TAU20 pytorch linear regression

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my_solution.py

@@ -34,13 +34,13 @@ X_testA = pandas.read_csv('test-A/in.tsv', sep='\t', header=None, names=names)
 X_testA.drop('opis', axis=1, inplace=True)
 X_testA = encoder(X_testA)
 
-data_train = X_train[['Powierzchnia w m2', 'Liczba pokoi', 'Miejsce parkingowe', 'Liczba pięter w budynku']].values
+data_train = X_train[['Powierzchnia w m2', 'Liczba pokoi']].values
 data_train = np.hstack((data_train, np.ones((data_train.shape[0], 1), dtype=data_train.dtype)))
 
 
 x = torch.tensor(data_train , dtype=torch.float)
 y = torch.tensor(y_train, dtype=torch.float)
-w = torch.tensor([0, 0, 0, 0, 0], dtype=torch.float, requires_grad=True)
+w = torch.tensor([0, 0, 0], dtype=torch.float, requires_grad=True)
 learning_rate  = torch.tensor(0.00000001)
 
 for _ in range(100000):
@@ -55,13 +55,13 @@ w.requires_grad_(False)
 
 
 
-data_dev = X_dev0[['Powierzchnia w m2', 'Liczba pokoi', 'Miejsce parkingowe', 'Liczba pięter w budynku']].values
+data_dev = X_dev0[['Powierzchnia w m2', 'Liczba pokoi']].values
 data_dev = np.hstack((data_dev, np.ones((data_dev.shape[0], 1), dtype=data_dev.dtype)))
 
 x_dev = torch.tensor(data_dev, dtype=torch.float)
 
 
-data_testA = X_testA[['Powierzchnia w m2', 'Liczba pokoi', 'Miejsce parkingowe', 'Liczba pięter w budynku']].values
+data_testA = X_testA[['Powierzchnia w m2', 'Liczba pokoi']].values
 data_testA = np.hstack((data_testA, np.ones((data_testA.shape[0], 1), dtype=data_testA.dtype)))
 x_test = torch.tensor(data_testA , dtype=torch.float)
 

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