#! /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 # DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE # GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, # 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.linalg import norm from numpy.testing import verbose, TestCase, run_module_suite, \ assert_raises, assert_array_equal, assert_equal, assert_almost_equal 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, minkowski, wminkowski, \ euclidean, sqeuclidean, cosine, correlation, mahalanobis, \ canberra, braycurtis, sokalmichener, _validate_vector _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) fp = open(fqfn) eo[name] = np.loadtxt(fp) fp.close() #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() 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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(within_tol(Y_test1, Y_right, eps)) def test_pdist_euclidean_random_u(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(within_tol(Y_test2, Y_right, eps)) def test_pdist_minkowski_3_2_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.assertTrue(within_tol(Y_test1, Y_right, eps)) def test_pdist_minkowski_3_2_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-3.2-iris'] Y_test1 = pdist(X, 'minkowski', 3.2) #print "minkowski-iris-3.2", np.abs(Y_test1 - Y_right).max() self.assertTrue(within_tol(Y_test1, Y_right, eps)) def test_pdist_minkowski_3_2_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.assertTrue(within_tol(Y_test2, Y_right, eps)) def test_pdist_minkowski_5_8_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.assertTrue(within_tol(Y_test1, Y_right, eps)) def test_pdist_minkowski_5_8_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.assertTrue(within_tol(Y_test1, Y_right, eps)) def test_pdist_minkowski_5_8_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.assertTrue(within_tol(Y_test2, Y_right, eps)) ################# wminkowski def test_pdist_wminkowski(self): x = np.array([[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 1.0]]) p2_expected = [1.0, 1.0, np.sqrt(3), np.sqrt(2), np.sqrt(2), np.sqrt(2)] p1_expected = [0.5, 1.0, 3.5, 1.5, 3.0, 2.5] dist = pdist(x, metric=wminkowski, w=[1.0, 1.0, 1.0]) assert_array_equal(dist, p2_expected) dist = pdist(x, metric=wminkowski, w=[0.5, 1.0, 2.0], p=1) assert_array_equal(dist, p1_expected) dist = pdist(x, metric='wminkowski', w=[1.0, 1.0, 1.0]) assert_array_equal(dist, p2_expected) dist = pdist(x, metric='wminkowski', w=[0.5, 1.0, 2.0], p=1) assert_array_equal(dist, p1_expected) ################### 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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(np.abs(m - 0.6) <= 1e-10) self.assertTrue(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.assertTrue(np.abs(m - (2.0/3.0)) <= 1e-10) self.assertTrue(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.assertTrue(within_tol(y1, y2, eps)) self.assertTrue(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.assertTrue(np.abs(m - 0.6) <= 1e-10) self.assertTrue(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.assertTrue(np.abs(m - (2.0/3.0)) <= 1e-10) self.assertTrue(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.assertTrue(within_tol(y1, y2, eps)) self.assertTrue(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.assertTrue(np.abs(m - 2.0) <= 1e-10) self.assertTrue(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.assertTrue(np.abs(m - 2.0) <= 1e-10) self.assertTrue(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.assertTrue(within_tol(y1, y2, eps)) self.assertTrue(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.assertTrue(np.abs(m - (3.0/7.0)) <= 1e-10) self.assertTrue(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.assertTrue(np.abs(m - 0.5) <= 1e-10) self.assertTrue(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.assertTrue(within_tol(y1, y2, eps)) self.assertTrue(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.assertTrue(np.abs(m - (3.0/4.0)) <= 1e-10) self.assertTrue(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.assertTrue(np.abs(m - (4.0/5.0)) <= 1e-10) self.assertTrue(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.assertTrue(within_tol(y1, y2, eps)) self.assertTrue(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.assertTrue(np.abs(m - (3.0/4.0)) <= 1e-10) self.assertTrue(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.assertTrue(np.abs(m - (4.0/5.0)) <= 1e-10) self.assertTrue(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.assertTrue(within_tol(y1, y2, eps)) self.assertTrue(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.assertTrue(np.abs(m - (3.0/5.0)) <= 1e-10) self.assertTrue(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.assertTrue(np.abs(m - (2.0/3.0)) <= 1e-10) self.assertTrue(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.assertTrue(within_tol(y1, y2, eps)) self.assertTrue(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.assertTrue(within_tol(y1, y2, eps)) self.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(within_tol(pdist_y, right_y, eps)) def within_tol(a, b, tol): return np.abs(a - b).max() < tol class TestSomeDistanceFunctions(TestCase): def setUp(self): # 1D arrays x = np.array([1.0, 2.0, 3.0]) y = np.array([1.0, 1.0, 5.0]) # 3x1 arrays x31 = x[:,np.newaxis] y31 = y[:,np.newaxis] # 1x3 arrays x13 = x31.T y13 = y31.T self.cases = [(x,y), (x31, y31), (x13, y13)] def test_minkowski(self): for x, y in self.cases: dist1 = minkowski(x, y, p=1) assert_almost_equal(dist1, 3.0) dist1p5 = minkowski(x, y, p=1.5) assert_almost_equal(dist1p5, (1.0+2.0**1.5)**(2./3)) dist2 = minkowski(x, y, p=2) assert_almost_equal(dist2, np.sqrt(5)) def test_wminkowski(self): w = np.array([1.0, 2.0, 0.5]) for x, y in self.cases: dist1 = wminkowski(x, y, p=1, w=w) assert_almost_equal(dist1, 3.0) dist1p5 = wminkowski(x, y, p=1.5, w=w) assert_almost_equal(dist1p5, (2.0**1.5+1.0)**(2./3)) dist2 = wminkowski(x, y, p=2, w=w) assert_almost_equal(dist2, np.sqrt(5)) def test_euclidean(self): for x, y in self.cases: dist = euclidean(x, y) assert_almost_equal(dist, np.sqrt(5)) def test_sqeuclidean(self): for x, y in self.cases: dist = sqeuclidean(x, y) assert_almost_equal(dist, 5.0) def test_cosine(self): for x, y in self.cases: dist = cosine(x, y) assert_almost_equal(dist, 1.0 - 18.0/(np.sqrt(14)*np.sqrt(27))) def test_correlation(self): xm = np.array([-1.0, 0, 1.0]) ym = np.array([-4.0/3, -4.0/3, 5.0-7.0/3]) for x, y in self.cases: dist = correlation(x, y) assert_almost_equal(dist, 1.0 - np.dot(xm, ym)/(norm(xm)*norm(ym))) def test_mahalanobis(self): x = np.array([1.0, 2.0, 3.0]) y = np.array([1.0, 1.0, 5.0]) vi = np.array([[2.0, 1.0, 0.0],[1.0, 2.0, 1.0], [0.0, 1.0, 2.0]]) for x, y in self.cases: dist = mahalanobis(x, y, vi) assert_almost_equal(dist, np.sqrt(6.0)) 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.assertTrue(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.assertTrue(rv.shape == (1,1)) self.assertTrue(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.assertTrue(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.assertTrue(rv.shape == (2,2)) self.assertTrue(rv[0,1] == 8.3) self.assertTrue(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.assertTrue(rA.shape == (1,)) self.assertTrue(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.assertTrue(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.assertTrue(len(s) == 2) self.assertTrue(len(Yr.shape) == 1) self.assertTrue(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.assertTrue(A[i, j] == Y[k]) k += 1 else: self.assertTrue(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.assertTrue(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.assertRaises(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.assertTrue(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.assertTrue(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.assertTrue(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.assertRaises(ValueError, self.bad_y, i) def minit(self, n): self.assertTrue(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.assertTrue(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.assertTrue(self.check_D(0)) def test_num_obs_dm_1(self): "Tests num_obs_dm(D) on a 1x1 distance matrix." self.assertTrue(self.check_D(1)) def test_num_obs_dm_2(self): "Tests num_obs_dm(D) on a 2x2 distance matrix." self.assertTrue(self.check_D(2)) def test_num_obs_dm_3(self): "Tests num_obs_dm(D) on a 3x3 distance matrix." self.assertTrue(self.check_D(2)) def test_num_obs_dm_4(self): "Tests num_obs_dm(D) on a 4x4 distance matrix." self.assertTrue(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.assertRaises(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.assertTrue(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.assertRaises(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.assertTrue(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.assertRaises(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.assertTrue(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.assertRaises(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.assertTrue(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.assertRaises(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertRaises(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.assertTrue(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.assertRaises(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.assertTrue(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.assertRaises(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertTrue(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.assertRaises(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 def test_bad_p(): """Raise ValueError if p < 1.""" p = 0.5 assert_raises(ValueError, minkowski, [1, 2], [3, 4], p) assert_raises(ValueError, wminkowski, [1, 2], [3, 4], p, [1, 1]) def test_sokalsneath_all_false(): """Regression test for ticket #876""" assert_raises(ValueError, sokalsneath, [False, False, False], [False, False, False]) def test_canberra(): """Regression test for ticket #1430.""" assert_equal(canberra([1,2,3], [2,4,6]), 1) assert_equal(canberra([1,1,0,0], [1,0,1,0]), 2) def test_braycurtis(): """Regression test for ticket #1430.""" assert_almost_equal(braycurtis([1,2,3], [2,4,6]), 1./3, decimal=15) assert_almost_equal(braycurtis([1,1,0,0], [1,0,1,0]), 0.5, decimal=15) def test_euclideans(): """Regression test for ticket #1328.""" x1 = np.array([1, 1, 1]) x2 = np.array([0, 0, 0]) # Basic test of the calculation. assert_almost_equal(sqeuclidean(x1, x2), 3.0, decimal=14) assert_almost_equal(euclidean(x1, x2), np.sqrt(3), decimal=14) # Check flattening for (1, N) or (N, 1) inputs assert_almost_equal(euclidean(x1[np.newaxis, :], x2[np.newaxis, :]), np.sqrt(3), decimal=14) assert_almost_equal(sqeuclidean(x1[np.newaxis, :], x2[np.newaxis, :]), 3.0, decimal=14) assert_almost_equal(sqeuclidean(x1[:, np.newaxis], x2[:, np.newaxis]), 3.0, decimal=14) # Distance metrics only defined for vectors (= 1-D) x = np.arange(4).reshape(2, 2) assert_raises(ValueError, euclidean, x, x) assert_raises(ValueError, sqeuclidean, x, x) # Another check, with random data. rs = np.random.RandomState(1234567890) x = rs.rand(10) y = rs.rand(10) d1 = euclidean(x, y) d2 = sqeuclidean(x, y) assert_almost_equal(d1**2, d2, decimal=14) def test_sokalmichener(): """Test that sokalmichener has the same result for bool and int inputs.""" p = [True, True, False] q = [True, False, True] x = [int(b) for b in p] y = [int(b) for b in q] dist1 = sokalmichener(p, q) dist2 = sokalmichener(x, y) # These should be exactly the same. assert_equal(dist1, dist2) def test__validate_vector(): """Assorted tests for _validate_vector.""" x = [1, 2, 3] y = _validate_vector(x) assert_array_equal(y, x) y = _validate_vector(x, dtype=np.float64) assert_array_equal(y, x) assert_equal(y.dtype, np.float64) x = [1] y = _validate_vector(x) assert_equal(y.ndim, 1) assert_equal(y, x) x = 1 y = _validate_vector(x) assert_equal(y.ndim, 1) assert_equal(y, [x]) x = np.arange(5).reshape(1, -1, 1) y = _validate_vector(x) assert_equal(y.ndim, 1) assert_array_equal(y, x[0, :, 0]) x = [[1, 2], [3, 4]] assert_raises(ValueError, _validate_vector, x) if __name__=="__main__": run_module_suite()