def _classifier(self, n):
"""
Return true or false based on a vector
:param n: vector to classify
:return:
"""
result = dot_product(self._inputList, self._weights)
#print ("DBG PERCEPTRON: " + str(self._weights))
#print ("DBG PERCEPTRON: " + str(result))
boolresult = result > self._threshold
return boolresult
def _classifier(self, n):
"""
Return true or false based on a vector
:param n: vector to classify
:return:
"""
result = dot_product(self._inputList, self._weights)
#print ("DBG PERCEPTRON: " + str(self._weights))
#print ("DBG PERCEPTRON: " + str(result))
boolresult = result > self._threshold
return boolresult
def _train(self):
## Undertrain as we are limiting the reps we are trianing for
for goWeightTraining in range(self._trainForAtLeastThisManyReps):
error_count = 0
#print ("DBG OptimusPERCEPTRON: _trainingset:" + str(self._training_set))
for input_vector, desired_output in self._training_set:
# debug print("_train: weights")
# debug print(weights)
result = dot_product(self._inputList, self._weights) > self._threshold
error = desired_output - result
if error != 0:
error_count += 1
for index, value in enumerate(self._inputList):
self._weights[index] += self._learning_rate * error * value
# debug print("_train: error_count:"+str(error_count))
# Don't overtrain
if error_count == 0:
break
return True
