update

doc/src/week40/week40.do.txt

@@ -236,11 +236,11 @@ import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.linear_model import SGDRegressor
 
-n = 100
+n = 1000
 x = 2*np.random.rand(n,1)
 y = 4+3*x+np.random.randn(n,1)
 
-X = np.c_[np.ones((m,1)), x]
+X = np.c_[np.ones((n,1)), x]
 theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)
 print("Own inversion")
 print(theta_linreg)
@@ -281,7 +281,7 @@ for epoch in range(n_epochs):
         random_index = np.random.randint(m)
         xi = X[random_index:random_index+1]
         yi = y[random_index:random_index+1]
-        gradients = (2.0/m) * xi.T @ ((xi @ theta)-yi)
+        gradients = 2.0* xi.T @ ((xi @ theta)-yi)
         eta = learning_schedule(epoch*m+i)
         theta = theta - eta*gradients
 print("theta from own sdg")