From 491cb3ebd654a99624f28cfc01ff9dd72ead6915 Mon Sep 17 00:00:00 2001
From: omkarcharatkar <132135901+omkarcharatkar@users.noreply.github.com>
Date: Sat, 6 May 2023 16:44:15 +0530
Subject: [PATCH] Update loss_func.py

added Root Mean Squared error function.
---
 MLlib/loss_func.py | 56 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)

diff --git a/MLlib/loss_func.py b/MLlib/loss_func.py
index f182f09..e89ed3c 100644
--- a/MLlib/loss_func.py
+++ b/MLlib/loss_func.py
@@ -425,3 +425,59 @@ def loss(X, Y, W):
         y_pred = np.dot(X, W).T
         L = np.sum(np.true_divide((np.abs(Y - y_pred) * 100), Y)) / X.shape[0]
         return L
+        
+        
+        
+        
+class RootMeanSquaredError():
+    """
+   Calculate Root Mean Squared Error.
+    """
+
+    @staticmethod
+    def loss(X, Y, W):
+        """
+        Calculate loss by Root Mean Squared Error method.
+
+        PARAMETERS
+        ==========
+
+        X:ndarray(dtype=float,ndim=1)
+          input vector
+        Y:ndarray(dtype=float)
+          output vector
+        W:ndarray(dtype=float)
+          Weights
+
+         RETURNS
+         =======
+
+         array of Root Mean Squared losses
+        """
+        M = X.shape[0]
+        return np.sqrt(np.sum(np.square(np.dot(X, W) - Y)) / M)
+
+    @staticmethod
+    def derivative(X, Y, W):
+        """
+        Calculate derivative for Root Mean Squared error method.
+
+        PARAMETERS
+        ==========
+
+        X:ndarray(dtype=float,ndim=1)
+          input vector
+        Y:ndarray(dtype=float)
+          output vector
+        W:ndarray(dtype=float)
+          Weights
+
+         RETURNS
+         =======
+
+         array of derivates
+        """
+        M = X.shape[0]
+        return np.dot(X.T, np.dot(X, W) - Y) / M
+
+