def run_comp_learning():
input_type = 'sin'
np.random.seed(123)
num_hidden_units = 20
noise = 0.1
lr = 0.2
n_epochs = 100
batch_train = False
cov = 0.5
x_train, y_train, x_test, y_test = Utils.create_dataset(input_type, noise)
mean = Utils.compute_rbf_centers(num_hidden_units)
model = rbf_model(x_train,
y_train,
mean,
cov,
n_epochs,
num_hidden_units,
lr,
batch_train=batch_train)
model.fit()
predictions = model.forward_pass(x_test, True)
error = model.evaluate(x_test, y_test, transform=True)
print(error)
def experimen_2d_data():
x_train, y_train, x_test, y_test = Utils.load_ballist_data()
lr = 0.1
n_epochs = 200
batch_train = False
threshold = 0
cov = 0.5
predictions_ = []
best_error = 100
best_node = -1
for i in np.arange(1, 15, 1):
mean = Utils.compute_rbf_centers_competitive_learning(
x_train, i, eta=0.2, iterations=600, threshold=threshold)
model = rbf_model(x_train,
y_train,
mean,
cov,
n_epochs,
i,
lr,
batch_train=batch_train)
model.fit()
predictions = model.forward_pass(x_test, True)
error = model.evaluate(x_test, y_test, transform=True)
print(error)
if error < best_error:
best_error = error
best_node = i
predictions_ = predictions
print("best error {0}, best node {1}".format(best_error, best_node))
print("best error {0}, best node {1}".format(best_error, best_node))
best_mean_cl = Utils.compute_rbf_centers_competitive_learning(
x_train, best_node, eta=0.2, iterations=200, threshold=threshold)
best_means = Utils.compute_rbf_centers(best_node, x_train)
# print(best_mean_cl)
Utils.plot_data_means(x_train, best_means, best_mean_cl,
'Plot data along with means')
Utils.plot_pred_actual(
predictions_, y_test,
'RBF on 2d data lr : {0}, sigma : {1}'.format(lr, cov))
def experiment_rbf_with_noise():
input_type = 'sin'
np.random.seed(123)
noise = 0.1
lr = 0.1
n_epochs = 200
batch_train = False
cov = 0.2
x_train, y_train, x_test, y_test = Utils.create_dataset(input_type,
noise=noise)
predictions_ = []
best_error = 100
best_node = -1
for i in np.arange(1, 49, 1):
# mean = Utils.compute_rbf_centers(i)
mean = Utils.compute_rbf_centers_competitive_learning(x_train,
i,
eta=0.2,
iterations=600,
threshold=0.2)
model = rbf_model(x_train,
y_train,
mean,
cov,
n_epochs,
i,
lr,
batch_train=batch_train)
model.fit()
predictions = model.forward_pass(x_test, True)
error = model.evaluate(x_test, y_test, transform=True)
if error < best_error:
best_error = error
best_node = i
predictions_ = predictions
print("best error {0}, best node {1}".format(best_error, best_node))
print("best error {0}, best node {1}".format(best_error, best_node))
Utils.plot_pred_actual(
predictions_, y_test,
'sequential noisy RBF , sin(2x) lr : {0}, sigma : {1}'.format(lr, cov))
def experiment_plot_error_with_nodes():
input_type = 'sin'
np.random.seed(123)
noise = 0.1
lr = 0.01
n_epochs = 300
batch_train = False
cov = 0.4
x_train, y_train, x_test, y_test = Utils.create_dataset(input_type,
noise=noise)
num_nodes = 50
predictions_ = []
best_error = 100
best_node = -1
errors = np.zeros(num_nodes)
for i in np.arange(1, num_nodes, 1):
mean = Utils.compute_rbf_centers(i)
# mean = Utils.compute_rbf_centers_competitive_learning(x_train, i, eta=0.2, iterations=100,threshold=1)
model = rbf_model(x_train,
y_train,
mean,
cov,
n_epochs,
i,
lr,
batch_train=batch_train)
model.fit()
predictions = model.forward_pass(x_test, True)
error = model.evaluate(x_test, y_test, transform=True)
errors[i] = error
if error < best_error:
best_error = error
best_node = i
predictions_ = predictions
print("best error {0}, best node {1}".format(best_error, best_node))
print("best error {0}, best node {1}".format(best_error, best_node))
# Utils.plot_pred_actual(predictions_, y_test, '')
Utils.plot_error_nodes(
errors, num_nodes, ['error'],
'seq noisy RBF , sin(2x) lr : {0}, sigma : {1}'.format(lr, cov))
def run_rbf_expe(units=9,
noise=0,
lr=0,
epochs=500,
batch=True,
cov=0.3,
competitive=True,
threshold=0):
input_type = 'sin'
np.random.seed(123)
num_hidden_units = units
n_epochs = epochs
batch_train = batch
cov = cov
x_train, y_train, x_test, y_test = Utils.create_dataset(input_type,
noise=noise)
if competitive:
mean = Utils.compute_rbf_centers_competitive_learning(
x_train,
num_hidden_units,
eta=0.2,
iterations=600,
threshold=threshold)
else:
mean = Utils.compute_rbf_centers(num_hidden_units)
model_competitive = rbf_model(x_train,
y_train,
mean,
cov,
n_epochs,
num_hidden_units,
lr,
batch_train=batch_train)
model_competitive.fit()
predictions_competitive = model_competitive.forward_pass(x_test, True)
error_competitive = model_competitive.evaluate(x_test,
y_test,
transform=True)
return [predictions_competitive, error_competitive, mean]
def experiment_batch_without_noise():
input_type = 'sin'
np.random.seed(123)
lr = 0.2
n_epochs = 300
batch_train = True
cov = 1
x_train, y_train, x_test, y_test = Utils.create_dataset(input_type)
predictions = []
best_error = 100
best_node = -1
for i in np.arange(1, len(x_train), 1):
mean = Utils.compute_rbf_centers(i)
model = rbf_model(x_train,
y_train,
mean,
cov,
n_epochs,
i,
lr,
batch_train=batch_train)
model.fit()
predictions = model.forward_pass(x_test, True)
error = model.evaluate(x_test, y_test, transform=True)
if error < best_error:
best_error = error
best_node = i
predictions = predictions
print("number of hidden units: {0} test Error: {1}".format(i, error))
print("best error {0}, best node {1}".format(best_error, best_node))
Utils.plot_pred_actual(predictions, y_test, '')