repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
r-mart/scikit-learn | examples/svm/plot_iris.py | 225 | 3252 | """
==================================================
Plot different SVM classifiers in the iris dataset
==================================================
Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:
- Sepal length
- Se... | bsd-3-clause |
hitszxp/scikit-learn | benchmarks/bench_sparsify.py | 28 | 3380 | """
Benchmark SGD prediction time with dense/sparse coefficients.
Invoke with
-----------
$ kernprof.py -l sparsity_benchmark.py
$ python -m line_profiler sparsity_benchmark.py.lprof
Typical output
--------------
input data sparsity: 0.050000
true coef sparsity: 0.000100
test data sparsity: 0.027400
model sparsity:... | bsd-3-clause |
meduz/scikit-learn | sklearn/neural_network/rbm.py | 46 | 12291 | """Restricted Boltzmann Machine
"""
# Authors: Yann N. Dauphin <dauphiya@iro.umontreal.ca>
# Vlad Niculae
# Gabriel Synnaeve
# Lars Buitinck
# License: BSD 3 clause
import time
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator
from ..base import TransformerMixi... | bsd-3-clause |
cbertinato/pandas | pandas/tests/io/sas/test_sas7bdat.py | 1 | 8025 | import io
import os
import numpy as np
import pytest
from pandas.errors import EmptyDataError
import pandas.util._test_decorators as td
import pandas as pd
import pandas.util.testing as tm
# https://github.com/cython/cython/issues/1720
@pytest.mark.filterwarnings("ignore:can't resolve package:ImportWarning")
class... | bsd-3-clause |
bharcode/Kaggle | JobSalaryPrediction/train.py | 4 | 1520 | import data_io
from features import FeatureMapper, SimpleTransform
import numpy as np
import pickle
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline
def feature_extractor():
features = [('FullDescription-Bag of Word... | gpl-2.0 |
leesavide/pythonista-docs | Documentation/matplotlib/examples/user_interfaces/wxcursor_demo.py | 9 | 2167 | """
Example to draw a cursor and report the data coords in wx
"""
import matplotlib
matplotlib.use('WXAgg')
from matplotlib.backends.backend_wxagg import FigureCanvasWxAgg as FigureCanvas
from matplotlib.backends.backend_wx import NavigationToolbar2Wx
from matplotlib.figure import Figure
from numpy import arange, sin... | apache-2.0 |
madjelan/scikit-learn | examples/ensemble/plot_bias_variance.py | 357 | 7324 | """
============================================================
Single estimator versus bagging: bias-variance decomposition
============================================================
This example illustrates and compares the bias-variance decomposition of the
expected mean squared error of a single estimator again... | bsd-3-clause |
datacommonsorg/api-python | datacommons_pandas/examples/df_builder.py | 1 | 4925 | # Copyright 2020 Google Inc.
#
# 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, so... | apache-2.0 |
bsipocz/glue | glue/clients/layer_artist.py | 1 | 20184 | """
LayerArtist classes handle the visualization of an individual subset
or dataset
"""
import logging
import numpy as np
from matplotlib.cm import gray
from ..core.exceptions import IncompatibleAttribute
from ..core.util import color2rgb, PropertySetMixin, Pointer
from ..core.subset import Subset
from .util import vi... | bsd-3-clause |
rtavenar/tslearn | tslearn/docs/examples/classification/plot_svm.py | 1 | 1387 | # -*- coding: utf-8 -*-
"""
SVM and GAK
===========
This example illustrates the use of the global alignment kernel (GAK) for
support vector classification.
This metric is defined in the :ref:`tslearn.metrics <mod-metrics>` module and
explained in details in [1].
In this example, a `TimeSeriesSVC` model that uses GA... | bsd-2-clause |
tawsifkhan/scikit-learn | examples/linear_model/lasso_dense_vs_sparse_data.py | 348 | 1862 | """
==============================
Lasso on dense and sparse data
==============================
We show that linear_model.Lasso provides the same results for dense and sparse
data and that in the case of sparse data the speed is improved.
"""
print(__doc__)
from time import time
from scipy import sparse
from scipy ... | bsd-3-clause |
MadsJensen/RP_scripts | graph_transitivity_ada_post.py | 1 | 2037 | import numpy as np
import bct
from sklearn.externals import joblib
from my_settings import (bands, source_folder)
from sklearn.ensemble import AdaBoostClassifier
from sklearn.cross_validation import (StratifiedKFold, cross_val_score)
from sklearn.grid_search import GridSearchCV
subjects = [
"0008", "0009", "0010"... | bsd-3-clause |
Jozhogg/iris | lib/iris/pandas.py | 1 | 6889 | # (C) British Crown Copyright 2013 - 2014, Met Office
#
# This file is part of Iris.
#
# Iris 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) any l... | lgpl-3.0 |
jramapuram/LSTM_Anomaly_Detector | data_generator.py | 1 | 2121 | __author__ = 'jramapuram'
import numpy as np
from data_source import DataSource
from random import randint
from math import sin, pi
from data_manipulator import window, split, normalize
# from sklearn.cross_validation import train_test_split
class DataGenerator(DataSource):
def __init__(self, conf, plotter):
... | mit |
macks22/scikit-learn | sklearn/grid_search.py | 103 | 36232 | """
The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters
of an estimator.
"""
from __future__ import print_function
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# ... | bsd-3-clause |
evelynegroen/evelynegroen.github.io | Code/SSRC_LCA_evelynegroen.py | 1 | 2933 | # Procedure: Global sensitivity analysis for matrix-based LCA
# Method: Squared standardized regression coefficients (SSRC)
# MCS: Monte Carlo simulation (normal random)
# Author: Evelyne Groen {evelyne [dot] groen [at] gmail [dot] com}
# Last update: 25/10/2016
import numpy as np
... | mit |
tacaswell/bokeh | bokeh/compat/mpl.py | 32 | 2834 | "Supporting objects and functions to convert Matplotlib objects into Bokeh."
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Continuum Analytics, Inc. All rights reserved.
#
# Powered by the Bokeh Development Team.
#
# The full license is in the file LICENSE.t... | bsd-3-clause |
jls713/jfactors | flattened/flattened.py | 1 | 19220 | ## Generates Figs 5, 6 & 7 of SEG 2016
## ============================================================================
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import quad
from matplotlib.patches import Ellipse
import flattened as fJ
from scipy.optimize import curve_fit
import seaborn as ... | mit |
SpectreJan/gnuradio | gr-filter/examples/fft_filter_ccc.py | 47 | 4363 | #!/usr/bin/env python
#
# Copyright 2013 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 |
eramirem/astroML | book_figures/chapter3/fig_chi2_distribution.py | 4 | 2407 | r"""
Example of a chi-squared distribution
---------------------------------------
Figure 3.14.
This shows an example of a :math:`\chi^2` distribution with various parameters.
We'll generate the distribution using::
dist = scipy.stats.chi2(...)
Where ... should be filled in with the desired distribution paramete... | bsd-2-clause |
chiffa/TcanAnalyzer | src/curve_fitting.py | 1 | 3961 | import numpy as np
from matplotlib import pyplot as plt
from scipy.optimize import minimize
from functools import partial
from adapters import hung_ji_adapter
from common import Plate
from configs import Locations
from csv import writer
def growth(timepoints, stepness, maxVal, midpoint, delay):
# maxVal = 50 # w... | bsd-3-clause |
ryandougherty/mwa-capstone | MWA_Tools/build/matplotlib/doc/mpl_toolkits/axes_grid/figures/axis_direction_demo_step04.py | 6 | 1465 | import matplotlib.pyplot as plt
import mpl_toolkits.axisartist as axisartist
def setup_axes(fig, rect):
ax = axisartist.Subplot(fig, rect)
fig.add_axes(ax)
ax.set_ylim(-0.1, 1.5)
ax.set_yticks([0, 1])
ax.axis[:].set_visible(False)
ax.axis["x1"] = ax.new_floating_axis(1, 0.3)
ax.axis["x1"... | gpl-2.0 |
MaxStrange/ArtieInfant | Artie/internals/vae/vae.py | 1 | 18464 | """
This module contains all the code necessary for the VAE.
The general use is to instantiate a VAE with the appropriate
hyper parameters, then to train it with a dataset. You can
then load the resulting weights into a VAE later.
Much of this code was taken from here: https://blog.keras.io/building-autoencoders-in-k... | mit |
clingsz/GAE | immuAnalysis/distribution_test.py | 1 | 5204 | # -*- coding: utf-8 -*-
"""
Created on Wed Mar 22 21:43:24 2017
@author: cling
"""
from scipy.stats import norm,lognorm,laplace,loglaplace,gamma,loggamma
import misc.data_gen as dg
import numpy
import misc.utils as utils
from sklearn.model_selection import KFold
from scipy import stats
import matplotlib.pyplot as plt... | gpl-3.0 |
lancezlin/ml_on_lc | templates/plots_templates.py | 1 | 5128 | # -*- coding: utf-8 -*-
## classification plotting templates
# Visualising the Training set results
from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start... | mit |
zentonllo/gcom | pr2/test_classify_plane_points.py | 2 | 3522 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Checking the implementation of MLP
Creates two concentric circles and checks whether
points near the middle circle are correctly classified.
@author: avaldes
"""
from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as plt
import... | mit |
liuwenf/moose | modules/tensor_mechanics/test/tests/drucker_prager/small_deform3.py | 23 | 3585 | #!/usr/bin/env python
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
def expected(scheme, sqrtj2):
cohesion = 10
friction_degrees = 35
tip_smoother = 8
friction = friction_degrees * np.pi / 180.0
if (scheme == "native"):
aaa = cohesion
bbb = np.tan(fricti... | lgpl-2.1 |
ishay2b/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/__init__.py | 79 | 2464 | # 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 |
vermouthmjl/scikit-learn | sklearn/tests/test_metaestimators.py | 57 | 4958 | """Common tests for metaestimators"""
import functools
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.externals.six import iterkeys
from sklearn.datasets import make_classification
from sklearn.utils.testing import assert_true, assert_false, assert_raises
from sklearn.pipeline import Pipeline... | bsd-3-clause |
hbhzwj/GAD | gad/Detector/BotDetector.py | 1 | 11724 | #!/usr/bin/env python
""" Community Detection Related Functions
"""
from __future__ import print_function, division, absolute_import
from subprocess import check_call
import itertools
import numpy as np
import scipy as sp
import sys
from .Correlation import TrafficCorrelationAnalyzer
from .. import util
# def com_de... | gpl-3.0 |
jjhelmus/wradlib | examples/verify_example.py | 1 | 3221 | # -*- coding: UTF-8 -*-
# -------------------------------------------------------------------------------
# Name: verify_example
# Purpose:
#
# Author: Maik Heistermann
#
# Created: 28.10.2011
# Copyright: (c) Maik Heistermann 2011
# Licence: The MIT License
# ---------------------------... | mit |
LevinJ/ud730-Deep-Learning | A1_notmnistdataset/p2_checkimage.py | 1 | 2762 | import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import pickle
import math
import numpy as np
class CheckImage:
def checkImageinPicle(self, randindexes):
with open('notMNIST_large/J.pickle', 'rb') as handle:
dataset = pickle.load(handle)
self.dispImages(dataset[randindex... | gpl-2.0 |
seansu4you87/kupo | projects/MOOCs/udacity/ud120-ml/projects/tools/email_preprocess.py | 5 | 2627 | #!/usr/bin/python
import pickle
import cPickle
import numpy
from sklearn import cross_validation
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_selection import SelectPercentile, f_classif
def preprocess(words_file = "../tools/word_data.pkl", authors_file="../tools/email_authors.p... | mit |
kuleshov/deep-learning-models | util/data.py | 1 | 10479 | import sys
import os
import pickle
import tarfile
import theano
import numpy as np
from scipy.ndimage import convolve
try:
from sklearn import datasets
from sklearn.cross_validation import train_test_split
except ImportError:
print "Warning: Couldn't load scikit-learn"
# ----------------------------------------... | mit |
betatim/BlackBox | skopt/tests/test_callbacks.py | 2 | 2072 | import pytest
import numpy as np
import os
from collections import namedtuple
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_less
from skopt import dummy_minimize
from skopt import gp_minimize
from skopt.benchmarks import bench1
from skopt.benchmarks import bench3
from skopt.... | bsd-3-clause |
iABC2XYZ/abc | DM_RFGAP_6/Test_Opti_phis2.py | 2 | 4583 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 31 14:29:03 2017
Author: Peiyong Jiang : jiangpeiyong@impcas.ac.cn
Function:
Test
完整可用，带优化
"""
from InputBeam import *
from InputLattice import *
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from EmitNG imp... | gpl-3.0 |
TeamHG-Memex/eli5 | tests/test_sklearn_utils.py | 1 | 4892 | # -*- coding: utf-8 -*-
from __future__ import absolute_import
import numpy as np
import pytest
from sklearn.datasets import make_classification, make_regression
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression, ElasticNet, SGDRegressor
fr... | mit |
dingocuster/scikit-learn | examples/model_selection/plot_roc_crossval.py | 247 | 3253 | """
=============================================================
Receiver Operating Characteristic (ROC) with cross validation
=============================================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
classifier output quality using cross-validation.
ROC curv... | bsd-3-clause |
vibhorag/scikit-learn | sklearn/tree/tests/test_tree.py | 48 | 47506 | """
Testing for the tree module (sklearn.tree).
"""
import pickle
from functools import partial
from itertools import product
import platform
import numpy as np
from scipy.sparse import csc_matrix
from scipy.sparse import csr_matrix
from scipy.sparse import coo_matrix
from sklearn.random_projection import sparse_rand... | bsd-3-clause |
chaluemwut/fbserver | venv/lib/python2.7/site-packages/sklearn/linear_model/__init__.py | 5 | 2876 | """
The :mod:`sklearn.linear_model` module implements generalized linear models. It
includes Ridge regression, Bayesian Regression, Lasso and Elastic Net
estimators computed with Least Angle Regression and coordinate descent. It also
implements Stochastic Gradient Descent related algorithms.
"""
# See http://scikit-le... | apache-2.0 |
Reagankm/KnockKnock | venv/lib/python3.4/site-packages/matplotlib/testing/jpl_units/Epoch.py | 11 | 7243 | #===========================================================================
#
# Epoch
#
#===========================================================================
"""Epoch module."""
#===========================================================================
# Place all imports after here.
#
from __future__ impo... | gpl-2.0 |
simon-pepin/scikit-learn | examples/svm/plot_svm_kernels.py | 329 | 1971 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
SVM-Kernels
=========================================================
Three different types of SVM-Kernels are displayed below.
The polynomial and RBF are especially useful when the
data-points are not linearly sep... | bsd-3-clause |
cbtolson/MakeUpMatch | WebApp/flask_mm/recommender.py | 1 | 7609 | import mysql.connector
import pandas as pd
import numpy as np
import re
####################################################
#Class for finding and returning products
####################################################
class Products():
################################################
#Function to initialze ... | apache-2.0 |
bibsian/database-development | test/test_qualitycontrol.py | 1 | 3500 | from pandas import DataFrame
import pytest
import re
import difflib
import ast
import shlex
import sys, os
if sys.platform == "darwin":
rootpath = (
"/Users/bibsian/Desktop/git/database-development/")
end = "/"
elif sys.platform == "win32":
rootpath = (
"C:\\Users\MillerLab\\Desktop\\databas... | mit |
shyamalschandra/scikit-learn | examples/cluster/plot_affinity_propagation.py | 349 | 2304 | """
=================================================
Demo of affinity propagation clustering algorithm
=================================================
Reference:
Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages
Between Data Points", Science Feb. 2007
"""
print(__doc__)
from sklearn.cluster impor... | bsd-3-clause |
gameduell/dask | dask/dataframe/tests/test_arithmetics_reduction.py | 4 | 42449 | from datetime import datetime
import pytest
import numpy as np
import pandas as pd
import dask.dataframe as dd
from dask.dataframe.utils import assert_eq, assert_dask_graph, make_meta
@pytest.mark.slow
def test_arithmetics():
dsk = {('x', 0): pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]},
... | bsd-3-clause |
UviDTE-UviSpace/UviSpace | docs/conf.py | 2 | 10251 | # -*- coding: utf-8 -*-
#
# UviSpace documentation build configuration file, created by
# sphinx-quickstart on Wed Oct 26 11:31:59 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-3.0 |
miloharper/neural-network-animation | matplotlib/tri/tritools.py | 10 | 12738 | """
Tools for triangular grids.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from matplotlib.tri import Triangulation
import numpy as np
class TriAnalyzer(object):
"""
Define basic tools for triangular mesh analysis and improveme... | mit |
mixturemodel-flow/tensorflow | tensorflow/contrib/learn/python/learn/learn_io/pandas_io_test.py | 111 | 7865 | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
raoulbq/scipy | scipy/spatial/_plotutils.py | 53 | 4034 | from __future__ import division, print_function, absolute_import
import numpy as np
from scipy._lib.decorator import decorator as _decorator
__all__ = ['delaunay_plot_2d', 'convex_hull_plot_2d', 'voronoi_plot_2d']
@_decorator
def _held_figure(func, obj, ax=None, **kw):
import matplotlib.pyplot as plt
if ax... | bsd-3-clause |
jjx02230808/project0223 | sklearn/neighbors/nearest_centroid.py | 38 | 7356 | # -*- coding: utf-8 -*-
"""
Nearest Centroid Classification
"""
# Author: Robert Layton <robertlayton@gmail.com>
# Olivier Grisel <olivier.grisel@ensta.org>
#
# License: BSD 3 clause
import warnings
import numpy as np
from scipy import sparse as sp
from ..base import BaseEstimator, ClassifierMixin
from ..met... | bsd-3-clause |
boada/HETDEXCluster | analysis/mkMLMasses_targetedOnly.py | 2 | 8189 | import numpy as np
import h5py as hdf
from sklearn.ensemble import RandomForestRegressor
from numpy.lib import recfunctions as rfns
from itertools import permutations
import multiprocessing
from halo_handler import find_indices
def child_initializer(_rf):
print('Starting', multiprocessing.current_process().name)
... | mit |
efce/voltPy | manager/helpers/alternatingSlicewiseDiagonalization.py | 1 | 5267 | import numpy as np
# import matplotlib.pyplot as plt
def asd(R, X0, Y0, I, J, K, F, lambdaa, eps, maxiter):
"""
Implements Alternating Slice-wise Decomposition.
Implementation based on:
N. M. Faber, R. Bro, and P. K. Hopke,
“Recent developments in CANDECOMP/PARAFAC algorithms:A critical review,”
... | gpl-3.0 |
gitten/python-emag | mom_capacitor.py | 1 | 2072 | import numpy as np
import matplotlib.pyplot as plt
import pylab
from scipy import linalg
from scipy import pi
#segmentation size
dl = .5
dx = dl
dy = dl
##physical parameters and constants
plate_len = 2
plate_width = plate_len
gap = .5
voltage = 1
ofx = 0
ofy = 0
e0 = 8.854e-12
col = 1.602176565e-19
##digitize p... | mit |
ofgulban/scikit-image | doc/examples/transform/plot_ransac3D.py | 19 | 1351 | """
============================================
Robust 3D line model estimation using RANSAC
============================================
In this example we see how to robustly fit a 3D line model to faulty data using
the RANSAC algorithm.
"""
import numpy as np
from matplotlib import pyplot as plt
from mpl_toolkits... | bsd-3-clause |
janhahne/nest-simulator | pynest/examples/pulsepacket.py | 12 | 11358 | # -*- coding: utf-8 -*-
#
# pulsepacket.py
#
# This file is part of NEST.
#
# Copyright (C) 2004 The NEST Initiative
#
# NEST is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or... | gpl-2.0 |
mne-tools/mne-tools.github.io | 0.18/_downloads/581d2726b5c2411659b07c43f23c972e/plot_brainstorm_phantom_ctf.py | 3 | 4731 | # -*- coding: utf-8 -*-
"""
.. _plot_brainstorm_phantom_ctf:
=======================================
Brainstorm CTF phantom dataset tutorial
=======================================
Here we compute the evoked from raw for the Brainstorm CTF phantom
tutorial dataset. For comparison, see [1]_ and:
https://neuroimag... | bsd-3-clause |
zaxtax/scikit-learn | sklearn/ensemble/voting_classifier.py | 4 | 8056 | """
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 |
berkeley-stat222/mousestyles | mousestyles/visualization/path_diversity_plotting.py | 3 | 2827 | from __future__ import (absolute_import, division,
print_function, unicode_literals)
import matplotlib.pyplot as plt
def plot_box(list_of_arrays, title="Box Plot of Distribution", width=4,
height=4):
r"""
Make a box plot of the desired metric (path length, speed, angle, e... | bsd-2-clause |
TomAugspurger/pandas | pandas/tests/indexing/test_partial.py | 1 | 22825 | """
test setting *parts* of objects both positionally and label based
TODO: these should be split among the indexer tests
"""
import numpy as np
import pytest
import pandas as pd
from pandas import DataFrame, Index, Period, Series, Timestamp, date_range, period_range
import pandas._testing as tm
class TestPartialS... | bsd-3-clause |
scls19fr/blaze | blaze/compute/tests/test_postgresql_compute.py | 6 | 4809 | from datetime import timedelta
import itertools
import re
import pytest
sa = pytest.importorskip('sqlalchemy')
pytest.importorskip('psycopg2')
import numpy as np
import pandas as pd
import pandas.util.testing as tm
from odo import odo, resource, drop, discover
from blaze import symbol, compute, concat
names = ('... | bsd-3-clause |
louisLouL/pair_trading | capstone_env/lib/python3.6/site-packages/matplotlib/tri/trirefine.py | 2 | 14271 | """
Mesh refinement for triangular grids.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
import numpy as np
from matplotlib.tri.triangulation import Triangulation
import matplotlib.tri.triinterpolate
class TriRefiner(object):
"""
A... | mit |
shusenl/scikit-learn | examples/decomposition/plot_sparse_coding.py | 247 | 3846 | """
===========================================
Sparse coding with a precomputed dictionary
===========================================
Transform a signal as a sparse combination of Ricker wavelets. This example
visually compares different sparse coding methods using the
:class:`sklearn.decomposition.SparseCoder` esti... | bsd-3-clause |
hansbrenna/NetCDF_postprocessor | HB_module/outsourced.py | 1 | 5152 | # -*- coding: utf-8 -*-
"""
Created on Tue Feb 23 09:46:10 2016
@author: hanbre
"""
import os.path
import sys
import numpy as np
import pandas as pd
import xarray
import re
def parse_time_axis(t,res,verbose=True):
#"""parse_time_axis(time_axis, resolution) parses an xray DataArray of netcdftime objects into a lis... | gpl-3.0 |
valexandersaulys/prudential_insurance_kaggle | venv/lib/python2.7/site-packages/sklearn/feature_extraction/tests/test_dict_vectorizer.py | 276 | 3790 | # Authors: Lars Buitinck <L.J.Buitinck@uva.nl>
# Dan Blanchard <dblanchard@ets.org>
# License: BSD 3 clause
from random import Random
import numpy as np
import scipy.sparse as sp
from numpy.testing import assert_array_equal
from sklearn.utils.testing import (assert_equal, assert_in,
... | gpl-2.0 |
lekston/ardupilot | Tools/mavproxy_modules/lib/magcal_graph_ui.py | 108 | 8248 | # Copyright (C) 2016 Intel Corporation. All rights reserved.
#
# This file 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 later version.
#
# This fi... | gpl-3.0 |
3drobotics/mavlink-solo | pymavlink/tools/mavgpslag.py | 43 | 3446 | #!/usr/bin/env python
'''
calculate GPS lag from DF log
'''
import sys, time, os
from argparse import ArgumentParser
parser = ArgumentParser(description=__doc__)
parser.add_argument("--plot", action='store_true', default=False, help="plot errors")
parser.add_argument("--minspeed", type=float, default=6, help="minimu... | lgpl-3.0 |
MartinDelzant/scikit-learn | sklearn/datasets/tests/test_rcv1.py | 322 | 2414 | """Test the rcv1 loader.
Skipped if rcv1 is not already downloaded to data_home.
"""
import errno
import scipy.sparse as sp
import numpy as np
from sklearn.datasets import fetch_rcv1
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing i... | bsd-3-clause |
BigDataforYou/movie_recommendation_workshop_1 | big_data_4_you_demo_1/venv/lib/python2.7/site-packages/pandas/core/panel4d.py | 2 | 1831 | """ Panel4D: a 4-d dict like collection of panels """
from pandas.core.panelnd import create_nd_panel_factory
from pandas.core.panel import Panel
Panel4D = create_nd_panel_factory(klass_name='Panel4D',
orders=['labels', 'items', 'major_axis',
... | mit |
soulmachine/scikit-learn | sklearn/manifold/tests/test_isomap.py | 31 | 3991 | from itertools import product
import numpy as np
from numpy.testing import assert_almost_equal, assert_array_almost_equal
from sklearn import datasets
from sklearn import manifold
from sklearn import neighbors
from sklearn import pipeline
from sklearn import preprocessing
from sklearn.utils.testing import assert_less
... | bsd-3-clause |
openpathsampling/openpathsampling | openpathsampling/numerics/resampling_statistics.py | 3 | 6058 | """
Tools for resampling functions that output pandas.DataFrame objects.
Typically, you use these tools in 3 steps:
1. Create resampling groups of data, using, e.g., BlockResampling
2. Create a function that maps a list of input data into the desired output
DataFrame
3. Create a ResamplingStatistics object using t... | mit |
saiwing-yeung/scikit-learn | sklearn/manifold/tests/test_spectral_embedding.py | 37 | 11168 | from nose.tools import assert_true
from nose.tools import assert_equal
from scipy.sparse import csr_matrix
from scipy.sparse import csc_matrix
from scipy.sparse import coo_matrix
from scipy.linalg import eigh
import numpy as np
from numpy.testing import assert_array_almost_equal
from numpy.testing import assert_array_... | bsd-3-clause |
alexsavio/scikit-learn | sklearn/datasets/mldata.py | 31 | 7856 | """Automatically download MLdata datasets."""
# Copyright (c) 2011 Pietro Berkes
# License: BSD 3 clause
import os
from os.path import join, exists
import re
import numbers
try:
# Python 2
from urllib2 import HTTPError
from urllib2 import quote
from urllib2 import urlopen
except ImportError:
# Pyt... | bsd-3-clause |
wrobstory/seaborn | seaborn/axisgrid.py | 20 | 66716 | from __future__ import division
from itertools import product
from distutils.version import LooseVersion
import warnings
from textwrap import dedent
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from six import string_types
from . import utils
from .palettes import c... | bsd-3-clause |
vishnumani2009/OpenSource-Open-Ended-Statistical-toolkit | FRONTEND/svmfront.py | 2 | 8728 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'svmui.ui'
#
# Created: Sun Mar 22 21:45:22 2015
# by: PyQt4 UI code generator 4.10.4
#
# WARNING! All changes made in this file will be lost!
from PyQt4 import QtCore, QtGui
from sklearn import svm
import numpy as np
try:
_fromUtf8... | gpl-3.0 |
ProkopHapala/ProbeParticleModel | examples/_bak/afm.py | 1 | 4476 | #!/usr/bin/python
import sys
import os
import ProbeParticle as PP
import elements
import basUtils
import numpy as np
import GridUtils as GU
import PPPlot as PL
import matplotlib.pyplot as plt
import sys
def query_yes_no(question, default="yes"):
"""Ask a yes/no question via raw_input() and return their answer.
... | mit |
mjsauvinen/P4UL | pyNetCDF/quadrantHoleNetCdf.py | 1 | 6023 | #!/usr/bin/env python3
import sys
import numpy as np
import argparse
import matplotlib.pyplot as plt
from plotTools import addContourf
from analysisTools import sensibleIds, groundOffset, quadrantAnalysis
from netcdfTools import read3dDataFromNetCDF
from utilities import filesFromList
from txtTools import openIOFile
''... | mit |
markfink/supercars | test/perftest/scripts/plot_jmeter.py | 1 | 17079 | #!/usr/bin/env python
"""
For performance testing you need a clear understanding of the transaction rates
of your target system configuration. From this information you need to design a
simplified load model for your performance test.
This script helps you to analyse access logs in order to extract information about
t... | mit |
Scapogo/zipline | zipline/utils/calendars/trading_calendar.py | 2 | 29586 | #
# Copyright 2016 Quantopian, Inc.
#
# 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 wr... | apache-2.0 |
pgromano/Markov_Models | Markov_Models/models.py | 1 | 8312 | from .base import MarkovChainMixin, MarkovStateModelMixin
from .estimation import count_matrix, count_vectorizer
from .utils import DiscreteSequence
import numpy as np
from sklearn.preprocessing import LabelEncoder
from copy import deepcopy
__all__ = ['MarkovChain', 'MarkovStateModel']
class MarkovChain(MarkovChain... | gpl-3.0 |
wavelets/office-nfl-pool | extra_code/make_datasheet.py | 2 | 4061 | """
make_datasheet.py
~~~~~~~~~~~~~~~~~~
A one-sheet log for the 2015 season.
Read in the CSV in '../data/nfl_season2015.csv' and
write out to '../excel_files/season2015_datasheet.xlsx'
The input file '../data/nfl_season2015.csv' looks like:
Season,Category,Week,Team,Opponent,AtHome,Points,PointsAllowed,Date,Stadiu... | mit |
dilawar/moose-full | moose-examples/snippets/insertSpines.py | 2 | 2798 | #########################################################################
## This program is part of 'MOOSE', the
## Messaging Object Oriented Simulation Environment.
## Copyright (C) 2015 Upinder S. Bhalla. and NCBS
## It is made available under the terms of the
## GNU Lesser General Public License version 2... | gpl-2.0 |
sanketloke/scikit-learn | sklearn/utils/tests/test_shortest_path.py | 303 | 2841 | from collections import defaultdict
import numpy as np
from numpy.testing import assert_array_almost_equal
from sklearn.utils.graph import (graph_shortest_path,
single_source_shortest_path_length)
def floyd_warshall_slow(graph, directed=False):
N = graph.shape[0]
#set nonzer... | bsd-3-clause |
masterkeywikz/seq2graph | src/theanets-0.6.1/examples/recurrent-sinusoid.py | 1 | 3784 | #!/usr/bin/env python
'''This example compares recurrent layer performance on a sine-generation task.
The task is to generate a complex sine wave that is constructed as a
superposition of a small set of pure frequencies. All networks are constructed
with one input (which receives all zero values), one recurrent hidde... | mit |
rgommers/pywt | pywt/_doc_utils.py | 3 | 5823 | """Utilities used to generate various figures in the documentation."""
from itertools import product
import numpy as np
from matplotlib import pyplot as plt
from ._dwt import pad
__all__ = ['wavedec_keys', 'wavedec2_keys', 'draw_2d_wp_basis',
'draw_2d_fswavedecn_basis', 'boundary_mode_subplot']
def wave... | mit |
mlyundin/scikit-learn | sklearn/ensemble/tests/test_weight_boosting.py | 83 | 17276 | """Testing for the boost module (sklearn.ensemble.boost)."""
import numpy as np
from sklearn.utils.testing import assert_array_equal, assert_array_less
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal, assert_true
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
booya-at/paraBEM | examples/openglider/alpha_cL_cD.py | 2 | 2600 | # -*- coding: utf-8 -*-
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
from openglider.jsonify import load
from openglider.utils.distribution import Distribution
from openglider.glider.in_out.export_3d import parabem_Panels
import parabem
from parabem.pan3d import DirichletD... | gpl-3.0 |
caganze/splat | splat/model.py | 1 | 230645 | from __future__ import print_function, division
"""
.. note::
These are the spectral modeling functions for SPLAT
"""
# imports: internal
import bz2
import copy
import glob
import gzip
import os
import requests
import shutil
import sys
import time
# imports: external
#import corner
import matplotlib; matplo... | mit |
lancezlin/ml_template_py | lib/python2.7/site-packages/pandas/io/s3.py | 7 | 3676 | """ s3 support for remote file interactivity """
import os
from pandas import compat
from pandas.compat import BytesIO
try:
import boto
from boto.s3 import key
except:
raise ImportError("boto is required to handle s3 files")
if compat.PY3:
from urllib.parse import urlparse as parse_url
else:
from... | mit |
hrjn/scikit-learn | examples/neighbors/plot_digits_kde_sampling.py | 108 | 2026 | """
=========================
Kernel Density Estimation
=========================
This example shows how kernel density estimation (KDE), a powerful
non-parametric density estimation technique, can be used to learn
a generative model for a dataset. With this generative model in place,
new samples can be drawn. These... | bsd-3-clause |
JasonKessler/scattertext | scattertext/categorytable/__init__.py | 1 | 6681 | import json
from bisect import bisect_left
import pandas as pd
import numpy as np
from scattertext.termranking import AbsoluteFrequencyRanker
from scattertext.termscoring.RankDifference import RankDifference
from scipy.stats import gmean
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.linear... | apache-2.0 |
alexlee-gk/visual_dynamics | visual_dynamics/algorithms/cem.py | 1 | 7754 | from __future__ import division, print_function
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import scipy
import scipy.stats
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
from visual_dynamics.algorithms import Algorithm, ServoingOptimizationAlgorithm
fr... | mit |
potash/scikit-learn | examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py | 87 | 3903 | """
==============================================
Feature agglomeration vs. univariate selection
==============================================
This example compares 2 dimensionality reduction strategies:
- univariate feature selection with Anova
- feature agglomeration with Ward hierarchical clustering
Both metho... | bsd-3-clause |
potash/scikit-learn | examples/linear_model/plot_robust_fit.py | 147 | 3050 | """
Robust linear estimator fitting
===============================
Here a sine function is fit with a polynomial of order 3, for values
close to zero.
Robust fitting is demoed in different situations:
- No measurement errors, only modelling errors (fitting a sine with a
polynomial)
- Measurement errors in X
- M... | bsd-3-clause |
CallaJun/hackprince | indico/mpl_toolkits/axisartist/axislines.py | 8 | 26139 | """
Axislines includes modified implementation of the Axes class. The
biggest difference is that the artists responsible to draw axis line,
ticks, ticklabel and axis labels are separated out from the mpl's Axis
class, which are much more than artists in the original
mpl. Originally, this change was motivated to support... | lgpl-3.0 |
ryfeus/lambda-packs | Sklearn_scipy_numpy/source/sklearn/linear_model/tests/test_theil_sen.py | 6 | 9943 | """
Testing for Theil-Sen module (sklearn.linear_model.theil_sen)
"""
# Author: Florian Wilhelm <florian.wilhelm@gmail.com>
# License: BSD 3 clause
from __future__ import division, print_function, absolute_import
import os
import sys
from contextlib import contextmanager
import numpy as np
from numpy.testing import ... | mit |
pprett/statsmodels | statsmodels/sandbox/nonparametric/kdecovclass.py | 5 | 5713 | '''subclassing kde
Author: josef pktd
'''
import numpy as np
import scipy
from scipy import stats
import matplotlib.pylab as plt
class gaussian_kde_set_covariance(stats.gaussian_kde):
'''
from Anne Archibald in mailinglist:
http://www.nabble.com/Width-of-the-gaussian-in-stats.kde.gaussian_kde---td1955892... | bsd-3-clause |
rmartinez-adc/ThinkStats2 | code/density.py | 67 | 2934 | """This file contains code used in "Think Stats",
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
import math
import random
import brfss
import first
import thinkstats2
import thinkp... | gpl-3.0 |
nikitasingh981/scikit-learn | examples/model_selection/plot_learning_curve.py | 76 | 4509 | """
========================
Plotting Learning Curves
========================
On the left side the learning curve of a naive Bayes classifier is shown for
the digits dataset. Note that the training score and the cross-validation score
are both not very good at the end. However, the shape of the curve can be found
in ... | bsd-3-clause |
binghongcha08/pyQMD | GWP/2D/1.0.8/plt.py | 14 | 1041 | ##!/usr/bin/python
import numpy as np
import pylab as plt
import seaborn as sns
sns.set_context('poster')
#with open("traj.dat") as f:
# data = f.read()
#
# data = data.split('\n')
#
# x = [row.split(' ')[0] for row in data]
# y = [row.split(' ')[1] for row in data]
#
# fig = plt.figure()
#
# ax1 ... | gpl-3.0 |
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