repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
biokit/biokit | biokit/viz/heatmap.py | 1 | 13458 | """Heatmap and dendograms"""
import matplotlib
import pylab
import scipy.cluster.hierarchy as hierarchy
import scipy.spatial.distance as distance
import numpy as np # get rid of this dependence
import easydev
import colormap
from biokit.viz.linkage import Linkage
__all__ = ['Heatmap']
def get_heatmap_df():
"""a... | bsd-2-clause |
michaelaye/pyciss | pyciss/plotting.py | 1 | 9363 | import logging
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
from astropy import units as u
from ipywidgets import fixed, interact
from ._utils import which_epi_janus_resonance
from .meta import get_all_resonances
from .ringcube import RingCube
logger = logging.getLogger(__name__)
res... | isc |
duttashi/Data-Analysis-Visualization | scripts/general/chiSquareTest.py | 1 | 3347 | __author__ = 'Ashoo'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.stats
import warnings
sns.set(color_codes=True)
# Reading the data where low_memory=False increases the program efficiency
data= pd.read_csv("gapminder.csv", low_memory=False)
# setting variab... | mit |
sgranitz/northwestern | predict420/grex2_Customer_Sales_Analysis.py | 2 | 7041 | # Stephan Granitz [ GrEx2 ]
# Import libraries
import pandas as pd
import numpy as np
import shelve
import sqlite3
# 1 Import each of the csv files you downloaded from the SSCC into a pandas DF
# Grab files
folder = "C:/Users/sgran/Desktop/GrEx2/"
io1 = folder + "seg6770cust.csv"
io2 = folder + "seg6770item.csv"
io3 ... | mit |
mehdidc/scikit-learn | benchmarks/bench_sample_without_replacement.py | 397 | 8008 | """
Benchmarks for sampling without replacement of integer.
"""
from __future__ import division
from __future__ import print_function
import gc
import sys
import optparse
from datetime import datetime
import operator
import matplotlib.pyplot as plt
import numpy as np
import random
from sklearn.externals.six.moves i... | bsd-3-clause |
jwlockhart/concept-networks | nlp.py | 1 | 2220 | # utility functions for NLP-based similarity metrics
# version 1.0
# code modified from:
# https://stackoverflow.com/questions/8897593/similarity-between-two-text-documents
import nltk
import string
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
stemmer = nltk.stem.porter.PorterStem... | gpl-3.0 |
QuantSoftware/QuantSoftwareToolkit | bin/investors_report.py | 5 | 6911 | #
# report.py
#
# Generates a html file containing a report based
# off a timeseries of funds from a pickle file.
#
# Drew Bratcher
#
from pylab import *
import numpy
from QSTK.qstkutil import DataAccess as da
from QSTK.qstkutil import qsdateutil as du
from QSTK.qstkutil import tsutil as tsu
from QSTK.q... | bsd-3-clause |
nschloe/matplotlib2tikz | test/test_rotated_labels.py | 1 | 3361 | # -*- coding: utf-8 -*-
#
import os
import tempfile
import pytest
from matplotlib import pyplot as plt
import matplotlib2tikz
def __plot():
fig, ax = plt.subplots()
x = [1, 2, 3, 4]
y = [1, 4, 9, 6]
plt.plot(x, y, "ro")
plt.xticks(x, rotation="horizontal")
return fig, ax
@pytest.mark.pa... | mit |
alekz112/statsmodels | statsmodels/iolib/tests/test_summary.py | 31 | 1535 | '''examples to check summary, not converted to tests yet
'''
from __future__ import print_function
if __name__ == '__main__':
from statsmodels.regression.tests.test_regression import TestOLS
#def mytest():
aregression = TestOLS()
TestOLS.setupClass()
results = aregression.res1
r_summary = s... | bsd-3-clause |
fadawar/election_wordcloud | generate.py | 1 | 2431 | #!/usr/bin/env python2
"""
Generate wordclouds with specific colors.
colors - list of rgb colors
file_name - path to file with source text
"""
from os import path
import random
import functools
from wordcloud import WordCloud
import matplotlib.pyplot as plt
# Smer
# colors = [(195, 27, 51)]
# file_name = 'progra... | mit |
brodoll/sms-tools | lectures/08-Sound-transformations/plots-code/FFT-filtering.py | 21 | 1723 | import math
import matplotlib.pyplot as plt
import numpy as np
import time, os, sys
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../../software/models/'))
import dftModel as DFT
import utilFunctions as UF
(fs, x) = UF.wavread('../../../sounds/orchestra.wav')
N = 2048
start = 1.0*fs
... | agpl-3.0 |
anurag313/scikit-learn | sklearn/utils/tests/test_murmurhash.py | 261 | 2836 | # Author: Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import numpy as np
from sklearn.externals.six import b, u
from sklearn.utils.murmurhash import murmurhash3_32
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_equal
from nose.tools import assert_equa... | bsd-3-clause |
frank-tancf/scikit-learn | examples/datasets/plot_random_multilabel_dataset.py | 278 | 3402 | """
==============================================
Plot randomly generated multilabel dataset
==============================================
This illustrates the `datasets.make_multilabel_classification` dataset
generator. Each sample consists of counts of two features (up to 50 in
total), which are differently distri... | bsd-3-clause |
anne-urai/serialDDM | graphicalModels/examples/classic.py | 7 | 1057 | """
The Quintessential PGM
======================
This is a demonstration of a very common structure found in graphical models.
It has been rendered using Daft's default settings for all the parameters
and it shows off how much beauty is baked in by default.
"""
from matplotlib import rc
rc("font", family="serif", s... | mit |
jfitzgerald79/gis-1 | sjoin.py | 2 | 4016 | import geopandas as gpd
from geopandas import tools
import numpy as np
import pandas as pd
import rtree
from shapely import prepared
r_df = gpd.GeoDataFrame.from_file('/home/akagi/GIS/2014_All_Parcel_Shapefiles/2014_Book400.shp')
l_df = gpd.GeoDataFrame.from_file('/home/akagi/GIS/census/cb_2013_04_tract_500k/cb_2013_... | gpl-2.0 |
perimosocordiae/scipy | scipy/linalg/basic.py | 4 | 67094 | #
# Author: Pearu Peterson, March 2002
#
# w/ additions by Travis Oliphant, March 2002
# and Jake Vanderplas, August 2012
from warnings import warn
import numpy as np
from numpy import atleast_1d, atleast_2d
from .flinalg import get_flinalg_funcs
from .lapack import get_lapack_funcs, _compute_lwork
from .... | bsd-3-clause |
Garrett-R/scikit-learn | sklearn/neighbors/unsupervised.py | 16 | 3198 | """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 |
phase4ground/DVB-receiver | modem/hdl/library/apsk_modulator/test/dut_control_apsk_modulator.py | 1 | 12998 | import cocotb
from cocotb.clock import Clock
from cocotb.triggers import Timer
from cocotb.triggers import RisingEdge
from cocotb.result import TestFailure
from cocotb.drivers.amba import AXI4LiteMaster
from cocotb.drivers.amba import AXI4StreamMaster
from cocotb.drivers.amba import AXI4StreamSlave
import sys, json
sy... | gpl-3.0 |
pyurdme/pyurdme | examples/cylinder_demo/cylinder_demo3D.py | 5 | 3718 | #!/usr/bin/env python
""" pyURDME model file for the annihilation cylinder 3D example. """
import os
import pyurdme
import dolfin
import mshr
import matplotlib.pyplot as plt
import numpy
# Global Constants
MAX_X_DIM = 5.0
MIN_X_DIM = -5.0
TOL = 1e-9
class Edge1(dolfin.SubDomain):
def inside(self, x, on_boundar... | gpl-3.0 |
xwolf12/scikit-learn | examples/svm/plot_custom_kernel.py | 171 | 1546 | """
======================
SVM with custom kernel
======================
Simple usage of Support Vector Machines to classify a sample. It will
plot the decision surface and the support vectors.
"""
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
# import some data... | bsd-3-clause |
benfrandsen/mPDFmodules_noDiffpy | fullfitmPDF_rhombo_nyquist_lmfit.py | 1 | 10627 | import scipy
from scipy import interpolate
from scipy.optimize.minpack import curve_fit
import numpy as np
import matplotlib.pyplot as plt
import sys
import random
from lmfit import Parameters, minimize, fit_report
from mcalculator_mod import calculateMPDF
from getmPDF import j0calc
def cv(x1,y1,x2,y2):... | gpl-3.0 |
bssrdf/sklearn-theano | examples/plot_mnist_generator.py | 9 | 1493 | """
=======================================================
Generative networks for random MNIST digits
=======================================================
This demo of an MNIST generator is based on the work of
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair,
A. Courville, Y.Bengio. *G... | bsd-3-clause |
JosmanPS/scikit-learn | examples/cluster/plot_ward_structured_vs_unstructured.py | 320 | 3369 | """
===========================================================
Hierarchical clustering: structured vs unstructured ward
===========================================================
Example builds a swiss roll dataset and runs
hierarchical clustering on their position.
For more information, see :ref:`hierarchical_clus... | bsd-3-clause |
alvarofierroclavero/scikit-learn | sklearn/covariance/tests/test_robust_covariance.py | 213 | 3359 | # 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 |
OpenDataDayBilbao/teseo2014 | data/analyzer.py | 2 | 25355 | # -*- coding: utf-8 -*-
"""
Created on Mon Sep 22 08:55:14 2014
@author: aitor
"""
import mysql.connector
import networkx as nx
from networkx.generators.random_graphs import barabasi_albert_graph
import json
import os.path
import numpy as np
import pandas as pd
from pandas import Series
from pandas import DataFrame
i... | apache-2.0 |
nushio3/cmaes | python/cma.py | 1 | 261903 | #!/usr/bin/env python2
"""Module cma implements the CMA-ES, Covariance Matrix Adaptation Evolution
Strategy, a stochastic optimizer for robust non-linear non-convex
derivative-free function minimization for Python versions 2.6 and 2.7
(for Python 2.5 class SolutionDict would need to be re-implemented, because
it d... | mit |
robin-lai/scikit-learn | sklearn/svm/classes.py | 126 | 40114 | import warnings
import numpy as np
from .base import _fit_liblinear, BaseSVC, BaseLibSVM
from ..base import BaseEstimator, RegressorMixin
from ..linear_model.base import LinearClassifierMixin, SparseCoefMixin, \
LinearModel
from ..feature_selection.from_model import _LearntSelectorMixin
from ..utils import check_X... | bsd-3-clause |
amolkahat/pandas | pandas/tests/api/test_api.py | 2 | 7464 | # -*- coding: utf-8 -*-
import sys
import pytest
import pandas as pd
from pandas import api
from pandas.util import testing as tm
class Base(object):
def check(self, namespace, expected, ignored=None):
# see which names are in the namespace, minus optional
# ignored ones
# compare vs the... | bsd-3-clause |
Jinkeycode/DeeplearningAI_AndrewNg | Course1 Neural Networks and Deep Learning/Week3 Shallow Neural Networks/planar_utils.py | 3 | 2253 | import matplotlib.pyplot as plt
import numpy as np
import sklearn
import sklearn.datasets
import sklearn.linear_model
def plot_decision_boundary(model, X, y):
# Set min and max values and give it some padding
x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1
y_min, y_max = X[1, :].min() - 1, X[1, :].max(... | mit |
adammenges/statsmodels | statsmodels/graphics/tukeyplot.py | 33 | 2473 | from statsmodels.compat.python import range
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.lines as lines
def tukeyplot(results, dim=None, yticklabels=None):
npairs = len(results)
fig = plt.figure()
fsp = fig.add_subplot(111)
fsp.axis([-50,50,... | bsd-3-clause |
dpshelio/sunpy | examples/map/plot_frameless_image.py | 1 | 1349 | """
===============================
Plotting a Map without any Axes
===============================
This examples shows you how to plot a Map without any annotations at all, i.e.
to save as an image.
"""
##############################################################################
# Start by importing the necessary m... | bsd-2-clause |
jstraub/dpMM | python/evalWikiWordVectors.py | 1 | 8101 | # Copyright (c) 2015, Julian Straub <jstraub@csail.mit.edu>
# Licensed under the MIT license. See the license file LICENSE.
import numpy as np
from scipy.linalg import eig, logm
import subprocess as subp
import matplotlib.pyplot as plt
import mayavi.mlab as mlab
from matplotlib.patches import Ellipse
import ipdb, re... | mit |
samuelleblanc/flight_planning_dist | flight_planning/write_utils.py | 2 | 20750 |
# coding: utf-8
# In[1]:
def __init__():
"""
Name:
write_utils
Purpose:
Module regrouping codes that are used to write out certain tyes of files. Example: ict
See each function within this module
Contains the following functions:
- write_ict: for writing... | gpl-3.0 |
Candihub/pixel | apps/data/io/parsers.py | 1 | 7594 | import logging
import pandas
from django.utils.translation import ugettext
from hashlib import blake2b
from ..models import Entry, Repository
logger = logging.getLogger(__name__)
class ChrFeatureParser(object):
def __init__(self, file_path, database_name, root_url, skip_rows=0):
self.file_path = file_... | bsd-3-clause |
joernhees/scikit-learn | examples/decomposition/plot_image_denoising.py | 70 | 6249 | """
=========================================
Image denoising using dictionary learning
=========================================
An example comparing the effect of reconstructing noisy fragments
of a raccoon face image using firstly online :ref:`DictionaryLearning` and
various transform methods.
The dictionary is fi... | bsd-3-clause |
mhdella/scikit-learn | sklearn/tree/tests/test_tree.py | 57 | 47417 | """
Testing for the tree module (sklearn.tree).
"""
import pickle
from functools import partial
from itertools import product
import platform
import numpy as np
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import coo_matrix
from sklearn.random_projection import sparse_rand... | bsd-3-clause |
wlamond/scikit-learn | sklearn/cluster/k_means_.py | 9 | 60297 | """K-means clustering"""
# Authors: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Thomas Rueckstiess <ruecksti@in.tum.de>
# James Bergstra <james.bergstra@umontreal.ca>
# Jan Schlueter <scikit-learn@jan-schlueter.de>
# Nelle Varoquaux
# Peter Prettenhofer <peter.prettenh... | bsd-3-clause |
blueskyjunkie/timeTools | timetools/synchronization/compliance/ituTG82611/__init__.py | 1 | 6978 | #
# Copyright 2017 Russell Smiley
#
# This file is part of timetools.
#
# timetools 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.
#
#... | gpl-3.0 |
shikhardb/scikit-learn | benchmarks/bench_plot_lasso_path.py | 301 | 4003 | """Benchmarks of Lasso regularization path computation using Lars and CD
The input data is mostly low rank but is a fat infinite tail.
"""
from __future__ import print_function
from collections import defaultdict
import gc
import sys
from time import time
import numpy as np
from sklearn.linear_model import lars_pat... | bsd-3-clause |
flightgong/scikit-learn | sklearn/utils/fixes.py | 1 | 8311 | """Compatibility fixes for older version of python, numpy and scipy
If you add content to this file, please give the version of the package
at which the fixe is no longer needed.
"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# ... | bsd-3-clause |
liangz0707/scikit-learn | sklearn/cluster/tests/test_spectral.py | 262 | 7954 | """Testing for Spectral Clustering methods"""
from sklearn.externals.six.moves import cPickle
dumps, loads = cPickle.dumps, cPickle.loads
import numpy as np
from scipy import sparse
from sklearn.utils import check_random_state
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
DailyActie/Surrogate-Model | 01-codes/scikit-learn-master/examples/tree/unveil_tree_structure.py | 1 | 4786 | """
=========================================
Understanding the decision tree structure
=========================================
The decision tree structure can be analysed to gain further insight on the
relation between the features and the target to predict. In this example, we
show how to retrieve:
- the binary t... | mit |
cogmission/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/offsetbox.py | 69 | 17728 | """
The OffsetBox is a simple container artist. The child artist are meant
to be drawn at a relative position to its parent. The [VH]Packer,
DrawingArea and TextArea are derived from the OffsetBox.
The [VH]Packer automatically adjust the relative postisions of their
children, which should be instances of the OffsetBo... | agpl-3.0 |
fzalkow/scikit-learn | benchmarks/bench_sparsify.py | 323 | 3372 | """
Benchmark SGD prediction time with dense/sparse coefficients.
Invoke with
-----------
$ kernprof.py -l sparsity_benchmark.py
$ python -m line_profiler sparsity_benchmark.py.lprof
Typical output
--------------
input data sparsity: 0.050000
true coef sparsity: 0.000100
test data sparsity: 0.027400
model sparsity:... | bsd-3-clause |
prheenan/Research | Perkins/Projects/WetLab/Demos/PCR_Optimizations/2016-7-8-DMSO-trials/main_dmso_opt.py | 1 | 1467 | # force floating point division. Can still use integer with //
from __future__ import division
# This file is used for importing the common utilities classes.
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("../../../../../../../")
from GeneralUtil.python import PlotUtilities as pPlotUti... | gpl-3.0 |
GeographicaGS/daynight2geojson | daynight2geojson/daynight2geojson.py | 2 | 3599 | # -*- coding: utf-8 -*-
#
# Author: Cayetano Benavent, 2015.
# https://github.com/GeographicaGS/daynight2geojson
#
# 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 ... | gpl-2.0 |
ctwj/crackCaptcha | captcha3.py | 1 | 4777 | #!/usr/bin/env python
# -*- coding: UTF-8 -*-
import random
from PIL import Image, ImageDraw, ImageFont, ImageFilter
import matplotlib.pyplot as plt
import numpy as np
default_font = "./font/DejaVuSans.ttf"
# 验证码中的字符, 就不用汉字了
number = ['0','1','2','3','4','5','6','7','8','9']
alphabet = ['a','b','c','d','e','f','g',... | apache-2.0 |
treycausey/scikit-learn | examples/covariance/plot_robust_vs_empirical_covariance.py | 8 | 6264 | """
=======================================
Robust vs Empirical covariance estimate
=======================================
The usual covariance maximum likelihood estimate is very sensitive to the
presence of outliers in the data set. In such a case, it would be better to
use a robust estimator of covariance to guara... | bsd-3-clause |
RapidApplicationDevelopment/tensorflow | tensorflow/contrib/learn/python/learn/tests/dataframe/feeding_queue_runner_test.py | 30 | 4727 | # 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 |
jayaneetha/crowdsource-platform | fixtures/createJson.py | 6 | 2462 | __author__ = 'Megha'
# Script to transfer csv containing data about various models to json
# Input csv file constituting of the model data
# Output json file representing the csv data as json object
# Assumes model name to be first line
# Field names of the model on the second line
# Data seperated by __DELIM__
# Examp... | mit |
jpautom/scikit-learn | sklearn/manifold/tests/test_mds.py | 324 | 1862 | import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_raises
from sklearn.manifold import mds
def test_smacof():
# test metric smacof using the data of "Modern Multidimensional Scaling",
# Borg & Groenen, p 154
sim = np.array([[0, 5, 3, 4],
... | bsd-3-clause |
coin-pan/Toodledo-graphical-activity-tracker | tracker.py | 1 | 12636 | #!/usr/bin/python
# -*- coding: UTF-8 -*-
#
#
# Toodledo Activity Tracker & Plotter
# Copyright (C) 2011 Marc Chauvet (marc DOT chauvet AT gmail DOT 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 ... | gpl-3.0 |
theislab/scanpy | scanpy/plotting/_matrixplot.py | 1 | 12979 | from typing import Optional, Union, Mapping # Special
from typing import Sequence # ABCs
from typing import Tuple # Classes
import numpy as np
import pandas as pd
from anndata import AnnData
from matplotlib import pyplot as pl
from matplotlib import rcParams
from matplotlib.colors import Normalize
from .. import l... | bsd-3-clause |
sbobovyc/JA-BiA-Tools | src/legacy/find_pkle.py | 1 | 4434 | """
Created on February 16, 2012
@author: sbobovyc
"""
"""
Copyright (C) 2012 Stanislav Bobovych
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, o... | gpl-3.0 |
probml/pyprobml | scripts/ising_image_denoise_demo.py | 1 | 2759 | # -*- coding: utf-8 -*-
"""
Author: Ang Ming Liang
Based on: https://github.com/probml/pmtk3/blob/master/demos/isingImageDenoiseDemo.m
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#from tqdm.notebook import tqdm
from tqdm import tqdm
from scipy.stats import norm
import pyprobml_utils a... | mit |
jlegendary/scikit-learn | sklearn/cross_validation.py | 96 | 58309 | """
The :mod:`sklearn.cross_validation` module includes utilities for cross-
validation and performance evaluation.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>,
# Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
from... | bsd-3-clause |
krafczyk/spack | var/spack/repos/builtin/packages/py-elephant/package.py | 4 | 2375 | ##############################################################################
# Copyright (c) 2013-2018, Lawrence Livermore National Security, LLC.
# Produced at the Lawrence Livermore National Laboratory.
#
# This file is part of Spack.
# Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved.
# LLNL-CODE-64... | lgpl-2.1 |
ambimanus/appsim | stats.py | 1 | 9689 | import sys
import os
from datetime import timedelta
import numpy as np
import scenario_factory
# http://www.javascripter.net/faq/hextorgb.htm
PRIMA = (148/256, 164/256, 182/256)
PRIMB = (101/256, 129/256, 164/256)
PRIM = ( 31/256, 74/256, 125/256)
PRIMC = ( 41/256, 65/256, 94/256)
PRIMD = ( 10/256, 42/256, 81... | mit |
Zhenxingzhang/kaggle-cdiscount-classification | src/data_preparation/dataset.py | 1 | 8215 | import pandas as pd
import numpy as np
from sklearn import preprocessing
from src.common import paths
import tensorflow as tf
from src.common import consts
from os import listdir
from os.path import isfile, join
from src.vgg_fine_tuning.vgg_preprocessing import _preprocess_for_train, _preprocess_for_val
def read_recor... | apache-2.0 |
swalter2/PersonalizationService | Service/feature.py | 1 | 14598 | # -*- coding: utf-8 -*-
from sklearn import svm
from sklearn.feature_extraction import DictVectorizer
from sklearn import cross_validation
import numpy as np
import pickle
import sys
#listen nötig für cross-feature-berechnungen
RESSORTS = ['Kultur','Bielefeld','Sport Bielefeld','Politik','Sport_Bund']
NORMALIZED_PAGE... | mit |
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 |
yyjiang/scikit-learn | examples/plot_digits_pipe.py | 250 | 1809 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Pipelining: chaining a PCA and a logistic regression
=========================================================
The PCA does an unsupervised dimensionality reduction, while the logistic
regression does the predictio... | bsd-3-clause |
jasonmccampbell/numpy-refactor-sprint | doc/sphinxext/plot_directive.py | 12 | 17831 | """
A special directive for generating a matplotlib plot.
.. warning::
This is a hacked version of plot_directive.py from Matplotlib.
It's very much subject to change!
Usage
-----
Can be used like this::
.. plot:: examples/example.py
.. plot::
import matplotlib.pyplot as plt
plt.plot... | bsd-3-clause |
ywcui1990/nupic.research | projects/sequence_prediction/continuous_sequence/run_tm_model.py | 3 | 16411 | ## ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013-2015, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This ... | agpl-3.0 |
oemof/reegis-hp | reegis_hp/waermetool/heat_demand.py | 3 | 4420 | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 23 14:35:28 2016
@author: uwe
"""
import logging
import time
import os
import pandas as pd
import numpy as np
from oemof.tools import logger
logger.define_logging()
start = time.time()
sync_path = '/home/uwe/chiba/RLI/data'
basic_path = os.path.join(os.path.expanduser... | gpl-3.0 |
bthirion/scikit-learn | examples/linear_model/plot_ransac.py | 103 | 1797 | """
===========================================
Robust linear model estimation using RANSAC
===========================================
In this example we see how to robustly fit a linear model to faulty data using
the RANSAC algorithm.
"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn import ... | bsd-3-clause |
josenavas/qiime | scripts/identify_paired_differences.py | 15 | 9191 | #!/usr/bin/env python
# File created on 19 Jun 2013
from __future__ import division
__author__ = "Greg Caporaso"
__copyright__ = "Copyright 2013, The QIIME project"
__credits__ = ["Greg Caporaso", "Jose Carlos Clemente Litran"]
__license__ = "GPL"
__version__ = "1.9.1-dev"
__maintainer__ = "Greg Caporaso"
__email__ = ... | gpl-2.0 |
AISpace2/AISpace2 | aipython/cspSLSPlot.py | 1 | 12720 | # cspSLS.py - Stochastic Local Search for Solving CSPs
# AIFCA Python3 code Version 0.7.1 Documentation at http://aipython.org
# Artificial Intelligence: Foundations of Computational Agents
# http://artint.info
# Copyright David L Poole and Alan K Mackworth 2017.
# This work is licensed under a Creative Commons
# Attr... | gpl-3.0 |
manifoldai/merf | setup.py | 1 | 1024 | from setuptools import setup, find_packages
def readme():
with open("README.md") as f:
return f.read()
def read_version(filename='VERSION'):
with open(filename, 'r') as f:
return f.readline()
setup(
name="merf",
version=read_version(),
description="Mixed Effects Random Forest",
... | mit |
amsjavan/nazarkav | nazarkav/tests/test_nazarkav.py | 1 | 4116 | import os.path as op
import numpy as np
import pandas as pd
import numpy.testing as npt
import nazarkav as sb
data_path = op.join(sb.__path__[0], 'data')
def test_transform_data():
"""
Testing the transformation of the data from raw data to functions
used for fitting a function.
... | mit |
AtsushiSakai/PythonRobotics | ArmNavigation/rrt_star_seven_joint_arm_control/rrt_star_seven_joint_arm_control.py | 1 | 14158 | """
RRT* path planner for a seven joint arm
Author: Mahyar Abdeetedal (mahyaret)
"""
import math
import os
import sys
import random
import numpy as np
from mpl_toolkits import mplot3d
import matplotlib.pyplot as plt
sys.path.append(os.path.dirname(os.path.abspath(__file__)) +
"/../n_joint_arm_3d/")
try:... | mit |
andrewnc/scikit-learn | sklearn/linear_model/stochastic_gradient.py | 65 | 50308 | # Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)
# Mathieu Blondel (partial_fit support)
#
# License: BSD 3 clause
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import scipy.sparse as sp
from abc import ABCMeta, abstractmethod
from ... | bsd-3-clause |
zorroblue/scikit-learn | examples/feature_selection/plot_rfe_with_cross_validation.py | 161 | 1380 | """
===================================================
Recursive feature elimination with cross-validation
===================================================
A recursive feature elimination example with automatic tuning of the
number of features selected with cross-validation.
"""
print(__doc__)
import matplotlib.p... | bsd-3-clause |
FowlerLab/Enrich2 | enrich2/barcodeid.py | 1 | 5459 | import logging
from .seqlib import SeqLib
from .barcode import BarcodeSeqLib
from .barcodemap import BarcodeMap
import pandas as pd
from .plots import barcodemap_plot
from matplotlib.backends.backend_pdf import PdfPages
import os.path
class BcidSeqLib(BarcodeSeqLib):
"""
Class for counting data from barcoded ... | bsd-3-clause |
samzhang111/scikit-learn | examples/bicluster/plot_spectral_biclustering.py | 403 | 2011 | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and
bicluster it using the Spectral Biclustering algorithm.
The data is generated with the ``make_checkerboard`... | bsd-3-clause |
louispotok/pandas | pandas/io/date_converters.py | 11 | 1901 | """This module is designed for community supported date conversion functions"""
from pandas.compat import range, map
import numpy as np
from pandas._libs.tslibs import parsing
def parse_date_time(date_col, time_col):
date_col = _maybe_cast(date_col)
time_col = _maybe_cast(time_col)
return parsing.try_pars... | bsd-3-clause |
pompiduskus/scikit-learn | examples/cluster/plot_mean_shift.py | 351 | 1793 | """
=============================================
A demo of the mean-shift clustering algorithm
=============================================
Reference:
Dorin Comaniciu and Peter Meer, "Mean Shift: A robust approach toward
feature space analysis". IEEE Transactions on Pattern Analysis and
Machine Intelligence. 2002. ... | bsd-3-clause |
cauchycui/scikit-learn | sklearn/feature_selection/variance_threshold.py | 238 | 2594 | # Author: Lars Buitinck <L.J.Buitinck@uva.nl>
# License: 3-clause BSD
import numpy as np
from ..base import BaseEstimator
from .base import SelectorMixin
from ..utils import check_array
from ..utils.sparsefuncs import mean_variance_axis
from ..utils.validation import check_is_fitted
class VarianceThreshold(BaseEstim... | bsd-3-clause |
nilmtk/nilmtk | docs/source/conf.py | 7 | 12943 | # -*- coding: utf-8 -*-
#
# NILMTK documentation build configuration file, created by
# sphinx-quickstart on Thu Jul 10 09:22:49 2014.
#
# 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.
#
# Al... | apache-2.0 |
m3drano/power-simulation | tools/plot_histogram.py | 1 | 3433 | #!/usr/bin/env python3
#
# Copyright 2017 Google 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 ... | apache-2.0 |
abhitopia/tensorflow | tensorflow/examples/learn/hdf5_classification.py | 60 | 2190 | # 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 |
chenyyx/scikit-learn-doc-zh | examples/en/linear_model/plot_multi_task_lasso_support.py | 77 | 2319 | #!/usr/bin/env python
"""
=============================================
Joint feature selection with multi-task Lasso
=============================================
The multi-task lasso allows to fit multiple regression problems
jointly enforcing the selected features to be the same across
tasks. This example simulates... | gpl-3.0 |
cBeaird/SemEval_Character-Identification-on-Multiparty-Dialogues | vcu_cmsc_516_semeval4_nn.py | 1 | 9181 | #!/usr/bin/env python
"""
Application start for the SemEval 2018 task 4 application: conference resolution for SemEval 2018
This file is the main starting point for the application. This files allow for the application to
be called with a series of input parameters to perform the different functions required by the
ap... | mit |
uglyboxer/linear_neuron | net-p3/lib/python3.5/site-packages/matplotlib/tests/test_triangulation.py | 9 | 39659 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.tri as mtri
from nose.tools import assert_equal, assert_raises
from numpy.testing import assert_array_equal, assert_array_almost_... | mit |
kirangonella/BuildingMachineLearningSystemsWithPython | ch09/01_fft_based_classifier.py | 24 | 3740 | # 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 collections import defaultdict
from sklearn.metrics import precision_recall_cu... | mit |
Myasuka/scikit-learn | examples/cluster/plot_segmentation_toy.py | 258 | 3336 | """
===========================================
Spectral clustering for image segmentation
===========================================
In this example, an image with connected circles is generated and
spectral clustering is used to separate the circles.
In these settings, the :ref:`spectral_clustering` approach solve... | bsd-3-clause |
PredictiveScienceLab/pysmc | examples/reaction_kinetics_run_nompi.py | 2 | 1378 | """
Solve the reaction kinetics inverse problem.
"""
import reaction_kinetics_model
import sys
import os
import pymc
sys.path.insert(0, os.path.abspath('..'))
import pysmc
import matplotlib.pyplot as plt
import pickle
if __name__ == '__main__':
model = reaction_kinetics_model.make_model()
# Construct the SM... | lgpl-3.0 |
deot95/Tesis | Proyecto de Grado Ingeniería Electrónica/Workspace/Comparison/Small Linear/plot_vols_and_perf_mpc.py | 1 | 4026 | import numpy as np
import matplotlib as mpl
import matplotlib.pylab as plt
import linear_env_small as linear_env
from numpy.matlib import repmat
state_dim = 5
Hs = 1800
A1 = 0.0020
mu1 = 250
sigma1 = 70
A2 = 0.0048
mu2 = 250
sigma2 = 70
dt = 1
x = np.arange(Hs)
d = np.zeros((2,Hs))
d[0,:] = A1*np.exp((-1*(x-mu1)**2)/(... | mit |
indranilsinharoy/PyZDDE | Examples/Scripts/couplingEfficiencySingleModeFibers.py | 2 | 4996 | #-------------------------------------------------------------------------------
# Name: couplingEfficiencySingleModeFibers.py
# Purpose: Demonstrate the following function related to POP in Zemax:
# zGetPOP(), zSetPOPSettings(), zModifyPOPSettings()
# Calculates the fiber coupling efficien... | mit |
lthurlow/Network-Grapher | proj/external/matplotlib-1.2.1/lib/mpl_examples/pylab_examples/fill_between_demo.py | 6 | 2268 | #!/usr/bin/env python
import matplotlib.mlab as mlab
from matplotlib.pyplot import figure, show
import numpy as np
x = np.arange(0.0, 2, 0.01)
y1 = np.sin(2*np.pi*x)
y2 = 1.2*np.sin(4*np.pi*x)
fig = figure()
ax1 = fig.add_subplot(311)
ax2 = fig.add_subplot(312, sharex=ax1)
ax3 = fig.add_subplot(313, sharex=ax1)
ax1.... | mit |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_epochs_to_data_frame.py | 1 | 8900 | """
.. _tut_io_export_pandas:
=================================
Export epochs to Pandas DataFrame
=================================
In this example the pandas exporter will be used to produce a DataFrame
object. After exploring some basic features a split-apply-combine
work flow will be conducted to examine the laten... | bsd-3-clause |
jesserobertson/pynoddy | pynoddy/experiment/TopologyAnalysis.py | 1 | 84153 | # -*- coding: utf-8 -*-
"""
Created on Wed Jul 15 12:14:08 2015
@author: Sam Thiele
"""
import os
import numpy as np
import scipy as sp
import math
from pynoddy.experiment.MonteCarlo import MonteCarlo
from pynoddy.output import NoddyTopology
from pynoddy.output import NoddyOutput
from pynoddy.history import NoddyHist... | gpl-2.0 |
gsprint23/sensor_data_preprocessing | src/main.py | 1 | 10003 | '''
Copyright (C) 2015 Gina L. Sprint
Email: Gina Sprint <gsprint@eecs.wsu.edu>
This file is part of sensor_data_preprocessing.
sensor_data_preprocessing 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, e... | gpl-3.0 |
dennisobrien/bokeh | sphinx/source/docs/user_guide/examples/extensions_example_latex.py | 6 | 2676 | """ The LaTex example was derived from: http://matplotlib.org/users/usetex.html
"""
import numpy as np
from bokeh.models import Label
from bokeh.plotting import figure, show
JS_CODE = """
import {Label, LabelView} from "models/annotations/label"
export class LatexLabelView extends LabelView
render: () ->
#--... | bsd-3-clause |
geodynamics/burnman | examples/example_gibbs_modifiers.py | 2 | 7755 | # This file is part of BurnMan - a thermoelastic and thermodynamic toolkit for the Earth and Planetary Sciences
# Copyright (C) 2012 - 2015 by the BurnMan team, released under the GNU
# GPL v2 or later.
"""
example_gibbs_modifiers
----------------
This example script demonstrates the modifications to
the gibbs free ... | gpl-2.0 |
poryfly/scikit-learn | sklearn/tests/test_cross_validation.py | 8 | 42537 | """Test the cross_validation module"""
from __future__ import division
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from scipy.sparse import csr_matrix
from scipy import stats
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.test... | bsd-3-clause |
valexandersaulys/prudential_insurance_kaggle | venv/lib/python2.7/site-packages/pandas/tseries/tests/test_converter.py | 13 | 5611 | from datetime import datetime, time, timedelta, date
import sys
import os
import nose
import numpy as np
from numpy.testing import assert_almost_equal as np_assert_almost_equal
from pandas import Timestamp, Period
from pandas.compat import u
import pandas.util.testing as tm
from pandas.tseries.offsets import Second, ... | gpl-2.0 |
totalgood/pug-data | pug/data/gmm.py | 1 | 4320 | """Generate synthetic samples from a mixture of gaussians or Gaussian Mixture Model (GMM)
Most of this code is derived from Nehalem Labs examples:
http://www.nehalemlabs.net/prototype/blog/2014/04/03/quick-introduction-to-gaussian-mixture-models-with-python/
References:
https://en.wikipedia.org/wiki/Metropolis%E2... | mit |
awni/tensorflow | tensorflow/examples/skflow/iris_val_based_early_stopping.py | 2 | 2221 | # 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 |
ShujiaHuang/AsmVar | src/AsmvarVarScore/modul/VariantRecalibrator.py | 2 | 3592 | """
===============================================
===============================================
Author: Shujia Huang
Date : 2014-05-23 11:21:53
"""
import sys
import matplotlib.pyplot as plt
# My own class
import VariantDataManager as vdm
import VariantRecalibratorEngine as vre
import VariantRecalibratorArgument... | mit |
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