Fixed all Returns

theano/tensor/basic.py

@@ -2261,7 +2261,7 @@ class Nonzero(gof.Op):
 
     Returns
     -------
-    result : matrix
+    matrix
         Matrix containing the indices of the non-zero elements of a.
 
     See Also
@@ -2323,7 +2323,7 @@ def nonzero(a, return_matrix=False):
 
     Returns
     -------
-    result : tuple of vectors or matrix
+    tuple of vectors or matrix
 
     See Also
     --------
@@ -2356,7 +2356,7 @@ def flatnonzero(a):
 
     Returns
     -------
-    res : vector
+    vector
         Output vector, containing the indices of the elements of `a.flatten()`
         that are non-zero.
 
@@ -2396,7 +2396,7 @@ def nonzero_values(a):
 
     Returns
     -------
-    res : vector
+    vector
         Output vector, containing the non-zero elements of a.
 
     See Also
@@ -2460,7 +2460,7 @@ def tri(N, M=None, k=0, dtype=None):
 
     Returns
     -------
-    tri : Array of shape (N, M)
+    Array of shape (N, M)
         Array with its lower triangle filled with ones and zero elsewhere;
         in other words ``T[i,j] == 1`` for ``i <= j + k``, 0 otherwise.
 
@@ -2489,7 +2489,7 @@ def tril(m, k=0):
 
     Returns
     -------
-    tril : array, shape (M, N)
+    array, shape (M, N)
         Lower triangle of `m`, of same shape and data-type as `m`.
 
     See Also
@@ -2569,7 +2569,7 @@ def eye(n, m=None, k=0, dtype=None):
 
     Returns
     -------
-    I : ndarray of shape (N,M)
+    ndarray of shape (N,M)
         An array where all elements are equal to zero, except for the `k`-th
         diagonal, whose values are equal to one.
 
@@ -3904,7 +3904,7 @@ def roll(x, shift, axis=None):
 
     Returns
     -------
-    res : tensor
+    tensor
         Output tensor, with the same shape as `x`.
 
     """
@@ -5520,6 +5520,7 @@ class Diagonal(Op):
 
     Returns
     -------
+    vector
         A vector representing the diagonal elements.
 
     """
@@ -5655,6 +5656,7 @@ def ptp(a, axis=None):
 
     Returns
     -------
+    array
         A new array holding the result.
 
     """

theano/tensor/blas.py

@@ -1449,7 +1449,10 @@ def _factor_canonicalized(lst):
 
 
 def _gemm_from_factored_list(lst):
-    """Returns None, or a list to replace node.outputs."""
+    """
+    Returns None, or a list to replace node.outputs.
+    
+    """
     lst2 = []
     # Remove the tuple that can't be cast correctly.
     # This can happen when we try to cast a complex to a real

theano/tensor/elemwise_cgen.py

@@ -5,8 +5,8 @@ import theano
 def make_declare(loop_orders, dtypes, sub):
     """
     Produce code to declare all necessary variables.
-    """
 
+    """
     decl = ""
     for i, (loop_order, dtype) in enumerate(zip(loop_orders, dtypes)):
         var = sub['lv%i' % i]  # input name corresponding to ith loop variable
@@ -342,6 +342,7 @@ def make_reordered_loop(init_loop_orders, olv_index, dtypes, inner_task, sub,
         Returns a list containing a C expression representing the
         stride for each dimension of the ith variable, in the
         specified loop_order.
+
         """
         var = sub["lv%i" % i]
         r = []

theano/tensor/extra_ops.py

@@ -539,8 +539,8 @@ def bincount(x, weights=None, minlength=None, assert_nonneg=False):
 
 
 def squeeze(x):
-    """Remove broadcastable dimensions from
-    the shape of an array.
+    """
+    Remove broadcastable dimensions from the shape of an array.
 
     It returns the input array, but with the
     broadcastable dimensions removed. This is
@@ -565,26 +565,27 @@ def squeeze(x):
 
 
 def compress(condition, x, axis=None):
-    """Return selected slices of an array along given axis.
+    """
+    Return selected slices of an array along given axis.
 
     It returns the input tensor, but with selected slices along a given axis
     retained. If no axis is provided, the tensor is flattened.
     Corresponds to numpy.compress
 
+    .. versionadded:: 0.7
+
     Parameters
     ----------
     x
         Input data, tensor variable.
-
     condition
          1 dimensional array of non-zero and zero values
          corresponding to indices of slices along a selected axis.
 
     Returns
     -------
-        `x` with selected slices
-
-    .. versionadded:: 0.7
+    object
+        `x` with selected slices.
 
     """
     indices = theano.tensor.basic.flatnonzero(condition)
@@ -802,12 +803,15 @@ bartlett_ = Bartlett()
 
 # I create a function only to have the doc show well.
 def bartlett(M):
-    """An instance of this class returns the Bartlett spectral window in the
+    """
+    An instance of this class returns the Bartlett spectral window in the
     time-domain. The Bartlett window is very similar to a triangular window,
     except that the end points are at zero. It is often used in signal
     processing for tapering a signal, without generating too much ripple in
     the frequency domain.
 
+    .. versionadded:: 0.6
+
     Parameters
     ----------
     M : integer scalar
@@ -821,8 +825,6 @@ def bartlett(M):
         (the value one appears only if the number of samples is odd), with
         the first and last samples equal to zero.
 
-    .. versionadded:: 0.6
-
     """
     return bartlett_(M)
 
@@ -889,20 +891,24 @@ fill_diagonal_ = FillDiagonal()
 
 # I create a function only to have the doc show well.
 def fill_diagonal(a, val):
-    """ Returns a copy of an array with all
+    """
+    Returns a copy of an array with all
     elements of the main diagonal set to a specified scalar value.
 
+    .. versionadded:: 0.6
+
     Parameters
     ----------
-    a :
+    a
         Rectangular array of at least two dimensions.
-    val :
+    val
         Scalar value to fill the diagonal whose type must be
         compatible with that of array 'a' (i.e. 'val' cannot be viewed
         as an upcast of 'a').
 
     Returns
     -------
+    array
         An array identical to 'a' except that its main diagonal
         is filled with scalar 'val'. (For an array 'a' with a.ndim >=
         2, the main diagonal is the list of locations a[i, i, ..., i]
@@ -911,7 +917,7 @@ def fill_diagonal(a, val):
     Support rectangular matrix and tensor with more than 2 dimensions
     if the later have all dimensions are equals.
 
-    .. versionadded:: 0.6
+
 
     """
     return fill_diagonal_(a, val)
@@ -1043,6 +1049,7 @@ def fill_diagonal_offset(a, val, offset):
 
     Returns
     -------
+    array
         An array identical to 'a' except that its offset diagonal
         is filled with scalar 'val'. The output is unwrapped.
 
@@ -1051,7 +1058,8 @@ def fill_diagonal_offset(a, val, offset):
 
 
 def to_one_hot(y, nb_class, dtype=None):
-    """Return a matrix where each row correspond to the one hot
+    """
+    Return a matrix where each row correspond to the one hot
     encoding of each element in y.
 
     Parameters
@@ -1069,7 +1077,7 @@ def to_one_hot(y, nb_class, dtype=None):
         A matrix of shape (y.shape[0], nb_class), where each row ``i`` is
         the one hot encoding of the corresponding ``y[i]`` value.
 
-   """
+    """
     ret = theano.tensor.zeros((y.shape[0], nb_class),
                               dtype=dtype)
     ret = theano.tensor.set_subtensor(ret[theano.tensor.arange(y.shape[0]), y],

theano/tensor/nlinalg.py

@@ -255,14 +255,18 @@ def trace(X):
     """
     Returns the sum of diagonal elements of matrix X.
 
-    :note: work on GPU since 0.6rc4.
+    Notes
+    -----
+    Works on GPU since 0.6rc4.
+
     """
     return extract_diag(X).sum()
 
 
 class Det(Op):
-    """Matrix determinant
-    Input should be a square matrix
+    """
+    Matrix determinant. Input should be a square matrix.
+
     """
 
     __props__ = ()
@@ -598,6 +602,7 @@ def qr(a, mode="full"):
 
 class SVD(Op):
     """
+
     Parameters
     ----------
     full_matrices : bool, optional
@@ -609,7 +614,7 @@ class SVD(Op):
         Whether or not to compute u and v in addition to s.
         True by default.
 
-        """
+    """
 
     # See doc in the docstring of the function just after this class.
     _numop = staticmethod(numpy.linalg.svd)

theano/tensor/nnet/conv.py

@@ -356,7 +356,8 @@ class ConvOp(OpenMPOp):
 
         Returns
         -------
-        (rows,cols) of output image.
+        object
+            (rows,cols) of output image.
 
         """
         # The formula would be ceil((i + s * k - s * 1) / float(d)),

theano/tensor/nnet/neighbours.py

@@ -13,27 +13,29 @@ from theano.gradient import grad_undefined
 
 
 class Images2Neibs(Op):
+    """
 
-    __props__ = ("mode",)
+    Parameters
+    ----------
+    mode : {'valid', 'ignore_borders', 'wrap_centered'}
+        'valid': Requires an input that is a multiple of the
+            pooling factor (in each direction).
+        'ignore_borders': Same as valid, but will ignore the borders
+            if the shape(s) of the input is not a multiple of the pooling
+            factor(s).
+        'wrap_centered' : ?? TODO comment
 
-    def __init__(self, mode='valid'):
-        """
-        Parameters
-        ----------
-        mode : {'valid', 'ignore_borders', 'wrap_centered'}
-           'valid': Requires an input that is a multiple of the
-                pooling factor (in each direction).
-            'ignore_borders': Same as valid, but will ignore the borders
-                if the shape(s) of the input is not a multiple of the pooling
-                factor(s).
-            'wrap_centered' : ?? TODO comment
+    Returns
+    -------
+    object
+        Reshapes the input as a 2D tensor where each row is an
+        pooling example.
 
-        Returns
-        -------
-            Reshapes the input as a 2D tensor where each row is an
-            pooling example.
+    """
 
-        """
+    __props__ = ("mode",)
+
+    def __init__(self, mode='valid'):
         if mode not in ['valid', 'wrap_centered', 'ignore_borders']:
             raise NotImplementedError("Only the mode valid, ignore_borders"
                                       " and wrap_centered have been"
@@ -456,16 +458,17 @@ def images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
             :math:`n * step\_size_i + neib\_shape_i` for some :math:`n`
     mode : {'valid', 'ignore_borders', 'wrap_centered}
         ``valid``
-           Requires an input that is a multiple of the
-           pooling factor (in each direction).
+        Requires an input that is a multiple of the
+        pooling factor (in each direction).
         ``ignore_borders``
-           Same as valid, but will ignore the borders if the shape(s) of
-           the input is not a multiple of the pooling factor(s).
+        Same as valid, but will ignore the borders if the shape(s) of
+        the input is not a multiple of the pooling factor(s).
         ``wrap_centered``
-           ?? TODO comment
+        ?? TODO comment
 
     Returns
     -------
+    object
         Reshapes the input as a 2D tensor where each row is an
         pooling example. Pseudo-code of the output:
 
@@ -531,6 +534,7 @@ def neibs2images(neibs, neib_shape, original_shape, mode='valid'):
 
     Returns
     -------
+    object
         Reconstructs the input of
         :func:`images2neibs <theano.sandbox.neigbours.neibs2images>`,
         a 4d tensor of shape `original_shape`.

theano/tensor/nnet/nnet.py

@@ -805,7 +805,7 @@ class CrossentropySoftmaxArgmax1HotWithBias(gof.Op):
     """
     A special compound L{Op} for the output of neural-net classifiers.
 
-    Attributes
+    Parameters
     ----------
     x : a matrix of floats (32 or 64)
     b : a [row] vector of floats (32 or 64), length is number of cols in x
@@ -813,6 +813,7 @@ class CrossentropySoftmaxArgmax1HotWithBias(gof.Op):
 
     Returns
     -------
+    object
         row-wise NLL, softmax(x+b), row-wise argmax of (x+b).
 
     @precondition: every entry in y_idx is a valid (non-negative)
@@ -1240,6 +1241,7 @@ def crossentropy_softmax_max_and_argmax_1hot_with_bias(x, b, y_idx, **kwargs):
     """
     Returns
     -------
+    object
         The cross-entropy, the softmax output, the max probability,
         and the argmax index.
 
@@ -1931,7 +1933,7 @@ def categorical_crossentropy(coding_dist, true_dist):
     ----------
     coding_dist : a dense matrix
         Each slice along axis represents one distribution.
-    true_dist: a dense matrix or sparse matrix or integer vector
+    true_dist : a dense matrix or sparse matrix or integer vector
         In the case of a matrix argument, each slice along axis represents one
         distribution. In the case of an integer vector argument, each element
         represents the position of the '1' in a 1-of-N encoding.
@@ -2091,7 +2093,7 @@ def relu(x, alpha=0):
     ----------
     x : symbolic tensor
         Tensor to compute the activation function for.
-    alpha: scalar or tensor, optional
+    alpha : scalar or tensor, optional
         Slope for negative input, usually between 0 and 1. The default value
         of 0 will lead to the standard rectifier, 1 will lead to
         a linear activation function, and any value in between will give a

theano/tensor/nnet/sigm.py

@@ -383,6 +383,7 @@ logsigm_to_softplus = gof.PatternSub(
 
 def _is_1(expr):
     """
+
     Returns
     -------
     bool
@@ -417,8 +418,10 @@ opt.register_stabilize(log1pexp_to_softplus, name='log1pexp_to_softplus')
 
 def is_1pexp(t):
     """
+
     Returns
     -------
+    object
         If 't' is of the form (1+exp(x)), return (False, x).
         Else return None.
 
@@ -471,6 +474,7 @@ def is_exp(var):
 
     Returns
     -------
+    tuple
         A pair (b, x) with `b` a boolean set to True if `var` is of the
         form `-exp(x)` and False if `var` is of the form `exp(x)`. If `var`
         cannot be cast into either form, then return `None`.
@@ -496,6 +500,7 @@ def is_mul(var):
 
     Returns
     -------
+    object
         A list [x, y, z, ...] if `var` is of the form `x * y * z * ...`,
         or None if `var` cannot be cast into this form.
 
@@ -538,6 +543,7 @@ def is_neg(var):
 
     Returns
     -------
+    object
         `x` if `var` is of the form `-x`, or None otherwise.
 
     """
@@ -627,6 +633,7 @@ def parse_mul_tree(root):
 
     Returns
     -------
+    object
         A tree where each non-leaf node corresponds to a multiplication
         in the computation of `root`, represented by the list of its inputs.
         Each input is a pair [n, x] with `n` a boolean value indicating whether
@@ -688,6 +695,7 @@ def replace_leaf(arg, leaves, new_leaves, op, neg):
 
     Returns
     -------
+    bool
         True if a replacement occurred, or False otherwise.
 
     """
@@ -712,6 +720,7 @@ def simplify_mul(tree):
 
     Returns
     -------
+    object
         A multiplication tree computing the same output as `tree` but without
         useless multiplications by 1 nor -1 (identified by leaves of the form
         [False, None] or [True, None] respectively). Useless multiplications
@@ -757,6 +766,7 @@ def compute_mul(tree):
 
     Returns
     -------
+    object
         A Variable that computes the multiplication represented by the tree.
 
     """

theano/tensor/opt.py

@@ -156,6 +156,7 @@ def _fill_chain(new_out, orig_inputs):
 
 def encompasses_broadcastable(b1, b2):
     """
+
     Parameters
     ----------
     b1
@@ -165,8 +166,9 @@ def encompasses_broadcastable(b1, b2):
 
     Returns
     -------
-    True if the broadcastable patterns b1 and b2 are such that b2 is
-    broadcasted to b1's shape and not the opposite.
+    bool
+        True if the broadcastable patterns b1 and b2 are such that b2 is
+        broadcasted to b1's shape and not the opposite.
 
     """
     if len(b1) < len(b2):
@@ -1693,10 +1695,10 @@ class Assert(T.Op):
 
     Examples
     --------
-      T = theano.tensor
-      x = T.vector('x')
-      assert_op = T.opt.Assert()
-      func = theano.function([x], assert_op(x, x.size<2))
+    T = theano.tensor
+    x = T.vector('x')
+    assert_op = T.opt.Assert()
+    func = theano.function([x], assert_op(x, x.size<2))
 
     """
 
@@ -3819,8 +3821,10 @@ class Canonizer(gof.LocalOptimizer):
     @staticmethod
     def get_constant(v):
         """
+
         Returns
         -------
+        object
             A numeric constant if v is a Constant or, well, a
             numeric constant. If v is a plain Variable, returns None.
 
@@ -5189,6 +5193,7 @@ def constant_folding(node):
 
 def _is_1(expr):
     """
+
     Returns
     -------
     bool
@@ -5204,6 +5209,7 @@ def _is_1(expr):
 
 def _is_minus1(expr):
     """
+
     Returns
     -------
     bool
@@ -5218,13 +5224,19 @@ def _is_minus1(expr):
 
 
 def get_clients(node):
-    """Used by erf/erfc opt to track less frequent op."""
+    """
+    Used by erf/erfc opt to track less frequent op.
+
+    """
     return [c for c, i in node.outputs[0].clients
             if c != "output"]
 
 
 def get_clients2(node):
-    """Used by erf/erfc opt to track less frequent op."""
+    """
+    Used by erf/erfc opt to track less frequent op.
+
+    """
     l = []
     for c, i in node.outputs[0].clients:
         if c != "output":

theano/tensor/raw_random.py

@@ -843,6 +843,7 @@ def multinomial(random_state, size=None, n=1, pvals=[0.5, 0.5],
 
     Returns
     -------
+    tensor
         Tensor of len(size)+1 dimensions, and shape[-1]==L, with
         the specified ``dtype``, with the experiment counts. See
         examples to understand the shape of the return value, which is

theano/tensor/shared_randomstreams.py

@@ -21,7 +21,10 @@ class RandomStateSharedVariable(SharedVariable):
 @shared_constructor
 def randomstate_constructor(value, name=None, strict=False,
                             allow_downcast=None, borrow=False):
-    """SharedVariable Constructor for RandomState"""
+    """
+    SharedVariable Constructor for RandomState.
+
+    """
     if not isinstance(value, numpy.random.RandomState):
         raise TypeError
     if not borrow:

theano/tensor/sort.py

@@ -103,6 +103,7 @@ class SortOp(theano.Op):
 
 def sort(a, axis=-1, kind='quicksort', order=None):
     """
+
     Parameters
     ----------
     a : Tensor
@@ -119,7 +120,8 @@ def sort(a, axis=-1, kind='quicksort', order=None):
 
     Returns
     -------
-    A sorted copy of an array.
+    array
+        A sorted copy of an array.
 
     """
     if axis is None:

theano/tensor/subtensor.py

@@ -309,8 +309,9 @@ class Subtensor(Op):
 
         Returns
         -------
-        idxs, with the slices flattened out into a list.
-        If cond is true for an entry, does not flatten it.
+        list
+            idxs, with the slices flattened out into a list.
+            If cond is true for an entry, does not flatten it.
 
         """
         ret = []
@@ -1484,7 +1485,8 @@ class IncSubtensor(Op):
 
         Returns
         -------
-        C code expression to make a copy of x.
+        object
+            C code expression to make a copy of x.
 
         Base class uses PyArrayObject *, subclasses may override for
         different types of arrays.
@@ -1547,8 +1549,8 @@ class IncSubtensor(Op):
 
         Returns
         -------
-        Returns a C code expression to copy source into view, and
-        return 0 on success.
+        object
+            C code expression to copy source into view, and 0 on success.
 
         """
         return """PyArray_CopyInto(%(view)s, %(source)s)""" % locals()
@@ -1911,12 +1913,13 @@ class AdvancedIncSubtensor1(Op):
         """
         Parameters
         ----------
-        x: string
+        x : string
             Gives the name of a C variable pointing to an array.
 
         Returns
         -------
-        C code expression to make a copy of x.
+        object
+            C code expression to make a copy of x.
 
         Base class uses PyArrayObject *, subclasses may override for
         different types of arrays.

theano/tensor/type.py

@@ -657,7 +657,8 @@ class TensorType(Type):
 
         Returns
         -------
-        Python object that ``self.get_size()`` understands.
+        object
+            Python object that ``self.get_size()`` understands.
 
         """
         return obj.shape
@@ -673,6 +674,7 @@ class TensorType(Type):
 
         Returns
         -------
+        int
             The number of bytes taken by the object described by ``shape_info``.
 
         """

theano/tensor/var.py

@@ -255,9 +255,11 @@ class _tensor_py_operators:
 
     def transpose(self, *axes):
         """
+
         Returns
         -------
-        `tensor.transpose(self, axes)` or `tensor.transpose(self, axes[0])`.
+        object
+            `tensor.transpose(self, axes)` or `tensor.transpose(self, axes[0])`.
 
         If only one `axes` argument is provided and it is iterable, then it is
         assumed to be the entire axes tuple, and passed intact to