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 |
|---|---|---|---|---|---|
aetilley/scikit-learn | examples/linear_model/plot_iris_logistic.py | 283 | 1678 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Logistic Regression 3-class Classifier
=========================================================
Show below is a logistic-regression classifiers decision boundaries on the
`iris <http://en.wikipedia.org/wiki/Iris_f... | bsd-3-clause |
ryfeus/lambda-packs | Sklearn_scipy_numpy/source/scipy/interpolate/tests/test_rbf.py | 45 | 4626 | #!/usr/bin/env python
# Created by John Travers, Robert Hetland, 2007
""" Test functions for rbf module """
from __future__ import division, print_function, absolute_import
import numpy as np
from numpy.testing import (assert_, assert_array_almost_equal,
assert_almost_equal, run_module_suit... | mit |
netodeolino/TCC | TCC 01/Extração/extrairMuitas.py | 1 | 8071 | # -*- coding: UTF-8 -*-
import numpy as np
import pandas
# - Variáveis globais
ENTIDADE_NAO_ENCOTRADA = -1
ENTIDADE_VAZIA = "NÃO POSSUI ESSA INFORMAÇÃO"
entidades = [
"LOCAL:", "SUSPEITO:", "VEÍCULO:", "VÍTIMA:", "VÍTIMAS:", "VÍTIMA FATAL:", "ARMA APREENDIDA:",
"MATERIAL APREENDIDO:", "PLACA:", "VÍTIMAS LESI... | mit |
kwailamchan/programming-languages | python/sklearn/examples/general/restricted_boltzmann_machine_features_for_digit_classification.py | 3 | 4757 | #---------------------------------------------------------------#
# Project: Restricted Boltzmann Machine features for digit classification
# Author: Kelly Chan
# Date: Apr 24 2014
#---------------------------------------------------------------#
from __future__ import print_function
print(__doc__)
import numpy as ... | mit |
kingjr/jr-tools | jr/gat/scorers.py | 1 | 3617 | # Author: Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
from nose.tools import assert_true
import numpy as np
from numpy.testing import assert_array_equal
from jr.stats import fast_mannwhitneyu
def _parallel_scorer(y_true, y_pred, func, n_jobs=1):
from nose.tools import assert_true
fro... | bsd-2-clause |
cellnopt/cellnopt | cno/io/midas_normalisation.py | 1 | 18714 | # -*- python -*-
#
# This file is part of cellnopt.core software
#
# Copyright (c) 2011-2013 - EBI-EMBL
#
# File author(s): Thomas Cokelaer <cokelaer@ebi.ac.uk>
#
# Distributed under the GPLv3 License.
# See accompanying file LICENSE.txt or copy at
# http://www.gnu.org/licenses/gpl-3.0.html
#
# website: www.... | bsd-2-clause |
jalabort/alabortcvpr2015 | alabortcvpr2015/clm/classifier.py | 1 | 5842 | from __future__ import division
import numpy as np
from numpy.fft import fft2, ifft2, fftshift
from sklearn import svm
from sklearn import linear_model
class MCF(object):
r"""
Multi-channel Correlation Filter
"""
def __init__(self, X, Y, l=0, cosine_mask=False):
if (X[0].shape[0],) + X[0].sha... | bsd-2-clause |
maminian/skewtools | scripts/animate_duct_flow_mc_projs_byframe.py | 1 | 5038 | #!/usr/bin/python
import numpy as np
import pylab
from numpy import transpose,size,sqrt
import sys
import matplotlib.pyplot as pyplot
from matplotlib.pyplot import cla,hold
import matplotlib.animation as anim
import h5py
from matplotlib.colors import LogNorm
from mpl_toolkits.mplot3d import Axes3D
from matplotlib ... | gpl-3.0 |
AlessandroCorsi/fibermodes | scripts/oscilloscope.py | 2 | 2071 |
import numpy
from matplotlib import pyplot
from datetime import datetime
def parseDate(dstr):
return datetime.strptime(dstr, '%d %b %Y').date()
def parseTime(dstr):
return datetime.strptime(dstr, '%H:%M:%S:%f').time()
TR = {
'Type': str,
'Points': int,
'Count': int,
'XInc': float,
'XO... | gpl-3.0 |
endolith/scikit-image | skimage/io/tests/test_mpl_imshow.py | 1 | 3275 | from __future__ import division
import numpy as np
from skimage import io
from skimage._shared._warnings import expected_warnings
import matplotlib.pyplot as plt
def setup():
io.reset_plugins()
# test images. Note that they don't have their full range for their dtype,
# but we still expect the display range to ... | bsd-3-clause |
apark263/tensorflow | tensorflow/python/kernel_tests/constant_op_eager_test.py | 33 | 21448 | # 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 |
zihua/scikit-learn | sklearn/utils/estimator_checks.py | 4 | 56697 | from __future__ import print_function
import types
import warnings
import sys
import traceback
import pickle
from copy import deepcopy
import numpy as np
from scipy import sparse
import struct
from sklearn.externals.six.moves import zip
from sklearn.externals.joblib import hash, Memory
from sklearn.utils.testing imp... | bsd-3-clause |
justincassidy/scikit-learn | benchmarks/bench_plot_neighbors.py | 287 | 6433 | """
Plot the scaling of the nearest neighbors algorithms with k, D, and N
"""
from time import time
import numpy as np
import pylab as pl
from matplotlib import ticker
from sklearn import neighbors, datasets
def get_data(N, D, dataset='dense'):
if dataset == 'dense':
np.random.seed(0)
return np.... | bsd-3-clause |
saiwing-yeung/scikit-learn | sklearn/neighbors/tests/test_nearest_centroid.py | 305 | 4121 | """
Testing for the nearest centroid module.
"""
import numpy as np
from scipy import sparse as sp
from numpy.testing import assert_array_equal
from numpy.testing import assert_equal
from sklearn.neighbors import NearestCentroid
from sklearn import datasets
from sklearn.metrics.pairwise import pairwise_distances
# t... | bsd-3-clause |
chunweiyuan/xarray | xarray/tests/test_dataarray.py | 1 | 161580 | import pickle
import warnings
from collections import OrderedDict
from copy import deepcopy
from textwrap import dedent
import sys
import numpy as np
import pandas as pd
import pytest
import xarray as xr
from xarray import (
DataArray, Dataset, IndexVariable, Variable, align, broadcast)
from xarray.coding.times i... | apache-2.0 |
mkraemer67/pylearn2 | pylearn2/models/tests/test_s3c_inference.py | 44 | 14386 | from __future__ import print_function
from pylearn2.models.s3c import S3C
from pylearn2.models.s3c import E_Step_Scan
from pylearn2.models.s3c import Grad_M_Step
from pylearn2.models.s3c import E_Step
from pylearn2.utils import contains_nan
from theano import function
import numpy as np
from theano.compat.six.moves im... | bsd-3-clause |
JosephKJ/SDD-RFCN-python | lib/objectness/utils.py | 1 | 5070 | import scipy
import os
import cv2
import numpy as np
from map import HeatMap
from sklearn.metrics import jaccard_similarity_score
from timer import Timer
from gc_executor import GC_executor
def generate_objectness_map(heatMapObj, image, hr_method='interpolation', use_gradcam=True):
"""
Generates the objectnes... | mit |
DANA-Laboratory/CoolProp | dev/TTSE/validate_TTSE.py | 5 | 5859 | import matplotlib
matplotlib.use('WXAgg')
import CoolProp
from CoolProp.Plots import Ph
from CoolProp.Plots.Plots import Trho,Ps,PT,Prho
import CoolProp.CoolProp as CP
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import random
import numpy as np
from math import log,exp
random.seed()
def check... | mit |
larsmans/scikit-learn | sklearn/tests/test_base.py | 19 | 6858 |
# Author: Gael Varoquaux
# License: BSD 3 clause
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing imp... | bsd-3-clause |
jeremander/AttrVN | nominate.py | 1 | 9874 | """After obtaining all the desired embeddings, stacks them and applies supervised learning to nominate nodes whose nomination_attr_type value is unknown. Optionally uses leave-one-out cross-validation to nominate the known nodes as well.
Usage: python3 nominate.py [path]
The directory [path] must include a fi... | apache-2.0 |
crslab/Inverse-Reinforcement-Learning | examples/lp_gridworld.py | 1 | 1234 | """
Run linear programming inverse reinforcement learning on the gridworld MDP.
Matthew Alger, 2015
matthew.alger@anu.edu.au
"""
import numpy as np
import matplotlib.pyplot as plt
import irl.linear_irl as linear_irl
import irl.mdp.gridworld as gridworld
def main(grid_size, discount):
"""
Run ... | mit |
poryfly/scikit-learn | sklearn/tree/tests/test_tree.py | 57 | 47417 | """
Testing for the tree module (sklearn.tree).
"""
import pickle
from functools import partial
from itertools import product
import platform
import numpy as np
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import coo_matrix
from sklearn.random_projection import sparse_rand... | bsd-3-clause |
victorbergelin/scikit-learn | examples/covariance/plot_robust_vs_empirical_covariance.py | 248 | 6359 | r"""
=======================================
Robust vs Empirical covariance estimate
=======================================
The usual covariance maximum likelihood estimate is very sensitive to the
presence of outliers in the data set. In such a case, it would be better to
use a robust estimator of covariance to guar... | bsd-3-clause |
sandeepkrjha/pgmpy | pgmpy/tests/test_estimators/test_ConstraintBasedEstimator.py | 6 | 6515 | import unittest
import pandas as pd
import numpy as np
from pgmpy.estimators import ConstraintBasedEstimator
from pgmpy.independencies import Independencies
from pgmpy.models import BayesianModel
from pgmpy.base import DirectedGraph, UndirectedGraph
class TestConstraintBasedEstimator(unittest.TestCase):
def tes... | mit |
TNT-Samuel/Coding-Projects | DNS Server/Source - Copy/Lib/site-packages/dask/dataframe/tseries/tests/test_resample.py | 2 | 2864 | from itertools import product
import pandas as pd
import pytest
from dask.dataframe.utils import assert_eq
import dask.dataframe as dd
def resample(df, freq, how='mean', **kwargs):
return getattr(df.resample(freq, **kwargs), how)()
@pytest.mark.parametrize(['obj', 'method', 'npartitions', 'freq', 'closed', 'l... | gpl-3.0 |
valexandersaulys/prudential_insurance_kaggle | venv/lib/python2.7/site-packages/pandas/tests/test_config.py | 13 | 16910 | #!/usr/bin/python
# -*- coding: utf-8 -*-
import pandas as pd
import unittest
import warnings
import nose
class TestConfig(unittest.TestCase):
_multiprocess_can_split_ = True
def __init__(self, *args):
super(TestConfig, self).__init__(*args)
from copy import deepcopy
self.cf = pd.cor... | gpl-2.0 |
bblais/Classy | debug/2020-11-09 - Debug NumpyNet MNIST.py | 2 | 4638 | #!/usr/bin/env python
# coding: utf-8
# In[1]:
get_ipython().magic('pylab inline')
# In[2]:
'''
Little example on how to use the Network class to create a model and perform
a basic classification of the MNIST dataset
'''
#from NumPyNet.layers.input_layer import Input_layer
from NumPyNet.layers.connected_layer i... | mit |
GaZ3ll3/scikit-image | doc/examples/applications/plot_morphology.py | 18 | 8229 | """
=======================
Morphological Filtering
=======================
Morphological image processing is a collection of non-linear operations related
to the shape or morphology of features in an image, such as boundaries,
skeletons, etc. In any given technique, we probe an image with a small shape or
template ca... | bsd-3-clause |
dmargala/tpcorr | examples/plugmap.py | 1 | 12679 | #!/usr/bin/env python
import matplotlib.pyplot as plt
import numpy as np
import sys,os
import string
import math
import argparse
def read_plugmap(filename):
debug=False
file=open(filename,"r")
doc={}
intypedef=False
indices={}
indices["HOLETYPE"]=8
indices["OBJECT"]=21
indices["ra"]=9... | mit |
rasbt/python-machine-learning-book | code/optional-py-scripts/ch11.py | 4 | 11413 | # Sebastian Raschka, 2015 (http://sebastianraschka.com)
# Python Machine Learning - Code Examples
#
# Chapter 11 - Working with Unlabeled Data – Clustering Analysis
#
# S. Raschka. Python Machine Learning. Packt Publishing Ltd., 2015.
# GitHub Repo: https://github.com/rasbt/python-machine-learning-book
#
# License: MIT... | mit |
rexshihaoren/scikit-learn | examples/model_selection/plot_roc_crossval.py | 247 | 3253 | """
=============================================================
Receiver Operating Characteristic (ROC) with cross validation
=============================================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality using cross-validation.
ROC curv... | bsd-3-clause |
willettk/decals | python/decals_dr2_tread_download.py | 1 | 5032 | from __future__ import division
from astropy.io import fits
from astropy.table import Table
from matplotlib import pyplot as plt
import numpy as np
from decals_dr2 import dstn_rgb
import progressbar as pb
import os,urllib
from multiprocessing.dummy import Pool as ThreadPool
from multiprocessing import Value, Lock
wid... | mit |
kdebrab/pandas | pandas/tests/test_errors.py | 3 | 2022 | # -*- coding: utf-8 -*-
import pytest
from warnings import catch_warnings
import pandas # noqa
import pandas as pd
from pandas.errors import AbstractMethodError
import pandas.util.testing as tm
@pytest.mark.parametrize(
"exc", ['UnsupportedFunctionCall', 'UnsortedIndexError',
'OutOfBoundsDatetime',
... | bsd-3-clause |
petosegan/scikit-learn | sklearn/datasets/base.py | 196 | 18554 | """
Base IO code for all datasets
"""
# Copyright (c) 2007 David Cournapeau <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
# 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
import os
import csv
import shutil
from os import environ
from os.pa... | bsd-3-clause |
depet/scikit-learn | sklearn/linear_model/tests/test_logistic.py | 16 | 5067 | import numpy as np
import scipy.sparse as sp
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_greater
from sklearn.utils.testing import assert_raises
from sklearn.util... | bsd-3-clause |
krzysztof/pykep | PyKEP/examples/_ex3.py | 5 | 8429 | try:
from PyGMO.problem import base as PyGMO_problem
"""
This example constructs, using PyGMO for optimization, an interplanetary low-thrust optimization
problem that can then be solved using one of the available PyGMO solvers. The problem is a non-linear constrained
problem that uses the Sims-Flan... | gpl-3.0 |
mramire8/structured | utilities/datautils.py | 1 | 18593 | from sklearn.datasets import load_files
from sklearn.datasets import fetch_20newsgroups
from sklearn.datasets import base as bunch
import numpy as np
class StemTokenizer(object):
def __init__(self):
from nltk import RegexpTokenizer
from nltk.stem import PorterStemmer
self.wnl = PorterStem... | apache-2.0 |
frank-tancf/scikit-learn | doc/datasets/mldata_fixture.py | 367 | 1183 | """Fixture module to skip the datasets loading when offline
Mock urllib2 access to mldata.org and create a temporary data folder.
"""
from os import makedirs
from os.path import join
import numpy as np
import tempfile
import shutil
from sklearn import datasets
from sklearn.utils.testing import install_mldata_mock
fr... | bsd-3-clause |
eubr-bigsea/tahiti | migrations/versions/54147db30380_fixing_some_sklearn_operations.py | 1 | 8064 | """fixing some sklearn operations.
Revision ID: 54147db30380
Revises: 29ecca388884
Create Date: 2020-01-23 12:51:44.638796
"""
from alembic import context
from alembic import op
from sqlalchemy import String, Integer, Text
from sqlalchemy.orm import sessionmaker
from sqlalchemy.sql import table, column
# revision i... | apache-2.0 |
eddowh/nyc-green-taxi-map-visualization | src/utils.py | 1 | 4086 | # -*- coding: utf-8 -*-
import pandas as pd
from functools import reduce
def reduce_taxi_df_memory_usage(df):
"""
Reduce memory footprint of the taxi data.
Parameters
----------
df : pandas.DataFrame
The dataframe that will have its memory footprint reduced.
Returns
-------
... | mit |
trevstanhope/agri-vision | test/camera.py | 1 | 1766 | import cv, cv2
from matplotlib import pyplot as plt
import numpy
CAMERA_INDEX = 0
HUE_MIN = 30
HUE_MAX = 120
PIXEL_WIDTH = 320
PIXEL_HEIGHT = 240
THRESHOLD_PERCENTILE = 95
camera = cv2.VideoCapture(CAMERA_INDEX)
camera.set(cv.CV_CAP_PROP_FRAME_WIDTH, PIXEL_WIDTH)
camera.set(cv.CV_CAP_PROP_FRAME_HEIGHT, PIXEL_HEIGHT)
... | mit |
massmutual/scikit-learn | sklearn/linear_model/tests/test_theil_sen.py | 234 | 9928 | """
Testing for Theil-Sen module (sklearn.linear_model.theil_sen)
"""
# Author: Florian Wilhelm <florian.wilhelm@gmail.com>
# License: BSD 3 clause
from __future__ import division, print_function, absolute_import
import os
import sys
from contextlib import contextmanager
import numpy as np
from numpy.testing import ... | bsd-3-clause |
dylanGeng/BuildingMachineLearningSystemsWithPython | ch12/image-classification.py | 21 | 3109 | # This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License
import mahotas as mh
import numpy as np
from glob import glob
from jug import TaskGenerator
# We need ... | mit |
mjbrodzik/ipython_notebooks | charis/dehra_dun/racovite_ablation_model.py | 1 | 3272 | #!/usr/bin/env python
import hypsometry
import pandas as pd
import numpy as np
class racovite_ablation_modelError( Exception ): pass
def run( clean_ice_km2, ela_m, ablation_gradient_m_per_100m, verbose=False ):
"""
Run the ablation gradient melt model as described by:
Racoviteanu et al., 2014, Evalua... | apache-2.0 |
FerranGarcia/shape_learning | scripts/set_optimal_letter.py | 3 | 5133 | #!/usr/bin/env python
# coding: utf-8
'''
This script just writes the shape of the letter the user draws at the top of the database.
This top-letter will be used as the optimal reference letter. We want childs to learn this letter
by playing with the robot.
'''
from shape_learning.shape_learner_manager import ShapeL... | isc |
temmeand/scikit-rf | qtapps/skrf_qtwidgets/qt.py | 6 | 10005 | from __future__ import print_function
import os
import time
import sys
import traceback
import platform
import ctypes
import sip
from . import cfg # must import cfg before qtpy to properly parse qt-bindings
from qtpy import QtCore, QtWidgets, QtGui
class QHLine(QtWidgets.QFrame):
def __init__(self):
su... | bsd-3-clause |
OpringaoDoTurno/airflow | airflow/hooks/hive_hooks.py | 3 | 28594 | # -*- coding: utf-8 -*-
#
# 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 writing, software
... | apache-2.0 |
asteca/ASteCA | packages/out/make_B2_plot.py | 1 | 3711 |
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from os.path import join
from . import mp_data_analysis
from . import add_version_plot
from . import prep_plots
from . prep_plots import figsize_x, figsize_y, grid_x, grid_y
def main(
npd, cld_i, pd, err_lst, cl_region_c, cl_region_rjct_c, st... | gpl-3.0 |
aterrel/blaze | blaze/compute/tests/test_pandas.py | 1 | 8386 | from __future__ import absolute_import, division, print_function
import pandas as pd
import numpy as np
from pandas import DataFrame, Series
from blaze.compute.pandas import *
from blaze.expr.table import *
from blaze.compatibility import builtins
t = TableSymbol('t', '{name: string, amount: int, id: int}')
df = D... | bsd-3-clause |
aabadie/scikit-learn | sklearn/decomposition/tests/test_nmf.py | 23 | 9736 | import numpy as np
from scipy import linalg
from sklearn.decomposition import (NMF, ProjectedGradientNMF,
non_negative_factorization)
from sklearn.decomposition import nmf # For testing internals
from scipy.sparse import csc_matrix
from sklearn.utils.testing import assert_true
from... | bsd-3-clause |
dvro/imbalanced-learn | imblearn/over_sampling/tests/test_smote.py | 2 | 12097 | """Test the module SMOTE."""
from __future__ import print_function
import os
import numpy as np
from numpy.testing import assert_raises
from numpy.testing import assert_equal
from numpy.testing import assert_array_equal
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_warns
from s... | mit |
ramansbach/cluster_analysis | clustering/scripts/analyze_length.py | 1 | 2968 | """
Created on Fri Oct 13 07:55:15 2017
@author: Rachael Mansbach
Script to run after analyze_clusters_serial.py, which computes the distribution
of lengths of contact and optical clusters, where "length" means the longest
distance between the COMs of two molecules in a cluster unwrapped over the
periodic boundary co... | mit |
lpsinger/astropy | astropy/io/misc/pandas/connect.py | 5 | 3378 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
# This file connects the readers/writers to the astropy.table.Table class
import functools
from astropy.table import Table
import astropy.io.registry as io_registry
__all__ = ['PANDAS_FMTS']
# Astropy users normally expect to not have an index, so defa... | bsd-3-clause |
mandli/multilayer-examples | 1d/setplot_drystate.py | 1 | 9573 | #!/usr/bin/env python
"""
Set up the plot figures, axes, and items to be done for each frame.
This module is imported by the plotting routines and then the
function setplot is called to set the plot parameters.
"""
import os
import numpy as np
# Plot customization
import matplotlib
# Markers and line widths... | mit |
r-mart/scikit-learn | sklearn/neighbors/nearest_centroid.py | 199 | 7249 | # -*- coding: utf-8 -*-
"""
Nearest Centroid Classification
"""
# Author: Robert Layton <robertlayton@gmail.com>
# Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import warnings
import numpy as np
from scipy import sparse as sp
from ..base import BaseEstimator, ClassifierMixin
from ..met... | bsd-3-clause |
carrillo/scikit-learn | examples/ensemble/plot_gradient_boosting_regression.py | 227 | 2520 | """
============================
Gradient Boosting regression
============================
Demonstrate Gradient Boosting on the Boston housing dataset.
This example fits a Gradient Boosting model with least squares loss and
500 regression trees of depth 4.
"""
print(__doc__)
# Author: Peter Prettenhofer <peter.prett... | bsd-3-clause |
boomsbloom/dtm-fmri | DTM/for_gensim/lib/python2.7/site-packages/matplotlib/type1font.py | 8 | 12515 | """
This module contains a class representing a Type 1 font.
This version reads pfa and pfb files and splits them for embedding in
pdf files. It also supports SlantFont and ExtendFont transformations,
similarly to pdfTeX and friends. There is no support yet for
subsetting.
Usage::
>>> font = Type1Font(filename)
... | mit |
robbymeals/scikit-learn | sklearn/utils/setup.py | 296 | 2884 | import os
from os.path import join
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
import numpy
from numpy.distutils.misc_util import Configuration
config = Configuration('utils', parent_package, top_path)
config.add_subpackage('sparsetools')
... | bsd-3-clause |
Akshay0724/scikit-learn | sklearn/gaussian_process/tests/test_gpc.py | 49 | 6016 | """Testing for Gaussian process classification """
# Author: Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# License: BSD 3 clause
import numpy as np
from scipy.optimize import approx_fprime
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF, Constant... | bsd-3-clause |
derrowap/MA490-MachineLearning-FinalProject | skParity_error.py | 1 | 1809 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import time
import multiprocessing
import numpy as np
from sklearn import cross_validation, metrics
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
from tensorflow.contr... | mit |
abimannans/scikit-learn | sklearn/utils/tests/test_estimator_checks.py | 202 | 3757 | import scipy.sparse as sp
import numpy as np
import sys
from sklearn.externals.six.moves import cStringIO as StringIO
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.testing import assert_raises_regex, assert_true
from sklearn.utils.estimator_checks import check_estimator
from sklearn.utils.... | bsd-3-clause |
winklerand/pandas | pandas/tests/io/test_packers.py | 1 | 32230 | import pytest
from warnings import catch_warnings
import os
import datetime
import numpy as np
import sys
from distutils.version import LooseVersion
from pandas import compat
from pandas.compat import u, PY3
from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
date_range, period_... | bsd-3-clause |
kipohl/ncanda-data-integration | scripts/redcap/scoring/casq/__init__.py | 2 | 2583 | #!/usr/bin/env python
##
## See COPYING file distributed along with the ncanda-data-integration package
## for the copyright and license terms
##
import pandas
import Rwrapper
#
# Variables from surveys needed for CASQ
#
# LimeSurvey field names
lime_fields = [ "casq_set1 [casq1]", "casq_set1 [casq2]", "casq_se... | bsd-3-clause |
radiasoft/radtrack | experimental/laserHeater/laserHeaterBenchmarkEnsemble.py | 1 | 3917 | """
Test for the laser heater infrastructure using the Gauss-Hermite laser mode
and a planar undulator. This test just checks a gaussian mode with the
parameters of the LCLS laser heater.
moduleauthor:: Stephen Webb <swebb@radiasoft.net>
Copyright (c) 2014 RadiaBeam Technologies. All rights reserved
"""
__author__ = ... | apache-2.0 |
aabadie/scikit-learn | doc/tutorial/text_analytics/skeletons/exercise_02_sentiment.py | 157 | 2409 | """Build a sentiment analysis / polarity model
Sentiment analysis can be casted as a binary text classification problem,
that is fitting a linear classifier on features extracted from the text
of the user messages so as to guess wether the opinion of the author is
positive or negative.
In this examples we will use a ... | bsd-3-clause |
dsquareindia/scikit-learn | sklearn/setup.py | 69 | 3201 | import os
from os.path import join
import warnings
from sklearn._build_utils import maybe_cythonize_extensions
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
lib... | bsd-3-clause |
olilarkin/faust | tools/physicalModeling/ir2dsp/ir2dsp.py | 1 | 3883 | #!/usr/bin/env python
# id2dsp.py
# Copyright Pierre-Amaury Grumiaux, Pierre Jouvelot, Emilio Jesus Gallego Arias,
# and Romain Michon
from __future__ import division
import math
import numpy as np
import matplotlib.pyplot as plt
from sys import argv
import subprocess
from scipy.io.wavfile import read
import peakutils... | gpl-2.0 |
net-titech/VidSum | src/packages/aistats-flid/code/ranking.py | 1 | 8310 | from __future__ import division, print_function
from matplotlib import pyplot as plt
import numpy as np
import IPython
from itertools import product
from itertools import chain, combinations
import ujson
import pickle
from amazon_utils import load_amazon_ranking_data_dpp
from amazon_experiment_specifications import da... | mit |
massmutual/scikit-learn | examples/decomposition/plot_faces_decomposition.py | 103 | 4394 | """
============================
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 |
phdowling/scikit-learn | examples/ensemble/plot_voting_probas.py | 316 | 2824 | """
===========================================================
Plot class probabilities calculated by the VotingClassifier
===========================================================
Plot the class probabilities of the first sample in a toy dataset
predicted by three different classifiers and averaged by the
`VotingC... | bsd-3-clause |
robintw/scikit-image | doc/examples/plot_tinting_grayscale_images.py | 14 | 5336 | """
=========================
Tinting gray-scale images
=========================
It can be useful to artificially tint an image with some color, either to
highlight particular regions of an image or maybe just to liven up a grayscale
image. This example demonstrates image-tinting by scaling RGB values and by
adjustin... | bsd-3-clause |
matousc89/padasip | padasip/filters/gngd.py | 1 | 6607 | """
.. versionadded:: 0.2
.. versionchanged:: 1.0.0
The generalized normalized gradient descent (GNGD) adaptive filter
:cite:`mandic2004generalized`
is an extension of the NLMS adaptive filter (:ref:`filter-nlms-label`).
The GNGD filter can be created as follows
>>> import padasip as pa
>>> pa.filters.Filter... | mit |
crichardson17/starburst_atlas | Low_resolution_sims/DustFree_LowRes/Geneva_Rot_cont/Geneva_Rot_cont_age2/Optical2.py | 33 | 7437 | import csv
import matplotlib.pyplot as plt
from numpy import *
import scipy.interpolate
import math
from pylab import *
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import matplotlib.patches as patches
from matplotlib.path import Path
import os
# --------------------------------------------------... | gpl-2.0 |
napjon/moocs_solution | ml-udacity/choose_your_own/your_algorithm.py | 6 | 1405 | #!/usr/bin/python
import matplotlib.pyplot as plt
from prep_terrain_data import makeTerrainData
from class_vis import prettyPicture
features_train, labels_train, features_test, labels_test = makeTerrainData()
### the training data (features_train, labels_train) have both "fast" and "slow" points mixed
### in togeth... | mit |
jackwong95/MMURandomStuff | TDS2101 - Intro To DS/Assignment/Data Cleaning and Exploratory Analysis/Source/execute.py | 1 | 19190 | import pandas as pd
import numpy as np
import os.path
import datetime as dt
import matplotlib.cm as cmx
import matplotlib.colors as colors
import matplotlib.pyplot as plt
import plotly.offline as offline
from matplotlib import style
# Global variables
outputDirPrefix = "../Plots/"
outputDirExtras = outputDirPrefix + ... | apache-2.0 |
shakamunyi/tensorflow | tensorflow/contrib/learn/python/learn/estimators/base.py | 7 | 19731 | # 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 |
mfjb/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 |
suyashbire1/pyhton_scripts_mom6 | plot_cont_budget.py | 1 | 5476 | import sys
import readParams_moreoptions as rdp1
import matplotlib.pyplot as plt
from mom_plot1 import m6plot, xdegtokm
import numpy as np
from netCDF4 import MFDataset as mfdset, Dataset as dset
import time
from pym6 import Domain, Variable, Plotter
import importlib
importlib.reload(Domain)
importlib.reload(Variable)
... | gpl-3.0 |
dpaiton/OpenPV | pv-core/analysis/python/plot_amoeba_response.py | 1 | 4052 | """
Make a histogram of normally distributed random numbers and plot the
analytic PDF over it
"""
import sys
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import matplotlib.cm as cm
import matplotlib.image as mpimg
import PVReadWeights as rw
import PVReadSparse as rs
import math
"""... | epl-1.0 |
jakirkham/bokeh | examples/models/file/anscombe.py | 5 | 3286 | from __future__ import print_function
import numpy as np
import pandas as pd
from bokeh.util.browser import view
from bokeh.document import Document
from bokeh.embed import file_html
from bokeh.layouts import column, gridplot
from bokeh.models import Circle, ColumnDataSource, Div, Grid, Line, LinearAxis, Plot, Range1... | bsd-3-clause |
jadsonjs/DataScience | statistics/avg-stddev.py | 1 | 1785 | #
# This program is distributed without any warranty and it
# can be freely redistributed for research, classes or private studies,
# since the copyright notices are not removed.
#
# This file was used to calc the average and std deviation of the results of
# ensembles generated by the software weka.
# The weka saves ... | apache-2.0 |
zorroblue/scikit-learn | examples/feature_selection/plot_select_from_model_boston.py | 146 | 1527 | """
===================================================
Feature selection using SelectFromModel and LassoCV
===================================================
Use SelectFromModel meta-transformer along with Lasso to select the best
couple of features from the Boston dataset.
"""
# Author: Manoj Kumar <mks542@nyu.edu>... | bsd-3-clause |
bert9bert/statsmodels | statsmodels/iolib/tests/test_summary.py | 31 | 1535 | '''examples to check summary, not converted to tests yet
'''
from __future__ import print_function
if __name__ == '__main__':
from statsmodels.regression.tests.test_regression import TestOLS
#def mytest():
aregression = TestOLS()
TestOLS.setupClass()
results = aregression.res1
r_summary = s... | bsd-3-clause |
mr-cloud/deep-learning-udacity | ocr.py | 1 | 24391 | import numpy as np
from scipy import ndimage
import pickle
import tensorflow as tf
from matplotlib import pyplot as plt
import sys
# 64 x 64, 95.64% at 98%
# architect: 8 convs + 3 FCL(LR, ReLU). maxout, channels: [48, 64, 128, 160], 192, 3072. dropout.
# conv: 5 x 5 zero padding, maxpooling 2 x 2, stride 2
# predict... | mit |
pystockhub/book | ch18/day04/Kiwoom.py | 2 | 8383 | import sys
from PyQt5.QtWidgets import *
from PyQt5.QAxContainer import *
from PyQt5.QtCore import *
import time
import pandas as pd
import sqlite3
TR_REQ_TIME_INTERVAL = 0.2
class Kiwoom(QAxWidget):
def __init__(self):
super().__init__()
self._create_kiwoom_instance()
self._set_signal_sl... | mit |
cfobel/thrust-timing | thrust_timing/path_timing.py | 1 | 6935 | import cythrust.device_vector as dv
from cythrust import DeviceDataFrame
from cythrust import DeviceVectorCollection
from thrust_timing.SORT_TIMING import (look_up_delay, step1, step2, step8,
step9, step10)
from thrust_timing.sort_timing import (compute_arrival_times,
... | gpl-2.0 |
bavardage/statsmodels | statsmodels/graphics/tests/test_regressionplots.py | 5 | 4406 | '''Tests for regressionplots, entire module is skipped
'''
import numpy as np
import nose
import statsmodels.api as sm
from statsmodels.graphics.regressionplots import (plot_fit, plot_ccpr,
plot_partregress, plot_regress_exog, abline_plot,
plot_partregress_grid, plot_ccpr_grid, ad... | bsd-3-clause |
jorisvandenbossche/geopandas | geopandas/tests/test_sindex.py | 1 | 4517 | import sys
from shapely.geometry import Polygon, Point
from geopandas import GeoSeries, GeoDataFrame, base, read_file
from geopandas.tests.util import unittest, download_nybb
@unittest.skipIf(sys.platform.startswith("win"), "fails on AppVeyor")
@unittest.skipIf(not base.HAS_SINDEX, 'Rtree absent, skipping')
class T... | bsd-3-clause |
brendancsmith/cohort-facebook | lib/word_cloud-master/doc/sphinxext/gen_rst.py | 17 | 33207 | """
Example generation for the python wordcloud project. Stolen from scikit-learn with modifications from PyStruct.
Generate the rst files for the examples by iterating over the python
example files.
Hacked to plot every example (not only those that start with 'plot').
"""
from time import time
import os
import shuti... | mit |
nokute78/fluent-bit | plugins/out_kafka/librdkafka-1.6.0/tests/performance_plot.py | 3 | 2902 | #!/usr/bin/env python3
#
import sys, json
import numpy as np
import matplotlib.pyplot as plt
from collections import defaultdict
def semver2int (semver):
if semver == 'trunk':
semver = '0.10.0.0'
vi = 0
i = 0
for v in reversed(semver.split('.')):
vi += int(v) * (i * 10)
i += 1... | apache-2.0 |
yonglehou/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 |
rkube/blob_tracking | blobtrail.py | 1 | 8352 | #!/opt/local/bin/python
# -*- Encoding: UTF-8 -*-
"""
=========
blobtrail
=========
.. codeauthor :: Ralph Kube <ralphkube@gmail.com>
A class that defines a blob event in a sequence of frames
from 2d turbulence imaging
"""
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
fro... | mit |
macks22/gensim | gensim/test/test_keras_integration.py | 1 | 6627 | import unittest
import os
import numpy as np
from gensim.models import word2vec
try:
from sklearn.datasets import fetch_20newsgroups
except ImportError:
raise unittest.SkipTest("Test requires sklearn to be installed, which is not available")
try:
import keras
from keras.engine import Input
from ke... | lgpl-2.1 |
kidaa/pySDC | examples/acoustic_2d_imex/playground.py | 1 | 2831 |
from pySDC import CollocationClasses as collclass
import numpy as np
from ProblemClass import acoustic_2d_imex
#from examples.sharpclaw_burgers1d.TransferClass import mesh_to_mesh_1d
from examples.acoustic_2d_imex.HookClass import plot_solution
from pySDC.datatype_classes.mesh import mesh, rhs_imex_mesh
from pySDC.... | bsd-2-clause |
sinhrks/darkcore | darkcore/components/tests/test_image.py | 1 | 1048 |
import pandas as pd
import pandas.util.testing as tm
import darkcore
class TestImage(tm.TestCase):
def test_image_detection(self):
df = pd.DataFrame({'A':[1, 2, 3]})
ax = df.plot()
self.assertTrue(darkcore.Image._maybe_image(ax))
self.assertTrue(darkcore.Image._maybe_image(ax.g... | bsd-3-clause |
petosegan/scikit-learn | sklearn/linear_model/tests/test_ransac.py | 216 | 13290 | import numpy as np
from numpy.testing import assert_equal, assert_raises
from numpy.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises_regexp
from scipy import sparse
from sklearn.utils.testing import assert_less
from sklearn.linear_model import LinearRegression, RANSACRegressor
f... | bsd-3-clause |
sonnyhu/scikit-learn | sklearn/externals/joblib/__init__.py | 4 | 5100 | """ Joblib is a set of tools to provide **lightweight pipelining in
Python**. In particular, joblib offers:
1. transparent disk-caching of the output values and lazy re-evaluation
(memoize pattern)
2. easy simple parallel computing
3. logging and tracing of the execution
Joblib is optimized to be **fast*... | bsd-3-clause |
biocore-ntnu/pyranges | pyranges/methods/coverage.py | 1 | 2032 | import numpy as np
import pandas as pd
from ncls import NCLS
def _number_overlapping(scdf, ocdf, **kwargs):
keep_nonoverlapping = kwargs.get("keep_nonoverlapping", True)
column_name = kwargs.get("overlap_col", True)
if scdf.empty:
return None
if ocdf.empty:
if keep_nonoverlapping:
... | mit |
Rambatino/Kruskals | Kruskals/__main__.py | 1 | 1129 | """
This package provides a python implementation of Kruskals Algorithm
"""
import argparse
import savReaderWriter as spss
import pandas as pd
from .kruskals import Kruskals
def main():
"""Entry point when module is run from command line"""
parser = argparse.ArgumentParser(description='Run Kruskal\'s Algorith... | mit |
r-mart/scikit-learn | sklearn/feature_selection/rfe.py | 64 | 17509 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Vincent Michel <vincent.michel@inria.fr>
# Gilles Louppe <g.louppe@gmail.com>
#
# License: BSD 3 clause
"""Recursive feature elimination for feature ranking"""
import warnings
import numpy as np
from ..utils import check_X_y, safe_sqr
fro... | bsd-3-clause |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.