Merge pull request #3179 from harlouci/props_sparse_sandbox

theano/sparse/sandbox/sp.py

@@ -41,6 +41,7 @@ class ConvolutionIndices(Op):
        patch. Convolution is then simply the dot product of (img x M)
        and the kernels.
     """
+    __props__ = ()
 
     @staticmethod
     def sparse_eval(inshp, kshp, nkern, strides=(1, 1), mode='valid'):

theano/sparse/sandbox/sp2.py

@@ -61,12 +61,7 @@ class Poisson(gof.op.Op):
     :return: A sparse matrix of random integers of a Poisson density
              with mean of `x` element wise.
     """
-
-    def __eq__(self, other):
-        return (type(self) == type(other))
-
-    def __hash__(self):
-        return hash(type(self))
+    __props__ = ()
 
     def make_node(self, x):
         x = as_sparse_variable(x)
@@ -91,8 +86,6 @@ class Poisson(gof.op.Op):
     def infer_shape(self, node, ins_shapes):
         return ins_shapes
 
-    def __str__(self):
-        return self.__class__.__name__
 poisson = Poisson()
 
 
@@ -112,19 +105,12 @@ class Binomial(gof.op.Op):
     :return: A sparse matrix of integers representing the number
              of success.
     """
+    __props__ = ("format", "dtype")
 
     def __init__(self, format, dtype):
         self.format = format
         self.dtype = dtype
 
-    def __eq__(self, other):
-        return ((type(self) == type(other)) and
-                self.format == other.format and
-                self.dtype == other.dtype)
-
-    def __hash__(self):
-        return hash(type(self)) ^ hash(self.format) ^ hash(self.dtype)
-
     def make_node(self, n, p, shape):
         n = tensor.as_tensor_variable(n)
         p = tensor.as_tensor_variable(p)
@@ -159,9 +145,6 @@ class Binomial(gof.op.Op):
     def infer_shape(self, node, ins_shapes):
         return [(node.inputs[2][0], node.inputs[2][1])]
 
-    def __str__(self):
-        return self.__class__.__name__
-
 csr_fbinomial = Binomial('csr', 'float32')
 csc_fbinomial = Binomial('csc', 'float32')
 csr_dbinomial = Binomial('csr', 'float64')
@@ -189,12 +172,7 @@ class Multinomial(gof.op.Op):
 
     :note: It will works only if `p` have csr format.
     """
-
-    def __eq__(self, other):
-        return (type(self) == type(other))
-
-    def __hash__(self):
-        return hash(type(self))
+    __props__ = ()
 
     def make_node(self, n, p):
         n = tensor.as_tensor_variable(n)
@@ -238,6 +216,4 @@ class Multinomial(gof.op.Op):
     def infer_shape(self, node, ins_shapes):
         return [ins_shapes[1]]
 
-    def __str__(self):
-        return self.__class__.__name__
 multinomial = Multinomial()