multivariable linear regression

Author: Jeffin Francis

Machine_Learning/MultivariateLinearRegression.py

@@ -19,6 +19,7 @@ def cost_function(self, x, y, n_weights):
 		return cost
 
 
+
 	def gradient(self, x, y, n_weights, epochs):
 		m = len(y)
 		for i in range(epochs):
@@ -31,6 +32,12 @@ def gradient(self, x, y, n_weights, epochs):
 				print("Loss of {}th epoch is {} ".format(i , model.cost_function(x, y, self.n_weights)))
 		return self.n_weights
 
+	def predict(self, x):
+		x=np.insert(x, 0 ,1)
+		print(x.T.dot(self.n_weights))
+
+
+
 	def plot(self):
 		plt.plot(self.loss_)
 		plt.xlabel("Epochs")
@@ -54,3 +61,4 @@ def plot(self):
 	model.fit(x, y, learning_rate)
 	print("Plotting loss")
 	model.plot()
+	model.predict(np.array([45,48]))