def binomial(random_state,
size=None,
n=1,
p=0.5,
ndim=None,
dtype='int64',
prob=None):
"""
Sample n times with probability of success prob for each trial,
return the number of successes.
If the size argument is ambiguous on the number of dimensions, ndim
may be a plain integer to supplement the missing information.
If size is None, the output shape will be determined by the shapes
of n and prob.
"""
if prob is not None:
p = prob
print >> sys.stderr, "DEPRECATION WARNING: the parameter prob to the binomal fct have been renamed to p to have the same name as numpy."
n = tensor.as_tensor_variable(n)
p = tensor.as_tensor_variable(p)
ndim, size, bcast = _infer_ndim_bcast(ndim, size, n, p)
if n.dtype == 'int64':
### THIS WORKS AROUND A NUMPY BUG on 32bit machine
### Erase when the following works on a 32bit machine:
### numpy.random.binomial(
# n=numpy.asarray([2,3,4], dtype='int64'),
# p=numpy.asarray([.1, .2, .3], dtype='float64'))
n = tensor.cast(n, 'int32')
op = RandomFunction(
'binomial',
tensor.TensorType(dtype=dtype, broadcastable=(False, ) * ndim))
return op(random_state, size, n, p)
def binomial(random_state, size=None, n=1, p=0.5, ndim=None, dtype='int64', prob=None):
"""
Sample n times with probability of success prob for each trial,
return the number of successes.
If the size argument is ambiguous on the number of dimensions, ndim
may be a plain integer to supplement the missing information.
If size is None, the output shape will be determined by the shapes
of n and prob.
"""
if prob is not None:
p = prob
print >> sys.stderr, "DEPRECATION WARNING: the parameter prob to the binomal fct have been renamed to p to have the same name as numpy."
n = tensor.as_tensor_variable(n)
p = tensor.as_tensor_variable(p)
ndim, size, bcast = _infer_ndim_bcast(ndim, size, n, p)
if n.dtype=='int64':
### THIS WORKS AROUND A NUMPY BUG on 32bit machine
### Erase when the following works on a 32bit machine:
### numpy.random.binomial(
# n=numpy.asarray([2,3,4], dtype='int64'),
# p=numpy.asarray([.1, .2, .3], dtype='float64'))
n = tensor.cast(n, 'int32')
op = RandomFunction('binomial',
tensor.TensorType(dtype = dtype, broadcastable = (False,)*ndim) )
return op(random_state, size, n, p)
def make_node(self, a, val):
a = tensor.as_tensor_variable(a)
val = tensor.as_tensor_variable(val)
if a.ndim < 2:
raise TypeError('%s: first parameter must have at least'
' two dimensions' % self.__class__.__name__)
elif val.ndim != 0:
raise TypeError('%s: second parameter must be a scalar'
% self.__class__.__name__)
val = tensor.cast(val, dtype=scalar.upcast(a.dtype, val.dtype))
if val.dtype != a.dtype:
raise TypeError('%s: type of second parameter must be compatible'
' with first\'s' % self.__class__.__name__)
return gof.Apply(self, [a, val], [a.type()])
def make_node(self, a, val):
a = tensor.as_tensor_variable(a)
val = tensor.as_tensor_variable(val)
if a.ndim < 2:
raise TypeError('%s: first parameter must have at least'
' two dimensions' % self.__class__.__name__)
elif val.ndim != 0:
raise TypeError('%s: second parameter must be a scalar' %
self.__class__.__name__)
val = tensor.cast(val, dtype=scalar.upcast(a.dtype, val.dtype))
if val.dtype != a.dtype:
raise TypeError('%s: type of second parameter must be compatible'
' with first\'s' % self.__class__.__name__)
return gof.Apply(self, [a, val], [a.type()])
def infer_shape(self, node, in_shapes):
temp = node.inputs[0]
M = tensor.switch(tensor.lt(temp, 0),
tensor.cast(0, temp.dtype), temp)
return [[M]]
def _infer_ndim_bcast(ndim, shape, *args):
"""
Infer the number of dimensions from the shape or the other arguments.
:rtype: (int, variable, tuple) triple, where the variable is an integer
vector, and the tuple contains Booleans.
:returns: the first element returned is the inferred number of dimensions.
The second element is the shape inferred (combining symbolic and constant
informations from shape and args).
The third element is a broadcasting pattern corresponding to that shape.
"""
# Find the minimum value of ndim required by the *args
if args:
args_ndim = max(arg.ndim for arg in args)
else:
args_ndim = 0
# there is a convention that -1 means the corresponding shape of a
# potentially-broadcasted symbolic arg
if (isinstance(shape, (tuple, list))
and numpy.all(numpy.asarray(shape) >= 0)):
bcast = [(s == 1) for s in shape]
v_shape = tensor.TensorConstant(type=tensor.lvector,
data=theano._asarray(shape,
dtype='int64'))
shape_ndim = len(shape)
if ndim is None:
ndim = shape_ndim
else:
if shape_ndim != ndim:
raise ValueError(
'ndim should be equal to len(shape), but\n',
'ndim = %s, len(shape) = %s, shape = %s' %
(ndim, shape_ndim, shape))
elif isinstance(shape, (tuple, list)):
# there is a convention that -1 means the corresponding shape of a
# potentially-broadcasted symbolic arg
#
# This case combines together symbolic and non-symbolic shape
# information
if ndim is None:
ndim = args_ndim
else:
ndim = max(args_ndim, ndim)
ndim = max(args_ndim, len(shape))
shape = [-1] * (ndim - len(shape)) + list(shape)
bcast = []
pre_v_shape = []
for i, s in enumerate(shape):
if hasattr(s, 'type'): # s is symbolic
bcast.append(False) # todo - introspect further
pre_v_shape.append(s)
else:
if s >= 0:
pre_v_shape.append(tensor.as_tensor_variable(s))
bcast.append((s == 1))
elif s == -1:
n_a_i = 0
for a in args:
# ndim: _ _ _ _ _ _
# ashp: s0 s1 s2 s3
# i
if i >= ndim - a.ndim:
n_a_i += 1
a_i = i + a.ndim - ndim
if not a.broadcastable[a_i]:
pre_v_shape.append(a.shape[a_i])
bcast.append(False)
break
else:
if n_a_i == 0:
raise ValueError(
('Auto-shape of -1 must overlap'
'with the shape of one of the broadcastable'
'inputs'))
else:
pre_v_shape.append(tensor.as_tensor_variable(1))
bcast.append(True)
else:
ValueError('negative shape', s)
# post-condition: shape may still contain both symbolic and
# non-symbolic things
v_shape = tensor.stack(*pre_v_shape)
elif shape is None:
# The number of drawn samples will be determined automatically,
# but we need to know ndim
if not args:
raise TypeError(('_infer_ndim_bcast cannot infer shape without'
' either shape or args'))
template = reduce(lambda a, b: a + b, args)
v_shape = template.shape
bcast = template.broadcastable
ndim = template.ndim
else:
v_shape = tensor.as_tensor_variable(shape)
if ndim is None:
ndim = tensor.get_vector_length(v_shape)
bcast = [False] * ndim
if (not (v_shape.dtype.startswith('int')
or v_shape.dtype.startswith('uint'))):
raise TypeError('shape must be an integer vector or list',
v_shape.dtype)
if args_ndim > ndim:
raise ValueError(
'ndim should be at least as big as required by args value',
(ndim, args_ndim), args)
assert ndim == len(bcast)
return ndim, tensor.cast(v_shape, 'int32'), tuple(bcast)
def _infer_ndim_bcast(ndim, shape, *args):
"""
Infer the number of dimensions from the shape or the other arguments.
:rtype: (int, variable, tuple) triple, where the variable is an integer
vector, and the tuple contains Booleans.
:returns: the first element returned is the inferred number of dimensions.
The second element is the shape inferred (combining symbolic and constant
informations from shape and args).
The third element is a broadcasting pattern corresponding to that shape.
"""
# Find the minimum value of ndim required by the *args
if args:
args_ndim = max(arg.ndim for arg in args)
else:
args_ndim = 0
# there is a convention that -1 means the corresponding shape of a
# potentially-broadcasted symbolic arg
if (isinstance(shape, (tuple, list))
and numpy.all(numpy.asarray(shape)>=0)):
bcast = [(s==1) for s in shape]
v_shape = tensor.TensorConstant(type=tensor.lvector, data=theano._asarray(shape, dtype='int64'))
shape_ndim = len(shape)
if ndim is None:
ndim = shape_ndim
else:
if shape_ndim != ndim:
raise ValueError('ndim should be equal to len(shape), but\n',
'ndim = %s, len(shape) = %s, shape = %s'
% (ndim, shape_ndim, shape))
elif isinstance(shape, (tuple, list)):
# there is a convention that -1 means the corresponding shape of a
# potentially-broadcasted symbolic arg
#
# This case combines together symbolic and non-symbolic shape
# information
if ndim is None:
ndim=args_ndim
else:
ndim = max(args_ndim, ndim)
ndim = max(args_ndim, len(shape))
shape = [-1]*(ndim - len(shape))+list(shape)
bcast = []
pre_v_shape = []
for i,s in enumerate(shape):
if hasattr(s, 'type'): # s is symbolic
bcast.append(False) # todo - introspect further
pre_v_shape.append(s)
else:
if s >= 0:
pre_v_shape.append(tensor.as_tensor_variable(s))
bcast.append((s==1))
elif s == -1:
n_a_i = 0
for a in args:
# ndim: _ _ _ _ _ _
# ashp: s0 s1 s2 s3
# i
if i >= ndim - a.ndim:
n_a_i += 1
a_i = i + a.ndim -ndim
if not a.broadcastable[a_i]:
pre_v_shape.append(a.shape[a_i])
bcast.append(False)
break
else:
if n_a_i == 0:
raise ValueError(('Auto-shape of -1 must overlap'
'with the shape of one of the broadcastable'
'inputs'))
else:
pre_v_shape.append(tensor.as_tensor_variable(1))
bcast.append(True)
else:
ValueError('negative shape', s)
# post-condition: shape may still contain both symbolic and non-symbolic things
v_shape = tensor.stack(*pre_v_shape)
elif shape is None:
# The number of drawn samples will be determined automatically,
# but we need to know ndim
if not args:
raise TypeError(('_infer_ndim_bcast cannot infer shape without'
' either shape or args'))
template = reduce(lambda a,b:a+b, args)
v_shape = template.shape
bcast = template.broadcastable
ndim = template.ndim
else:
v_shape = tensor.as_tensor_variable(shape)
if ndim is None:
ndim = tensor.get_vector_length(v_shape)
bcast = [False]*ndim
if not (v_shape.dtype.startswith('int') or v_shape.dtype.startswith('uint')):
raise TypeError('shape must be an integer vector or list', v_shape.dtype)
if args_ndim > ndim:
raise ValueError('ndim should be at least as big as required by args value',
(ndim, args_ndim), args)
assert ndim == len(bcast)
return ndim, tensor.cast(v_shape, 'int32'), tuple(bcast)
def infer_shape(self, node, in_shapes):
temp = node.inputs[0]
M = tensor.switch(tensor.lt(temp, 0), tensor.cast(0, temp.dtype), temp)
return [[M]]
