def binary_wine():
"""
Uploads crisp data, fuzzifies it, and implements ec to try and find the best network
:return: None
"""
training_file = 'cross_data (3 inputs - 2 outputs).csv' # 3 inputs, 2 outputs
num_inputs = 3
num_outputs = 2
training_data = ForwardNet.load_training_data(training_file, num_inputs)
random.shuffle(training_data) # Shuffle the training data to keep results useful
num_samples = None # Number of samples from the training data to use (reduces amount of computation)
population_size = 50 # Number of parents to start out with
selection_size = 50 # Number of survivors between the parents and the offspring (mu + lambda)
generations = 15 # Iterations
min_weight = -1 # Min value for weights and biases
max_weight = 1 # Max value for weights and biases
min_hidden = 5 # Min number of hidden nodes in a layer
max_hidden = 10 # Max number of hidden nodes in a layer
min_layer = 1 # Min number of layers
max_layer = 3 # Max number of layers
mut_chance = 50 # Mutation chance for each individual weight
ec = EvoComp(init_population_size=population_size, selection_size=selection_size, generations=generations,
training_data=training_data, num_samples=num_samples, num_inputs=num_inputs, num_outputs=num_outputs,
min_weight=min_weight, max_weight=max_weight, min_hidden=min_hidden, max_hidden=max_hidden,
min_layer=min_layer, max_layer=max_layer, input_type='raw_input', output_type='raw_output',
type_init='select', mut_chance=mut_chance, mutate_data=False)
ec.evolutionary_computation()
coordinate_x = []
coordinate_y = []
colors = []
precision = 0.001 # Concentration of dots on graph
multiplier = int(1 / (precision * 100))
for i in range(42 * multiplier + 1): # -2.1 to +2.1 on graph
for j in range(42 * multiplier + 1): # -2.1 to +2.1 on graph
num1 = (float(i) / (10 * multiplier)) - 2.1
num2 = (float(j) / (10 * multiplier)) - 2.1
coordinate_x.append(num1)
coordinate_y.append(num2)
coordinate = [num1, num2, 0] # 3rd coordinate is 0 because it doesn't alter output
network_input = {'raw_input': coordinate} # How ForwardNet takes an input into neural network
outputs = ec.best_nn.forward_computation(network_input) # Outputs[-1] is output layer's outputs
colors.append('r' if outputs[-1].index(max(outputs[-1])) == 0 else 'b') # Red if class 1, else blue
plt.scatter(coordinate_x, coordinate_y, s=10, c=colors, alpha=0.5)
plt.show()
def fuzzy_wine():
"""
Uploads fuzzy data and implements ec to try and find the best network
:return: None
"""
training_file = 'fuzzy_wine_inputs.csv' # 22 inputs, 13 outputs
num_inputs = 22
num_outputs = 13
training_data = ForwardNet.load_training_data(training_file, num_inputs)
random.shuffle(
training_data) # Shuffle the training data to keep results useful
num_samples = 750 # Number of samples from the training data to use (reduces amount of computation)
init_population_size = 50 # Number of parents to start out with
selection_size = 50 # Number of survivors between the parents and the offspring (mu + lambda)
generations = 15 # Iterations
min_weight = -1 # Min value for weights and biases
max_weight = 1 # Max value for weights and biases
min_hidden = 5 # Min number of hidden nodes in a layer
max_hidden = 10 # Max number of hidden nodes in a layer
min_layer = 1 # Min number of layers
max_layer = 3 # Max number of layers
mut_chance = 50 # Mutation chance for each individual weight
ec = EvoComp(init_population_size=init_population_size,
selection_size=selection_size,
generations=generations,
training_data=training_data,
num_samples=num_samples,
num_inputs=num_inputs,
num_outputs=num_outputs,
min_weight=min_weight,
max_weight=max_weight,
min_hidden=min_hidden,
max_hidden=max_hidden,
min_layer=min_layer,
max_layer=max_layer,
input_type='raw_input',
output_type='raw_output',
type_init='select',
mut_chance=mut_chance,
mutate_data=False)
ec.evolutionary_computation()