[Scipy-svn] r5133 - in trunk/scipy: cluster linalg optimize

scipy-svn@scip... scipy-svn@scip...
Sun Nov 16 07:34:06 CST 2008


Author: ptvirtan
Date: 2008-11-16 07:33:51 -0600 (Sun, 16 Nov 2008)
New Revision: 5133

Modified:
   trunk/scipy/cluster/hierarchy.py
   trunk/scipy/linalg/basic.py
   trunk/scipy/linalg/info.py
   trunk/scipy/optimize/info.py
Log:
Fix some docstring offending Sphinx Latex generation

Modified: trunk/scipy/cluster/hierarchy.py
===================================================================
--- trunk/scipy/cluster/hierarchy.py	2008-11-16 13:12:04 UTC (rev 5132)
+++ trunk/scipy/cluster/hierarchy.py	2008-11-16 13:33:51 UTC (rev 5133)
@@ -966,7 +966,9 @@
            deviation of the link heights, respectively; ``R[i,2]`` is
            the number of links included in the calculation; and
            ``R[i,3]`` is the inconsistency coefficient,
-           .. math:
+           
+           .. math::
+           
                \frac{\mathtt{Z[i,2]}-\mathtt{R[i,0]}}
                     {R[i,1]}.
 

Modified: trunk/scipy/linalg/basic.py
===================================================================
--- trunk/scipy/linalg/basic.py	2008-11-16 13:12:04 UTC (rev 5132)
+++ trunk/scipy/linalg/basic.py	2008-11-16 13:33:51 UTC (rev 5133)
@@ -400,14 +400,14 @@
         ord    norm for matrices             norm for vectors
         =====  ============================  ==========================
         None   Frobenius norm                2-norm
-        'fro'  Frobenius norm                -
+        'fro'  Frobenius norm                --
         inf    max(sum(abs(x), axis=1))      max(abs(x))
         -inf   min(sum(abs(x), axis=1))      min(abs(x))
         1      max(sum(abs(x), axis=0))      as below
         -1     min(sum(abs(x), axis=0))      as below
         2      2-norm (largest sing. value)  as below
         -2     smallest singular value       as below
-        other  -                             sum(abs(x)**ord)**(1./ord)
+        other  --                            sum(abs(x)**ord)**(1./ord)
         =====  ============================  ==========================
 
     Returns

Modified: trunk/scipy/linalg/info.py
===================================================================
--- trunk/scipy/linalg/info.py	2008-11-16 13:12:04 UTC (rev 5132)
+++ trunk/scipy/linalg/info.py	2008-11-16 13:33:51 UTC (rev 5133)
@@ -2,7 +2,7 @@
 Linear algebra routines
 =======================
 
- Linear Algebra Basics:
+Linear Algebra Basics::
 
    inv        --- Find the inverse of a square matrix
    solve      --- Solve a linear system of equations
@@ -14,7 +14,7 @@
    pinv       --- Pseudo-inverse (Moore-Penrose) using lstsq
    pinv2      --- Pseudo-inverse using svd
 
- Eigenvalues and Decompositions:
+Eigenvalues and Decompositions::
 
    eig        --- Find the eigenvalues and vectors of a square matrix
    eigvals    --- Find the eigenvalues of a square matrix
@@ -36,7 +36,7 @@
    rsf2csf    --- Real to complex schur form
    hessenberg --- Hessenberg form of a matrix
 
- matrix Functions:
+matrix Functions::
 
    expm       --- matrix exponential using Pade approx.
    expm2      --- matrix exponential using Eigenvalue decomp.
@@ -52,7 +52,7 @@
    sqrtm      --- matrix square root
    funm       --- Evaluating an arbitrary matrix function.
 
- Iterative linear systems solutions
+Iterative linear systems solutions::
 
    cg         --- Conjugate gradient (symmetric systems only)
    cgs        --- Conjugate gradient squared

Modified: trunk/scipy/optimize/info.py
===================================================================
--- trunk/scipy/optimize/info.py	2008-11-16 13:12:04 UTC (rev 5132)
+++ trunk/scipy/optimize/info.py	2008-11-16 13:33:51 UTC (rev 5133)
@@ -2,7 +2,7 @@
 Optimization Tools
 ==================
 
- A collection of general-purpose optimization routines.
+A collection of general-purpose optimization routines.::
 
    fmin        --  Nelder-Mead Simplex algorithm
                      (uses only function calls)
@@ -17,9 +17,8 @@
    leastsq     --  Minimize the sum of squares of M equations in
                      N unknowns given a starting estimate.
 
+Constrained Optimizers (multivariate)::
 
-  Constrained Optimizers (multivariate)
-
    fmin_l_bfgs_b -- Zhu, Byrd, and Nocedal's L-BFGS-B constrained optimizer
                       (if you use this please quote their papers -- see help)
 
@@ -28,28 +27,24 @@
 
    fmin_cobyla   -- Constrained Optimization BY Linear Approximation
 
+Global Optimizers::
 
-  Global Optimizers
-
    anneal      --  Simulated Annealing
    brute       --  Brute force searching optimizer
 
+Scalar function minimizers::
 
-  Scalar function minimizers
-
    fminbound   --  Bounded minimization of a scalar function.
    brent       --  1-D function minimization using Brent method.
    golden      --  1-D function minimization using Golden Section method
    bracket     --  Bracket a minimum (given two starting points)
 
+Also a collection of general-purpose root-finding routines::
 
- Also a collection of general-purpose root-finding routines.
-
    fsolve      --  Non-linear multi-variable equation solver.
 
+Scalar function solvers::
 
-  Scalar function solvers
-
    brentq      --  quadratic interpolation Brent method
    brenth      --  Brent method (modified by Harris with hyperbolic
                      extrapolation)
@@ -59,7 +54,7 @@
 
    fixed_point --  Single-variable fixed-point solver.
 
- A collection of general-purpose nonlinear multidimensional solvers.
+A collection of general-purpose nonlinear multidimensional solvers::
 
    broyden1            --  Broyden's first method - is a quasi-Newton-Raphson
                            method for updating an approximate Jacobian and then
@@ -83,7 +78,7 @@
    anderson2           --  the Anderson method, the same as anderson, but
                            formulated differently
 
- Utility Functions
+Utility Functions::
 
    line_search --  Return a step that satisfies the strong Wolfe conditions.
    check_grad  --  Check the supplied derivative using finite difference



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