def make_node(self, M):
M = tensor.as_tensor_variable(M)
if M.ndim != 0:
raise TypeError('%s only works on scalar input'
% self.__class__.__name__)
elif (not M.dtype.startswith('int')) and \
(not M.dtype.startswith('uint')):
# dtype is a theano attribute here
raise TypeError('%s only works on integer input'
% self.__class__.__name__)
return gof.Apply(self, [M], [tensor.dvector()])
def make_node(self, M):
M = tensor.as_tensor_variable(M)
if M.ndim != 0:
raise TypeError('%s only works on scalar input' %
self.__class__.__name__)
elif (not M.dtype.startswith('int')) and \
(not M.dtype.startswith('uint')):
# dtype is a theano attribute here
raise TypeError('%s only works on integer input' %
self.__class__.__name__)
return gof.Apply(self, [M], [tensor.dvector()])
def make_node(self, x, weights):
x = basic.as_tensor_variable(x)
if x.dtype not in BinCountOp.compatible_type:
raise TypeError("Inputs dtype must be an integer.")
# Some dtypes are not supported by numpy's implementation of bincount.
# Until another one is available, we should fail at graph construction
# time, not wait for execution.
int_bitwidth = theano.gof.cmodule.python_int_bitwidth()
if int_bitwidth == 64:
numpy_unsupported_dtypes = ('uint64',)
if int_bitwidth == 32:
numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
intp_bitwidth = theano.gof.cmodule.local_bitwidth()
if intp_bitwidth == 32:
out_type = basic.ivector()
elif intp_bitwidth == 64:
out_type = basic.lvector()
if x.dtype in numpy_unsupported_dtypes:
raise TypeError(
("Input dtypes %s are not supported by numpy.bincount, "
% numpy_unsupported_dtypes), x.dtype)
if x.ndim != 1:
raise TypeError("Inputs must be of dimension 1.")
if weights is None:
weights = theano.gof.Constant(theano.gof.Generic(), None)
else:
weights = basic.as_tensor_variable(weights)
out_type = basic.dvector()
if weights.ndim != 1:
raise TypeError("Weights cannot have a number of"
"dimension different of 1.")
return theano.Apply(self, [x, weights], [out_type])
def make_node(self, x, weights):
x = basic.as_tensor_variable(x)
if x.dtype not in BinCountOp.compatible_type:
raise TypeError("Inputs dtype must be an integer.")
# Some dtypes are not supported by numpy's implementation of bincount.
# Until another one is available, we should fail at graph construction
# time, not wait for execution.
int_bitwidth = theano.gof.cmodule.python_int_bitwidth()
if int_bitwidth == 64:
numpy_unsupported_dtypes = ('uint64', )
if int_bitwidth == 32:
numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
intp_bitwidth = theano.gof.cmodule.local_bitwidth()
if intp_bitwidth == 32:
out_type = basic.ivector()
elif intp_bitwidth == 64:
out_type = basic.lvector()
if x.dtype in numpy_unsupported_dtypes:
raise TypeError(
("Input dtypes %s are not supported by numpy.bincount, " %
numpy_unsupported_dtypes), x.dtype)
if x.ndim != 1:
raise TypeError("Inputs must be of dimension 1.")
if weights is None:
weights = theano.gof.Constant(theano.gof.Generic(), None)
else:
weights = basic.as_tensor_variable(weights)
out_type = basic.dvector()
if weights.ndim != 1:
raise TypeError("Weights cannot have a number of"
"dimension different of 1.")
return theano.Apply(self, [x, weights], [out_type])
