#!/usr/bin/python3

import torch
import pandas


data = pandas.read_csv('mieszkania.tsv', sep='\t')

x1 = torch.tensor(data['powierzchnia'], dtype=torch.float)
x0 = torch.ones(x1.size(0))
x = torch.stack((x0, x1)).transpose(0, 1)
y = torch.tensor(data['cena'], dtype=torch.float)

w = torch.rand(2, requires_grad=True)

learning_rate = torch.tensor(0.000002)

for _ in range(400000):
    ypredicted = x @ w
    cost = torch.sum((ypredicted - y) ** 2)

    print(w, " => ", cost)

    cost.backward()

    with torch.no_grad():
        w = w - learning_rate * w.grad

    w.requires_grad_(True)