repo_name stringlengths 6 67 | path stringlengths 5 185 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 1.02k 962k | license stringclasses 15
values |
|---|---|---|---|---|---|
rahuldhote/scikit-learn | sklearn/decomposition/__init__.py | 147 | 1421 | """
The :mod:`sklearn.decomposition` module includes matrix decomposition
algorithms, including among others PCA, NMF or ICA. Most of the algorithms of
this module can be regarded as dimensionality reduction techniques.
"""
from .nmf import NMF, ProjectedGradientNMF
from .pca import PCA, RandomizedPCA
from .incrementa... | bsd-3-clause |
ThomasMiconi/nupic.research | htmresearch/support/sp_paper_utils.py | 6 | 11432 | #!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2016, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions ... | agpl-3.0 |
chrsrds/scikit-learn | examples/semi_supervised/plot_label_propagation_versus_svm_iris.py | 21 | 2391 | """
=====================================================================
Decision boundary of label propagation versus SVM on the Iris dataset
=====================================================================
Comparison for decision boundary generated on iris dataset
between Label Propagation and SVM.
This demon... | bsd-3-clause |
rudhir-upretee/Sumo_With_Netsim | tools/visualization/mpl_dump_twoAgainst.py | 3 | 7534 | #!/usr/bin/env python
"""
@file mpl_dump_twoAgainst.py
@author Daniel Krajzewicz
@author Michael Behrisch
@date 2007-10-25
@version $Id: mpl_dump_twoAgainst.py 11671 2012-01-07 20:14:30Z behrisch $
This script reads two dump files and plots one of the values
stored therein as an x-/y- plot.
matplotlib has t... | gpl-3.0 |
rhyolight/nupic | src/nupic/algorithms/monitor_mixin/plot.py | 20 | 5229 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2014-2015, 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 p... | agpl-3.0 |
shanzhenren/ClusType | src/algorithm.py | 1 | 10630 | from collections import defaultdict
from operator import itemgetter
from math import log, sqrt
import random as rn
import time
from numpy import * # install numpy
from scipy import * # install scipy
from numpy.linalg import norm
import numpy.linalg as npl
from scipy.sparse import *
import scipy.sparse.linalg as spsl
fr... | gpl-3.0 |
neale/softmax | softmax_regression.py | 1 | 7400 | #!/usr/bin/env python
import sys
import time
import numpy as np
import random
import matplotlib.pyplot as plt
from collections import defaultdict
cost = 0.0
lrate = .1
grad = np.array([])
lambda_factor = 0.0
nclasses = 2
def vector_to_collumn(vec):
length = len(vec)
A = np.array(vec)
for i in range(lengt... | unlicense |
OFAI/million-post-corpus | experiments/src/evaluate_lstm.py | 1 | 13644 | import math
import multiprocessing
import os
import warnings
from gensim.models.word2vec import Word2Vec
import numpy
import tensorflow as tf
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from sklearn.exceptions import UndefinedMetricWarning
from sklearn.metrics import precision_score, recall... | mit |
agutieda/QuantEcon.py | quantecon/estspec.py | 7 | 4856 | """
Filename: estspec.py
Authors: Thomas Sargent, John Stachurski
Functions for working with periodograms of scalar data.
"""
from __future__ import division, print_function
import numpy as np
from numpy.fft import fft
from pandas import ols, Series
def smooth(x, window_len=7, window='hanning'):
"""
Smoot... | bsd-3-clause |
jakereimer/pipeline | python/pipeline/notify.py | 5 | 1997 | import datajoint as dj
from datajoint.jobs import key_hash
from . import experiment
schema = dj.schema('pipeline_notification', locals())
# Decorator for notification functions. Ignores exceptions.
def ignore_exceptions(f):
def wrapper(*args, **kwargs):
try:
return f(*args, **kwargs)
e... | lgpl-3.0 |
shusenl/scikit-learn | sklearn/tests/test_common.py | 70 | 7717 | """
General tests for all estimators in sklearn.
"""
# Authors: Andreas Mueller <amueller@ais.uni-bonn.de>
# Gael Varoquaux gael.varoquaux@normalesup.org
# License: BSD 3 clause
from __future__ import print_function
import os
import warnings
import sys
import pkgutil
from sklearn.externals.six import PY3
fr... | bsd-3-clause |
JaviMerino/bart | bart/common/Utils.py | 1 | 5498 | # Copyright 2015-2015 ARM Limited
#
# 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 w... | apache-2.0 |
softwaresaved/SSINetworkGraphics | Fellows/Python/home_inst_map.py | 1 | 1379 | #!/usr/bin/python2.7
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np
# set up orthographic map projection with
# perspective of satellite looking down at 50N, 100W.
# use low resolution coastlines.
# don't plot features that are smaller than 1000 square km.
map = Basemap(pro... | bsd-3-clause |
nmayorov/scikit-learn | sklearn/linear_model/stochastic_gradient.py | 34 | 50761 | # Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)
# Mathieu Blondel (partial_fit support)
#
# License: BSD 3 clause
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
from abc import ABCMeta, abstractmethod
from ..externals.joblib import ... | bsd-3-clause |
andrewnc/scikit-learn | sklearn/cluster/tests/test_spectral.py | 262 | 7954 | """Testing for Spectral Clustering methods"""
from sklearn.externals.six.moves import cPickle
dumps, loads = cPickle.dumps, cPickle.loads
import numpy as np
from scipy import sparse
from sklearn.utils import check_random_state
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_a... | bsd-3-clause |
don-willingham/Sonoff-Tasmota | tools/serial-plotter.py | 2 | 5157 | #!/usr/bin/env python3
"""
serial-plotter.py - for Tasmota
Copyright (C) 2020 Christian Baars
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at yo... | gpl-3.0 |
ThiagoGarciaAlves/intellij-community | python/helpers/pydev/pydevd.py | 3 | 69138 | '''
Entry point module (keep at root):
This module starts the debugger.
'''
import sys
if sys.version_info[:2] < (2, 6):
raise RuntimeError('The PyDev.Debugger requires Python 2.6 onwards to be run. If you need to use an older Python version, use an older version of the debugger.')
import atexit
import os
import... | apache-2.0 |
rcharp/toyota-flask | venv/lib/python2.7/site-packages/numpy/lib/polynomial.py | 35 | 37641 | """
Functions to operate on polynomials.
"""
from __future__ import division, absolute_import, print_function
__all__ = ['poly', 'roots', 'polyint', 'polyder', 'polyadd',
'polysub', 'polymul', 'polydiv', 'polyval', 'poly1d',
'polyfit', 'RankWarning']
import re
import warnings
import numpy.core.... | apache-2.0 |
pravsripad/mne-python | mne/io/edf/tests/test_edf.py | 4 | 21326 | # -*- coding: utf-8 -*-
# Authors: Teon Brooks <teon.brooks@gmail.com>
# Martin Billinger <martin.billinger@tugraz.at>
# Alan Leggitt <alan.leggitt@ucsf.edu>
# Alexandre Barachant <alexandre.barachant@gmail.com>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
# Joan Massic... | bsd-3-clause |
datapythonista/pandas | pandas/tests/frame/methods/test_is_homogeneous_dtype.py | 4 | 1422 | import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import (
Categorical,
DataFrame,
)
# _is_homogeneous_type always returns True for ArrayManager
pytestmark = td.skip_array_manager_invalid_test
@pytest.mark.parametrize(
"data, expected",
[
# empty
... | bsd-3-clause |
krez13/scikit-learn | sklearn/ensemble/weight_boosting.py | 23 | 40739 | """Weight Boosting
This module contains weight boosting estimators for both classification and
regression.
The module structure is the following:
- The ``BaseWeightBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regression and classification
only differ from each ot... | bsd-3-clause |
StefReck/Km3-Autoencoder | scripts/convergence_diagnosis.py | 1 | 13188 | # -*- coding: utf-8 -*-
"""
Create layer output histograms of the last layers of a network, while training.
"""
#import matplotlib
#matplotlib.use('Agg')
from keras.layers import Activation, Input, Lambda, Dropout, Dense, Flatten, Conv3D, MaxPooling3D, UpSampling3D,BatchNormalization, ZeroPadding3D, Conv3DTranspose, ... | mit |
Aerolyzer/Aerolyzer | horizon.py | 1 | 4599 | import sys
import cv2
import os
import numpy as np
from matplotlib import pyplot as plt
def sigm(x):
return 1 / (1 + np.exp(-x))
def is_sky(img):
syn0 = np.array([[0.6106635051820115, -1.2018987127529588, -10.344605820189082, 1.1911213385074928, -6.818421664371254, 0.7888012143578024, 0.1930026599192343, 2.... | apache-2.0 |
zooniverse/aggregation | experimental/dkMeansPaper/dk1.py | 2 | 3219 | #!/usr/bin/env python
__author__ = 'greghines'
import numpy as np
import os
import pymongo
import sys
import cPickle as pickle
import bisect
import csv
import matplotlib.pyplot as plt
import random
import math
import urllib
import matplotlib.cbook as cbook
from scipy.stats.stats import pearsonr
from scipy.stats import ... | apache-2.0 |
bgris/ODL_bgris | lib/python3.5/site-packages/scipy/stats/_binned_statistic.py | 28 | 25272 | from __future__ import division, print_function, absolute_import
import warnings
import numpy as np
from scipy._lib.six import callable, xrange
from collections import namedtuple
__all__ = ['binned_statistic',
'binned_statistic_2d',
'binned_statistic_dd']
BinnedStatisticResult = namedtuple('B... | gpl-3.0 |
idaholab/raven | tests/framework/Samplers/Categorical/StringVars/proj_second.py | 2 | 1667 | import numpy as np
def run(self,Input):
v0 = self.v0
y0 = self.y0
ang = 45.*np.pi/180.
times = np.linspace(0,2,5)
mode = self.mode
if mode == 'stepper':
y = stepper(v0,y0,ang,times)
elif mode == 'analytic':
y = analytic(v0,y0,ang,times)
else:
raise IOError('Unrecognized mode:',mode)
self.... | apache-2.0 |
pavanramkumar/pyglmnet | examples/plot_tikhonov.py | 1 | 8672 | # -*- coding: utf-8 -*-
"""
========================
Tikhonov Regularization
========================
Tikhonov regularization is a generalized form of L2-regularization. It allows
us to articulate our prior knowlege about correlations between
different predictors with a multivariate Gaussian prior. Here, we demonstrat... | mit |
arielmakestuff/loadlimit | test/unit/stat/test_timeseries.py | 1 | 6054 | # -*- coding: utf-8 -*-
# test/unit/stat/test_timeseries.py
# Copyright (C) 2016 authors and contributors (see AUTHORS file)
#
# This module is released under the MIT License.
"""Test timeseries()"""
# ============================================================================
# Imports
# ===========================... | mit |
caisq/tensorflow | tensorflow/examples/learn/multiple_gpu.py | 39 | 3957 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | apache-2.0 |
lpryszcz/bin | sam2hist.py | 1 | 2337 | #!/usr/bin/env python
desc="""Report histogram for given insert size data
"""
epilog="""Author:
l.p.pryszcz+git@gmail.com
Barcelona, 18/10/2012
"""
import argparse, os, sys
from datetime import datetime
def plot( isizes,outfn ):
"""
"""
import matplotlib.pyplot as plt
# the histogram of the data
... | gpl-3.0 |
kazemakase/scikit-learn | examples/linear_model/plot_ols.py | 220 | 1940 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Linear Regression Example
=========================================================
This example uses the only the first feature of the `diabetes` dataset, in
order to illustrate a two-dimensional plot of this regre... | bsd-3-clause |
justincassidy/scikit-learn | examples/applications/face_recognition.py | 191 | 5513 | """
===================================================
Faces recognition example using eigenfaces and SVMs
===================================================
The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:
http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (2... | bsd-3-clause |
JasonKessler/scattertext | demo_category_frequencies.py | 1 | 1662 | import pandas as pd
import scattertext as st
'''
Sample genre frequencies from the Corpus of Contemporary American English via
https://www.wordfrequency.info/100k_compare_to_60k_etc.asp .
We'll examine the difference between spoken and fiction, and just consider the top 1000
words in the sample.
'''
df = (pd.read_e... | apache-2.0 |
MurphysLab/ADAblock | Data_Sort_and_Plot_Script.py | 1 | 70948 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Data_Sort_and_Plot_Script.py
Script created by Jeffrey N. Murphy (2015); Email: jnmurphy@ualberta.ca
Provided without any warranty.
The directory locations will need to be modified, depending on the location of the input file.
The input for this script is the file pro... | mit |
matthiaskoenig/sbmlutils | src/sbmlutils/manipulation/interpolation.py | 1 | 13801 | """Create SBML/antimony files for interpolation of datasets.
https://github.com/allyhume/SBMLDataTools
https://github.com/allyhume/SBMLDataTools.git
TODO: fix composition with existing models
TODO: support coupling with existing models via comp
The functionality is very useful, but only if this can be applied to exis... | lgpl-3.0 |
fabianp/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_02_sentiment.py | 254 | 2795 | """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 |
uci-cbcl/tree-hmm | treehmm/__init__.py | 2 | 65101 | #!/usr/bin/env python
"""
Variational Bayes method to solve phylgoenetic HMM for histone modifications
Need to:
* Preprocess
** load each dataset
** call significant sites for each dataset (vs. one control dataset)
** save out resulting histogrammed data
* Learn
** Init parameters randomly
** E step: optimize each q... | bsd-3-clause |
joernhees/scikit-learn | examples/plot_compare_reduction.py | 45 | 4959 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
=================================================================
Selecting dimensionality reduction with Pipeline and GridSearchCV
=================================================================
This example constructs a pipeline that does dimensionality
reduction f... | bsd-3-clause |
ChanChiChoi/scikit-learn | examples/applications/plot_prediction_latency.py | 234 | 11277 | """
==================
Prediction Latency
==================
This is an example showing the prediction latency of various scikit-learn
estimators.
The goal is to measure the latency one can expect when doing predictions
either in bulk or atomic (i.e. one by one) mode.
The plots represent the distribution of the pred... | bsd-3-clause |
elkingtonmcb/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 |
BrechtBa/plottools | doc/source/conf.py | 1 | 10429 | # -*- coding: utf-8 -*-
#
# Someproject documentation build configuration file, created by
# sphinx-quickstart on Mon Sep 05 08:56:23 2016.
#
# 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.
#... | gpl-2.0 |
ssaeger/scikit-learn | sklearn/tree/tests/test_export.py | 31 | 9588 | """
Testing for export functions of decision trees (sklearn.tree.export).
"""
from re import finditer
from numpy.testing import assert_equal
from nose.tools import assert_raises
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.ensemble import GradientBoostingClassifier
from sklearn... | bsd-3-clause |
xzh86/scikit-learn | examples/cluster/plot_kmeans_assumptions.py | 270 | 2040 | """
====================================
Demonstration of k-means assumptions
====================================
This example is meant to illustrate situations where k-means will produce
unintuitive and possibly unexpected clusters. In the first three plots, the
input data does not conform to some implicit assumptio... | bsd-3-clause |
MatthewThe/kaggle | titanic/bin/random_forest.py | 1 | 2193 | #!/usr/bin/python
import csv
import numpy as np
from sklearn import svm
from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
# 0: PassengerId, 1: Survived, 2: Pclass, 3: Name, 4: Sex (male,female), 5: Age, 6: SibSp, 7: ParCh, 8: Ticket, 9: Fare, 10: Cabin, 11: Embarked (S,C,Q)
# 1,0,3,"... | apache-2.0 |
nickdex/cosmos | code/artificial_intelligence/src/particle_swarm_optimization/gbestPSO/Gbest3D.py | 3 | 2194 | import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def f(args):
return np.sum([x ** 2 for x in args])
dimensions = 2
boundary = (-10, 10)
particles = 20
positions = []
velocities = []
w_min = 0.01
w_max = 0.1
c1 = 0
c2 = 0
num_iters = 20
for i in range(particles):
p... | gpl-3.0 |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/skimage/transform/tests/test_radon_transform.py | 2 | 15936 | from __future__ import print_function, division
import os
import itertools
import numpy as np
from skimage import data_dir
from skimage.io import imread
from skimage.transform import radon, iradon, iradon_sart, rescale
from skimage._shared import testing
from skimage._shared.testing import test_parallel
from skimage... | gpl-3.0 |
sunzhxjs/JobGIS | lib/python2.7/site-packages/pandas/core/dtypes.py | 9 | 5492 | """ define extension dtypes """
import re
import numpy as np
from pandas import compat
class ExtensionDtype(object):
"""
A np.dtype duck-typed class, suitable for holding a custom dtype.
THIS IS NOT A REAL NUMPY DTYPE
"""
name = None
names = None
type = None
subdtype = None
kind =... | mit |
freeman-lab/dask | dask/dataframe/tests/test_multi.py | 5 | 5300 | import dask.dataframe as dd
import pandas as pd
from dask.dataframe.multi import (align_partitions, join_indexed_dataframes,
hash_join, concat_indexed_dataframes)
import pandas.util.testing as tm
from dask.async import get_sync
def test_align_partitions():
A = pd.DataFrame({'x': [1, 2, 3, 4, 5, 6], 'y': li... | bsd-3-clause |
habi/GlobalDiagnostiX | AngularOpening.py | 1 | 6875 | # -*- coding: utf-8 -*-
"""
Calculate the angular opening of the lens, including the shades that we have
to build in between.
"""
from __future__ import division
import optparse
import sys
import os
import numpy
import matplotlib.pyplot as plt
from matplotlib.patches import Wedge
from matplotlib.patches import Rectang... | unlicense |
rafaelvalle/MDI | nnet_lasagne.py | 1 | 10609 | # code adapted from lasagne tutorial
# http://lasagne.readthedocs.org/en/latest/user/tutorial.html
import time
import os
from itertools import product
import numpy as np
from sklearn.cross_validation import KFold
import theano
from theano import tensor as T
import lasagne
from params import nnet_params_dict, feats_tra... | mit |
andresmeh/pylibnidaqmx | nidaqmx/wxagg_plot.py | 16 | 4515 |
import os
import sys
import time
import traceback
import matplotlib
matplotlib.use('WXAgg')
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg
from matplotlib.backends.backend_wx import NavigationToolbar2Wx
import wx
from matplotlib.figure import Figure
class PlotFigure(wx.Frame):
def OnKeyPresse... | bsd-3-clause |
alephu5/Soundbyte | environment/lib/python3.3/site-packages/matplotlib/gridspec.py | 1 | 14943 | """
:mod:`~matplotlib.gridspec` is a module which specifies the location
of the subplot in the figure.
``GridSpec``
specifies the geometry of the grid that a subplot will be
placed. The number of rows and number of columns of the grid
need to be set. Optionally, the subplot layout parameter... | gpl-3.0 |
louisLouL/pair_trading | capstone_env/lib/python3.6/site-packages/pandas/core/reshape/merge.py | 3 | 53914 | """
SQL-style merge routines
"""
import copy
import warnings
import string
import numpy as np
from pandas.compat import range, lzip, zip, map, filter
import pandas.compat as compat
from pandas import (Categorical, Series, DataFrame,
Index, MultiIndex, Timedelta)
from pandas.core.frame import _mer... | mit |
dhwang99/statistics_introduction | probility/dirichlet.py | 1 | 2446 | #encoding: utf8
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
from mpl_toolkits.mplot3d import Axes3D
from gamma_dist import gamma_values
import pdb
'''
Dir(X;alpha_vector) = (Gamma(sum(alpha_vector))/Multi(Gamma(alpha_i)) * Multi(xi^alpha_i)
'''
def dir_pdf(alpha_vector, X_vector): ... | gpl-3.0 |
prasanna08/oppia-ml | core/classifiers/TextClassifier/TextClassifier.py | 1 | 7667 | # coding: utf-8
#
# Copyright 2017 The Oppia 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 requi... | apache-2.0 |
yunfeilu/scikit-learn | sklearn/utils/estimator_checks.py | 21 | 51976 | from __future__ import print_function
import types
import warnings
import sys
import traceback
import inspect
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.ut... | bsd-3-clause |
ScopeFoundry/FoundryDataBrowser | viewers/hyperspec_h5.py | 1 | 9105 | #from ScopeFoundry.data_browser import HyperSpectralBaseView
from FoundryDataBrowser.viewers.hyperspec_base_view import HyperSpectralBaseView
from FoundryDataBrowser.viewers.plot_n_fit import PeakUtilsFitter
import numpy as np
import h5py
import pyqtgraph as pg
class HyperSpecH5View(HyperSpectralBaseView):
name... | bsd-3-clause |
bejar/kemlglearn | kemlglearn/cluster/consensus/SimpleConsensusClustering.py | 1 | 4669 | """
.. module:: SimpleConsensusClustering
SimpleConsensusClustering
*************
:Description: SimpleConsensusClustering
:Authors: bejar
:Version:
:Created on: 22/01/2015 10:46
"""
__author__ = 'bejar'
import numpy as np
from sklearn.base import BaseEstimator, ClusterMixin, TransformerMixin
from s... | mit |
femtotrader/arctic-updater | samples/unique.py | 1 | 2387 | import time
import logging
from collections import OrderedDict
import pandas as pd
pd.set_option('max_rows', 10)
pd.set_option('expand_frame_repr', False)
pd.set_option('max_columns', 6)
from arctic_updater.updaters.truefx import TrueFXUpdater
logging.Formatter.converter = time.gmtime
logging.basicConfig(format='%(asc... | isc |
adammenges/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 |
dsc381/yahoo_cqa | fex.py | 2 | 5492 | import os
import json
import numpy
import codecs
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn import svm
from sklearn.metrics import accuracy_score, precision_score
def extract(f):
def sample(f):
hits = []
mis... | gpl-2.0 |
qingshuimonk/STA663 | Vanilla_GAN.py | 1 | 3445 | import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib
# matplotlib.use('PS')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import os
from time import gmtime, strftime
def xavier_init(size):
in_dim = size[0]
xavier_stdde... | mit |
Vvucinic/Wander | venv_2_7/lib/python2.7/site-packages/pandas/core/algorithms.py | 9 | 18486 | """
Generic data algorithms. This module is experimental at the moment and not
intended for public consumption
"""
from __future__ import division
from warnings import warn
import numpy as np
from pandas import compat, lib, _np_version_under1p8
import pandas.core.common as com
import pandas.algos as algos
import panda... | artistic-2.0 |
arunchaganty/presidential-debates | python/doc2vec.py | 2 | 5109 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Label documents with doc2vec.
"""
import numpy as np
import os
from collections import Counter
from pprint import pprint
import csv
import sys
import gensim
from gensim.corpora import Dictionary, HashDictionary, MmCorpus
from gensim.models.doc2vec import TaggedDocume... | mit |
anirudhjayaraman/scikit-learn | sklearn/datasets/samples_generator.py | 103 | 56423 | """
Generate samples of synthetic data sets.
"""
# Authors: B. Thirion, G. Varoquaux, A. Gramfort, V. Michel, O. Grisel,
# G. Louppe, J. Nothman
# License: BSD 3 clause
import numbers
import array
import numpy as np
from scipy import linalg
import scipy.sparse as sp
from ..preprocessing import MultiLabelBin... | bsd-3-clause |
John-Jumper/Upside-MD | py/sim_timeseries.py | 1 | 5583 | #!/usr/bin/env python
from multiprocessing import Pool
import numpy as np
import tables as tb
import collections
from glob import glob
import re
import sys,os
import cPickle as cp
from glob import glob
import pandas as pd
from gzip import GzipFile
import time
upside_dir = os.path.expanduser('~/upside/')
if upside_d... | gpl-2.0 |
jmuhlich/pysb | pysb/tests/test_simulator_scipy.py | 5 | 19613 | from pysb.testing import *
import sys
import copy
import numpy as np
from pysb import Monomer, Parameter, Initial, Observable, Rule, Expression
from pysb.simulator import ScipyOdeSimulator, InconsistentParameterError
from pysb.simulator.scipyode import CythonRhsBuilder
from pysb.examples import robertson, earm_1_0, tys... | bsd-2-clause |
ningchi/scikit-learn | examples/neural_networks/plot_rbm_logistic_classification.py | 258 | 4609 | """
==============================================================
Restricted Boltzmann Machine features for digit classification
==============================================================
For greyscale image data where pixel values can be interpreted as degrees of
blackness on a white background, like handwritten... | bsd-3-clause |
PatrickChrist/scikit-learn | benchmarks/bench_isotonic.py | 268 | 3046 | """
Benchmarks of isotonic regression performance.
We generate a synthetic dataset of size 10^n, for n in [min, max], and
examine the time taken to run isotonic regression over the dataset.
The timings are then output to stdout, or visualized on a log-log scale
with matplotlib.
This alows the scaling of the algorith... | bsd-3-clause |
with-git/tensorflow | tensorflow/contrib/labeled_tensor/python/ops/ops.py | 77 | 46403 | # 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 |
vdrhtc/Measurement-automation | lib2/correlatorMeasurement.py | 1 | 19512 | from copy import deepcopy
import numpy as np
import tqdm
from lib2.MeasurementResult import MeasurementResult
from lib2.stimulatedEmission import StimulatedEmission
from datetime import datetime as dt
import matplotlib.pyplot as plt
from matplotlib import colorbar
from scipy import signal
import numba
@numba.njit(p... | gpl-3.0 |
sirex/jsontableschema-pandas-py | tableschema_pandas/storage.py | 1 | 4592 | # -*- coding: utf-8 -*-
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import six
import collections
import tableschema
import pandas as pd
from .mapper import Mapper
# Module API
class Storage(tableschema.Storage)... | lgpl-3.0 |
js7558/pyBinance | tests/test-createOrder.py | 1 | 4354 | #!/usr/bin/python
import pandas as pd
import sys
sys.path.append('../')
from Binance import Binance
import logging.config
import logging.handlers
import logging
import os
# this logging configuration is sketchy
binance = logging.getLogger(__name__)
logging.config.fileConfig('logging.ini')
# create Binance object
b... | mit |
iZehan/spatial-pbs | setup.py | 1 | 4732 | """
Created on 21 May 2014
@author: Zehan Wang
Copyright (C) Zehan Wang 2011-2014 and onwards
Library for patch-based segmentation with spatial context. (Spatially Aware Patch-based Segmentation)
"""
import sys
import os
import shutil
from distutils.command.clean import clean as Clean
DISTNAME = 'spatch'
DESCRIPTIO... | bsd-3-clause |
rexthompson/axwx | axwx/wu_metadata_scraping.py | 1 | 5613 | """
Weather Underground PWS Metadata Scraping Module
Code to scrape PWS network metadata
"""
import pandas as pd
import urllib3
from bs4 import BeautifulSoup as BS
import numpy as np
import requests
# import time
def scrape_station_info(state="WA"):
"""
A script to scrape the station information published ... | mit |
deepfield/ibis | ibis/pandas/execution/tests/test_cast.py | 1 | 3838 | import pytest
import decimal
import pandas as pd
import pandas.util.testing as tm # noqa: E402
import ibis.expr.datatypes as dt # noqa: E402
import ibis
pytestmark = pytest.mark.pandas
@pytest.mark.parametrize('from_', ['plain_float64', 'plain_int64'])
@pytest.mark.parametrize(
('to', 'expected'),
[
... | apache-2.0 |
amandalund/openmc | openmc/plotter.py | 6 | 38919 | from itertools import chain
from numbers import Integral, Real
import string
import numpy as np
import openmc.checkvalue as cv
import openmc.data
# Supported keywords for continuous-energy cross section plotting
PLOT_TYPES = ['total', 'scatter', 'elastic', 'inelastic', 'fission',
'absorption', 'capture... | mit |
per-andersen/Deltamu | Visualisation.py | 1 | 30055 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.patheffects as patheffects
from matplotlib.font_manager import FontProperties
from mpl_toolkits.mplot3d import axes3d
import pickle as pick
import Contour
import Deltamu
'''
Program to analyse and plot output from... | gpl-3.0 |
askielboe/JAVELIN | examples/demo.py | 1 | 13965 | #Last-modified: 08 Dec 2013 15:54:47
import numpy as np
import matplotlib.pyplot as plt
from javelin.predict import PredictSignal, PredictRmap, generateLine, generateError, PredictSpear
from javelin.lcio import *
from javelin.zylc import LightCurve, get_data
from javelin.lcmodel import Cont_Model, Rmap_Model, Pmap_Mode... | gpl-2.0 |
bzero/statsmodels | statsmodels/tsa/tests/test_stattools.py | 26 | 12110 | from statsmodels.compat.python import lrange
from statsmodels.tsa.stattools import (adfuller, acf, pacf_ols, pacf_yw,
pacf, grangercausalitytests,
coint, acovf,
arma_order_select_... | bsd-3-clause |
Huyuwei/tvm | nnvm/tutorials/from_mxnet.py | 2 | 5160 | # 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 u... | apache-2.0 |
DiamondLightSource/auto_tomo_calibration-experimental | old_code_scripts/measure_resolution/lmfit-py/doc/sphinx/numpydoc/docscrape_sphinx.py | 154 | 7759 | import re, inspect, textwrap, pydoc
import sphinx
from docscrape import NumpyDocString, FunctionDoc, ClassDoc
class SphinxDocString(NumpyDocString):
def __init__(self, docstring, config={}):
self.use_plots = config.get('use_plots', False)
NumpyDocString.__init__(self, docstring, config=config)
... | apache-2.0 |
kenshay/ImageScripter | ProgramData/SystemFiles/Python/Lib/site-packages/pandas/io/tests/parser/python_parser_only.py | 7 | 7755 | # -*- coding: utf-8 -*-
"""
Tests that apply specifically to the Python parser. Unless specifically
stated as a Python-specific issue, the goal is to eventually move as many of
these tests out of this module as soon as the C parser can accept further
arguments when parsing.
"""
import csv
import sys
import nose
impo... | gpl-3.0 |
michaelaye/scikit-image | doc/examples/plot_local_binary_pattern.py | 17 | 6774 | """
===============================================
Local Binary Pattern for texture classification
===============================================
In this example, we will see how to classify textures based on LBP (Local
Binary Pattern). LBP looks at points surrounding a central point and tests
whether the surroundin... | bsd-3-clause |
themrmax/scikit-learn | sklearn/feature_selection/tests/test_feature_select.py | 43 | 26651 | """
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 numpy.testing import run_module_suite
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from... | bsd-3-clause |
MartinThoma/algorithms | ML/movielens-20m/ml-20m/movies_analysis.py | 1 | 2395 | from collections import Counter
from itertools import combinations
import clana.io
import clana.visualize_cm
import networkx as nx
import numpy as np
import pandas as pd
import progressbar
# Load the data
df = pd.read_csv("movies.csv")
df["genres"] = df["genres"].str.split("|")
# Analyze the data
list_values = [valu... | mit |
shenzebang/scikit-learn | sklearn/utils/tests/test_sparsefuncs.py | 157 | 13799 | import numpy as np
import scipy.sparse as sp
from scipy import linalg
from numpy.testing import assert_array_almost_equal, assert_array_equal
from sklearn.datasets import make_classification
from sklearn.utils.sparsefuncs import (mean_variance_axis,
inplace_column_scale,
... | bsd-3-clause |
andrewnc/scikit-learn | sklearn/datasets/lfw.py | 141 | 19372 | """Loader for the Labeled Faces in the Wild (LFW) dataset
This dataset is a collection of JPEG pictures of famous people collected
over the internet, all details are available on the official website:
http://vis-www.cs.umass.edu/lfw/
Each picture is centered on a single face. The typical task is called
Face Veri... | bsd-3-clause |
MyRookie/SentimentAnalyse | venv/lib/python2.7/site-packages/nltk/probability.py | 3 | 89919 | # -*- coding: utf-8 -*-
# Natural Language Toolkit: Probability and Statistics
#
# Copyright (C) 2001-2015 NLTK Project
# Author: Edward Loper <edloper@gmail.com>
# Steven Bird <stevenbird1@gmail.com> (additions)
# Trevor Cohn <tacohn@cs.mu.oz.au> (additions)
# Peter Ljunglöf <peter.ljung... | mit |
RichardTMR/homework | week3/Codelab2/tool_show_size.py | 1 | 1681 |
import tensorflow as tf
import matplotlib.pyplot as plt
import tools
cat = plt.imread('cat.jpg') #unit8
plt.imshow(cat)
cat = tf.cast(cat, tf.float32) #[360, 300, 3]
x = tf.reshape(cat, [1, 360, 300, 3]) #[1, 360, 300, 3]
# First conv
with tf.variable_scope('conv1'):
w = tools.weight([3,3,3,16], is_uniform=True)... | apache-2.0 |
kyleabeauchamp/HMCNotes | code/correctness/run_samples_grid_ljbox.py | 1 | 3519 | import lb_loader
import pandas as pd
import simtk.openmm.app as app
import numpy as np
import simtk.openmm as mm
from simtk import unit as u
from openmmtools import hmc_integrators, testsystems
from collections import OrderedDict
def get_grid(sysname, temperature, timestep, langevin_timestep, groups, steps_per_hmc=100... | gpl-2.0 |
bchappet/dnfpy | src/dnfpy/cellular/rsdnf2LayerConvolutionTest.py | 1 | 5065 | from dnfpy.core.constantMap import ConstantMap
import unittest
import numpy as np
import dnfpy.view.staticViewMatplotlib as view
from dnfpy.cellular.rsdnf2LayerConvolution import Rsdnf2LayerConvolution
import matplotlib.pyplot as plt
class Rsdnf2LayerConvolutionTest(unittest.TestCase):
def setUp(self):
... | gpl-2.0 |
muxiaobai/CourseExercises | python/kaggle/learn/RandomForestRegressor.py | 1 | 2390 | #!/usr/bin/python
# -*- coding: UTF-8 -*-
#https://www.kaggle.com/dansbecker/random-forests
# vary trees
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_sp... | gpl-2.0 |
live-clones/dolfin-adjoint | examples/time-distributed-control/time-distributed-control.py | 1 | 6656 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# .. _klein:
#
# .. py:currentmodule:: dolfin_adjoint
#
# Time-distributed controls
# =========================
#
# .. sectionauthor:: Simon W. Funke <simon@simula.no>
#
#
# Background
# **********
# Some time-dependent problems have control variables that are distributed ... | lgpl-3.0 |
lnls-fac/apsuite | apsuite/dynap/dynap_xy.py | 1 | 10945 | """Calculate dynamic aperture."""
import numpy as _np
import matplotlib.pyplot as _mpyplot
import matplotlib.gridspec as _mgridspec
import matplotlib.colors as _mcolors
import matplotlib.text as _mtext
import pyaccel.tracking as _pytrack
from .base import BaseClass as _BaseClass
class DynapXYParams():
"""."""... | mit |
jmmease/pandas | pandas/core/algorithms.py | 6 | 51528 | """
Generic data algorithms. This module is experimental at the moment and not
intended for public consumption
"""
from __future__ import division
from warnings import warn, catch_warnings
import numpy as np
from pandas.core.dtypes.cast import maybe_promote
from pandas.core.dtypes.generic import (
ABCSeries, ABCIn... | bsd-3-clause |
Odingod/mne-python | mne/epochs.py | 1 | 85604 | """Tools for working with epoched data"""
# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
# Denis Engemann <denis.engemann@gmail.com>
# Mainak Jas <mainak@neuro... | bsd-3-clause |
ual/urbansim | urbansim/developer/developer.py | 1 | 10647 | import pandas as pd
import numpy as np
class Developer(object):
"""
Pass the dataframe that is returned by feasibility here
Can also be a dictionary where keys are building forms and values are
the individual data frames returned by the proforma lookup routine.
"""
def __init__(self, feasib... | bsd-3-clause |
vkscool/nupic | external/linux32/lib/python2.6/site-packages/matplotlib/collections.py | 69 | 39876 | """
Classes for the efficient drawing of large collections of objects that
share most properties, e.g. a large number of line segments or
polygons.
The classes are not meant to be as flexible as their single element
counterparts (e.g. you may not be able to select all line styles) but
they are meant to be fast for com... | gpl-3.0 |
rubikloud/scikit-learn | sklearn/metrics/__init__.py | 214 | 3440 | """
The :mod:`sklearn.metrics` module includes score functions, performance metrics
and pairwise metrics and distance computations.
"""
from .ranking import auc
from .ranking import average_precision_score
from .ranking import coverage_error
from .ranking import label_ranking_average_precision_score
from .ranking imp... | bsd-3-clause |
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