Project struct

src/learning/supervised.py

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+import math as math
+import numpy as np
+
+from ml import MLAlgorithm
+from learning.data import Dataset
+
+class LinearRegression(MLAlgorithm):
+    def __init__(self, dataset:Dataset, learning_rate:float=0.1) -> None:
+        self._set_dataset(dataset)
+
+        parameters = dataset.data.shape[1] - 1 #removing the result
+        self.theta = np.random.rand(parameters)
+        self.alpha = max(0, learning_rate)
+
+    def learning_step(self) -> float:
+        theta = self.theta
+        alpha = self.alpha
+        x, y, m = self._split_data_target(self.learnset)
+
+        self.theta -= alpha * (1/m) * np.sum((x.dot(theta) - y) * x.T, axis=1)
+        return self._error(x, y, m)
+
+    def test_error(self) -> float:
+        x, y, m = self._split_data_target(self.testset)
+        return self._error(x, y, m)
+
+    def _error(self, x:np.ndarray, y:np.ndarray, m:int) -> float:
+        diff = (x.dot(self.theta) - y)
+        return 1/(2*m) * np.sum(diff ** 2)