# [Scipy-svn] r5243 - trunk/scipy/spatial

scipy-svn@scip... scipy-svn@scip...
Thu Dec 11 20:54:27 CST 2008

Author: damian.eads
Date: 2008-12-11 20:54:24 -0600 (Thu, 11 Dec 2008)
New Revision: 5243

Modified:
trunk/scipy/spatial/distance.py
Log:
Fixed bugs in LaTeX math in distance documentation.

Modified: trunk/scipy/spatial/distance.py
===================================================================
--- trunk/scipy/spatial/distance.py	2008-12-12 00:50:03 UTC (rev 5242)
+++ trunk/scipy/spatial/distance.py	2008-12-12 02:54:24 UTC (rev 5243)
@@ -370,8 +370,8 @@

.. math::

-       \frac{c_{TF} + c_{FT}}
-            {c_{TT} + c_{FT} + c_{TF}}
+         \frac{c_{TF} + c_{FT}}
+              {c_{TT} + c_{FT} + c_{TF}}

where :math:c_{ij} is the number of occurrences of
:math:\mathtt{u[k]} = i and :math:\mathtt{v[k]} = j for
@@ -400,8 +400,8 @@

.. math::

-       \frac{c_{TF} + c_{FT} - c_{TT} + n}
-            {c_{FT} + c_{TF} + n}
+         \frac{c_{TF} + c_{FT} - c_{TT} + n}
+              {c_{FT} + c_{TF} + n}

where :math:c_{ij} is the number of occurrences of
:math:\mathtt{u[k]} = i and :math:\mathtt{v[k]} = j for
@@ -455,7 +455,7 @@

.. math::

-       \sum_i {u_i-v_i}.
+       \sum_i {(u_i-v_i)}.

:Parameters:
u : ndarray
@@ -872,7 +872,7 @@

5. Y = pdist(X, 'sqeuclidean')

-       Computes the squared Euclidean distance ||u-v||_2^2 between
+       Computes the squared Euclidean distance :math:||u-v||_2^2 between
the vectors.

6. Y = pdist(X, 'cosine')
@@ -920,7 +920,7 @@

.. math::

-          d(u,v) = max_i {|u_i-v_i|}.
+          d(u,v) = \max_i {|u_i-v_i|}.

11. Y = pdist(X, 'canberra')

@@ -929,8 +929,8 @@

.. math::

-         d(u,v) = \sum_u {|u_i-v_i|}
-                         {|u_i|+|v_i|}
+         d(u,v) = \sum_u \frac{|u_i-v_i|}
+                              {(|u_i|+|v_i|)}

12. Y = pdist(X, 'braycurtis')
@@ -1043,8 +1043,11 @@
Y : ndarray
A condensed distance matrix.

+    :SeeAlso:

-       """
+       squareform : converts between condensed distance matrices and
+                    square distance matrices.
+    """

#         21. Y = pdist(X, 'test_Y')
@@ -1603,7 +1606,7 @@

5. Y = cdist(XA, XB, 'sqeuclidean')

-       Computes the squared Euclidean distance ||u-v||_2^2 between
+       Computes the squared Euclidean distance :math:||u-v||_2^2 between
the vectors.

6. Y = cdist(XA, XB, 'cosine')
@@ -1615,7 +1618,7 @@
\frac{1 - uv^T}
{{|u|}_2 {|v|}_2}

-       where |*|_2 is the 2 norm of its argument *.
+       where :math:|*|_2 is the 2-norm of its argument *.

7. Y = cdist(XA, XB, 'correlation')

@@ -1653,7 +1656,7 @@

.. math::

-          d(u,v) = max_i {|u_i-v_i|}.
+          d(u,v) = \max_i {|u_i-v_i|}.

11. Y = cdist(XA, XB, 'canberra')

@@ -1662,8 +1665,8 @@

.. math::

-         d(u,v) = \sum_u {|u_i-v_i|}
-                         {|u_i|+|v_i|}
+         d(u,v) = \sum_u \frac{|u_i-v_i|}
+                              {(|u_i|+|v_i|)}

12. Y = cdist(XA, XB, 'braycurtis')
@@ -1674,8 +1677,8 @@

.. math::

-            d(u,v) = \frac{\sum_i {u_i-v_i}}
-                          {\sum_i {u_i+v_i}}
+            d(u,v) = \frac{\sum_i (u_i-v_i)}
+                          {\sum_i (u_i+v_i)}

13. Y = cdist(XA, XB, 'mahalanobis', VI=None)

@@ -1687,38 +1690,38 @@

14. Y = cdist(XA, XB, 'yule')

-       Computes the Yule distance between each pair of boolean
+       Computes the Yule distance between the boolean
vectors. (see yule function documentation)

-    15. Y = cdist(XA, 'matching')
+    15. Y = cdist(XA, XB, 'matching')

-       Computes the matching distance between each pair of boolean
+       Computes the matching distance between the boolean
vectors. (see matching function documentation)

-    16. Y = cdist(XA, 'dice')
+    16. Y = cdist(XA, XB, 'dice')

-       Computes the Dice distance between each pair of boolean
-       vectors. (see dice function documentation)
+       Computes the Dice distance between the boolean vectors. (see
+       dice function documentation)

17. Y = cdist(XA, XB, 'kulsinski')

-       Computes the Kulsinski distance between each pair of
-       boolean vectors. (see kulsinski function documentation)
+       Computes the Kulsinski distance between the boolean
+       vectors. (see kulsinski function documentation)

18. Y = cdist(XA, XB, 'rogerstanimoto')

-       Computes the Rogers-Tanimoto distance between each pair of
-       boolean vectors. (see rogerstanimoto function documentation)
+       Computes the Rogers-Tanimoto distance between the boolean
+       vectors. (see rogerstanimoto function documentation)

19. Y = cdist(XA, XB, 'russellrao')

-       Computes the Russell-Rao distance between each pair of
-       boolean vectors. (see russellrao function documentation)
+       Computes the Russell-Rao distance between the boolean
+       vectors. (see russellrao function documentation)

20. Y = cdist(XA, XB, 'sokalmichener')

-       Computes the Sokal-Michener distance between each pair of
-       boolean vectors. (see sokalmichener function documentation)
+       Computes the Sokal-Michener distance between the boolean
+       vectors. (see sokalmichener function documentation)

21. Y = cdist(XA, XB, 'sokalsneath')