30 lines
594 B
Python
30 lines
594 B
Python
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#!/usr/bin/python3
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import torch
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import pandas
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data = pandas.read_csv('mieszkania.tsv', sep='\t')
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x1 = torch.tensor(data['powierzchnia'], dtype=torch.float)
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x0 = torch.ones(x1.size(0))
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x = torch.stack((x0, x1)).transpose(0, 1)
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y = torch.tensor(data['cena'], dtype=torch.float)
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w = torch.rand(2, requires_grad=True)
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learning_rate = torch.tensor(0.000002)
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for _ in range(400000):
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ypredicted = x @ w
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cost = torch.sum((ypredicted - y) ** 2)
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print(w, " => ", cost)
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cost.backward()
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with torch.no_grad():
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w = w - learning_rate * w.grad
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w.requires_grad_(True)
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