def test_reset_compute(self):
network = RbfNetwork(2, 1, 1)
total = 0
for i in xrange(0, len(network.long_term_memory)):
total += network.long_term_memory[i]
self.assertEquals(0, total)
network.reset()
total = 0
for i in xrange(0, len(network.long_term_memory)):
total += network.long_term_memory[i]
self.assertTrue(total > 1)
def test_reset_compute(self):
network = RbfNetwork(2, 1, 1)
total = 0
for i in xrange(0, len(network.long_term_memory)):
total += network.long_term_memory[i]
self.assertEquals(0, total)
network.reset()
total = 0
for i in xrange(0, len(network.long_term_memory)):
total += network.long_term_memory[i]
self.assertTrue(total > 1)
# Normalize iris species using one-of-n.
# We could have used equilateral as well. For an example of equilateral, see the example_nm_iris example.
norm.norm_col_one_of_n(iris_work, 4, classes, 0, 1)
# Prepare training data. Separate into input and ideal.
training = np.array(iris_work)
training_input = training[:, 0:4]
training_ideal = training[:, 4:7]
# Create an RBF network. There are four inputs and two outputs.
# There are also five RBF functions used internally.
# You can experiment with different numbers of internal RBF functions.
# However, the input and output must match the data set.
network = RbfNetwork(4, 4, 3)
network.reset()
def score_funct(x):
"""
The score function for Iris anneal.
@param x:
@return:
"""
global best_score
global input_data
global output_data
# 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 = []
# Normalize iris species using one-of-n.
# We could have used equilateral as well. For an example of equilateral, see the example_nm_iris example.
norm.norm_col_one_of_n(iris_work, 4, classes, 0, 1)
# Prepare training data. Separate into input and ideal.
training = np.array(iris_work)
training_input = training[:, 0:4]
training_ideal = training[:, 4:7]
# Create an RBF network. There are four inputs and two outputs.
# There are also five RBF functions used internally.
# You can experiment with different numbers of internal RBF functions.
# However, the input and output must match the data set.
network = RbfNetwork(4, 4, 3)
network.reset()
def score_funct(x):
"""
The score function for Iris anneal.
@param x:
@return:
"""
global best_score
global input_data
global output_data
# 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:
