#!/usr/bin/env python3
import numpy as np

c = 0.1
LIMIT = 100
print_epoches = [5, 10, 100, 1000]
DATA_SLICE = 5
NUM_CLASS = 4
INPUT_FEATURE = 6

def load():
    raw_data = np.loadtxt('car.data', delimiter=',')
    data_size = len(raw_data)
    np.random.shuffle(raw_data)
    data = np.array_split(raw_data, DATA_SLICE)
    ret = [np.split(i, [1,], axis=1) for i in data]
    return ret

data_pieces = load()

val_idx = 0

train_label = np.concatenate(
  [data_pieces[i][0] for i in list(range(DATA_SLICE))[:val_idx] + list(range(DATA_SLICE))[val_idx+1:]]
)
train_label = train_label - 1
train_label = train_label.flatten().astype(int)

train_data = np.concatenate(
  [data_pieces[i][1] for i in list(range(DATA_SLICE))[:val_idx] + list(range(DATA_SLICE))[val_idx+1:]]
)


val_label = data_pieces[val_idx][0]
val_data = data_pieces[val_idx][1]
val_label = val_label - 1
val_label = val_label.flatten().astype(int)

v = np.array(val_data, dtype='float32')
v = np.concatenate((v, np.ones((len(v), 1))), axis=1)

def pla(train_data, train_label, val_data, val_label, picked_class):
    x = np.array(train_data, dtype='float32')
    x = np.concatenate((x, np.ones((len(x), 1))), axis=1)

    for i in range(len(x)):
        if train_label[i] != picked_class:
            x[i] *= -1
    w = np.random.rand(INPUT_FEATURE + 1)
    for j in range(LIMIT):
        flag = False
        for i in x:
            z = sum(i * w)
            if z <= 0:
                w = w + c * i
                flag = True
        if not flag:
            print('')
            break
        #if j in print_epoches:
        print(j)
        print('train acc:', np.sum(np.sum(x * w, axis=1) > 0) / len(x))
        print('val acc:', np.sum((np.sum(v * w, axis=1) > 0) == (val_label == picked_class)) / len(v))
    return w


w = [pla(train_data, train_label, val_data, val_label, i) for i in range(NUM_CLASS)]
pred = [np.sum(v * w[i], axis=1) > 0 for i in range(NUM_CLASS)]
print('val acc:', sum([all([pred[j][i] == (j == val_label[i]) for j in range(NUM_CLASS)]) for i in range(len(val_label))]) / len(val_label))