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implementation/artificial_intelligence/lasso_regession.py

@@ -1,28 +1,28 @@
 import numpy as np
+
 # refence:
 # https://stats.stackexchange.com/questions/\
 # 502376/\
 # what-is-gradient-of-the-objective-function-of-lasso-regression
 
 
-def costFunction(X_data: np.array, y_data: np.array,
-                 alpha: float,
-                 theta: np.ndarray,
-                 panjang_data: int) -> float:
-
+def costFunction(
+    X_data: np.array,
+    y_data: np.array,
+    alpha: float,
+    theta: np.ndarray,
+    panjang_data: int,
+) -> float:
     prod = np.dot(theta, X_data)
     prod = prod - y_data
-    result = np.sum(prod ** 2) + (alpha * np.sum(np.abs(theta.T)))
+    result = np.sum(prod**2) + (alpha * np.sum(np.abs(theta.T)))
     result = (result) / (2 * panjang_data)
     return result
 
 
-def gradient_descent_step(X_data: np.array,
-                          y_data: np.array,
-                          alpha: float,
-                          theta: np.array,
-                          panjang_data: int) -> float:
-
+def gradient_descent_step(
+    X_data: np.array, y_data: np.array, alpha: float, theta: np.array, panjang_data: int
+) -> float:
     prod = np.dot(theta, X_data)
     prod = prod - y_data
     sum_grad = np.dot(prod, X_data.T) + alpha * np.sign(theta.T)
@@ -31,11 +31,13 @@ def gradient_descent_step(X_data: np.array,
 
 
 class Lasso:
-    def __init__(self,
-                 aplha: float = 1.0,
-                 fit_intercept: bool = True,
-                 iterable: int = 1000,
-                 learning_path: float = .0001) -> None:
+    def __init__(
+        self,
+        aplha: float = 1.0,
+        fit_intercept: bool = True,
+        iterable: int = 1000,
+        learning_path: float = 0.0001,
+    ) -> None:
         """_summary_
         lasso regession merupakan regulasi alternatif
         pemodelan linear regession yang bertujuan agar model regessi
@@ -68,9 +70,8 @@ def fit(self, x: np.array, y: np.array) -> object:
         Y = np.array(y)
         theta = np.zeros(panjang_fitur)
         for _ in range(self.iterable):
-            theta = gradient_descent_step(
-                X, Y, self.alpha, theta, panjang_data)
-            costFunction(X, Y, self.alpha , theta , panjang_data)
+            theta = gradient_descent_step(X, Y, self.alpha, theta, panjang_data)
+            costFunction(X, Y, self.alpha, theta, panjang_data)
         return theta
 
     def transforms(self, x: np.array) -> np.array:
@@ -95,4 +96,5 @@ def main():
 
 if __name__ == "__main__":
     import doctest
+
     doctest.testmod(verbose=True)