def average():
# Raises one number to the power of another number
random.seed(time.time())
power_train = TrainingData()
power_test = TrainingData()
for i in range(100):
a = random.random()
b = random.random()
power_train.append(DataPoint([a, b], [(a + b) / 2]))
a = random.random()
b = random.random()
power_test.append(DataPoint([a, b], [(a + b) / 2]))
return power_train, power_test
def adding():
# Adds two random numbers together
random.seed(time.time())
adder = TrainingData()
adder_testing = TrainingData()
for i in range(100):
a = random.random()
b = random.random()
adder.append(DataPoint([a, b], [a + b]))
a = random.random()
b = random.random()
adder_testing.append(DataPoint([a, b], [a + b]))
# return a tuple of the training data and testing data
return adder, adder_testing
def subtract():
# Subtracts two random numbers
random.seed(time.time())
subtract_train = TrainingData()
subtract_test = TrainingData()
for i in range(100):
a = random.random()
b = random.random()
subtract_train.append(DataPoint([a, b], [a - b]))
a = random.random()
b = random.random()
subtract_test.append(DataPoint([a, b], [a - b]))
# return a tuple of the training data and testing data
return subtract_train, subtract_test
from Network import Network
from TrainingData import TrainingData, DataPoint
print("Creating network.")
net = Network([2, 8, 8, 4])
training_data = TrainingData()
# Inputs: 0 and 1 Expected outputs:and, or, nand, xor
training_data.append(DataPoint([0, 0], [0, 0, 1, 0]))
training_data.append(DataPoint([0, 1], [0, 1, 1, 1]))
training_data.append(DataPoint([1, 0], [0, 1, 1, 1]))
training_data.append(DataPoint([1, 1], [1, 1, 0, 0]))
print("Training network...")
current_error = net.train_network_gen_alg(training_data.get_inputs(), training_data.get_expected())
print("Current network error: " + str(current_error))
i = 0
while current_error[1] > 0.001: # * len(training_data[1]) * len(training_data[1][0]):
i += 1
prev_error = current_error
current_error = net.train_network_gen_alg(training_data.get_inputs(), training_data.get_expected())
if i == 1:
print()
print("100 more iterations...")
print("Current network error: " + str(current_error[0]))
print("Current max error: " + str(current_error[1]))
print("Temperature: " + str(net.temperature))
i = 0
for data_input in training_data.get_inputs():
def decoder():
# Makes a binary decoder of size size
decoder_training = TrainingData()
decoder_testing = TrainingData()
decoder_training.append(DataPoint([0.0],
[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]))
decoder_training.append(DataPoint([0.1],
[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]))
decoder_training.append(DataPoint([0.2],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]))
decoder_training.append(DataPoint([0.3],
[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]))
decoder_training.append(DataPoint([0.4],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]))
decoder_training.append(DataPoint([0.5],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]))
decoder_training.append(DataPoint([0.6],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]))
decoder_training.append(DataPoint([0.7],
[0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]))
decoder_training.append(DataPoint([0.8],
[0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]))
decoder_training.append(DataPoint([0.9],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]))
decoder_training.append(DataPoint([1.0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]))
return decoder_training, decoder_testing
def and_or_nand_xor():
truth_table = TrainingData()
truth_table_testing = TrainingData()
##Inputs: 0 and 1 Expected outputs:and, or, nand, xor
truth_table.append(DataPoint([0, 0, 0, 0], [0, 0, 1, 0]))
truth_table.append(DataPoint([0, 0, 0, 1], [0, 1, 1, 1]))
truth_table.append(DataPoint([0, 0, 1, 0], [0, 1, 1, 1]))
truth_table.append(DataPoint([0, 0, 1, 1], [0, 1, 1, 0]))
truth_table.append(DataPoint([0, 1, 0, 0], [0, 1, 1, 1]))
truth_table.append(DataPoint([0, 1, 0, 1], [0, 1, 1, 0]))
truth_table.append(DataPoint([0, 1, 1, 0], [0, 1, 1, 0]))
truth_table.append(DataPoint([0, 1, 1, 1], [0, 1, 1, 1]))
truth_table.append(DataPoint([1, 0, 0, 0], [0, 1, 1, 1]))
truth_table.append(DataPoint([1, 0, 0, 1], [0, 1, 1, 0]))
truth_table.append(DataPoint([1, 0, 1, 0], [0, 1, 1, 0]))
truth_table.append(DataPoint([1, 0, 1, 1], [0, 1, 1, 1]))
truth_table.append(DataPoint([1, 1, 0, 0], [0, 1, 1, 0]))
truth_table.append(DataPoint([1, 1, 0, 1], [0, 1, 1, 1]))
truth_table.append(DataPoint([1, 1, 1, 0], [0, 1, 1, 1]))
truth_table.append(DataPoint([1, 1, 1, 1], [1, 1, 0, 0]))
return truth_table, truth_table_testing
learnAdd.update() # network2 = network1.getReverse() # network2.display() #============================================# ############################################## # Training Data # #===============# truth_table = TrainingData() truth_table_testing = TrainingData() ##Inputs: 0 and 1 Expected outputs:and, or, nand, xor truth_table.append(DataPoint([0, 0, 0, 0], [0, 0, 1, 0])) truth_table.append(DataPoint([0, 0, 0, 1], [0, 1, 1, 1])) truth_table.append(DataPoint([0, 0, 1, 0], [0, 1, 1, 1])) truth_table.append(DataPoint([0, 0, 1, 1], [0, 1, 1, 0])) truth_table.append(DataPoint([0, 1, 0, 0], [0, 1, 1, 1])) truth_table.append(DataPoint([0, 1, 0, 1], [0, 1, 1, 0])) truth_table.append(DataPoint([0, 1, 1, 0], [0, 1, 1, 0])) truth_table.append(DataPoint([0, 1, 1, 1], [0, 1, 1, 1])) truth_table.append(DataPoint([1, 0, 0, 0], [0, 1, 1, 1])) truth_table.append(DataPoint([1, 0, 0, 1], [0, 1, 1, 0])) truth_table.append(DataPoint([1, 0, 1, 0], [0, 1, 1, 0])) truth_table.append(DataPoint([1, 0, 1, 1], [0, 1, 1, 1])) truth_table.append(DataPoint([1, 1, 0, 0], [0, 1, 1, 0])) truth_table.append(DataPoint([1, 1, 0, 1], [0, 1, 1, 1])) truth_table.append(DataPoint([1, 1, 1, 0], [0, 1, 1, 1])) truth_table.append(DataPoint([1, 1, 1, 1], [1, 1, 0, 0]))