pocket_perceptron.py

def pocket_perceptron(weights, binary_train, binary_test):  
    best_weights =  weights
    best_accuracy = 0
    count = 0
    K = 100
    k = 0
    while True:
        #    print(weights)
        for row in binary_train:
            predicted = predict(row, weights)
    #        print("Class : "+ str(row[-1]) +" Predicted: "+ str(predicted))
            if predicted != row[-1]:
                k += 1
                count = 0
                if predicted == 1:
                    weights = sub_from_weights(weights, row)
                else:
                    weights = add_to_weights(weights, row)
                
                print(weights)
                
                accuracy = test_accuracy(binary_train, weights)
                if accuracy > best_accuracy:
                    best_weights, best_accuracy = weights, accuracy 
                    
                print(accuracy)
    
            if k == K or count == binary_train.shape[0]:
                break
            
            count += 1
            
        if k == K or count == binary_train.shape[0]:
            break
    
    weights = best_weights
    
    test_accuracy(binary_test, weights)