def activate(self, inputs):
"""Return the model outputs for given inputs."""
# Calculate similarity between input and each stored input
# (gaussian of each distance)
similarities = calculate.gaussian(
_distances(inputs, self._input_matrix), self._variance)
# Then scale each stored target by corresponding similarity, and sum
output_vec = _weighted_sum_rows(self._target_matrix, similarities)
if self._scale_by_similarity:
output_vec /= numpy.sum(similarities)
if self._scale_by_class:
# Scale output by number of classes (sum of targets)
# This minimizes the effect of unbalanced classes
# Return 0 when target total is 0
output_vec[:] = calculate.protvecdiv(output_vec,
self._target_totals)
# Convert output to probabilities, and return
output_vec /= sum(output_vec)
return output_vec
def test_protvecdiv_zero_den():
# Returns 0 for position with 0 denominator
# Also for 0 / 0
assert (calculate.protvecdiv(numpy.array([1.0, 2.0, 0.0]),
numpy.array([2.0, 0.0, 0.0])) == numpy.array(
[0.5, 0.0, 0.0])).all()
def test_protvecdiv_no_zero():
assert (calculate.protvecdiv(numpy.array([1.0, 2.0, 3.0]),
numpy.array([2.0, 2.0, 2.0])) == numpy.array(
[0.5, 1.0, 1.5])).all()