def get_error(self, data_set: DataSet):
squared_sum = 0
# Sum up the squared sup across all squared differences between the actual class value and the expected value.
for example_array, expected_class in data_set.get_data():
output = self.run(example_array)
squared_sum += (output - expected_class)**2
return math.sqrt(squared_sum) / len(data_set.get_data())
def get_accuracy(self, data_set: DataSet):
correct = 0
# Sum the number of correctly classified examples.
for example_array, expected_class in data_set.get_data():
output = self.run(example_array)
if output == expected_class:
correct += 1
# Divide the number of correct examples by the total number of examples.
return correct / len(data_set.get_data())