import numpy as np
import datetime
import random
from liblinear.liblinearutil import *
import matplotlib.pyplot as plt

FILENAME = "hw4_train.dat"

def read_data(filename):
    with open(filename) as fp:
        lines = fp.readlines()
        x, y = [], []
        for line in lines:
            numbers = [ float(i) for i in line.split() ]
            x.append(numbers[:-1])
            y.append(int(numbers[-1]))
    return x, y

def format(features):
    '''
        change to LIBSVM format
    '''
    results = []
    for feature in features:
        result = {}
        for index, value in enumerate(feature):
            if value != 0.0:
                result[index+1] = value
        results.append(result)
    return results
    
def error(gt, pred):
    err = 0
    for index in range(len(gt)):
        err = (err+1) if gt[index]!=pred[index] else err
    return err/len(gt)

def new_split(x, y):
    random.seed(datetime.datetime.now().timestamp())
    data = list(zip(x, y))
    random.shuffle(data)
    x, y = zip(*data)
    train_x, val_x = x[:120], x[120:]
    train_y, val_y = y[:120], y[120:]
    return (train_x, train_y), (val_x, val_y)

x, y = read_data(FILENAME)
x = format(x)
log_lambda = []
for _ in range(128):
    (train_x, train_y), (val_x, val_y) = new_split(x, y)
    prob = problem(train_y, train_x)

    lambda_powers = [-6, -4, -2, 0, 2]
    results = []
    for lambda_power in lambda_powers:
        lambda_value = 10 ** lambda_power
        param_C = 1/(2*lambda_value) 
        param = parameter('-s 0 -c {} -e 0.000001 -q'.format(param_C))
        model = train(prob, param)
        p_label, p_acc, p_val = predict(val_y, val_x, model)
        err = error(val_y, p_label)
        print("0/1 error: ", err)
        print()
        results.append({'lambda': lambda_power, 'error': err})

    ans, min_err = None, 1
    for i in results:
        if i['error'] <= min_err:
            min_err = i['error']
            ans = i

    print("the largest lambda: {}, log_10(lambda*): {}".format(10**ans['lambda'], ans['lambda']))
    print()
    log_lambda.append(ans['lambda'])


plt.hist(log_lambda)
plt.savefig("hw4_11.png")