Merge pull request #494 from lamblin/fix_doc_syntax

doc/NEWS.txt

@@ -54,8 +54,7 @@ Interface Features Removed (most were deprecated):
       to return has been removed. Instead, apply a subtensor to the output
       returned by scan to select a certain slice.
     * The inner function (that scan receives) should return its outputs and
-      updates following this order:
-        [outputs], [updates], [condition].
+      updates following this order: [outputs], [updates], [condition].
       One can skip any of the three if not used, but the order has to stay unchanged.
 
 Interface bug fix:
@@ -74,8 +73,10 @@ Bug fixes (incorrect results):
    Dieleman)
  * Theoretical bug: in some case we could have GPUSum return bad value.
    We were not able to reproduce this problem
-     * patterns affected ({0,1}*nb dim, 0 no reduction on this dim, 1 reduction on this dim):
-       01, 011, 0111, 010, 10, 001, 0011, 0101 (Frederic)
+
+   * patterns affected ({0,1}*nb dim, 0 no reduction on this dim, 1 reduction on this dim):
+     01, 011, 0111, 010, 10, 001, 0011, 0101 (Frederic)
+
  * div by zero in verify_grad. This hid a bug in the grad of Images2Neibs. (James)
  * theano.sandbox.neighbors.Images2Neibs grad was returning a wrong value.
    The grad is now disabled and returns an error. (Frederic)
@@ -98,12 +99,13 @@ Scan fixes:
    before : most of the time crash, but could be wrong value with bad number of dimensions (so a visible bug)
    now : do the right thing.
  * gradient with respect to outputs using multiple taps (reported by Timothy, fix by Razvan)
-   before : it used to return wrong values
-   now : do the right thing.
-   Note: The reported case of this bug was happening in conjunction with the
-         save optimization of scan that give run time errors. So if you didn't
-         manually disable the same memory optimization (number in the list4),
-         you are fine if you didn't manually request multiple taps.
+
+   * before : it used to return wrong values
+   * now : do the right thing.
+   * Note: The reported case of this bug was happening in conjunction with the
+     save optimization of scan that give run time errors. So if you didn't
+     manually disable the same memory optimization (number in the list4),
+     you are fine if you didn't manually request multiple taps.
  * Rop of gradient of scan (reported by Timothy and Justin Bayer, fix by Razvan)
    before : compilation error when computing R-op
    now : do the right thing.

doc/core_development_guide.txt

@@ -20,7 +20,7 @@ some of them might be outdated though:
 
 * :ref:`pipeline` -- Describes the steps of compiling a Theano Function.
 
-* :ref:`graphstructure` -- Describes the symbolic graphs generated by
+* :ref:`graphstructures` -- Describes the symbolic graphs generated by
   :mod:`theano.scan`.
 
 * :ref:`unittest` -- Tutorial on how to use unittest in testing Theano.

doc/developer/scan.txt

@@ -91,7 +91,7 @@ Naming conventions
 * ``input_state`` will stand for a state :math:`\mathbf{x}`, when it is
   provided as an input to the recurrent formula (the inner function) that 
   will generate the new value of the state
-* ``output_state`` will stand for a state :math:`\math{x}` when it refers 
+* ``output_state`` will stand for a state :math:`\mathbf{x}` when it refers 
   to the result of the recurrent formula (the output of the inner function)
 * ``output`` will stand for an output :math:`\mathbf{y}`
 * ``input`` will be an input :math:`\mathbf{u}`

doc/introduction.txt

@@ -174,29 +174,35 @@ Here is the state of that vision as of 24 October 2011 (after Theano release
 * We have many graph transformations that cover the 4 categories listed above.
 * We can improve the graph transformation with better storage optimization
   and instruction selection.
-    * Similar to auto-tuning during the optimization phase, but this
-      doesn't apply to only 1 op.
-    * Example of use: Determine if we should move computation to the
-      GPU or not depending on the input size.
-    * Possible implementation note: allow Theano Variable in the env to
-      have more then 1 owner.
+
+  * Similar to auto-tuning during the optimization phase, but this
+    doesn't apply to only 1 op.
+  * Example of use: Determine if we should move computation to the
+    GPU or not depending on the input size.
+  * Possible implementation note: allow Theano Variable in the env to
+    have more then 1 owner.
+
 * We have a CUDA backend for tensors of type `float32` only.
 * Efforts have begun towards a generic GPU ndarray (GPU tensor) (started in the
   `compyte <https://github.com/inducer/compyte/wiki>`_ project)
-    * Move GPU backend outside of Theano (on top of PyCUDA/PyOpenCL)
-    * Will provide better support for GPU on Windows and use an OpenCL backend on CPU.
+
+  * Move GPU backend outside of Theano (on top of PyCUDA/PyOpenCL)
+  * Will provide better support for GPU on Windows and use an OpenCL backend on CPU.
+
 * Loops work, but not all related optimizations are currently done.
 * The cvm linker allows lazy evaluation. It works, but some work is still
   needed before enabling it by default.
-    * All tests pass with linker=cvm?
-    * How to have `DEBUG_MODE` check it? Right now, DebugMode checks the computation non-lazily.
-    * The profiler used by cvm is less complete than `PROFILE_MODE`.
+
+  * All tests pass with linker=cvm?
+  * How to have `DEBUG_MODE` check it? Right now, DebugMode checks the computation non-lazily.
+  * The profiler used by cvm is less complete than `PROFILE_MODE`.
+
 * SIMD parallelism on the CPU comes from the compiler.
 * Multi-core parallelism is only supported for gemv and gemm, and only
   if the external BLAS implementation supports it.
 * No multi-node support.
 * Many, but not all NumPy functions/aliases are implemented.
-    * http://www.assembla.com/spaces/theano/tickets/781
+  * http://www.assembla.com/spaces/theano/tickets/781
 * Wrapping an existing Python function in easy, but better documentation of
   it would make it even easier.
 * We need to find a way to separate the shared variable memory

doc/sandbox/randomnumbers.txt

-.. sandbox_randnb:
+.. _sandbox_randnb:
 
 ==============
 Random Numbers

theano/sandbox/cuda/rng_curand.py

@@ -311,7 +311,7 @@ class CURAND_RandomStreams(object):
         Return symbolic tensor of normally-distributed numbers.
 
         :param: size: Can be a list of integer or Theano variable(ex: the shape
-        of other Theano Variable)
+            of other Theano Variable)
         """
         if isinstance(size, tuple):
             msg = "size must be a tuple of int or a Theano variable"

theano/sandbox/linalg/ops.py

@@ -480,8 +480,8 @@ class MatrixPinv(Op):
 
     The pseudo-inverse of a matrix A, denoted :math:`A^+`, is
     defined as: "the matrix that 'solves' [the least-squares problem]
-    :math:`Ax = b`," i.e., if :math:`\bar{x}` is said solution, then
-    :math:`A^+` is that matrix such that :math:`\bar{x} = A^+b`.
+    :math:`Ax = b`," i.e., if :math:`\\bar{x}` is said solution, then
+    :math:`A^+` is that matrix such that :math:`\\bar{x} = A^+b`.
 
     Note that :math:`Ax=AA^+b`, so :math:`AA^+` is close to the identity matrix.
     This method is not faster then `matrix_inverse`. Its strength comes from

theano/sparse/basic.py

@@ -861,9 +861,9 @@ class GetItem2d(gof.op.Op):
     class GetItemScalar that return a tensor scalar.
 
     :note: that subtensor selection always returns a matrix so
-    indexing with [a:b, c:d] is forced.  If one index is a scalar,
-    e.g. x[a:b, c] and x[a, b:c], generate an error. Use instead
-    x[a:b, c:c+1] and x[a:a+1, b:c].
+        indexing with [a:b, c:d] is forced.  If one index is a scalar,
+        e.g. x[a:b, c] and x[a, b:c], generate an error. Use instead
+        x[a:b, c:c+1] and x[a:a+1, b:c].
 
     The above indexing methods are not supported because the rval
     would be a sparse matrix rather than a sparse vector, which is a
@@ -2136,7 +2136,7 @@ class Usmm(gof.op.Op):
     alpha is a scalar
 
     :note: We don't implement the infer_shape as it is inserted by optimization
-    only
+        only
     """
     def __eq__(self, other):
         return type(self) == type(other)
@@ -2204,7 +2204,7 @@ class UsmmCscDense(gof.Op):
     alpha is a scalar
 
     :note: We don't implement the infer_shape as it is inserted by optimization
-    only
+        only
     """
     def __init__(self, inplace):
         self.inplace = inplace

theano/tensor/raw_random.py

@@ -563,7 +563,7 @@ def permutation(random_state, size=None, n=1, ndim=None, dtype='int64'):
     the size argument and the shape of n, but you may always specify it
     with the `ndim` parameter.
 
-    .. note::
+    :note:
         Note that the output will then be of dimension ndim+1.
     """
     ndim, size, bcast = _infer_ndim_bcast(ndim, size)