[Scipy-svn] r5209 - in trunk/scipy: cluster spatial

scipy-svn@scip... scipy-svn@scip...
Sun Nov 30 09:19:26 CST 2008

Author: ptvirtan
Date: 2008-11-30 09:19:11 -0600 (Sun, 30 Nov 2008)
New Revision: 5209

Modified:
trunk/scipy/cluster/hierarchy.py
trunk/scipy/spatial/distance.py
Log:

Modified: trunk/scipy/cluster/hierarchy.py
===================================================================
--- trunk/scipy/cluster/hierarchy.py	2008-11-30 14:48:09 UTC (rev 5208)
+++ trunk/scipy/cluster/hierarchy.py	2008-11-30 15:19:11 UTC (rev 5209)
@@ -506,7 +506,7 @@

.. math::
d(u,v) = \sum_{ij} \frac{d(u[i], v[j])}
-                                  {(|u|*|v|)
+                                  {(|u|*|v|)}

for all points :math:i and :math:j where :math:|u|
and :math:|v| are the cardinalities of clusters :math:u

Modified: trunk/scipy/spatial/distance.py
===================================================================
--- trunk/scipy/spatial/distance.py	2008-11-30 14:48:09 UTC (rev 5208)
+++ trunk/scipy/spatial/distance.py	2008-11-30 15:19:11 UTC (rev 5209)
@@ -449,7 +449,7 @@
return np.sqrt(((u-v)**2 / V).sum())

def cityblock(u, v):
-    """
+    r"""
Computes the Manhattan distance between two n-vectors u and v,
which is defined as

@@ -472,7 +472,7 @@
return abs(u-v).sum()

def mahalanobis(u, v, VI):
-    """
+    r"""
Computes the Mahalanobis distance between two n-vectors u and v,
which is defiend as

@@ -498,7 +498,7 @@
return np.sqrt(np.dot(np.dot((u-v),VI),(u-v).T).sum())

def chebyshev(u, v):
-    """
+    r"""
Computes the Chebyshev distance between two n-vectors u and v,
which is defined as

@@ -521,7 +521,7 @@
return max(abs(u-v))

def braycurtis(u, v):
-    """
+    r"""
Computes the Bray-Curtis distance between two n-vectors u and
v, which is defined as

@@ -544,7 +544,7 @@
return abs(u-v).sum() / abs(u+v).sum()

def canberra(u, v):
-    """
+    r"""
Computes the Canberra distance between two n-vectors u and v,
which is defined as

@@ -607,15 +607,14 @@
return (nft, ntf)

def yule(u, v):
-    """
+    r"""
Computes the Yule dissimilarity between two boolean n-vectors u and v,
which is defined as

.. math::

-         \frac{R}
-         \frac{c_{TT} + c_{FF} + \frac{R}{2}}
+         \frac{R}{c_{TT} + c_{FF} + \frac{R}{2}}

where :math:c_{ij} is the number of occurrences of
:math:\mathtt{u[k]} = i and :math:\mathtt{v[k]} = j for
@@ -637,7 +636,7 @@
return float(2.0 * ntf * nft) / float(ntt * nff + ntf * nft)

def matching(u, v):
-    """
+    r"""
Computes the Matching dissimilarity between two boolean n-vectors
u and v, which is defined as

@@ -665,7 +664,7 @@
return float(nft + ntf) / float(len(u))

def dice(u, v):
-    """
+    r"""
Computes the Dice dissimilarity between two boolean n-vectors
u and v, which is

@@ -698,7 +697,7 @@
return float(ntf + nft) / float(2.0 * ntt + ntf + nft)

def rogerstanimoto(u, v):
-    """
+    r"""
Computes the Rogers-Tanimoto dissimilarity between two boolean
n-vectors u and v, which is defined as

@@ -727,7 +726,7 @@
return float(2.0 * (ntf + nft)) / float(ntt + nff + (2.0 * (ntf + nft)))

def russellrao(u, v):
-    """
+    r"""
Computes the Russell-Rao dissimilarity between two boolean n-vectors
u and v, which is defined as

@@ -759,7 +758,7 @@
return float(len(u) - ntt) / float(len(u))

def sokalmichener(u, v):
-    """
+    r"""
Computes the Sokal-Michener dissimilarity between two boolean vectors
u and v, which is defined as

@@ -795,7 +794,7 @@
return float(2.0 * (ntf + nft))/float(ntt + nff + 2.0 * (ntf + nft))

def sokalsneath(u, v):
-    """
+    r"""
Computes the Sokal-Sneath dissimilarity between two boolean vectors
u and v,

@@ -829,7 +828,7 @@

def pdist(X, metric='euclidean', p=2, V=None, VI=None):
-    """
+    r"""
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
@@ -1263,7 +1262,7 @@
return dm

def squareform(X, force="no", checks=True):
-    """
+    r"""
Converts a vector-form distance vector to a square-form distance
matrix, and vice-versa.

@@ -1456,7 +1455,7 @@
return valid

def is_valid_y(y, warning=False, throw=False, name=None):
-    """
+    r"""
Returns True if the variable y passed is a valid condensed
distance matrix. Condensed distance matrices must be 1-dimensional
numpy arrays containing doubles. Their length must be a binomial
@@ -1558,7 +1557,7 @@

def cdist(XA, XB, metric='euclidean', p=2, V=None, VI=None, w=None):
-    """
+    r"""
Computes distance between each pair of observation vectors in the
Cartesian product of two collections of vectors. XA is a
:math:m_A by :math:n array while XB is a :math:m_B by
@@ -1782,7 +1781,7 @@
:Returns:
Y : ndarray
A :math:m_A by :math:m_B distance matrix.
-       """
+    """

#         21. Y = cdist(XA, XB, 'test_Y')