[Scipy-svn] r4148 - in trunk/scipy/cluster: . src tests
scipy-svn at scipy.org
scipy-svn at scipy.org
Thu Apr 17 22:13:29 EDT 2008
Author: damian.eads
Date: 2008-04-17 21:13:12 -0500 (Thu, 17 Apr 2008)
New Revision: 4148
Added:
trunk/scipy/cluster/tests/pdist-minkowski-3.2-ml-iris.txt
trunk/scipy/cluster/tests/pdist-minkowski-5.8-ml-iris.txt
trunk/scipy/cluster/tests/test_hierarchy.py
Removed:
trunk/scipy/cluster/tests/pdist-minkowski-0.6-ml-iris.txt
trunk/scipy/cluster/tests/pdist-minkowski-0.6-ml.txt
trunk/scipy/cluster/tests/pdist-minkowski-0.8-ml-iris.txt
Modified:
trunk/scipy/cluster/hierarchy.py
trunk/scipy/cluster/src/hierarchy.c
trunk/scipy/cluster/tests/iris.txt
Log:
Wrote tests for about half the distance functions. Fixed Jaccard -- the C implementation was a direct copy of Hamming. It now functions as desired. Fixed check for float64 arrays in pdist so that boolean observation matrices don't generate an exception.
Modified: trunk/scipy/cluster/hierarchy.py
===================================================================
--- trunk/scipy/cluster/hierarchy.py 2008-04-17 21:15:51 UTC (rev 4147)
+++ trunk/scipy/cluster/hierarchy.py 2008-04-18 02:13:12 UTC (rev 4148)
@@ -175,7 +175,7 @@
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""
-import _hierarchy_wrap, scipy, scipy.stats, numpy, types, math, sys
+import _hierarchy_wrap, scipy, numpy, types, math, sys, scipy.stats
_cpy_non_euclid_methods = {'single': 0, 'complete': 1, 'average': 2, 'weighted': 6}
_cpy_euclid_methods = {'centroid': 3, 'median': 4, 'ward': 5}
@@ -190,6 +190,15 @@
def _warning(s):
print ('[WARNING] scipy-cluster: %s' % s)
+def _unbiased_variance(X):
+ """
+ Computes the unbiased variance of each dimension of a collection of
+ observation vectors, represented by a matrix where the rows are the
+ observations.
+ """
+ #n = numpy.double(X.shape[1])
+ return scipy.stats.var(X, axis=0) # * n / (n - 1.0)
+
def _copy_array_if_base_present(a):
"""
Copies the array if its base points to a parent array.
@@ -235,7 +244,7 @@
matrix Z. See linkage for more information on the return structure
and algorithm.
- (a condensed alias for linkage)
+ (a condensed alias for single)
"""
return linkage(y, method='single', metric='euclidean')
@@ -869,7 +878,7 @@
for k < n.
"""
- return ((scipy.bitwise_and((u != v), scipy.bitwise_or(u != 0, v != 0))).sum()) / scipy.bitwise_or(u != 0, v != 0).sum()
+ return numpy.double(scipy.bitwise_and((u != v), scipy.bitwise_or(u != 0, v != 0)).sum()) / numpy.double(scipy.bitwise_or(u != 0, v != 0).sum())
def kulsinski(u, v):
"""
@@ -1292,8 +1301,8 @@
if type(X) is not _array_type:
raise TypeError('The parameter passed must be an array.')
- if X.dtype != 'double':
- raise TypeError('X must be a float64 array')
+ if X.dtype == 'float32':
+ raise TypeError('Floating point arrays must be 64-bit.')
# The C code doesn't do striding.
[X] = _copy_arrays_if_base_present([X])
@@ -1301,7 +1310,7 @@
s = X.shape
if len(s) != 2:
- raise ValueError('A matrix must be passed.');
+ raise ValueError('A 2-dimensional array must be passed.');
m = s[0]
n = s[1]
@@ -1310,10 +1319,27 @@
mtype = type(metric)
if mtype is types.FunctionType:
k = 0
- for i in xrange(0, m - 1):
- for j in xrange(i+1, m):
- dm[k] = metric(X[i, :], X[j, :])
- k = k + 1
+ if metric == minkowski:
+ for i in xrange(0, m - 1):
+ for j in xrange(i+1, m):
+ dm[k] = minkowski(X[i, :], X[j, :], p)
+ k = k + 1
+ elif metric == seuclidean:
+ for i in xrange(0, m - 1):
+ for j in xrange(i+1, m):
+ dm[k] = seuclidean(X[i, :], X[j, :], V)
+ k = k + 1
+ elif metric == mahalanobis:
+ for i in xrange(0, m - 1):
+ for j in xrange(i+1, m):
+ dm[k] = mahalanobis(X[i, :], X[j, :], V)
+ k = k + 1
+ else:
+ for i in xrange(0, m - 1):
+ for j in xrange(i+1, m):
+ dm[k] = metric(X[i, :], X[j, :])
+ k = k + 1
+
elif mtype is types.StringType:
mstr = metric.lower()
@@ -1332,14 +1358,14 @@
elif X.dtype == 'bool':
_hierarchy_wrap.pdist_hamming_bool_wrap(X, dm)
else:
- raise TypeError('Invalid input matrix type %s for hamming.' % str(X.dtype))
+ raise TypeError('Invalid input array value type %s for hamming.' % str(X.dtype))
elif mstr in set(['jaccard', 'jacc', 'ja', 'j']):
if X.dtype == 'double':
- _hierarchy_wrap.pdist_hamming_wrap(X, dm)
+ _hierarchy_wrap.pdist_jaccard_wrap(X, dm)
elif X.dtype == 'bool':
- _hierarchy_wrap.pdist_hamming_bool_wrap(X, dm)
+ _hierarchy_wrap.pdist_jaccard_bool_wrap(X, dm)
else:
- raise TypeError('Invalid input matrix type %s for jaccard.' % str(X.dtype))
+ raise TypeError('Invalid input array value type %s for jaccard.' % str(X.dtype))
elif mstr in set(['chebyshev', 'cheby', 'cheb', 'ch']):
_hierarchy_wrap.pdist_chebyshev_wrap(X, dm)
elif mstr in set(['minkowski', 'mi', 'm']):
@@ -1357,7 +1383,7 @@
# The C code doesn't do striding.
[VV] = _copy_arrays_if_base_present([V])
else:
- VV = scipy.stats.var(X, axis=0)
+ VV = _unbiased_variance(X)
_hierarchy_wrap.pdist_seuclidean_wrap(X, VV, dm)
# Need to test whether vectorized cosine works better.
# Find out: Is there a dot subtraction operator so I can
@@ -1417,7 +1443,7 @@
dm = pdist(X, euclidean)
elif metric == 'test_sqeuclidean':
if V is None:
- V = scipy.stats.var(X, axis=0)
+ V = _unbiased_variance(X)
dm = pdist(X, lambda u, v: seuclidean(u, v, V))
elif metric == 'test_braycurtis':
dm = pdist(X, braycurtis)
@@ -1431,7 +1457,7 @@
elif metric == 'test_cityblock':
dm = pdist(X, cityblock)
elif metric == 'test_minkowski':
- dm = pdist(X, minkowski)
+ dm = pdist(X, minkowski, p)
elif metric == 'test_cosine':
dm = pdist(X, cosine)
elif metric == 'test_correlation':
Modified: trunk/scipy/cluster/src/hierarchy.c
===================================================================
--- trunk/scipy/cluster/src/hierarchy.c 2008-04-17 21:15:51 UTC (rev 4147)
+++ trunk/scipy/cluster/src/hierarchy.c 2008-04-18 02:13:12 UTC (rev 4148)
@@ -285,19 +285,20 @@
int i = 0;
double denom = 0.0, num = 0.0;
for (i = 0; i < n; i++) {
- num += (u[i] != v[i]) && ((u[i] != 0) || (v[i] != 0));
- denom += (u[i] != 0) || (v[i] != 0);
+ num += (u[i] != v[i]) && ((u[i] != 0.0) || (v[i] != 0.0));
+ denom += (u[i] != 0.0) || (v[i] != 0.0);
}
return num / denom;
}
double jaccard_distance_bool(const char *u, const char *v, int n) {
int i = 0;
- double s = 0.0;
+ double s = 0.0, num = 0.0, denom = 0.0;
for (i = 0; i < n; i++) {
- s = s + (u[i] != v[i]);
+ num += (u[i] != v[i]) && ((u[i] != 0) || (v[i] != 0));
+ denom += (u[i] != 0) || (v[i] != 0);
}
- return s / (double)n;
+ return num / denom;
}
double dot_product(const double *u, const double *v, int n) {
Modified: trunk/scipy/cluster/tests/iris.txt
===================================================================
--- trunk/scipy/cluster/tests/iris.txt 2008-04-17 21:15:51 UTC (rev 4147)
+++ trunk/scipy/cluster/tests/iris.txt 2008-04-18 02:13:12 UTC (rev 4148)
@@ -1,151 +1,150 @@
-5.1,3.5,1.4,0.2
-4.9,3.0,1.4,0.2
-4.7,3.2,1.3,0.2
-4.6,3.1,1.5,0.2
-5.0,3.6,1.4,0.2
-5.4,3.9,1.7,0.4
-4.6,3.4,1.4,0.3
-5.0,3.4,1.5,0.2
-4.4,2.9,1.4,0.2
-4.9,3.1,1.5,0.1
-5.4,3.7,1.5,0.2
-4.8,3.4,1.6,0.2
-4.8,3.0,1.4,0.1
-4.3,3.0,1.1,0.1
-5.8,4.0,1.2,0.2
-5.7,4.4,1.5,0.4
-5.4,3.9,1.3,0.4
-5.1,3.5,1.4,0.3
-5.7,3.8,1.7,0.3
-5.1,3.8,1.5,0.3
-5.4,3.4,1.7,0.2
-5.1,3.7,1.5,0.4
-4.6,3.6,1.0,0.2
-5.1,3.3,1.7,0.5
-4.8,3.4,1.9,0.2
-5.0,3.0,1.6,0.2
-5.0,3.4,1.6,0.4
-5.2,3.5,1.5,0.2
-5.2,3.4,1.4,0.2
-4.7,3.2,1.6,0.2
-4.8,3.1,1.6,0.2
-5.4,3.4,1.5,0.4
-5.2,4.1,1.5,0.1
-5.5,4.2,1.4,0.2
-4.9,3.1,1.5,0.1
-5.0,3.2,1.2,0.2
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-4.9,3.1,1.5,0.1
-4.4,3.0,1.3,0.2
-5.1,3.4,1.5,0.2
-5.0,3.5,1.3,0.3
-4.5,2.3,1.3,0.3
-4.4,3.2,1.3,0.2
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-7.0,3.2,4.7,1.4
-6.4,3.2,4.5,1.5
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-6.5,2.8,4.6,1.5
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-6.3,3.3,4.7,1.6
-4.9,2.4,3.3,1.0
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-5.2,2.7,3.9,1.4
-5.0,2.0,3.5,1.0
-5.9,3.0,4.2,1.5
-6.0,2.2,4.0,1.0
-6.1,2.9,4.7,1.4
-5.6,2.9,3.6,1.3
-6.7,3.1,4.4,1.4
-5.6,3.0,4.5,1.5
-5.8,2.7,4.1,1.0
-6.2,2.2,4.5,1.5
-5.6,2.5,3.9,1.1
-5.9,3.2,4.8,1.8
-6.1,2.8,4.0,1.3
-6.3,2.5,4.9,1.5
-6.1,2.8,4.7,1.2
-6.4,2.9,4.3,1.3
-6.6,3.0,4.4,1.4
-6.8,2.8,4.8,1.4
-6.7,3.0,5.0,1.7
-6.0,2.9,4.5,1.5
-5.7,2.6,3.5,1.0
-5.5,2.4,3.8,1.1
-5.5,2.4,3.7,1.0
-5.8,2.7,3.9,1.2
-6.0,2.7,5.1,1.6
-5.4,3.0,4.5,1.5
-6.0,3.4,4.5,1.6
-6.7,3.1,4.7,1.5
-6.3,2.3,4.4,1.3
-5.6,3.0,4.1,1.3
-5.5,2.5,4.0,1.3
-5.5,2.6,4.4,1.2
-6.1,3.0,4.6,1.4
-5.8,2.6,4.0,1.2
-5.0,2.3,3.3,1.0
-5.6,2.7,4.2,1.3
-5.7,3.0,4.2,1.2
-5.7,2.9,4.2,1.3
-6.2,2.9,4.3,1.3
-5.1,2.5,3.0,1.1
-5.7,2.8,4.1,1.3
-6.3,3.3,6.0,2.5
-5.8,2.7,5.1,1.9
-7.1,3.0,5.9,2.1
-6.3,2.9,5.6,1.8
-6.5,3.0,5.8,2.2
-7.6,3.0,6.6,2.1
-4.9,2.5,4.5,1.7
-7.3,2.9,6.3,1.8
-6.7,2.5,5.8,1.8
-7.2,3.6,6.1,2.5
-6.5,3.2,5.1,2.0
-6.4,2.7,5.3,1.9
-6.8,3.0,5.5,2.1
-5.7,2.5,5.0,2.0
-5.8,2.8,5.1,2.4
-6.4,3.2,5.3,2.3
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-7.7,3.8,6.7,2.2
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-6.3,2.7,4.9,1.8
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-7.9,3.8,6.4,2.0
-6.4,2.8,5.6,2.2
-6.3,2.8,5.1,1.5
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Deleted: trunk/scipy/cluster/tests/pdist-minkowski-0.6-ml-iris.txt
===================================================================
--- trunk/scipy/cluster/tests/pdist-minkowski-0.6-ml-iris.txt 2008-04-17 21:15:51 UTC (rev 4147)
+++ trunk/scipy/cluster/tests/pdist-minkowski-0.6-ml-iris.txt 2008-04-18 02:13:12 UTC (rev 4148)
@@ -1 +0,0 @@
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6.6316535e+00 6.7574575e+00 3.8708609e+00 1.5319850e+00 2.6519650e+00 4.4437762e+00 9.8003160e-01 1.4773595e+00 1.8347096e+00 6.1811463e+00 3.6072442e+00 2.6058219e+00 5.7420681e+00 2.0228388e+00 6.2233978e+00 3.6815270e+00 2.6076804e+00 5.9098113e+00 3.7062982e+00 7.8728875e-01 2.2289625e+00 2.6281695e+00 2.3563262e+00 4.1364091e+00 2.5152850e+00 2.9891849e+00 1.6856955e+00 3.7440234e+00
Deleted: trunk/scipy/cluster/tests/pdist-minkowski-0.6-ml.txt
===================================================================
--- trunk/scipy/cluster/tests/pdist-minkowski-0.6-ml.txt 2008-04-17 21:15:51 UTC (rev 4147)
+++ trunk/scipy/cluster/tests/pdist-minkowski-0.6-ml.txt 2008-04-18 02:13:12 UTC (rev 4148)
@@ -1 +0,0 @@
- 6.1585489e+02 6.2321817e+02 5.8818412e+02 6.7863863e+02 6.0771510e+02 6.9131089e+02 6.2439260e+02 7.1004702e+02 6.4565616e+02 6.5614478e+02 6.1511455e+02 6.2860242e+02 6.4943432e+02 7.1272806e+02 6.5685389e+02 6.2442150e+02 6.6505781e+02 6.6563043e+02 6.5960729e+02 6.8610088e+02 5.7808638e+02 6.1778875e+02 6.3862183e+02 6.2928553e+02 6.1812973e+02 5.9637134e+02 6.2055904e+02 6.2265891e+02 4.9440236e+02 6.4015022e+02 6.2838510e+02 6.5307398e+02 5.3986370e+02 6.4941961e+02 6.0463616e+02 6.3148720e+02 6.0308734e+02 6.0506766e+02 6.2190656e+02 6.5950715e+02 6.2248928e+02 6.2725172e+02 6.4807454e+02 5.7578035e+02 8.3827038e+02 7.1063040e+02 5.4478600e+02 6.7440334e+02 7.3220343e+02 6.1389081e+02 6.3014002e+02 6.5931791e+02 7.8153691e+02 6.8695874e+02 5.1250688e+02 7.0378289e+02 5.6254491e+02 6.4842853e+02 6.9060029e+02 6.0045029e+02 6.2365908e+02 4.6473547e+02 5.7836117e+02 6.6987139e+02 6.3212657e+02 5.5707081e+02 6.4115762e+02 6.5663457e+02 6.6269023e+02 6.8210531e+02 6.1382960e+02 6.3382190e+02 5.9540062e+02 5.5933269e+02 6.4209430e+02 5.2358429e+02 5.7886008e+02 5.8103514e+02 5.7774802e+02 5.9722361e+02 5.4489073e+02 6.3528629e+02 6.4604143e+02 6.5393880e+02 7.0267325e+02 7.1504213e+02 6.5799742e+02 7.4683271e+02 6.0347074e+02 6.5778541e+02 6.6513775e+02 6.8814952e+02 7.0301623e+02 6.2527972e+02 6.8267156e+02 5.4010932e+02 7.0611774e+02 5.6459584e+02 6.3041182e+02 6.3605235e+02 6.7600054e+02 5.4577351e+02 7.0929347e+02 5.9178309e+02 6.1850390e+02 6.4481363e+02 6.3110933e+02 5.5940455e+02 5.6734092e+02 6.5381272e+02 7.1624576e+02 6.3193705e+02 6.6240603e+02 6.5731415e+02 6.5015656e+02 5.9508845e+02 6.6741407e+02 6.8757498e+02 6.6762533e+02 6.5036333e+02 6.6798992e+02 5.7082376e+02 6.2171263e+02 5.8471396e+02 6.3814192e+02 6.1738265e+02 5.8671693e+02 5.8080905e+02 6.1314083e+02 5.9496681e+02 5.7419618e+02 5.6938212e+02 5.9554798e+02 6.8401851e+02 6.6784590e+02 6.3260832e+02 6.5948824e+02 5.8215353e+02 6.5090954e+02 5.6125819e+02 6.0728529e+02 6.3023598e+02 6.0123106e+02 5.3359706e+02 5.8579452e+02 5.9743445e+02 6.8559330e+02 6.1808386e+02 5.4070268e+02 6.6481624e+02 6.8773735e+02 6.3323647e+02 5.8871137e+02 6.6999350e+02 6.7738537e+02 5.5644352e+02 6.6779435e+02 5.1484535e+02 6.5022821e+02 5.3865596e+02 6.3170314e+02 6.1494903e+02 6.2657009e+02 6.2255510e+02 5.9046375e+02 6.2911230e+02 5.8085299e+02 7.3934668e+02 5.8435174e+02 6.7690574e+02 5.5336944e+02 6.7962701e+02 5.8526484e+02 6.1967171e+02 6.3942520e+02 6.5540238e+02 7.0212254e+02 6.5926444e+02 6.1750355e+02 7.0941803e+02 6.9104083e+02 6.2177029e+02 6.0252775e+02 5.6810664e+02 6.4965351e+02 6.8120808e+02 7.2772619e+02 6.3309152e+02 5.8067697e+02 5.2359879e+02
Deleted: trunk/scipy/cluster/tests/pdist-minkowski-0.8-ml-iris.txt
===================================================================
--- trunk/scipy/cluster/tests/pdist-minkowski-0.8-ml-iris.txt 2008-04-17 21:15:51 UTC (rev 4147)
+++ trunk/scipy/cluster/tests/pdist-minkowski-0.8-ml-iris.txt 2008-04-18 02:13:12 UTC (rev 4148)
@@ -1 +0,0 @@
- 5.5129254e-01 5.3331654e-01 6.7676105e-01 1.4697345e-01 6.6234335e-01 5.2994606e-01 1.8410575e-01 9.5758668e-01 4.9210778e-01 3.9226391e-01 3.9226391e-01 6.1555822e-01 1.0472948e+00 9.2471857e-01 1.1554378e+00 5.8223100e-01 1.0000000e-01 7.8283081e-01 3.4362433e-01 4.5762068e-01 3.1669465e-01 6.7676105e-01 4.9711212e-01 6.1555822e-01 5.6532807e-01 3.3820388e-01 1.4697345e-01 1.4697345e-01 5.6852959e-01 5.6852959e-01 4.1053944e-01 6.3875221e-01 8.3216069e-01 4.9210778e-01 3.9226391e-01 4.1800334e-01 4.9210778e-01 9.0121124e-01 1.4697345e-01 1.8410575e-01 1.3942248e+00 7.9157919e-01 4.7637957e-01 6.4713428e-01 6.1555822e-01 3.7328014e-01 6.0246555e-01 3.1669465e-01 2.3011223e-01 4.2028240e+00 3.7864855e+00 4.3687291e+00 3.2516340e+00 3.9910490e+00 3.5488716e+00 3.9497402e+00 2.4686636e+00 3.9353547e+00 3.0120410e+00 2.8437807e+00 3.3771153e+00 3.3228842e+00 3.8583830e+00 2.7076091e+00 3.8151170e+00 3.5621318e+00 3.1396480e+00 3.9876247e+00 3.0214409e+00 4.0070659e+00 3.2398535e+00 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2.5047066e+00 2.5354493e+00 9.7383980e-01 1.9684311e+00 2.0003862e+00 2.7629628e+00 1.6792104e+00 1.7633931e+00 1.9229112e+00 2.8550332e+00 1.4646467e+00 1.6596462e+00 2.0248875e+00 2.6508549e+00 2.1269404e+00 2.1316761e+00 2.6579753e+00 9.0121124e-01 9.0702094e-01 1.4540804e+00 6.5721874e-01 5.3331654e-01 1.0590327e+00 6.5951158e-01 4.9711212e-01 8.2330060e-01 8.9333246e-01 6.9144557e-01 7.0427522e-01 5.8223100e-01 7.8283081e-01 5.2951637e-01 6.9905366e-01 8.9665187e-01 6.1217218e-01 2.0237038e+00 1.2587344e+00 6.3137460e-01 5.9997944e-01 1.0146116e+00 1.2156057e+00 1.0975698e+00 6.7676105e-01 1.3937623e+00 1.4192640e+00 9.6187679e-01 5.3331654e-01 6.1217218e-01 1.1057186e+00 5.6852959e-01 2.0488924e+00 1.3535221e+00 7.5966619e-01 1.0379897e+00 1.2700883e+00 1.3366492e+00 1.4984189e+00 8.2330060e-01 1.4144838e+00 1.5212536e+00 1.3030263e+00 8.5666462e-01 9.8618111e-01 1.2836546e+00 8.3749635e-01 1.5989791e+00 1.5904535e+00 2.3032863e+00 1.1402466e+00 1.1662169e+00 1.3362791e+00 2.3010008e+00 9.6485354e-01 1.1871900e+00 1.3977943e+00 2.0053385e+00 1.5999496e+00 1.7694181e+00 2.2100095e+00 7.1370782e-01 1.1933491e+00 8.0906455e-01 5.1859441e-01 8.9333246e-01 1.1996723e+00 6.5951158e-01 4.5227024e-01 7.4760192e-01 1.2587344e+00 7.7478955e-01 2.5735380e-01 1.0384427e+00 8.4231031e-01 6.1555822e-01 7.0210005e-01 8.6267904e-01 8.8275255e-01 8.1199808e-01 8.5299662e-01 7.0427522e-01 8.3163633e-01 4.1053944e-01 6.5951158e-01 6.7676105e-01 1.1845013e+00 1.3028825e+00 1.1293499e+00 5.1232775e-01 1.5491559e+00 1.4673712e+00 1.0030992e+00 6.5951158e-01 7.0612170e-01 9.6235823e-01 3.2240315e-01 4.3649025e-01 3.7328014e-01 1.2752289e+00 5.7909019e-01 6.1517707e-01 3.8739477e-01 1.0246555e+00 4.9210778e-01 8.1758743e-01 1.1293899e+00 5.6532807e-01 1.2955673e+00 3.6384497e-01 2.5735380e-01 4.3540658e-01 1.1454011e+00 6.4597730e-01 6.5951158e-01 1.1915830e+00 1.2854807e+00 8.2083495e-01 7.2768994e-01 2.5735380e-01 1.0030389e+00 5.4772047e-01 8.5559597e-01 1.1584102e+00 1.5294202e+00 1.4792717e+00 1.0806255e+00 5.6532807e-01 8.0213820e-01 1.0138644e+00 3.4362433e-01 3.3820388e-01 7.5101750e-01 1.4001664e+00 8.8978585e-01 8.4665275e-01 1.4027057e+00 6.4713428e-01 1.3803227e+00 8.4852602e-01 6.5951158e-01 1.3466484e+00 8.3749635e-01 3.7328014e-01 7.0612170e-01 9.8447732e-01 6.1217218e-01 1.1205937e+00 6.8870991e-01 6.6234335e-01 6.5721874e-01 8.2567325e-01
Added: trunk/scipy/cluster/tests/pdist-minkowski-3.2-ml-iris.txt
===================================================================
--- trunk/scipy/cluster/tests/pdist-minkowski-3.2-ml-iris.txt 2008-04-17 21:15:51 UTC (rev 4147)
+++ trunk/scipy/cluster/tests/pdist-minkowski-3.2-ml-iris.txt 2008-04-18 02:13:12 UTC (rev 4148)
@@ -0,0 +1 @@
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1.9782093e+00 9.0454394e-01 1.0244319e+00 1.1833480e+00 1.5948732e+00 1.3360558e+00 1.5461469e+00 1.8900319e+00 6.2081167e-01 9.1750357e-01 6.4290921e-01 4.4417983e-01 7.0429250e-01 8.7229670e-01 5.3665999e-01 4.0438741e-01 5.6454040e-01 1.0054794e+00 5.7015910e-01 2.1269358e-01 7.5705927e-01 7.3496673e-01 5.2942799e-01 6.2024833e-01 6.6491075e-01 6.8801986e-01 6.1947990e-01 7.2172678e-01 5.5492130e-01 6.9006418e-01 3.2816937e-01 5.3665999e-01 5.6700421e-01 9.7779835e-01 1.0014633e+00 9.4854455e-01 3.8934542e-01 1.2342162e+00 1.1043332e+00 7.9580667e-01 5.3665999e-01 5.7257017e-01 7.2852070e-01 3.0275928e-01 3.4378533e-01 3.2352160e-01 1.1199472e+00 5.0991930e-01 4.6472023e-01 2.8507955e-01 7.7869083e-01 4.1586001e-01 7.1799256e-01 1.0132664e+00 5.0905001e-01 9.9519977e-01 3.0811765e-01 2.1269358e-01 4.0293660e-01 8.3060013e-01 5.0592043e-01 5.3665999e-01 9.3049742e-01 1.1263042e+00 8.0064372e-01 6.1623531e-01 2.1269358e-01 7.7588000e-01 4.4651726e-01 7.2783368e-01 1.0329901e+00 1.1697902e+00 1.0851476e+00 9.2859317e-01 5.0905001e-01 7.1504098e-01 7.7763126e-01 3.0546431e-01 2.5651975e-01 7.0437330e-01 1.0573285e+00 7.3145860e-01 6.9325418e-01 1.0901359e+00 5.3588338e-01 1.0175773e+00 6.4405773e-01 5.3665999e-01 1.0078327e+00 6.2407309e-01 3.2352160e-01 5.7257017e-01 8.5205778e-01 5.1691876e-01 9.4022486e-01 5.6769031e-01 4.8927739e-01 6.0611244e-01 6.0900723e-01
Added: trunk/scipy/cluster/tests/pdist-minkowski-5.8-ml-iris.txt
===================================================================
--- trunk/scipy/cluster/tests/pdist-minkowski-5.8-ml-iris.txt 2008-04-17 21:15:51 UTC (rev 4147)
+++ trunk/scipy/cluster/tests/pdist-minkowski-5.8-ml-iris.txt 2008-04-18 02:13:12 UTC (rev 4148)
@@ -0,0 +1 @@
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1.6885111e+00 1.8315348e+00 8.0097499e-01 1.6050900e+00 1.5160591e+00 2.0074169e+00 1.1389082e+00 1.2275665e+00 1.3502668e+00 2.1298991e+00 1.1075720e+00 1.2113964e+00 1.3632538e+00 1.7639471e+00 1.4922544e+00 1.7133283e+00 2.0290146e+00 7.1621748e-01 8.0051036e-01 1.3008813e+00 6.0017982e-01 4.1210927e-01 8.0492246e-01 5.0477564e-01 3.4080442e-01 5.6595908e-01 6.3309258e-01 4.5784410e-01 5.0855778e-01 4.1317535e-01 6.0366256e-01 4.0246123e-01 6.0035621e-01 5.7630313e-01 5.0085236e-01 1.4407390e+00 9.1892413e-01 4.2270142e-01 3.6452132e-01 6.7824250e-01 9.0668287e-01 8.2458409e-01 5.2133179e-01 9.0792879e-01 1.0124729e+00 8.0250202e-01 4.1210927e-01 5.0085236e-01 8.2458478e-01 4.1315633e-01 1.6100639e+00 1.0426636e+00 5.2491734e-01 8.0488008e-01 8.6054545e-01 1.0116721e+00 9.7291273e-01 5.6595908e-01 9.4532171e-01 1.1186499e+00 9.1681464e-01 6.3178782e-01 6.2656178e-01 9.6574369e-01 5.3943256e-01 1.4007831e+00 1.3033860e+00 1.7572550e+00 8.5406674e-01 1.0030724e+00 1.0426513e+00 1.9078843e+00 9.0005048e-01 1.0010209e+00 1.0776188e+00 1.4549432e+00 1.2363278e+00 1.5032156e+00 1.8103044e+00 6.0184934e-01 8.2671175e-01 6.0383105e-01 4.1209001e-01 6.3309258e-01 7.4418186e-01 5.0477564e-01 4.0006662e-01 4.8342635e-01 9.1892413e-01 4.8391482e-01 2.0121983e-01 6.4620889e-01 7.0462697e-01 5.0436965e-01 6.0184622e-01 5.7608844e-01 6.1830764e-01 5.3914287e-01 7.0105084e-01 5.0855778e-01 6.3165225e-01 3.0490481e-01 5.0477564e-01 5.2133179e-01 9.1446938e-01 8.7209348e-01 9.0532093e-01 3.4085233e-01 1.1298636e+00 9.6150595e-01 7.2036819e-01 5.0477564e-01 5.2167208e-01 6.3925756e-01 3.0008832e-01 3.0915245e-01 3.0474106e-01 1.1006468e+00 5.0043842e-01 4.1420960e-01 2.4195741e-01 6.8170466e-01 4.0127250e-01 7.0096708e-01 1.0003198e+00 5.0043084e-01 9.1132198e-01 3.0026460e-01 2.0121983e-01 4.0004442e-01 6.9987517e-01 4.5148429e-01 5.0477564e-01 8.3183672e-01 1.1010711e+00 8.0000160e-01 6.0052920e-01 2.0121983e-01 6.8161057e-01 4.1212852e-01 7.0176121e-01 1.0030721e+00 1.0458540e+00 9.3984267e-01 9.0166476e-01 5.0043084e-01 7.0088627e-01 7.0993998e-01 3.0017653e-01 2.2608083e-01 7.0008584e-01 9.3848935e-01 7.0184453e-01 6.3178534e-01 9.6936870e-01 5.0476836e-01 8.7372177e-01 5.6618864e-01 5.0477564e-01 8.7240114e-01 5.3943256e-01 3.0474106e-01 5.2167208e-01 8.0879701e-01 5.0085236e-01 9.0279268e-01 5.2133802e-01 4.2362917e-01 6.0017982e-01 5.2838320e-01
Added: trunk/scipy/cluster/tests/test_hierarchy.py
===================================================================
--- trunk/scipy/cluster/tests/test_hierarchy.py 2008-04-17 21:15:51 UTC (rev 4147)
+++ trunk/scipy/cluster/tests/test_hierarchy.py 2008-04-18 02:13:12 UTC (rev 4148)
@@ -0,0 +1,401 @@
+#! /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 sys
+import os.path
+from scipy.testing import *
+from scipy.cluster.hierarchy import pdist
+
+import numpy
+#import math
+
+#from scipy.cluster.hierarchy import pdist, euclidean
+
+_filenames = ["iris.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"]
+
+# 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():
+ for fn in _filenames:
+ name = fn.replace(".txt", "").replace("-ml", "")
+ fqfn = os.path.join(os.path.dirname(__file__), fn)
+ eo[name] = numpy.loadtxt(open(fqfn))
+ #print "%s: %s %s" % (name, str(eo[name].shape), str(eo[name].dtype))
+ eo['pdist-boolean-inp'] = numpy.bool_(eo['pdist-boolean-inp'])
+
+load_testing_files()
+
+#print eo.keys()
+
+
+#print numpy.abs(Y_test2 - Y_right).max()
+#print numpy.abs(Y_test1 - Y_right).max()
+
+class TestPdist(TestCase):
+
+ ################### 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_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(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_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_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_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_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", numpy.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", numpy.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", numpy.abs(Y_test1 - Y_right).max()
+
+ 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", numpy.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", numpy.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", numpy.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", numpy.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", numpy.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", numpy.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", numpy.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", numpy.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", numpy.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", numpy.abs(Y_test2 - Y_right).max()
+ self.failUnless(within_tol(Y_test2, Y_right, eps))
+
+def within_tol(a, b, tol):
+ return numpy.abs(a - b).max() < tol
+
+if __name__ == "__main__":
+ nose.run(argv=['', __file__])
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