Project struct

src/app.py

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+from learning.data import Dataset
+from learning.supervised import LinearRegression
+from learning.ml import MLAlgorithm
+from plot import Plot
+
+def auto_mpg() -> MLAlgorithm:
+    df = Dataset("datasets\\auto-mpg.csv", "MPG")
+
+    df.to_numbers(["HP"])
+    df.handle_na()
+    df.regularize(excepts=["Cylinders","Year","Origin"])
+
+    return LinearRegression(df, learning_rate=0.0001)
+
+def automobile() -> MLAlgorithm:
+    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.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:])
+
+"""
+for _ in range(0, epoch):
+    train_err = lr.learning_step()
+    test_err = lr.test_error()
+
+    plot.update("training", train_err)
+    plot.update("test", test_err)
+    plot.update_limits()
+"""
+
+plot.wait()
+
+