learning_loop_lrml.R

for (iteration in seq_len(n_iterations)) { 
  yhat               <- dot(x_b, theta)          # predict using weights in theta
  residuals_b        <- yhat - y                 # calculate the residuals
  gradients          <- 2/n * dot(t(x_b), residuals_b) # calculate the gradients of MSE w.r.t model weights 
  theta              <- theta - learning_rate * gradients # update theta 
  
  sse_i[[iteration]] <- sum((y - dot(x_b, theta))**2)
  b0[[iteration]]    <- theta[2]
  b1[[iteration]]    <- theta[1]
  
}

model_i <- data.frame(model_iter = 1:n_iterations, 
                      sse = sse_i, 
                      b0 = b0, 
                      b1 = b1)