Adaptive Learning, Stock Market, Predictions, Machine Learning, Artificial Intelligence.
for n in range(0,M): xn = np.block([[xd[n]], [xn[0:N]]]); y[n]= np.matmul(wn.T, xn); if(n>M-l-1): e =0; else: e=int(x[n]-y[n]); wn = wn + 2*u*e*xn; return y,wn; Now, we will define our vector x, that has the 178 values of ABEV3 (ON): x = np.array([1655, 1648, 1615, 1638, 1685, 1729, 1754, 1770,