#! /usr/bin/env python
#
# Author: Damian Eads
# Date: April 17, 2008
#
# Copyright (C) 2008 Damian Eads
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
#
# 3. The name of the author may not be used to endorse or promote
# products derived from this software without specific prior
# written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
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# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os.path
import numpy as np
from numpy.testing import *
from scipy.spatial.distance import squareform,pdist,cdist,matching,\
jaccard, dice, sokalsneath, rogerstanimoto, \
russellrao, yule, num_obs_y, num_obs_dm, \
is_valid_dm, is_valid_y
_filenames = ["iris.txt",
"cdist-X1.txt",
"cdist-X2.txt",
"pdist-hamming-ml.txt",
"pdist-boolean-inp.txt",
"pdist-jaccard-ml.txt",
"pdist-cityblock-ml-iris.txt",
"pdist-minkowski-3.2-ml-iris.txt",
"pdist-cityblock-ml.txt",
"pdist-correlation-ml-iris.txt",
"pdist-minkowski-5.8-ml-iris.txt",
"pdist-correlation-ml.txt",
"pdist-minkowski-3.2-ml.txt",
"pdist-cosine-ml-iris.txt",
"pdist-seuclidean-ml-iris.txt",
"pdist-cosine-ml.txt",
"pdist-seuclidean-ml.txt",
"pdist-double-inp.txt",
"pdist-spearman-ml.txt",
"pdist-euclidean-ml.txt",
"pdist-euclidean-ml-iris.txt",
"pdist-chebychev-ml.txt",
"pdist-chebychev-ml-iris.txt",
"random-bool-data.txt"]
_tdist = np.array([[0, 662, 877, 255, 412, 996],
[662, 0, 295, 468, 268, 400],
[877, 295, 0, 754, 564, 138],
[255, 468, 754, 0, 219, 869],
[412, 268, 564, 219, 0, 669],
[996, 400, 138, 869, 669, 0 ]], dtype='double')
_ytdist = squareform(_tdist)
# A hashmap of expected output arrays for the tests. These arrays
# come from a list of text files, which are read prior to testing.
eo = {}
def load_testing_files():
"Loading test data files for the scipy.spatial.distance tests."
for fn in _filenames:
name = fn.replace(".txt", "").replace("-ml", "")
fqfn = os.path.join(os.path.dirname(__file__), fn)
eo[name] = np.loadtxt(open(fqfn))
#print "%s: %s %s" % (name, str(eo[name].shape), str(eo[name].dtype))
eo['pdist-boolean-inp'] = np.bool_(eo['pdist-boolean-inp'])
load_testing_files()
#print eo.keys()
#print np.abs(Y_test2 - Y_right).max()
#print np.abs(Y_test1 - Y_right).max()
class TestCdist(TestCase):
"""
Test suite for the cdist function.
"""
def test_cdist_euclidean_random(self):
"Tests cdist(X, 'euclidean') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'euclidean')
Y2 = cdist(X1, X2, 'test_euclidean')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_euclidean_random_unicode(self):
"Tests cdist(X, u'euclidean') using unicode metric string"
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, u'euclidean')
Y2 = cdist(X1, X2, u'test_euclidean')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_sqeuclidean_random(self):
"Tests cdist(X, 'sqeuclidean') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'sqeuclidean')
Y2 = cdist(X1, X2, 'test_sqeuclidean')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_cityblock_random(self):
"Tests cdist(X, 'cityblock') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'cityblock')
Y2 = cdist(X1, X2, 'test_cityblock')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_hamming_double_random(self):
"Tests cdist(X, 'hamming') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'hamming')
Y2 = cdist(X1, X2, 'test_hamming')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_hamming_bool_random(self):
"Tests cdist(X, 'hamming') on random boolean data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1'] < 0.5
X2 = eo['cdist-X2'] < 0.5
Y1 = cdist(X1, X2, 'hamming')
Y2 = cdist(X1, X2, 'test_hamming')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_jaccard_double_random(self):
"Tests cdist(X, 'jaccard') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'jaccard')
Y2 = cdist(X1, X2, 'test_jaccard')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_jaccard_bool_random(self):
"Tests cdist(X, 'jaccard') on random boolean data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1'] < 0.5
X2 = eo['cdist-X2'] < 0.5
Y1 = cdist(X1, X2, 'jaccard')
Y2 = cdist(X1, X2, 'test_jaccard')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_chebychev_random(self):
"Tests cdist(X, 'chebychev') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'chebychev')
Y2 = cdist(X1, X2, 'test_chebychev')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_minkowski_random_p3d8(self):
"Tests cdist(X, 'minkowski') on random data. (p=3.8)"
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'minkowski', p=3.8)
Y2 = cdist(X1, X2, 'test_minkowski', p=3.8)
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_minkowski_random_p4d6(self):
"Tests cdist(X, 'minkowski') on random data. (p=4.6)"
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'minkowski', p=4.6)
Y2 = cdist(X1, X2, 'test_minkowski', p=4.6)
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_minkowski_random_p1d23(self):
"Tests cdist(X, 'minkowski') on random data. (p=1.23)"
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'minkowski', p=1.23)
Y2 = cdist(X1, X2, 'test_minkowski', p=1.23)
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_wminkowski_random_p3d8(self):
"Tests cdist(X, 'wminkowski') on random data. (p=3.8)"
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
w = 1.0 / X1.std(axis=0)
Y1 = cdist(X1, X2, 'wminkowski', p=3.8, w=w)
Y2 = cdist(X1, X2, 'test_wminkowski', p=3.8, w=w)
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_wminkowski_random_p4d6(self):
"Tests cdist(X, 'wminkowski') on random data. (p=4.6)"
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
w = 1.0 / X1.std(axis=0)
Y1 = cdist(X1, X2, 'wminkowski', p=4.6, w=w)
Y2 = cdist(X1, X2, 'test_wminkowski', p=4.6, w=w)
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_wminkowski_random_p1d23(self):
"Tests cdist(X, 'wminkowski') on random data. (p=1.23)"
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
w = 1.0 / X1.std(axis=0)
Y1 = cdist(X1, X2, 'wminkowski', p=1.23, w=w)
Y2 = cdist(X1, X2, 'test_wminkowski', p=1.23, w=w)
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_seuclidean_random(self):
"Tests cdist(X, 'seuclidean') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'seuclidean')
Y2 = cdist(X1, X2, 'test_seuclidean')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_sqeuclidean_random(self):
"Tests cdist(X, 'sqeuclidean') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'sqeuclidean')
Y2 = cdist(X1, X2, 'test_sqeuclidean')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_cosine_random(self):
"Tests cdist(X, 'cosine') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'cosine')
Y2 = cdist(X1, X2, 'test_cosine')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_correlation_random(self):
"Tests cdist(X, 'correlation') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'correlation')
Y2 = cdist(X1, X2, 'test_correlation')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_mahalanobis_random(self):
"Tests cdist(X, 'mahalanobis') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1']
X2 = eo['cdist-X2']
Y1 = cdist(X1, X2, 'mahalanobis')
Y2 = cdist(X1, X2, 'test_mahalanobis')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_canberra_random(self):
"Tests cdist(X, 'canberra') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1'] < 0.5
X2 = eo['cdist-X2'] < 0.5
Y1 = cdist(X1, X2, 'canberra')
Y2 = cdist(X1, X2, 'test_canberra')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_braycurtis_random(self):
"Tests cdist(X, 'braycurtis') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1'] < 0.5
X2 = eo['cdist-X2'] < 0.5
Y1 = cdist(X1, X2, 'braycurtis')
Y2 = cdist(X1, X2, 'test_braycurtis')
if verbose > 2:
print Y1, Y2
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_yule_random(self):
"Tests cdist(X, 'yule') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1'] < 0.5
X2 = eo['cdist-X2'] < 0.5
Y1 = cdist(X1, X2, 'yule')
Y2 = cdist(X1, X2, 'test_yule')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_matching_random(self):
"Tests cdist(X, 'matching') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1'] < 0.5
X2 = eo['cdist-X2'] < 0.5
Y1 = cdist(X1, X2, 'matching')
Y2 = cdist(X1, X2, 'test_matching')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_kulsinski_random(self):
"Tests cdist(X, 'kulsinski') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1'] < 0.5
X2 = eo['cdist-X2'] < 0.5
Y1 = cdist(X1, X2, 'kulsinski')
Y2 = cdist(X1, X2, 'test_kulsinski')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_dice_random(self):
"Tests cdist(X, 'dice') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1'] < 0.5
X2 = eo['cdist-X2'] < 0.5
Y1 = cdist(X1, X2, 'dice')
Y2 = cdist(X1, X2, 'test_dice')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_rogerstanimoto_random(self):
"Tests cdist(X, 'rogerstanimoto') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1'] < 0.5
X2 = eo['cdist-X2'] < 0.5
Y1 = cdist(X1, X2, 'rogerstanimoto')
Y2 = cdist(X1, X2, 'test_rogerstanimoto')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_russellrao_random(self):
"Tests cdist(X, 'russellrao') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1'] < 0.5
X2 = eo['cdist-X2'] < 0.5
Y1 = cdist(X1, X2, 'russellrao')
Y2 = cdist(X1, X2, 'test_russellrao')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_sokalmichener_random(self):
"Tests cdist(X, 'sokalmichener') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1'] < 0.5
X2 = eo['cdist-X2'] < 0.5
Y1 = cdist(X1, X2, 'sokalmichener')
Y2 = cdist(X1, X2, 'test_sokalmichener')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
def test_cdist_sokalsneath_random(self):
"Tests cdist(X, 'sokalsneath') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X1 = eo['cdist-X1'] < 0.5
X2 = eo['cdist-X2'] < 0.5
Y1 = cdist(X1, X2, 'sokalsneath')
Y2 = cdist(X1, X2, 'test_sokalsneath')
if verbose > 2:
print (Y1-Y2).max()
self.failUnless(within_tol(Y1, Y2, eps))
class TestPdist(TestCase):
"""
Test suite for the pdist function.
"""
################### pdist: euclidean
def test_pdist_euclidean_random(self):
"Tests pdist(X, 'euclidean') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-euclidean']
Y_test1 = pdist(X, 'euclidean')
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_euclidean_random(self):
"Tests pdist(X, 'euclidean') with unicode metric string"
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-euclidean']
Y_test1 = pdist(X, u'euclidean')
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_euclidean_random_float32(self):
"Tests pdist(X, 'euclidean') on random data (float32)."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = np.float32(eo['pdist-double-inp'])
Y_right = eo['pdist-euclidean']
Y_test1 = pdist(X, 'euclidean')
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_euclidean_random_nonC(self):
"Tests pdist(X, 'test_euclidean') [the non-C implementation] on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-euclidean']
Y_test2 = pdist(X, 'test_euclidean')
self.failUnless(within_tol(Y_test2, Y_right, eps))
def test_pdist_euclidean_iris_double(self):
"Tests pdist(X, 'euclidean') on the Iris data set."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-euclidean-iris']
Y_test1 = pdist(X, 'euclidean')
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_euclidean_iris_float32(self):
"Tests pdist(X, 'euclidean') on the Iris data set. (float32)"
eps = 1e-06
# Get the data: the input matrix and the right output.
X = np.float32(eo['iris'])
Y_right = eo['pdist-euclidean-iris']
Y_test1 = pdist(X, 'euclidean')
if verbose > 2:
print np.abs(Y_right - Y_test1).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_euclidean_iris_nonC(self):
"Tests pdist(X, 'test_euclidean') [the non-C implementation] on the Iris data set."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-euclidean-iris']
Y_test2 = pdist(X, 'test_euclidean')
self.failUnless(within_tol(Y_test2, Y_right, eps))
################### pdist: seuclidean
def test_pdist_seuclidean_random(self):
"Tests pdist(X, 'seuclidean') on random data."
eps = 1e-05
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-seuclidean']
Y_test1 = pdist(X, 'seuclidean')
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_seuclidean_random_float32(self):
"Tests pdist(X, 'seuclidean') on random data (float32)."
eps = 1e-05
# Get the data: the input matrix and the right output.
X = np.float32(eo['pdist-double-inp'])
Y_right = eo['pdist-seuclidean']
Y_test1 = pdist(X, 'seuclidean')
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_seuclidean_random_nonC(self):
"Tests pdist(X, 'test_sqeuclidean') [the non-C implementation] on random data."
eps = 1e-05
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-seuclidean']
Y_test2 = pdist(X, 'test_sqeuclidean')
self.failUnless(within_tol(Y_test2, Y_right, eps))
def test_pdist_seuclidean_iris(self):
"Tests pdist(X, 'seuclidean') on the Iris data set."
eps = 1e-05
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-seuclidean-iris']
Y_test1 = pdist(X, 'seuclidean')
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_seuclidean_iris_float32(self):
"Tests pdist(X, 'seuclidean') on the Iris data set (float32)."
eps = 1e-05
# Get the data: the input matrix and the right output.
X = np.float32(eo['iris'])
Y_right = eo['pdist-seuclidean-iris']
Y_test1 = pdist(X, 'seuclidean')
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_seuclidean_iris_nonC(self):
"Tests pdist(X, 'test_seuclidean') [the non-C implementation] on the Iris data set."
eps = 1e-05
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-seuclidean-iris']
Y_test2 = pdist(X, 'test_sqeuclidean')
self.failUnless(within_tol(Y_test2, Y_right, eps))
################### pdist: cosine
def test_pdist_cosine_random(self):
"Tests pdist(X, 'cosine') on random data."
eps = 1e-08
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-cosine']
Y_test1 = pdist(X, 'cosine')
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_cosine_random_float32(self):
"Tests pdist(X, 'cosine') on random data. (float32)"
eps = 1e-08
# Get the data: the input matrix and the right output.
X = np.float32(eo['pdist-double-inp'])
Y_right = eo['pdist-cosine']
Y_test1 = pdist(X, 'cosine')
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_cosine_random_nonC(self):
"Tests pdist(X, 'test_cosine') [the non-C implementation] on random data."
eps = 1e-08
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-cosine']
Y_test2 = pdist(X, 'test_cosine')
self.failUnless(within_tol(Y_test2, Y_right, eps))
def test_pdist_cosine_iris(self):
"Tests pdist(X, 'cosine') on the Iris data set."
eps = 1e-08
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-cosine-iris']
Y_test1 = pdist(X, 'cosine')
self.failUnless(within_tol(Y_test1, Y_right, eps))
#print "cosine-iris", np.abs(Y_test1 - Y_right).max()
def test_pdist_cosine_iris_float32(self):
"Tests pdist(X, 'cosine') on the Iris data set."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = np.float32(eo['iris'])
Y_right = eo['pdist-cosine-iris']
Y_test1 = pdist(X, 'cosine')
if verbose > 2:
print np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
#print "cosine-iris", np.abs(Y_test1 - Y_right).max()
def test_pdist_cosine_iris_nonC(self):
"Tests pdist(X, 'test_cosine') [the non-C implementation] on the Iris data set."
eps = 1e-08
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-cosine-iris']
Y_test2 = pdist(X, 'test_cosine')
self.failUnless(within_tol(Y_test2, Y_right, eps))
################### pdist: cityblock
def test_pdist_cityblock_random(self):
"Tests pdist(X, 'cityblock') on random data."
eps = 1e-06
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-cityblock']
Y_test1 = pdist(X, 'cityblock')
#print "cityblock", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_cityblock_random_float32(self):
"Tests pdist(X, 'cityblock') on random data. (float32)"
eps = 1e-06
# Get the data: the input matrix and the right output.
X = np.float32(eo['pdist-double-inp'])
Y_right = eo['pdist-cityblock']
Y_test1 = pdist(X, 'cityblock')
#print "cityblock", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_cityblock_random_nonC(self):
"Tests pdist(X, 'test_cityblock') [the non-C implementation] on random data."
eps = 1e-06
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-cityblock']
Y_test2 = pdist(X, 'test_cityblock')
self.failUnless(within_tol(Y_test2, Y_right, eps))
def test_pdist_cityblock_iris(self):
"Tests pdist(X, 'cityblock') on the Iris data set."
eps = 1e-14
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-cityblock-iris']
Y_test1 = pdist(X, 'cityblock')
self.failUnless(within_tol(Y_test1, Y_right, eps))
#print "cityblock-iris", np.abs(Y_test1 - Y_right).max()
def test_pdist_cityblock_iris_float32(self):
"Tests pdist(X, 'cityblock') on the Iris data set. (float32)"
eps = 1e-06
# Get the data: the input matrix and the right output.
X = np.float32(eo['iris'])
Y_right = eo['pdist-cityblock-iris']
Y_test1 = pdist(X, 'cityblock')
if verbose > 2:
print "cityblock-iris-float32", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_cityblock_iris_nonC(self):
"Tests pdist(X, 'test_cityblock') [the non-C implementation] on the Iris data set."
eps = 1e-14
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-cityblock-iris']
Y_test2 = pdist(X, 'test_cityblock')
self.failUnless(within_tol(Y_test2, Y_right, eps))
################### pdist: correlation
def test_pdist_correlation_random(self):
"Tests pdist(X, 'correlation') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-correlation']
Y_test1 = pdist(X, 'correlation')
#print "correlation", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_correlation_random_float32(self):
"Tests pdist(X, 'correlation') on random data. (float32)"
eps = 1e-07
# Get the data: the input matrix and the right output.
X = np.float32(eo['pdist-double-inp'])
Y_right = eo['pdist-correlation']
Y_test1 = pdist(X, 'correlation')
#print "correlation", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_correlation_random_nonC(self):
"Tests pdist(X, 'test_correlation') [the non-C implementation] on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-correlation']
Y_test2 = pdist(X, 'test_correlation')
self.failUnless(within_tol(Y_test2, Y_right, eps))
def test_pdist_correlation_iris(self):
"Tests pdist(X, 'correlation') on the Iris data set."
eps = 1e-08
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-correlation-iris']
Y_test1 = pdist(X, 'correlation')
#print "correlation-iris", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_correlation_iris_float32(self):
"Tests pdist(X, 'correlation') on the Iris data set. (float32)"
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = np.float32(eo['pdist-correlation-iris'])
Y_test1 = pdist(X, 'correlation')
if verbose > 2:
print "correlation-iris", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_correlation_iris_nonC(self):
"Tests pdist(X, 'test_correlation') [the non-C implementation] on the Iris data set."
eps = 1e-08
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-correlation-iris']
Y_test2 = pdist(X, 'test_correlation')
#print "test-correlation-iris", np.abs(Y_test2 - Y_right).max()
self.failUnless(within_tol(Y_test2, Y_right, eps))
################# minkowski
def test_pdist_minkowski_random(self):
"Tests pdist(X, 'minkowski') on random data."
eps = 1e-05
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-minkowski-3.2']
Y_test1 = pdist(X, 'minkowski', 3.2)
#print "minkowski", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_minkowski_random_float32(self):
"Tests pdist(X, 'minkowski') on random data. (float32)"
eps = 1e-05
# Get the data: the input matrix and the right output.
X = np.float32(eo['pdist-double-inp'])
Y_right = eo['pdist-minkowski-3.2']
Y_test1 = pdist(X, 'minkowski', 3.2)
#print "minkowski", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_minkowski_random_nonC(self):
"Tests pdist(X, 'test_minkowski') [the non-C implementation] on random data."
eps = 1e-05
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-minkowski-3.2']
Y_test2 = pdist(X, 'test_minkowski', 3.2)
self.failUnless(within_tol(Y_test2, Y_right, eps))
def test_pdist_minkowski_iris(self):
"Tests pdist(X, 'minkowski') on iris data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-minkowski-3.2-iris']
Y_test1 = pdist(X, 'minkowski', 3.2)
#print "minkowski-iris-3.2", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_minkowski_iris_float32(self):
"Tests pdist(X, 'minkowski') on iris data. (float32)"
eps = 1e-07
# Get the data: the input matrix and the right output.
X = np.float32(eo['iris'])
Y_right = eo['pdist-minkowski-3.2-iris']
Y_test1 = pdist(X, 'minkowski', 3.2)
#print "minkowski-iris-3.2", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_minkowski_iris_nonC(self):
"Tests pdist(X, 'test_minkowski') [the non-C implementation] on iris data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-minkowski-3.2-iris']
Y_test2 = pdist(X, 'test_minkowski', 3.2)
self.failUnless(within_tol(Y_test2, Y_right, eps))
def test_pdist_minkowski_iris(self):
"Tests pdist(X, 'minkowski') on iris data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-minkowski-5.8-iris']
Y_test1 = pdist(X, 'minkowski', 5.8)
#print "minkowski-iris-5.8", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_minkowski_iris_float32(self):
"Tests pdist(X, 'minkowski') on iris data. (float32)"
eps = 1e-06
# Get the data: the input matrix and the right output.
X = np.float32(eo['iris'])
Y_right = eo['pdist-minkowski-5.8-iris']
Y_test1 = pdist(X, 'minkowski', 5.8)
if verbose > 2:
print "minkowski-iris-5.8", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_minkowski_iris_nonC(self):
"Tests pdist(X, 'test_minkowski') [the non-C implementation] on iris data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-minkowski-5.8-iris']
Y_test2 = pdist(X, 'test_minkowski', 5.8)
self.failUnless(within_tol(Y_test2, Y_right, eps))
################### pdist: hamming
def test_pdist_hamming_random(self):
"Tests pdist(X, 'hamming') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['pdist-boolean-inp']
Y_right = eo['pdist-hamming']
Y_test1 = pdist(X, 'hamming')
#print "hamming", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_hamming_random_float32(self):
"Tests pdist(X, 'hamming') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = np.float32(eo['pdist-boolean-inp'])
Y_right = eo['pdist-hamming']
Y_test1 = pdist(X, 'hamming')
#print "hamming", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_hamming_random_nonC(self):
"Tests pdist(X, 'test_hamming') [the non-C implementation] on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = eo['pdist-boolean-inp']
Y_right = eo['pdist-hamming']
Y_test2 = pdist(X, 'test_hamming')
#print "test-hamming", np.abs(Y_test2 - Y_right).max()
self.failUnless(within_tol(Y_test2, Y_right, eps))
################### pdist: hamming (double)
def test_pdist_dhamming_random(self):
"Tests pdist(X, 'hamming') on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = np.float64(eo['pdist-boolean-inp'])
Y_right = eo['pdist-hamming']
Y_test1 = pdist(X, 'hamming')
#print "hamming", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_dhamming_random_float32(self):
"Tests pdist(X, 'hamming') on random data. (float32)"
eps = 1e-07
# Get the data: the input matrix and the right output.
X = np.float32(eo['pdist-boolean-inp'])
Y_right = eo['pdist-hamming']
Y_test1 = pdist(X, 'hamming')
#print "hamming", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_dhamming_random_nonC(self):
"Tests pdist(X, 'test_hamming') [the non-C implementation] on random data."
eps = 1e-07
# Get the data: the input matrix and the right output.
X = np.float64(eo['pdist-boolean-inp'])
Y_right = eo['pdist-hamming']
Y_test2 = pdist(X, 'test_hamming')
#print "test-hamming", np.abs(Y_test2 - Y_right).max()
self.failUnless(within_tol(Y_test2, Y_right, eps))
################### pdist: jaccard
def test_pdist_jaccard_random(self):
"Tests pdist(X, 'jaccard') on random data."
eps = 1e-08
# Get the data: the input matrix and the right output.
X = eo['pdist-boolean-inp']
Y_right = eo['pdist-jaccard']
Y_test1 = pdist(X, 'jaccard')
#print "jaccard", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_jaccard_random_float32(self):
"Tests pdist(X, 'jaccard') on random data. (float32)"
eps = 1e-08
# Get the data: the input matrix and the right output.
X = np.float32(eo['pdist-boolean-inp'])
Y_right = eo['pdist-jaccard']
Y_test1 = pdist(X, 'jaccard')
#print "jaccard", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_jaccard_random_nonC(self):
"Tests pdist(X, 'test_jaccard') [the non-C implementation] on random data."
eps = 1e-08
# Get the data: the input matrix and the right output.
X = eo['pdist-boolean-inp']
Y_right = eo['pdist-jaccard']
Y_test2 = pdist(X, 'test_jaccard')
#print "test-jaccard", np.abs(Y_test2 - Y_right).max()
self.failUnless(within_tol(Y_test2, Y_right, eps))
################### pdist: jaccard (double)
def test_pdist_djaccard_random(self):
"Tests pdist(X, 'jaccard') on random data."
eps = 1e-08
# Get the data: the input matrix and the right output.
X = np.float64(eo['pdist-boolean-inp'])
Y_right = eo['pdist-jaccard']
Y_test1 = pdist(X, 'jaccard')
#print "jaccard", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_djaccard_random_float32(self):
"Tests pdist(X, 'jaccard') on random data. (float32)"
eps = 1e-08
# Get the data: the input matrix and the right output.
X = np.float32(eo['pdist-boolean-inp'])
Y_right = eo['pdist-jaccard']
Y_test1 = pdist(X, 'jaccard')
#print "jaccard", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_djaccard_random_nonC(self):
"Tests pdist(X, 'test_jaccard') [the non-C implementation] on random data."
eps = 1e-08
# Get the data: the input matrix and the right output.
X = np.float64(eo['pdist-boolean-inp'])
Y_right = eo['pdist-jaccard']
Y_test2 = pdist(X, 'test_jaccard')
#print "test-jaccard", np.abs(Y_test2 - Y_right).max()
self.failUnless(within_tol(Y_test2, Y_right, eps))
################### pdist: chebychev
def test_pdist_chebychev_random(self):
"Tests pdist(X, 'chebychev') on random data."
eps = 1e-08
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-chebychev']
Y_test1 = pdist(X, 'chebychev')
#print "chebychev", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_chebychev_random_float32(self):
"Tests pdist(X, 'chebychev') on random data. (float32)"
eps = 1e-07
# Get the data: the input matrix and the right output.
X = np.float32(eo['pdist-double-inp'])
Y_right = eo['pdist-chebychev']
Y_test1 = pdist(X, 'chebychev')
if verbose > 2:
print "chebychev", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_chebychev_random_nonC(self):
"Tests pdist(X, 'test_chebychev') [the non-C implementation] on random data."
eps = 1e-08
# Get the data: the input matrix and the right output.
X = eo['pdist-double-inp']
Y_right = eo['pdist-chebychev']
Y_test2 = pdist(X, 'test_chebychev')
#print "test-chebychev", np.abs(Y_test2 - Y_right).max()
self.failUnless(within_tol(Y_test2, Y_right, eps))
def test_pdist_chebychev_iris(self):
"Tests pdist(X, 'chebychev') on the Iris data set."
eps = 1e-15
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-chebychev-iris']
Y_test1 = pdist(X, 'chebychev')
#print "chebychev-iris", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_chebychev_iris_float32(self):
"Tests pdist(X, 'chebychev') on the Iris data set. (float32)"
eps = 1e-06
# Get the data: the input matrix and the right output.
X = np.float32(eo['iris'])
Y_right = eo['pdist-chebychev-iris']
Y_test1 = pdist(X, 'chebychev')
if verbose > 2:
print "chebychev-iris", np.abs(Y_test1 - Y_right).max()
self.failUnless(within_tol(Y_test1, Y_right, eps))
def test_pdist_chebychev_iris_nonC(self):
"Tests pdist(X, 'test_chebychev') [the non-C implementation] on the Iris data set."
eps = 1e-15
# Get the data: the input matrix and the right output.
X = eo['iris']
Y_right = eo['pdist-chebychev-iris']
Y_test2 = pdist(X, 'test_chebychev')
#print "test-chebychev-iris", np.abs(Y_test2 - Y_right).max()
self.failUnless(within_tol(Y_test2, Y_right, eps))
def test_pdist_matching_mtica1(self):
"Tests matching(*,*) with mtica example #1 (nums)."
m = matching(np.array([1, 0, 1, 1, 0]),
np.array([1, 1, 0, 1, 1]))
m2 = matching(np.array([1, 0, 1, 1, 0], dtype=np.bool),
np.array([1, 1, 0, 1, 1], dtype=np.bool))
self.failUnless(np.abs(m - 0.6) <= 1e-10)
self.failUnless(np.abs(m2 - 0.6) <= 1e-10)
def test_pdist_matching_mtica2(self):
"Tests matching(*,*) with mtica example #2."
m = matching(np.array([1, 0, 1]),
np.array([1, 1, 0]))
m2 = matching(np.array([1, 0, 1], dtype=np.bool),
np.array([1, 1, 0], dtype=np.bool))
self.failUnless(np.abs(m - (2.0/3.0)) <= 1e-10)
self.failUnless(np.abs(m2 - (2.0/3.0)) <= 1e-10)
def test_pdist_matching_match(self):
"Tests pdist(X, 'matching') to see if the two implementations match on random boolean input data."
D = eo['random-bool-data']
B = np.bool_(D)
if verbose > 2:
print B.shape, B.dtype
eps = 1e-10
y1 = pdist(B, "matching")
y2 = pdist(B, "test_matching")
y3 = pdist(D, "test_matching")
if verbose > 2:
print np.abs(y1-y2).max()
print np.abs(y1-y3).max()
self.failUnless(within_tol(y1, y2, eps))
self.failUnless(within_tol(y2, y3, eps))
def test_pdist_jaccard_mtica1(self):
"Tests jaccard(*,*) with mtica example #1."
m = jaccard(np.array([1, 0, 1, 1, 0]),
np.array([1, 1, 0, 1, 1]))
m2 = jaccard(np.array([1, 0, 1, 1, 0], dtype=np.bool),
np.array([1, 1, 0, 1, 1], dtype=np.bool))
self.failUnless(np.abs(m - 0.6) <= 1e-10)
self.failUnless(np.abs(m2 - 0.6) <= 1e-10)
def test_pdist_jaccard_mtica2(self):
"Tests jaccard(*,*) with mtica example #2."
m = jaccard(np.array([1, 0, 1]),
np.array([1, 1, 0]))
m2 = jaccard(np.array([1, 0, 1], dtype=np.bool),
np.array([1, 1, 0], dtype=np.bool))
self.failUnless(np.abs(m - (2.0/3.0)) <= 1e-10)
self.failUnless(np.abs(m2 - (2.0/3.0)) <= 1e-10)
def test_pdist_jaccard_match(self):
"Tests pdist(X, 'jaccard') to see if the two implementations match on random double input data."
D = eo['random-bool-data']
if verbose > 2:
print D.shape, D.dtype
eps = 1e-10
y1 = pdist(D, "jaccard")
y2 = pdist(D, "test_jaccard")
y3 = pdist(np.bool_(D), "test_jaccard")
if verbose > 2:
print np.abs(y1-y2).max()
print np.abs(y2-y3).max()
self.failUnless(within_tol(y1, y2, eps))
self.failUnless(within_tol(y2, y3, eps))
def test_pdist_yule_mtica1(self):
"Tests yule(*,*) with mtica example #1."
m = yule(np.array([1, 0, 1, 1, 0]),
np.array([1, 1, 0, 1, 1]))
m2 = yule(np.array([1, 0, 1, 1, 0], dtype=np.bool),
np.array([1, 1, 0, 1, 1], dtype=np.bool))
if verbose > 2:
print m
self.failUnless(np.abs(m - 2.0) <= 1e-10)
self.failUnless(np.abs(m2 - 2.0) <= 1e-10)
def test_pdist_yule_mtica2(self):
"Tests yule(*,*) with mtica example #2."
m = yule(np.array([1, 0, 1]),
np.array([1, 1, 0]))
m2 = yule(np.array([1, 0, 1], dtype=np.bool),
np.array([1, 1, 0], dtype=np.bool))
if verbose > 2:
print m
self.failUnless(np.abs(m - 2.0) <= 1e-10)
self.failUnless(np.abs(m2 - 2.0) <= 1e-10)
def test_pdist_yule_match(self):
"Tests pdist(X, 'yule') to see if the two implementations match on random double input data."
D = eo['random-bool-data']
if verbose > 2:
print D.shape, D.dtype
eps = 1e-10
y1 = pdist(D, "yule")
y2 = pdist(D, "test_yule")
y3 = pdist(np.bool_(D), "test_yule")
if verbose > 2:
print np.abs(y1-y2).max()
print np.abs(y2-y3).max()
self.failUnless(within_tol(y1, y2, eps))
self.failUnless(within_tol(y2, y3, eps))
def test_pdist_dice_mtica1(self):
"Tests dice(*,*) with mtica example #1."
m = dice(np.array([1, 0, 1, 1, 0]),
np.array([1, 1, 0, 1, 1]))
m2 = dice(np.array([1, 0, 1, 1, 0], dtype=np.bool),
np.array([1, 1, 0, 1, 1], dtype=np.bool))
if verbose > 2:
print m
self.failUnless(np.abs(m - (3.0/7.0)) <= 1e-10)
self.failUnless(np.abs(m2 - (3.0/7.0)) <= 1e-10)
def test_pdist_dice_mtica2(self):
"Tests dice(*,*) with mtica example #2."
m = dice(np.array([1, 0, 1]),
np.array([1, 1, 0]))
m2 = dice(np.array([1, 0, 1], dtype=np.bool),
np.array([1, 1, 0], dtype=np.bool))
if verbose > 2:
print m
self.failUnless(np.abs(m - 0.5) <= 1e-10)
self.failUnless(np.abs(m2 - 0.5) <= 1e-10)
def test_pdist_dice_match(self):
"Tests pdist(X, 'dice') to see if the two implementations match on random double input data."
D = eo['random-bool-data']
if verbose > 2:
print D.shape, D.dtype
eps = 1e-10
y1 = pdist(D, "dice")
y2 = pdist(D, "test_dice")
y3 = pdist(D, "test_dice")
if verbose > 2:
print np.abs(y1-y2).max()
print np.abs(y2-y3).max()
self.failUnless(within_tol(y1, y2, eps))
self.failUnless(within_tol(y2, y3, eps))
def test_pdist_sokalsneath_mtica1(self):
"Tests sokalsneath(*,*) with mtica example #1."
m = sokalsneath(np.array([1, 0, 1, 1, 0]),
np.array([1, 1, 0, 1, 1]))
m2 = sokalsneath(np.array([1, 0, 1, 1, 0], dtype=np.bool),
np.array([1, 1, 0, 1, 1], dtype=np.bool))
if verbose > 2:
print m
self.failUnless(np.abs(m - (3.0/4.0)) <= 1e-10)
self.failUnless(np.abs(m2 - (3.0/4.0)) <= 1e-10)
def test_pdist_sokalsneath_mtica2(self):
"Tests sokalsneath(*,*) with mtica example #2."
m = sokalsneath(np.array([1, 0, 1]),
np.array([1, 1, 0]))
m2 = sokalsneath(np.array([1, 0, 1], dtype=np.bool),
np.array([1, 1, 0], dtype=np.bool))
if verbose > 2:
print m
self.failUnless(np.abs(m - (4.0/5.0)) <= 1e-10)
self.failUnless(np.abs(m2 - (4.0/5.0)) <= 1e-10)
def test_pdist_sokalsneath_match(self):
"Tests pdist(X, 'sokalsneath') to see if the two implementations match on random double input data."
D = eo['random-bool-data']
if verbose > 2:
print D.shape, D.dtype
eps = 1e-10
y1 = pdist(D, "sokalsneath")
y2 = pdist(D, "test_sokalsneath")
y3 = pdist(np.bool_(D), "test_sokalsneath")
if verbose > 2:
print np.abs(y1-y2).max()
print np.abs(y2-y3).max()
self.failUnless(within_tol(y1, y2, eps))
self.failUnless(within_tol(y2, y3, eps))
def test_pdist_rogerstanimoto_mtica1(self):
"Tests rogerstanimoto(*,*) with mtica example #1."
m = rogerstanimoto(np.array([1, 0, 1, 1, 0]),
np.array([1, 1, 0, 1, 1]))
m2 = rogerstanimoto(np.array([1, 0, 1, 1, 0], dtype=np.bool),
np.array([1, 1, 0, 1, 1], dtype=np.bool))
if verbose > 2:
print m
self.failUnless(np.abs(m - (3.0/4.0)) <= 1e-10)
self.failUnless(np.abs(m2 - (3.0/4.0)) <= 1e-10)
def test_pdist_rogerstanimoto_mtica2(self):
"Tests rogerstanimoto(*,*) with mtica example #2."
m = rogerstanimoto(np.array([1, 0, 1]),
np.array([1, 1, 0]))
m2 = rogerstanimoto(np.array([1, 0, 1], dtype=np.bool),
np.array([1, 1, 0], dtype=np.bool))
if verbose > 2:
print m
self.failUnless(np.abs(m - (4.0/5.0)) <= 1e-10)
self.failUnless(np.abs(m2 - (4.0/5.0)) <= 1e-10)
def test_pdist_rogerstanimoto_match(self):
"Tests pdist(X, 'rogerstanimoto') to see if the two implementations match on random double input data."
D = eo['random-bool-data']
if verbose > 2:
print D.shape, D.dtype
eps = 1e-10
y1 = pdist(D, "rogerstanimoto")
y2 = pdist(D, "test_rogerstanimoto")
y3 = pdist(np.bool_(D), "test_rogerstanimoto")
if verbose > 2:
print np.abs(y1-y2).max()
print np.abs(y2-y3).max()
self.failUnless(within_tol(y1, y2, eps))
self.failUnless(within_tol(y2, y3, eps))
def test_pdist_russellrao_mtica1(self):
"Tests russellrao(*,*) with mtica example #1."
m = russellrao(np.array([1, 0, 1, 1, 0]),
np.array([1, 1, 0, 1, 1]))
m2 = russellrao(np.array([1, 0, 1, 1, 0], dtype=np.bool),
np.array([1, 1, 0, 1, 1], dtype=np.bool))
if verbose > 2:
print m
self.failUnless(np.abs(m - (3.0/5.0)) <= 1e-10)
self.failUnless(np.abs(m2 - (3.0/5.0)) <= 1e-10)
def test_pdist_russellrao_mtica2(self):
"Tests russellrao(*,*) with mtica example #2."
m = russellrao(np.array([1, 0, 1]),
np.array([1, 1, 0]))
m2 = russellrao(np.array([1, 0, 1], dtype=np.bool),
np.array([1, 1, 0], dtype=np.bool))
if verbose > 2:
print m
self.failUnless(np.abs(m - (2.0/3.0)) <= 1e-10)
self.failUnless(np.abs(m2 - (2.0/3.0)) <= 1e-10)
def test_pdist_russellrao_match(self):
"Tests pdist(X, 'russellrao') to see if the two implementations match on random double input data."
D = eo['random-bool-data']
if verbose > 2:
print D.shape, D.dtype
eps = 1e-10
y1 = pdist(D, "russellrao")
y2 = pdist(D, "test_russellrao")
y3 = pdist(np.bool_(D), "test_russellrao")
if verbose > 2:
print np.abs(y1-y2).max()
print np.abs(y2-y3).max()
self.failUnless(within_tol(y1, y2, eps))
self.failUnless(within_tol(y2, y3, eps))
def test_pdist_sokalmichener_match(self):
"Tests pdist(X, 'sokalmichener') to see if the two implementations match on random double input data."
D = eo['random-bool-data']
if verbose > 2:
print D.shape, D.dtype
eps = 1e-10
y1 = pdist(D, "sokalmichener")
y2 = pdist(D, "test_sokalmichener")
y3 = pdist(np.bool_(D), "test_sokalmichener")
if verbose > 2:
print np.abs(y1-y2).max()
print np.abs(y2-y3).max()
self.failUnless(within_tol(y1, y2, eps))
self.failUnless(within_tol(y2, y3, eps))
def test_pdist_kulsinski_match(self):
"Tests pdist(X, 'kulsinski') to see if the two implementations match on random double input data."
D = eo['random-bool-data']
if verbose > 2:
print D.shape, D.dtype
eps = 1e-10
y1 = pdist(D, "kulsinski")
y2 = pdist(D, "test_kulsinski")
y3 = pdist(np.bool_(D), "test_kulsinski")
if verbose > 2:
print np.abs(y1-y2).max()
self.failUnless(within_tol(y1, y2, eps))
def test_pdist_canberra_match(self):
"Tests pdist(X, 'canberra') to see if the two implementations match on the Iris data set."
D = eo['iris']
if verbose > 2:
print D.shape, D.dtype
eps = 1e-10
y1 = pdist(D, "canberra")
y2 = pdist(D, "test_canberra")
if verbose > 2:
print np.abs(y1-y2).max()
self.failUnless(within_tol(y1, y2, eps))
def test_pdist_canberra_ticket_711(self):
"Tests pdist(X, 'canberra') to see if Canberra gives the right result as reported in Scipy bug report 711."
eps = 1e-8
pdist_y = pdist(([3.3], [3.4]), "canberra")
right_y = 0.01492537
if verbose > 2:
print np.abs(pdist_y-right_y).max()
self.failUnless(within_tol(pdist_y, right_y, eps))
def within_tol(a, b, tol):
return np.abs(a - b).max() < tol
class TestSquareForm(TestCase):
################### squareform
def test_squareform_empty_matrix(self):
"Tests squareform on an empty matrix."
A = np.zeros((0,0))
rA = squareform(np.array(A, dtype='double'))
self.failUnless(rA.shape == (0,))
def test_squareform_empty_vector(self):
"Tests squareform on an empty vector."
v = np.zeros((0,))
rv = squareform(np.array(v, dtype='double'))
self.failUnless(rv.shape == (1,1))
self.failUnless(rv[0, 0] == 0)
def test_squareform_1by1_matrix(self):
"Tests squareform on a 1x1 matrix."
A = np.zeros((1,1))
rA = squareform(np.array(A, dtype='double'))
self.failUnless(rA.shape == (0,))
def test_squareform_one_vector(self):
"Tests squareform on a 1-D array, length=1."
v = np.ones((1,)) * 8.3
rv = squareform(np.array(v, dtype='double'))
self.failUnless(rv.shape == (2,2))
self.failUnless(rv[0,1] == 8.3)
self.failUnless(rv[1,0] == 8.3)
def test_squareform_2by2_matrix(self):
"Tests squareform on a 2x2 matrix."
A = np.zeros((2,2))
A[0,1]=0.8
A[1,0]=0.8
rA = squareform(np.array(A, dtype='double'))
self.failUnless(rA.shape == (1,))
self.failUnless(rA[0] == 0.8)
def test_squareform_multi_matrix(self):
"Tests squareform on a square matrices of multiple sizes."
for n in xrange(2, 5):
yield self.check_squareform_multi_matrix(n)
def check_squareform_multi_matrix(self, n):
X = np.random.rand(n, 4)
Y = pdist(X)
self.failUnless(len(Y.shape) == 1)
A = squareform(Y)
Yr = squareform(A)
s = A.shape
k = 0
if verbose >= 3:
print A.shape, Y.shape, Yr.shape
self.failUnless(len(s) == 2)
self.failUnless(len(Yr.shape) == 1)
self.failUnless(s[0] == s[1])
for i in xrange(0, s[0]):
for j in xrange(i+1, s[1]):
if i != j:
#print i, j, k, A[i, j], Y[k]
self.failUnless(A[i, j] == Y[k])
k += 1
else:
self.failUnless(A[i, j] == 0)
class TestNumObsY(TestCase):
def test_num_obs_y_multi_matrix(self):
"Tests num_obs_y with observation matrices of multiple sizes."
for n in xrange(2, 10):
X = np.random.rand(n, 4)
Y = pdist(X)
#print A.shape, Y.shape, Yr.shape
self.failUnless(num_obs_y(Y) == n)
def test_num_obs_y_1(self):
"Tests num_obs_y(y) on a condensed distance matrix over 1 observations. Expecting exception."
self.failUnlessRaises(ValueError, self.check_y, 1)
def test_num_obs_y_2(self):
"Tests num_obs_y(y) on a condensed distance matrix over 2 observations."
self.failUnless(self.check_y(2))
def test_num_obs_y_3(self):
"Tests num_obs_y(y) on a condensed distance matrix over 3 observations."
self.failUnless(self.check_y(3))
def test_num_obs_y_4(self):
"Tests num_obs_y(y) on a condensed distance matrix over 4 observations."
self.failUnless(self.check_y(4))
def test_num_obs_y_5_10(self):
"Tests num_obs_y(y) on a condensed distance matrix between 5 and 15 observations."
for i in xrange(5, 16):
self.minit(i)
def test_num_obs_y_2_100(self):
"Tests num_obs_y(y) on 100 improper condensed distance matrices. Expecting exception."
a = set([])
for n in xrange(2, 16):
a.add(n*(n-1)/2)
for i in xrange(5, 105):
if i not in a:
self.failUnlessRaises(ValueError, self.bad_y, i)
def minit(self, n):
self.failUnless(self.check_y(n))
def bad_y(self, n):
y = np.random.rand(n)
return num_obs_y(y)
def check_y(self, n):
return num_obs_y(self.make_y(n)) == n
def make_y(self, n):
return np.random.rand((n*(n-1)/2))
class TestNumObsDM(TestCase):
############## num_obs_dm
def test_num_obs_dm_multi_matrix(self):
"Tests num_obs_dm with observation matrices of multiple sizes."
for n in xrange(1, 10):
X = np.random.rand(n, 4)
Y = pdist(X)
A = squareform(Y)
if verbose >= 3:
print A.shape, Y.shape
self.failUnless(num_obs_dm(A) == n)
def test_num_obs_dm_0(self):
"Tests num_obs_dm(D) on a 0x0 distance matrix. Expecting exception."
self.failUnless(self.check_D(0))
def test_num_obs_dm_1(self):
"Tests num_obs_dm(D) on a 1x1 distance matrix."
self.failUnless(self.check_D(1))
def test_num_obs_dm_2(self):
"Tests num_obs_dm(D) on a 2x2 distance matrix."
self.failUnless(self.check_D(2))
def test_num_obs_dm_3(self):
"Tests num_obs_dm(D) on a 3x3 distance matrix."
self.failUnless(self.check_D(2))
def test_num_obs_dm_4(self):
"Tests num_obs_dm(D) on a 4x4 distance matrix."
self.failUnless(self.check_D(4))
def check_D(self, n):
return num_obs_dm(self.make_D(n)) == n
def make_D(self, n):
return np.random.rand(n, n)
def is_valid_dm_throw(D):
return is_valid_dm(D, throw=True)
class TestIsValidDM(TestCase):
def test_is_valid_dm_int16_array_E(self):
"Tests is_valid_dm(*) on an int16 array. Exception expected."
D = np.zeros((5, 5), dtype='i')
self.failUnlessRaises(TypeError, is_valid_dm_throw, (D))
def test_is_valid_dm_int16_array_F(self):
"Tests is_valid_dm(*) on an int16 array. False expected."
D = np.zeros((5, 5), dtype='i')
self.failUnless(is_valid_dm(D) == False)
def test_is_valid_dm_improper_shape_1D_E(self):
"Tests is_valid_dm(*) on a 1D array. Exception expected."
D = np.zeros((5,), dtype=np.double)
self.failUnlessRaises(ValueError, is_valid_dm_throw, (D))
def test_is_valid_dm_improper_shape_1D_F(self):
"Tests is_valid_dm(*) on a 1D array. False expected."
D = np.zeros((5,), dtype=np.double)
self.failUnless(is_valid_dm(D) == False)
def test_is_valid_dm_improper_shape_3D_E(self):
"Tests is_valid_dm(*) on a 3D array. Exception expected."
D = np.zeros((3,3,3), dtype=np.double)
self.failUnlessRaises(ValueError, is_valid_dm_throw, (D))
def test_is_valid_dm_improper_shape_3D_F(self):
"Tests is_valid_dm(*) on a 3D array. False expected."
D = np.zeros((3,3,3), dtype=np.double)
self.failUnless(is_valid_dm(D) == False)
def test_is_valid_dm_nonzero_diagonal_E(self):
"Tests is_valid_dm(*) on a distance matrix with a nonzero diagonal. Exception expected."
y = np.random.rand(10)
D = squareform(y)
for i in xrange(0, 5):
D[i, i] = 2.0
self.failUnlessRaises(ValueError, is_valid_dm_throw, (D))
def test_is_valid_dm_nonzero_diagonal_F(self):
"Tests is_valid_dm(*) on a distance matrix with a nonzero diagonal. False expected."
y = np.random.rand(10)
D = squareform(y)
for i in xrange(0, 5):
D[i, i] = 2.0
self.failUnless(is_valid_dm(D) == False)
def test_is_valid_dm_assymetric_E(self):
"Tests is_valid_dm(*) on an assymetric distance matrix. Exception expected."
y = np.random.rand(10)
D = squareform(y)
D[1,3] = D[3,1] + 1
self.failUnlessRaises(ValueError, is_valid_dm_throw, (D))
def test_is_valid_dm_assymetric_F(self):
"Tests is_valid_dm(*) on an assymetric distance matrix. False expected."
y = np.random.rand(10)
D = squareform(y)
D[1,3] = D[3,1] + 1
self.failUnless(is_valid_dm(D) == False)
def test_is_valid_dm_correct_1_by_1(self):
"Tests is_valid_dm(*) on a correct 1x1. True expected."
D = np.zeros((1,1), dtype=np.double)
self.failUnless(is_valid_dm(D) == True)
def test_is_valid_dm_correct_2_by_2(self):
"Tests is_valid_dm(*) on a correct 2x2. True expected."
y = np.random.rand(1)
D = squareform(y)
self.failUnless(is_valid_dm(D) == True)
def test_is_valid_dm_correct_3_by_3(self):
"Tests is_valid_dm(*) on a correct 3x3. True expected."
y = np.random.rand(3)
D = squareform(y)
self.failUnless(is_valid_dm(D) == True)
def test_is_valid_dm_correct_4_by_4(self):
"Tests is_valid_dm(*) on a correct 4x4. True expected."
y = np.random.rand(6)
D = squareform(y)
self.failUnless(is_valid_dm(D) == True)
def test_is_valid_dm_correct_5_by_5(self):
"Tests is_valid_dm(*) on a correct 5x5. True expected."
y = np.random.rand(10)
D = squareform(y)
self.failUnless(is_valid_dm(D) == True)
def is_valid_y_throw(y):
return is_valid_y(y, throw=True)
class TestIsValidY(TestCase):
def test_is_valid_y_int16_array_E(self):
"Tests is_valid_y(*) on an int16 array. Exception expected."
y = np.zeros((10,), dtype='i')
self.failUnlessRaises(TypeError, is_valid_y_throw, (y))
def test_is_valid_y_int16_array_F(self):
"Tests is_valid_y(*) on an int16 array. False expected."
y = np.zeros((10,), dtype='i')
self.failUnless(is_valid_y(y) == False)
def test_is_valid_y_improper_shape_2D_E(self):
"Tests is_valid_y(*) on a 2D array. Exception expected."
y = np.zeros((3,3,), dtype=np.double)
self.failUnlessRaises(ValueError, is_valid_y_throw, (y))
def test_is_valid_y_improper_shape_2D_F(self):
"Tests is_valid_y(*) on a 2D array. False expected."
y = np.zeros((3,3,), dtype=np.double)
self.failUnless(is_valid_y(y) == False)
def test_is_valid_y_improper_shape_3D_E(self):
"Tests is_valid_y(*) on a 3D array. Exception expected."
y = np.zeros((3,3,3), dtype=np.double)
self.failUnlessRaises(ValueError, is_valid_y_throw, (y))
def test_is_valid_y_improper_shape_3D_F(self):
"Tests is_valid_y(*) on a 3D array. False expected."
y = np.zeros((3,3,3), dtype=np.double)
self.failUnless(is_valid_y(y) == False)
def test_is_valid_y_correct_2_by_2(self):
"Tests is_valid_y(*) on a correct 2x2 condensed. True expected."
y = self.correct_n_by_n(2)
self.failUnless(is_valid_y(y) == True)
def test_is_valid_y_correct_3_by_3(self):
"Tests is_valid_y(*) on a correct 3x3 condensed. True expected."
y = self.correct_n_by_n(3)
self.failUnless(is_valid_y(y) == True)
def test_is_valid_y_correct_4_by_4(self):
"Tests is_valid_y(*) on a correct 4x4 condensed. True expected."
y = self.correct_n_by_n(4)
self.failUnless(is_valid_y(y) == True)
def test_is_valid_y_correct_5_by_5(self):
"Tests is_valid_y(*) on a correct 5x5 condensed. True expected."
y = self.correct_n_by_n(5)
self.failUnless(is_valid_y(y) == True)
def test_is_valid_y_2_100(self):
"Tests is_valid_y(*) on 100 improper condensed distance matrices. Expecting exception."
a = set([])
for n in xrange(2, 16):
a.add(n*(n-1)/2)
for i in xrange(5, 105):
if i not in a:
self.failUnlessRaises(ValueError, self.bad_y, i)
def bad_y(self, n):
y = np.random.rand(n)
return is_valid_y(y, throw=True)
def correct_n_by_n(self, n):
y = np.random.rand(n*(n-1)/2)
return y
if __name__=="__main__":
run_module_suite()
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