def compare_models():
simulations_number = 30
weight_limit = 0.5
error_margin = 0.3
dataset = get_dataset(noise_data_number=5, unipolar=False)
powers = np.arange(-2, -5, -1)
learning_rates = np.concatenate([np.power([10.] * len(powers), powers),
5 * np.power([10.] * len(powers[1:]), powers[1:])])
learning_rates = np.sort(learning_rates)[1:]
perceptron_avg_epochs_numbers = []
adaline_avg_epochs_numbers = []
for lr in learning_rates:
lr_epochs_num = []
for _ in range(simulations_number):
perceptron = Perceptron(2, weight_limit=weight_limit, activation_fn=bipolar_activation)
epoch_num, _ = train_model(perceptron, dataset, lr, verbose=False)
lr_epochs_num.append(epoch_num)
perceptron_avg_epochs_numbers.append(lr_epochs_num)
lr_epochs_num = []
for _ in range(simulations_number):
adaline = Adaline(2, weight_limit=weight_limit, error_margin=error_margin)
epoch_num, mse = train_model(adaline, dataset, lr, verbose=False)
print(f'MSE: {mse[-1]}')
lr_epochs_num.append(epoch_num)
adaline_avg_epochs_numbers.append(lr_epochs_num)
plot_result(learning_rates, perceptron_avg_epochs_numbers, adaline_avg_epochs_numbers)
def analyze(simulations_num: int = 10):
powers = np.arange(-1, -5, -1)
w_limit = 0.5
l_rates = np.concatenate([[0.9, 0.5, 0.25],
np.power([10.] * len(powers), powers),
5 * np.power([10.] * len(powers[1:]), powers[1:])
])
l_rates = np.sort(l_rates)[1:]
dataset = get_dataset(noise_data_number=5, unipolar=True)
epochs_numbers = []
for lr in l_rates:
lr_epochs_num = []
for _ in range(simulations_num):
perceptron = Perceptron(2,
weight_limit=w_limit,
activation_fn=unipolar_activation)
epoch_num, _ = train_model(perceptron, dataset, lr, verbose=False)
lr_epochs_num.append(epoch_num)
epochs_numbers.append(lr_epochs_num)
plot_results(l_rates, epochs_numbers)
def run_training(weight_limit: float, unipolar: bool = True):
learning_rate = 0.01
dataset = get_dataset(noise_data_number=10, unipolar=unipolar)
if unipolar:
print('UNIPOLAR training')
else:
print('BIPOLAR training')
if unipolar:
perceptron = Perceptron(2,
weight_limit=weight_limit,
activation_fn=unipolar_activation)
else:
perceptron = Perceptron(2,
weight_limit=weight_limit,
activation_fn=bipolar_activation)
train_model(perceptron, dataset, learning_rate)
evaluate_model(perceptron,
get_dataset(noise_data_number=2, unipolar=unipolar),
unipolar=unipolar)
def run_training():
weight_limit = 1.0
learning_rate = 0.01
error_margin = 0.3
max_epoch = 500
dataset = get_dataset(noise_data_number=20, unipolar=False)
adaline = Adaline(2, weight_limit=weight_limit, error_margin=error_margin)
epoch_num, mean_squared_errors = train_model(adaline, dataset, learning_rate,
max_epoch=max_epoch)
plot_mean_square_errors(epoch_num, mean_squared_errors, error_margin)
evaluate_model(adaline, get_dataset(noise_data_number=2, unipolar=False), unipolar=False)
def analyze(simulations_num: int = 10):
lr = 0.01
weight_limit = np.array([0.1, 0.3, 0.5, 0.8, 1.0])
dataset = get_dataset(noise_data_number=5, unipolar=True)
epochs_numbers = []
for w_limit in weight_limit:
w_epochs_num = []
for _ in range(simulations_num):
perceptron = Perceptron(2,
weight_limit=w_limit,
activation_fn=unipolar_activation)
epoch_num, _ = train_model(perceptron, dataset, lr, verbose=False)
w_epochs_num.append(epoch_num)
epochs_numbers.append(w_epochs_num)
plot_results(weight_limit, epochs_numbers)
unipolar_dataset = get_dataset(noise_data_number=5, unipolar=True)
bipolar_dataset = get_dataset(noise_data_number=5, unipolar=False)
simulations_num = 50
lr = 0.01
weight_limit = 0.5
unipolar_epochs_num = []
bipolar_epochs_num = []
for i in range(simulations_num):
unipolar_perceptron = Perceptron(2,
weight_limit=weight_limit,
activation_fn=unipolar_activation)
epoch_num, _ = train_model(unipolar_perceptron,
unipolar_dataset,
lr,
verbose=False)
unipolar_epochs_num.append(epoch_num)
unipolar_perceptron = Perceptron(2,
weight_limit=weight_limit,
activation_fn=bipolar_activation)
epoch_num, _ = train_model(unipolar_perceptron,
bipolar_dataset,
lr,
verbose=False)
bipolar_epochs_num.append(epoch_num)
plt.boxplot([unipolar_epochs_num, bipolar_epochs_num],
labels=['Aktywacja unipolarna', 'Aktywacja bipolarna'])
plt.ylabel('Liczba epok')