def contains_inf(arr, node=None, var=None):
"""
Test whether a numpy.ndarray contains any `np.inf` values.
Parameters
----------
arr : np.ndarray or output of any Theano op
node : None or an Apply instance.
If the output of a Theano op, the node associated to it.
var : The Theano symbolic variable.
Returns
-------
contains_inf : bool
`True` if the array contains any `np.inf` values, `False` otherwise.
Notes
-----
Tests for the presence of `np.inf`'s by determining whether the
values returned by `np.nanmin(arr)` and `np.nanmax(arr)` are finite.
This approach is more memory efficient than the obvious alternative,
calling `np.any(np.isinf(ndarray))`, which requires the construction of a
boolean array with the same shape as the input array.
"""
if isinstance(arr, theano.gof.type._cdata_type):
return False
elif isinstance(arr, np.random.mtrand.RandomState):
return False
elif var and getattr(var.tag, 'is_rng', False):
return False
elif isinstance(arr, slice):
return False
elif arr.size == 0:
return False
elif cuda.cuda_available and isinstance(arr, cuda.CudaNdarray):
if (node and hasattr(theano.sandbox, 'rng_mrg') and
isinstance(
node.op,
# It store ints in float container
theano.sandbox.rng_mrg.GPU_mrg_uniform)):
return False
else:
compile_gpu_func(False, True, False)
return (np.isinf(f_gpumin(arr.reshape(arr.size))) or
np.isinf(f_gpumax(arr.reshape(arr.size))))
elif pygpu_available and isinstance(arr, GpuArray):
return (np.isinf(f_gpua_min(arr.reshape(arr.size))) or
np.isinf(f_gpua_max(arr.reshape(arr.size))))
return np.isinf(np.nanmax(arr)) or np.isinf(np.nanmin(arr))
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