## ----Example_1_Training, fig.width=7, fig.asp = .62---------------------- library(ahnr) # Create data set.seed(123) x <- 2 * runif(1000) - 1; x <- sort(x) y <- (x < 0.1) * (0.05 * runif(1000) + atan(pi*x)) + (x >= 0.1 & x < 0.6) * (0.05 * runif(1000) + sin(pi*x)) + (x >= 0.6) * (0.05 * runif(1000) + cos(pi*x)) plot(x, y, type = 'l') # Create the Sigma dataset Sigma <- list(X = data.frame(x = x), Y = data.frame(y = y)) # Create network ahn <- fit(Sigma, 4, 0.01, 2000) ## ----Example_1_Simulation, fig.width=7, fig.asp = .62-------------------- # Create test data X <- data.frame(x = x) # Simulate ysim <- predict(ahn, X) plot(x, y, type = 'l') lines(x, ysim, type = 'l', lty = 2, col = 'red') legend(-1, 1, c('Original', 'Simulation'), col = c(1,2), lty = c(1,2), cex = 0.8) ## ----Example_1_Summary--------------------------------------------------- summary(ahn) ## ----Example_1_Network_Visualization------------------------------------- visualize(ahn) ## ----Example_2_Training_and_Simulation, fig.width=7, fig.asp = .62------- # Create data set.seed(12321) t <- seq(0, 15, 0.01) X <- data.frame(x1 = cos(t), x2 = t) Y <- data.frame(y = sin(t)) # Create the Sigma dataset Sigma <- list(X = X, Y = Y) # Create network ahn <- fit(Sigma, 5, 0.01, 2000) # Simulate ysim <- predict(ahn, X) plot(t, Y$y, type = 'l', col = 'black', xlab = 't', ylab = 'y') lines(t, ysim, type = 'l', lty = 2, col = 'red') legend(0, -0.5, c('Original', 'Simulation'), col = c(1,2), lty = c(1,2), cex = 0.6) ## ----Example_2_Summary--------------------------------------------------- summary(ahn) ## ----Example_2_Network_Visualization------------------------------------- visualize(ahn)