Given the following function where:
- y is a ND array
- g has one less dimension than y
- x is a 1D array
- the result is the same number of dimensions as g
How do I write a Theano operation that wraps this function? I can get it
to work for the case where y is a 1D array and g is a scalar (see below).
However, when y is a 2D array, I get the following error. I understand why
I'm getting the error, but I'm not clear how to write a more suitably
polymorphic function that can handle broadcasting. Any pointers would be
appreciated.
TypeError: We expected inputs of types '[TensorType(float64, vector),
TensorType(float64, scalar)]' but got types '[TensorType(float64, matrix),
TensorType(float64, vector)]'
class Threshold(theano.Op):
__props__ = ('threshold', 'lower', 'upper', 'step')
itypes = [theano.tensor.dvector, theano.tensor.dscalar]
otypes = [theano.tensor.dscalar]
def __init__(self, threshold, lower, upper, step):
self.threshold = threshold
self.lower = lower
self.upper = upper
self.step = step
self.x = np.arange(lower, upper, step)
super().__init__()
def perform(self, node, inputs, output_storage):
y = inputs[0]
g = inputs[1]
z = output_storage[0]
y = norm.ppf(y)-norm.ppf(g)
fx = lambda y: np.interp([1], y, x)[0]
z[0] = np.apply_along_axis(fx, 0, y)
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