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 |
|---|---|---|---|---|---|
chebee7i/twitter | scripts/rates.py | 1 | 2320 | """
Plot tweet rates.
"""
import operator
from collections import OrderedDict
import twitterproj
import json
import matplotlib.pyplot as plt
import numpy as np
import seaborn
def user_rates(ax=None):
if ax is None:
ax = plt.gca()
rates = twitterproj.tweet_rates__users()
import json
with open(... | unlicense |
rishikksh20/scikit-learn | benchmarks/bench_plot_neighbors.py | 101 | 6469 | """
Plot the scaling of the nearest neighbors algorithms with k, D, and N
"""
from time import time
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import ticker
from sklearn import neighbors, datasets
def get_data(N, D, dataset='dense'):
if dataset == 'dense':
np.random.seed(0)
... | bsd-3-clause |
nasa/CrisisMappingToolkit | bin/lake_measure.py | 1 | 25777 | # -----------------------------------------------------------------------------
# Copyright * 2014, United States Government, as represented by the
# Administrator of the National Aeronautics and Space Administration. All
# rights reserved.
#
# The Crisis Mapping Toolkit (CMT) v1 platform is licensed under the Apache
#... | apache-2.0 |
pmelchior/shear-stacking-tests | shear_stacking.py | 2 | 16172 | import numpy as np
from math import pi, sqrt
import os, fitsio
def skyAngle(ra, dec, ra_ref, dec_ref):
# CAUTION: this needs to be a pseudo-Cartesian coordinate frame
# (not pure RA/DEC), otherwise angles are skewed
return np.arctan2(dec-dec_ref, (ra-ra_ref)*np.cos(dec*pi/180))
def skyDistance(ra, dec, ra... | mit |
awanke/bokeh | bokeh/charts/builder/line_builder.py | 43 | 5360 | """This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the Line class which lets you build your Line charts just
passing the arguments to the Chart class and calling the proper functions.
"""
#-----------------------------------------------------------------... | bsd-3-clause |
vidalalcala/ml-tools | mltools/metrics.py | 1 | 4159 | import sklearn.metrics
import numpy as np
import xgboost
import pandas as pd
import rpy2.robjects.packages as packages
import rpy2.robjects.pandas2ri as pandas2ri
import matplotlib
matplotlib.use('Agg')
import matplotlib.backends.backend_pdf as backend_pdf
import seaborn
# R import and interfaces
p_roc = packages.impo... | mit |
joshloyal/scikit-learn | sklearn/tree/tests/test_tree.py | 12 | 60577 | """
Testing for the tree module (sklearn.tree).
"""
import copy
import pickle
from functools import partial
from itertools import product
import struct
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 s... | bsd-3-clause |
billy-inn/scikit-learn | doc/sphinxext/numpy_ext/docscrape_sphinx.py | 408 | 8061 | import re
import inspect
import textwrap
import pydoc
from .docscrape import NumpyDocString
from .docscrape import FunctionDoc
from .docscrape import ClassDoc
class SphinxDocString(NumpyDocString):
def __init__(self, docstring, config=None):
config = {} if config is None else config
self.use_plots... | bsd-3-clause |
mikebenfield/scikit-learn | examples/classification/plot_lda_qda.py | 32 | 5381 | """
====================================================================
Linear and Quadratic Discriminant Analysis with covariance ellipsoid
====================================================================
This example plots the covariance ellipsoids of each class and
decision boundary learned by LDA and QDA. The... | bsd-3-clause |
dsullivan7/scikit-learn | sklearn/feature_selection/tests/test_base.py | 170 | 3666 | import numpy as np
from scipy import sparse as sp
from nose.tools import assert_raises, assert_equal
from numpy.testing import assert_array_equal
from sklearn.base import BaseEstimator
from sklearn.feature_selection.base import SelectorMixin
from sklearn.utils import check_array
class StepSelector(SelectorMixin, Ba... | bsd-3-clause |
sinhrks/japandas | doc/source/conf.py | 1 | 8760 | # -*- coding: utf-8 -*-
#
# japandas documentation build configuration file, created by
# sphinx-quickstart on Sun Feb 8 19:30:56 2015.
#
# 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.
#
# ... | bsd-3-clause |
holsety/tushare | tushare/datayes/future.py | 17 | 1740 | # -*- coding:utf-8 -*-
"""
通联数据
Created on 2015/08/24
@author: Jimmy Liu
@group : waditu
@contact: jimmysoa@sina.cn
"""
from pandas.compat import StringIO
import pandas as pd
from tushare.util import vars as vs
from tushare.util.common import Client
from tushare.util import upass as up
class Future():
def _... | bsd-3-clause |
ngoix/OCRF | sklearn/gaussian_process/tests/test_kernels.py | 24 | 11602 | """Testing for kernels for Gaussian processes."""
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Licence: BSD 3 clause
from collections import Hashable
from sklearn.externals.funcsigs import signature
import numpy as np
from sklearn.gaussian_process.kernels import _approx_fprime
from sklearn.metrics... | bsd-3-clause |
jorik041/scikit-learn | examples/text/document_classification_20newsgroups.py | 222 | 10500 | """
======================================================
Classification of text documents using sparse features
======================================================
This is an example showing how scikit-learn can be used to classify documents
by topics using a bag-of-words approach. This example uses a scipy.spars... | bsd-3-clause |
FreeSchoolHackers/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 |
fbagirov/scikit-learn | examples/mixture/plot_gmm_classifier.py | 250 | 3918 | """
==================
GMM classification
==================
Demonstration of Gaussian mixture models for classification.
See :ref:`gmm` for more information on the estimator.
Plots predicted labels on both training and held out test data using a
variety of GMM classifiers on the iris dataset.
Compares GMMs with sp... | bsd-3-clause |
MyRookie/SentimentAnalyse | venv/lib/python2.7/site-packages/nltk/draw/dispersion.py | 3 | 1802 | # Natural Language Toolkit: Dispersion Plots
#
# Copyright (C) 2001-2015 NLTK Project
# Author: Steven Bird <stevenbird1@gmail.com>
# URL: <http://nltk.org/>
# For license information, see LICENSE.TXT
"""
A utility for displaying lexical dispersion.
"""
def dispersion_plot(text, words, ignore_case=False, t... | mit |
roman-dvorak/SolarForecast | tools/rename.py | 1 | 13299 | #!/usr/bin/python
import os
import datetime
import time
import ftplib
import ConfigParser
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
import operator
from scipy.interpolate import interp1d
from array import array
import subprocess
import Image
import pyfits
arrA = []
arrB = []
... | gpl-3.0 |
calliope-project/calliope | calliope/time/clustering.py | 1 | 17528 | """
Copyright (C) since 2013 Calliope contributors listed in AUTHORS.
Licensed under the Apache 2.0 License (see LICENSE file).
clustering.py
~~~~~~~~~~~~~
Functions to cluster data along the time dimension.
"""
import numpy as np
import pandas as pd
import xarray as xr
from sklearn.metrics import mean_squared_erro... | apache-2.0 |
466152112/scikit-learn | sklearn/datasets/tests/test_lfw.py | 230 | 7880 | """This test for the LFW require medium-size data dowloading and processing
If the data has not been already downloaded by running the examples,
the tests won't run (skipped).
If the test are run, the first execution will be long (typically a bit
more than a couple of minutes) but as the dataset loader is leveraging
... | bsd-3-clause |
carrillo/scikit-learn | examples/classification/plot_digits_classification.py | 289 | 2397 | """
================================
Recognizing hand-written digits
================================
An example showing how the scikit-learn can be used to recognize images of
hand-written digits.
This example is commented in the
:ref:`tutorial section of the user manual <introduction>`.
"""
print(__doc__)
# Autho... | bsd-3-clause |
Connexions/cnx-mathml2svg | tests.py | 2 | 9765 | # -*- coding: utf-8 -*-
# ###
# Copyright (c) 2015, Rice University
# This software is subject to the provisions of the GNU Affero General
# Public License version 3 (AGPLv3).
# See LICENCE.txt for details.
# ###
import os
import unittest
from pyramid import httpexceptions
from pyramid import testing as pyramid_testin... | agpl-3.0 |
d00d/quantNotebooks | Notebooks/strategies/from quantopian.algorithm import attach_pipeline,.py | 1 | 4644 | from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import CustomFactor, SimpleMovingAverage
from quantopian.pipeline.data import morningstar
import pandas as pd
import n... | unlicense |
beepee14/scikit-learn | sklearn/preprocessing/tests/test_data.py | 71 | 38516 | import warnings
import numpy as np
import numpy.linalg as la
from scipy import sparse
from distutils.version import LooseVersion
from sklearn.utils.testing import assert_almost_equal, clean_warning_registry
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal... | bsd-3-clause |
simmimourya1/cyvlfeat | cyvlfeat/sift/plotsiftdescriptor.py | 1 | 7655 | import math
import numpy as np
from numpy import newaxis
from numpy import matlib
from matplotlib import collections as mc
from matplotlib import pyplot as plt
from cyvlfeat.utils import utils as utils
def plotsiftdescriptor(d, f=None, magnification=3.0, num_spatial_bins=4, num_orientation_bins=8, max_value=0):
r... | bsd-2-clause |
lo-co/atm-py | atmPy/for_removal/miniSASP/miniSASP.py | 6 | 46804 | # -*- coding: utf-8 -*-
"""
Created on Thu Mar 19 21:23:22 2015
@author: htelg
"""
import warnings
import numpy as np
import pandas as pd
import pylab as plt
from scipy import stats
from atmPy.tools import array_tools, plt_tools
from atmPy.tools import math_linear_algebra as mla
# from scipy impor... | mit |
RayMick/scikit-learn | sklearn/cluster/setup.py | 263 | 1449 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
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
cblas_libs, blas_info = ... | bsd-3-clause |
rs2/pandas | pandas/tests/series/methods/test_isin.py | 2 | 2751 | import numpy as np
import pytest
import pandas as pd
from pandas import Series, date_range
import pandas._testing as tm
class TestSeriesIsIn:
def test_isin(self):
s = Series(["A", "B", "C", "a", "B", "B", "A", "C"])
result = s.isin(["A", "C"])
expected = Series([True, False, True, False,... | bsd-3-clause |
kraemerd17/kraemerd17.github.io | static/files/brownian/brownian.py | 1 | 1492 | # tools for numerical computation and basic plotting
import numpy as np
from scipy.stats import gaussian_kde
import matplotlib.pyplot as plt
# seaborn is for improving the aesthetic of matplotlib's default
# plotting it's also a pretty powerful statistical plotting
# library, which we do not take advantage of here
... | mit |
terrycojones/dark-matter | dark/graphics.py | 2 | 37967 | import os
from copy import deepcopy
from stat import S_ISDIR
from math import ceil
from collections import defaultdict
from time import ctime, time
from textwrap import fill
try:
import matplotlib
if not os.environ.get('DISPLAY'):
# Use non-interactive Agg backend
matplotlib.use('Agg')
impo... | mit |
scenarios/tensorflow | tensorflow/examples/learn/iris.py | 19 | 1651 | # 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 |
ClimbsRocks/scikit-learn | examples/feature_selection/plot_f_test_vs_mi.py | 75 | 1647 | """
===========================================
Comparison of F-test and mutual information
===========================================
This example illustrates the differences between univariate F-test statistics
and mutual information.
We consider 3 features x_1, x_2, x_3 distributed uniformly over [0, 1], the
targ... | bsd-3-clause |
BeiLuoShiMen/nupic | examples/opf/clients/hotgym/anomaly/one_gym/nupic_anomaly_output.py | 49 | 9450 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, 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 progra... | agpl-3.0 |
ammarkhann/FinalSeniorCode | lib/python2.7/site-packages/pandas/core/groupby.py | 3 | 146721 | import types
from functools import wraps
import numpy as np
import datetime
import collections
import warnings
import copy
from textwrap import dedent
from pandas.compat import (
zip, range, lzip,
callable, map
)
from pandas import compat
from pandas.compat.numpy import function as nv, _np_version_under1p8
fr... | mit |
thunderhoser/GewitterGefahr | gewittergefahr/scripts/find_normalization_params_test.py | 1 | 13957 | """Unit tests for find_normalization_params.py."""
import copy
import unittest
import numpy
import pandas
from gewittergefahr.deep_learning import deep_learning_utils as dl_utils
from gewittergefahr.scripts import find_normalization_params as find_norm_params
TOLERANCE = 1e-6
# The following constants are used to te... | mit |
anurag313/scikit-learn | sklearn/neighbors/tests/test_ball_tree.py | 159 | 10196 | import pickle
import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.neighbors.ball_tree import (BallTree, NeighborsHeap,
simultaneous_sort, kernel_norm,
nodeheap_sort, DTYPE, ITYPE)
from sklearn.neighbors.dis... | bsd-3-clause |
ashutoshvt/psi4 | psi4/driver/diatomic.py | 1 | 11372 | #
# @BEGIN LICENSE
#
# Psi4: an open-source quantum chemistry software package
#
# Copyright (c) 2007-2021 The Psi4 Developers.
#
# The copyrights for code used from other parties are included in
# the corresponding files.
#
# This file is part of Psi4.
#
# Psi4 is free software; you can redistribute it and/or modify
#... | lgpl-3.0 |
yavalvas/yav_com | build/matplotlib/examples/event_handling/lasso_demo.py | 9 | 2365 | """
Show how to use a lasso to select a set of points and get the indices
of the selected points. A callback is used to change the color of the
selected points
This is currently a proof-of-concept implementation (though it is
usable as is). There will be some refinement of the API.
"""
from matplotlib.widgets import... | mit |
Windy-Ground/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 |
astropy/astropy | astropy/visualization/wcsaxes/tests/test_grid_paths.py | 6 | 1050 | import numpy as np
import pytest
from matplotlib.lines import Path
from astropy.visualization.wcsaxes.grid_paths import get_lon_lat_path
@pytest.mark.parametrize('step_in_degrees', [10, 1, 0.01])
def test_round_trip_visibility(step_in_degrees):
zero = np.zeros(100)
# The pixel values are irrelevant for this... | bsd-3-clause |
SMTorg/smt | smt/problems/tests/test_problem_examples.py | 3 | 7369 | import unittest
import matplotlib
import matplotlib.pyplot
matplotlib.use("Agg")
matplotlib.pyplot.switch_backend("Agg")
class Test(unittest.TestCase):
def test_cantilever_beam(self):
import numpy as np
import matplotlib.pyplot as plt
from smt.problems import CantileverBeam
ndi... | bsd-3-clause |
JsNoNo/scikit-learn | sklearn/linear_model/tests/test_least_angle.py | 98 | 20870 | from nose.tools import assert_equal
import numpy as np
from scipy import linalg
from sklearn.cross_validation import train_test_split
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing impor... | bsd-3-clause |
SylvioL/PHYMOBAT | Precision_moba.py | 1 | 8682 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# This file is part of PHYMOBAT 1.2.
# Copyright 2016 Sylvio Laventure (IRSTEA - UMR TETIS)
#
# PHYMOBAT 1.2 is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, eit... | gpl-3.0 |
simontorres/bravo | gui/gui_con_pdm_play.py | 1 | 7734 | #import sys
import matplotlib
matplotlib.use('QT4Agg')
from matplotlib.widgets import Button
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import UnivariateSpline
from matplotlib.widgets import MultiCursor
import os
import argparse
from astropy.stats import LombScargle
def get_args(argu... | gpl-3.0 |
chris-chris/tensorflow | tensorflow/contrib/learn/python/learn/tests/dataframe/in_memory_source_test.py | 62 | 3960 | # 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 |
kthyng/tracpy | tracpy/tools.py | 1 | 18989 | """
Tools for dealing with drifter stuff.
Functions include:
* interpolate2d
* interpolate3d
* find_final
* convert_indices
* check_points
* seed
"""
import numpy as np
from scipy import ndimage
import time
import matplotlib.tri as mtri
# from matplotlib.mlab import Path, find
from matplotlib.path import Path
def ... | mit |
louispotok/pandas | pandas/tests/indexes/timedeltas/test_construction.py | 3 | 3568 | import pytest
import numpy as np
from datetime import timedelta
import pandas as pd
import pandas.util.testing as tm
from pandas import TimedeltaIndex, timedelta_range, to_timedelta
class TestTimedeltaIndex(object):
def test_construction_base_constructor(self):
arr = [pd.Timedelta('1 days'), pd.NaT, pd... | bsd-3-clause |
heyfaraday/CMB_test | planck_viewer.py | 1 | 1097 | import matplotlib.pyplot as plt
import numpy as np
import healpy as hp
map_I = hp.read_map('data/COM_CMB_IQU-smica_1024_R2.02_full.fits')
hp.mollview(map_I, norm='hist', min=-0.1, max=0.1, xsize=2000)
plt.show()
map_Q = hp.read_map('data/COM_CMB_IQU-smica_1024_R2.02_full.fits', field=1)
hp.mollview(map_Q, norm='hist... | mit |
kaichogami/scikit-learn | sklearn/ensemble/tests/test_bagging.py | 34 | 25693 | """
Testing for the bagging ensemble module (sklearn.ensemble.bagging).
"""
# Author: Gilles Louppe
# License: BSD 3 clause
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.te... | bsd-3-clause |
oscarbranson/latools | Supplement/comparison_tools/plots_zircon.py | 1 | 2913 | import re
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
from .stats import fmt_RSS
from .plots import rangecalcx, bland_altman, get_panel_bounds
def fmt_el(el):
e = re.match('.*?([A-z]+).*?', el).groups()[0]
m = re.match('.*?([0-9]+).*?', el).groups()[0]
return e + m
def bland... | mit |
ankurankan/scikit-learn | examples/classification/plot_classification_probability.py | 242 | 2624 | """
===============================
Plot classification probability
===============================
Plot the classification probability for different classifiers. We use a 3
class dataset, and we classify it with a Support Vector classifier, L1
and L2 penalized logistic regression with either a One-Vs-Rest or multinom... | bsd-3-clause |
captiosus/treadmill | treadmill/reports.py | 1 | 10706 | """Handles reports over scheduler data."""
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import
import time
import datetime
import itertools
import logging
import fnmatch
import numpy as np
import pandas as pd
import six... | apache-2.0 |
oaklandanalytics/cutting_board | scripts/fetch_buildings.py | 1 | 1841 | import geopandas as gpd
import osmnx
import time
import sys
import numpy as np
args = sys.argv[1:]
juris = args[0]
# this is nasty - these are cities without building footprints in OSM
# that crashes osmnx (at the time of this writing) - so we switch to a
# city that doesn't crash and the joins will fail in the next ... | bsd-3-clause |
kedz/cuttsum | trec2014/python/cuttsum/salience.py | 1 | 18804 | import os
import re
import gzip
import pandas as pd
from cuttsum.data import get_resource_manager, MultiProcessWorker
from cuttsum.misc import ProgressBar
import random
import GPy
import numpy as np
from collections import defaultdict
import multiprocessing
import signal
import sys
import Queue
from sklearn.preprocessi... | apache-2.0 |
nguyentu1602/statsmodels | statsmodels/sandbox/nonparametric/kde2.py | 34 | 3158 | # -*- coding: utf-8 -*-
from __future__ import print_function
from statsmodels.compat.python import lzip, zip
import numpy as np
from . import kernels
#TODO: should this be a function?
class KDE(object):
"""
Kernel Density Estimator
Parameters
----------
x : array-like
N-dimensional array... | bsd-3-clause |
jeffschulte/protein | pyplots/single-image-creation.py | 2 | 3203 | from __future__ import division
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import sys
import time
import file_loader as load
import Image
import math
import re
f_shape = sys.argv[1]
f_param1 = sys.argv[2]
f_param2 = sys.argv[3]
f_param3 = sys.argv[4]
f_param4 =... | mit |
jreback/pandas | pandas/tests/arrays/categorical/test_algos.py | 6 | 2589 | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize("ordered", [True, False])
@pytest.mark.parametrize("categories", [["b", "a", "c"], ["a", "b", "c", "d"]])
def test_factorize(categories, ordered):
cat = pd.Categorical(
["b", "b", "a", "c", None], c... | bsd-3-clause |
jeepsterboy/waveletanalysis | wavelets_bams/tests/test_wavelets.py | 1 | 10164 | from __future__ import division
from nose.tools import *
import numpy.testing as npt
import numpy as np
import scipy.signal
from scipy.io import wavfile
import matplotlib.pyplot as plt
import wavelets
from wavelets import WaveletAnalysis
__all__ = ['test_N', 'compare_cwt', 'compare_morlet', 'test_Cd',
't... | mit |
andrewcbennett/iris | docs/iris/example_code/General/projections_and_annotations.py | 6 | 5249 | """
Plotting in different projections
=================================
This example shows how to overlay data and graphics in different projections,
demonstrating various features of Iris, Cartopy and matplotlib.
We wish to overlay two datasets, defined on different rotated-pole grids.
To display both together, we m... | gpl-3.0 |
gpersistence/tstop | scripts/bar_plot_from_csv.py | 1 | 2145 | #TSTOP
#
#This program is free software: you can redistribute it and/or modify
#it under the terms of the GNU General Public License as published by
#the Free Software Foundation, either version 3 of the License, or
#(at your option) any later version.
#
#This program is distributed in the hope that it will be useful,
... | gpl-3.0 |
robbymeals/scikit-learn | examples/linear_model/plot_lasso_coordinate_descent_path.py | 254 | 2639 | """
=====================
Lasso and Elastic Net
=====================
Lasso and elastic net (L1 and L2 penalisation) implemented using a
coordinate descent.
The coefficients can be forced to be positive.
"""
print(__doc__)
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import num... | bsd-3-clause |
chenyyx/scikit-learn-doc-zh | examples/en/linear_model/plot_omp.py | 385 | 2263 | """
===========================
Orthogonal Matching Pursuit
===========================
Using orthogonal matching pursuit for recovering a sparse signal from a noisy
measurement encoded with a dictionary
"""
print(__doc__)
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import OrthogonalM... | gpl-3.0 |
bnaul/scikit-learn | sklearn/tests/test_isotonic.py | 3 | 18868 | import warnings
import numpy as np
import pickle
import copy
import pytest
from sklearn.isotonic import (check_increasing, isotonic_regression,
IsotonicRegression, _make_unique)
from sklearn.utils.validation import check_array
from sklearn.utils._testing import (assert_raises, assert_al... | bsd-3-clause |
ephes/scikit-learn | examples/gaussian_process/plot_gp_regression.py | 253 | 4054 | #!/usr/bin/python
# -*- coding: utf-8 -*-
r"""
=========================================================
Gaussian Processes regression: basic introductory example
=========================================================
A simple one-dimensional regression exercise computed in two different ways:
1. A noise-free cas... | bsd-3-clause |
wolfiex/ropacode | revamp/ropa_tool.py | 1 | 2766 | '''
A tool to calculate the fluxes from DSMACC
D.Ellis 2016
'''
#functions
global specs,reactants
xlen = lambda x: xrange(len(x))
def getcoef (x): # '''gets specie coefficients from data'''
try: return int(re.sub(r'([\.\d]*)\s*\D[\d\D]*', r'\1', x))
except: return 1 #assume coeff are Z+
def getspec (x)... | cc0-1.0 |
amandersillinois/landlab | landlab/components/chi_index/channel_chi.py | 3 | 27201 | # -*- coding: utf-8 -*-
"""Created March 2016.
@author: dejh
"""
import numpy as np
from landlab import Component, RasterModelGrid
try:
from itertools import izip
except ImportError:
izip = zip
class ChiFinder(Component):
"""Calculate Chi Indices.
This component calculates chi indices, sensu Per... | mit |
ml-playground/data-science-from-scratch | code/nearest_neighbors.py | 57 | 7357 | from __future__ import division
from collections import Counter
from linear_algebra import distance
from statistics import mean
import math, random
import matplotlib.pyplot as plt
def raw_majority_vote(labels):
votes = Counter(labels)
winner, _ = votes.most_common(1)[0]
return winner
def majority_vote(lab... | unlicense |
Fireblend/chromium-crosswalk | chrome/test/data/nacl/gdb_rsp.py | 99 | 2431 | # Copyright (c) 2012 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.
# This file is based on gdb_rsp.py file from NaCl repository.
import re
import socket
import time
def RspChecksum(data):
checksum = 0
for char in ... | bsd-3-clause |
glouppe/scikit-learn | sklearn/gaussian_process/tests/test_kernels.py | 23 | 11813 | """Testing for kernels for Gaussian processes."""
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Licence: BSD 3 clause
from collections import Hashable
from sklearn.externals.funcsigs import signature
import numpy as np
from scipy.optimize import approx_fprime
from sklearn.metrics.pairwise \
imp... | bsd-3-clause |
zhoulingjun/zipline | zipline/finance/trading.py | 15 | 19380 | #
# Copyright 2014 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | apache-2.0 |
hainm/statsmodels | statsmodels/sandbox/distributions/examples/matchdist.py | 33 | 9822 | '''given a 1D sample of observation, find a matching distribution
* estimate maximum likelihood paramater for each distribution
* rank estimated distribution by Kolmogorov-Smirnov and Anderson-Darling
test statistics
Author: Josef Pktd
License: Simplified BSD
original December 2008
TODO:
* refactor to result clas... | bsd-3-clause |
AlexRobson/scikit-learn | sklearn/setup.py | 225 | 2856 | import os
from os.path import join
import warnings
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info, BlasNotFoundError
import numpy
libraries = []
if os.name == 'posix':
libraries.appe... | bsd-3-clause |
tonysyu/mpltools | mpltools/sphinx/plot2rst.py | 2 | 20147 | """
Generate reStructuredText example from python files.
Generate the rst files for the examples by iterating over the python
example files. Files that generate images should start with 'plot'.
To generate your own examples, add ``'mpltools.sphinx.plot2rst'`` to the list
of ``extensions`` in your Sphinx configuration... | bsd-3-clause |
siutanwong/scikit-learn | examples/exercises/plot_cv_digits.py | 232 | 1206 | """
=============================================
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 |
tody411/ImageViewerFramework | ivf/batch/segmentation.py | 1 | 4213 | # -*- coding: utf-8 -*-
## @package ivf.batch.segmentation
#
# ivf.batch.segmentation utility package.
# @author tody
# @date 2016/02/15
import numpy as np
import cv2
import matplotlib.pyplot as plt
from PyQt4.QtGui import *
from PyQt4.QtCore import *
import sys
import os
from ivf.batch.batch import ... | mit |
bigdataelephants/scikit-learn | examples/manifold/plot_manifold_sphere.py | 258 | 5101 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=============================================
Manifold Learning methods on a severed sphere
=============================================
An application of the different :ref:`manifold` techniques
on a spherical data-set. Here one can see the use of
dimensionality reducti... | bsd-3-clause |
edux300/research | script_full_images_view.py | 1 | 16557 | # -*- coding: utf-8 -*-
"""
Created on Thu Aug 10 13:22:54 2017
@author: eduardo
"""
from matplotlib import pyplot as plt
import dicom as dcm
import pickle as pkl
import read_inbreast as readin
import scipy
import scipy.ndimage
import cnn_models as models
import tensorflow as tf
import numpy as np
import utils as ut
... | apache-2.0 |
bigfootproject/OSMEF | data_processing/graphs/jain.py | 1 | 1315 | #!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
import json
data = json.load(open("../../osmef/data.json"))
N = 6
MS = 10
#xpoints = (1, 5, 10, 20, 30, 50)
xpoints = (1, 5, 10, 15, 20, 25, 30, 35, 40, 50)
def calc_jain(values):
return (sum(values)**2)/(len(values)*sum(values**2))
fig = pl... | apache-2.0 |
buncem/deep-learning | image-classification/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... | mit |
dopplershift/MetPy | tests/plots/test_declarative.py | 1 | 41498 | # Copyright (c) 2019 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Test the simplified plotting interface."""
from datetime import datetime, timedelta
from io import BytesIO
import warnings
import matplotlib
import numpy as np
import pandas ... | bsd-3-clause |
shenzebang/scikit-learn | sklearn/utils/tests/test_sparsefuncs.py | 157 | 13799 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from numpy.testing import assert_array_almost_equal, assert_array_equal
from sklearn.datasets import make_classification
from sklearn.utils.sparsefuncs import (mean_variance_axis,
inplace_column_scale,
... | bsd-3-clause |
muxiaobai/CourseExercises | python/kaggle/competition/house-price/house_price.py | 1 | 8403 |
# coding: utf-8
# # 房价预测案例
#
# ## Step 1: 检视源数据集
# In[5]:
import numpy as np
import pandas as pd
# #### 读入数据
#
# * 一般来说源数据的index那一栏没什么用，我们可以用来作为我们pandas dataframe的index。这样之后要是检索起来也省事儿。
#
# * 有人的地方就有鄙视链。跟知乎一样。Kaggle的也是个处处呵呵的危险地带。Kaggle上默认把数据放在*input*文件夹下。所以我们没事儿写个教程什么的，也可以依据这个convention来，显得自己很有逼格。。
# In[6]:
... | gpl-2.0 |
arashzamani/lstm_nlg_ver1 | test_cases/embedding_al1.py | 1 | 3778 | import collections
import operator
import numpy
import random
import sys
from keras.layers import Dense, Activation, Dropout, Embedding, LSTM, Input
from keras.layers.recurrent import GRU
from keras.models import Sequential
from keras.wrappers.scikit_learn import KerasRegressor
from keras.utils import np_utils
from sk... | gpl-3.0 |
CVML/scikit-learn | sklearn/qda.py | 140 | 7682 | """
Quadratic Discriminant Analysis
"""
# Author: Matthieu Perrot <matthieu.perrot@gmail.com>
#
# License: BSD 3 clause
import warnings
import numpy as np
from .base import BaseEstimator, ClassifierMixin
from .externals.six.moves import xrange
from .utils import check_array, check_X_y
from .utils.validation import ... | bsd-3-clause |
akionakamura/scikit-learn | examples/decomposition/plot_kernel_pca.py | 353 | 2011 | """
==========
Kernel PCA
==========
This example shows that Kernel PCA is able to find a projection of the data
that makes data linearly separable.
"""
print(__doc__)
# Authors: Mathieu Blondel
# Andreas Mueller
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomp... | bsd-3-clause |
anntzer/scikit-learn | sklearn/feature_selection/tests/test_chi2.py | 19 | 2987 | """
Tests for chi2, currently the only feature selection function designed
specifically to work with sparse matrices.
"""
import warnings
import numpy as np
import pytest
from scipy.sparse import coo_matrix, csr_matrix
import scipy.stats
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.feature_se... | bsd-3-clause |
amolkahat/pandas | pandas/plotting/_core.py | 2 | 128319 | # being a bit too dynamic
# pylint: disable=E1101
from __future__ import division
import warnings
import re
from collections import namedtuple
from distutils.version import LooseVersion
import numpy as np
from pandas.util._decorators import cache_readonly, Appender
from pandas.compat import range, lrange, map, zip, ... | bsd-3-clause |
jkorell/PTVS | Python/Product/Analyzer/BuiltinScraperTests.py | 18 | 18954 | # ############################################################################
#
# Copyright (c) Microsoft Corporation.
#
# This source code is subject to terms and conditions of the Apache License, Version 2.0. A
# copy of the license can be found in the License.html file at the root of this distribution.... | apache-2.0 |
rohit21122012/DCASE2013 | runs/2016/baseline64/task1_scene_classification.py | 6 | 34635 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# DCASE 2016::Acoustic Scene Classification / Baseline System
from src.ui import *
from src.general import *
from src.files import *
from src.features import *
from src.dataset import *
from src.evaluation import *
import numpy
import csv
import argparse
import textwra... | mit |
hbar/python-BeamDynamicsTools | applications/Trajectory-BFieldRippleStudy.py | 1 | 7085 | import sys
sys.path.append('../lib/')
from BeamDynamicsTools import *
import pylab as pl
import matplotlib as mpl
# Define array of injection angles
# (x,y,z) = (1.798m, -0.052m, 0.243m)
# alpha = 12.6 degrees (X-Z plane)
# beta = 8.0 degrees (X-Y plane)
alpha0 = 12.6
beta0 = 8.0
alpha = alpha0/180.0*pi; beta = bet... | mit |
kaichogami/scikit-learn | examples/tree/unveil_tree_structure.py | 67 | 4824 | """
=========================================
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... | bsd-3-clause |
joshloyal/scikit-learn | examples/applications/svm_gui.py | 124 | 11251 | """
==========
Libsvm GUI
==========
A simple graphical frontend for Libsvm mainly intended for didactic
purposes. You can create data points by point and click and visualize
the decision region induced by different kernels and parameter settings.
To create positive examples click the left mouse button; to create
neg... | bsd-3-clause |
saiwing-yeung/scikit-learn | examples/ensemble/plot_gradient_boosting_quantile.py | 392 | 2114 | """
=====================================================
Prediction Intervals for Gradient Boosting Regression
=====================================================
This example shows how quantile regression can be used
to create prediction intervals.
"""
import numpy as np
import matplotlib.pyplot as plt
from skle... | bsd-3-clause |
StartupsPoleEmploi/labonneboite | labonneboite/scripts/impact_retour_emploi/make_report.py | 1 | 12252 | import numpy
import pandas as pd
from labonneboite.conf import settings
from labonneboite.importer import util as import_util
from labonneboite.importer.jobs.common import logger
from labonneboite.importer import settings as importer_settings
from labonneboite.scripts.impact_retour_emploi.google_sheets_report import G... | agpl-3.0 |
davidgbe/scikit-learn | examples/plot_kernel_ridge_regression.py | 230 | 6222 | """
=============================================
Comparison of kernel ridge regression and SVR
=============================================
Both kernel ridge regression (KRR) and SVR learn a non-linear function by
employing the kernel trick, i.e., they learn a linear function in the space
induced by the respective k... | bsd-3-clause |
q1ang/seaborn | examples/elaborate_violinplot.py | 30 | 1055 | """
Violinplot from a wide-form dataset
===================================
_thumb: .6, .45
"""
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="whitegrid")
# Load the example dataset of brain network correlations
df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)
# Pull out a... | bsd-3-clause |
ulikoehler/cv_algorithms | cv_algorithms/neighbours.py | 1 | 6843 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Thinning algorithms
"""
import numpy as np
from ._ffi import *
from ._checks import *
import enum
__all__ = ["binary_neighbours", "Neighbours", "Direction"]
_ffi.cdef('''
int binary_neighbours(uint8_t* dst, const uint8_t* src, int width, int height);
''')
def binary... | apache-2.0 |
gevero/deap | examples/coev/coop_evol.py | 12 | 6361 | # This file is part of DEAP.
#
# DEAP is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# DEAP is distributed ... | lgpl-3.0 |
aleksandr-bakanov/astropy | astropy/visualization/wcsaxes/axislabels.py | 4 | 5905 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
import numpy as np
from matplotlib import rcParams
from matplotlib.text import Text
import matplotlib.transforms as mtransforms
from .frame import RectangularFrame
class AxisLabels(Text):
def __init__(self, frame, minpad=1, *args, **kwargs):
... | bsd-3-clause |
Clyde-fare/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 |
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