Delete AdamSGD.py

doc/src/week39/codes/AdamSGD.py

@@ -1,61 +0,0 @@
-# Using Autograd to calculate gradients using Adam and Stochastic Gradient descent
-# OLS example
-from random import random, seed
-import numpy as np
-import autograd.numpy as np
-import matplotlib.pyplot as plt
-from autograd import grad
-
-# Note change from previous example
-def CostOLS(y,X,theta):
-    return np.sum((y-X @ theta)**2)
-
-n = 10000
-x = np.random.rand(n,1)
-y = 2.0+3*x +4*x*x# +np.random.randn(n,1)
-
-X = np.c_[np.ones((n,1)), x, x*x]
-XT_X = X.T @ X
-theta_linreg = np.linalg.pinv(XT_X) @ (X.T @ y)
-print("Own inversion")
-print(theta_linreg)
-
-
-# Note that we request the derivative wrt third argument (theta, 2 here)
-training_gradient = grad(CostOLS,2)
-# Define parameters for Stochastic Gradient Descent
-n_epochs = 50
-M = 5   #size of each minibatch
-m = int(n/M) #number of minibatches
-# Guess for unknown parameters theta
-theta = np.random.randn(3,1)
-
-# Value for learning rate
-eta = 0.01
-rho1 = 0.9
-rho2 = 0.99
-# Including AdaGrad parameter to avoid possible division by zero
-delta  = 1e-8
-for epoch in range(n_epochs):
-    Giter = np.zeros(shape=(3,3))
-    t = 0
-    s = np.zeros(shape=(3,1))
-    for i in range(m):
-        random_index = M*np.random.randint(m)
-        xi = X[random_index:random_index+M]
-        yi = y[random_index:random_index+M]
-        gradients = (1.0/M)*training_gradient(yi, xi, theta)
-        t += 1
-        Previous = Giter
-        Giter +=gradients @ gradients.T
-        Gnew = (rho2*Previous+(1-rho2)*Giter)
-        Gnew = Gnew#/(1.0-rho2*t)
-        Ginverse = np.c_[eta/(delta+np.sqrt(np.diagonal(Gnew)))]
-        s += rho1*s+(1-rho1)*gradients
-#        snew = s/(1.0-rho1*t)
-        theta -= Ginverse.T @ s
-print("theta from own Adam")
-print(theta)
-
-
-from math import sqrt