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 |
|---|---|---|---|---|---|
WarrenWeckesser/scipy | scipy/spatial/_spherical_voronoi.py | 7 | 13692 | """
Spherical Voronoi Code
.. versionadded:: 0.18.0
"""
#
# Copyright (C) Tyler Reddy, Ross Hemsley, Edd Edmondson,
# Nikolai Nowaczyk, Joe Pitt-Francis, 2015.
#
# Distributed under the same BSD license as SciPy.
#
import warnings
import numpy as np
import scipy
from . import _voronoi
from scipy.spat... | bsd-3-clause |
petebachant/MakeWaves | makewaves/wavemakerlimits.py | 1 | 3085 | """This module replicates the logic of the Regular Waves VI to calculate a
safe wave height.
"""
from __future__ import division, print_function, absolute_import
import numpy as np
import os
import sys
_thisdir = os.path.dirname(os.path.abspath(__file__))
settings_dir = os.path.join(_thisdir, "settings")
max_halfstr... | gpl-3.0 |
ageron/tensorflow | tensorflow/contrib/learn/__init__.py | 18 | 2695 | # 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 |
LCAV/pyroomacoustics | pyroomacoustics/tests/test_build_rir.py | 1 | 4247 | from __future__ import division, print_function
import pyroomacoustics as pra
import numpy as np
try:
from pyroomacoustics import build_rir
build_rir_available = True
except:
print('build_rir not available')
build_rir_available = False
# tolerance for test success (1%)
tol = 0.01
fdl = 81
fs = 16000
... | mit |
prashantvyas/cuda-convnet2 | convdata.py | 174 | 14675 | # Copyright 2014 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | apache-2.0 |
depet/scikit-learn | examples/applications/face_recognition.py | 12 | 5368 | """
===================================================
Faces recognition example using eigenfaces and SVMs
===================================================
The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:
http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (2... | bsd-3-clause |
minrk/oauthenticator | docs/source/conf.py | 2 | 4506 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup --------------------------------------------------------------
# If extensions (or module... | bsd-3-clause |
mlyundin/scikit-learn | examples/applications/plot_model_complexity_influence.py | 323 | 6372 | """
==========================
Model Complexity Influence
==========================
Demonstrate how model complexity influences both prediction accuracy and
computational performance.
The dataset is the Boston Housing dataset (resp. 20 Newsgroups) for
regression (resp. classification).
For each class of models we m... | bsd-3-clause |
yousrabk/mne-python | mne/viz/tests/test_3d.py | 4 | 7511 | # 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 |
achim1/HErmes | HErmes/selection/variables.py | 2 | 26753 | """
Container classes for variables
"""
import numpy as np
import os
import pandas as pd
import tables
import abc
import enum
import array
import numbers
from ..utils import files as f
from ..utils import Logger
from copy import deepcopy as copy
from collections import Sequence
from itertools import chain, count
DEF... | gpl-2.0 |
louisLouL/pair_trading | capstone_env/lib/python3.6/site-packages/pandas/core/nanops.py | 7 | 23743 | import itertools
import functools
import operator
import numpy as np
from pandas import compat
from pandas._libs import tslib, algos, lib
from pandas.core.dtypes.common import (
_get_dtype,
is_float, is_scalar,
is_integer, is_complex, is_float_dtype,
is_complex_dtype, is_integer_dtype,
is_bool_dtyp... | mit |
aabadie/scikit-learn | sklearn/neighbors/classification.py | 15 | 14359 | """Nearest Neighbor Classification"""
# Authors: Jake Vanderplas <vanderplas@astro.washington.edu>
# Fabian Pedregosa <fabian.pedregosa@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Sparseness support by Lars Buitinck
# Multi-output support by Arnaud Joly <a.joly@ul... | bsd-3-clause |
mbayon/TFG-MachineLearning | vbig/lib/python2.7/site-packages/sklearn/datasets/tests/test_lfw.py | 42 | 7253 | """This test for the LFW require medium-size data downloading 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... | mit |
devanshdalal/scikit-learn | sklearn/datasets/tests/test_rcv1.py | 322 | 2414 | """Test the rcv1 loader.
Skipped if rcv1 is not already downloaded to data_home.
"""
import errno
import scipy.sparse as sp
import numpy as np
from sklearn.datasets import fetch_rcv1
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing i... | bsd-3-clause |
treycausey/scikit-learn | examples/plot_precision_recall.py | 3 | 6114 | """
================
Precision-Recall
================
Example of Precision-Recall metric to evaluate classifier output quality.
In information retrieval, precision is a measure of result relevancy, while
recall is a measure of how many truly relevant results are returned. A high
area under the curve represents both ... | bsd-3-clause |
elenbert/allsky | src/webdatagen/sensors-graphgen.py | 1 | 13361 | #!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import csv
import MySQLdb
import sys
import config
def plot_sky_temp(sensor_data, output_file, output_csv_file, one_day=False):
xdata = []
ydata = []
print 'Plotting skytemp graph using ' + str(len(sensor_data... | gpl-2.0 |
q1ang/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 |
mattgiguere/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 |
porterdf/porterdf.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 |
datapythonista/pandas | pandas/core/arrays/timedeltas.py | 1 | 36958 | from __future__ import annotations
from datetime import timedelta
from typing import TYPE_CHECKING
import numpy as np
from pandas._libs import (
lib,
tslibs,
)
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.tslibs import (
BaseOffset,
NaT,
NaTType,
Period,
Tick,
Timed... | bsd-3-clause |
phobson/statsmodels | statsmodels/tsa/ar_model.py | 1 | 33913 | from __future__ import division
from statsmodels.compat.python import iteritems, range, string_types, lmap, long
import numpy as np
from numpy import dot, identity
from numpy.linalg import inv, slogdet
from scipy.stats import norm
from statsmodels.regression.linear_model import OLS
from statsmodels.tsa.tsatools import... | bsd-3-clause |
icdishb/scikit-learn | examples/svm/plot_custom_kernel.py | 115 | 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 |
ChanChiChoi/scikit-learn | examples/text/mlcomp_sparse_document_classification.py | 292 | 4498 | """
========================================================
Classification of text documents: using a MLComp dataset
========================================================
This is an example showing how the scikit-learn can be used to classify
documents by topics using a bag-of-words approach. This example uses
a s... | bsd-3-clause |
frank-tancf/scikit-learn | examples/neighbors/plot_approximate_nearest_neighbors_scalability.py | 85 | 5728 | """
============================================
Scalability of Approximate Nearest Neighbors
============================================
This example studies the scalability profile of approximate 10-neighbors
queries using the LSHForest with ``n_estimators=20`` and ``n_candidates=200``
when varying the number of sa... | bsd-3-clause |
Bollegala/MLIB | utils/genArtificialData.py | 1 | 4811 | '''
Created on Aug 5, 2011
@author: danu
'''
from matplotlib import pyplot as plt
import numpy as np
def twoGaussians(n):
"""
Generates 2d n data points from two Gaussians.
Returns an array of 2D vectors corresponding to the data points
and a 1d array indicating the true labels of those data points.
"""
flag ... | bsd-3-clause |
neilpanchal/iPython-Notebook-Profile | profile_neil/ipython_notebook_config.py | 1 | 19985 | # Configuration file for ipython-notebook.
c = get_config()
#------------------------------------------------------------------------------
# NotebookApp configuration
#------------------------------------------------------------------------------
# NotebookApp will inherit config from: BaseIPythonApplication, Appli... | mit |
Alan-Robertson/python-qinfer | src/qinfer/examples/rebit_plotting_example.py | 1 | 12182 | #!/usr/bin/python
# -*- coding: utf-8 -*-
##
# rebit_plotting_example.py: rebit tomography illustration module
##
# © 2013 Chris Ferrie (csferrie@gmail.com) and
# Christopher E. Granade (cgranade@gmail.com)
#
# This file is a part of the Qinfer project.
# Licensed under the AGPL version 3.
##
# This program... | agpl-3.0 |
ryfeus/lambda-packs | LightGBM_sklearn_scipy_numpy/source/sklearn/exceptions.py | 50 | 5276 | """
The :mod:`sklearn.exceptions` module includes all custom warnings and error
classes used across scikit-learn.
"""
__all__ = ['NotFittedError',
'ChangedBehaviorWarning',
'ConvergenceWarning',
'DataConversionWarning',
'DataDimensionalityWarning',
'EfficiencyWarn... | mit |
ishanic/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 |
pjryan126/solid-start-careers | store/api/zillow/venv/lib/python2.7/site-packages/numpy/lib/twodim_base.py | 26 | 26904 | """ Basic functions for manipulating 2d arrays
"""
from __future__ import division, absolute_import, print_function
from numpy.core.numeric import (
asanyarray, arange, zeros, greater_equal, multiply, ones, asarray,
where, int8, int16, int32, int64, empty, promote_types, diagonal,
)
from numpy.core import... | gpl-2.0 |
mantidproject/mantid | qt/python/mantidqt/widgets/plotconfigdialog/test/test_plotconfigdialogpresenter.py | 3 | 12339 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2019 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
# T... | gpl-3.0 |
kenshay/ImageScript | ProgramData/SystemFiles/Python/Lib/site-packages/pandas/io/tests/json/test_pandas.py | 7 | 42394 | # pylint: disable-msg=W0612,E1101
import nose
from pandas.compat import range, lrange, StringIO, OrderedDict
import os
import numpy as np
from pandas import (Series, DataFrame, DatetimeIndex, Timestamp,
read_json, compat)
from datetime import timedelta
import pandas as pd
from pandas.util.testing ... | gpl-3.0 |
nborggren/zipline | zipline/pipeline/loaders/blaze/earnings.py | 2 | 4491 | from datashape import istabular
import pandas as pd
from toolz import valmap
from .core import (
TS_FIELD_NAME,
SID_FIELD_NAME,
bind_expression_to_resources,
ffill_query_in_range,
)
from zipline.pipeline.data import EarningsCalendar
from zipline.pipeline.loaders.base import PipelineLoader
from zipline.... | apache-2.0 |
camptocamp/QGIS | python/plugins/processing/algs/BarPlot.py | 1 | 2901 | # -*- coding: utf-8 -*-
"""
***************************************************************************
BarPlot.py
---------------------
Date : January 2013
Copyright : (C) 2013 by Victor Olaya
Email : volayaf at gmail dot com
******************************... | gpl-2.0 |
MostafaGazar/tensorflow | tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py | 4 | 6151 | # 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 |
harshaneelhg/scikit-learn | sklearn/tests/test_dummy.py | 129 | 17774 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from sklearn.base import clone
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_eq... | bsd-3-clause |
stanmoore1/lammps | examples/SPIN/test_problems/validation_damped_exchange/plot_precession.py | 9 | 1111 | #!/usr/bin/env python3
import numpy as np, pylab, tkinter
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from decimal import *
import sys, string, os
argv = sys.argv
if len(argv) != 3:
print("Syntax: ./plot_precession.py res_lammps.dat res_llg.dat")
sys.exit()
lammps_file = sys.argv[1]
llg... | gpl-2.0 |
rhyolight/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/colors.py | 69 | 31676 | """
A module for converting numbers or color arguments to *RGB* or *RGBA*
*RGB* and *RGBA* are sequences of, respectively, 3 or 4 floats in the
range 0-1.
This module includes functions and classes for color specification
conversions, and for mapping numbers to colors in a 1-D array of
colors called a colormap. Color... | agpl-3.0 |
marcocaccin/scikit-learn | examples/plot_isotonic_regression.py | 303 | 1767 | """
===================
Isotonic Regression
===================
An illustration of the isotonic regression on generated data. The
isotonic regression finds a non-decreasing approximation of a function
while minimizing the mean squared error on the training data. The benefit
of such a model is that it does not assume a... | bsd-3-clause |
kmather73/content | labs/lab2/cs109style.py | 38 | 1293 | from __future__ import print_function
from IPython.core.display import HTML
from matplotlib import rcParams
#colorbrewer2 Dark2 qualitative color table
dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),
(0.8509803921568627, 0.37254901960784315, 0.00784313725490196),
... | mit |
ChanChiChoi/scikit-learn | examples/manifold/plot_mds.py | 261 | 2616 | """
=========================
Multi-dimensional scaling
=========================
An illustration of the metric and non-metric MDS on generated noisy data.
The reconstructed points using the metric MDS and non metric MDS are slightly
shifted to avoid overlapping.
"""
# Author: Nelle Varoquaux <nelle.varoquaux@gmail.... | bsd-3-clause |
jpautom/scikit-learn | examples/neighbors/plot_species_kde.py | 282 | 4059 | """
================================================
Kernel Density Estimate of Species Distributions
================================================
This shows an example of a neighbors-based query (in particular a kernel
density estimate) on geospatial data, using a Ball Tree built upon the
Haversine distance metric... | bsd-3-clause |
flyingbanana1024102/transmission-line-simulator | src/views/sourceeditor.py | 1 | 5620 | #
# Transmission Line Simulator
#
# Author(s): Jiacong Xu
# Created: Jul-14-2017
#
from popupeditor import PopupEditor
from kivy.properties import *
from materialbutton import MaterialButton
from util.constants import *
from kivy.animation import Animation
from models.powersource import *
from kivy.metrics import *
i... | mit |
dtiarks/ThesisPlot | Chap5/Giovanni/post_selected/giovanni.py | 1 | 8588 | import numpy as np
from matplotlib import pyplot as plt
from datetime import datetime
import re
from scipy.optimize import curve_fit
from matplotlib import rc
#curve=np.loadtxt("curve.tsv")
histoLst=[]
histoPostLst=[]
histoRefLst=[]
xLst=np.arange(-8,-12.5,-1)
#print xLst
name="ref001"
nameRef="ref001"
##for n in ... | mit |
FRESNA/PyPSA | pypsa/pf.py | 1 | 51617 | ## Copyright 2015-2018 Tom Brown (FIAS), Jonas Hoersch (FIAS)
## 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.
## T... | gpl-3.0 |
hannorein/rebound | python_examples/megno/problem.py | 1 | 3476 | #!/usr/bin/python
# This example integrates Jupiter and Saturn in the Solar system for a variety of initial conditions.
# Alongside the normal equations of motions, IAS15 is used to integrate the variational equations.
# These can be used to measure the Mean Exponential Growth of Nearby Orbits (MEGNO), a chaos indicato... | gpl-3.0 |
evanbiederstedt/RRBSfun | epiphen/cll_tests/total_CLL_chr04.py | 1 | 8306 | import glob
import pandas as pd
import numpy as np
pd.set_option('display.max_columns', 50) # print all rows
import os
os.chdir("/gpfs/commons/home/biederstedte-934/evan_projects/correct_phylo_files")
cw154 = glob.glob("binary_position_RRBS_cw154*")
trito = glob.glob("binary_position_RRBS_trito_pool*")
print(le... | mit |
macks22/scikit-learn | sklearn/decomposition/tests/test_dict_learning.py | 85 | 8565 | import numpy as np
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
wlamond/scikit-learn | examples/calibration/plot_calibration_multiclass.py | 95 | 6971 | """
==================================================
Probability Calibration for 3-class classification
==================================================
This example illustrates how sigmoid calibration changes predicted
probabilities for a 3-class classification problem. Illustrated is the
standard 2-simplex, wher... | bsd-3-clause |
ClimbsRocks/scikit-learn | examples/linear_model/plot_polynomial_interpolation.py | 168 | 2088 | #!/usr/bin/env python
"""
========================
Polynomial interpolation
========================
This example demonstrates how to approximate a function with a polynomial of
degree n_degree by using ridge regression. Concretely, from n_samples 1d
points, it suffices to build the Vandermonde matrix, which is n_samp... | bsd-3-clause |
aspiringfastlaner/spx_options_backtesting | Python Code/black_scholes.py | 1 | 1821 | # Importing modules
import smtplib
import pandas as pd
import datetime as dt
import pandas.stats.moments as st
import matplotlib.pyplot as plt
import os
import quandl as qd
import seaborn as sns
from scipy.stats import norm
from math import *
import matplotlib.animation as animation
import matplotlib.pyplot as plt
impo... | apache-2.0 |
Capstone2017/Machine-Learning-NLP | notebook/2nd_RNN.py | 1 | 3452 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Nov 8 23:07:21 2016
@author: Yu Zhipeng
"""
from __future__ import print_function
import numpy as np
np.random.seed(1337) # for reproducibility
from keras.preprocessing import sequence
from keras.utils import np_utils
from keras.models import Sequen... | mit |
bdh1011/wau | venv/lib/python2.7/site-packages/pandas/tests/test_expressions.py | 4 | 16414 | # -*- coding: utf-8 -*-
from __future__ import print_function
# pylint: disable-msg=W0612,E1101
import nose
import re
from numpy.random import randn
import operator
import numpy as np
from numpy.testing import assert_array_equal
from pandas.core.api import DataFrame, Panel
from pandas.computation import expressions... | mit |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/doc/mpl_toolkits/axes_grid/figures/demo_colorbar_of_inset_axes.py | 5 | 1150 | import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid.inset_locator import inset_axes, zoomed_inset_axes
from mpl_toolkits.axes_grid.colorbar import colorbar
def get_demo_image():
from matplotlib.cbook import get_sample_data
import numpy as np
f = get_sample_data("axes_grid/bivariate_normal.npy", as... | gpl-2.0 |
mhue/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 |
stuart-knock/bokeh | bokeh/charts/builder/step_builder.py | 43 | 5445 | """This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the Step class which lets you build your Step charts just
passing the arguments to the Chart class and calling the proper functions.
"""
#-----------------------------------------------------------------... | bsd-3-clause |
jdmcbr/geopandas | geopandas/io/tests/generate_legacy_storage_files.py | 2 | 2736 | """
Script to create the data and write legacy storage (pickle) files.
Based on pandas' generate_legacy_storage_files.py script.
To use this script, create an environment for which you want to
generate pickles, activate the environment, and run this script as:
$ python geopandas/geopandas/io/tests/generate_legacy_st... | bsd-3-clause |
vybstat/scikit-learn | sklearn/datasets/__init__.py | 176 | 3671 | """
The :mod:`sklearn.datasets` module includes utilities to load datasets,
including methods to load and fetch popular reference datasets. It also
features some artificial data generators.
"""
from .base import load_diabetes
from .base import load_digits
from .base import load_files
from .base import load_iris
from .... | bsd-3-clause |
ndingwall/scikit-learn | sklearn/datasets/tests/test_openml.py | 5 | 51973 | """Test the openml loader.
"""
import gzip
import json
import numpy as np
import os
import re
import scipy.sparse
import sklearn
import pytest
from sklearn import config_context
from sklearn.datasets import fetch_openml
from sklearn.datasets._openml import (_open_openml_url,
_arff... | bsd-3-clause |
khkaminska/scikit-learn | examples/calibration/plot_calibration_curve.py | 225 | 5903 | """
==============================
Probability Calibration curves
==============================
When performing classification one often wants to predict not only the class
label, but also the associated probability. This probability gives some
kind of confidence on the prediction. This example demonstrates how to di... | bsd-3-clause |
petebachant/seaborn | seaborn/tests/test_utils.py | 11 | 11537 | """Tests for plotting utilities."""
import warnings
import tempfile
import shutil
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import nose
import nose.tools as nt
from nose.tools import assert_equal, raises
import numpy.testing as npt
import pandas.util.testing as pdt
from distutils.version ... | bsd-3-clause |
intel-analytics/analytics-zoo | pyzoo/zoo/orca/learn/tf2/estimator.py | 1 | 21473 | #
# Copyright 2018 Analytics Zoo Authors.
#
# 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... | apache-2.0 |
datapythonista/pandas | pandas/tests/frame/methods/test_count.py | 3 | 1081 | from pandas import (
DataFrame,
Series,
)
import pandas._testing as tm
class TestDataFrameCount:
def test_count(self):
# corner case
frame = DataFrame()
ct1 = frame.count(1)
assert isinstance(ct1, Series)
ct2 = frame.count(0)
assert isinstance(ct2, Series)
... | bsd-3-clause |
lujunyan1118/SimpleBool | BoolSimu.py | 1 | 14680 | #!/bin/env python
'''
SimpleBool is a python package for dynamic simulations of boolean network
Copyright (C) 2013 Junyan Lu
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, eithe... | gpl-3.0 |
alphaBenj/zipline | tests/test_algorithm.py | 1 | 175570 | #
# 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 |
altairpearl/scikit-learn | sklearn/tree/export.py | 12 | 16020 | """
This module defines export functions for decision trees.
"""
# Authors: Gilles Louppe <g.louppe@gmail.com>
# Peter Prettenhofer <peter.prettenhofer@gmail.com>
# Brian Holt <bdholt1@gmail.com>
# Noel Dawe <noel@dawe.me>
# Satrajit Gosh <satrajit.ghosh@gmail.com>
# Trevor... | bsd-3-clause |
daniaki/Enrich2 | enrich2/base/utils.py | 1 | 8780 | # Copyright 2016-2017 Alan F Rubin, Daniel C Esposito
#
# This file is part of Enrich2.
#
# Enrich2 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) ... | gpl-3.0 |
wzbozon/statsmodels | statsmodels/sandbox/examples/try_quantile_regression1.py | 33 | 1188 | '''Example to illustrate Quantile Regression
Author: Josef Perktold
polynomial regression with systematic deviations above
'''
import numpy as np
from statsmodels.compat.python import zip
from scipy import stats
import statsmodels.api as sm
from statsmodels.regression.quantile_regression import QuantReg
sige = 0.... | bsd-3-clause |
mattcaldwell/zipline | tests/test_history.py | 2 | 36566 | #
# 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 |
bitemyapp/ggplot | ggplot/stats/stat_bar.py | 12 | 1322 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import pandas as pd
from .stat import stat
_MSG_LABELS = """There are more than 30 unique values mapped to x.
If you want a histogram instead, use 'geom_histogram()'.
"""
class stat_bar(stat):
REQUIRE... | bsd-2-clause |
a4a881d4/6FSK | modu.py | 1 | 1695 | import const
import freqTab
import math
import numpy as np
import utils
import matplotlib.pyplot as plt
def dpmap(D,P):
r = []
p = 1
for k in range(len(D)):
r.append((D[k]*const.DataA+P[k]*const.PilotA*1j)*p)
p = p * 1j
return r
def toFreq(D,P):
m = dpmap(D,P)
p = [const.constellationIndex[c] for c in m]
r... | gpl-3.0 |
sahat/bokeh | bokeh/tests/test_protocol.py | 4 | 3772 | import unittest
from unittest import skipIf
import numpy as np
from .test_utils import skipIfPyPy
try:
import pandas as pd
is_pandas = True
except ImportError as e:
is_pandas = False
class TestBokehJSONEncoder(unittest.TestCase):
def setUp(self):
from bokeh.protocol import BokehJSONEncoder... | bsd-3-clause |
auDeep/auDeep | audeep/backend/parsers/compare19_sd.py | 1 | 4730 | # Copyright (C) 2017-2019 Michael Freitag, Shahin Amiriparian, Sergey Pugachevskiy, Nicholas Cummins, Björn Schuller
#
# This file is part of auDeep.
#
# auDeep 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, ... | gpl-3.0 |
jakobworldpeace/scikit-learn | examples/ensemble/plot_voting_decision_regions.py | 86 | 2386 | """
==================================================
Plot the decision boundaries of a VotingClassifier
==================================================
Plot the decision boundaries of a `VotingClassifier` for
two features of the Iris dataset.
Plot the class probabilities of the first sample in a toy dataset
pred... | bsd-3-clause |
IndraVikas/scikit-learn | sklearn/metrics/ranking.py | 75 | 25426 | """Metrics to assess performance on classification task given scores
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.... | bsd-3-clause |
MatthieuBizien/scikit-learn | sklearn/gaussian_process/tests/test_kernels.py | 6 | 11602 | """Testing for kernels for Gaussian processes."""
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: 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 |
montoyjh/pymatgen | pymatgen/phonon/tests/test_plotter.py | 3 | 3658 |
import unittest
import os
import json
import scipy
from io import open
from pymatgen.phonon.dos import CompletePhononDos
from pymatgen.phonon.plotter import PhononDosPlotter, PhononBSPlotter, ThermoPlotter
from pymatgen.phonon.bandstructure import PhononBandStructureSymmLine
test_dir = os.path.join(os.path.dirname(... | mit |
yagmursato/RobotOrHuman | mainn.py | 1 | 1464 | import pandas
import numpy as np
from pandas import DataFrame
from sklearn.cross_validation import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
deneme = []
human = []
with open("human.txt","r") as f:
for line in f:
... | apache-2.0 |
RBDA-F17/crime | code_drop_2/filter_clean_taxi.py | 1 | 3471 | import os
import sys
import pandas as pd
from pyspark.sql.types import *
from pyspark.sql import Row, Column
from pyspark.sql.functions import *
from datetime import datetime
from pyspark import SparkConf, SparkContext
from pyspark.sql import SQLContext
from pyspark.sql.functions import udf
user = os. environ['USER']
... | gpl-3.0 |
maheshakya/scikit-learn | examples/applications/plot_prediction_latency.py | 25 | 11317 | """
==================
Prediction Latency
==================
This is an example showing the prediction latency of various scikit-learn
estimators.
The goal is to measure the latency one can expect when doing predictions
either in bulk or atomic (i.e. one by one) mode.
The plots represent the distribution of the pred... | bsd-3-clause |
hlin117/scikit-learn | examples/hetero_feature_union.py | 81 | 6241 | """
=============================================
Feature Union with Heterogeneous Data Sources
=============================================
Datasets can often contain components of that require different feature
extraction and processing pipelines. This scenario might occur when:
1. Your dataset consists of hetero... | bsd-3-clause |
cactusbin/nyt | matplotlib/lib/matplotlib/contour.py | 4 | 66434 | """
These are classes to support contour plotting and
labelling for the axes class
"""
from __future__ import division, print_function
import warnings
import matplotlib as mpl
import numpy as np
from numpy import ma
import matplotlib._cntr as _cntr
import matplotlib.path as mpath
import matplotlib.ticker as ticker
imp... | unlicense |
lpenguin/pandas-qt | pandasqt/models/DataFrameModel.py | 3 | 21416 | # -*- coding: utf-8 -*-
"""Easy integration of DataFrame into pyqt framework
@author: Jev Kuznetsov, Matthias Ludwig - Datalyze Solutions
"""
from datetime import datetime
from pandasqt.compat import Qt, QtCore, QtGui, Slot, Signal
import pandas
import numpy
import parser
import re
from pandasqt.models.ColumnDty... | mit |
Erotemic/ibeis | ibeis/main_module.py | 1 | 21403 | # -*- coding: utf-8 -*-
"""
This module defines the entry point into the IBEIS system
ibeis.opendb and ibeis.main are the main entry points
"""
from __future__ import absolute_import, division, print_function
#from six.moves import builtins
import sys
import multiprocessing
#try:
import utool as ut
profile = ut.profil... | apache-2.0 |
phdowling/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 |
mattilyra/scikit-learn | sklearn/utils/tests/test_random.py | 85 | 7349 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from scipy.misc import comb as combinations
from numpy.testing import assert_array_almost_equal
from sklearn.utils.random import sample_without_replacement
from sklearn.utils.random import random_choice_csc
from sklearn.utils.testing import ... | bsd-3-clause |
JingheZ/shogun | applications/easysvm/tutpaper/svm_params.py | 26 | 12935 |
#from matplotlib import rc
#rc('text', usetex=True)
fontsize = 16
contourFontsize = 12
showColorbar = False
xmin = -1
xmax = 1
ymin = -1.05
ymax = 1
import sys,os
import numpy
import shogun
from shogun.Kernel import GaussianKernel, LinearKernel, PolyKernel
from shogun.Features import RealFeatures, BinaryLabels
from... | gpl-3.0 |
MaxNoe/pyhexgrid | hexgrid/utils.py | 1 | 2136 | import numpy as np
import pandas as pd
from .hexpoints import HexPoints
DIRECTIONS = {
orientation: HexPoints.from_points([
(1, -1, 0),
(1, 0, -1),
(0, 1, -1),
(-1, 1, 0),
(-1, 0, 1),
(0, -1, 1)
], orientation=orientation)
for orientation in ('flat_top', 'po... | mit |
Ffisegydd/python-examples | examples/scipy/using odr to fit data with errors.py | 1 | 1301 | '''Use ODR to fit a quadratic function to data with errors in
both x and y.'''
import numpy as np
import matplotlib.pyplot as plt
from scipy.odr import *
import random
# Initiate some data, giving some randomness using random.random().
x = np.array([0, 1, 2, 3, 4, 5])
y = np.array([i**2 + random.random() for i in... | mit |
dungvtdev/upsbayescpm | bayespy/inference/vmp/nodes/GaussianProcesses.py | 5 | 25953 | ################################################################################
# Copyright (C) 2011-2012 Jaakko Luttinen
#
# This file is licensed under the MIT License.
################################################################################
import itertools
import numpy as np
#import scipy as sp
#import s... | mit |
nmayorov/scikit-learn | sklearn/feature_selection/tests/test_feature_select.py | 103 | 22297 | """
Todo: cross-check the F-value with stats model
"""
from __future__ import division
import itertools
import warnings
import numpy as np
from scipy import stats, sparse
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
openego/ego.io | egoio/db_tables/model_draft.py | 1 | 302053 | # coding: utf-8
from sqlalchemy import ARRAY, BigInteger, Boolean, CHAR, CheckConstraint, Column, Date, DateTime, Float, ForeignKey, ForeignKeyConstraint, Index, Integer, JSON, Numeric, SmallInteger, String, Table, Text, UniqueConstraint, text
from geoalchemy2.types import Geometry, Raster
from sqlalchemy.dialects.post... | agpl-3.0 |
phobson/statsmodels | statsmodels/datasets/statecrime/data.py | 3 | 3141 | #! /usr/bin/env python
"""Statewide Crime Data"""
__docformat__ = 'restructuredtext'
COPYRIGHT = """Public domain."""
TITLE = """Statewide Crime Data 2009"""
SOURCE = """
All data is for 2009 and was obtained from the American Statistical Abstracts except as indicated below.
"""
DESCRSHORT = """State ... | bsd-3-clause |
minesense/VisTrails | scripts/dist/windows/Input/startup.py | 2 | 7926 | ###############################################################################
##
## Copyright (C) 2014-2016, New York University.
## Copyright (C) 2011-2014, NYU-Poly.
## Copyright (C) 2006-2011, University of Utah.
## All rights reserved.
## Contact: contact@vistrails.org
##
## This file is part of VisTrails.
##
## ... | bsd-3-clause |
tosolveit/scikit-learn | sklearn/__check_build/__init__.py | 345 | 1671 | """ Module to give helpful messages to the user that did not
compile the scikit properly.
"""
import os
INPLACE_MSG = """
It appears that you are importing a local scikit-learn source tree. For
this, you need to have an inplace install. Maybe you are in the source
directory and you need to try from another location.""... | bsd-3-clause |
djgagne/scikit-learn | sklearn/tree/tests/test_tree.py | 11 | 47506 | """
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 |
liyu1990/sklearn | sklearn/cluster/birch.py | 22 | 22730 | # Authors: Manoj Kumar <manojkumarsivaraj334@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Joel Nothman <joel.nothman@gmail.com>
# License: BSD 3 clause
from __future__ import division
import warnings
import numpy as np
from scipy import sparse
from math import sqrt
fro... | bsd-3-clause |
Newman101/scipy | scipy/integrate/quadrature.py | 33 | 28087 | from __future__ import division, print_function, absolute_import
import numpy as np
import math
import warnings
# trapz is a public function for scipy.integrate,
# even though it's actually a numpy function.
from numpy import trapz
from scipy.special.orthogonal import p_roots
from scipy.special import gammaln
from sc... | bsd-3-clause |
f3r/scikit-learn | sklearn/datasets/twenty_newsgroups.py | 35 | 13626 | """Caching loader for the 20 newsgroups text classification dataset
The description of the dataset is available on the official website at:
http://people.csail.mit.edu/jrennie/20Newsgroups/
Quoting the introduction:
The 20 Newsgroups data set is a collection of approximately 20,000
newsgroup documents,... | bsd-3-clause |
UCSD-CCAL/ccal | ccal/correlate.py | 1 | 1960 | from numpy import full, nan
from numpy.random import seed, shuffle
from sklearn.linear_model import LinearRegression
from .COLOR_CATEGORICAL import COLOR_CATEGORICAL
from .compute_empirical_p_value import compute_empirical_p_value
from .plot_points import plot_points
def correlate(
x,
y,
n_permutation=0,... | mit |
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