def perceptron_predict(test_set, model, threshold=0.5):
prediction = []
for sample in test_set:
sample.append(1)
if inner_product(sample, model) > threshold:
prediction.append(1)
else:
prediction.append(-1)
return prediction
def perceptron_train(training_set, label, learning_rate = 1.0):
num = len(training_set)
if num == 0:
raise "the training set is empty"
if len(label) != num:
raise "the number of training sample is not the same with labels"
for sample in training_set:
sample.append(1)
dim = len(training_set[0])
model = [0] * dim
while True:
error_count = 0
for i in range(0, dim):
if label[i]*inner_product(model, training_set[i]) <= 0:
model = dot_product_add(label[i]*learning_rate, training_set[i], model)
error_count = error_count + 1
if error_count == 0:
return model
