library(deepnet) Var1 <- c(rnorm(50, 1, 0.5), rnorm(50, -0.6, 0.2)) Var2 <- c(rnorm(50, -0.8, 0.2), rnorm(50, 2, 1)) x <- matrix(c(Var1, Var2), nrow = 100, ncol = 2) y <- c(rep(1, 50), rep(0, 50)) nn <- nn.train(x, y, hidden = c(5)) ## predict by nn test_Var1 <- c(rnorm(50, 1, 0.5), rnorm(50, -0.6, 0.2)) test_Var2 <- c(rnorm(50, -0.8, 0.2), rnorm(50, 2, 1)) test_x <- matrix(c(test_Var1, test_Var2), nrow = 100, ncol = 2) yy <- nn.predict(nn, test_x)
https://stackoverflow.com/questions/38500473/how-to-use-deepnet-for-classification-in-r
https://stackoverflow.com/a/38501656/5270873
library(deepnet) Var1 <- c(rnorm(50, 1, 0.5), rnorm(50, -0.6, 0.2)) Var2 <- c(rnorm(50, -0.8, 0.2), rnorm(50, 2, 1)) x <- matrix(c(Var1, Var2), nrow = 100, ncol = 2) y <- c(rep(1, 50), rep(0, 50)) nn <- dbn.dnn.train(x, y, hidden = c(5))
Var1 <- c(rnorm(50, 1, 0.5), rnorm(50, -0.6, 0.2)) Var2 <- c(rnorm(50, -0.8, 0.2), rnorm(50, 2, 1)) x <- matrix(c(Var1, Var2), nrow = 100, ncol = 2) y <- c(rep(1, 50), rep(0, 50))
dnn <- dbn.dnn.train(x, y, hidden = c(5, 5))
test_Var1 <- c(rnorm(50, 1, 0.5), rnorm(50, -0.6, 0.2)) test_Var2 <- c(rnorm(50, -0.8, 0.2), rnorm(50, 2, 1)) test_x <- matrix(c(test_Var1, test_Var2), nrow = 100, ncol = 2) nn.test(dnn, test_x, y) #[1] 0.25
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.