process_data.py

import random


def get_data():
    file = open('data.txt', 'r')
    file_data = file.read().split("\n")
    data_bunch = [i for i in file_data]
    data_bunch.remove(data_bunch[-1])
    random.shuffle(data_bunch)
    pre_processed_features = []
    pre_processed_labels = []
    for j in data_bunch:
        d = j.split(",")
        t_d = [i for i in d]
        t_d.remove(t_d[-1])
        pre_processed_features.append(t_d)
        pre_processed_labels.append(d[-1])
    processed_features = pre_processed_features
    processed_labels = []
    for label in pre_processed_labels:
        label = label.replace("Iris-versicolor", "versicolor")
        label = label.replace("Iris-setosa", "setosa")
        processed_labels.append(label)
    data = (processed_features, processed_labels)
    return data


def process_data(pre_data):
    train_data = []
    test_data = []
    for d in pre_data[0]:
        sub_data = []
        for entry in d:
            sub_data.append(float(entry))
        train_data.append(sub_data)
    for d in pre_data[1]:
        if d == "setosa":
            test_data.append(1)
        elif d == "versicolor":
            test_data.append(0)
    return (train_data, test_data)



def process_data_2(pre_data):
    train_data = []
    test_data = []
    for d in pre_data[0]:
        sub_data = []
        for entry in d:
            sub_data.append(float(entry))
        train_data.append(sub_data)
    for d in pre_data[1]:
        if d == "setosa":
            test_data.append(np.array([1, 0]).reshape(2,1))
        elif d == "versicolor":
            test_data.append(np.array([0, 1]).reshape(2, 1))
    return (train_data, test_data)