Adaptive Learning, Stock Market, Predictions, Machine Learning, Artificial Intelligence.
100*(pred.T-realvalues)/realvalues print(abs(error)) So, the full code: import numpy as np import matplotlib.pyplot as plt def lmsPred(x,l,u,N): xd= np.block([np.zeros((1,l)), x]).T y=np.zeros((len(xd),1)) xn=np.zeros((N+1,1)) xn = np.matrix(xn) wn=np.random.rand(N+1,1)/10 M=len(xd) for n in