fix making out files

linreg.py

@@ -0,0 +1,37 @@
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+from math import sqrt
+
+# trenowanie
+train_data = pd.read_csv('train/train.tsv', sep='\t', names=['price', 'mileage', 'year', 'brand', 'engine_type', 'engine_capacity'])
+train_y = train_data[['price']]
+train_x = train_data[['mileage', 'year', 'engine_capacity']]
+lin_reg = LinearRegression()
+lin_reg.fit(train_x, train_y)
+
+# predykcja dev-0
+dev_data = pd.read_csv('dev-0/in.tsv', sep='\t', names=['mileage', 'year', 'brand', 'engine_type', 'engine_capacity'])
+dev_x = dev_data[['mileage', 'year', 'engine_capacity']]
+dev_prediction = lin_reg.predict(dev_x)
+
+# ewaluacja dev-0 przy pomocy rmse
+dev_y = pd.read_csv('dev-0/expected.tsv', sep='\n', header=None)
+dev_rmse = sqrt(mean_squared_error(dev_y, dev_prediction))
+print(dev_rmse)
+
+# predycja test-A
+test_data = pd.read_csv('test-A/in.tsv', sep='\t', names=['mileage', 'year', 'brand', 'engine_type', 'engine_capacity'])
+test_x = test_data[['mileage', 'year', 'engine_capacity']]
+test_prediction = lin_reg.predict(test_x)
+
+# zapisane predykcji do plików out
+with open('test-A/out.tsv', 'w') as test_output:
+    for prediction in test_prediction:
+        test_output.write(str(prediction[0]) + '\n')
+
+with open('dev-0/out.tsv', 'w') as dev_output:
+    for prediction in dev_prediction:
+        dev_output.write(str(prediction[0]) + '\n')
+
+