moved other_ops to pylearn/nnet_ops

sandbox/others_ops.py

-## This file contain ops that are not currently integrated in the core of threano. 
-## Not all of those ops have been thoroughly tested.
-
-from theano.scalar import *
-import theano.tensor as T
-import theano
-from numpy import *
-
-class Prepend_scalar_constant_to_each_row(Op):
-    def __init__(self, val = 0):
-        if isinstance(val, float):
-            val = constant(val)
-        self.val = val
-
-    def make_node(self, mat):
-        #check type of input
-        if not isinstance(mat,Result) or not mat.type==T.matrix().type:
-            raise TypeError("Expected a matrix as input")
-        x = T.as_tensor(mat)
-        y = T.as_tensor(self.val)
-        if x.type.dtype != y.type.dtype:
-            TypeError("the value to prepend don't have the same type as the matrix")
-        
-        node = Apply(op=self, inputs=[mat], outputs=[T.matrix()])
-        return node
-
-    def perform(self, node, (mat, ), (output, )):
-        new_shape=(mat.shape[0],mat.shape[1]+1)
-        if output[0] == None:
-            output[0]=numpy.empty(new_shape,dtype=mat.dtype)
-            out=output[0]
-        else:
-            if output[0].shape!=new_shape:
-                try:
-                    output[0].resize(new_shape)
-                except:
-                    output[0]=numpy.empty(new_shape, dtype=mat.dtype)
-            out=output[0]
-
-        out[:,0].fill(self.val.data)
-        out[:,1:]=mat
-
-    def grad(self, (mat,), (goutput,)):
-        return goutput[:,1:]
-
-class Prepend_scalar_to_each_row(Op):        
-    def make_node(self, val, mat):
-        #check type of input
-        if isinstance(val, float):
-            val = constant(val)
-        if not isinstance(mat,Result) or not mat.type==T.matrix().type:
-            raise TypeError("Expected a matrix as input")
-        x = T.as_tensor(mat)
-        y = T.as_tensor(val)
-        if x.type.dtype != y.type.dtype:
-            TypeError("the value to prepend don't have the same type as the matrix")
-        
-        node = Apply(op=self, inputs=[val,mat], outputs=[T.matrix()])
-        return node
-
-    def perform(self, node, (val,mat), (output, )):
-        new_shape=(mat.shape[0],mat.shape[1]+1)
-        if output[0] == None:
-            output[0]=numpy.empty(new_shape,dtype=mat.dtype)
-            out=output[0]
-        else:
-            if output[0].shape!=new_shape:
-                try:
-                    output[0].resize(new_shape)
-                except:
-                    output[0]=numpy.empty(new_shape, dtype=mat.dtype)
-            out=output[0]
-        out[:,0].fill(val)
-        out[:,1:]=mat
-
-    def grad(self, (val, mat), (goutput,)):
-        return goutput[:,0], goutput[:,1:]
-
-prepend_scalar_to_each_row = Prepend_scalar_to_each_row()
-prepend_0_to_each_row = Prepend_scalar_constant_to_each_row(0.)
-prepend_1_to_each_row = Prepend_scalar_constant_to_each_row(1.)
-
-class solve(Op):
-    """
-    Find the solution to the linear equation Ax=b,
-    where A is a 2d matrix and b is a 1d or 2d matrix.
-    It use numpy.solve to find the solution.
-    """
-
-    def make_node(self, A, b):
-        if not isinstance(A, Result) or not A.type==T.matrix().type:
-            raise TypeError("We expected that A had a matrix type")
-        if not isinstance(B, Result) or not B.type==T.matrix().type:
-            raise TypeError("We expected that B had a matrix type")
-
-        node = Apply(op=self, inputs=[A, B], outputs=[T.matrix()])
-        return node
-
-    def perform(self, node, (A, B), (output, )):
-        ret=numpy.solve(A,B)
-        output[0]=ret
-
-    def grad(self, (theta, A, B), (gtheta,)):
-        raise NotImplementedError()
-
-if __name__ == '__main__':
-
-    x=T.matrix('x')
-    y=Prepend_scalar_constant_to_each_row(4.)(x)
-    f=theano.function([x],[y])
-    mat=numpy.random.rand(3,5)
-    gradient(f(mat))
-    print f(mat)
-
-    x=T.matrix('x')
-    y=Prepend_scalar_to_each_row()(5.,x)
-    f=theano.function([x],[y])
-    mat=numpy.ones((3,5),dtype="float32")
-    print f(mat)
-
-    A=numpy.random.randn(5,5)
-    b=numpy.array(range(5),dtype=float)
-    x=linalg.solve(A,b)
-    print A,b
-    print numpy.dot(A,x)
-