import statistics

import pandas as pd
from sklearn.model_selection import train_test_split

from funcClassifier import random_state_fit

names = ['D1', 'TI1', 'V1', 'RH', 'P']


def bring(data):
    return data[names], data['T'].astype('int')


def worker():
    data = pd.read_csv('WindData.csv')

    x, y = bring(data)

    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=100)

    funcs = ['relu', 'identity', 'tanh', 'logistic']
    acc = []
    for i in range(0, len(funcs)):
        acc.append(random_state_fit(i, x_train, y_train, x_test, y_test, funcs[i]))

    print('\n Results: ')
    print(f' Min is {min(acc)}, Median is {statistics.median(acc)}, Max is {max(acc)}')