def test_compute_classification(self):
network = RbfNetwork(2, 1, 2)
network.copy_memory([
2.0, # input 1 to RBF 1
2.0, # input 2 to RBF 1
5.0, # RBF width
2.0, # RBF, center-0
4.0, # RBF, center-1
3.0, # RBF1 to Output 1
4.0, # Bias to Output 1
5.0, # RBF1 to Output 2
6.0]) # Bias to Output 2
x = [1, 2]
y = network.compute_regression(x)
# Inputs: (2*1) + (2*2) = 6
# RBF: Gaussian(6) = 1
# Outputs: (1*3) + (1*4) = 7
self.assertAlmostEqual(7, y[0], 3)
# Inputs: (2*1) + (2*2) = 6
# RBF: Gaussian(6) = 1
# Outputs: (1*5) + (1*6) = 11
self.assertAlmostEqual(11, y[1], 3)
cls = network.compure_classification(x)
# class 1 is higher than class 0
self.assertEqual(1, cls)
def test_compute_classification(self):
network = RbfNetwork(2, 1, 2)
network.copy_memory([
2.0, # input 1 to RBF 1
2.0, # input 2 to RBF 1
5.0, # RBF width
2.0, # RBF, center-0
4.0, # RBF, center-1
3.0, # RBF1 to Output 1
4.0, # Bias to Output 1
5.0, # RBF1 to Output 2
6.0
]) # Bias to Output 2
x = [1, 2]
y = network.compute_regression(x)
# Inputs: (2*1) + (2*2) = 6
# RBF: Gaussian(6) = 1
# Outputs: (1*3) + (1*4) = 7
self.assertAlmostEqual(7, y[0], 3)
# Inputs: (2*1) + (2*2) = 6
# RBF: Gaussian(6) = 1
# Outputs: (1*5) + (1*6) = 11
self.assertAlmostEqual(11, y[1], 3)
cls = network.compure_classification(x)
# class 1 is higher than class 0
self.assertEqual(1, cls)
def test_compute_regression(self):
network = RbfNetwork(2, 1, 1)
network.copy_memory([
2.0, # input 1 to RBF 1
2.0, # input 2 to RBF 1
5.0, # RBF width
2.0, # RBF, center-0
4.0, # RBF, center-1
3.0, # RBF1 to Output 1
4.0 # Bias to Output 1
])
x = [1, 2]
y = network.compute_regression(x)[0]
# Inputs: (2*1) + (2*2) = 6
# RBF: Gaussian(6) = 1
# Outputs: (1*3) + (1*4) = 7
self.assertAlmostEqual(7, y, 3)
def test_compute_regression(self):
network = RbfNetwork(2, 1, 1)
network.copy_memory([
2.0, # input 1 to RBF 1
2.0, # input 2 to RBF 1
5.0, # RBF width
2.0, # RBF, center-0
4.0, # RBF, center-1
3.0, # RBF1 to Output 1
4.0 # Bias to Output 1
])
x = [1, 2]
y = network.compute_regression(x)[0]
# Inputs: (2*1) + (2*2) = 6
# RBF: Gaussian(6) = 1
# Outputs: (1*3) + (1*4) = 7
self.assertAlmostEqual(7, y, 3)
# Update the network's long term memory to the vector we need to score.
network.copy_memory(x)
# Loop over the training set and calculate the output for each.
actual_output = []
for input_data in training_input:
output_data = network.compute_regression(input_data)
actual_output.append(output_data)
# Calculate the error with MSE.
result = ErrorCalculation.mse(np.array(actual_output), training_ideal)
return result
# Create a copy of the long-term memory. This becomes the initial state.
x0 = list(network.long_term_memory)
# Perform the annealing
train = TrainAnneal()
train.display_iteration = True
train.train(x0, score_funct)
# Display the final validation. We show all of the iris data as well as the predicted species.
for i in range(0, len(training_input)):
input_data = training_input[i]
# Compute the output from the RBF network
output_data = network.compute_regression(input_data)
ideal_data = training_ideal[i]
# Decode the three output neurons into a class number.
class_id = norm.denorm_one_of_n(output_data)
print(
str(input_data) + " -> " + inv_classes[class_id] + ", Ideal: " +
ideal_species[i])
# Update the network's long term memory to the vector we need to score.
network.copy_memory(x)
# Loop over the training set and calculate the output for each.
actual_output = []
for input_data in training_input:
output_data = network.compute_regression(input_data)
actual_output.append(output_data)
# Calculate the error with MSE.
result = ErrorCalculation.mse(np.array(actual_output), training_ideal)
return result
# Create a copy of the long-term memory. This becomes the initial state.
x0 = list(network.long_term_memory)
# Perform the annealing
train = TrainAnneal()
train.display_iteration = True
train.train(x0, score_funct)
# Display the final validation. We show all of the iris data as well as the predicted species.
for i in range(0, len(training_input)):
input_data = training_input[i]
# Compute the output from the RBF network
output_data = network.compute_regression(input_data)
ideal_data = training_ideal[i]
# Decode the three output neurons into a class number.
class_id = norm.denorm_one_of_n(output_data)
print(str(input_data) + " -> " + inv_classes[class_id] + ", Ideal: " + ideal_species[i])
