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)
The score function. Calculate the MSE error between the actual network output and the ideal values for the XOR.
@param x: The long term memory that we are to score.
@return: The MSE error.
"""
global input_data
global output_data
network.copy_memory(x)
actual_output = []
for input_data in training_input:
output_data = network.compute_regression(input_data)
actual_output.append(output_data)
return ErrorCalculation.mse(np.array(actual_output), training_ideal)
# Create a copy of the long-term memory. This becomes the initial state.
x0 = list(network.long_term_memory)
# Train with hill climbing.
train = TrainHillClimb()
train.display_iteration = True
train.max_iterations = 100
train.stop_score = 0.05
result = train.train(x0, score_funct)
# Copy our final state to the long term memory of the network.
network.copy_memory(result)
# Display the output for the XOR. XOR will not be trained perfectly. You should see that the (0,1) and (1,0) inputs
# are both close to 1.0, whereas the (1,1) and (0,0) are close to 0.0.
for input_data in training_input:
output_data = network.compute_regression(input_data)
print(str(input_data) + " -> " + str(output_data))
@param x: The long term memory that we are to score.
@return: The MSE error.
"""
global input_data
global output_data
network.copy_memory(x)
actual_output = []
for input_data in training_input:
output_data = network.compute_regression(input_data)
actual_output.append(output_data)
return ErrorCalculation.mse(np.array(actual_output), training_ideal)
# Create a copy of the long-term memory. This becomes the initial state.
x0 = list(network.long_term_memory)
# Train with hill climbing.
train = TrainHillClimb()
train.display_iteration = True
train.max_iterations = 100
train.stop_score = 0.05
result = train.train(x0, score_funct)
# Copy our final state to the long term memory of the network.
network.copy_memory(result)
# Display the output for the XOR. XOR will not be trained perfectly. You should see that the (0,1) and (1,0) inputs
# are both close to 1.0, whereas the (1,1) and (0,0) are close to 0.0.
for input_data in training_input:
output_data = network.compute_regression(input_data)
print(str(input_data) + " -> " + str(output_data))
