[Scipy-svn] r4944 - in trunk/scipy: optimize spatial

scipy-svn@scip... scipy-svn@scip...
Sun Nov 2 22:53:39 CST 2008


Author: damian.eads
Date: 2008-11-02 22:53:36 -0600 (Sun, 02 Nov 2008)
New Revision: 4944

Modified:
   trunk/scipy/optimize/optimize.py
   trunk/scipy/spatial/distance.py
   trunk/scipy/spatial/info.py
Log:
Working on doc fixes.

Modified: trunk/scipy/optimize/optimize.py
===================================================================
--- trunk/scipy/optimize/optimize.py	2008-11-03 03:54:26 UTC (rev 4943)
+++ trunk/scipy/optimize/optimize.py	2008-11-03 04:53:36 UTC (rev 4944)
@@ -101,7 +101,7 @@
          full_output=0, disp=1, retall=0, callback=None):
     """Minimize a function using the downhill simplex algorithm.
 
-    *Parameters*:
+    :Parameters:
 
       func : callable func(x,*args)
           The objective function to be minimized.
@@ -113,7 +113,7 @@
           Called after each iteration, as callback(xk), where xk is the
           current parameter vector.
 
-    *Returns*: (xopt, {fopt, iter, funcalls, warnflag})
+    :Returns: (xopt, {fopt, iter, funcalls, warnflag})
 
       xopt : ndarray
           Parameter that minimizes function.
@@ -146,7 +146,7 @@
       retall : bool
           Set to True to return list of solutions at each iteration.
 
-    *Notes*
+    :Notes:
 
         Uses a Nelder-Mead simplex algorithm to find the minimum of
         function of one or more variables.
@@ -406,7 +406,7 @@
                 args=(), c1=1e-4, c2=0.9, amax=50):
     """Find alpha that satisfies strong Wolfe conditions.
 
-    *Parameters*:
+    :Parameters:
 
         f : callable f(x,*args)
             Objective function.
@@ -426,7 +426,7 @@
         c2 : float
             Parameter for curvature condition rule.
 
-    *Returns*:
+    :Returns:
 
         alpha0 : float
             Alpha for which ``x_new = x0 + alpha * pk``.
@@ -435,7 +435,7 @@
         gc : int
             Number of gradient evaluations made.
 
-    *Notes*
+    :Notes:
 
         Uses the line search algorithm to enforce strong Wolfe
         conditions.  See Wright and Nocedal, 'Numerical Optimization',
@@ -550,7 +550,7 @@
     Uses the interpolation algorithm (Armiijo backtracking) as suggested by
     Wright and Nocedal in 'Numerical Optimization', 1999, pg. 56-57
 
-    *Returns*: (alpha, fc, gc)
+    :Returns: (alpha, fc, gc)
 
     """
 
@@ -627,7 +627,7 @@
               retall=0, callback=None):
     """Minimize a function using the BFGS algorithm.
 
-    *Parameters*:
+    :Parameters:
 
       f : callable f(x,*args)
           Objective function to be minimized.
@@ -648,7 +648,7 @@
           iteration.  Called as callback(xk), where xk is the
           current parameter vector.
 
-    *Returns*: (xopt, {fopt, gopt, Hopt, func_calls, grad_calls, warnflag}, <allvecs>)
+    :Returns: (xopt, {fopt, gopt, Hopt, func_calls, grad_calls, warnflag}, <allvecs>)
 
         xopt : ndarray
             Parameters which minimize f, i.e. f(xopt) == fopt.
@@ -679,7 +679,7 @@
         retall : bool
             Return a list of results at each iteration if True.
 
-    *Notes*
+    :Notes:
 
         Optimize the function, f, whose gradient is given by fprime
         using the quasi-Newton method of Broyden, Fletcher, Goldfarb,
@@ -801,7 +801,7 @@
               maxiter=None, full_output=0, disp=1, retall=0, callback=None):
     """Minimize a function using a nonlinear conjugate gradient algorithm.
 
-    *Parameters*:
+    :Parameters:
         f : callable f(x,*args)
             Objective function to be minimized.
         x0 : ndarray
@@ -822,7 +822,7 @@
             iteration.  Called as callback(xk), where xk is the
             current parameter vector.
 
-    *Returns*: (xopt, {fopt, func_calls, grad_calls, warnflag}, {allvecs})
+    :Returns: (xopt, {fopt, func_calls, grad_calls, warnflag}, {allvecs})
 
         xopt : ndarray
             Parameters which minimize f, i.e. f(xopt) == fopt.
@@ -850,7 +850,7 @@
       retall : bool
         return a list of results at each iteration if True.
 
-    *Notes*
+    :Notes:
 
         Optimize the function, f, whose gradient is given by fprime
         using the nonlinear conjugate gradient algorithm of Polak and
@@ -955,7 +955,7 @@
              callback=None):
     """Minimize a function using the Newton-CG method.
 
-    *Parameters*:
+    :Parameters:
 
         f : callable f(x,*args)
             Objective function to be minimized.
@@ -979,7 +979,7 @@
             each iteration.  Called as callback(xk), where xk is the
             current parameter vector.
 
-    *Returns*: (xopt, {fopt, fcalls, gcalls, hcalls, warnflag},{allvecs})
+    :Returns: (xopt, {fopt, fcalls, gcalls, hcalls, warnflag},{allvecs})
 
         xopt : ndarray
             Parameters which minimizer f, i.e. ``f(xopt) == fopt``.
@@ -1011,7 +1011,7 @@
         retall : bool
             If True, return a list of results at each iteration.
 
-    *Notes*
+    :Notes:
       1. scikits.openopt offers a unified syntax to call this and other solvers.
       2. Only one of `fhess_p` or `fhess` need to be given.  If `fhess`
       is provided, then `fhess_p` will be ignored.  If neither `fhess`
@@ -1132,7 +1132,7 @@
               full_output=0, disp=1):
     """Bounded minimization for scalar functions.
 
-    *Parameters*:
+    :Parameters:
 
       func : callable f(x,*args)
           Objective function to be minimized (must accept and return scalars).
@@ -1154,7 +1154,7 @@
               3 : print iteration results.
 
 
-    *Returns*: (xopt, {fval, ierr, numfunc})
+    :Returns: (xopt, {fval, ierr, numfunc})
 
       xopt : ndarray
           Parameters (over given interval) which minimize the
@@ -1168,7 +1168,7 @@
         The number of function calls made.
 
 
-    *Notes*
+    :Notes:
 
         Finds a local minimizer of the scalar function `func` in the
         interval x1 < xopt < x2 using Brent's method.  (See `brent`
@@ -1429,7 +1429,7 @@
     return the minimum of the function isolated to a fractional precision of
     tol.
 
-    *Parameters*:
+    :Parameters:
 
         func : callable f(x,*args)
             Objective function.
@@ -1445,7 +1445,7 @@
             If True, return all output args (xmin, fval, iter,
             funcalls).
 
-    *Returns*:
+    :Returns:
 
         xmin : ndarray
             Optimum point.
@@ -1477,7 +1477,7 @@
     return the minimum of the function isolated to a fractional precision of
     tol.
 
-    *Parameters*:
+    :Parameters:
 
         func : callable func(x,*args)
             Objective function to minimize.
@@ -1494,7 +1494,7 @@
         full_output : bool
             If True, return optional outputs.
 
-    *Notes*
+    :Notes:
 
         Uses analog of bisection method to decrease the bracketed
         interval.
@@ -1557,7 +1557,7 @@
     f(xa) > f(xb) < f(xc). It doesn't always mean that obtained
     solution will satisfy xa<=x<=xb
 
-    *Parameters*:
+    :Parameters:
 
         func : callable f(x,*args)
             Objective function to minimize.
@@ -1570,7 +1570,7 @@
         maxiter : int
             Maximum number of iterations to perform.
 
-    *Returns*: xa, xb, xc, fa, fb, fc, funcalls
+    :Returns: xa, xb, xc, fa, fb, fc, funcalls
 
         xa, xb, xc : float
             Bracket.
@@ -1655,7 +1655,7 @@
                 direc=None):
     """Minimize a function using modified Powell's method.
 
-    *Parameters*:
+    :Parameters:
 
       func : callable f(x,*args)
           Objective function to be minimized.
@@ -1670,7 +1670,7 @@
       direc : ndarray
           Initial direction set.
 
-    *Returns*: (xopt, {fopt, xi, direc, iter, funcalls, warnflag}, {allvecs})
+    :Returns: (xopt, {fopt, xi, direc, iter, funcalls, warnflag}, {allvecs})
 
         xopt : ndarray
             Parameter which minimizes `func`.
@@ -1708,7 +1708,7 @@
           If True, return a list of the solution at each iteration.
 
 
-    *Notes*
+    :Notes:
 
         Uses a modification of Powell's method to find the minimum of
         a function of N variables.
@@ -1825,9 +1825,9 @@
 def brute(func, ranges, args=(), Ns=20, full_output=0, finish=fmin):
     """Minimize a function over a given range by brute force.
 
-    *Parameters*:
+    :Parameters:
 
-        func : callable f(x,*args)
+        func : callable ``f(x,*args)``
             Objective function to be minimized.
         ranges : tuple
             Each element is a tuple of parameters or a slice object to
@@ -1839,7 +1839,7 @@
         full_output : bool
             If True, return the evaluation grid.
 
-    *Returns*: (x0, fval, {grid, Jout})
+    :Returns: (x0, fval, {grid, Jout})
 
         x0 : ndarray
             Value of arguments to `func`, giving minimum over the grid.
@@ -1851,7 +1851,7 @@
         Jout : ndarray
             Function values over grid:  ``Jout = func(*grid)``.
 
-    *Notes*
+    :Notes:
 
         Find the minimum of a function evaluated on a grid given by
         the tuple ranges.

Modified: trunk/scipy/spatial/distance.py
===================================================================
--- trunk/scipy/spatial/distance.py	2008-11-03 03:54:26 UTC (rev 4943)
+++ trunk/scipy/spatial/distance.py	2008-11-03 04:53:36 UTC (rev 4944)
@@ -1563,48 +1563,19 @@
     thrown if ``XA`` and ``XB`` do not have the same number of
     columns.
 
-    A rectangular distance matrix Y is returned. For each :math:`$i$`
+    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 :math:`$n$`-dimensional space.
-       XB : ndarray
-           An :math:`$m_B$` by :math:`$n$` array of :math:`$m_B$`
-           original observations in an :math:`$n$`-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', 'wminkowski',
-           'yule'.
-       w : ndarray
-           The weight vector (for weighted Minkowski).
-       p : double
-           The p-norm to apply (for Minkowski, weighted and unweighted)
-       V : ndarray
-           The variance vector (for standardized Euclidean).
-       VI : ndarray
-           The inverse of the covariance matrix (for Mahalanobis).
-
-
-    :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.
+       Computes the distance between :math:`$m$` points using
+       Euclidean distance (2-norm) as the distance metric between the
+       points. The points are arranged as :math:`$m$`
+       :math:`$n$`-dimensional row vectors in the matrix X.
 
     2. ``Y = cdist(X, 'minkowski', p)``
 
@@ -1781,6 +1752,34 @@
 
          dm = cdist(X, 'sokalsneath')
 
+    :Parameters:
+       XA : ndarray
+           An :math:`$m_A$` by :math:`$n$` array of :math:`$m_A$`
+           original observations in an :math:`$n$`-dimensional space.
+       XB : ndarray
+           An :math:`$m_B$` by :math:`$n$` array of :math:`$m_B$`
+           original observations in an :math:`$n$`-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', 'wminkowski',
+           'yule'.
+       w : ndarray
+           The weight vector (for weighted Minkowski).
+       p : double
+           The p-norm to apply (for Minkowski, weighted and unweighted)
+       V : ndarray
+           The variance vector (for standardized Euclidean).
+       VI : ndarray
+           The inverse of the covariance matrix (for Mahalanobis).
+
+
+    :Returns:
+       Y : ndarray
+           A :math:`$m_A$` by :math:`$m_B$` distance matrix.
        """
 
 

Modified: trunk/scipy/spatial/info.py
===================================================================
--- trunk/scipy/spatial/info.py	2008-11-03 03:54:26 UTC (rev 4943)
+++ trunk/scipy/spatial/info.py	2008-11-03 04:53:36 UTC (rev 4944)
@@ -1,21 +1,4 @@
 """
-Vector Quantization / Kmeans
-============================
-
-    Clustering algorithms are useful in information theory, target detection,
-    communications, compression, and other areas. The vq module only
-    supports vector quantization and the k-means algorithms. Development
-    of self-organizing maps (SOM) and other approaches is underway.
-
-Hierarchical Clustering
-=======================
-
-    The hierarchy module provides functions for hierarchical and agglomerative
-    clustering. Its features include generating hierarchical clusters from
-    distance matrices, computing distance matrices from observation vectors,
-    calculating statistics on clusters, cutting linkages to generate flat
-    clusters, and visualizing clusters with dendrograms.
-
 Distance Computation
 ====================
 



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