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 |
|---|---|---|---|---|---|
toastedcornflakes/scikit-learn | examples/svm/plot_svm_margin.py | 318 | 2328 | #!/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 |
hsuantien/scikit-learn | sklearn/ensemble/tests/test_gradient_boosting.py | 127 | 37672 | """
Testing for the gradient boosting module (sklearn.ensemble.gradient_boosting).
"""
import warnings
import numpy as np
from sklearn import datasets
from sklearn.base import clone
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble.grad... | bsd-3-clause |
teonlamont/mne-python | mne/viz/tests/test_3d.py | 2 | 18155 | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Denis Engemann <denis.engemann@gmail.com>
# Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Eric Larson <larson.eric.d@gmail.com>
# Mainak Jas <mainak@neuro.hut.fi>
# Mark Wronkiewicz <wronk.mark@gmail.c... | bsd-3-clause |
yuyu2172/chainercv | tests/visualizations_tests/test_vis_bbox.py | 3 | 4256 | import unittest
import numpy as np
from chainer import testing
from chainercv.utils import generate_random_bbox
from chainercv.visualizations import vis_bbox
try:
import matplotlib # NOQA
_available = True
except ImportError:
_available = False
@testing.parameterize(
*testing.product_dict([
... | mit |
eg-zhang/scikit-learn | examples/cluster/plot_color_quantization.py | 297 | 3443 | # -*- coding: utf-8 -*-
"""
==================================
Color Quantization using K-Means
==================================
Performs a pixel-wise Vector Quantization (VQ) of an image of the summer palace
(China), reducing the number of colors required to show the image from 96,615
unique colors to 64, while pre... | bsd-3-clause |
fzalkow/scikit-learn | sklearn/neighbors/tests/test_kd_tree.py | 129 | 7848 | import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.neighbors.kd_tree import (KDTree, NeighborsHeap,
simultaneous_sort, kernel_norm,
nodeheap_sort, DTYPE, ITYPE)
from sklearn.neighbors.dist_metrics import Dista... | bsd-3-clause |
vivekmishra1991/scikit-learn | examples/linear_model/plot_robust_fit.py | 238 | 2414 | """
Robust linear estimator fitting
===============================
Here a sine function is fit with a polynomial of order 3, for values
close to zero.
Robust fitting is demoed in different situations:
- No measurement errors, only modelling errors (fitting a sine with a
polynomial)
- Measurement errors in X
- M... | bsd-3-clause |
radiasoft/radtrack | experimental/hermite/testHermite02.py | 1 | 6919 | #
# Test executable #2 to exercise the Gauss-Hermite class
# Here, we fit a Gauss-Hermite expansion to an arbitrary profile.
# The SciPy least squares method is used.
#
# Copyright (c) 2013 RadiaBeam Technologies. All rights reserved
#
# python imports
import math
# SciPy imports
import numpy as np
import matplotlib.p... | apache-2.0 |
pauliacomi/pyGAPS | src/pygaps/parsing/json.py | 1 | 8713 | """
Parse to and from a JSON string/file format for isotherms.
The _parser_version variable documents any changes to the format,
and is used to check for any deprecations.
"""
import json
import warnings
import pandas
from pygaps.core.baseisotherm import BaseIsotherm
from pygaps.core.modelisotherm import ModelIsot... | mit |
airanmehr/bio | Scripts/TimeSeriesPaper/RealData/Friday.py | 1 | 16916 | '''
Copyleft Jan 27, 2017 Arya Iranmehr, PhD Student, Bafna Lab, UC San Diego, Email: airanmehr@gmail.com
'''
import numpy as np;
np.set_printoptions(linewidth=200, precision=5, suppress=True)
import pandas as pd;
pd.options.display.max_rows = 40;
pd.options.display.expand_frame_repr = False
import pylab as plt;
imp... | mit |
brianquinlan/learn-tensorflow | learning1/learn.py | 1 | 1334 | import pandas
import numpy
from IPython import display
import tensorflow
from tensorflow.contrib import learn
import numpy
def get_features(dataframe):
return dataframe[['a', 'b', 'c']]
def get_targets(dataframe):
return dataframe[['d']]
data = pandas.read_csv(open('linear.csv'), sep=',').astype(numpy.float3... | mit |
classicboyir/BuildingMachineLearningSystemsWithPython | ch07/figure1_2.py | 22 | 1848 | # 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 numpy as np
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegr... | mit |
saiwing-yeung/scikit-learn | examples/cross_decomposition/plot_compare_cross_decomposition.py | 55 | 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 |
mkness/TheCannon | code/makeplot_coeff_general.py | 1 | 2724 | #!/usr/bin/python
import numpy
from numpy import savetxt
import matplotlib
from matplotlib import pyplot
import scipy
from scipy import interpolate
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
s = matplotlib.font_manager.FontProperties()
s.set_family('serif')
s.set_size(14)
from matplotlib import r... | mit |
andrescodas/casadi | docs/examples/python/direct_single_shooting.py | 2 | 3248 | #
# This file is part of CasADi.
#
# CasADi -- A symbolic framework for dynamic optimization.
# Copyright (C) 2010-2014 Joel Andersson, Joris Gillis, Moritz Diehl,
# K.U. Leuven. All rights reserved.
# Copyright (C) 2011-2014 Greg Horn
#
# CasADi is free software; you can... | lgpl-3.0 |
shakamunyi/tensorflow | tensorflow/contrib/learn/python/learn/tests/test_custom_decay.py | 7 | 2270 | # 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 |
jat255/hyperspy | hyperspy/drawing/_markers/rectangle.py | 4 | 3742 | # -*- coding: utf-8 -*-
# Copyright 2007-2020 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 |
oxpeter/small_fry | concordance.py | 1 | 13262 | #!/usr/bin/env python
"""
This program is a simulation of gene expression studies performed in four different ant species,
in order to determine the likelihood of achieving given congruence between studies by chance.
The starting point is the Cerapachys biroi, Acromyrmex echinatior, Solenopsis invicta and
Dinoponera qu... | gpl-2.0 |
khancyr/ardupilot | Tools/mavproxy_modules/lib/magcal_graph_ui.py | 108 | 8248 | # Copyright (C) 2016 Intel Corporation. All rights reserved.
#
# This file 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 fi... | gpl-3.0 |
assad2012/ggplot | ggplot/tests/test_stat_summary.py | 12 | 1028 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
from ggplot.tests import image_comparison
from ggplot import *
import numpy as np
import pandas as pd
@image_comparison(baseline_images=['default'])
def test_stat_summary_default():
print(ggplot(aes(x='... | bsd-2-clause |
PatrickOReilly/scikit-learn | examples/svm/plot_svm_regression.py | 120 | 1520 | """
===================================================================
Support Vector Regression (SVR) using linear and non-linear kernels
===================================================================
Toy example of 1D regression using linear, polynomial and RBF kernels.
"""
print(__doc__)
import numpy as np
... | bsd-3-clause |
lvniqi/tianchi_power | code/preprocess.py | 1 | 32964 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat May 13 17:36:18 2017
@author: boweiy
"""
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import os
import xgboost as xgb
from multiprocessing import Pool as m_Pool
from sklearn import preprocessing
from sklea... | mit |
XueqingLin/tensorflow | tensorflow/contrib/learn/python/learn/estimators/rnn.py | 4 | 10267 | # 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 |
anurag313/scikit-learn | sklearn/datasets/tests/test_samples_generator.py | 181 | 15664 | from __future__ import division
from collections import defaultdict
from functools import partial
import numpy as np
import scipy.sparse as sp
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing imp... | bsd-3-clause |
syl20bnr/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/projections/polar.py | 69 | 20981 | import math
import numpy as npy
import matplotlib
rcParams = matplotlib.rcParams
from matplotlib.artist import kwdocd
from matplotlib.axes import Axes
from matplotlib import cbook
from matplotlib.patches import Circle
from matplotlib.path import Path
from matplotlib.ticker import Formatter, Locator
from matplotlib.tr... | gpl-3.0 |
ProkopHapala/SimpleSimulationEngine | python/pyMolecular/test_CLCFGO.py | 1 | 26748 | import os
import sys
import numpy as np
#sys.path.append("../")
sys.path.append('../')
from pyMeta import cpp_utils
cpp_utils.clean_build = False # Recompile only if changed
#import eFF
import CLCFGO as effmc
import eFF_terms as effpy
# ========== Globals
iNorm = -1
bDebug = 0
natom=0; norb=2; perOrb=1; nqOr... | mit |
MDAnalysis/mdanalysis | package/MDAnalysis/analysis/encore/clustering/__init__.py | 1 | 1447 | # -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 -*-
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
#
# MDAnalysis --- https://www.mdanalysis.org
# Copyright (c) 2006-2017 The MDAnalysis Development Team and contributors
# (see the file AUTHORS for the full list of names)
#
# Released under t... | gpl-2.0 |
barney-NG/pyCAMTracker | src/filterpy/kalman/tests/test_kf.py | 1 | 13439 | # -*- coding: utf-8 -*-
"""Copyright 2015 Roger R Labbe Jr.
FilterPy library.
http://github.com/rlabbe/filterpy
Documentation at:
https://filterpy.readthedocs.org
Supporting book at:
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
This is licensed under an MIT license. See the readme.MD file
for mor... | mit |
rsignell-usgs/notebook | WCS/WCS_sciencebase_test.py | 1 | 2121 | # -*- coding: utf-8 -*-
# <nbformat>3.0</nbformat>
# <headingcell level=1>
# Extract data from USGS ScienceBase
# <codecell>
%matplotlib inline
# <codecell>
from owslib.wcs import WebCoverageService
import numpy as np
import numpy.ma as ma
endpoint='https://www.sciencebase.gov/catalogMaps/mapping/ows/5638cf1fe4b0... | mit |
NunoEdgarGub1/scikit-learn | examples/linear_model/plot_sgd_weighted_samples.py | 344 | 1458 | """
=====================
SGD: Weighted samples
=====================
Plot decision function of a weighted dataset, where the size of points
is proportional to its weight.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import linear_model
# we create 20 points
np.random.seed(0)
X ... | bsd-3-clause |
matteobachetti/srt-single-dish-tools | srttools/scan.py | 1 | 35441 | """Scan class."""
from __future__ import (absolute_import, division,
print_function)
from .io import read_data, root_name, get_chan_columns, get_channel_feed
from .io import mkdir_p
import glob
from .read_config import read_config, get_config_file
from .fit import ref_mad, contiguous_regions
im... | bsd-3-clause |
DistrictDataLabs/yellowbrick | yellowbrick/style/utils.py | 1 | 2199 | # yellowbrick.style.utils
# Utility functions for styles
#
# Author: Neal Humphrey
# Created: Wed Mar 22 12:39:35 2017 -0400
#
# Copyright (C) 2017 The scikit-yb developers
# For license information, see LICENSE.txt
#
# ID: utils.py [45268fc] humphrey.neal@gmail.com $
"""
Utility functions for styles
"""
#########... | apache-2.0 |
henry-ngo/VIP | vip_hci/stats/utils_stats.py | 1 | 1208 | #! /usr/bin/env python
"""
Various stat functions.
"""
from __future__ import division, print_function
__author__ = 'C. Gomez @ ULg'
__all__ = ['descriptive_stats']
import numpy as np
from matplotlib.pyplot import boxplot
def descriptive_stats(array, verbose=True, label='', mean=False, plot=False):
""" Simple... | mit |
behrtam/wine-quality-prediction | naive-red.py | 1 | 1207 | from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import MultinomialNB
from sklearn.naive_bayes import BernoulliNB
from sklearn.cross_validation import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.metrics import classification_report
from sklearn.metrics import confusio... | mit |
nicholasmalaya/arcanus | exp/press_trans/code/read_incline_error.py | 2 | 2212 | #!/bin/py
#
# open file
# read contents
# (re)start when third column found
#
import sys
#
# open and read file
#
path="../data/statistics_incl.lvm"
file = open(path, "r+")
#
# data objects
#
set_names = []
voltage = []
std = []
height = []
voltage2 = []
std2 = []
height2 = []
for line in file:
... | mit |
ThomasHSmith/PhosphoProTools | src/phosphoprotools/piscoreanalysis.py | 1 | 10612 | # Filename: piscoreanalysis.py
# Author: Thomas H. Smith 2017
"""
Small collection of functions used for statistical analysis
of phosphoproteomics datasets with row-wise intensity values
with at least two replicates from each experimental condition
"""
from scipy import stats
from numpy import var, mean, log2, log10, ... | mit |
sellberg/SACLA2016A8015 | scripts/04_ADUs_mean_hist.py | 2 | 1706 | #!/home/doniach/dermen/epd731/bin/python
import numpy as np
import h5py
import matplotlib
import matplotlib.pyplot as plt
import argparse
import time
import pandas as pd
# -- default parameters
run = 448539
file_folder = '/UserData/fperakis/2016_6/01_test/'
# -- files and folders
file_name = '%d.h5'%(run)
file_path... | bsd-2-clause |
tbarchyn/flow_ninja | flow_ninja_utilities.py | 1 | 2227 | # FLOW NINJA
# Copyright 2016-2017 Thomas E. Barchyn
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
# associated documentation files (the "Software"), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge,... | mit |
vinodkc/spark | python/pyspark/pandas/tests/indexes/test_base.py | 14 | 97964 | #
# 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 |
pratapvardhan/scikit-learn | examples/model_selection/plot_underfitting_overfitting.py | 53 | 2668 | """
============================
Underfitting vs. Overfitting
============================
This example demonstrates the problems of underfitting and overfitting and
how we can use linear regression with polynomial features to approximate
nonlinear functions. The plot shows the function that we want to approximate,
wh... | bsd-3-clause |
willettk/rgz-analysis | python/rgz_sdss_gz.py | 2 | 4629 | import numpy as np
from matplotlib import pyplot as plt
from astropy.io import ascii
from astropy.io import fits
from scipy import stats
from astropy.io.votable import parse_single_table
from astropy.cosmology import WMAP9
rgzdir = '/Users/willettk/Astronomy/Research/GalaxyZoo/rgz-analysis'
def votable_read(fname):
... | mit |
ZENGXH/scikit-learn | examples/cluster/plot_agglomerative_clustering_metrics.py | 402 | 4492 | """
Agglomerative clustering with different metrics
===============================================
Demonstrates the effect of different metrics on the hierarchical clustering.
The example is engineered to show the effect of the choice of different
metrics. It is applied to waveforms, which can be seen as
high-dimens... | bsd-3-clause |
asnorkin/sentiment_analysis | site/lib/python2.7/site-packages/sklearn/linear_model/setup.py | 83 | 1719 | 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... | mit |
mehdidc/scikit-learn | sklearn/tests/test_cross_validation.py | 3 | 45125 | """Test the cross_validation module"""
from __future__ import division
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from scipy import stats
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
from sklearn... | bsd-3-clause |
LilyFG/LilyFG.github.io | markdown_generator/publications.py | 197 | 3887 |
# coding: utf-8
# # Publications markdown generator for academicpages
#
# Takes a TSV of publications with metadata and converts them for use with [academicpages.github.io](academicpages.github.io). This is an interactive Jupyter notebook, with the core python code in publications.py. Run either from the `markdown_g... | mit |
gfyoung/pandas | pandas/tests/frame/methods/test_to_records.py | 6 | 14349 | from collections import abc
import numpy as np
import pytest
from pandas import (
CategoricalDtype,
DataFrame,
MultiIndex,
Series,
Timestamp,
date_range,
)
import pandas._testing as tm
class TestDataFrameToRecords:
def test_to_records_timeseries(self):
index = date_range("1/1/200... | bsd-3-clause |
kashif/scikit-learn | sklearn/metrics/cluster/tests/test_unsupervised.py | 26 | 3305 | import numpy as np
from scipy.sparse import csr_matrix
from sklearn import datasets
from sklearn.metrics.cluster.unsupervised import silhouette_score
from sklearn.metrics import pairwise_distances
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.te... | bsd-3-clause |
vibhorag/scikit-learn | sklearn/ensemble/tests/test_base.py | 284 | 1328 | """
Testing for the base module (sklearn.ensemble.base).
"""
# Authors: Gilles Louppe
# License: BSD 3 clause
from numpy.testing import assert_equal
from nose.tools import assert_true
from sklearn.utils.testing import assert_raise_message
from sklearn.datasets import load_iris
from sklearn.ensemble import BaggingCla... | bsd-3-clause |
cactusbin/nyt | matplotlib/lib/matplotlib/rcsetup.py | 4 | 31862 | """
The rcsetup module contains the default values and the validation code for
customization using matplotlib's rc settings.
Each rc setting is assigned a default value and a function used to validate
any attempted changes to that setting. The default values and validation
functions are defined in the rcsetup module, ... | unlicense |
wavelets/deepnet | deepnet/visualize.py | 10 | 5738 | import numpy as np
import cPickle as pickle
import matplotlib.pyplot as plt
plt.ion()
fig_id = 0
def GetFigId():
globals()['fig_id'] += 1
return globals()['fig_id'] - 1
def show_model_state(model, step):
for i, node in enumerate(model.node_list):
dims = int(np.floor(np.sqrt(node.state.shape[0])))
displa... | bsd-3-clause |
jswoboda/GeoDataPython | GeoData/utilityfuncs.py | 1 | 25206 | #!/usr/bin/env python
"""
Note: "cartesian" column order is x,y,z in the Nx3 matrix
This module holds a number of functions that can be used to read data into
GeoData objects. All of the function s have the following outputs
(data,coordnames,dataloc,sensorloc,times)
Outputs
data - A dictionary with keys that are t... | mit |
hlin117/scikit-learn | examples/cluster/plot_kmeans_stability_low_dim_dense.py | 338 | 4324 | """
============================================================
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 |
lin-credible/scikit-learn | sklearn/linear_model/tests/test_coordinate_descent.py | 40 | 23697 | # Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
from sys import version_info
import numpy as np
from scipy import interpolate, sparse
from copy import deepcopy
from sklearn.datasets import load_boston
from sklearn.utils.testing ... | bsd-3-clause |
zfrenchee/pandas | pandas/tests/series/test_timeseries.py | 1 | 32356 | # coding=utf-8
# pylint: disable-msg=E1101,W0612
import pytest
import numpy as np
from datetime import datetime, timedelta, time
import pandas as pd
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas._libs.tslib import iNaT
from pandas.compat import lrange, StringIO, product
from ... | bsd-3-clause |
jmschrei/scikit-learn | examples/linear_model/plot_robust_fit.py | 26 | 2701 | """
Robust linear estimator fitting
===============================
Here a sine function is fit with a polynomial of order 3, for values
close to zero.
Robust fitting is demoed in different situations:
- No measurement errors, only modelling errors (fitting a sine with a
polynomial)
- Measurement errors in X
- M... | bsd-3-clause |
mxjl620/scikit-learn | examples/cluster/plot_kmeans_digits.py | 230 | 4524 | """
===========================================================
A demo of K-Means clustering on the handwritten digits data
===========================================================
In this example we compare the various initialization strategies for
K-means in terms of runtime and quality of the results.
As the gr... | bsd-3-clause |
JasonKessler/scattertext | scattertext/test/test_word2VecFromParsedCorpus.py | 1 | 1889 | from unittest import TestCase
import pandas as pd
from scattertext.CorpusFromParsedDocuments import CorpusFromParsedDocuments
from scattertext.WhitespaceNLP import whitespace_nlp
from scattertext.representations.Word2VecFromParsedCorpus import Word2VecFromParsedCorpus, \
Word2VecFromParsedCorpusBigrams
from scattert... | apache-2.0 |
Jay-Oh-eN/topmodel | web/views/pages.py | 1 | 2302 | from flask import render_template, g, request, redirect
from topmodel import plots
from topmodel.hmetrics import auc
from web import app
import matplotlib.pyplot as plt
@app.route("/")
def home():
model_paths = sorted(g.model_data_manager.models.keys())
return render_template("index.html", model_paths=model... | mit |
fredhusser/scikit-learn | examples/calibration/plot_calibration.py | 225 | 4795 | """
======================================
Probability calibration of classifiers
======================================
When performing classification you often want to predict not only
the class label, but also the associated probability. This probability
gives you some kind of confidence on the prediction. However,... | bsd-3-clause |
jeremymchacon/colony_counter | cc_n.py | 1 | 9065 | #! /usr/bin/Rscript
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 3 21:09:17 2016
@author: Jeremy
"""
import sys
import numpy as np
import matplotlib.pyplot as plt
import skimage
from skimage import io
from skimage import filters
from skimage import feature
from skimage import segmentation
import sklearn
from skl... | mit |
rytaft/h-store | scripts/anticache/plotter.py | 9 | 2968 | #!/usr/bin/env python
import os
import sys
import csv
import logging
import matplotlib.pyplot as plot
import pylab
OPT_GRAPH_WIDTH = 1200
OPT_GRAPH_HEIGHT = 600
OPT_GRAPH_DPI = 100
## ==============================================
## main
## ==============================================
if __name__ == '__main__':
... | gpl-3.0 |
jorik041/scikit-learn | sklearn/linear_model/least_angle.py | 42 | 49357 | """
Least Angle Regression algorithm. See the documentation on the
Generalized Linear Model for a complete discussion.
"""
from __future__ import print_function
# Author: Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Gael Varoquaux
#
# License: BSD 3 ... | bsd-3-clause |
milapour/palm | palm/state_collection.py | 2 | 4073 | import pandas
class StateCollectionFactory(object):
"""
Factory class that builds a StateCollection.
Attributes
----------
state_list : list
The states in the model.
state_id_list : list
The identifier strings for each state.
"""
def __init__(self):
super(StateC... | bsd-2-clause |
rolando/ClickSecurity-data_hacking | data_hacking/simple_stats/simple_stats.py | 6 | 7475 | # Contingency Table, Two-way table, Joint Distribution, G-Scores
# Going off the reservation here, just couldn't find the right functionality elsewhere
# References: http://en.wikipedia.org/wiki/Contingency_table
# http://en.wikipedia.org/wiki/G_test (Wikipedia)
# http://udel.edu/~mcdonald/stath... | mit |
yandex/rep | rep/estimators/interface.py | 1 | 8670 | """
**REP** wrappers are derived from :class:`Classifier` and :class:`Regressor`
depending on the problem of interest.
Below you can see the standard methods available in the wrappers.
"""
from __future__ import division, print_function, absolute_import
from abc import ABCMeta, abstractmethod
import numpy
import pan... | apache-2.0 |
USCDataScience/NN-fileTypeDetection | classifiers/supportVectorMachine.py | 1 | 2438 | #
# 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 |
softsr/paparazzi | sw/airborne/test/ahrs/ahrs_utils.py | 15 | 5172 | #! /usr/bin/env python
# $Id$
# Copyright (C) 2011 Antoine Drouin
#
# This file is part of Paparazzi.
#
# Paparazzi 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, or (at your option)
# an... | gpl-2.0 |
olologin/scikit-learn | examples/preprocessing/plot_function_transformer.py | 158 | 1993 | """
=========================================================
Using FunctionTransformer to select columns
=========================================================
Shows how to use a function transformer in a pipeline. If you know your
dataset's first principle component is irrelevant for a classification task,
you ca... | bsd-3-clause |
loli/semisupervisedforests | examples/text/hashing_vs_dict_vectorizer.py | 284 | 3265 | """
===========================================
FeatureHasher and DictVectorizer Comparison
===========================================
Compares FeatureHasher and DictVectorizer by using both to vectorize
text documents.
The example demonstrates syntax and speed only; it doesn't actually do
anything useful with the e... | bsd-3-clause |
TimoRoth/oggm | benchmarks/track_model_results.py | 2 | 10718 | import os
import numpy as np
import oggm
import geopandas as gpd
import xarray as xr
from oggm import tasks
from oggm import cfg, utils, workflow
from oggm.utils import get_demo_file
from oggm.tests.funcs import get_test_dir
from oggm.core import climate, massbalance
from oggm.workflow import execute_entity_task
from ... | bsd-3-clause |
pianomania/scikit-learn | examples/applications/plot_tomography_l1_reconstruction.py | 23 | 5460 | """
======================================================================
Compressive sensing: tomography reconstruction with L1 prior (Lasso)
======================================================================
This example shows the reconstruction of an image from a set of parallel
projections, acquired along dif... | bsd-3-clause |
aewhatley/scikit-learn | examples/tree/plot_tree_regression_multioutput.py | 206 | 1800 | """
===================================================================
Multi-output Decision Tree Regression
===================================================================
An example to illustrate multi-output regression with decision tree.
The :ref:`decision trees <tree>`
is used to predict simultaneously the ... | bsd-3-clause |
kgullikson88/LasCampanas-MIKE | ConvertToExtensions.py | 1 | 2043 | import FittingUtilities
from astropy.io import fits as pyfits
import sys
import os
import numpy
import matplotlib.pyplot as plt
import HelperFunctions
left_trim = 8
right_trim = 0
bad_regions = {}
if __name__ == "__main__":
fileList = []
for arg in sys.argv[1:]:
fileList.append(arg)
for fname in fileL... | gpl-3.0 |
nguyentu1602/statsmodels | statsmodels/genmod/tests/test_gee.py | 19 | 55589 | """
Test functions for GEE
External comparisons are to R and Stata. The statmodels GEE
implementation should generally agree with the R GEE implementation
for the independence and exchangeable correlation structures. For
other correlation structures, the details of the correlation
estimation differ among implementat... | bsd-3-clause |
akrherz/iem | htdocs/json/sbw_by_point.py | 1 | 5353 | """
Get storm based warnings by lat lon point, optionally a time
"""
import sys
from io import BytesIO, StringIO
import json
import datetime
import numpy as np
from paste.request import parse_formvars
from pyiem.util import get_dbconn, utc
from pyiem.nws.vtec import VTEC_PHENOMENA, VTEC_SIGNIFICANCE, get_ps_string
fro... | mit |
FRESNA/PyPSA | test/test_opf_storage.py | 1 | 1107 |
import pypsa
import pandas as pd
import sys
import os
from numpy.testing import assert_array_almost_equal as equal
solvers = ['glpk'] if sys.platform == 'win32' else ['cbc', 'glpk']
def test_opf():
csv_folder_name = os.path.join(os.path.dirname(__file__), "..", "examples",
"o... | gpl-3.0 |
TakayukiSakai/tensorflow | tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py | 1 | 3793 | # 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 |
BhallaLab/moose-examples | tutorials/ChemicalOscillators/relaxationOsc.py | 4 | 3265 | #########################################################################
## This program is part of 'MOOSE', the
## Messaging Object Oriented Simulation Environment.
## Copyright (C) 2014 Upinder S. Bhalla. and NCBS
## It is made available under the terms of the
## GNU Lesser General Public License version 2... | gpl-2.0 |
solin319/incubator-mxnet | example/speech_recognition/stt_utils.py | 44 | 5892 | # 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 u... | apache-2.0 |
lsolanka/simtools | simtools/plotting/figsavers.py | 3 | 2426 | '''Automatic figure savers.'''
from __future__ import absolute_import, print_function, division
from matplotlib.backends.backend_pdf import PdfPages
class MultiFigureSaver(object):
'''Matplotlib figure saver that processes several images.
This is an abstract class that defines basic functionality. Use some ... | gpl-2.0 |
WGierke/git_better | app/main.py | 1 | 6434 | from __future__ import division
def warn(*args, **kwargs):
pass
import warnings
warnings.warn = warn
import argparse
import os
import pandas as pd
import sys
from tqdm import tqdm
from classifier import get_text_pipeline, get_voting_classifier, DescriptionClassifier, ReadmeClassifier, NumericEnsembleClassifier, nor... | apache-2.0 |
bzero/arctic | arctic/scripts/arctic_copy_data.py | 4 | 4999 | import argparse
import os
import logging
from multiprocessing import Pool
import pwd
from arctic.decorators import _get_host
from arctic.store.audit import ArcticTransaction
from ..hosts import get_arctic_lib
from ..date import DateRange, to_pandas_closed_closed, CLOSED_OPEN, OPEN_CLOSED, mktz
from .utils import setu... | lgpl-2.1 |
openthings/zeppelin | interpreter/lib/python/backend_zinline.py | 61 | 11831 | # 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 use ... | apache-2.0 |
shakamunyi/tensorflow | tensorflow/contrib/learn/__init__.py | 42 | 2596 | # 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 |
vespero89/Snoring_Challenge | Supervectors/gridsearch_ivec.py | 1 | 6323 | import sys
sys.path.append('..')
import numpy as np
from sklearn.svm import SVC
import sklearn.preprocessing as preprocessing
from sklearn.metrics import recall_score, accuracy_score, confusion_matrix, classification_report
import os
import sys
import utils.dataset_manupulation as dm
import utils.utils as utl
import w... | gpl-3.0 |
mattgiguere/scikit-learn | sklearn/mixture/gmm.py | 7 | 31031 | """
Gaussian Mixture Models.
This implementation corresponds to frequentist (non-Bayesian) formulation
of Gaussian Mixture Models.
"""
# Author: Ron Weiss <ronweiss@gmail.com>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Bertrand Thirion <bertrand.thirion@inria.fr>
import warnings
import numpy as... | bsd-3-clause |
jseabold/scikit-learn | sklearn/grid_search.py | 6 | 38441 | """
The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters
of an estimator.
"""
from __future__ import print_function
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# ... | bsd-3-clause |
PhE/dask | dask/dataframe/tests/test_optimize_dataframe.py | 3 | 2007 | import pytest
from operator import getitem
from toolz import valmap, merge
from dask.dataframe.optimize import dataframe_from_ctable
import dask.dataframe as dd
import pandas as pd
dsk = {('x', 0): pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]},
index=[0, 1, 3]),
('x', 1): pd.DataFr... | bsd-3-clause |
ruchee/vimrc | vimfiles/bundle/vim-python/submodules/pydocstyle/src/tests/test_cases/canonical_numpy_examples.py | 3 | 5315 | """This is the docstring for the example.py module. Modules names should
have short, all-lowercase names. The module name may have underscores if
this improves readability.
Every module should have a docstring at the very top of the file. The
module's docstring may extend over multiple lines. If your docstring doe... | mit |
rohanp/scikit-learn | examples/decomposition/plot_ica_vs_pca.py | 306 | 3329 | """
==========================
FastICA on 2D point clouds
==========================
This example illustrates visually in the feature space a comparison by
results using two different component analysis techniques.
:ref:`ICA` vs :ref:`PCA`.
Representing ICA in the feature space gives the view of 'geometric ICA':
ICA... | bsd-3-clause |
pcm17/tensorflow | tensorflow/examples/learn/boston.py | 11 | 1978 | # 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 |
kejbaly2/metrique | metrique/reporting.py | 1 | 3359 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
# Author: "Juraj Niznan" <jniznan@redhat.com>
# Author: "Chris Ward <cward@redhat.com>
'''
metrique.reporting
~~~~~~~~~~~~~~~~~~
This module contains a basic reporting class
for quickly generating textual reports.
'''
... | gpl-3.0 |
RPGOne/scikit-learn | sklearn/neural_network/rbm.py | 46 | 12291 | """Restricted Boltzmann Machine
"""
# Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca>
# Vlad Niculae
# Gabriel Synnaeve
# Lars Buitinck
# License: BSD 3 clause
import time
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator
from ..base import TransformerMixi... | bsd-3-clause |
untom/scikit-learn | examples/applications/plot_species_distribution_modeling.py | 254 | 7434 | """
=============================
Species distribution modeling
=============================
Modeling species' geographic distributions is an important
problem in conservation biology. In this example we
model the geographic distribution of two south american
mammals given past observations and 14 environmental
varia... | bsd-3-clause |
widdowquinn/pyani | tests/test_anim.py | 1 | 7630 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# (c) The James Hutton Institute 2017-2019
# (c) University of Strathclyde 2019-2021
# Author: Leighton Pritchard
#
# Contact:
# leighton.pritchard@strath.ac.uk
#
# Leighton Pritchard,
# Strathclyde Institute for Pharmacy and Biomedical Sciences,
# 161 Cathedral Street,
# G... | mit |
ndingwall/scikit-learn | examples/datasets/plot_random_multilabel_dataset.py | 23 | 3379 | """
==============================================
Plot randomly generated multilabel dataset
==============================================
This illustrates the :func:`~sklearn.datasets.make_multilabel_classification`
dataset generator. Each sample consists of counts of two features (up to 50 in
total), which are dif... | bsd-3-clause |
AZMAG/urbansim | urbansim/utils/yamlio.py | 3 | 6649 | """
Utilities for doing IO to YAML files.
"""
try:
from itertools import izip as zip
except ImportError:
pass
import os
import sys
import numpy as np
import yaml
from collections import OrderedDict
if sys.version_info[0] < 3:
def __represent_long(dumper, data):
"""
Strips away extraneou... | bsd-3-clause |
jrderuiter/ngs-tk | ngs_tk/cnv/resample.py | 1 | 1837 | from __future__ import (absolute_import, division,
print_function, unicode_literals)
# noinspection PyUnresolvedReferences
from builtins import (ascii, bytes, chr, dict, filter, hex, input,
int, map, next, oct, open, pow, range, round,
str, super, zip... | gpl-2.0 |
fredhusser/scikit-learn | sklearn/linear_model/tests/test_passive_aggressive.py | 169 | 8809 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal, assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_rais... | bsd-3-clause |
rmhyman/DataScience | Lesson4/lineplot_compare_with_ggplot.py | 1 | 1282 |
import pandas
from ggplot import *
def lineplot_compare(hr_by_team_year_sf_la_csv):
# Write a function, lineplot_compare, that will read a csv file
# called hr_by_team_year_sf_la.csv and plot it using pandas and ggplot.
#
# This csv file has three columns: yearID, HR, and teamID. The data ... | mit |
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