added other err fn

Assignment1/demo.py

+import matplotlib.pyplot as plt
+import csv
+
+x = []
+y = []
+i=1
+
+with open('error.log','r') as csvfile:
+    for row in csvfile:
+        x.append(i)
+        i+=1
+        y.append(float(row[:-1]))
+
+plt.plot(x,y, label='Loaded from file!')
+plt.xlabel('x')
+plt.ylabel('y')
+plt.title('Interesting Graph\nCheck it out')
+plt.legend()
+plt.show()
\ No newline at end of file

Assignment1/main.py

 import numpy as np
 import argparse
 import csv
-import matplotlib.pyplot as plt
+# import matplotlib.pyplot as plt
 ''' 
 You are only required to fill the following functions
 mean_squared_loss
@@ -24,7 +24,7 @@ def mean_squared_loss(xdata, ydata, weights):
 
 	guess = np.dot(xdata,weights)
 	samples = np.shape(guess)[0]
-	err = 0.5*samples*np.sum(np.square(ydata.T-guess))
+	err = (0.5/samples)*np.sum(np.square(ydata-guess))
 	return err
 	raise NotImplementedError
 
@@ -32,33 +32,61 @@ def mean_squared_gradient(xdata, ydata, weights):
 
 	samples = np.shape(xdata)[0]
 	guess = np.dot(xdata,weights)
-	gradient = (1/samples)*np.dot(xdata.T,(guess-ydata.T))
+	gradient = (1/samples)*np.dot(xdata.T,(guess-ydata))
 	return gradient
 
 	raise NotImplementedError
 
 def mean_absolute_loss(xdata, ydata, weights):
 
+	guess = np.dot(xdata,weights)
+	samples = np.shape(guess)[0]
+	err = (1/samples)*np.sum(np.absolute(ydata-guess))
+	return err
 	raise NotImplementedError
 
 def mean_absolute_gradient(xdata, ydata, weights):
 
+	guess = np.dot(xdata,weights)
+	if np.sum(ydata-guess) < 0:
+		gradient = np.random.randint(0,10,np.shape(weights)[0])
+	else:
+		gradient = np.random.randint(-10,0,np.shape(weights)[0])
+	return gradient
+
 	raise NotImplementedError
 
 def mean_log_cosh_loss(xdata, ydata, weights):
 
+	guess = np.dot(xdata,weights)
+	samples = np.shape(guess)[0]
+	err = (1/samples)*np.sum(np.square(ydata-guess))
+	return err
 	raise NotImplementedError
 
 def mean_log_cosh_gradient(xdata, ydata, weights):
 
+	guess = np.dot(xdata,weights)
+	samples = np.shape(guess)[0]
+	gradient = np.dot(xdata.T,np.tanh(guess-ydata))
+	return gradient
+
 	raise NotImplementedError
 
 def root_mean_squared_loss(xdata, ydata, weights):
 
+	guess = np.dot(xdata,weights)
+	samples = np.shape(guess)[0]
+	err = np.sqrt(np.divide(np.sum(np.square(ydata.T-guess)),samples))
+	return err
 	raise NotImplementedError
 
 def root_mean_squared_gradient(xdata, ydata, weights):
 
+	samples = np.shape(xdata)[0]
+	gradient = -weights.T/np.sqrt(samples)
+	return gradient
+
 	raise NotImplementedError
 
 class LinearRegressor: