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 |
|---|---|---|---|---|---|
StuntsPT/pyRona | setup.py | 1 | 2535 | #!/usr/bin/python3
# Copyright 2017-2020 Francisco Pina Martins <f.pinamartins@gmail.com>
# This file is part of pyRona.
# pyRona 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 Licens... | gpl-3.0 |
jundongl/PyFeaST | skfeature/function/sparse_learning_based/MCFS.py | 3 | 2089 | import scipy
import numpy as np
from sklearn import linear_model
from skfeature.utility.construct_W import construct_W
def mcfs(X, n_selected_features, **kwargs):
"""
This function implements unsupervised feature selection for multi-cluster data.
Input
-----
X: {numpy array}, shape (n_samples, n_... | gpl-2.0 |
andrewnc/scikit-learn | sklearn/linear_model/__init__.py | 270 | 3096 | """
The :mod:`sklearn.linear_model` module implements generalized linear models. It
includes Ridge regression, Bayesian Regression, Lasso and Elastic Net
estimators computed with Least Angle Regression and coordinate descent. It also
implements Stochastic Gradient Descent related algorithms.
"""
# See http://scikit-le... | bsd-3-clause |
valexandersaulys/prudential_insurance_kaggle | venv/lib/python2.7/site-packages/sklearn/datasets/species_distributions.py | 64 | 7917 | """
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_... | gpl-2.0 |
harishkrao/Python-for-Data-Analysis | chapter 07/analysis.py | 4 | 2414 | from pandas import *
from pandas.util.decorators import cache_readonly
import numpy as np
import os
base = 'ml-100k'
class IndexedFrame(object):
"""
"""
def __init__(self, frame, field):
self.frame = frame
def _build_index(self):
pass
class Movielens(object):
def __init__(self... | mit |
glorizen/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/backends/backend_qt4.py | 69 | 20664 | from __future__ import division
import math
import os
import sys
import matplotlib
from matplotlib import verbose
from matplotlib.cbook import is_string_like, onetrue
from matplotlib.backend_bases import RendererBase, GraphicsContextBase, \
FigureManagerBase, FigureCanvasBase, NavigationToolbar2, IdleEvent, curso... | agpl-3.0 |
proto-n/Alpenglow | python/alpenglow/OnlineExperiment.py | 1 | 11711 | from .Getter import Getter as rs
from .utils.DataframeData import DataframeData
from .ParameterDefaults import ParameterDefaults
import pandas as pd
import alpenglow.sip as sip
class OnlineExperiment(ParameterDefaults):
"""OnlineExperiment(seed=254938879,top_k=100)
This is the base class of every online exper... | apache-2.0 |
coldmanck/fast-rcnn | tools/train_svms.py | 3 | 13479 | #!/usr/bin/env python
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""
Train post-hoc SVMs using the algorithm and ... | mit |
zlongshen/InertialNav | code/plot_glitchOffset.py | 6 | 3806 | #!/bin/python
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.cbook as cbook
import numpy as np
import math
# timestamp, velN, velE, velD, posN, posE, posOffN, posOffE, velOffN, velOffE
data = np.genfromtxt('GlitchOffsetOut.txt', delimiter=' ', skip_header=1,
skip_footer=1, names=['time', ... | bsd-3-clause |
datacommonsorg/data | scripts/india_census/primary_census_abstract_houseless_population/preprocess.py | 1 | 3249 | # Copyright 2020 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | apache-2.0 |
themrmax/scikit-learn | examples/model_selection/plot_nested_cross_validation_iris.py | 46 | 4415 | """
=========================================
Nested versus non-nested cross-validation
=========================================
This example compares non-nested and nested cross-validation strategies on a
classifier of the iris data set. Nested cross-validation (CV) is often used to
train a model in which hyperparam... | bsd-3-clause |
xiaoxiamii/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 |
robin-lai/scikit-learn | sklearn/ensemble/tests/test_partial_dependence.py | 365 | 6996 | """
Testing for the partial dependence module.
"""
import numpy as np
from numpy.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import if_matplotlib
from sklearn.ensemble.partial_dependence import partial_dependence
from sklearn.ensemble.partial_dependence... | bsd-3-clause |
kaichogami/scikit-learn | examples/semi_supervised/plot_label_propagation_structure.py | 45 | 2433 | """
==============================================
Label Propagation learning a complex structure
==============================================
Example of LabelPropagation learning a complex internal structure
to demonstrate "manifold learning". The outer circle should be
labeled "red" and the inner circle "blue". Be... | bsd-3-clause |
wasade/qiime | setup.py | 1 | 9528 | #!/usr/bin/env python
# File created on 17 Feb 2010
from __future__ import division
from setuptools import setup
from stat import S_IEXEC
from os import (chdir, getcwd, listdir, chmod, walk, rename, remove, chmod,
stat, devnull)
from os.path import join, abspath
from sys import platform, argv
from subpr... | gpl-2.0 |
rrohan/scikit-learn | sklearn/datasets/species_distributions.py | 198 | 7923 | """
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_... | bsd-3-clause |
florian-f/sklearn | examples/ensemble/plot_forest_multioutput.py | 4 | 2004 | """
=========================================
Face completion with multi-output forests
=========================================
This example shows the use of multi-output forests to complete images.
The goal is to predict the lower half of a face given its upper half.
The first row of images shows true faces. The s... | bsd-3-clause |
wolfiex/DSMACC-testing | dsmacc/examples/spectral_embedding_.py | 1 | 22572 | """Spectral Embedding"""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# Wei LI <kuantkid@gmail.com>
# License: BSD 3 clause
#import warnings
class warnings:None
warnings.warn=lambda x:1
import warnings
import numpy as np
from scipy import sparse
from scipy.linalg import eigh
from scipy.sparse.... | gpl-3.0 |
mattgiguere/scikit-learn | examples/ensemble/plot_adaboost_hastie_10_2.py | 355 | 3576 | """
=============================
Discrete versus Real AdaBoost
=============================
This example is based on Figure 10.2 from Hastie et al 2009 [1] and illustrates
the difference in performance between the discrete SAMME [2] boosting
algorithm and real SAMME.R boosting algorithm. Both algorithms are evaluate... | bsd-3-clause |
cython-testbed/pandas | pandas/tests/frame/test_asof.py | 4 | 4662 | # coding=utf-8
import numpy as np
import pytest
from pandas import (DataFrame, date_range, Timestamp, Series,
to_datetime)
import pandas.util.testing as tm
from .common import TestData
class TestFrameAsof(TestData):
def setup_method(self, method):
self.N = N = 50
self.rng = ... | bsd-3-clause |
shubhomoydas/pyaad | pyalad/data_plotter.py | 1 | 3636 | import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.markers
from r_support import cbind
class DataPlotter(object):
"""Encapsulates the layout of PDF plots
The PDF will have a grid layout of rows x cols subplots.
When a new plot is adde... | mit |
feranick/SpectralMachine | Archive/SpectraLearnPredict/SpectraLearnPredict/slp/slp_kmeans.py | 1 | 3607 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
'''
**********************************************************
*
* SpectraLearnPredict - kmeans
* Perform Machine Learning on Spectroscopy Data.
*
* Uses: Deep Neural Networks, TensorFlow, SVM, PCA, K-Means
*
* By: Nicola Ferralis <feranick@hotmail.com>
*
*****************... | gpl-3.0 |
pratapvardhan/scikit-learn | examples/ensemble/plot_isolation_forest.py | 65 | 2363 | """
==========================================
IsolationForest example
==========================================
An example using IsolationForest for anomaly detection.
The IsolationForest 'isolates' observations by randomly selecting a feature
and then randomly selecting a split value between the maximum and minimu... | bsd-3-clause |
ezekial4/atomic_neu | examples/rate_equations.py | 1 | 1649 | import numpy as np
import matplotlib.pyplot as plt
import atomic.atomic as atomic
ad = atomic.element('carbon')
temperature = np.logspace(0, 3, 50)
density = 1e20
tau = 1e20 / density
t_normalized = np.logspace(-7, 0, 50)
t_normalized -= t_normalized[0]
times = t_normalized * tau
rt = atomic.RateEquations(ad)
yy =... | mit |
dvro/imbalanced-learn | examples/under-sampling/plot_random_under_sampler.py | 3 | 1918 | """
=====================
Random under-sampling
=====================
An illustration of the random under-sampling method.
"""
print(__doc__)
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
# Define some color for the plotting
almost_black = '#262626'
palette = sns.color_palette()
from sklearn.dat... | mit |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_stats_spatio_temporal_cluster_sensors.py | 3 | 7514 | """
.. _stats_cluster_sensors_2samp_spatial:
=====================================================
Spatiotemporal permutation F-test on full sensor data
=====================================================
Tests for differential evoked responses in at least
one condition using a permutation clustering test.
The Fiel... | bsd-3-clause |
bjornsturmberg/NumBAT | lit_examples/simo-lit_06_2-Florez-NatComm_2016-d1160nm.py | 1 | 3419 | """ Replicating the results of
Brillouin scattering self-cancellation
Florez et al.
http://dx.doi.org/10.1038/ncomms11759
"""
import time
import datetime
import numpy as np
import sys
import matplotlib
matplotlib.use('pdf')
import matplotlib.pyplot as plt
sys.path.append("../backend/")
import materials
im... | gpl-3.0 |
JosmanPS/scikit-learn | sklearn/metrics/tests/test_regression.py | 272 | 6066 | from __future__ import division, print_function
import numpy as np
from itertools import product
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.... | bsd-3-clause |
bassio/omicexperiment | omicexperiment/transforms/filters/sample.py | 1 | 1678 | import pandas as pd
from omicexperiment.transforms.transform import Filter, AttributeFilter, GroupByTransform, FlexibleOperatorMixin, AttributeFlexibleOperatorMixin, TransformObjectsProxy
from omicexperiment.transforms.sample import SampleGroupBy, SampleSumCounts
class SampleMinCount(Filter):
def __dapply__(self,... | bsd-3-clause |
ehogan/iris | docs/iris/example_code/General/SOI_filtering.py | 6 | 3196 | """
Applying a filter to a time-series
==================================
This example demonstrates low pass filtering a time-series by applying a
weighted running mean over the time dimension.
The time-series used is the Darwin-only Southern Oscillation index (SOI),
which is filtered using two different Lanczos filt... | lgpl-3.0 |
FrederikDiehl/apsis | code/apsis/assistants/experiment_assistant.py | 2 | 16607 | __author__ = 'Frederik Diehl'
from apsis.models.experiment import Experiment
from apsis.models.candidate import Candidate
from apsis.utilities.optimizer_utils import check_optimizer
from apsis.utilities.file_utils import ensure_directory_exists
import numpy as np
import datetime
import os
import time
from apsis.utilit... | mit |
rahul-c1/scikit-learn | examples/decomposition/plot_faces_decomposition.py | 204 | 4452 | """
============================
Faces dataset decompositions
============================
This example applies to :ref:`olivetti_faces` different unsupervised
matrix decomposition (dimension reduction) methods from the module
:py:mod:`sklearn.decomposition` (see the documentation chapter
:ref:`decompositions`) .
"""... | bsd-3-clause |
ThorJonsson/IceTourists | HelpFunctions.py | 3 | 3361 | # -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import shapefile
import bokeh.plotting as bpl
import matplotlib.cm as mpc
import pdb
import matplotlib.pyplot as plt
# read the shapefile and initialize the map
def initialize_map(filename, plot_width):
# Read the shapefile
dat = shapefile.Reader... | gpl-2.0 |
kiran/bart-sign | venv/lib/python2.7/site-packages/numpy/lib/function_base.py | 14 | 134708 | from __future__ import division, absolute_import, print_function
import warnings
import sys
import collections
import operator
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import linspace, atleast_1d, atleast_2d
from numpy.core.numeric import (
ones, zeros, arange, concatenate, array, asarr... | mit |
pizzathief/scipy | scipy/ndimage/fourier.py | 8 | 11239 | # Copyright (C) 2003-2005 Peter J. Verveer
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following d... | bsd-3-clause |
janvanrijn/openml-pimp | examples/animation/generate_priors_example.py | 1 | 9181 | import argparse
import matplotlib.pyplot as plt
import matplotlib.animation
import matplotlib.patches
import numpy as np
import os
import openml
import pickle
import scipy.stats
def parse_args():
parser = argparse.ArgumentParser(description='Generate svm boundary gif')
# output file related
parser.add_arg... | bsd-3-clause |
RachitKansal/scikit-learn | sklearn/ensemble/tests/test_gradient_boosting_loss_functions.py | 221 | 5517 | """
Testing for the gradient boosting loss functions and initial estimators.
"""
import numpy as np
from numpy.testing import assert_array_equal
from numpy.testing import assert_almost_equal
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.utils import check_random_state
from ... | bsd-3-clause |
yungyuc/solvcon | sandbox/gas/tube/sodtube1d/handler_data.py | 1 | 6683 | #
# handler_data.py
#
# Description:
# helper functions to handle, caculate the solutions.
#
import sys
import scipy.optimize as so
import matplotlib.pyplot as plt
class DataManager():
"""
Manage how to get extended information by input data.
"""
def __init__(self):
pass
def get_errorNo... | bsd-3-clause |
cl4rke/scikit-learn | sklearn/cluster/tests/test_bicluster.py | 226 | 9457 | """Testing for Spectral Biclustering methods"""
import numpy as np
from scipy.sparse import csr_matrix, issparse
from sklearn.grid_search import ParameterGrid
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from... | bsd-3-clause |
jzt5132/scikit-learn | sklearn/cluster/tests/test_k_means.py | 63 | 26190 | """Testing for K-means"""
import sys
import numpy as np
from scipy import sparse as sp
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.testing i... | bsd-3-clause |
kazemakase/scikit-learn | examples/covariance/plot_covariance_estimation.py | 250 | 5070 | """
=======================================================================
Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood
=======================================================================
When working with covariance estimation, the usual approach is to use
a maximum likelihood estimator,... | bsd-3-clause |
trungnt13/scikit-learn | sklearn/covariance/__init__.py | 389 | 1157 | """
The :mod:`sklearn.covariance` module includes methods and algorithms to
robustly estimate the covariance of features given a set of points. The
precision matrix defined as the inverse of the covariance is also estimated.
Covariance estimation is closely related to the theory of Gaussian Graphical
Models.
"""
from ... | bsd-3-clause |
antoinecarme/pyaf | tests/bugs/issue_36/display_version_info.py | 1 | 1141 |
def getVersions():
import os, platform, pyaf
lVersionDict = {};
lVersionDict["PyAF_version"] = pyaf.__version__;
lVersionDict["system_platform"] = platform.platform();
lVersionDict["system_uname"] = platform.uname();
lVersionDict["system_processor"] = platform.processor();
lVersion... | bsd-3-clause |
h2educ/scikit-learn | sklearn/grid_search.py | 61 | 37197 | """
The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters
of an estimator.
"""
from __future__ import print_function
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# ... | bsd-3-clause |
HBPNeurorobotics/nest-simulator | examples/nest/Potjans_2014/spike_analysis.py | 4 | 6267 | # -*- coding: utf-8 -*-
#
# spike_analysis.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License,... | gpl-2.0 |
StevenReitsma/kaggle-galaxyzoo | graphs/per_class.py | 1 | 3765 | import matplotlib
matplotlib.use('Agg')
import prettyplotlib as ppl
import matplotlib.pyplot as plt
import numpy as np
data1 = np.genfromtxt('error_inv.csv', dtype=float, delimiter=',')
data2 = np.genfromtxt('error_var.csv', dtype=float, delimiter=',')
for i in range(0, data1.shape[0]):
if np.sum(data1[i, 0:2]) != ... | gpl-2.0 |
CyclotronResearchCentre/forward | examples/example_sphere_summary.py | 1 | 2382 | import numpy as np
from matplotlib.pyplot import plot, show, legend, close
import matplotlib.pyplot as plt
close("all")
import seaborn as sns
#sns.set(style="whitegrid")
sns.set(style="ticks")
# Allow text to be edited in Illustrator
import matplotlib as mpl
mpl.rcParams['pdf.fonttype'] = 42
res = np.load("SphereResu... | gpl-2.0 |
arcolife/scholarec | corpus/data_modelling/training_skel.py | 1 | 2615 | import pandas as pd
# steps to form a skeleton for sample training data of ratings
'''
# here fields used are: Physics, Probability & Mathematics (in order)
# total entries ~43 (around 15 for each)
id = ['1401.0828v1',
'1010.5976v1',
'0706.0750v2',
'1103.4289v1',
'0611347v1',
'1304.3144... | gpl-3.0 |
jm-begon/scikit-learn | sklearn/cross_decomposition/tests/test_pls.py | 215 | 11427 | import numpy as np
from sklearn.utils.testing import (assert_array_almost_equal,
assert_array_equal, assert_true, assert_raise_message)
from sklearn.datasets import load_linnerud
from sklearn.cross_decomposition import pls_
from nose.tools import assert_equal
def test_pls():
d =... | bsd-3-clause |
sandeepgupta2k4/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator_test.py | 14 | 46097 | # 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 |
ninotoshi/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator.py | 1 | 24626 | # Copyright 2015 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 o... | apache-2.0 |
Britefury/scikit-image | doc/examples/plot_gabor.py | 11 | 4450 | """
=============================================
Gabor filter banks for texture classification
=============================================
In this example, we will see how to classify textures based on Gabor filter
banks. Frequency and orientation representations of the Gabor filter are similar
to those of the huma... | bsd-3-clause |
hpcbg/metaheuristic | gradient.py | 1 | 1125 | # -*- coding: utf-8 -*-
"""
Hill Climbing aka Gradient Descent is local search method.
@author: Simeon Tsvetanov
"""
import random
import matplotlib.pyplot as plt
solutions = [3, 5, 6, 7, 8, 6, 4, 3, 2, 5, 6, 7, 9, 23, 35, 12, 6, 5, 4, 10, 15, 13, 8, 9, 12, 15, 4]
x_axis_len = len(solutions)
plt.axis([0, x_axis_len, ... | gpl-2.0 |
simonsfoundation/CaImAn | use_cases/custom/rois_Hendrik.py | 2 | 50709 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 22 13:22:26 2015
@author: agiovann
"""
from builtins import map
from builtins import zip
from builtins import str
from builtins import range
import cv2
import json
import logging
import matplotlib.pyplot as pl
import matplotlib.patches as mpatches... | gpl-2.0 |
gengliangwang/spark | python/pyspark/pandas/internal.py | 1 | 57972 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | apache-2.0 |
pombredanne/dask | dask/dataframe/utils.py | 1 | 4423 | from __future__ import absolute_import, division, print_function
from collections import Iterator
import numpy as np
import pandas as pd
import toolz
def shard_df_on_index(df, divisions):
""" Shard a DataFrame by ranges on its index
Examples
--------
>>> df = pd.DataFrame({'a': [0, 10, 20, 30, 40]... | bsd-3-clause |
fyffyt/scikit-learn | sklearn/metrics/tests/test_ranking.py | 127 | 40813 | from __future__ import division, print_function
import numpy as np
from itertools import product
import warnings
from scipy.sparse import csr_matrix
from sklearn import datasets
from sklearn import svm
from sklearn import ensemble
from sklearn.datasets import make_multilabel_classification
from sklearn.random_projec... | bsd-3-clause |
jmetzen/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 |
waynenilsen/statsmodels | statsmodels/examples/ex_feasible_gls_het.py | 34 | 4267 | # -*- coding: utf-8 -*-
"""Examples for linear model with heteroscedasticity estimated by feasible GLS
These are examples to check the results during developement.
The assumptions:
We have a linear model y = X*beta where the variance of an observation depends
on some explanatory variable Z (`exog_var`).
linear_model... | bsd-3-clause |
jniediek/mne-python | tutorials/plot_modifying_data_inplace.py | 4 | 2936 | """
.. _tut_modifying_data_inplace:
Modifying data in-place
=======================
"""
from __future__ import print_function
import mne
import os.path as op
import numpy as np
from matplotlib import pyplot as plt
###############################################################################
# It is often necessar... | bsd-3-clause |
alexei-matveev/ase-local | ase/calculators/eam.py | 1 | 28788 | """Calculator for the Embedded Atom Method Potential"""
# eam.py
# Embedded Atom Method Potential
# These routines integrate with the ASE simulation environment
# Paul White (Oct 2012)
# UNCLASSIFIED
# License: See accompanying license files for details
import os
import numpy as np
from ase.test import NotAvailable... | gpl-2.0 |
dcprojects/CoolProp | dev/scripts/fit_ancillary_ODRPACK.py | 5 | 11720 | from __future__ import division
import numpy as np
from scipy.odr import *
import scipy.optimize, random
import matplotlib.pyplot as plt
import textwrap
import random
from CoolProp.CoolProp import Props, get_REFPROPname
def rsquared(x, y):
"""
Return R^2 where x and y are array-like.
from http://stac... | mit |
srowen/spark | python/pyspark/pandas/data_type_ops/null_ops.py | 5 | 1936 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | apache-2.0 |
shllybkwrm/swarm_gesture_control | recognition/dataProcessing.py | 1 | 47533 | # -*- coding: utf-8 -*-
"""
Created on Thu Feb 12 18:29:30 2015
@author: Shelly
"""
#import cv2 ### NOTE: Needs OpenCV 2.4(.10) if CV2-KNN used
import math
import numpy as np
from sklearn import cross_validation
from sklearn.svm import SVC
from sklearn.learning_curve import learning_curve
from sklearn.metrics import c... | gpl-2.0 |
ajdagokcen/madlyambiguous-repo | python/embcat_server.py | 1 | 12141 | import argparse
import rpyc
from rpyc.utils.server import ThreadedServer
import csv
import gensim
from gensim.models import Word2Vec
import numpy as np
from numpy import linalg
from sklearn.cluster import SpectralClustering
from nltk.tokenize import TreebankWordTokenizer
from nltk.corpus import stopwords
from nltk.prob... | gpl-3.0 |
Lawrence-Liu/scikit-learn | sklearn/feature_selection/tests/test_feature_select.py | 143 | 22295 | """
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 |
danieldmm/minerva | scripts/explore_statistics.py | 1 | 1441 | import pandas as pd
import os
import numpy as np
import matplotlib.pyplot as plt
# %matplotlib inline
plt.rcParams["figure.figsize"] = (8, 5)
AZ_ZONES = ['AIM', 'BAS', 'BKG', 'CTR', 'OTH', 'OWN', 'TXT']
CORESC_LIST = ["Hyp", "Mot", "Bac", "Goa", "Obj", "Met", "Exp", "Mod", "Obs", "Res", "Con"]
CORESC_LIST = [x.lower(... | gpl-3.0 |
numenta/nupic.research | nupic/research/support/elastic_logger.py | 3 | 9319 | # Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2019, 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 program is free software: you can redistribute it and/or modify
# it unde... | agpl-3.0 |
pytorch/fairseq | examples/speech_to_text/prep_mtedx_data.py | 1 | 8995 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import logging
import os
from pathlib import Path
import shutil
from itertools import groupby
from temp... | mit |
TheHonestGene/risk-predictor | setup.py | 1 | 1882 | from setuptools import setup, find_packages # Always prefer setuptools over distutils
from codecs import open # To use a consistent encoding
from os import path
here = path.abspath(path.dirname(__file__))
# Get the long description from the relevant file
with open(path.join(here, 'README.rst'), encoding='utf-8') as... | mit |
smartscheduling/scikit-learn-categorical-tree | examples/applications/wikipedia_principal_eigenvector.py | 233 | 7819 | """
===============================
Wikipedia principal eigenvector
===============================
A classical way to assert the relative importance of vertices in a
graph is to compute the principal eigenvector of the adjacency matrix
so as to assign to each vertex the values of the components of the first
eigenvect... | bsd-3-clause |
oew1v07/scikit-image | doc/examples/plot_seam_carving.py | 13 | 2351 | """
============
Seam Carving
============
This example demonstrates how images can be resized using seam carving [1]_.
Resizing to a new aspect ratio distorts image contents. Seam carving attempts
to resize *without* distortion, by removing regions of an image which are less
important. In this example we are using th... | bsd-3-clause |
rl-institut/smenos | reegis_hp/berlin_hp/berlin_brdbg_example_plot.py | 7 | 3580 | #!/usr/bin/python3
# -*- coding: utf-8
import logging
import matplotlib.pyplot as plt
from oemof.outputlib import to_pandas as tpd
from oemof.tools import logger
from oemof.core import energy_system as es
# The following dictionaries are a workaround due to issue #26
rename = {
"(val, ('sink', 'Landkreis Witten... | gpl-3.0 |
gibiansky/tensorflow | tensorflow/contrib/learn/__init__.py | 11 | 1763 | # 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 |
LiaoPan/scikit-learn | sklearn/linear_model/setup.py | 169 | 1567 | import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('linear_model', parent_package, top_path)
cblas_libs, blas_info = get_blas_info... | bsd-3-clause |
twankim/weaksemi | utils.py | 1 | 7546 | # -*- coding: utf-8 -*-
# @Author: twankim
# @Date: 2017-05-05 20:22:13
# @Last Modified by: twankim
# @Last Modified time: 2017-10-26 03:25:34
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pandas as pd
def accuracy(y_true,y_pred):
return 100*np.sum(y_true==... | mit |
haanme/DWIProstateMotionCorrection | motioncorrection_transformix.py | 1 | 11807 | #!/usr/bin/env python
elastix_dir = '/Users/eija/Documents/SW/Elastix/elastix_sources_v4.7/bin/bin/'
experiment_dir = '/Users/eija/Desktop/prostate_MR/pipelinedata'
mask_matfile_basedir_hB = '/Users/eija/Desktop/prostate_MR/PET_MR_dwis/Carimas27projectfiles_Hb_work_all_noGS_for_Motion_Cor/ROI_mat_files'
mask_matfile_b... | mit |
louispotok/pandas | pandas/tests/series/test_timeseries.py | 3 | 35025 | # coding=utf-8
# pylint: disable-msg=E1101,W0612
import pytest
import numpy as np
from datetime import datetime, timedelta, time
import pandas as pd
import pandas.util.testing as tm
import pandas.util._test_decorators as td
from pandas._libs.tslib import iNaT
from pandas.compat import lrange, StringIO, product
from ... | bsd-3-clause |
pythonvietnam/scikit-learn | sklearn/cluster/tests/test_hierarchical.py | 230 | 19795 | """
Several basic tests for hierarchical clustering procedures
"""
# Authors: Vincent Michel, 2010, Gael Varoquaux 2012,
# Matteo Visconti di Oleggio Castello 2014
# License: BSD 3 clause
from tempfile import mkdtemp
import shutil
from functools import partial
import numpy as np
from scipy import sparse
from... | bsd-3-clause |
mrshu/scikit-learn | examples/cluster/plot_mini_batch_kmeans.py | 2 | 4072 | """
=====================================================
A demo of the K Means clustering algorithm
=====================================================
We want to compare the performance of the MiniBatchKMeans and KMeans:
the MiniBatchKMeans is faster, but gives slightly different results (see
:ref:`mini_batch_kmea... | bsd-3-clause |
OpenPHDGuiding/phd2 | contributions/MPI_IS_gaussian_process/tools/plot_dec_data.py | 1 | 1316 | #!/usr/bin/env python
import matplotlib.pyplot as plt
from numpy import genfromtxt
def read_data():
measurement_data = genfromtxt('tm_data.csv', delimiter=',') # read GP data from csv
measurement_data = measurement_data[1:,:] # strip first line to remove header text
location = measurement_data... | bsd-3-clause |
gustavovaliati/ci724-ppginfufpr-2016 | exerc-3c/main.py | 1 | 1897 | import numpy as np
import argparse, cv2, glob, sys
import datetime
ap = argparse.ArgumentParser()
ap.add_argument("-t", "--target", required = True, help = "Is the file used as reference for the comparison")
ap.add_argument("-d", "--dataset", required = True, help = "Path to the directory of images")
args = vars(ap.pa... | gpl-3.0 |
UltronAI/Deep-Learning | Pattern-Recognition/hw3-Unsupervised-Learning/main.py | 1 | 1579 | import numpy as np
import utils
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.cluster import KMeans as kmeans
for n in range(2, 5):
x = np.loadtxt("dataset.txt")
N, d = x.shape
x_train, var, selected = PCA(x, n)
var_selected = np.zeros(d)
var_... | mit |
lmallin/coverage_test | python_venv/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 |
hubert667/AIR | src/scripts/fakeQueryData.py | 1 | 2040 | from sklearn import svm
import sys, random, ast, os
try:
import include, copy, pickle
except:
pass
import retrieval_system, environment, evaluation,comparison,ranker
import query as queryClass
import ranker as rankerClass
from clusterData import *
import numpy as np
from queryRankers import *
from queryFeatures... | gpl-3.0 |
cmick/sklearn-rri | sklearn_rri/rri.py | 1 | 6938 | """Reflective Random Indexing (RRI) algorithm implementation."""
import math
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import normalize
from sklearn.random_projection import sparse_random_matrix
from sklearn.utils.extmath import safe_sparse_dot
from sklearn.u... | bsd-3-clause |
pratapvardhan/pandas | pandas/tests/test_lib.py | 7 | 7887 | # -*- coding: utf-8 -*-
import pytest
import numpy as np
from pandas import Index
from pandas._libs import lib, writers as libwriters
import pandas.util.testing as tm
class TestMisc(object):
def test_max_len_string_array(self):
arr = a = np.array(['foo', 'b', np.nan], dtype='object')
assert li... | bsd-3-clause |
xiaoxiamii/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 |
sertansenturk/tomato | src/tomato/symbolic/symbtr/extras/txt.py | 1 | 11400 |
import os
import warnings
import pandas as pd
from ....io import IO
from ..dataextractor import DataExtractor
from ..reader.mu2 import Mu2Reader
class Txt:
symbtr_cols = ['Sira', 'Kod', 'Nota53', 'NotaAE', 'Koma53', 'KomaAE',
'Pay', 'Payda', 'Ms', 'LNS', 'Bas', 'Soz1', 'Offset']
@classm... | agpl-3.0 |
lawlietfans/tushare | tushare/stock/reference.py | 27 | 25190 | # -*- coding:utf-8 -*-
"""
投资参考数据接口
Created on 2015/03/21
@author: Jimmy Liu
@group : waditu
@contact: jimmysoa@sina.cn
"""
from __future__ import division
from tushare.stock import cons as ct
from tushare.stock import ref_vars as rv
from tushare.util import dateu as dt
import pandas as pd
import time
i... | bsd-3-clause |
feranick/GES_AT | Other/Cursors/cursors.py | 1 | 1136 | import sys
from PyQt5 import QtWidgets
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
import matplotlib.pyplot as plt
from matplotlib.widgets import Cursor
class Window(QtWidgets.QDialog):
def __init__(self, parent=None):
super(Window, self).__init__(parent)
self.f... | gpl-3.0 |
jblackburne/scikit-learn | examples/cluster/plot_cluster_iris.py | 350 | 2593 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
K-means Clustering
=========================================================
The plots display firstly what a K-means algorithm would yield
using three clusters. It is then shown what the effect of a bad
initializa... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.21/_downloads/704841087184bb64030fba1c58ce8038/plot_topo_compare_conditions.py | 20 | 1828 | """
=================================================
Compare evoked responses for different conditions
=================================================
In this example, an Epochs object for visual and auditory responses is created.
Both conditions are then accessed by their respective names to create a sensor
layout... | bsd-3-clause |
webmasterraj/GaSiProMo | flask/lib/python2.7/site-packages/pandas/stats/fama_macbeth.py | 16 | 6740 | from pandas.core.base import StringMixin
from pandas.compat import StringIO, range
import numpy as np
from pandas.core.api import Series, DataFrame
import pandas.stats.common as common
from pandas.util.decorators import cache_readonly
def fama_macbeth(**kwargs):
"""Runs Fama-MacBeth regression.
Parameters
... | gpl-2.0 |
mailhexu/pyDFTutils | examples/abinit_phonon_unfolding/unfold_example.py | 1 | 4006 | #!/usr/bin/env python
import abipy.abilab as abilab
import numpy as np
from ase.build import bulk
from ase.dft.kpoints import get_special_points, bandpath
from pyDFTutils.unfolding.phonon_unfolder import phonon_unfolder
from pyDFTutils.phonon.plotphon import plot_band_weight
import matplotlib.pyplot as plt
atomic_mass... | lgpl-3.0 |
mojoboss/scikit-learn | examples/cluster/plot_dict_face_patches.py | 337 | 2747 | """
Online learning of a dictionary of parts of faces
==================================================
This example uses a large dataset of faces to learn a set of 20 x 20
images patches that constitute faces.
From the programming standpoint, it is interesting because it shows how
to use the online API of the sciki... | bsd-3-clause |
dsm054/pandas | scripts/find_undoc_args.py | 2 | 5101 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Script that compares the signature arguments with the ones in the docsting
and returns the differences in plain text or GitHub task list format.
Usage::
$ ./find_undoc_args.py (see arguments below)
"""
from __future__ import print_function
import sys
from collecti... | bsd-3-clause |
tmhm/scikit-learn | sklearn/feature_extraction/tests/test_dict_vectorizer.py | 276 | 3790 | # Authors: Lars Buitinck <L.J.Buitinck@uva.nl>
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from random import Random
import numpy as np
import scipy.sparse as sp
from numpy.testing import assert_array_equal
from sklearn.utils.testing import (assert_equal, assert_in,
... | bsd-3-clause |
lbishal/scikit-learn | sklearn/utils/tests/test_linear_assignment.py | 421 | 1349 | # Author: Brian M. Clapper, G Varoquaux
# License: BSD
import numpy as np
# XXX we should be testing the public API here
from sklearn.utils.linear_assignment_ import _hungarian
def test_hungarian():
matrices = [
# Square
([[400, 150, 400],
[400, 450, 600],
[300, 225, 300]],
... | bsd-3-clause |
vybstat/scikit-learn | sklearn/metrics/cluster/unsupervised.py | 230 | 8281 | """ Unsupervised evaluation metrics. """
# Authors: Robert Layton <robertlayton@gmail.com>
#
# License: BSD 3 clause
import numpy as np
from ...utils import check_random_state
from ..pairwise import pairwise_distances
def silhouette_score(X, labels, metric='euclidean', sample_size=None,
random... | bsd-3-clause |
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