repo_name stringlengths 7 92 | path stringlengths 5 149 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 911 693k | license stringclasses 15
values |
|---|---|---|---|---|---|
soulmachine/scikit-learn | examples/cluster/plot_dbscan.py | 346 | 2479 | # -*- coding: utf-8 -*-
"""
===================================
Demo of DBSCAN clustering algorithm
===================================
Finds core samples of high density and expands clusters from them.
"""
print(__doc__)
import numpy as np
from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn... | bsd-3-clause |
BillFoland/daisyluAMR | system/daisylu_system.py | 1 | 13721 |
import os
import sys
import pickle
import pandas as pd
import numpy as np
import hashlib
import os.path
from daisylu_config import *
from daisylu_vectors import *
from sentences import *
from daisylu_output import *
""
def addWikificationToDFrames(sents, sTypes, sentenceAttr):
# nee... | mit |
mmottahedi/neuralnilm_prototype | scripts/e351.py | 2 | 6885 | from __future__ import print_function, division
import matplotlib
import logging
from sys import stdout
matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!
from neuralnilm import (Net, RealApplianceSource,
BLSTMLayer, DimshuffleLayer,
Bidirectio... | mit |
macks22/scikit-learn | sklearn/cluster/spectral.py | 233 | 18153 | # -*- coding: utf-8 -*-
"""Algorithms for spectral clustering"""
# Author: Gael Varoquaux gael.varoquaux@normalesup.org
# Brian Cheung
# Wei LI <kuantkid@gmail.com>
# License: BSD 3 clause
import warnings
import numpy as np
from ..base import BaseEstimator, ClusterMixin
from ..utils import check_rand... | bsd-3-clause |
molpopgen/fwdpy11 | examples/discrete_demography/localadaptation.py | 1 | 7832 | #
# Copyright (C) 2019 Kevin Thornton <krthornt@uci.edu>
#
# This file is part of fwdpy11.
#
# fwdpy11 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... | gpl-3.0 |
OPU-Surveillance-System/monitoring | master/scripts/planner/solvers/test_penalization_plot.py | 1 | 1040 | import matplotlib.pyplot as plt
with open("test_pen", "r") as f:
data = f.read()
data = data.split("\n")[:-1]
data = [data[i].split(" ") for i in range(0, len(data))]
pen = [float(data[i][0]) for i in range(len(data))]
u = [float(data[i][1]) for i in range(len(data))]
d = [float(data[i][2]) for i in range(len(data... | mit |
NDManh/numbbo | code-postprocessing/bbob_pproc/pptex.py | 4 | 14442 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
"""Routines for writing TeX for tables."""
from __future__ import absolute_import
import os
import sys
import string
import numpy
from . import toolsstats
from pdb import set_trace
#GLOBAL VARIABLES DEFINITION
alphabet = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuv... | bsd-3-clause |
huobaowangxi/scikit-learn | sklearn/calibration.py | 137 | 18876 | """Calibration of predicted probabilities."""
# Author: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Balazs Kegl <balazs.kegl@gmail.com>
# Jan Hendrik Metzen <jhm@informatik.uni-bremen.de>
# Mathieu Blondel <mathieu@mblondel.org>
#
# License: BSD 3 clause
from __future__ impo... | bsd-3-clause |
pbrod/scipy | scipy/special/basic.py | 3 | 70421 | #
# Author: Travis Oliphant, 2002
#
from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
import math
from scipy._lib.six import xrange
from numpy import (pi, asarray, floor, isscalar, iscomplex, real,
imag, sqrt, where, mgrid, sin, place, issubdtype,... | bsd-3-clause |
joernhees/scikit-learn | sklearn/feature_selection/tests/test_from_model.py | 26 | 6935 | import numpy as np
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_array_equal... | bsd-3-clause |
boada/astLib | astLib/astSED.py | 2 | 55763 | """module for performing calculations on Spectral Energy Distributions (SEDs)
(c) 2007-2013 Matt Hilton
U{http://astlib.sourceforge.net}
This module provides classes for manipulating SEDs, in particular the Bruzual &
Charlot 2003, Maraston 2005, and Percival et al 2009 stellar population
synthesis models are current... | lgpl-2.1 |
wkew/FTMSVisualization | 3-HeteroClassPlotter.py | 1 | 10441 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 22 11:42:36 2016
@author: Will Kew
will.kew@gmail.com
Copyright Will Kew, 2016
This file is part of FTMS Visualisation (also known as i-van Krevelen).
FTMS Visualisation is free software: you can redistribute it and/or modify
it under the terms of the G... | gpl-3.0 |
Fireblend/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 |
henkhaus/wow | testing/plotter.py | 1 | 1278 | from pymongo import MongoClient
from matplotlib import pyplot as plt
import os
from datetime import datetime, date, time, timedelta
client = MongoClient()
# using wowtest.auctiondata
db = client.wowtest
posts = db.auctiondata
auctions = posts.find().limit(10)
#time.time() into datetime --->
#datetime.datetime.fromti... | apache-2.0 |
mikebenfield/scikit-learn | sklearn/neighbors/regression.py | 26 | 10999 | """Nearest Neighbor Regression"""
# 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@ulg.ac... | bsd-3-clause |
MLWave/auto-sklearn | autosklearn/wrapper_for_SMAC.py | 5 | 3119 | try:
import cPickle as pickle
except:
import pickle
import os
import time
import signal
import sys
import lockfile
from HPOlibConfigSpace import configuration_space
from autosklearn.data.data_manager import DataManager
import autosklearn.models.holdout_evaluator
from autosklearn.models.paramsklearn import g... | bsd-3-clause |
badlogicmanpreet/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/rcsetup.py | 69 | 23344 | """
The rcsetup module contains the default values and the validation code for
customization using matplotlib's rc settings.
Each rc setting is assigned a default value and a function used to validate any
attempted changes to that setting. The default values and validation functions
are defined in the rcsetup module, ... | agpl-3.0 |
williamdlees/TRIgS | PlotIdentity.py | 2 | 6306 | # The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND... | mit |
banesullivan/ParaViewGeophysics | PVGeo/ubc/tensor.py | 1 | 21910 | __all__ = [
'TensorMeshReader',
'TensorMeshAppender',
'TopoMeshAppender',
]
__displayname__ = 'Tensor Mesh'
import os
import sys
import numpy as np
import pandas as pd
import vtk
from .. import _helpers, interface
from ..base import AlgorithmBase
from .two_file_base import ModelAppenderBase, ubcMeshRead... | bsd-3-clause |
jseabold/scikit-learn | sklearn/feature_selection/rfe.py | 4 | 15662 | # 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 numpy as np
from ..utils import check_X_y, safe_sqr
from ..utils.metaes... | bsd-3-clause |
cuemacro/chartpy | chartpy_examples/subplot_example.py | 1 | 2359 | __author__ = 'saeedamen' # Saeed Amen
#
# Copyright 2016 Cuemacro
#
# 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 ... | apache-2.0 |
nvoron23/statsmodels | statsmodels/sandbox/examples/try_multiols.py | 33 | 1243 | # -*- coding: utf-8 -*-
"""
Created on Sun May 26 13:23:40 2013
Author: Josef Perktold, based on Enrico Giampieri's multiOLS
"""
#import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.sandbox.multilinear import multiOLS, multigroup
data = sm.datasets.longley.load_pandas()
df = data.e... | bsd-3-clause |
jeffery-do/Vizdoombot | doom/lib/python3.5/site-packages/skimage/viewer/utils/core.py | 19 | 6555 | import numpy as np
from ..qt import QtWidgets, has_qt, FigureManagerQT, FigureCanvasQTAgg
from ..._shared.utils import warn
import matplotlib as mpl
from matplotlib.figure import Figure
from matplotlib import _pylab_helpers
from matplotlib.colors import LinearSegmentedColormap
if has_qt and 'agg' not in mpl.get_backen... | mit |
ilyes14/scikit-learn | examples/preprocessing/plot_function_transformer.py | 161 | 1949 | """
=========================================================
Using FunctionTransformer to select columns
=========================================================
Shows how to use a function transformer in a pipeline. If you know your
dataset's first principle component is irrelevant for a classification task,
you ca... | bsd-3-clause |
gclenaghan/scikit-learn | examples/applications/topics_extraction_with_nmf_lda.py | 18 | 3768 | """
=======================================================================================
Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
=======================================================================================
This is an example of applying Non-negative Matrix ... | bsd-3-clause |
anhaidgroup/py_stringsimjoin | py_stringsimjoin/tests/test_size_filter.py | 1 | 25474 | import unittest
from nose.tools import assert_equal, assert_list_equal, nottest, raises
from py_stringmatching.tokenizer.delimiter_tokenizer import DelimiterTokenizer
from py_stringmatching.tokenizer.qgram_tokenizer import QgramTokenizer
import numpy as np
import pandas as pd
from py_stringsimjoin.filter.size_filter ... | bsd-3-clause |
ZENGXH/scikit-learn | sklearn/metrics/tests/test_classification.py | 42 | 52642 | from __future__ import division, print_function
import numpy as np
from scipy import linalg
from functools import partial
from itertools import product
import warnings
from sklearn import datasets
from sklearn import svm
from sklearn.datasets import make_multilabel_classification
from sklearn.preprocessing import La... | bsd-3-clause |
magne-max/zipline-ja | zipline/pipeline/loaders/utils.py | 1 | 9840 | import datetime
import numpy as np
import pandas as pd
from zipline.utils.pandas_utils import mask_between_time
def is_sorted_ascending(a):
"""Check if a numpy array is sorted."""
return (np.fmax.accumulate(a) <= a).all()
def validate_event_metadata(event_dates,
event_timestamps... | apache-2.0 |
swatlab/uplift-analysis | src_code_metrics.py | 1 | 4848 | import re, csv, pytz, json, subprocess
from dateutil import parser
import pandas as pd
import get_bugs
from libmozdata import patchanalysis
# Execute a shell command
def shellCommand(command_str):
cmd =subprocess.Popen(command_str.split(' '), stdout=subprocess.PIPE)
cmd_out, cmd_err = cmd.communicate()
ret... | mpl-2.0 |
rdipietro/tensorflow | tensorflow/contrib/learn/python/learn/estimators/classifier_test.py | 16 | 5175 | # 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 |
rs2/pandas | pandas/tests/indexing/multiindex/test_xs.py | 1 | 9100 | import numpy as np
import pytest
from pandas import DataFrame, Index, IndexSlice, MultiIndex, Series, concat, date_range
import pandas._testing as tm
import pandas.core.common as com
@pytest.fixture
def four_level_index_dataframe():
arr = np.array(
[
[-0.5109, -2.3358, -0.4645, 0.05076, 0.364... | bsd-3-clause |
Lab603/PicEncyclopedias | jni-build/jni-build/jni/include/tensorflow/contrib/learn/python/learn/tests/multioutput_test.py | 5 | 1679 | # 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... | mit |
timmie/cartopy | lib/cartopy/mpl/ticker.py | 3 | 10493 | # (C) British Crown Copyright 2014 - 2016, Met Office
#
# This file is part of cartopy.
#
# cartopy is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the
# Free Software Foundation, either version 3 of the License, or
# (at your option)... | gpl-3.0 |
ifcharming/voltdb2.1 | tools/vis.py | 1 | 5697 | #!/usr/bin/env python
# This is a visualizer which pulls TPC-C benchmark results from the MySQL
# databases and visualizes them. Four graphs will be generated, latency graph on
# sinigle node and multiple nodes, and throughput graph on single node and
# multiple nodes.
#
# Run it without any arguments to see what argu... | gpl-3.0 |
gdetor/SI-RF-Structure | Statistics/clear_data.py | 1 | 5369 | # Copyright (c) 2014, Georgios Is. Detorakis (gdetor@gmail.com) and
# Nicolas P. Rougier (nicolas.rougier@inria.fr)
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redis... | gpl-3.0 |
cheral/orange3 | doc/development/source/orange-demo/orangedemo/OWLearningCurveB.py | 2 | 13882 | import sys
from collections import OrderedDict
from functools import reduce
import numpy
import sklearn.cross_validation
from PyQt4.QtGui import QTableWidget, QTableWidgetItem
import Orange.data
import Orange.classification
from Orange.widgets import widget, gui, settings
from Orange.evaluation.testing import Resul... | bsd-2-clause |
rizac/gfz-reportgen | gfzreport/sphinxbuild/map/__init__.py | 2 | 43603 | '''
This module implements the function `plotmap` which plots scattered points on a map
retrieved using ArgGIS Server REST API. The function is highly customizable and is basically a
wrapper around the `Basemap` library (for the map background)
plus matplotlib utilities (for plotting points, shapes, labels and legend)
... | gpl-3.0 |
yanlend/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 |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/matplotlib/dates.py | 6 | 52305 | """
Matplotlib provides sophisticated date plotting capabilities, standing on the
shoulders of python :mod:`datetime`, the add-on modules :mod:`pytz` and
:mod:`dateutil`. :class:`datetime` objects are converted to floating point
numbers which represent time in days since 0001-01-01 UTC, plus 1. For
example, 0001-01-0... | gpl-3.0 |
RachitKansal/scikit-learn | examples/ensemble/plot_gradient_boosting_regularization.py | 355 | 2843 | """
================================
Gradient Boosting regularization
================================
Illustration of the effect of different regularization strategies
for Gradient Boosting. The example is taken from Hastie et al 2009.
The loss function used is binomial deviance. Regularization via
shrinkage (``lear... | bsd-3-clause |
bhargav/scikit-learn | doc/conf.py | 26 | 8446 | # -*- coding: utf-8 -*-
#
# scikit-learn documentation build configuration file, created by
# sphinx-quickstart on Fri Jan 8 09:13:42 2010.
#
# This file is execfile()d with the current directory set to its containing
# dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
... | bsd-3-clause |
JamesSample/ecosystem_services_impacts | Code/01_es_lu_cc.py | 1 | 21539 | #------------------------------------------------------------------------------
# Name: 01_es_lu_cc.py
# Purpose: Processing for the CREW project on ES, LUC and CC.
#
# Author: James Sample
#
# Created: 14/01/2015
# Copyright: (c) James Sample and JHI, 2015
# License: https://github.com/JamesS... | mit |
silky/ProbablyOverthinkingIt | thinkstats2.py | 1 | 69096 | """This file contains code for use with "Think Stats" and
"Think Bayes", both by Allen B. Downey, available from greenteapress.com
Copyright 2014 Allen B. Downey
License: GNU GPLv3 http://www.gnu.org/licenses/gpl.html
"""
from __future__ import print_function, division
"""This file contains class definitions for:
H... | mit |
sniemi/SamPy | sandbox/src1/examples/multi_image.py | 1 | 1769 | #!/usr/bin/env python
'''
Make a set of images with a single colormap, norm, and colorbar.
It also illustrates colorbar tick labelling with a multiplier.
'''
from matplotlib.pyplot import figure, show, sci
from matplotlib import cm, colors
from matplotlib.font_manager import FontProperties
from numpy import amin, ama... | bsd-2-clause |
suranap/qiime | qiime/quality_scores_plot.py | 9 | 6918 | #!/usr/bin/env python
# File created Sept 29, 2010
from __future__ import division
__author__ = "William Walters"
__copyright__ = "Copyright 2011, The QIIME Project"
__credits__ = ["William Walters", "Greg Caporaso"]
__license__ = "GPL"
__version__ = "1.9.1-dev"
__maintainer__ = "William Walters"
__email__ = "William.... | gpl-2.0 |
jeffery-do/Vizdoombot | doom/lib/python3.5/site-packages/numpy/core/function_base.py | 23 | 6891 | from __future__ import division, absolute_import, print_function
__all__ = ['logspace', 'linspace']
from . import numeric as _nx
from .numeric import result_type, NaN, shares_memory, MAY_SHARE_BOUNDS, TooHardError
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None):
"""
Return evenly... | mit |
RomainBrault/scikit-learn | sklearn/decomposition/tests/test_incremental_pca.py | 43 | 10272 | """Tests for Incremental PCA."""
import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn import datasets
from sklearn.decomposition import PCA, IncrementalPCA
iris = datasets.load... | bsd-3-clause |
GuessWhoSamFoo/pandas | pandas/tests/tseries/test_frequencies.py | 1 | 29684 | from datetime import datetime, timedelta
import numpy as np
import pytest
from pandas._libs.tslibs import frequencies as libfrequencies, resolution
from pandas._libs.tslibs.ccalendar import MONTHS
from pandas._libs.tslibs.frequencies import (
INVALID_FREQ_ERR_MSG, FreqGroup, _period_code_map, get_freq, get_freq_c... | bsd-3-clause |
jcrudy/sklearntools | sklearntools/test/test_transformers.py | 1 | 3613 | from sklearntools.transformers import Constant, VariableTransformer, Identity,\
Censor, NanMap, Log
import numpy as np
import pandas
from numpy.testing.utils import assert_array_almost_equal
from sklearn.datasets.base import load_boston
from pyearth.earth import Earth
from sklearntools.calibration import ResponseTr... | bsd-3-clause |
skdaccess/skdaccess | skdaccess/geo/srtm/cache/data_fetcher.py | 2 | 10677 | # The MIT License (MIT)
# Copyright (c) 2016 Massachusetts Institute of Technology
#
# Authors: Cody Rude, Guillaume Rongier
# This software has been created in projects supported by the US National
# Science Foundation and NASA (PI: Pankratius)
#
# Permission is hereby granted, free of charge, to any person obtaining ... | mit |
yilei0620/3D_Conditional_Gan | GenSample_obj.py | 1 | 4544 | import sys
sys.path.append('..')
import os
import json
from time import time
import numpy as np
from sklearn.externals import joblib
import scipy
from scipy import io
# from matplotlib import pyplot as plt
# from sklearn.externals import joblib
import theano
import theano.tensor as T
from lib import activations
fro... | mit |
felipemontefuscolo/bitme | tactic/bitmex_dummy_tactic.py | 1 | 1028 | from common.quote import Quote
from tactic import TacticInterface, ExchangeInterface, Symbol, OrderCommon, Fill
import pandas as pd
class BitmexDummyTactic(TacticInterface):
"""
This class is associated to orders issued by Bitmex
"""
def finalize(self) -> None:
pass
def handle_quote(self... | mpl-2.0 |
peastman/msmbuilder | msmbuilder/tests/test_kernel_approximation.py | 9 | 1158 | from __future__ import absolute_import
import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.kernel_approximation import Nystroem as NystroemR
from msmbuilder.decomposition.kernel_approximation import Nystroem, LandmarkNystroem
def test_nystroem_vs_sklearn():
np.random.seed(42)
... | lgpl-2.1 |
nmayorov/scikit-learn | examples/applications/plot_outlier_detection_housing.py | 243 | 5577 | """
====================================
Outlier detection on a real data set
====================================
This example illustrates the need for robust covariance estimation
on a real data set. It is useful both for outlier detection and for
a better understanding of the data structure.
We selected two sets o... | bsd-3-clause |
moutai/scikit-learn | examples/neural_networks/plot_mlp_training_curves.py | 56 | 3596 | """
========================================================
Compare Stochastic learning strategies for MLPClassifier
========================================================
This example visualizes some training loss curves for different stochastic
learning strategies, including SGD and Adam. Because of time-constrai... | bsd-3-clause |
garywu/pypedream | pypedream/plot/_filt.py | 1 | 2685 | import numpy
has_matplotlib = True
try:
from matplotlib import pyplot, figure
except ImportError:
has_matplotlib = False
from dagpype._core import filters
def _make_relay_call(fn, name):
def new_fn(*args, **kwargs):
@filters
def _dagpype_internal_fn_act(target):
try:
... | bsd-3-clause |
nliolios24/textrank | share/doc/networkx-1.9.1/examples/graph/unix_email.py | 62 | 2683 | #!/usr/bin/env python
"""
Create a directed graph, allowing multiple edges and self loops, from
a unix mailbox. The nodes are email addresses with links
that point from the sender to the recievers. The edge data
is a Python email.Message object which contains all of
the email message data.
This example shows the po... | mit |
imaculate/scikit-learn | sklearn/linear_model/randomized_l1.py | 11 | 24849 | """
Randomized Lasso/Logistic: feature selection based on Lasso and
sparse Logistic Regression
"""
# Author: Gael Varoquaux, Alexandre Gramfort
#
# License: BSD 3 clause
import itertools
from abc import ABCMeta, abstractmethod
import warnings
import numpy as np
from scipy.sparse import issparse
from scipy import spar... | bsd-3-clause |
prabhjyotsingh/incubator-zeppelin | flink/interpreter/src/main/resources/python/zeppelin_pyflink.py | 10 | 2806 | #
# 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 |
prasunroypr/digit-recognizer | source/defs.py | 1 | 6607 | ################################################################################
"""
Functions for Digit Recognition
Created on Wed Jun 01 00:00:00 2016
@author: Prasun Roy
@e-mail: prasunroy.pr@gmail.com
"""
################################################################################
# import modules
import ma... | gpl-3.0 |
endolith/scikit-image | skimage/feature/tests/test_util.py | 35 | 2818 | import numpy as np
try:
import matplotlib.pyplot as plt
except ImportError:
plt = None
from numpy.testing import assert_equal, assert_raises
from skimage.feature.util import (FeatureDetector, DescriptorExtractor,
_prepare_grayscale_input_2D,
_mask... | bsd-3-clause |
alexsavio/scikit-learn | examples/model_selection/plot_roc_crossval.py | 21 | 3477 | """
=============================================================
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 |
chaowu2009/stereo-vo | tools/capture_TwoCameras_saveImagesOnly.py | 1 | 2289 | import numpy as np
import cv2
import time
import matplotlib.pylab as plt
"""
Make sure that you hold the checkerboard horizontally (more checkers horizontally than vertically).
In order to get a good calibration you will need to move the checkerboard around in the camera frame such that:
the checkerboard is det... | mit |
kevin-intel/scikit-learn | sklearn/feature_selection/tests/test_rfe.py | 10 | 16467 | """
Testing Recursive feature elimination
"""
from operator import attrgetter
import pytest
import numpy as np
from numpy.testing import assert_array_almost_equal, assert_array_equal
from scipy import sparse
from sklearn.feature_selection import RFE, RFECV
from sklearn.datasets import load_iris, make_friedman1
from s... | bsd-3-clause |
Mako-kun/mangaki | mangaki/mangaki/utils/svd.py | 2 | 5410 | from django.contrib.auth.models import User
from mangaki.models import Rating, Work, Recommendation
from mangaki.utils.chrono import Chrono
from mangaki.utils.values import rating_values
from scipy.sparse import lil_matrix
from sklearn.utils.extmath import randomized_svd
import numpy as np
from django.db import connect... | agpl-3.0 |
MartialD/hyperspy | hyperspy/drawing/tiles.py | 4 | 2899 | # -*- coding: utf-8 -*-
# Copyright 2007-2011 The HyperSpy developers
#
# This file is part of HyperSpy.
#
# HyperSpy 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... | gpl-3.0 |
Rocamadour7/ml_tutorial | 05. Clustering/titanic-data-example.py | 1 | 1721 | import numpy as np
from sklearn.cluster import KMeans
from sklearn import preprocessing
import pandas as pd
'''
Pclass Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)
survival Survival (0 = No; 1 = Yes)
name Name
sex Sex
age Age
sibsp Number of Siblings/Spouses Aboard
parch Number of Parents/Children Aboard
ticket Ticket ... | mit |
huytd/dejavu | dejavu/fingerprint.py | 1 | 6020 | import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from scipy.ndimage.filters import maximum_filter
from scipy.ndimage.morphology import (generate_binary_structure,
iterate_structure, binary_erosion)
import hashlib
from operator import itemgetter
IDX... | mit |
xuleiboy1234/autoTitle | tensorflow/tensorflow/examples/learn/wide_n_deep_tutorial.py | 18 | 8111 | # 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... | mit |
joshloyal/scikit-learn | sklearn/cluster/tests/test_affinity_propagation.py | 341 | 2620 | """
Testing for Clustering methods
"""
import numpy as np
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_raises
from sklearn.cluster.affinity_propagation_ import AffinityPropagation
from sklearn.cluster.affinity_propagatio... | bsd-3-clause |
eriksonJAguiar/TCC-UENP-Codigos | My_codes/tools-sentiment/word_freq.py | 1 | 4759 | import nltk
import pandas as pd
import re
from googletrans import Translator
from unicodedata import normalize
def read_csv(file):
df1 = pd.DataFrame.from_csv('files_extern/%s.csv'%(file),sep=';',index_col=0,encoding ='ISO-8859-1')
df1 = df1.reset_index()
return df1
def write_csv(data,file):
d... | gpl-3.0 |
pypot/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 |
hahnicity/ace | chapter1/problem3.py | 1 | 1222 | """
Problem 3.
calculate the time series
yt = 5 + .05 * t + Et (Where E is epsilon)
for years 1960, 1961, ..., 2001 assuming Et independently and
identically distributed with mean 0 and sigma 0.2.
"""
from random import uniform
from matplotlib.pyplot import plot, show
from numpy import array, polyfit, poly1d
def ... | unlicense |
gnu-sandhi/sandhi | modules/gr36/gnuradio-core/src/examples/pfb/interpolate.py | 17 | 8253 | #!/usr/bin/env python
#
# Copyright 2009 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio 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, or (at your option)
# ... | gpl-3.0 |
gclenaghan/scikit-learn | sklearn/decomposition/tests/test_truncated_svd.py | 73 | 6086 | """Test truncated SVD transformer."""
import numpy as np
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from sklearn.utils import check_random_state
from sklearn.utils.testing import (assert_array_almost_equal, assert_equal,
assert_raises, assert_greater,
... | bsd-3-clause |
tbtraltaa/medianshape | medianshape/simplicial/surfgen.py | 1 | 10038 | # encoding: utf-8
'''
2D surface embedded in 3D
-------------------------
'''
from __future__ import absolute_import
import importlib
import os
import numpy as np
from medianshape.simplicial import pointgen3d, mesh, utils
from medianshape.simplicial.meshgen import meshgen2d
import matplotlib.pyplot as plt
from mpl_t... | gpl-3.0 |
rbharath/pande-gas | vs_utils/utils/dragon_utils.py | 3 | 5800 | """
Dragon utilities.
"""
__author__ = "Steven Kearnes"
__copyright__ = "Copyright 2014, Stanford University"
__license__ = "BSD 3-clause"
from cStringIO import StringIO
import numpy as np
import os
import pandas as pd
import subprocess
import tempfile
from vs_utils.utils import SmilesGenerator
class Dragon(object... | bsd-3-clause |
suraj-jayakumar/lstm-rnn-ad | src/testdata/random_data_time_series/generate_data.py | 1 | 1042 | # -*- coding: utf-8 -*-
"""
Created on Tue Feb 23 11:15:12 2016
@author: suraj
"""
import random
import numpy as np
import pickle
import matplotlib.pyplot as plt
attachRateList = []
for i in range(3360):
attachRateList.append(random.uniform(4,6))
attachRateList = np.array(attachRateList)
encoded_attach_rate... | apache-2.0 |
hackthemarket/pystrat | sim.py | 1 | 10697 | # simple trading strategy simulator
import pandas as pd
from pandas.tools.plotting import autocorrelation_plot
from pandas.tools.plotting import scatter_matrix
import numpy as np
from scipy import stats
import sklearn
from sklearn import preprocessing as pp
import matplotlib as mpl
import matplotlib.pyplot as plt
f... | gpl-3.0 |
alexeyum/scikit-learn | sklearn/datasets/mlcomp.py | 289 | 3855 | # Copyright (c) 2010 Olivier Grisel <olivier.grisel@ensta.org>
# License: BSD 3 clause
"""Glue code to load http://mlcomp.org data as a scikit.learn dataset"""
import os
import numbers
from sklearn.datasets.base import load_files
def _load_document_classification(dataset_path, metadata, set_=None, **kwargs):
if ... | bsd-3-clause |
Gabriel-p/mcs_rot_angles | aux_modules/validation_set.py | 1 | 10176 |
import os
from astropy.io import ascii
from astropy.table import Table
from astropy.coordinates import Distance, Angle, SkyCoord
from astropy import units as u
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import sys
# Change path so that we can import functions from the '... | gpl-3.0 |
annahs/atmos_research | LEO_calc_coating_from_meas_scat_amp_and_write_to_db.py | 1 | 3857 | import sys
import os
import datetime
import pickle
import numpy as np
import matplotlib.pyplot as plt
from pprint import pprint
import sqlite3
import calendar
from datetime import datetime
#id INTEGER PRIMARY KEY AUTOINCREMENT,
#sp2b_file TEXT,
#file_index INT,
#instr TEXT,
#instr_locn TEXT,
#particle_type TEXT,
#... | mit |
great-expectations/great_expectations | great_expectations/expectations/core/expect_column_values_to_be_in_type_list.py | 1 | 17690 | import logging
from typing import Dict, Optional
import numpy as np
import pandas as pd
from great_expectations.core import ExpectationConfiguration
from great_expectations.exceptions import InvalidExpectationConfigurationError
from great_expectations.execution_engine import (
ExecutionEngine,
PandasExecution... | apache-2.0 |
bongtrop/peach | tutorial/neural-networks/linear-prediction.py | 6 | 3386 | ################################################################################
# Peach - Computational Intelligence for Python
# Jose Alexandre Nalon
#
# This file: tutorial/linear-prediction.py
# Using neural networks to predict number sequences
#######################################################################... | lgpl-2.1 |
cs207-project/TimeSeries | procs/_corr.py | 1 | 4794 | import numpy.fft as nfft
import numpy as np
import timeseries as ts
from scipy.stats import norm
# import pyfftw
import sys
#sys.path.append("/Users/yuhantang/CS207/TimeSeries/procs")
from .interface import *
def createfromlist(l):
d = new_darray(len(l))
for i in range(0,len(l)):
darray_set(d,i,l[i])
... | mit |
benjaminoh1/tensorflowcookbook | Chapter 07/bag_of_words.py | 1 | 6082 | # Working with Bag of Words
#---------------------------------------
#
# In this example, we will download and preprocess the ham/spam
# text data. We will then use a one-hot-encoding to make a
# bag of words set of features to use in logistic regression.
#
# We will use these one-hot-vectors for logistic regression... | mit |
dvro/scikit-protopy | protopy/base.py | 1 | 4528 | """Base and mixin classes for instance reduction techniques"""
# Author: Dayvid Victor <dvro@cin.ufpe.br>
# License: BSD Style
import warnings
from abc import ABCMeta, abstractmethod
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.neighbors.classification import KNeighborsClassifier
from sklearn... | bsd-2-clause |
ZenDevelopmentSystems/scikit-learn | sklearn/linear_model/tests/test_sgd.py | 68 | 43439 | import pickle
import unittest
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing ... | bsd-3-clause |
SP2RC-Coding-Club/Codes | 13_07_2017/3D_slab_modes.py | 1 | 35096 |
#import pdb # pause code for debugging at pdb.set_trace()
import numpy as np
import toolbox as tool
import slab_functions as sf
from pysac.plot.mayavi_seed_streamlines import SeedStreamline
import matplotlib.pyplot as plt
from mayavi import mlab
import gc
#import move_seed_points as msp
import mayavi_plotting_function... | mit |
iShoto/testpy | codes/20200104_metric_learning_mnist/src/train_mnist_original_center.py | 1 | 5545 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import torch.optim.lr_scheduler as lr_scheduler
from torch.autograd.function import Function
import torchvision
import os
import matplotlib... | mit |
reinaH/osf.io | scripts/analytics/email_invites.py | 55 | 1332 | # -*- coding: utf-8 -*-
import os
import matplotlib.pyplot as plt
from framework.mongo import database
from website import settings
from utils import plot_dates, mkdirp
user_collection = database['user']
FIG_PATH = os.path.join(settings.ANALYTICS_PATH, 'figs', 'features')
mkdirp(FIG_PATH)
def analyze_email_invi... | apache-2.0 |
roshchupkin/hase | tools/VCF2hdf5.py | 1 | 4024 |
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from config import PYTHON_PATH
if PYTHON_PATH is not None:
for i in PYTHON_PATH: sys.path.insert(0,i)
import argparse
import h5py
import pandas as pd
import numpy as np
from hdgwas.tools import Timer
import tables
import... | gpl-3.0 |
moutai/scikit-learn | sklearn/manifold/locally_linear.py | 37 | 25852 | """Locally Linear Embedding"""
# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
# Jake Vanderplas -- <vanderplas@astro.washington.edu>
# License: BSD 3 clause (C) INRIA 2011
import numpy as np
from scipy.linalg import eigh, svd, qr, solve
from scipy.sparse import eye, csr_matrix
from ..base import B... | bsd-3-clause |
moutai/scikit-learn | sklearn/datasets/tests/test_mldata.py | 384 | 5221 | """Test functionality of mldata fetching utilities."""
import os
import shutil
import tempfile
import scipy as sp
from sklearn import datasets
from sklearn.datasets import mldata_filename, fetch_mldata
from sklearn.utils.testing import assert_in
from sklearn.utils.testing import assert_not_in
from sklearn.utils.test... | bsd-3-clause |
google-research/disentanglement_lib | disentanglement_lib/data/ground_truth/cars3d.py | 1 | 4067 | # coding=utf-8
# Copyright 2018 The DisentanglementLib 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
#
# Un... | apache-2.0 |
Lawrence-Liu/scikit-learn | sklearn/cluster/setup.py | 263 | 1449 | # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import os
from os.path import join
import numpy
from sklearn._build_utils import get_blas_info
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
cblas_libs, blas_info = ... | bsd-3-clause |
mohitreddy1996/Gender-Detection-from-Signature | src/train_test/random_forests.py | 1 | 1140 | from sklearn.metrics import precision_recall_fscore_support
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import MinMaxScaler, normalize
df = pd.read_csv('../../Dataset/dataset.csv', delimiter='\t')
dataset = df.values
mask = np.random.rand(len... | mit |
AlexanderFabisch/scikit-learn | sklearn/decomposition/tests/test_pca.py | 21 | 11810 | import numpy as np
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_rai... | bsd-3-clause |
eramirem/astroML | book_figures/chapter9/fig_photoz_tree.py | 3 | 3637 | """
Photometric Redshifts by Decision Trees
---------------------------------------
Figure 9.14
Photometric redshift estimation using decision-tree regression. The data is
described in Section 1.5.5. The training set consists of u, g , r, i, z
magnitudes of 60,000 galaxies from the SDSS spectroscopic sample.
Cross-val... | bsd-2-clause |
vitale232/ves | ves/VESinverse_vectorized.py | 1 | 12839 | # -*- coding: utf-8 -*-
"""
Created on Thu Jan 28 16:32:48 2016
@author: jclark
this code uses the Ghosh method to determine the apparent resistivities
for a layered earth model. Either schlumberger or Wenner configurations
can be used
"""
import numpy as np
import random
import matplotlib
matplotlib... | lgpl-3.0 |
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