Fix bugs

src/app.py

@@ -1,9 +1,8 @@
 from learning.data import Dataset
 from learning.supervised import LinearRegression
-from learning.ml import MLAlgorithm
-from plot import Plot
+from learning.ml import MLRegression
 
-def auto_mpg() -> MLAlgorithm:
+def auto_mpg() -> MLRegression:
     df = Dataset("datasets\\auto-mpg.csv", "MPG")
 
     df.to_numbers(["HP"])
@@ -12,29 +11,22 @@ def auto_mpg() -> MLAlgorithm:
 
     return LinearRegression(df, learning_rate=0.0001)
 
-def automobile() -> MLAlgorithm:
+def automobile() -> MLRegression:
     df = Dataset("datasets\\regression\\automobile.csv", "symboling")
 
     attributes_to_modify = ["fuel-system", "engine-type", "drive-wheels", "body-style", "make", "engine-location", "aspiration", "fuel-type", "num-of-cylinders", "num-of-doors"]
     df.factorize(attributes_to_modify)
-    df.to_numbers()
+    df.to_numbers(["normalized-losses", "bore", "stroke", "horsepower", "peak-rpm", "price"])
     df.handle_na()
     df.regularize(excepts=attributes_to_modify)
 
     return LinearRegression(df, learning_rate=0.001)
 
 
-
-
-epoch = 50000
-skip = 1000
-lr = automobile()
-
-train, test = lr.learn(epoch)
-
-plot = Plot("Error", "Time", "Mean Error")
-plot.line("training", "red", data=train[skip:])
-plot.line("test", "blue", data=test[skip:])
+epoch = 15000
+ml = automobile()
+ml.learn(epoch)
+ml.plot()
 
 """
 for _ in range(0, epoch):
@@ -45,7 +37,3 @@ for _ in range(0, epoch):
     plot.update("test", test_err)
     plot.update_limits()
 """
-
-plot.wait()
-
-