def backpropagate(self, source):
"""
Performs the backpropagation of error through this neuron.
This method will collect all of the errors from its output neurons
and gated neurons before calculating performing weight adjustments.
"""
# Check off source.
try: self.outputSet.remove(source)
except KeyError: pass
# Only continue if we have been notified by all output neurons.
if len(self.outputSet) == 0:
# Reset output set.
self.resetOutputSet()
## Projected error.
self.projectedError = \
calculateProjectedError(self.derivative, self.projectedConnections)
## Gate error.
gateError = calculateGateError(self.derivative, self.gatedConnections)
# Calculate total error.
self.error = gateError + self.projectedError
# Backpropagate.
for neuron in self.inputSet: neuron.backpropagate(self)
def test_projected_error(self):
# Zero derivative
error = functions.calculateProjectedError(0.0, [self.c1])
self.assertAlmostEqual(0.0, error)
# One ungated connection.
error = functions.calculateProjectedError(self.derivative, [self.c1])
self.assertAlmostEqual(22.5, error)
# Two ungated connections.
error = functions.calculateProjectedError(self.derivative, [self.c1, self.c2])
self.assertAlmostEqual(62.5, error)
# One ungated and one gated connection.
error = functions.calculateProjectedError(self.derivative, [self.c1, self.g1])
self.assertAlmostEqual(97.5, error)
