dependencies3-2

import numpy as np
from itertools import combinations


Alearn=np.array([
[5.1, 3.5, 1.4, 0.2],
[4.9, 3.0, 1.4, 0.2],
[4.7, 3.2, 1.3, 0.2],
[4.6, 3.1, 1.5, 0.2],
[5.0, 3.6, 1.4, 0.2],
[5.4, 3.9, 1.7, 0.4],
[4.6, 3.4, 1.4, 0.3],
[5.0, 3.4, 1.5, 0.2],
[4.4, 2.9, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.1],
[5.4, 3.7, 1.5, 0.2],
[4.8, 3.4, 1.6, 0.2],
[4.8, 3.0, 1.4, 0.1],
[4.3, 3.0, 1.1, 0.1],
[5.8, 4.0, 1.2, 0.2],
[5.7, 4.4, 1.5, 0.4],
[5.4, 3.9, 1.3, 0.4],
[5.1, 3.5, 1.4, 0.3],
[5.7, 3.8, 1.7, 0.3],
[5.1, 3.8, 1.5, 0.3]]);
Aexam=np.array([
[5.4, 3.4, 1.7, 0.2],
[5.1, 3.7, 1.5, 0.4],
[4.6, 3.6, 1.0, 0.2],
[5.1, 3.3, 1.7, 0.5],
[4.8, 3.4, 1.9, 0.2],
[5.0, 3.0, 1.6, 0.2],
[5.0, 3.4, 1.6, 0.4],
[5.2, 3.5, 1.5, 0.2],
[5.2, 3.4, 1.4, 0.2],
[4.7, 3.2, 1.6, 0.2],
[4.8, 3.1, 1.6, 0.2],
[5.4, 3.4, 1.5, 0.4],
[5.2, 4.1, 1.5, 0.1],
[5.5, 4.2, 1.4, 0.2],
[4.9, 3.1, 1.5, 0.2],
[5.0, 3.2, 1.2, 0.2],
[5.5, 3.5, 1.3, 0.2],
[4.9, 3.6, 1.4, 0.1],
[4.4, 3.0, 1.3, 0.2],
[5.1, 3.4, 1.5, 0.2],
[5.0, 3.5, 1.3, 0.3],
[4.5, 2.3, 1.3, 0.3],
[4.4, 3.2, 1.3, 0.2],
[5.0, 3.5, 1.6, 0.6],
[5.1, 3.8, 1.9, 0.4],
[4.8, 3.0, 1.4, 0.3],
[5.1, 3.8, 1.6, 0.2],
[4.6, 3.2, 1.4, 0.2],
[5.3, 3.7, 1.5, 0.2],
[5.0, 3.3, 1.4, 0.2]]);
Blearn=np.array([
[7.0, 3.2, 4.7, 1.4],
[6.4, 3.2, 4.5, 1.5],
[6.9, 3.1, 4.9, 1.5],
[5.5, 2.3, 4.0, 1.3],
[6.5, 2.8, 4.6, 1.5],
[5.7, 2.8, 4.5, 1.3],
[6.3, 3.3, 4.7, 1.6],
[4.9, 2.4, 3.3, 1.0],
[6.6, 2.9, 4.6, 1.3],
[5.2, 2.7, 3.9, 1.4],
[5.0, 2.0, 3.5, 1.0],
[5.9, 3.0, 4.2, 1.5],
[6.0, 2.2, 4.0, 1.0],
[6.1, 2.9, 4.7, 1.4],
[5.6, 2.9, 3.6, 1.3],
[6.7, 3.1, 4.4, 1.4],
[5.6, 3.0, 4.5, 1.5],
[5.8, 2.7, 4.1, 1.0],
[6.2, 2.2, 4.5, 1.5],
[5.6, 2.5, 3.9, 1.1]]);
Bexam=np.array([
[5.9, 3.2, 4.8, 1.8],
[6.1, 2.8, 4.0, 1.3],
[6.3, 2.5, 4.9, 1.5],
[6.1, 2.8, 4.7, 1.2],
[6.4, 2.9, 4.3, 1.3],
[6.6, 3.0, 4.4, 1.4],
[6.8, 2.8, 4.8, 1.4],
[6.7, 3.0, 5.0, 1.7],
[6.0, 2.9, 4.5, 1.5],
[5.7, 2.6, 3.5, 1.0],
[5.5, 2.4, 3.8, 1.1],
[5.5, 2.4, 3.7, 1.0],
[5.8, 2.7, 3.9, 1.2],
[6.0, 2.7, 5.1, 1.6],
[5.4, 3.0, 4.5, 1.5],
[6.0, 3.4, 4.5, 1.6],
[6.7, 3.1, 4.7, 1.5],
[6.3, 2.3, 4.4, 1.3],
[5.6, 3.0, 4.1, 1.3],
[5.5, 2.5, 4.0, 1.3],
[5.5, 2.6, 4.4, 1.2],
[6.1, 3.0, 4.6, 1.4],
[5.8, 2.6, 4.0, 1.2],
[5.0, 2.3, 3.3, 1.0],
[5.6, 2.7, 4.2, 1.3],
[5.7, 3.0, 4.2, 1.2],
[5.7, 2.9, 4.2, 1.3],
[6.2, 2.9, 4.3, 1.3],
[5.1, 2.5, 3.0, 1.1],
[5.7, 2.8, 4.1, 1.3]]);
Clearn=np.array([
[6.3, 3.3, 6.0, 2.5],
[5.8, 2.7, 5.1, 1.9],
[7.1, 3.0, 5.9, 2.1],
[6.3, 2.9, 5.6, 1.8],
[6.5, 3.0, 5.8, 2.2],
[7.6, 3.0, 6.6, 2.1],
[4.9, 2.5, 4.5, 1.7],
[7.3, 2.9, 6.3, 1.8],
[6.7, 2.5, 5.8, 1.8],
[7.2, 3.6, 6.1, 2.5],
[6.5, 3.2, 5.1, 2.0],
[6.4, 2.7, 5.3, 1.9],
[6.8, 3.0, 5.5, 2.1],
[5.7, 2.5, 5.0, 2.0],
[5.8, 2.8, 5.1, 2.4],
[6.4, 3.2, 5.3, 2.3],
[6.5, 3.0, 5.5, 1.8],
[7.7, 3.8, 6.7, 2.2],
[7.7, 2.6, 6.9, 2.3],
[6.0, 2.2, 5.0, 1.5]]);
Cexam=np.array([
[6.9, 3.2, 5.7, 2.3],
[5.6, 2.8, 4.9, 2.0],
[7.7, 2.8, 6.7, 2.0],
[6.3, 2.7, 4.9, 1.8],
[6.7, 3.3, 5.7, 2.1],
[7.2, 3.2, 6.0, 1.8],
[6.2, 2.8, 4.8, 1.8],
[6.1, 3.0, 4.9, 1.8],
[6.4, 2.8, 5.6, 2.1],
[7.2, 3.0, 5.8, 1.6],
[7.4, 2.8, 6.1, 1.9],
[7.9, 3.8, 6.4, 2.0],
[6.4, 2.8, 5.6, 2.2],
[6.3, 2.8, 5.1, 1.5],
[6.1, 2.6, 5.6, 1.4],
[7.7, 3.0, 6.1, 2.3],
[6.3, 3.4, 5.6, 2.4],
[6.4, 3.1, 5.5, 1.8],
[6.0, 3.0, 4.8, 1.8],
[6.9, 3.1, 5.4, 2.1],
[6.7, 3.1, 5.6, 2.4],
[6.9, 3.1, 5.1, 2.3],
[5.8, 2.7, 5.1, 1.9],
[6.8, 3.2, 5.9, 2.3],
[6.7, 3.3, 5.7, 2.5],
[6.7, 3.0, 5.2, 2.3],
[6.3, 2.5, 5.0, 1.9],
[6.5, 3.0, 5.2, 2.0],
[6.2, 3.4, 5.4, 2.3],
[5.9, 3.0, 5.1, 1.8]]);


phi = np.ones((1, len(Alearn[0]) + 1))
def predict(phi, X_augmented):
    labels = (np.multiply(phi, X_augmented).sum(axis=1) >= 0).astype(int)
    return labels.reshape((len(labels), 1))

def updated_phi(phi_old, X_augmented, y_true, y_predicted):
    X_err_1 = (X_augmented * ((y_true != y_predicted) & (y_true == 1))).sum(axis=0)
    X_err_0 = (X_augmented * ((y_true != y_predicted) & (y_true == 0))).sum(axis=0)
    grad = X_err_0 - X_err_1
    
    return phi_old - grad

def loss(phi, X_augmented, y_true, y_predicted):
    X_err_1 = (X_augmented * ((y_true != y_predicted) & (y_true == 1)))
    X_err_0 = (X_augmented * ((y_true != y_predicted) & (y_true == 0)))

    value = (X_err_0.dot(phi) - X_err_1.dot(phi)).sum()
    return value


def train(phi_init, X_augmented, y_true):
    phi_old = phi_init
    for i in range(1000):
        phi_new = updated_phi(phi_old, X_augmented, y_true, predict(phi_old, X_augmented))
        loss_ = loss(phi_new, X_augmented, y_true, predict(phi_new, X_augmented))
#         print('iteration #%s, loss: %s' % (i, loss_))
        if not loss_:
            break
        phi_old = phi_new
    
    return phi_new


if __name__ == '__main__':
    input_ = combinations([(Alearn, Aexam), (Blearn, Bexam), (Clearn, Cexam)], 2)
    labels = combinations('ABC', 2)
    results = {}
    for label, ((first_learn, first_exam), (second_learn, second_exam)) in zip(labels, input_):

        X = np.concatenate((first_learn, second_learn), axis=0)
        X_augmented = np.concatenate((X, np.ones((len(X), 1))), axis=1)
        y_true = np.concatenate((np.zeros((len(first_learn), 1)), np.ones((len(second_learn), 1))), axis=0)
        phi_init = np.array([1, 1, 1, 1, 1])

        phi_star = train(phi_init, X_augmented, y_true)

        exam = np.concatenate((first_exam, second_exam), axis=0)
        exam_augmented = np.concatenate((exam, np.ones((len(exam), 1))), axis=1)
        y_true = np.concatenate((np.zeros((len(first_exam), 1)), np.ones((len(second_exam), 1))), axis=0)
        y_predicted = predict(phi_star, exam_augmented)

        loss_ = loss(phi_star, X_augmented=exam_augmented, y_predicted=y_predicted, y_true=y_true)
        mis_0 = ((y_true != y_predicted) & (y_true == 0)).sum()
        mis_1 = ((y_true != y_predicted) & (y_true == 1)).sum()
        results[' vs '.join(label)] =  'loss={:.2f}, misclassified zero class:{}, first cass:{}'.format(loss_, mis_0, mis_1)

    for item in results.items():
        print("%s: %s" % item)