feat: add linear regression machine learning algo through OLS method

Python_codes/Linear Regression/ols.py

@@ -0,0 +1,48 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# Copyright 2021 Jude Michael Teves
+#
+# This is my implementation of the linear regression machine learning algorithm through ordinary least squares method.
+#
+# This program is free software; you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation; either version 2 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program; if not, see <http://www.gnu.org/licenses/>.
+
+import numpy as np
+
+class OLS():
+  def fit(self, X, y):
+    b = np.ones(X.shape[0]).reshape(-1,1)
+    X = np.concatenate([b, X], axis=1)
+    theta = np.linalg.inv(np.dot(X.T, X)) @ X.T @ y.T
+    self.theta = theta
+    return theta
+
+  def predict(self, X):
+    b = np.ones(X.shape[0]).reshape(-1,1)
+    X = np.concatenate([b, X], axis=1)
+    return X @ self.theta
+
+  @staticmethod
+  def r_squared(y_true:np.array, y_pred:np.array) -> float:
+    '''
+    Returns the score using R-squared metric.
+    '''
+    u = ((y_true - y_pred) ** 2).sum() # residual sum of squares
+    v = ((y_true - y_true.mean()) ** 2).sum() # total sum of squares
+    score = 1 - (u/v)
+    return score
+
+  def score(self, X, y) -> float:
+    y_pred = self.predict(X)
+    return self.r_squared(y, y_pred)