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 |
|---|---|---|---|---|---|
shreyas15/Product-Recommender-Engine | metadata_preprocessor.py | 1 | 2879 | # metadata_preprocessor.py
#
# Standalone Python/Spark program to perform data pre-processing..
# Reads Ratings data and meta data to combine where necessary
# and encode labels to a form fit for processing.
#
#
# Usage: spark-submit data_preprocessor.py <inputdatafile>
# Example usage: spark-submit data_preprocessor.p... | mit |
agomariz/scikit-neuralnetwork | sknn/tests/test_sklearn.py | 5 | 2706 | import unittest
from nose.tools import (assert_equal, assert_raises, assert_in, assert_not_in)
import numpy
from scipy.stats import randint, uniform
from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
from sklearn.cross_validation import cross_val_score
from sknn.mlp import Regressor as MLPR, Classifier... | bsd-3-clause |
zaxliu/deepnap | experiments/kdd-exps/experiment_DynaQNN_130_Feb12_2215.py | 1 | 5180 | # System built-in modules
import time
from datetime import datetime
import sys
import os
from multiprocessing import Pool
# Project dependency modules
import pandas as pd
pd.set_option('mode.chained_assignment', None) # block warnings due to DataFrame value assignment
import lasagne
# Project modules
sys.path.append('... | bsd-3-clause |
clairetang6/bokeh | bokeh/charts/builders/line_builder.py | 8 | 9446 | """This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the Line class which lets you build your Line charts just
passing the arguments to the Chart class and calling the proper functions.
"""
# ----------------------------------------------------------------... | bsd-3-clause |
matteorr/coco-analyze | analysisAPI/sizeSensitivity.py | 1 | 8981 | ## imports
import os, time
import numpy as np
import matplotlib.pyplot as plt
def sizeSensitivity( coco_analyze, oks, saveDir ):
loc_dir = saveDir + '/benchmarks_sensitivity/size'
if not os.path.exists(loc_dir):
os.makedirs(loc_dir)
os.makedirs(loc_dir + '/all_plots')
f = open('%s/std_out.t... | mit |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/doc/mpl_toolkits/axes_grid/examples/inset_locator_demo2.py | 8 | 1255 | import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid1.inset_locator import mark_inset
import numpy as np
def get_demo_image():
from matplotlib.cbook import get_sample_data
import numpy as np
f = get_sample_data("axes_grid/bivariat... | gpl-2.0 |
idaholab/raven | plugins/ExamplePlugin/src/CorrelationPlot.py | 1 | 3426 | """
Author: talbpaul
Date : 2021-04-02
"""
import os
import numpy as np
import matplotlib.pyplot as plt
from PluginBaseClasses.OutStreamPlotPlugin import PlotPlugin, InputTypes, InputData
class Correlation(PlotPlugin):
# Example Plot plugin class
@classmethod
def getInputSpecification(cls):
"""
... | apache-2.0 |
RPGOne/Skynet | scikit-learn-0.18.1/examples/semi_supervised/plot_label_propagation_versus_svm_iris.py | 50 | 2378 | """
=====================================================================
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 |
luamct/WebSci14 | features/color/stats.py | 1 | 1332 | '''
Created on 13/08/2013
@author: Felipe Costa
'''
from math import sqrt, atan2, pi
import matplotlib.pyplot as pp
import numpy as np
import utils
class Statistics :
def __init__(self):
self.table_name = "stats"
def get_table_name(self):
return self.table_name
def process(self,rgb_img):
#... | gpl-3.0 |
tapomayukh/projects_in_python | classification/Classification_with_kNN/Single_Contact_Classification/Scaled_Features/results/2_categories/test10_cross_validate_categories_mov_fixed_1200ms_scaled_method_v.py | 1 | 4633 |
# Principal Component Analysis Code :
from numpy import mean,cov,double,cumsum,dot,linalg,array,rank,size,flipud
from pylab import *
import numpy as np
import matplotlib.pyplot as pp
#from enthought.mayavi import mlab
import scipy.ndimage as ni
import roslib; roslib.load_manifest('sandbox_tapo_darpa_m3')
import ro... | mit |
ycaihua/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 |
yl565/statsmodels | statsmodels/sandbox/tsa/fftarma.py | 30 | 16438 | # -*- coding: utf-8 -*-
"""
Created on Mon Dec 14 19:53:25 2009
Author: josef-pktd
generate arma sample using fft with all the lfilter it looks slow
to get the ma representation first
apply arma filter (in ar representation) to time series to get white noise
but seems slow to be useful for fast estimation for nobs=1... | bsd-3-clause |
neurotechuoft/MindType | Code/V1/src/p300_service/tests/plot_data.py | 1 | 1662 | import numpy as np
import scipy.stats as st
import pickle
from p300_service import ml
import matplotlib.pyplot as plt
N = 120 # number of trials
M = 4 # number of channels
F = 256 # number of features
with open('data/train_data.pickle', 'rb') as f:
train_data = pickle.load(f)
with open('data/test_... | agpl-3.0 |
rahul-c1/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 |
jblackburne/scikit-learn | examples/cluster/plot_cluster_iris.py | 350 | 2593 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
K-means Clustering
=========================================================
The plots display firstly what a K-means algorithm would yield
using three clusters. It is then shown what the effect of a bad
initializa... | bsd-3-clause |
michrawson/SVM_Implicit_Surface_Reconstruction | KernelRidgeRegression.py | 2 | 1721 | from sklearn.datasets import make_regression
from sklearn.cross_validation import train_test_split
from sklearn.kernel_ridge import KernelRidge
import sys
import numpy as np
def kernel(a,b):
return np.dot(a,b)
assert kernel([1,1],[1,-1]) == 0
def kernel_ridge_regression(X_train,y_train, Lambda):
y_train = np... | mit |
ARudiuk/mne-python | tutorials/plot_sensors_time_frequency.py | 3 | 5104 | """
.. _tut_sensors_time_frequency:
=============================================
Frequency and time-frequency sensors analysis
=============================================
The objective is to show you how to explore the spectral content
of your data (frequency and time-frequency). Here we'll work on Epochs.
We wi... | bsd-3-clause |
XueqingLin/tensorflow | tensorflow/contrib/learn/python/learn/estimators/linear_test.py | 1 | 57602 | # 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 |
dimroc/tensorflow-mnist-tutorial | lib/python3.6/site-packages/matplotlib/backends/tkagg.py | 10 | 1250 | from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import tkinter as Tk
import numpy as np
from matplotlib.backends import _tkagg
def blit(photoimage, aggimage, bbox=None, colormode=1):
tk = photoimage.tk
if bbox is not Non... | apache-2.0 |
joernhees/scikit-learn | examples/ensemble/plot_forest_iris.py | 18 | 6190 | """
====================================================================
Plot the decision surfaces of ensembles of trees on the iris dataset
====================================================================
Plot the decision surfaces of forests of randomized trees trained on pairs of
features of the iris dataset.
... | bsd-3-clause |
michaelaye/scikit-image | skimage/viewer/canvastools/base.py | 43 | 3877 | import numpy as np
from matplotlib import lines
__all__ = ['CanvasToolBase', 'ToolHandles']
def _pass(*args):
pass
class CanvasToolBase(object):
"""Base canvas tool for matplotlib axes.
Parameters
----------
manager : Viewer or PlotPlugin.
Skimage viewer or plot plugin object.
on_m... | bsd-3-clause |
adamtiger/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/pandas_io.py | 92 | 4535 | # 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 |
eickenberg/scikit-learn | sklearn/tests/test_naive_bayes.py | 16 | 12584 | import pickle
from io import BytesIO
import numpy as np
import scipy.sparse
from sklearn.datasets import load_digits
from sklearn.cross_validation import cross_val_score
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equ... | bsd-3-clause |
jjx02230808/project0223 | sklearn/utils/tests/test_random.py | 230 | 7344 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from scipy.misc import comb as combinations
from numpy.testing import assert_array_almost_equal
from sklearn.utils.random import sample_without_replacement
from sklearn.utils.random import random_choice_csc
from sklearn.utils.testing import ... | bsd-3-clause |
bjlittle/iris | docs/gallery_code/general/plot_inset.py | 1 | 2280 | """
Test Data Showing Inset Plots
=============================
This example demonstrates the use of a single 3D data cube with time, latitude
and longitude dimensions to plot a temperature series for a single latitude
coordinate, with an inset plot of the data region.
"""
import cartopy.crs as ccrs
import matplotli... | lgpl-3.0 |
zhenv5/scikit-learn | sklearn/utils/fixes.py | 39 | 13318 | """Compatibility fixes for older version of python, numpy and scipy
If you add content to this file, please give the version of the package
at which the fixe is no longer needed.
"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# ... | bsd-3-clause |
robin-lai/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 |
craigcitro/pydatalab | datalab/bigquery/_table.py | 4 | 35527 | # Copyright 2015 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | apache-2.0 |
yask123/scikit-learn | examples/ensemble/plot_adaboost_regression.py | 311 | 1529 | """
======================================
Decision Tree Regression with AdaBoost
======================================
A decision tree is boosted using the AdaBoost.R2 [1] algorithm on a 1D
sinusoidal dataset with a small amount of Gaussian noise.
299 boosts (300 decision trees) is compared with a single decision tr... | bsd-3-clause |
hjanime/bcbio-nextgen | bcbio/pipeline/qcsummary.py | 1 | 44083 | """Quality control and summary metrics for next-gen alignments and analysis.
"""
import collections
import contextlib
import csv
import os
import shutil
import subprocess
import pandas as pd
import lxml.html
import yaml
from datetime import datetime
# allow graceful during upgrades
try:
import matplotlib
matpl... | mit |
prasadtalasila/MailingListParser | lib/input/mbox/keyword_clustering.py | 1 | 7021 | import json
import mailbox
import numpy as np
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from sklearn.cluster import KMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from lib.analysis.author import ranking
from lib.util import custom_stopwords
from lib.util.... | gpl-3.0 |
gviejo/ThalamusPhysio | python/main_test_final_classification_XGB_KL.py | 1 | 15477 | import ternary
import numpy as np
import pandas as pd
from functions import *
import sys
from functools import reduce
from sklearn.manifold import *
from sklearn.cluster import *
from sklearn.linear_model import *
from sklearn.ensemble import *
from pylab import *
import _pickle as cPickle
from skimage.filters import g... | gpl-3.0 |
quantumlib/Cirq | cirq-core/cirq/experiments/cross_entropy_benchmarking_test.py | 1 | 5377 | # Copyright 2019 The Cirq Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | apache-2.0 |
shijieice/cuda-convnet2 | convdata.py | 174 | 14675 | # Copyright 2014 Google Inc. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | apache-2.0 |
poidl/yassy | doc/python/frei_appendix_B1.py | 1 | 1579 | #!/bin/python
# pylint: disable=C0103
"""Python translation of Frei Appendix B1."""
# Frei, B.: Digital sound generation. Institute for Computer Music and
# Sound Technology (ICST) Zurich University of the Arts.
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
# parameters
... | gpl-2.0 |
numenta-archive/htmresearch | projects/vehicle-control/agent/run_sm.py | 6 | 7819 | #!/usr/bin/env python
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2015, 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 |
andyraib/data-storage | python_scripts/env/lib/python3.6/site-packages/matplotlib/contour.py | 10 | 68919 | """
These are classes to support contour plotting and
labelling for the axes class
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six.moves import xrange
import warnings
import matplotlib as mpl
import numpy as np
from numpy import ma
... | apache-2.0 |
kiwifb/numpy | numpy/lib/npyio.py | 1 | 73745 | from __future__ import division, absolute_import, print_function
import sys
import os
import re
import itertools
import warnings
import weakref
from operator import itemgetter, index as opindex
import numpy as np
from . import format
from ._datasource import DataSource
from numpy.core.multiarray import packbits, unpa... | bsd-3-clause |
NICTA/dora | dora/regressors/gp/demo.py | 1 | 3125 | """ simple_regression.py
This demo shows how to construct a simple regression by composing a kernel
and optimising its hyperparameters.
"""
import numpy as np
import matplotlib.pyplot as pl
import dora.regressors.gp as gp
def main():
nTrain = 20
nQuery = 100
nDraws = 20
nDims = 1
seed = 100... | apache-2.0 |
MatthieuBizien/scikit-learn | sklearn/ensemble/gradient_boosting.py | 25 | 71089 | """Gradient Boosted Regression Trees
This module contains methods for fitting gradient boosted regression trees for
both classification and regression.
The module structure is the following:
- The ``BaseGradientBoosting`` base class implements a common ``fit`` method
for all the estimators in the module. Regressio... | bsd-3-clause |
udacity/deep-learning | image-classification/helper.py | 155 | 5631 | import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelBinarizer
def _load_label_names():
"""
Load the label names from file
"""
return ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
def load_cfar10_batch(ci... | mit |
changyy/py-MLHelper | org/changyy/ml/feature_engineering.py | 1 | 3172 | def fill_value_via_statistics_handler(panda_obj, column_name='age', max_value=120):
import pandas
import numpy
if type(panda_obj) is not pandas.core.frame.DataFrame:
return panda_obj
panda_obj.loc[ panda_obj[column_name][panda_obj[column_name] > max_value ].index , column_name ] = numpy.nan
value_avg = panda_ob... | mit |
wdurhamh/statsmodels | statsmodels/sandbox/examples/example_garch.py | 31 | 2294 | import numpy as np
import matplotlib.pyplot as plt
#import scikits.timeseries as ts
#import scikits.timeseries.lib.plotlib as tpl
import statsmodels.api as sm
#from statsmodels.sandbox import tsa
from statsmodels.sandbox.tsa.garch import * # local import
#dta2 = ts.tsfromtxt(r'gspc_table.csv',
# datecols=0, ... | bsd-3-clause |
astocko/statsmodels | statsmodels/tools/testing.py | 23 | 1443 | """assert functions from numpy and pandas testing
"""
import re
from distutils.version import StrictVersion
import numpy as np
import numpy.testing as npt
import pandas
import pandas.util.testing as pdt
# for pandas version check
def strip_rc(version):
return re.sub(r"rc\d+$", "", version)
def is_pandas_min_v... | bsd-3-clause |
Neurita/darwin | darwin/gini.py | 1 | 4714 | # -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as stats
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.cross_validation import LeaveOneOut
from .pipeline import ClassificationPipeline
class FeaturesGiniIndex(object):
"""This class wraps a classificat... | bsd-3-clause |
WarrenWeckesser/scipy | scipy/signal/bsplines.py | 12 | 19509 | from numpy import (logical_and, asarray, pi, zeros_like,
piecewise, array, arctan2, tan, zeros, arange, floor)
from numpy.core.umath import (sqrt, exp, greater, less, cos, add, sin,
less_equal, greater_equal)
# From splinemodule.c
from .spline import cspline2d, sepfir2d... | bsd-3-clause |
caseyclements/blaze | blaze/compute/pandas.py | 2 | 16522 | """
>>> from blaze.expr import symbol
>>> from blaze.compute.pandas import compute
>>> accounts = symbol('accounts', 'var * {name: string, amount: int}')
>>> deadbeats = accounts[accounts['amount'] < 0]['name']
>>> from pandas import DataFrame
>>> data = [['Alice', 100], ['Bob', -50], ['Charlie', -20]]
>>> df = Data... | bsd-3-clause |
johnmgregoire/vanDover_CHESS | xrdUI.py | 1 | 428115 | global XRFALLOWED
try:
from xrf_analysis import *
XRFALLOWED=True
except:
XRFALLOWED=False
from PyQt4.QtCore import *
from PyQt4.QtGui import *
from XRDdefaults import *
from xrd_fileIO_fcns import *
from xrd_math_fcns import *
from xrdPLOT import *
from xrd_diffraction_conversion_fcns import *
from xrf_de... | bsd-3-clause |
gfyoung/pandas | pandas/tests/io/parser/test_c_parser_only.py | 1 | 21552 | """
Tests that apply specifically to the CParser. Unless specifically stated
as a CParser-specific issue, the goal is to eventually move as many of
these tests out of this module as soon as the Python parser can accept
further arguments when parsing.
"""
from io import BytesIO, StringIO, TextIOWrapper
import mmap
impo... | bsd-3-clause |
bloyl/mne-python | mne/datasets/sleep_physionet/tests/test_physionet.py | 12 | 8388 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Joan Massich <mailsik@gmail.com>
#
# License: BSD Style.
import os.path as op
import numpy as np
import pytest
from numpy.testing import assert_array_equal
import mne
from mne.utils import requires_good_network
from mne.utils import requires_pand... | bsd-3-clause |
pypot/scikit-learn | examples/datasets/plot_random_dataset.py | 348 | 2254 | """
==============================================
Plot randomly generated classification dataset
==============================================
Plot several randomly generated 2D classification datasets.
This example illustrates the :func:`datasets.make_classification`
:func:`datasets.make_blobs` and :func:`datasets.... | bsd-3-clause |
jjx02230808/project0223 | examples/ensemble/plot_voting_decision_regions.py | 230 | 2386 | """
==================================================
Plot the decision boundaries of a VotingClassifier
==================================================
Plot the decision boundaries of a `VotingClassifier` for
two features of the Iris dataset.
Plot the class probabilities of the first sample in a toy dataset
pred... | bsd-3-clause |
lin-credible/scikit-learn | sklearn/decomposition/base.py | 313 | 5647 | """Principal Component Analysis Base Classes"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Denis A. Engemann <d.engemann@fz-juelich.de>
# Kyle Kastner <kastnerkyle@gmail.com>
#
# Licen... | bsd-3-clause |
raincoatrun/basemap | lib/mpl_toolkits/basemap/proj.py | 1 | 17835 | import numpy as np
from mpl_toolkits.basemap import pyproj
import math
from matplotlib.cbook import dedent
__version__ = '1.2.2'
_dg2rad = math.radians(1.)
_rad2dg = math.degrees(1.)
_cylproj = ['cyl','merc','mill','gall']
_pseudocyl = ['moll','kav7','eck4','robin','sinu','mbtfpq','vandg','hammer']
_upper_right_out_... | gpl-2.0 |
florian-f/sklearn | sklearn/hmm.py | 3 | 45124 | # Hidden Markov Models
#
# Author: Ron Weiss <ronweiss@gmail.com>
# and Shiqiao Du <lucidfrontier.45@gmail.com>
# API changes: Jaques Grobler <jaquesgrobler@gmail.com>
"""
The :mod:`sklearn.hmm` module implements hidden Markov models.
**Warning:** :mod:`sklearn.hmm` is orphaned, undocumented and has known
numerical s... | bsd-3-clause |
ZiqianXY/MLEN | src/p0_titanic_survival_exploration/titanic_visualizations.py | 24 | 5425 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def filter_data(data, condition):
"""
Remove elements that do not match the condition provided.
Takes a data list as input and returns a filtered list.
Conditions should be a list of strings of the following format:
'<field> <... | mit |
BubuLK/sfepy | probe.py | 5 | 9831 | #!/usr/bin/env python
# 12.01.2007, c
"""
Probe finite element solutions in points defined by various geometrical probes.
Generation mode
---------------
python probe.py [generation options] <input file> <results file>
Probe the data in the results file corresponding to the problem defined in the
input file. The inpu... | bsd-3-clause |
IQSS/geoconnect | gc_apps/gis_tabular/tab_file_stats.py | 1 | 6000 | """
Gather tabular file information: number of rows, column names, etc
"""
from csv import QUOTE_NONNUMERIC
import pandas as pd
from django.core.files.base import ContentFile
from gc_apps.gis_tabular.models import TabularFileInfo
from gc_apps.geo_utils.file_field_helper import get_file_path_or_url
from gc_apps.geo_ut... | apache-2.0 |
cactusbin/nyt | matplotlib/examples/pylab_examples/annotation_demo.py | 6 | 5582 | """
Some examples of how to annotate points in figures. You specify an
annotation point xy=(x,y) and a text point xytext=(x,y) for the
annotated points and text location, respectively. Optionally, you can
specify the coordinate system of xy and xytext with one of the
following strings for xycoords and textcoords (def... | unlicense |
palash1992/GEM | tests/test_karate.py | 1 | 2625 | '''
Run the graph embedding methods on Karate graph and evaluate them on
graph reconstruction and visualization. Please copy the
gem/data/karate.edgelist to the working directory
'''
import matplotlib.pyplot as plt
from time import time
from gem.utils import graph_util, plot_util
from gem.evaluation import visu... | bsd-3-clause |
Unidata/MetPy | v0.6/_downloads/NEXRAD_Level_3_File.py | 2 | 1569 | # Copyright (c) 2015 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
NEXRAD Level 3 File
===================
Use MetPy to read information from a NEXRAD Level 3 (NIDS product) file and plot
"""
import matplotlib.pyplot as plt
import numpy as np
... | bsd-3-clause |
RPGOne/Skynet | scikit-learn-0.18.1/examples/plot_johnson_lindenstrauss_bound.py | 67 | 7474 | r"""
=====================================================================
The Johnson-Lindenstrauss bound for embedding with random projections
=====================================================================
The `Johnson-Lindenstrauss lemma`_ states that any high dimensional
dataset can be randomly projected i... | bsd-3-clause |
adamgreenhall/scikit-learn | examples/decomposition/plot_incremental_pca.py | 244 | 1878 | """
===============
Incremental PCA
===============
Incremental principal component analysis (IPCA) is typically used as a
replacement for principal component analysis (PCA) when the dataset to be
decomposed is too large to fit in memory. IPCA builds a low-rank approximation
for the input data using an amount of memo... | bsd-3-clause |
sonnyhu/scikit-learn | examples/covariance/plot_robust_vs_empirical_covariance.py | 73 | 6451 | 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 |
wiki2014/Learning-Summary | alps/cts/apps/CameraITS/tests/scene1/test_param_sensitivity.py | 3 | 2539 | # Copyright 2013 The Android Open Source Project
#
# 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 a... | gpl-3.0 |
petosegan/scikit-learn | sklearn/ensemble/voting_classifier.py | 178 | 8006 | """
Soft Voting/Majority Rule classifier.
This module contains a Soft Voting/Majority Rule classifier for
classification estimators.
"""
# Authors: Sebastian Raschka <se.raschka@gmail.com>,
# Gilles Louppe <g.louppe@gmail.com>
#
# Licence: BSD 3 clause
import numpy as np
from ..base import BaseEstimator
f... | bsd-3-clause |
gaborfodor/TNP | extract_features.py | 1 | 17673 | import pandas as pd
import numpy as np
from os import listdir, makedirs, getcwd, remove
from os.path import isfile, join, abspath, exists, isdir
import datetime as dt
MIN_FREQ, LOCATION_CAT, FEATURE_CAT, FAULT_LOOKBACK, SHIFT = 20, 4, 5, 10, 1
print MIN_FREQ, LOCATION_CAT, FEATURE_CAT, FAULT_LOOKBACK, SHIFT
... | mit |
ntucllab/striatum | setup.py | 1 | 1415 | #!/usr/bin/env python
import os
from setuptools import setup
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
# read the docs could not compile numpy and c extensions
if on_rtd:
setup_requires = []
install_requires = []
else:
setup_requires = [
'nose',
'coverage',
]
install_r... | bsd-2-clause |
asljivo1/802.11ah-ns3 | ns-3/src/core/examples/sample-rng-plot.py | 188 | 1246 | # -*- Mode:Python; -*-
# /*
# * This program is free software; you can redistribute it and/or modify
# * it under the terms of the GNU General Public License version 2 as
# * published by the Free Software Foundation
# *
# * This program is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRA... | gpl-2.0 |
1a1a11a/mimircache | PyMimircache/profiler/profilerUtils.py | 1 | 8030 | # coding=utf-8
"""
This module provides some common utils shared by profilers
Author: Jason Yang <peter.waynechina@gmail.com> 2017/10
"""
import os
import math
import time
import pickle
import numpy as np
import matplotlib
# matplotlib.use("Agg")
# pypy3 fails on this
import matplotlib.pyplot as plt
impo... | gpl-3.0 |
bccp/bananaplots | bananas/bananas.py | 1 | 12737 | import numpy
__version__ = "0.0.3"
from .model import GMM, Confidence, CombinedModel
from functools import reduce
def _sorteditems(d, orderby):
""" return items from a dict of dict, sorted by the orderby item of the dict """
s = sorted([(i[orderby], k) for k, i in d.items()])
return [(k, d[k]) for i, k in... | apache-2.0 |
gregsharp/vowpal_wabbit | python/tests/test_sklearn_vw.py | 6 | 5431 | from collections import namedtuple
import numpy as np
import pytest
from vowpalwabbit.sklearn_vw import VW, VWClassifier, VWRegressor, tovw
from sklearn import datasets
from sklearn.utils.validation import NotFittedError
from scipy.sparse import csr_matrix
"""
Test utilities to support integration of Vowpal Wabbit a... | bsd-3-clause |
henrytao-me/openerp.positionq | openerp/addons/resource/faces/timescale.py | 170 | 3902 | ############################################################################
# Copyright (C) 2005 by Reithinger GmbH
# mreithinger@web.de
#
# This file is part of faces.
#
# faces is free software; you can redistribute it and/or modify
# ... | agpl-3.0 |
baudren/montepython_public | montepython/likelihoods/BK14/__init__.py | 2 | 17975 | """
.. module:: BK14
:synopsis: BK14 likelihood from http://arxiv.org/pdf/1510.09217.pdf, http://bicepkeck.org/bk14_2015_release.html
.. moduleauthor:: Thomas Tram <thomas.tram@port.ac.uk>
Last updated July 20, 2016. Based on the CosmoMC module.
"""
import numpy as np
import pandas as pd
import scipy.linalg as la
... | mit |
pramodh-bn/learn-data-edx | Week 7/qp100.py | 1 | 5618 | import numpy as np
from sklearn.svm import SVC
def getSample(pointA, pointB, numberOfPoints):
pointList = list(zip(np.random.uniform(-1,1.00,numberOfPoints),np.random.uniform(-1,1.00,numberOfPoints)))
sample = np.array([(i[0], i[1], isLeft(pointA, pointB, i)) for i in pointList])
y = sample[:,2]
breakp... | unlicense |
arjoly/scikit-learn | sklearn/tests/test_dummy.py | 186 | 17778 | from __future__ import division
import numpy as np
import scipy.sparse as sp
from sklearn.base import clone
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_eq... | bsd-3-clause |
sonnyhu/scikit-learn | examples/linear_model/plot_ridge_path.py | 55 | 2138 | """
===========================================================
Plot Ridge coefficients as a function of the regularization
===========================================================
Shows the effect of collinearity in the coefficients of an estimator.
.. currentmodule:: sklearn.linear_model
:class:`Ridge` Regressi... | bsd-3-clause |
ChristianSch/skml | test/test_dataset.py | 1 | 1443 | from chai import Chai
from scipy import sparse
from sklearn.linear_model import LogisticRegression
from skml.problem_transformation.probabilistic_classifier_chain \
import ProbabilisticClassifierChain
from skml.datasets import load_dataset, sample_down_label_space
class TestDataset(Chai):
def test_load_yeast(... | mit |
larsoner/mne-python | examples/simulation/plot_simulate_raw_data.py | 19 | 2830 | """
===========================
Generate simulated raw data
===========================
This example generates raw data by repeating a desired source activation
multiple times.
"""
# Authors: Yousra Bekhti <yousra.bekhti@gmail.com>
# Mark Wronkiewicz <wronk.mark@gmail.com>
# Eric Larson <larson.eric.... | bsd-3-clause |
jjx02230808/project0223 | sklearn/decomposition/tests/test_kernel_pca.py | 32 | 8066 | import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import (assert_array_almost_equal, assert_less,
assert_equal, assert_not_equal,
assert_raises)
from sklearn.decomposition import PCA, KernelPCA
from sklearn.datasets import mak... | bsd-3-clause |
daureg/illalla | utils.py | 1 | 6495 | #! /usr/bin/python2
# vim: set fileencoding=utf-8
from collections import Counter
from persistent import load_var
import json
import arguments
from random import uniform
import CommonMongo as cm
from geographiclib.geodesic import Geodesic
EARTH = Geodesic.WGS84
from datetime import datetime as dt
import numpy as np
d... | mit |
ddervs/GreenGraph | greengraph/classes/Map.py | 1 | 1580 | import numpy as np
import requests
from StringIO import StringIO
from matplotlib import image as img
class Map(object):
def __init__(self, latitude, longitude, satellite=True,
zoom=10, size=(400, 400), sensor=False):
base = "http://maps.googleapis.com/maps/api/staticmap?"
params... | mit |
cython-testbed/pandas | pandas/tests/io/parser/compression.py | 2 | 4740 | # -*- coding: utf-8 -*-
"""
Tests compressed data parsing functionality for all
of the parsers defined in parsers.py
"""
import pytest
import pandas as pd
import pandas.compat as compat
import pandas.util.testing as tm
import pandas.util._test_decorators as td
import gzip
import bz2
try:
lzma = compat.import_lz... | bsd-3-clause |
biln/airflow | airflow/hooks/base_hook.py | 18 | 2571 | # -*- 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 |
landmanbester/Copernicus | Plotter.py | 1 | 19513 | #!/usr/bin/env python
import numpy as np
from scipy.interpolate import UnivariateSpline as uvs
import matplotlib as mpl
mpl.use('Agg')
mpl.rcParams.update({'font.size': 14, 'font.family': 'serif'})
import matplotlib.pyplot as plt
from statsmodels.distributions.empirical_distribution import ECDF
from genFLRW import FLRW... | gpl-3.0 |
anirudhjayaraman/scikit-learn | examples/applications/svm_gui.py | 287 | 11161 | """
==========
Libsvm GUI
==========
A simple graphical frontend for Libsvm mainly intended for didactic
purposes. You can create data points by point and click and visualize
the decision region induced by different kernels and parameter settings.
To create positive examples click the left mouse button; to create
neg... | bsd-3-clause |
mantidproject/mantid | qt/python/mantidqt/widgets/test/test_fitpropertybrowserplotinteraction.py | 3 | 10600 | # Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2020 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
impo... | gpl-3.0 |
tata-antares/jet_tagging_LHCb | utils/utils.py | 1 | 10379 | from __future__ import print_function, division
import numpy
import pandas
from matplotlib import pyplot as plt
import matplotlib
from matplotlib import cm
from sklearn.metrics import roc_auc_score
from collections import OrderedDict
from rep.utils import get_efficiencies
from rep.plotting import ErrorPlot
from rep.uti... | apache-2.0 |
popgengui/negui | setup.py | 1 | 1916 | '''
Description
'''
__filename__ = "setup.py"
__date__ = "20171105"
__author__ = "Ted Cosart<ted.cosart@umontana.edu>"
import os
from setuptools import setup, find_packages
def get_version():
PARAMNAME="progversion"
PARAM_VAL_DELIMIT="="
IDX_VAL=1
STARTUP_INFO_LOC="/agestrucne/resources/startup.info"
s_versio... | agpl-3.0 |
jniediek/combinato | combinato/plot/plot_sorted.py | 1 | 4903 | #!/usr/bin/env python3
# JN 2015-02-18
# pylint: disable=E1101,star-args
"""
plot all clusters from a channel in one overview figure
"""
from __future__ import print_function, division, absolute_import
import os
import numpy as np
import matplotlib.pyplot as mpl
from matplotlib.gridspec import GridSpec
from .. i... | mit |
lbdreyer/cartopy | lib/cartopy/examples/effects_of_the_ellipse.py | 5 | 4876 | """
The effect of badly referencing an ellipse
------------------------------------------
This example demonstrates the effect of referencing your data to an incorrect
ellipse.
First we define two coordinate systems - one using the World Geodetic System
established in 1984 and the other using a spherical globe. Next ... | lgpl-3.0 |
Chuban/moose | modules/porous_flow/doc/tests/dirackernels.py | 10 | 7355 | #!/usr/bin/env python
import os
import sys
import numpy as np
from scipy.special import erf
import matplotlib.pyplot as plt
def bh02_expected(pressure):
perm = 1.0E-12
ele_length = 2
radius = 0.1
bh_length = 1
re = 0.28
r0 = re * np.sqrt(ele_length**2 + ele_length**2) / 2.0
wc = 2 * np.pi ... | lgpl-2.1 |
sriharshams/mlnd | customer_segments/visuals.py | 21 | 6047 | ###########################################
# Suppress matplotlib user warnings
# Necessary for newer version of matplotlib
import warnings
warnings.filterwarnings("ignore", category = UserWarning, module = "matplotlib")
#
# Display inline matplotlib plots with IPython
from IPython import get_ipython
get_ipython().run_... | apache-2.0 |
rameez3333/skylab | doc/examples/weighted_sensitivity.py | 2 | 1818 | # -*-coding:utf8-*-
import data
import numpy as np
from skylab.ps_injector import PointSourceInjector
from skylab.utils import poisson_weight
import matplotlib
matplotlib.use("QT4Agg")
import matplotlib.pyplot as plt
if __name__=="__main__":
# init likelihood class
llh = data.init(1000, 1000, ncpu=4)
... | gpl-3.0 |
baojianzhou/DLReadingGroup | keras/keras/callbacks.py | 1 | 39787 | from __future__ import absolute_import
from __future__ import print_function
import os
import csv
import six
import numpy as np
import time
import json
import warnings
from collections import deque
from collections import OrderedDict
from collections import Iterable
from .utils.generic_utils import Progbar
from . im... | apache-2.0 |
jblackburne/scikit-learn | sklearn/tests/test_base.py | 2 | 11255 | # Author: Gael Varoquaux
# License: BSD 3 clause
import sys
import numpy as np
import scipy.sparse as sp
import sklearn
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... | bsd-3-clause |
ehogan/iris | docs/iris/example_code/Meteorology/wind_speed.py | 11 | 2361 | """
Plotting wind direction using quiver
===========================================================
This example demonstrates using quiver to plot wind speed contours and wind
direction arrows from wind vector component input data. The vector components
are co-located in space in this case.
For the second plot, the ... | lgpl-3.0 |
WalkingMachine/sara_commun | wm_ork/capture/sandbox/orb_template_gen.py | 1 | 2597 | #!/usr/bin/env python
import ecto
from ecto_opencv import cv_bp
from ecto_opencv.highgui import imread, MatWriter
from ecto_opencv.features2d import ORB, ORBstats, DescriptorAccumulator, KeypointsToMat
from ecto_opencv.imgproc import cvtColor, Conversion
from ecto_opencv.calib import PointsTo3d
from ecto.opts import sc... | apache-2.0 |
ZENGXH/scikit-learn | doc/tutorial/text_analytics/solutions/exercise_01_language_train_model.py | 254 | 2253 | """Build a language detector model
The goal of this exercise is to train a linear classifier on text features
that represent sequences of up to 3 consecutive characters so as to be
recognize natural languages by using the frequencies of short character
sequences as 'fingerprints'.
"""
# Author: Olivier Grisel <olivie... | bsd-3-clause |
lifei96/Medium-crawler-with-data-analyzer | User_Crawler/xgb_pr.py | 2 | 1512 | # -*- coding: utf-8 -*-
import pandas as pd
import xgboost as xgb
from sklearn.metrics import classification_report
from sklearn.metrics import f1_score
import numpy as np
def f1(preds, dtrain):
return 'f1-score', -f1_score(dtrain.get_label(), preds, average='weighted')
def xgb_pr():
train_set = pd.read_cs... | mit |
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