From 3d254244e564e40fdf348600d168777fa8a8f43a Mon Sep 17 00:00:00 2001
From: kayabaakihiko13 <iqbalramad75@gmail.com>
Date: Sun, 21 May 2023 01:02:35 +0700
Subject: [PATCH 1/2] feat: penambahan algoritma ridge Regession

---
 .../rigde_regession.py                        | 97 +++++++++++++++++++
 1 file changed, 97 insertions(+)
 create mode 100644 implementation/artificial_intelligence/rigde_regession.py

diff --git a/implementation/artificial_intelligence/rigde_regession.py b/implementation/artificial_intelligence/rigde_regession.py
new file mode 100644
index 00000000..caffb55a
--- /dev/null
+++ b/implementation/artificial_intelligence/rigde_regession.py
@@ -0,0 +1,97 @@
+# ridge regession adalah dimana merupakan tipe
+# regessi untuk modifikasi OLS regesi yang bertujuan
+# agar membantu untuk menolak overfitting
+# overfiting itu adalah pemodelan matematis
+# bahwa koresponden yang terlalu dekat dengan nilai
+# actual yang bisa menggangu akurasi pemodelan kita
+import numpy as np
+
+
+class Ridge:
+    def __init__(self, ridge_parameter: float = 1.0) -> None:
+        """
+        Parameter:
+        ---
+        :param rigde_parameter: adalah merupakan alpha
+                                yang berfungsi untuk bias
+                                di OLS
+        """
+        self.alpha = ridge_parameter
+        self.intercept = None
+        self.coef_ = None
+        self.theta = None
+
+    def fit(self, x: np.array, y: np.array):
+        """
+        Parameters:
+        ---
+        :param X: merupakan data untuk melatih algoritma
+        :param y: merupakan data target
+
+        Return:
+        ---
+        :param self: merupakan estimator
+        """
+        X = np.array(x)
+        X = np.c_[np.ones((X.shape[0], 1)), X]
+        Y = np.array(y)
+        W = X.T @ X
+        identity = np.identity(Y.shape[0])
+        bias = self.alpha * identity
+        self.theta = np.linalg.inv(W + bias) @ X.T @ Y
+        self.coef_ = self.theta[1:]
+        self.intercept = self.theta[0]
+        return self
+
+    def transform(self, x: np.array):
+        """
+        Parameter:
+        ---
+        :param x:merupakan data input yang ingin mendapatkan
+                ouput dari hasil algoritma
+        Returns:
+        ---
+        :param self.predictions: merupakan hasil dari outputnya
+        """
+        thetas = self.theta
+        X = np.array(x)
+        X_predictor = np.c_[np.ones((X.shape[0], 1)), X]
+        self.predictions = X_predictor.dot(thetas)
+        return self.predictions
+
+    def error_value(self, x: np.array, y: np.array):
+        X = np.array(x)
+        y = np.array(y)
+        self.fit(X, y)
+        error = y - self.transform(X)
+        return error
+
+    def score(self, y_actual: np.array, y_predict: np.array) -> float:
+        tss = np.sum((y_actual - np.mean(y_actual))**2)
+        rss = np.sum((y_actual - y_predict)**2)
+        r_squared = 1 - rss / tss
+        return r_squared
+
+
+def main():
+    import matplotlib.pyplot as plt
+    X = np.array([[1, 2], [2, 3], [4, 5]])
+    y = np.array([[1], [7], [9]])
+    print(X)
+    print(y.shape)
+    model = Ridge()
+    # modeling=Ridge()
+    model.fit(X, y)
+    y_predict = model.transform(X)
+    print(y_predict)
+    print(model.score(y, y_predict))
+    plt.scatter(X[:, 0], y, color='black')
+    plt.plot(X[:, 0], y_predict)
+    plt.title('Low-Alpha Ridge on Three Points')
+    plt.show()
+
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod(verbose=True)
+    main()

From a1e79165b6789e5d710ade8c936e98aaa574882b Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Sat, 20 May 2023 18:04:09 +0000
Subject: [PATCH 2/2] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../artificial_intelligence/rigde_regession.py         | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/implementation/artificial_intelligence/rigde_regession.py b/implementation/artificial_intelligence/rigde_regession.py
index caffb55a..ea243637 100644
--- a/implementation/artificial_intelligence/rigde_regession.py
+++ b/implementation/artificial_intelligence/rigde_regession.py
@@ -67,14 +67,15 @@ def error_value(self, x: np.array, y: np.array):
         return error
 
     def score(self, y_actual: np.array, y_predict: np.array) -> float:
-        tss = np.sum((y_actual - np.mean(y_actual))**2)
-        rss = np.sum((y_actual - y_predict)**2)
+        tss = np.sum((y_actual - np.mean(y_actual)) ** 2)
+        rss = np.sum((y_actual - y_predict) ** 2)
         r_squared = 1 - rss / tss
         return r_squared
 
 
 def main():
     import matplotlib.pyplot as plt
+
     X = np.array([[1, 2], [2, 3], [4, 5]])
     y = np.array([[1], [7], [9]])
     print(X)
@@ -85,13 +86,14 @@ def main():
     y_predict = model.transform(X)
     print(y_predict)
     print(model.score(y, y_predict))
-    plt.scatter(X[:, 0], y, color='black')
+    plt.scatter(X[:, 0], y, color="black")
     plt.plot(X[:, 0], y_predict)
-    plt.title('Low-Alpha Ridge on Three Points')
+    plt.title("Low-Alpha Ridge on Three Points")
     plt.show()
 
 
 if __name__ == "__main__":
     import doctest
+
     doctest.testmod(verbose=True)
     main()