- coding: utf-8 -*-
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
from copy import deepcopy
from functools import partial
from io import BytesIO
import os
import os.path as op
import pathlib
import pickle
import shutil
imp... | bsd-3-clause |
AlexisEidelman/Til | til/data/utils/utils.py | 2 | 8328 | # -*- coding:utf-8 -*-
from __future__ import print_function
'''
Created on 2 août 2013
@author: a.eidelman
'''
import numpy as np
from pandas import Series, DataFrame
from numpy.lib.stride_tricks import as_strided
import pandas as pd
import pdb
of_name_to_til= {'ind':'person','foy':'declar','men':'menage', 'fam':'... | gpl-3.0 |
sergpolly/Thermal_adapt_scripts | ArchNew/DONE_proteome_arch_analysis.py | 1 | 3215 | import re
import os
import sys
from Bio import Seq
from Bio import SeqIO
from Bio import SeqUtils
import pandas as pd
from functools import partial
import time
from multiprocessing import Pool
aacids = list('CMFILVWYAGTSNQDEHRKP')
# def get_aausage_proteome(seqrec):
# # seqrec = db[seqrec_id]
# features = se... | mit |
TheKingInYellow/PySeidon | pyseidon/utilities/save_FlowFile_BPFormat.py | 2 | 12569 | from __future__ import division
import numpy as np
#from rawADCPclass import rawADCP
from datetime import datetime
from datetime import timedelta
import scipy.io as sio
import scipy.interpolate as sip
import matplotlib.pyplot as plt
import seaborn
def date2py(matlab_datenum):
"""
Converts matlab's datenum time... | agpl-3.0 |
eg-zhang/scikit-learn | examples/svm/plot_svm_nonlinear.py | 268 | 1091 | """
==============
Non-linear SVM
==============
Perform binary classification using non-linear SVC
with RBF kernel. The target to predict is a XOR of the
inputs.
The color map illustrates the decision function learned by the SVC.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn imp... | bsd-3-clause |
edhuckle/statsmodels | statsmodels/examples/try_tukey_hsd.py | 33 | 6616 | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 28 15:34:18 2012
Author: Josef Perktold
"""
from __future__ import print_function
from statsmodels.compat.python import StringIO
import numpy as np
from numpy.testing import assert_almost_equal, assert_equal
from statsmodels.stats.libqsturng import qsturng
ss = '''\
... | bsd-3-clause |
Obus/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 |
DhrubajyotiDas/PyAbel | examples/example_linbasex_hansenlaw.py | 1 | 3148 | # -*- coding: utf-8 -*-
import numpy as np
import abel
import matplotlib.pyplot as plt
IM = np.loadtxt("data/VMI_art1.txt.bz2")
legendre_orders = [0, 2, 4] # Legendre polynomial orders
proj_angles = np.arange(0, np.pi/2, np.pi/10) # projection angles in 10 degree steps
radial_step = 1 # pixel grid
smoothing = 1 #... | mit |
arasuarun/shogun | examples/undocumented/python_modular/graphical/so_multiclass_director_BMRM.py | 16 | 4362 | #!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from modshogun import RealFeatures
from modshogun import MulticlassModel, MulticlassSOLabels, RealNumber, DualLibQPBMSOSVM, DirectorStructuredModel
from modshogun import BMRM, PPBMRM, P3BMRM, ResultSet, RealVector
from modshogun import Structure... | gpl-3.0 |
CompPhysics/ThesisProjects | doc/MSc/msc_students/former/ChristianF/ThesisCodes/vmc-solver/AnalyseData/blocking.py | 1 | 1908 | import numpy as np
import matplotlib.pyplot as plt
import sys
def readData(filename):
infile = open("%s" %filename, 'r')
energies = []
for line in infile:
energies.append(float(line))
infile.close()
return np.asarray(energies)
def blocking(energies, nBlocks, blockSize):
... | cc0-1.0 |
vladpopovici/WSItk | WSItk/segm/tissue.py | 1 | 5399 | # -*- coding: utf-8 -*-
"""
SEGM.TISSUE: try to segment the tissue regions from a pathology slide.
@author: vlad
"""
from __future__ import (absolute_import, division, print_function, unicode_literals)
__version__ = 0.01
__author__ = 'Vlad Popovici'
__all__ = ['tissue_region_from_rgb', 'tissue_fat', 'tissue_chromatin'... | mit |
uglyboxer/linear_neuron | net-p3/lib/python3.5/site-packages/sklearn/covariance/graph_lasso_.py | 11 | 23920 | """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... | mit |
carlthome/librosa | docs/examples/plot_pcen_stream.py | 1 | 3937 | # coding: utf-8
# Code source: Brian McFee
# License: ISC
"""
==============
PCEN Streaming
==============
This notebook demonstrates how to use streaming IO with `librosa.pcen`
to do dynamic per-channel energy normalization on a spectrogram incrementally.
This is useful when processing long audio files that are too ... | isc |
SheffieldML/GPy | GPy/plotting/matplot_dep/img_plots.py | 15 | 2159 | # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
"""
The module contains the tools for ploting 2D image visualizations
"""
import numpy as np
from matplotlib.cm import jet
width_max = 15
height_max = 12
def _calculateFigureSize(x_size, y_size, fig_ncols... | bsd-3-clause |
adithyaselv/face-expression-detect | EmoDetect.py | 1 | 3169 | #!/usr/bin/python
#Title: Script to find emotion using facial expression
#Date:25/10/2015
#Author:Adithya Selvaprithiviraj
#PS: Not trained for nuetral expression
import argparse,sys
try:
from FeatureGen import*
except ImportError:
print "Make sure FeatureGen.pyc file is in the current directory"
exit()
... | gpl-2.0 |
RomainBrault/scikit-learn | examples/svm/plot_svm_margin.py | 88 | 2540 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
SVM Margins Example
=========================================================
The plots below illustrate the effect the parameter `C` has
on the separation line. A large value of `C` basically tells
our model that w... | bsd-3-clause |
clemkoa/scikit-learn | examples/ensemble/plot_gradient_boosting_early_stopping.py | 23 | 5049 | """
===================================
Early stopping of Gradient Boosting
===================================
Gradient boosting is an ensembling technique where several weak learners
(regression trees) are combined to yield a powerful single model, in an
iterative fashion.
Early stopping support in Gradient Boostin... | bsd-3-clause |
rohit21122012/DCASE2013 | runs/2016/dnn2016med_traps/traps14/src/evaluation.py | 56 | 43426 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import numpy
import sys
from sklearn import metrics
class DCASE2016_SceneClassification_Metrics():
"""DCASE 2016 scene classification metrics
Examples
--------
>>> dcase2016_scene_metric = DCASE2016_SceneClassification_Metrics(class_lis... | mit |
zfrenchee/pandas | pandas/tests/indexing/test_multiindex.py | 1 | 50115 | from warnings import catch_warnings
import pytest
import numpy as np
import pandas as pd
from pandas import (Panel, Series, MultiIndex, DataFrame,
Timestamp, Index, date_range)
from pandas.util import testing as tm
from pandas.errors import PerformanceWarning, UnsortedIndexError
from pandas.tests.in... | bsd-3-clause |
cg123/craigslist-cupid | cupid.py | 1 | 2287 | #!/usr/bin/env python
# 4/7/2014
# Charles O. Goddard
import sys
import numpy
import itertools
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
import craigslist
def all_personals(city='boston'):
generators = []
for a in 'wm':
for b in 'mw':
generators.append((craigslist.postings(... | bsd-3-clause |
costypetrisor/scikit-learn | sklearn/tests/test_learning_curve.py | 225 | 10791 | # Author: Alexander Fabisch <afabisch@informatik.uni-bremen.de>
#
# License: BSD 3 clause
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
import numpy as np
import warnings
from sklearn.base import BaseEstimator
from sklearn.learning_curve import learning_curve, validation_curve
from sklearn.u... | bsd-3-clause |
attacc/lumen | PythonPP/current.py | 1 | 1621 | #!/usr/bin/python3
import argparse
import numpy as np
import re
import sys
from scipy.interpolate import InterpolatedUnivariateSpline
import matplotlib.pyplot as plt
"""
Calculate current using finite differences from the polarization
Author: C. Attaccalite
"""
#
# parse command line
#
parser = argparse.ArgumentParse... | gpl-2.0 |
cdegroc/scikit-learn | examples/cluster/plot_lena_ward_segmentation.py | 5 | 1905 | """
===============================================================
A demo of structured Ward hierarchical clustering on Lena image
===============================================================
Compute the segmentation of a 2D image with Ward hierarchical
clustering. The clustering is spatially constrained in order
... | bsd-3-clause |
russel1237/scikit-learn | examples/mixture/plot_gmm.py | 248 | 2817 | """
=================================
Gaussian Mixture Model Ellipsoids
=================================
Plot the confidence ellipsoids of a mixture of two Gaussians with EM
and variational Dirichlet process.
Both models have access to five components with which to fit the
data. Note that the EM model will necessari... | bsd-3-clause |
rmcgibbo/msmbuilder | msmbuilder/utils/nearest.py | 12 | 6505 | # Author: Matthew Harrigan <matthew.p.harrigan@gmail.com>
# Contributors:
# Copyright (c) 2015, Stanford University and the Authors
# All rights reserved.
from __future__ import absolute_import, print_function, division
from scipy.spatial import KDTree as sp_KDTree
import numpy as np
from . import check_iter_of_seque... | lgpl-2.1 |
dpinney/omf | omf/scratch/dispatchStrategy/dispatchStrategy.py | 1 | 8921 | ''' Apply PNNL VirtualBatteries (VBAT) load model to day ahead forecast.'''
from os.path import isdir, join as pJoin
import pandas as pd
import numpy as np
from sklearn import linear_model
import pulp
from omf.solvers import VB
#from . import VB
from omf.models import __neoMetaModel__
from omf.models.__neoMetaModel__ i... | gpl-2.0 |
micmn/shogun | examples/undocumented/python/graphical/interactive_svr_demo.py | 6 | 11220 | """
Shogun demo, based on PyQT Demo by Eli Bendersky
Christian Widmer
Soeren Sonnenburg
License: GPLv3
"""
import numpy
import sys, os, csv
from PyQt4.QtCore import *
from PyQt4.QtGui import *
import matplotlib
from matplotlib.colorbar import make_axes, Colorbar
from matplotlib.backends.backend_qt4agg import FigureCa... | gpl-3.0 |
BioNinja/gseapy | gseapy/enrichr.py | 1 | 18033 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# see: http://amp.pharm.mssm.edu/Enrichr/help#api for API docs
import sys, json, os, logging
import requests
import pandas as pd
from io import StringIO
from collections import OrderedDict
from pkg_resources import resource_filename
from time import sleep
from tempfile imp... | mit |
ssaeger/scikit-learn | examples/ensemble/plot_gradient_boosting_regression.py | 87 | 2510 | """
============================
Gradient Boosting regression
============================
Demonstrate Gradient Boosting on the Boston housing dataset.
This example fits a Gradient Boosting model with least squares loss and
500 regression trees of depth 4.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prett... | bsd-3-clause |
herilalaina/scikit-learn | sklearn/cluster/tests/test_birch.py | 14 | 5691 | """
Tests for the birch clustering algorithm.
"""
from scipy import sparse
import numpy as np
from sklearn.cluster.tests.common import generate_clustered_data
from sklearn.cluster.birch import Birch
from sklearn.cluster.hierarchical import AgglomerativeClustering
from sklearn.datasets import make_blobs
from sklearn.l... | bsd-3-clause |
bayesimpact/bob-emploi | data_analysis/lib/read_data.py | 1 | 6334 | """Module to bundle data queries that are used over several notebooks.
Created by Stephan on Nov 3, 2015
"""
import codecs
import copy
import glob
import os
import re
import typing
from typing import Any, Dict, Iterable, List
import pandas as pd
import xmltodict
from bob_emploi.data_analysis.lib import migration_he... | gpl-3.0 |
stinebuu/nest-simulator | extras/ConnPlotter/tcd_nest.py | 20 | 6959 | # -*- coding: utf-8 -*-
#
# tcd_nest.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 |
zzcclp/spark | python/pyspark/pandas/tests/data_type_ops/test_base.py | 13 | 3913 | #
# 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 "License"); you may not us... | apache-2.0 |
Clyde-fare/scikit-learn | examples/linear_model/plot_theilsen.py | 232 | 3615 | """
====================
Theil-Sen Regression
====================
Computes a Theil-Sen Regression on a synthetic dataset.
See :ref:`theil_sen_regression` for more information on the regressor.
Compared to the OLS (ordinary least squares) estimator, the Theil-Sen
estimator is robust against outliers. It has a breakd... | bsd-3-clause |
zrhans/pythonanywhere | .virtualenvs/django19/lib/python3.4/site-packages/pandas/io/tests/test_parsers.py | 9 | 162328 | # -*- coding: utf-8 -*-
# pylint: disable=E1101
from datetime import datetime
import csv
import os
import sys
import re
import nose
import platform
from numpy import nan
import numpy as np
from pandas.io.common import DtypeWarning
from pandas import DataFrame, Series, Index, MultiIndex, DatetimeIndex
from pandas.com... | apache-2.0 |
ZENGXH/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 |
mkliegl/custom-sklearn | flexible_linear.py | 1 | 8775 | # Author: Markus Kliegl
# License: MIT
r"""Regularized linear regression with custom training and regularization costs.
:class:`FlexibleLinearRegression` is a scikit-learn-compatible linear
regression estimator that allows specification of arbitrary
training and regularization cost functions.
For a linear model:
..... | mit |
JosmanPS/scikit-learn | sklearn/feature_extraction/text.py | 110 | 50157 | # -*- coding: utf-8 -*-
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Robert Layton <robertlayton@gmail.com>
# Jochen Wersdörfer <jochen@wersdoerfer.de>
# Roman Sinayev <roman.sinayev@gma... | bsd-3-clause |
0x0all/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 |
schoolie/bokeh | bokeh/charts/builders/horizon_builder.py | 6 | 6668 | """This is the Bokeh charts interface. It gives you a high level API
to build complex plot is a simple way.
This is the Horizon class which lets you build your Horizon charts
just passing the arguments to the Chart class and calling the proper
functions.
"""
#-----------------------------------------------------------... | bsd-3-clause |
kartikkumar/pagmo | PyGMO/problem/_gtop.py | 4 | 32258 | from PyGMO.problem._problem_space import cassini_1, gtoc_1, gtoc_2, cassini_2, rosetta, messenger_full, tandem, laplace, sagas, mga_1dsm_alpha, mga_1dsm_tof, mga_incipit, mga_incipit_cstrs, mga_part, _gtoc_2_objective
# Redefining the constructors of all problems to obtain good documentation
# and allowing kwargs
de... | gpl-3.0 |
mfjb/scikit-learn | examples/linear_model/plot_sgd_iris.py | 286 | 2202 | """
========================================
Plot multi-class SGD on the iris dataset
========================================
Plot decision surface of multi-class SGD on iris dataset.
The hyperplanes corresponding to the three one-versus-all (OVA) classifiers
are represented by the dashed lines.
"""
print(__doc__)
... | bsd-3-clause |
rseubert/scikit-learn | examples/linear_model/plot_ridge_path.py | 254 | 1655 | """
===========================================================
Plot Ridge coefficients as a function of the regularization
===========================================================
Shows the effect of collinearity in the coefficients of an estimator.
.. currentmodule:: sklearn.linear_model
:class:`Ridge` Regressi... | bsd-3-clause |
mblondel/scikit-learn | sklearn/utils/tests/test_multiclass.py | 11 | 15420 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from itertools import product
from functools import partial
from sklearn.externals.six.moves import xrange
from sklearn.externals.six import iteritems
from scipy.sparse import issparse
from scipy.sparse import csc_matrix
from scipy.sparse im... | bsd-3-clause |
nitish-tripathi/Simplery | ANN/MultiNeuralNetwork.py | 1 | 6387 |
import os.path
import sys
import cPickle
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.datasets import make_moons, make_circles
class MultiNeuralNetwork(object):
""" Multiple Neural Network Implementation """
def __init__(self, num_outputs=1, hid... | mit |
m4rx9/rna-pdb-tools | rna_tools/tools/rna_alignment/utils/rna_alignment_get_species.py | 1 | 9721 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
This is an improved version of the script that uses the Rfam MySQL database online interface (thanks @akaped for this idea) (so you need to be connected to the Internet, of course). Redirect the output to the file.
.. image:: ../pngs/species.png
.. warning :: This scr... | mit |
arabenjamin/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 |
ScreamingUdder/mantid | scripts/FilterEvents/eventFilterGUI.py | 1 | 41849 | #pylint: disable=invalid-name, too-many-lines, too-many-instance-attributes
from __future__ import (absolute_import, division, print_function)
import numpy
from FilterEvents.ui_MainWindow import Ui_MainWindow #import line for the UI python class
from PyQt4 import QtCore, QtGui
from PyQt4.QtCore import *
from PyQt4.QtG... | gpl-3.0 |
kghiasi/cuda-convnet2 | convdata.py | 174 | 14675 | # Copyright 2014 Google Inc. 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 applicable law or... | apache-2.0 |
sckott/pyalm_scott | pyalm/stuff.py | 2 | 2931 | import sys
import requests
import csv
import pandas as pd
class Alm:
# def __init__(self):
def alm2(self, doi = None, pmid = None, pmcid = None, mdid = None,
url = 'http://alm.plos.org/api/v3/articles', info = "totals",
months = None, days = None, year = None, source = None, key = None):
'''
shit = p... | mit |
mathewlee11/lmfm | lmfm/lmfm.py | 1 | 7318 | from __future__ import absolute_import
import numpy as np
from als_fast import FMRegressor
from sgd_fast import FMClassifier
from scipy.sparse import dok_matrix
from sklearn.base import BaseEstimator, RegressorMixin, ClassifierMixin
from sklearn.utils import check_X_y, assert_all_finite
__author__ = "mathewlee11"
cla... | mit |
aburrell/davitpy | davitpy/pydarn/proc/signal/xcor.py | 3 | 15116 | # -*- coding: utf-8 -*-
import copy
import datetime
from matplotlib import pyplot as mp
import numpy as np
import scipy as sp
from signalCommon import *
import logging
# Cross Correlation Objects Start Here
class xcor(object):
def __init__(self, sig0, sig1, mode='full', comment=None, **metadata):
"""Def... | gpl-3.0 |
KaiSzuttor/espresso | samples/lb_profile.py | 1 | 2744 | # Copyright (C) 2010-2019 The ESPResSo project
#
# This file is part of ESPResSo.
#
# ESPResSo 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 v... | gpl-3.0 |
brajagopalcse/SAIL_CodeMixed-ICON-2017 | evalSAIL.py | 1 | 3978 | #!/usr/bin/env python3
"""
Script to calculate different metrices from the labelled test dataset using the gold dataset for the SAIL (Codemixed) 2017 shared task @ICON-2017.
This script requires the gold annotated file provided by the organizers.
If your system is unable to predict sentiment of a sentence, then tag ... | mit |
yarikoptic/pystatsmodels | statsmodels/datasets/statecrime/data.py | 3 | 2985 | #! /usr/bin/env python
"""Statewide Crime Data"""
__docformat__ = 'restructuredtext'
COPYRIGHT = """Public domain."""
TITLE = """Statewide Crime Data 2009"""
SOURCE = """
All data is for 2009 and was obtained from the American Statistical Abstracts except as indicated below.
"""
DESCRSHORT = """State ... | bsd-3-clause |
georgewinstone/figures2canvas | figures2canvas/__init__.py | 1 | 2915 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 28 16:36:12 2017
@author: george
"""
import numpy as np
import matplotlib.pyplot as plt
import os
import matplotlib._pylab_helpers
import os
import pip
from PIL import Image
def install(package):
pip.main(['install', package])
def merge_images(file1, file2):
... | mit |
mfalkiewicz/pyTotalActivation | examples/sg_tutorial/plot_sg_tutorial.py | 7 | 3828 | """
======================================
A quick tour of sphinx-gallery and rST
======================================
One of the most important components of any package is its documentation.
For packages that involve data analysis, visualization of results / data is
a key element of the docs. Sphinx-gallery is an ... | mit |
bhargav/scikit-learn | sklearn/ensemble/__init__.py | 153 | 1382 | """
The :mod:`sklearn.ensemble` module includes ensemble-based methods for
classification, regression and anomaly detection.
"""
from .base import BaseEnsemble
from .forest import RandomForestClassifier
from .forest import RandomForestRegressor
from .forest import RandomTreesEmbedding
from .forest import ExtraTreesCla... | bsd-3-clause |
pkruskal/scikit-learn | sklearn/preprocessing/data.py | 113 | 56747 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Eric Martin <eric@ericmart.in>
# License: BSD 3 clause
from itertools import chain, combina... | bsd-3-clause |
endolith/scikit-image | doc/examples/transform/plot_ransac3D.py | 19 | 1351 | """
============================================
Robust 3D line model estimation using RANSAC
============================================
In this example we see how to robustly fit a 3D line model to faulty data using
the RANSAC algorithm.
"""
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits... | bsd-3-clause |
sgenoud/scikit-learn | examples/neighbors/plot_regression.py | 7 | 1372 | """
============================
Nearest Neighbors regression
============================
Demonstrate the resolution of a regression problem
using a k-Nearest Neighbor and the interpolation of the
target using both barycenter and constant weights.
"""
print __doc__
# Author: Alexandre Gramfort <alexandre.gramfort@i... | bsd-3-clause |
arjoly/scikit-learn | sklearn/ensemble/tests/test_voting_classifier.py | 140 | 6926 | """Testing for the boost module (sklearn.ensemble.boost)."""
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestCl... | bsd-3-clause |
GuessWhoSamFoo/pandas | pandas/tests/indexes/test_base.py | 1 | 101609 | # -*- coding: utf-8 -*-
from collections import defaultdict
from datetime import datetime, timedelta
import math
import operator
import sys
import numpy as np
import pytest
from pandas._libs.tslib import Timestamp
from pandas.compat import (
PY3, PY35, PY36, StringIO, lrange, lzip, range, text_type, u, zip)
from... | bsd-3-clause |
xyjin/Program_trade_system | strategies/trending.py | 1 | 4604 | # A module for all built-in commands.
# vim: sw=4: et
LICENSE="""
Copyright (C) 2011 Michael Ihde
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)... | gpl-2.0 |
mprego/NBA | Regression/tests/ELO_tests.py | 1 | 6119 | import pandas as pd
from unittest import TestCase
from Regression.ELO import ELO
class Test_ELO(TestCase):
def test_get_dates(self):
schedule = pd.DataFrame(dict(Date={0: '1/1/2000', 1: '1/2/2000', 2: '1/2/2000', 3: '1/3/2000'},
Home={0: 'SA', 1: 'SA', 2: 'HOU', 3: 'DAL... | mit |
fedspendingtransparency/data-act-broker-backend | dataactvalidator/scripts/loader_utils.py | 1 | 7703 | import logging
import pandas as pd
import numpy as np
import csv
from io import StringIO
from datetime import datetime
from pandas import isnull
from pandas.io.sql import SQLTable
from sqlalchemy.engine import Connection
from typing import List, Iterable
from dataactcore.utils.failure_threshold_exception import Failur... | cc0-1.0 |
haya14busa/alc-etm-searcher | nltk-3.0a3/build/lib/nltk/probability.py | 2 | 87772 | # -*- coding: utf-8 -*-
# Natural Language Toolkit: Probability and Statistics
#
# Copyright (C) 2001-2013 NLTK Project
# Author: Edward Loper <edloper@gmail.com>
# Steven Bird <stevenbird1@gmail.com> (additions)
# Trevor Cohn <tacohn@cs.mu.oz.au> (additions)
# Peter Ljunglöf <peter.ljunglof@hea... | mit |
Eric-Gaudiello/tensorflow_dev | tensorflow_home/tensorflow_venv/lib/python3.4/site-packages/numpy/lib/polynomial.py | 82 | 37957 | """
Functions to operate on polynomials.
"""
from __future__ import division, absolute_import, print_function
__all__ = ['poly', 'roots', 'polyint', 'polyder', 'polyadd',
'polysub', 'polymul', 'polydiv', 'polyval', 'poly1d',
'polyfit', 'RankWarning']
import re
import warnings
import numpy.core.... | gpl-3.0 |
altairpearl/scikit-learn | examples/exercises/plot_cv_digits.py | 135 | 1223 | """
=============================================
Cross-validation on Digits Dataset Exercise
=============================================
A tutorial exercise using Cross-validation with an SVM on the Digits dataset.
This exercise is used in the :ref:`cv_generators_tut` part of the
:ref:`model_selection_tut` section... | bsd-3-clause |
alutu/revelio | Revelio-parser/run_cgndetect_v6.1.py | 1 | 39801 | #!/usr/bin/env python
"""
parse the raw data from REVELIO tests that ran in the MICROWORKERS:
table header:
boxid, revelio_type, timestamp, local_IP, upnp_wan_ip, STUN, trace_packetsize, traceroute_results
--first line is the traceroute to the mapped address (using 100 bytes packets)
-- example:
3f6eb30ced2211e5aefa90... | gpl-2.0 |
ric2b/Vivaldi-browser | chromium/tools/perf/cli_tools/soundwave/worker_pool.py | 10 | 2508 | # Copyright 2018 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""
Use a pool of workers to concurrently process a sequence of items.
Example usage:
from soundwave import worker_pool
def MyWorker(args):
... | bsd-3-clause |
marcsans/cnn-physics-perception | phy/lib/python2.7/site-packages/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... | mit |
leonardolepus/pubmad | toolbox/miscellaneous/main.py | 3 | 4086 | import copy
import csv
import networkx as nx
import matplotlib.pyplot as plt
import pickle
import time
import operator
def plot(g, file = None, weighted = False, weighted_edge = False):
pos=nx.spring_layout(g)
if weighted:
weights = [g.node[n][weighted] for n in g]
max_weight = max(weights)... | gpl-2.0 |
rgerkin/pyNeuroML | pyneuroml/pynml.py | 1 | 51611 | #!/usr/bin/env python
"""
Python wrapper around jnml command.
Also a number of helper functions for
handling/generating/running LEMS/NeuroML2 files
Thanks to Werner van Geit for an initial version of a python wrapper for jnml.
"""
from __future__ import absolute_import
from __future__ import print_function
from _... | lgpl-3.0 |
sarathid/Python-works | Deep_learning_ND/helper.py | 155 | 5631 | import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelBinarizer
def _load_label_names():
"""
Load the label names from file
"""
return ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
def load_cfar10_batch(ci... | gpl-3.0 |
joshgabriel/dft-crossfilter | CompleteApp/crossfilter_prec_app/old_mains/old_main.py | 3 | 10263 | # main.py that controls the whole app
# to run: just run bokeh serve --show crossfilter_app in the benchmark-view repo
from random import random
import os
from bokeh.layouts import column
from bokeh.models import Button
from bokeh.models.widgets import Select, MultiSelect, Slider
from bokeh.palettes import RdYlBu3
fr... | mit |
ZenDevelopmentSystems/scikit-learn | sklearn/linear_model/setup.py | 146 | 1713 | import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('linear_model', parent_package, top_path)
cblas_libs, blas_info = get_blas_info... | bsd-3-clause |
bnaul/scikit-learn | sklearn/datasets/tests/test_kddcup99.py | 3 | 1490 | """Test kddcup99 loader, if the data is available,
or if specifically requested via environment variable
(e.g. for travis cron job).
Only 'percent10' mode is tested, as the full data
is too big to use in unit-testing.
"""
from sklearn.datasets.tests.test_common import check_return_X_y
from functools import partial
... | bsd-3-clause |
lancezlin/ml_template_py | lib/python2.7/site-packages/matplotlib/spines.py | 8 | 19263 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from matplotlib.externals import six
import matplotlib
import matplotlib.artist as martist
from matplotlib.artist import allow_rasterization
from matplotlib import docstring
import matplotlib.transforms as mt... | mit |
vigilv/scikit-learn | sklearn/cross_decomposition/cca_.py | 209 | 3150 | from .pls_ import _PLS
__all__ = ['CCA']
class CCA(_PLS):
"""CCA Canonical Correlation Analysis.
CCA inherits from PLS with mode="B" and deflation_mode="canonical".
Read more in the :ref:`User Guide <cross_decomposition>`.
Parameters
----------
n_components : int, (default 2).
numb... | bsd-3-clause |
Sammers21/math_stat_python | problem6.py | 2 | 17603 | import pandas as pd
import statsmodels.formula.api as smf
import math
from lib import rss, ess
from scipy.stats import f, norm, chi
import numpy as np
from lib import mk_data_var, read_column_from_csv
# TODO alter this to your variant
v_number = 1
mk_data_var(v_number)
class_1 = read_column_from_csv(0, 'data/6probl... | apache-2.0 |
morrigan/user-behavior-anomaly-detector | src/prepare_data.py | 1 | 5564 | #!/usr/bin/python
import os, csv
import tensorflow as tf
import numpy as np
import pandas as pd
import helpers
# fix random seed for reproducibility
np.random.seed(7)
#-------------------------- Constants --------------------------#
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string(
"input_dir", os.path.abspath("../d... | mit |
redfern314/eeproto | fskplot.py | 1 | 1031 | import csv
import matplotlib.pyplot as plt
# NOTE: change this to reflect the sampling period for this data
timescale = 0.0002
with open('fsk.csv', 'r') as csvfile:
reader = csv.reader(csvfile)
time = []
samples = []
for row in reader:
time.append(round(float(row[0]),7))
samples.appen... | mit |
wmvanvliet/mne-python | mne/viz/tests/test_circle.py | 14 | 5024 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: Simplified BSD
import numpy as np
import pytest
import matplotlib.pyplot as plt
from mne.viz import plot_connectivity_circle, circular_l... | bsd-3-clause |
flyingpoops/kaggle-digit-recognizer-team-learning | model/cnn/cnn2.py | 1 | 7015 | # Multi Convolutional Layer Nerual Network (keras part is mostly adapted from kaggle script at https://www.kaggle.com/somshubramajumdar/digit-recognizer/deep-convolutional-network-using-keras)
import os
os.environ["THEANO_FLAGS"] = "mode=FAST_RUN,device=gpu,floatX=float32,lib.cnmem=1,dnn.enabled=False"
# temp files wil... | apache-2.0 |
samfpetersen/gnuradio | gr-filter/examples/synth_to_chan.py | 18 | 3875 | #!/usr/bin/env python
#
# Copyright 2010,2012,2013 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your ... | gpl-3.0 |
LaceyChen17/instacart-market-basket-analysis | lgb_cv.py | 1 | 4447 | import gc; import pickle; from copy import deepcopy
import numpy as np; import pandas as pd
import lightgbm as lgb
from matplotlib import pyplot as plt
from sklearn.model_selection import GroupKFold
from sklearn.metrics import f1_score, roc_auc_score
import constants, feats, transactions, utils, evaluation, inference... | mit |
orezpraw/unnaturalcode | unnaturalcode/testdata/makehuman/docs/sphinx/source/conf.py | 2 | 8127 | # -*- coding: utf-8 -*-
#
# MakeHuman documentation build configuration file, created by
# sphinx-quickstart on Sun Oct 30 12:49:31 2011.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# A... | agpl-3.0 |
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