[Scipy-svn] r4694 - in trunk/scipy/cluster: . src tests
scipy-svn at scipy.org
scipy-svn at scipy.org
Sun Sep 7 15:28:15 EDT 2008
Author: damian.eads
Date: 2008-09-07 14:28:06 -0500 (Sun, 07 Sep 2008)
New Revision: 4694
Added:
trunk/scipy/cluster/tests/cdist-X1.txt
trunk/scipy/cluster/tests/cdist-X2.txt
Modified:
trunk/scipy/cluster/distance.py
trunk/scipy/cluster/src/distance.c
trunk/scipy/cluster/src/distance.h
trunk/scipy/cluster/src/distance_wrap.c
trunk/scipy/cluster/tests/test_distance.py
Log:
Added cdist function for computing distances between two collections of vectors. Added tests for the cdist function.
Modified: trunk/scipy/cluster/distance.py
===================================================================
--- trunk/scipy/cluster/distance.py 2008-09-07 07:46:29 UTC (rev 4693)
+++ trunk/scipy/cluster/distance.py 2008-09-07 19:28:06 UTC (rev 4694)
@@ -796,7 +796,7 @@
def pdist(X, metric='euclidean', p=2, V=None, VI=None):
"""
- Computes the distance between m original observations in
+ Computes the pairwise distances between m original observations in
n-dimensional space. Returns a condensed distance matrix Y. For
each :math:`$i$` and :math:`$j$` (where :math:`$i<j<n$), the
metric ``dist(u=X[i], v=X[j])`` is computed and stored in the
@@ -1001,6 +1001,8 @@
dm = pdist(X, 'sokalsneath')
"""
+
+
# 21. Y = pdist(X, 'test_Y')
#
# Computes the distance between all pairs of vectors in X
@@ -1131,7 +1133,7 @@
elif mstr == 'canberra':
_distance_wrap.pdist_canberra_wrap(_convert_to_double(X), dm)
elif mstr == 'braycurtis':
- _distance_wrap.pdist_bray_curtis_wrap(_convert_to_bool(X), dm)
+ _distance_wrap.pdist_bray_curtis_wrap(_convert_to_double(X), dm)
elif mstr == 'yule':
_distance_wrap.pdist_yule_bool_wrap(_convert_to_bool(X), dm)
elif mstr == 'matching':
@@ -1204,6 +1206,7 @@
else:
raise TypeError('2nd argument metric must be a string identifier or a function.')
return dm
+
def squareform(X, force="no", checks=True):
"""
Converts a vector-form distance vector to a square-form distance
@@ -1497,3 +1500,456 @@
is_valid_y(Y, throw=True, name='Y')
d = int(np.ceil(np.sqrt(Y.shape[0] * 2)))
return d
+
+
+def cdist(XA, XB, metric='euclidean', p=2, V=None, VI=None):
+ """
+ Computes distance between each pair of observations between two
+ collections of vectors. ``XA`` is a :math:`$m_A$` by :math:`$n$`
+ array while ``XB`` is a :math:`$m_B$` by :math:`$n$` array. A
+ :math:`$m_A$` by :math:`$m_B$` array is returned. An exception is
+ thrown if ``XA`` and ``XB`` do not have the same number of
+ columns.
+
+ A rectangular distance matrix Y is returned. For each :math:`$i$`
+ and :math:`$j$`, the metric ``dist(u=XA[i], v=XB[j])`` is computed
+ and stored in the :math:`ij`th entry.
+
+
+ :Parameters:
+ XA : ndarray
+ An :math:`$m_A$` by :math:`$n$` array of :math:`$m_A$`
+ original observations in an n-dimensional space.
+ XB : ndarray
+ An :math:`$m_B$` by :math:`$n$` array of :math:`$m_B$`
+ original observations in an n-dimensional space.
+ metric : string or function
+ The distance metric to use. The distance function can
+ be 'braycurtis', 'canberra', 'chebyshev', 'cityblock',
+ 'correlation', 'cosine', 'dice', 'euclidean', 'hamming',
+ 'jaccard', 'kulsinski', 'mahalanobis', 'matching',
+ 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean',
+ 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'.
+
+ :Returns:
+ Y : ndarray
+ A :math:`$m_A$` by :math:`$m_B$` distance matrix.
+
+ Calling Conventions
+ -------------------
+
+ 1. ``Y = cdist(X, 'euclidean')``
+
+ Computes the distance between m points using Euclidean distance
+ (2-norm) as the distance metric between the points. The points
+ are arranged as m n-dimensional row vectors in the matrix X.
+
+ 2. ``Y = cdist(X, 'minkowski', p)``
+
+ Computes the distances using the Minkowski distance
+ :math:`$||u-v||_p$` (p-norm) where :math:`$p \geq 1$`.
+
+ 3. ``Y = cdist(X, 'cityblock')``
+
+ Computes the city block or Manhattan distance between the
+ points.
+
+ 4. ``Y = cdist(X, 'seuclidean', V=None)``
+
+ Computes the standardized Euclidean distance. The standardized
+ Euclidean distance between two n-vectors ``u`` and ``v`` is
+
+ .. math:
+
+ sqrt(\sum {(u_i-v_i)^2 / V[x_i]}).
+
+ V is the variance vector; V[i] is the variance computed over all
+ the i'th components of the points. If not passed, it is
+ automatically computed.
+
+ 5. ``Y = cdist(X, 'sqeuclidean')``
+
+ Computes the squared Euclidean distance ||u-v||_2^2 between
+ the vectors.
+
+ 6. ``Y = cdist(X, 'cosine')``
+
+ Computes the cosine distance between vectors u and v,
+
+ .. math:
+
+ \frac{1 - uv^T}
+ {{|u|}_2 {|v|}_2}
+
+ where |*|_2 is the 2 norm of its argument *.
+
+ 7. ``Y = cdist(X, 'correlation')``
+
+ Computes the correlation distance between vectors u and v. This is
+
+ .. math:
+
+ \frac{1 - (u - n{|u|}_1){(v - n{|v|}_1)}^T}
+ {{|(u - n{|u|}_1)|}_2 {|(v - n{|v|}_1)|}^T}
+
+ where :math:`$|*|_1$` is the Manhattan (or 1-norm) of its
+ argument, and :math:`$n$` is the common dimensionality of the
+ vectors.
+
+ 8. ``Y = cdist(X, 'hamming')``
+
+ Computes the normalized Hamming distance, or the proportion of
+ those vector elements between two n-vectors ``u`` and ``v``
+ which disagree. To save memory, the matrix ``X`` can be of type
+ boolean.
+
+ 9. ``Y = cdist(X, 'jaccard')``
+
+ Computes the Jaccard distance between the points. Given two
+ vectors, ``u`` and ``v``, the Jaccard distance is the
+ proportion of those elements ``u[i]`` and ``v[i]`` that
+ disagree where at least one of them is non-zero.
+
+ 10. ``Y = cdist(X, 'chebyshev')``
+
+ Computes the Chebyshev distance between the points. The
+ Chebyshev distance between two n-vectors ``u`` and ``v`` is the
+ maximum norm-1 distance between their respective elements. More
+ precisely, the distance is given by
+
+ .. math:
+
+ d(u,v) = max_i {|u_i-v_i|}.
+
+ 11. ``Y = cdist(X, 'canberra')``
+
+ Computes the Canberra distance between the points. The
+ Canberra distance between two points ``u`` and ``v`` is
+
+ .. math:
+
+ d(u,v) = \sum_u {|u_i-v_i|}
+ {|u_i|+|v_i|}
+
+
+ 12. ``Y = cdist(X, 'braycurtis')``
+
+ Computes the Bray-Curtis distance between the points. The
+ Bray-Curtis distance between two points ``u`` and ``v`` is
+
+
+ .. math:
+
+ d(u,v) = \frac{\sum_i {u_i-v_i}}
+ {\sum_i {u_i+v_i}}
+
+ 13. ``Y = cdist(X, 'mahalanobis', VI=None)``
+
+ Computes the Mahalanobis distance between the points. The
+ Mahalanobis distance between two points ``u`` and ``v`` is
+ :math:`$(u-v)(1/V)(u-v)^T$` where :math:`$(1/V)$` (the ``VI``
+ variable) is the inverse covariance. If ``VI`` is not None,
+ ``VI`` will be used as the inverse covariance matrix.
+
+ 14. ``Y = cdist(X, 'yule')``
+
+ Computes the Yule distance between each pair of boolean
+ vectors. (see yule function documentation)
+
+ 15. ``Y = cdist(X, 'matching')``
+
+ Computes the matching distance between each pair of boolean
+ vectors. (see matching function documentation)
+
+ 16. ``Y = cdist(X, 'dice')``
+
+ Computes the Dice distance between each pair of boolean
+ vectors. (see dice function documentation)
+
+ 17. ``Y = cdist(X, 'kulsinski')``
+
+ Computes the Kulsinski distance between each pair of
+ boolean vectors. (see kulsinski function documentation)
+
+ 18. ``Y = cdist(X, 'rogerstanimoto')``
+
+ Computes the Rogers-Tanimoto distance between each pair of
+ boolean vectors. (see rogerstanimoto function documentation)
+
+ 19. ``Y = cdist(X, 'russellrao')``
+
+ Computes the Russell-Rao distance between each pair of
+ boolean vectors. (see russellrao function documentation)
+
+ 20. ``Y = cdist(X, 'sokalmichener')``
+
+ Computes the Sokal-Michener distance between each pair of
+ boolean vectors. (see sokalmichener function documentation)
+
+ 21. ``Y = cdist(X, 'sokalsneath')``
+
+ Computes the Sokal-Sneath distance between each pair of
+ boolean vectors. (see sokalsneath function documentation)
+
+ 22. ``Y = cdist(X, f)``
+
+ Computes the distance between all pairs of vectors in X
+ using the user supplied 2-arity function f. For example,
+ Euclidean distance between the vectors could be computed
+ as follows::
+
+ dm = cdist(X, (lambda u, v: np.sqrt(((u-v)*(u-v).T).sum())))
+
+ Note that you should avoid passing a reference to one of
+ the distance functions defined in this library. For example,::
+
+ dm = cdist(X, sokalsneath)
+
+ would calculate the pair-wise distances between the vectors in
+ X using the Python function sokalsneath. This would result in
+ sokalsneath being called :math:`${n \choose 2}$` times, which
+ is inefficient. Instead, the optimized C version is more
+ efficient, and we call it using the following syntax.::
+
+ dm = cdist(X, 'sokalsneath')
+
+ """
+
+
+# 21. Y = cdist(X, 'test_Y')
+#
+# Computes the distance between all pairs of vectors in X
+# using the distance metric Y but with a more succint,
+# verifiable, but less efficient implementation.
+
+
+ XA = np.asarray(XA)
+ XB = np.asarray(XB)
+
+ #if np.issubsctype(X, np.floating) and not np.issubsctype(X, np.double):
+ # raise TypeError('Floating point arrays must be 64-bit (got %r).' %
+ # (X.dtype.type,))
+
+ # The C code doesn't do striding.
+ [XA] = _copy_arrays_if_base_present([_convert_to_double(XA)])
+ [XB] = _copy_arrays_if_base_present([_convert_to_double(XB)])
+
+ s = XA.shape
+ sB = XB.shape
+
+ if len(s) != 2:
+ raise ValueError('XA must be a 2-dimensional array.');
+ if len(sB) != 2:
+ raise ValueError('XB must be a 2-dimensional array.');
+ if s[1] != sB[1]:
+ raise ValueError('XA and XB must have the same number of columns (i.e. feature dimension.)')
+
+ mA = s[0]
+ mB = sB[0]
+ n = s[1]
+ dm = np.zeros((mA, mB), dtype=np.double)
+
+ mtype = type(metric)
+ if mtype is types.FunctionType:
+ if metric == minkowski:
+ for i in xrange(0, mA):
+ for j in xrange(0, mB):
+ dm[i, j] = minkowski(XA[i, :], XB[j, :], p)
+ elif metric == seuclidean:
+ for i in xrange(0, mA):
+ for j in xrange(0, mB):
+ dm[i, j] = seuclidean(XA[i, :], XB[j, :], V)
+ elif metric == mahalanobis:
+ for i in xrange(0, mA):
+ for j in xrange(0, mB):
+ dm[i, j] = mahalanobis(XA[i, :], XB[j, :], V)
+ else:
+ for i in xrange(0, mA):
+ for j in xrange(0, mB):
+ dm[i, j] = metric(XA[i, :], XB[j, :])
+ elif mtype is types.StringType:
+ mstr = metric.lower()
+
+ #if XA.dtype != np.double and \
+ # (mstr != 'hamming' and mstr != 'jaccard'):
+ # TypeError('A double array must be passed.')
+ if mstr in set(['euclidean', 'euclid', 'eu', 'e']):
+ _distance_wrap.cdist_euclidean_wrap(_convert_to_double(XA),
+ _convert_to_double(XB), dm)
+ elif mstr in set(['sqeuclidean', 'sqe', 'sqeuclid']):
+ _distance_wrap.cdist_euclidean_wrap(_convert_to_double(XA),
+ _convert_to_double(XB), dm)
+ dm **= 2.0
+ elif mstr in set(['cityblock', 'cblock', 'cb', 'c']):
+ _distance_wrap.cdist_city_block_wrap(_convert_to_double(XA),
+ _convert_to_double(XB), dm)
+ elif mstr in set(['hamming', 'hamm', 'ha', 'h']):
+ if XA.dtype == np.bool:
+ _distance_wrap.cdist_hamming_bool_wrap(_convert_to_bool(XA),
+ _convert_to_bool(XB), dm)
+ else:
+ _distance_wrap.cdist_hamming_wrap(_convert_to_double(XA),
+ _convert_to_double(XB), dm)
+ elif mstr in set(['jaccard', 'jacc', 'ja', 'j']):
+ if XA.dtype == np.bool:
+ _distance_wrap.cdist_jaccard_bool_wrap(_convert_to_bool(XA),
+ _convert_to_bool(XB), dm)
+ else:
+ _distance_wrap.cdist_jaccard_wrap(_convert_to_double(XA),
+ _convert_to_double(XB), dm)
+ elif mstr in set(['chebychev', 'chebyshev', 'cheby', 'cheb', 'ch']):
+ _distance_wrap.cdist_chebyshev_wrap(_convert_to_double(XA),
+ _convert_to_double(XB), dm)
+ elif mstr in set(['minkowski', 'mi', 'm']):
+ _distance_wrap.cdist_minkowski_wrap(_convert_to_double(XA),
+ _convert_to_double(XB), dm, p)
+ elif mstr in set(['seuclidean', 'se', 's']):
+ if V is not None:
+ V = np.asarray(V)
+ if type(V) != np.ndarray:
+ raise TypeError('Variance vector V must be a numpy array')
+ if V.dtype != np.double:
+ raise TypeError('Variance vector V must contain doubles.')
+ if len(V.shape) != 1:
+ raise ValueError('Variance vector V must be one-dimensional.')
+ if V.shape[0] != n:
+ raise ValueError('Variance vector V must be of the same dimension as the vectors on which the distances are computed.')
+ # The C code doesn't do striding.
+ [VV] = _copy_arrays_if_base_present([_convert_to_double(V)])
+ else:
+ X = np.vstack([XA, XB])
+ VV = np.var(X, axis=0, ddof=1)
+ X = None
+ del X
+ _distance_wrap.cdist_seuclidean_wrap(_convert_to_double(XA),
+ _convert_to_double(XB), VV, dm)
+ # Need to test whether vectorized cosine works better.
+ # Find out: Is there a dot subtraction operator so I can
+ # subtract matrices in a similar way to multiplying them?
+ # Need to get rid of as much unnecessary C code as possible.
+ elif mstr in set(['cosine', 'cos']):
+ normsA = np.sqrt(np.sum(XA * XA, axis=1))
+ normsB = np.sqrt(np.sum(XB * XB, axis=1))
+ _distance_wrap.cdist_cosine_wrap(_convert_to_double(XA),
+ _convert_to_double(XB), dm,
+ normsA,
+ normsB)
+ elif mstr in set(['correlation', 'co']):
+ XA2 = XA - XA.mean(1)[:,np.newaxis]
+ XB2 = XB - XB.mean(1)[:,np.newaxis]
+ #X2 = X - np.matlib.repmat(np.mean(X, axis=1).reshape(m, 1), 1, n)
+ normsA = np.sqrt(np.sum(XA2 * XA2, axis=1))
+ normsB = np.sqrt(np.sum(XB2 * XB2, axis=1))
+ _distance_wrap.cdist_cosine_wrap(_convert_to_double(XA2),
+ _convert_to_double(XB2),
+ _convert_to_double(dm),
+ _convert_to_double(normsA),
+ _convert_to_double(normsB))
+ elif mstr in set(['mahalanobis', 'mahal', 'mah']):
+ if VI is not None:
+ VI = _convert_to_double(np.asarray(VI))
+ if type(VI) != np.ndarray:
+ raise TypeError('VI must be a numpy array.')
+ if VI.dtype != np.double:
+ raise TypeError('The array must contain 64-bit floats.')
+ [VI] = _copy_arrays_if_base_present([VI])
+ else:
+ X = np.vstack([XA, XB])
+ V = np.cov(X.T)
+ X = None
+ del X
+ VI = _convert_to_double(np.linalg.inv(V).T.copy())
+ # (u-v)V^(-1)(u-v)^T
+ _distance_wrap.cdist_mahalanobis_wrap(_convert_to_double(XA),
+ _convert_to_double(XB), VI, dm)
+ elif mstr == 'canberra':
+ _distance_wrap.cdist_canberra_wrap(_convert_to_double(XA),
+ _convert_to_double(XB), dm)
+ elif mstr == 'braycurtis':
+ _distance_wrap.cdist_bray_curtis_wrap(_convert_to_double(XA),
+ _convert_to_double(XB), dm)
+ elif mstr == 'yule':
+ _distance_wrap.cdist_yule_bool_wrap(_convert_to_bool(XA),
+ _convert_to_bool(XB), dm)
+ elif mstr == 'matching':
+ _distance_wrap.cdist_matching_bool_wrap(_convert_to_bool(XA),
+ _convert_to_bool(XB), dm)
+ elif mstr == 'kulsinski':
+ _distance_wrap.cdist_kulsinski_bool_wrap(_convert_to_bool(XA),
+ _convert_to_bool(XB), dm)
+ elif mstr == 'dice':
+ _distance_wrap.cdist_dice_bool_wrap(_convert_to_bool(XA),
+ _convert_to_bool(XB), dm)
+ elif mstr == 'rogerstanimoto':
+ _distance_wrap.cdist_rogerstanimoto_bool_wrap(_convert_to_bool(XA),
+ _convert_to_bool(XB), dm)
+ elif mstr == 'russellrao':
+ _distance_wrap.cdist_russellrao_bool_wrap(_convert_to_bool(XA),
+ _convert_to_bool(XB), dm)
+ elif mstr == 'sokalmichener':
+ _distance_wrap.cdist_sokalmichener_bool_wrap(_convert_to_bool(XA),
+ _convert_to_bool(XB), dm)
+ elif mstr == 'sokalsneath':
+ _distance_wrap.cdist_sokalsneath_bool_wrap(_convert_to_bool(XA),
+ _convert_to_bool(XB), dm)
+ elif metric == 'test_euclidean':
+ dm = cdist(XA, XB, euclidean)
+ elif metric == 'test_seuclidean':
+ if V is None:
+ V = np.var(np.vstack([XA, XB]), axis=0, ddof=1)
+ else:
+ V = np.asarray(V)
+ dm = cdist(XA, XB, lambda u, v: seuclidean(u, v, V))
+ elif metric == 'test_sqeuclidean':
+ dm = cdist(XA, XB, lambda u, v: sqeuclidean(u, v))
+ elif metric == 'test_braycurtis':
+ dm = cdist(XA, XB, braycurtis)
+ elif metric == 'test_mahalanobis':
+ if VI is None:
+ X = np.vstack([XA, XB])
+ V = np.cov(X.T)
+ VI = np.linalg.inv(V)
+ X = None
+ del X
+ else:
+ VI = np.asarray(VI)
+ [VI] = _copy_arrays_if_base_present([VI])
+ # (u-v)V^(-1)(u-v)^T
+ dm = cdist(XA, XB, (lambda u, v: mahalanobis(u, v, VI)))
+ elif metric == 'test_canberra':
+ dm = cdist(XA, XB, canberra)
+ elif metric == 'test_cityblock':
+ dm = cdist(XA, XB, cityblock)
+ elif metric == 'test_minkowski':
+ dm = cdist(XA, XB, minkowski, p)
+ elif metric == 'test_cosine':
+ dm = cdist(XA, XB, cosine)
+ elif metric == 'test_correlation':
+ dm = cdist(XA, XB, correlation)
+ elif metric == 'test_hamming':
+ dm = cdist(XA, XB, hamming)
+ elif metric == 'test_jaccard':
+ dm = cdist(XA, XB, jaccard)
+ elif metric == 'test_chebyshev' or metric == 'test_chebychev':
+ dm = cdist(XA, XB, chebyshev)
+ elif metric == 'test_yule':
+ dm = cdist(XA, XB, yule)
+ elif metric == 'test_matching':
+ dm = cdist(XA, XB, matching)
+ elif metric == 'test_dice':
+ dm = cdist(XA, XB, dice)
+ elif metric == 'test_kulsinski':
+ dm = cdist(XA, XB, kulsinski)
+ elif metric == 'test_rogerstanimoto':
+ dm = cdist(XA, XB, rogerstanimoto)
+ elif metric == 'test_russellrao':
+ dm = cdist(XA, XB, russellrao)
+ elif metric == 'test_sokalsneath':
+ dm = cdist(XA, XB, sokalsneath)
+ elif metric == 'test_sokalmichener':
+ dm = cdist(XA, XB, sokalmichener)
+ else:
+ raise ValueError('Unknown Distance Metric: %s' % mstr)
+ else:
+ raise TypeError('2nd argument metric must be a string identifier or a function.')
+ return dm
Modified: trunk/scipy/cluster/src/distance.c
===================================================================
--- trunk/scipy/cluster/src/distance.c 2008-09-07 07:46:29 UTC (rev 4693)
+++ trunk/scipy/cluster/src/distance.c 2008-09-07 19:28:06 UTC (rev 4694)
@@ -700,7 +700,7 @@
}
void cdist_hamming_bool(const char *XA,
- const char *XB, const char *X, double *dm, int mA, int mB, int n) {
+ const char *XB, double *dm, int mA, int mB, int n) {
int i, j;
const char *u, *v;
double *it = dm;
Modified: trunk/scipy/cluster/src/distance.h
===================================================================
--- trunk/scipy/cluster/src/distance.h 2008-09-07 07:46:29 UTC (rev 4693)
+++ trunk/scipy/cluster/src/distance.h 2008-09-07 19:28:06 UTC (rev 4694)
@@ -74,7 +74,7 @@
void cdist_hamming(const double *XA,
const double *XB, double *dm, int mA, int mB, int n);
void cdist_hamming_bool(const char *XA,
- const char *XB, const char *X, double *dm,
+ const char *XB, double *dm,
int mA, int mB, int n);
void cdist_jaccard(const double *XA,
const double *XB, double *dm, int mA, int mB, int n);
Modified: trunk/scipy/cluster/src/distance_wrap.c
===================================================================
--- trunk/scipy/cluster/src/distance_wrap.c 2008-09-07 07:46:29 UTC (rev 4693)
+++ trunk/scipy/cluster/src/distance_wrap.c 2008-09-07 19:28:06 UTC (rev 4694)
@@ -40,6 +40,506 @@
#include <numpy/arrayobject.h>
#include <stdio.h>
+extern PyObject *cdist_euclidean_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const double *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const double*)XA_->data;
+ XB = (const double*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_euclidean(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+extern PyObject *cdist_canberra_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const double *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const double*)XA_->data;
+ XB = (const double*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_canberra(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+extern PyObject *cdist_bray_curtis_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const double *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const double*)XA_->data;
+ XB = (const double*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_bray_curtis(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+
+extern PyObject *cdist_mahalanobis_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *covinv_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const double *XA, *XB;
+ const double *covinv;
+ if (!PyArg_ParseTuple(args, "O!O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &covinv_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const double*)XA_->data;
+ XB = (const double*)XB_->data;
+ covinv = (const double*)covinv_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_mahalanobis(XA, XB, covinv, dm, mA, mB, n);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+
+extern PyObject *cdist_chebyshev_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const double *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const double*)XA_->data;
+ XB = (const double*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_chebyshev(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+
+extern PyObject *cdist_cosine_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_, *normsA_, *normsB_;
+ int mA, mB, n;
+ double *dm;
+ const double *XA, *XB, *normsA, *normsB;
+ if (!PyArg_ParseTuple(args, "O!O!O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_,
+ &PyArray_Type, &normsA_,
+ &PyArray_Type, &normsB_)) {
+ return 0;
+ }
+ else {
+ XA = (const double*)XA_->data;
+ XB = (const double*)XB_->data;
+ dm = (double*)dm_->data;
+ normsA = (const double*)normsA_->data;
+ normsB = (const double*)normsB_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_cosine(XA, XB, dm, mA, mB, n, normsA, normsB);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+extern PyObject *cdist_seuclidean_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_, *var_;
+ int mA, mB, n;
+ double *dm;
+ const double *XA, *XB, *var;
+ if (!PyArg_ParseTuple(args, "O!O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &var_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const double*)XA_->data;
+ XB = (const double*)XB_->data;
+ dm = (double*)dm_->data;
+ var = (double*)var_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_seuclidean(XA, XB, var, dm, mA, mB, n);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+extern PyObject *cdist_city_block_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const double *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const double*)XA_->data;
+ XB = (const double*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_city_block(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+extern PyObject *cdist_hamming_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const double *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const double*)XA_->data;
+ XB = (const double*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_hamming(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+extern PyObject *cdist_hamming_bool_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const char *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const char*)XA_->data;
+ XB = (const char*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_hamming_bool(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+extern PyObject *cdist_jaccard_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const double *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const double*)XA_->data;
+ XB = (const double*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_jaccard(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+extern PyObject *cdist_jaccard_bool_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const char *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const char*)XA_->data;
+ XB = (const char*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_jaccard_bool(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+extern PyObject *cdist_minkowski_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const double *XA, *XB;
+ double p;
+ if (!PyArg_ParseTuple(args, "O!O!O!d",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_,
+ &p)) {
+ return 0;
+ }
+ else {
+ XA = (const double*)XA_->data;
+ XB = (const double*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_minkowski(XA, XB, dm, mA, mB, n, p);
+ }
+ return Py_BuildValue("d", 0.0);
+}
+
+
+extern PyObject *cdist_yule_bool_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const char *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const char*)XA_->data;
+ XB = (const char*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_yule_bool(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("");
+}
+
+extern PyObject *cdist_matching_bool_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const char *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const char*)XA_->data;
+ XB = (const char*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_matching_bool(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("");
+}
+
+extern PyObject *cdist_dice_bool_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const char *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const char*)XA_->data;
+ XB = (const char*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_dice_bool(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("");
+}
+
+extern PyObject *cdist_rogerstanimoto_bool_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const char *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const char*)XA_->data;
+ XB = (const char*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_rogerstanimoto_bool(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("");
+}
+
+extern PyObject *cdist_russellrao_bool_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const char *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const char*)XA_->data;
+ XB = (const char*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_russellrao_bool(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("");
+}
+
+extern PyObject *cdist_kulsinski_bool_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const char *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const char*)XA_->data;
+ XB = (const char*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_kulsinski_bool(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("");
+}
+
+extern PyObject *cdist_sokalmichener_bool_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const char *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const char*)XA_->data;
+ XB = (const char*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_sokalmichener_bool(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("");
+}
+
+extern PyObject *cdist_sokalsneath_bool_wrap(PyObject *self, PyObject *args) {
+ PyArrayObject *XA_, *XB_, *dm_;
+ int mA, mB, n;
+ double *dm;
+ const char *XA, *XB;
+ if (!PyArg_ParseTuple(args, "O!O!O!",
+ &PyArray_Type, &XA_, &PyArray_Type, &XB_,
+ &PyArray_Type, &dm_)) {
+ return 0;
+ }
+ else {
+ XA = (const char*)XA_->data;
+ XB = (const char*)XB_->data;
+ dm = (double*)dm_->data;
+ mA = XA_->dimensions[0];
+ mB = XB_->dimensions[0];
+ n = XA_->dimensions[1];
+
+ cdist_sokalsneath_bool(XA, XB, dm, mA, mB, n);
+ }
+ return Py_BuildValue("");
+}
+
+/***************************** pdist ***/
+
extern PyObject *pdist_euclidean_wrap(PyObject *self, PyObject *args) {
PyArrayObject *X_, *dm_;
int m, n;
@@ -533,6 +1033,27 @@
static PyMethodDef _distanceWrapMethods[] = {
+ {"cdist_bray_curtis_wrap", cdist_bray_curtis_wrap, METH_VARARGS},
+ {"cdist_canberra_wrap", cdist_canberra_wrap, METH_VARARGS},
+ {"cdist_chebyshev_wrap", cdist_chebyshev_wrap, METH_VARARGS},
+ {"cdist_city_block_wrap", cdist_city_block_wrap, METH_VARARGS},
+ {"cdist_cosine_wrap", cdist_cosine_wrap, METH_VARARGS},
+ {"cdist_dice_bool_wrap", cdist_dice_bool_wrap, METH_VARARGS},
+ {"cdist_euclidean_wrap", cdist_euclidean_wrap, METH_VARARGS},
+ {"cdist_hamming_wrap", cdist_hamming_wrap, METH_VARARGS},
+ {"cdist_hamming_bool_wrap", cdist_hamming_bool_wrap, METH_VARARGS},
+ {"cdist_jaccard_wrap", cdist_jaccard_wrap, METH_VARARGS},
+ {"cdist_jaccard_bool_wrap", cdist_jaccard_bool_wrap, METH_VARARGS},
+ {"cdist_kulsinski_bool_wrap", cdist_kulsinski_bool_wrap, METH_VARARGS},
+ {"cdist_mahalanobis_wrap", cdist_mahalanobis_wrap, METH_VARARGS},
+ {"cdist_matching_bool_wrap", cdist_matching_bool_wrap, METH_VARARGS},
+ {"cdist_minkowski_wrap", cdist_minkowski_wrap, METH_VARARGS},
+ {"cdist_rogerstanimoto_bool_wrap", cdist_rogerstanimoto_bool_wrap, METH_VARARGS},
+ {"cdist_russellrao_bool_wrap", cdist_russellrao_bool_wrap, METH_VARARGS},
+ {"cdist_seuclidean_wrap", cdist_seuclidean_wrap, METH_VARARGS},
+ {"cdist_sokalmichener_bool_wrap", cdist_sokalmichener_bool_wrap, METH_VARARGS},
+ {"cdist_sokalsneath_bool_wrap", cdist_sokalsneath_bool_wrap, METH_VARARGS},
+ {"cdist_yule_bool_wrap", cdist_yule_bool_wrap, METH_VARARGS},
{"pdist_bray_curtis_wrap", pdist_bray_curtis_wrap, METH_VARARGS},
{"pdist_canberra_wrap", pdist_canberra_wrap, METH_VARARGS},
{"pdist_chebyshev_wrap", pdist_chebyshev_wrap, METH_VARARGS},
Added: trunk/scipy/cluster/tests/cdist-X1.txt
===================================================================
--- trunk/scipy/cluster/tests/cdist-X1.txt 2008-09-07 07:46:29 UTC (rev 4693)
+++ trunk/scipy/cluster/tests/cdist-X1.txt 2008-09-07 19:28:06 UTC (rev 4694)
@@ -0,0 +1,10 @@
+1.147593763490969421e-01 8.926156143344999849e-01 1.437758624645746330e-02 1.803435962879929022e-02 5.533046214065578949e-01 5.554315640747428118e-01 4.497546637814608950e-02 4.438089247948049376e-01 7.984582810220538507e-01 2.752880789161644692e-01 1.344667112315823809e-01 9.230479561452992199e-01 6.040471462941819913e-01 3.797251652770228247e-01 4.316042735592399149e-01 5.312356915348823705e-01 4.348143005129563310e-01 3.111531488508799681e-01 9.531194313908697424e-04 8.212995023500069269e-02 6.689953269869852726e-01 9.914864535288493430e-01 8.037556036341153565e-01
+9.608925123801395074e-01 2.974451233678974127e-01 9.001110330654185088e-01 5.824163330415995654e-01 7.308574928293812834e-01 2.276154562412870952e-01 7.306791076039623745e-01 8.677244866905511333e-01 9.160806456176984192e-01 6.157216959991280714e-01 5.149053524695440531e-01 3.056427344890983999e-01 9.790557366933895223e-01 4.484995861076724877e-01 4.776550391081165747e-01 7.210436977670631187e-01 9.136399501661039979e-01 4.260275733550000776e-02 5.943900041968954717e-01 3.864571606342745991e-01 9.442027665110838131e-01 4.779949058608601309e-02 6.107551944250865228e-01
+3.297286578103622023e-01 5.980207401936733502e-01 3.673301293561567205e-01 2.585830520887681949e-01 4.660558746104259686e-01 6.083795956610364986e-01 4.535206368070313632e-01 6.873989778785424276e-01 5.130152688495458468e-01 7.665877846542720198e-01 3.444402973525138023e-01 3.583658123644906102e-02 7.924818220986856732e-01 8.746685720522412444e-01 3.010105569182431884e-01 6.012239357385538163e-01 6.233737362204671006e-01 4.830438698668915176e-01 2.317286885842551047e-02 7.585989958123050547e-01 7.108257632278830451e-01 1.551024884178199281e-01 2.665485998155288083e-01
+2.456278068903017253e-02 4.148739837711815648e-01 1.986372227934196655e-01 6.920408530298168825e-01 1.003067576685774398e-01 7.421560456480125190e-01 1.808453980608998313e-01 4.251297882537475870e-01 6.773002683522370004e-01 4.084108792570182445e-01 7.462888013191590897e-01 8.069930220529277776e-01 9.211110587681808903e-01 4.141491046181076108e-01 7.486318689260342829e-01 9.515405507589296263e-01 4.634288892577109742e-03 8.027593488166355762e-01 3.010346805217798405e-01 8.663248877242523127e-01 2.479968181181605447e-01 5.619851096054278017e-01 3.903886764590250857e-01
+7.122019976035700584e-01 6.188878051047785878e-01 7.290897087051201320e-01 6.334802157757637442e-01 5.523084734954342156e-01 5.614937129563645213e-01 2.496741051791574462e-01 5.972227939599233926e-01 1.786590597761109622e-01 2.609525984850900038e-01 7.210438943286010538e-01 2.211429064605652250e-01 9.140497572472672250e-02 1.430242193668443962e-01 7.856446942916397447e-01 4.635256358156553125e-01 5.278744289813760426e-01 3.702808015407184072e-01 5.527073830480792038e-01 6.370732917599846168e-01 9.953487928925482953e-01 3.021789770611936765e-01 3.354901923998221402e-02
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Added: trunk/scipy/cluster/tests/cdist-X2.txt
===================================================================
--- trunk/scipy/cluster/tests/cdist-X2.txt 2008-09-07 07:46:29 UTC (rev 4693)
+++ trunk/scipy/cluster/tests/cdist-X2.txt 2008-09-07 19:28:06 UTC (rev 4694)
@@ -0,0 +1,20 @@
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Modified: trunk/scipy/cluster/tests/test_distance.py
===================================================================
--- trunk/scipy/cluster/tests/test_distance.py 2008-09-07 07:46:29 UTC (rev 4693)
+++ trunk/scipy/cluster/tests/test_distance.py 2008-09-07 19:28:06 UTC (rev 4694)
@@ -40,11 +40,13 @@
import numpy as np
from numpy.testing import *
from scipy.cluster.hierarchy import linkage, from_mlab_linkage, numobs_linkage
-from scipy.cluster.distance import squareform, pdist, matching, jaccard, dice, sokalsneath, rogerstanimoto, russellrao, yule, numobs_dm, numobs_y
+from scipy.cluster.distance import squareform, pdist, cdist, matching, jaccard, dice, sokalsneath, rogerstanimoto, russellrao, yule, numobs_dm, numobs_y
#from scipy.cluster.hierarchy import pdist, euclidean
_filenames = ["iris.txt",
+ "cdist-X1.txt",
+ "cdist-X2.txt",
"pdist-hamming-ml.txt",
"pdist-boolean-inp.txt",
"pdist-jaccard-ml.txt",
@@ -97,6 +99,298 @@
#print np.abs(Y_test2 - Y_right).max()
#print np.abs(Y_test1 - Y_right).max()
+class TestCdist(TestCase):
+ """
+ Test suite for the pdist 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')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_sqeuclidean_random(self):
+ "Tests cdist(X, 'sqeuclidean') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1']
+ X2 = eo['cdist-X2']
+ Y1 = cdist(X1, X2, 'sqeuclidean')
+ Y2 = cdist(X1, X2, 'test_sqeuclidean')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_cityblock_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, 'cityblock')
+ Y2 = cdist(X1, X2, 'test_cityblock')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_hamming_double_random(self):
+ "Tests cdist(X, 'hamming') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1']
+ X2 = eo['cdist-X2']
+ Y1 = cdist(X1, X2, 'hamming')
+ Y2 = cdist(X1, X2, 'test_hamming')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_hamming_bool_random(self):
+ "Tests cdist(X, 'hamming') on random boolean data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1'] < 0.5
+ X2 = eo['cdist-X2'] < 0.5
+ Y1 = cdist(X1, X2, 'hamming')
+ Y2 = cdist(X1, X2, 'test_hamming')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_jaccard_double_random(self):
+ "Tests cdist(X, 'jaccard') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1']
+ X2 = eo['cdist-X2']
+ Y1 = cdist(X1, X2, 'jaccard')
+ Y2 = cdist(X1, X2, 'test_jaccard')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_jaccard_bool_random(self):
+ "Tests cdist(X, 'jaccard') on random boolean data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1'] < 0.5
+ X2 = eo['cdist-X2'] < 0.5
+ Y1 = cdist(X1, X2, 'jaccard')
+ Y2 = cdist(X1, X2, 'test_jaccard')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_chebychev_random(self):
+ "Tests cdist(X, 'chebychev') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1']
+ X2 = eo['cdist-X2']
+ Y1 = cdist(X1, X2, 'chebychev')
+ Y2 = cdist(X1, X2, 'test_chebychev')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_minkowski_random_p3d8(self):
+ "Tests cdist(X, 'minkowski') on random data. (p=3.8)"
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1']
+ X2 = eo['cdist-X2']
+ Y1 = cdist(X1, X2, 'minkowski', p=3.8)
+ Y2 = cdist(X1, X2, 'test_minkowski', p=3.8)
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_minkowski_random_p4d6(self):
+ "Tests cdist(X, 'minkowski') on random data. (p=4.6)"
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1']
+ X2 = eo['cdist-X2']
+ Y1 = cdist(X1, X2, 'minkowski', p=4.6)
+ Y2 = cdist(X1, X2, 'test_minkowski', p=4.6)
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_minkowski_random_p1d23(self):
+ "Tests cdist(X, 'minkowski') on random data. (p=1.23)"
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1']
+ X2 = eo['cdist-X2']
+ Y1 = cdist(X1, X2, 'minkowski', p=1.23)
+ Y2 = cdist(X1, X2, 'test_minkowski', p=1.23)
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_seuclidean_random(self):
+ "Tests cdist(X, 'seuclidean') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1']
+ X2 = eo['cdist-X2']
+ Y1 = cdist(X1, X2, 'seuclidean')
+ Y2 = cdist(X1, X2, 'test_seuclidean')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_sqeuclidean_random(self):
+ "Tests cdist(X, 'sqeuclidean') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1']
+ X2 = eo['cdist-X2']
+ Y1 = cdist(X1, X2, 'sqeuclidean')
+ Y2 = cdist(X1, X2, 'test_sqeuclidean')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_cosine_random(self):
+ "Tests cdist(X, 'cosine') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1']
+ X2 = eo['cdist-X2']
+ Y1 = cdist(X1, X2, 'cosine')
+ Y2 = cdist(X1, X2, 'test_cosine')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_correlation_random(self):
+ "Tests cdist(X, 'correlation') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1']
+ X2 = eo['cdist-X2']
+ Y1 = cdist(X1, X2, 'correlation')
+ Y2 = cdist(X1, X2, 'test_correlation')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_mahalanobis_random(self):
+ "Tests cdist(X, 'mahalanobis') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1']
+ X2 = eo['cdist-X2']
+ Y1 = cdist(X1, X2, 'mahalanobis')
+ Y2 = cdist(X1, X2, 'test_mahalanobis')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_canberra_random(self):
+ "Tests cdist(X, 'canberra') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1'] < 0.5
+ X2 = eo['cdist-X2'] < 0.5
+ Y1 = cdist(X1, X2, 'canberra')
+ Y2 = cdist(X1, X2, 'test_canberra')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_braycurtis_random(self):
+ "Tests cdist(X, 'braycurtis') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1'] < 0.5
+ X2 = eo['cdist-X2'] < 0.5
+ Y1 = cdist(X1, X2, 'braycurtis')
+ Y2 = cdist(X1, X2, 'test_braycurtis')
+ print Y1, Y2
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_yule_random(self):
+ "Tests cdist(X, 'yule') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1'] < 0.5
+ X2 = eo['cdist-X2'] < 0.5
+ Y1 = cdist(X1, X2, 'yule')
+ Y2 = cdist(X1, X2, 'test_yule')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_matching_random(self):
+ "Tests cdist(X, 'matching') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1'] < 0.5
+ X2 = eo['cdist-X2'] < 0.5
+ Y1 = cdist(X1, X2, 'matching')
+ Y2 = cdist(X1, X2, 'test_matching')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_kulsinski_random(self):
+ "Tests cdist(X, 'kulsinski') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1'] < 0.5
+ X2 = eo['cdist-X2'] < 0.5
+ Y1 = cdist(X1, X2, 'kulsinski')
+ Y2 = cdist(X1, X2, 'test_kulsinski')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_dice_random(self):
+ "Tests cdist(X, 'dice') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1'] < 0.5
+ X2 = eo['cdist-X2'] < 0.5
+ Y1 = cdist(X1, X2, 'dice')
+ Y2 = cdist(X1, X2, 'test_dice')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_rogerstanimoto_random(self):
+ "Tests cdist(X, 'rogerstanimoto') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1'] < 0.5
+ X2 = eo['cdist-X2'] < 0.5
+ Y1 = cdist(X1, X2, 'rogerstanimoto')
+ Y2 = cdist(X1, X2, 'test_rogerstanimoto')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_russellrao_random(self):
+ "Tests cdist(X, 'russellrao') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1'] < 0.5
+ X2 = eo['cdist-X2'] < 0.5
+ Y1 = cdist(X1, X2, 'russellrao')
+ Y2 = cdist(X1, X2, 'test_russellrao')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_sokalmichener_random(self):
+ "Tests cdist(X, 'sokalmichener') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1'] < 0.5
+ X2 = eo['cdist-X2'] < 0.5
+ Y1 = cdist(X1, X2, 'sokalmichener')
+ Y2 = cdist(X1, X2, 'test_sokalmichener')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
+ def test_cdist_sokalsneath_random(self):
+ "Tests cdist(X, 'sokalsneath') on random data."
+ eps = 1e-07
+ # Get the data: the input matrix and the right output.
+ X1 = eo['cdist-X1'] < 0.5
+ X2 = eo['cdist-X2'] < 0.5
+ Y1 = cdist(X1, X2, 'sokalsneath')
+ Y2 = cdist(X1, X2, 'test_sokalsneath')
+ print (Y1-Y2).max()
+ self.failUnless(within_tol(Y1, Y2, eps))
+
class TestPdist(TestCase):
"""
Test suite for the pdist function.
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