Merge pull request #1924 from delallea/minor

doc/install_ubuntu.txt

@@ -3,8 +3,8 @@
 Easy Installation of an optimized Theano on Ubuntu
 ==================================================
 
-These instruction was done for Ubuntu 11.04, 11.10, 12.04, 12.10, 13.04
-and 13.10. You can probably do something similar on older computer.
+These instructions were written for Ubuntu 11.04, 11.10, 12.04, 12.10, 13.04
+and 13.10. You can probably do something similar on older versions.
 
 .. note::
 

doc/library/sparse/index.txt

@@ -124,7 +124,7 @@ List of Implemented Operations
     - :func:`csr_from_dense <theano.sparse.basic.csr_from_dense>`,
       :func:`csc_from_dense <theano.sparse.basic.csc_from_dense>`.
       The grad implemented is structured.
-    - Theano SparseVariable object have a method ``toarray()`` that is the same as
+    - Theano SparseVariable objects have a method ``toarray()`` that is the same as
       :func:`dense_from_sparse <theano.sparse.basic.dense_from_sparse>`.
 
 - Construction of Sparses and their Properties

doc/library/tensor/basic.txt

@@ -1346,7 +1346,7 @@ Mathematical
    sometimes more accurate).
 
    C code is provided in the Theano_lgpl repository.
-   This make it faster.
+   This makes it faster.
 
    https://github.com/Theano/Theano_lgpl.git
 

doc/library/typed_list.txt

@@ -22,7 +22,7 @@ the same Theano type. Here is an example::
 
 A second example with Scan. Scan doesn't yet have direct support of
 TypedList, so you can only use it as non_sequences (not in sequences or
-as outputs).::
+as outputs)::
 
     import theano.typed_list
 

theano/compile/ops.py

@@ -16,9 +16,9 @@ import numpy
 def register_view_op_c_code(type, code, version=()):
     """ Tell ViewOp how to generate C code for a Theano Type
 
-    :param typ: A Theano type. It must be the Theano class itself and not an
+    :param type: A Theano type. It must be the Theano class itself and not an
                 instance of the class.
-    :param code: C code that return a view for the Theano type 'typ'.
+    :param code: C code that returns a view for the Theano type 'type'.
                  Use %(iname)s and %(oname)s for the input and output C
                  variable names respectively.
     :param version: A number indicating the version of the code, for cache.
@@ -396,7 +396,7 @@ def register_shape_i_c_code(typ, code, version=()):
 
     :param typ: A Theano type. It must be the Theano class itself and not an
                 instance of the class.
-    :param code: C code that get the shape of dimensions %(i)s for the Theano type 'typ'.
+    :param code: C code that gets the shape of dimensions %(i)s for the Theano type 'typ'.
                  Use %(iname)s and %(oname)s for the input and output C
                  variable names respectively.
     :param version: A number indicating the version of the code, for cache.
@@ -508,11 +508,11 @@ def register_rebroadcast_c_code(typ, code, version=()):
     :param typ: A Theano type. It must be the Theano class itself and not an
                 instance of the class.
 
-    :param code: C code that check if the dimensions %(axis) is of
+    :param code: C code that checks if the dimension %(axis)s is of
                  shape 1 for the Theano type 'typ'.  Use %(iname)s and
                  %(oname)s for the input and output C variable names
-                 respectively.  %(axis)s for the axis that we need to
-                 check. This code is put in a loop for all axis
+                 respectively, and %(axis)s for the axis that we need to
+                 check. This code is put in a loop for all axes.
 
     :param version: A number indicating the version of the code, for cache.
     """
@@ -546,14 +546,13 @@ class Rebroadcast(gof.Op):
         self.axis = dict(axis)
         for axis, broad in self.axis.iteritems():
             assert isinstance(axis, (numpy.integer, int)), (
-                "Rebroadcast need integers axis. Got ", axis)
+                "Rebroadcast needs integer axes. Got ", axis)
 
     def __eq__(self, other):
         return type(self) == type(other) and self.axis == other.axis
 
     def __hash__(self):
-        items = self.axis.items()
-        items.sort()  # no ambiguity because each item key is unique
+        items = sorted(self.axis.iteritems())  # no ambiguity because each item key is unique
         return hash(type(self)) ^ hash(tuple(items))
 
     def __str__(self):
@@ -654,7 +653,7 @@ def register_specify_shape_c_code(typ, code, version=(),
 
     :param typ: A Theano type. It must be the Theano class itself and not an
                 instance of the class.
-    :param code: C code that check the shape and return a view for the Theano type 'typ'.
+    :param code: C code that checks the shape and returns a view for the Theano type 'typ'.
                  Use %(iname)s and %(oname)s for the input and output C
                  variable names respectively.
                  %(shape)s is the vector of shape of %(iname)s.

theano/sparse/basic.py

@@ -693,7 +693,7 @@ CSC = CSM('csc')
 representation.
 
 :param data: One dimensional tensor representing
-    the data of the sparse to construct.
+    the data of the sparse matrix to construct.
 :param indices: One dimensional tensor of integers
     representing the indices of the sparse
     matrix to construct.
@@ -716,7 +716,7 @@ CSR = CSM('csr')
 representation.
 
 :param data: One dimensional tensor representing
-    the data of the sparse to construct.
+    the data of the sparse matrix to construct.
 :param indices: One dimensional tensor of integers
     representing the indices of the sparse
     matrix to construct.
@@ -1096,18 +1096,18 @@ class GetItem2d(gof.op.Op):
         return self.__class__.__name__
 
 get_item_2d = GetItem2d()
-"""Implement a subtensor of sparse variable and that return a
+"""Implement a subtensor of sparse variable, returning a
 sparse matrix.
 
 If you want to take only one element of a sparse matrix see
-`GetItemScalar` that return a tensor scalar.
+`GetItemScalar` that returns a tensor scalar.
 
 .. note::
 
     Subtensor selection always returns a matrix, so indexing
-    with [a:b, c:d] is forced.  If one index is a scalar. For
-    instance, 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].
+    with [a:b, c:d] is forced.  If one index is a scalar, for
+    instance, x[a:b, c] or x[a, b:c], an error will be raised. Use
+    instead x[a:b, c:c+1] or x[a:a+1, b:c].
 
 The above indexing methods are not supported because the return value
 would be a sparse matrix rather than a sparse vector, which is a
@@ -1118,7 +1118,7 @@ when sparse vectors are supported.
 :param x: Sparse matrix.
 :param index: Tuple of slice object.
 
-:return: The slice corresponding in `x`.
+:return: The corresponding slice in `x`.
 
 :note: The grad is not implemented for this op.
 """
@@ -1168,8 +1168,8 @@ class GetItemScalar(gof.op.Op):
         return self.__class__.__name__
 
 get_item_scalar = GetItemScalar()
-"""Implement a subtensor of a sparse variable that take
-two scalars as index and return a scalar.
+"""Implement a subtensor of a sparse variable that takes
+two scalars as index and returns a scalar.
 
 If you want to take a slice of a sparse matrix see
 `GetItem2d` that returns a sparse matrix.
@@ -1177,7 +1177,7 @@ If you want to take a slice of a sparse matrix see
 :param x: Sparse matrix.
 :param index: Tuple of scalars.
 
-:return: The item corresponding in `x`.
+:return: The corresponding item in `x`.
 
 :note:  The grad is not implemented for this op.
 """
@@ -1498,14 +1498,14 @@ class SpSum(gof.op.Op):
 
 
 def sp_sum(x, axis=None, sparse_grad=False):
-    """Calculate the sum of a sparse matrix along a specify
+    """Calculate the sum of a sparse matrix along the specified
     axis.
 
-    It operates a reduction along the axis specified. When
-    `axis` is `None`, it is apply along all axes.
+    It operates a reduction along the specified axis. When
+    `axis` is `None`, it is applied along all axes.
 
     :param x: Sparse matrix.
-    :param axis: Axis along which the sum is applied. Integers or `None`.
+    :param axis: Axis along which the sum is applied. Integer or `None`.
     :param sparse_grad: `True` to have a structured grad. Boolean.
 
     :return: The sum of `x` in a dense format.
@@ -1645,7 +1645,7 @@ class EnsureSortedIndices(gof.op.Op):
         else:
             return self.__class__.__name__ + "{no_inplace}"
 ensure_sorted_indices = EnsureSortedIndices(inplace=False)
-"""Resort indices of a sparse matrix.
+"""Re-sort indices of a sparse matrix.
 
 CSR column indices are not necessarily sorted. Likewise
 for CSC row indices. Use `ensure_sorted_indices` when sorted
@@ -1662,12 +1662,12 @@ libraries).
 
 def clean(x):
     """Remove explicit zeros from a sparse matrix, and
-    resort indices.
+    re-sort indices.
 
     CSR column indices are not necessarily sorted. Likewise
     for CSC row indices. Use `clean` when sorted
     indices are required (e.g. when passing data to other
-    libraries) and to ensure there is no zeros in the data.
+    libraries) and to ensure there are no zeros in the data.
 
     :param x: A sparse matrix.
 
@@ -1860,9 +1860,9 @@ class StructuredAddSV(gof.op.Op):
         return self.__class__.__name__
 structured_add_s_v = StructuredAddSV()
 """Structured addition of a sparse matrix and a dense vector.
-The elements of the vector are are only added to the corresponding
-non-zero elements. Therefore, this operation outputs another sparse
-matrix.
+The elements of the vector are only added to the corresponding
+non-zero elements of the sparse matrix. Therefore, this operation
+outputs another sparse matrix.
 
 :param x: Sparse matrix.
 :param y: Tensor type vector.
@@ -3696,7 +3696,7 @@ class Usmm(gof.op.Op):
 
         out[0] = rval
 usmm = Usmm()
-"""Performs the expression is `alpha` * `x` `y` + `z`.
+"""Performs the expression `alpha` * `x` `y` + `z`.
 
 :param x: Matrix variable.
 :param y: Matrix variable.

theano/tensor/basic.py

@@ -3151,7 +3151,7 @@ class Split(Op):
             raise ValueError('The splits sum to %s, expected %s' %
                              (numpy.sum(splits), len_along_axis))
         if python_any([nb < 0 for nb in splits]):
-            raise ValueError('Split: you try to make an ndarray with'
+            raise ValueError('Split: you tried to make an ndarray with a '
                              'negative number of elements.')
 
         # Checking is done, let's roll the splitting algorithm!

theano/tensor/signal/conv.py

@@ -88,7 +88,7 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
     if input.ndim == 2 and filters.ndim == 2:
         if theano.config.warn.signal_conv2d_interface:
             warnings.warn(
-                "theano.tensor.signal.conv2d() now output 2d tensor when both"
+                "theano.tensor.signal.conv2d() now outputs a 2d tensor when both"
                 " inputs are 2d. To disable this warning, set the Theano flag"
                 " warn.signal_conv2d_interface to False",
                 stacklevel=3)

theano/typed_list/basic.py

@@ -101,7 +101,7 @@ getitem = GetItem()
 """
 Get specified slice of a typed list.
 
-:param x: type type list.
+:param x: typed list.
 :param index: the index of the value to return from `x`.
 """
 
@@ -227,7 +227,7 @@ class Extend(Op):
 
 extend = Extend()
 """
-Append all element of a list at the end of another list.
+Append all elements of a list at the end of another list.
 
 :param x: The typed list to extend.
 :param toAppend: The typed list that will be added at the end of `x`.
@@ -296,7 +296,7 @@ insert = Insert()
 """
 Insert an element at an index in a typed list.
 
-:param x: the typed list to modified.
+:param x: the typed list to modify.
 :param index: the index where to put the new element in `x`.
 :param toInsert: The new element to insert.
 """
@@ -475,11 +475,11 @@ class Count(Op):
 
 count = Count()
 """
-Count the number of time an element is in the typed list.
+Count the number of times an element is in the typed list.
 
 :param x: The typed list to look into.
 :param elem: The element we want to count in list.
-    The element are compared with equals.
+    The elements are compared with equals.
 
 :note: Python implementation of count doesn't work when we want to
     count an ndarray from a list. This implementation works in that