我想在R Markdown / Presentation / ioslides或Slidy中显示以下情节。
x1<-rnorm(1000,mean=0,sd=1)
x2<-rnorm(1000,mean=0,sd=1)
e<-rnorm(1000,mean=0,sd=1)
y<-x1*2-x2*3+e
y<-(y-min(y))/(max(y)-min(y))
library(neuralnet)
model<-neuralnet(y~x1+x2,data=cbind(y,x1,x2))
plot(model)
str(model$net.result)
..这就是，原则上的代码有效;情节显示。 （..和Markdown：
## Plot
```{r plot}
x1<-rnorm(1000,mean=0,sd=1)
x2<-rnorm(1000,mean=0,sd=1)
e<-rnorm(1000,mean=0,sd=1)
y<-x1*2-x2*3+e
y<-(y-min(y))/(max(y)-min(y))
library(neuralnet)
model<-neuralnet(y~x1+x2,data=cbind(y,x1,x2))
plot(model)
str(model$net.result)
```
RESP）
..输出在那里，但不是情节。
我的问题是，在R Markdown中绘制神经网络库结果是否存在根本错误？
Thx＆amp;亲切的问候
答案 0 :(得分：1)
是的，我可以重现您的问题。 plot.nn()的函数neuralnet打开一个新的绘图设备（R / plot.nn.r的第21行：grDevices::dev.new()）。我无法正确解释这种行为（也许有人能够胜任？），但是如果你避开这一行，那么你会在渲染的Markdown结果中得到一个情节。
你可以像这样实施这个hacky解决方案：
```{r echo = FALSE}
library(neuralnet)
plot.nn <-
function (x, rep = NULL, x.entry = NULL, x.out = NULL, radius = 0.15,
arrow.length = 0.2, intercept = TRUE, intercept.factor = 0.4,
information = TRUE, information.pos = 0.1, col.entry.synapse = "black",
col.entry = "black", col.hidden = "black", col.hidden.synapse = "black",
col.out = "black", col.out.synapse = "black", col.intercept = "blue",
fontsize = 12, dimension = 6, show.weights = TRUE, file = NULL,
...)
{
net <- x
if (is.null(net$weights))
stop("weights were not calculated")
if (!is.null(file) && !is.character(file))
stop("'file' must be a string")
if (is.null(rep)) {
for (i in 1:length(net$weights)) {
if (!is.null(file))
file.rep <- paste(file, ".", i, sep = "")
else file.rep <- NULL
#grDevices::dev.new()
plot.nn(net, rep = i, x.entry, x.out, radius, arrow.length,
intercept, intercept.factor, information, information.pos,
col.entry.synapse, col.entry, col.hidden, col.hidden.synapse,
col.out, col.out.synapse, col.intercept, fontsize,
dimension, show.weights, file.rep, ...)
}
}
else {
if (is.character(file) && file.exists(file))
stop(sprintf("%s already exists", sQuote(file)))
result.matrix <- t(net$result.matrix)
if (rep == "best")
rep <- as.integer(which.min(result.matrix[, "error"]))
if (rep > length(net$weights))
stop("'rep' does not exist")
weights <- net$weights[[rep]]
if (is.null(x.entry))
x.entry <- 0.5 - (arrow.length/2) * length(weights)
if (is.null(x.out))
x.out <- 0.5 + (arrow.length/2) * length(weights)
width <- max(x.out - x.entry + 0.2, 0.8) * 8
radius <- radius/dimension
entry.label <- net$model.list$variables
out.label <- net$model.list$response
neuron.count <- array(0, length(weights) + 1)
neuron.count[1] <- nrow(weights[[1]]) - 1
neuron.count[2] <- ncol(weights[[1]])
x.position <- array(0, length(weights) + 1)
x.position[1] <- x.entry
x.position[length(weights) + 1] <- x.out
if (length(weights) > 1)
for (i in 2:length(weights)) {
neuron.count[i + 1] <- ncol(weights[[i]])
x.position[i] <- x.entry + (i - 1) * (x.out -
x.entry)/length(weights)
}
y.step <- 1/(neuron.count + 1)
y.position <- array(0, length(weights) + 1)
y.intercept <- 1 - 2 * radius
information.pos <- min(min(y.step) - 0.1, 0.2)
if (length(entry.label) != neuron.count[1]) {
if (length(entry.label) < neuron.count[1]) {
tmp <- NULL
for (i in 1:(neuron.count[1] - length(entry.label))) {
tmp <- c(tmp, "no name")
}
entry.label <- c(entry.label, tmp)
}
}
if (length(out.label) != neuron.count[length(neuron.count)]) {
if (length(out.label) < neuron.count[length(neuron.count)]) {
tmp <- NULL
for (i in 1:(neuron.count[length(neuron.count)] -
length(out.label))) {
tmp <- c(tmp, "no name")
}
out.label <- c(out.label, tmp)
}
}
grid::grid.newpage()
for (k in 1:length(weights)) {
for (i in 1:neuron.count[k]) {
y.position[k] <- y.position[k] + y.step[k]
y.tmp <- 0
for (j in 1:neuron.count[k + 1]) {
y.tmp <- y.tmp + y.step[k + 1]
result <- calculate.delta(c(x.position[k],
x.position[k + 1]), c(y.position[k], y.tmp),
radius)
x <- c(x.position[k], x.position[k + 1] - result[1])
y <- c(y.position[k], y.tmp + result[2])
grid::grid.lines(x = x, y = y, arrow = grid::arrow(length = grid::unit(0.15,
"cm"), type = "closed"), gp = grid::gpar(fill = col.hidden.synapse,
col = col.hidden.synapse, ...))
if (show.weights)
draw.text(label = weights[[k]][neuron.count[k] -
i + 2, neuron.count[k + 1] - j + 1], x = c(x.position[k],
x.position[k + 1]), y = c(y.position[k],
y.tmp), xy.null = 1.25 * result, color = col.hidden.synapse,
fontsize = fontsize - 2, ...)
}
if (k == 1) {
grid::grid.lines(x = c((x.position[1] - arrow.length),
x.position[1] - radius), y = y.position[k],
arrow = grid::arrow(length = grid::unit(0.15, "cm"),
type = "closed"), gp = grid::gpar(fill = col.entry.synapse,
col = col.entry.synapse, ...))
draw.text(label = entry.label[(neuron.count[1] +
1) - i], x = c((x.position - arrow.length),
x.position[1] - radius), y = c(y.position[k],
y.position[k]), xy.null = c(0, 0), color = col.entry.synapse,
fontsize = fontsize, ...)
grid::grid.circle(x = x.position[k], y = y.position[k],
r = radius, gp = grid::gpar(fill = "white", col = col.entry,
...))
}
else {
grid::grid.circle(x = x.position[k], y = y.position[k],
r = radius, gp = grid::gpar(fill = "white", col = col.hidden,
...))
}
}
}
out <- length(neuron.count)
for (i in 1:neuron.count[out]) {
y.position[out] <- y.position[out] + y.step[out]
grid::grid.lines(x = c(x.position[out] + radius, x.position[out] +
arrow.length), y = y.position[out], arrow = grid::arrow(length = grid::unit(0.15,
"cm"), type = "closed"), gp = grid::gpar(fill = col.out.synapse,
col = col.out.synapse, ...))
draw.text(label = out.label[(neuron.count[out] +
1) - i], x = c((x.position[out] + radius), x.position[out] +
arrow.length), y = c(y.position[out], y.position[out]),
xy.null = c(0, 0), color = col.out.synapse, fontsize = fontsize,
...)
grid::grid.circle(x = x.position[out], y = y.position[out],
r = radius, gp = grid::gpar(fill = "white", col = col.out,
...))
}
if (intercept) {
for (k in 1:length(weights)) {
y.tmp <- 0
x.intercept <- (x.position[k + 1] - x.position[k]) *
intercept.factor + x.position[k]
for (i in 1:neuron.count[k + 1]) {
y.tmp <- y.tmp + y.step[k + 1]
result <- calculate.delta(c(x.intercept, x.position[k +
1]), c(y.intercept, y.tmp), radius)
x <- c(x.intercept, x.position[k + 1] - result[1])
y <- c(y.intercept, y.tmp + result[2])
grid::grid.lines(x = x, y = y, arrow = grid::arrow(length = grid::unit(0.15,
"cm"), type = "closed"), gp = grid::gpar(fill = col.intercept,
col = col.intercept, ...))
xy.null <- cbind(x.position[k + 1] - x.intercept -
2 * result[1], -(y.tmp - y.intercept + 2 *
result[2]))
if (show.weights)
draw.text(label = weights[[k]][1, neuron.count[k +
1] - i + 1], x = c(x.intercept, x.position[k +
1]), y = c(y.intercept, y.tmp), xy.null = xy.null,
color = col.intercept, alignment = c("right",
"bottom"), fontsize = fontsize - 2, ...)
}
grid::grid.circle(x = x.intercept, y = y.intercept,
r = radius, gp = grid::gpar(fill = "white", col = col.intercept,
...))
grid::grid.text(1, x = x.intercept, y = y.intercept,
gp = grid::gpar(col = col.intercept, ...))
}
}
if (information)
grid::grid.text(paste("Error: ", round(result.matrix[rep,
"error"], 6), " Steps: ", result.matrix[rep,
"steps"], sep = ""), x = 0.5, y = information.pos,
just = "bottom", gp = grid::gpar(fontsize = fontsize +
2, ...))
if (!is.null(file)) {
weight.plot <- grDevices::recordPlot()
save(weight.plot, file = file)
}
}
}
calculate.delta <-
function (x, y, r)
{
delta.x <- x[2] - x[1]
delta.y <- y[2] - y[1]
x.null <- r/sqrt(delta.x^2 + delta.y^2) * delta.x
if (y[1] < y[2])
y.null <- -sqrt(r^2 - x.null^2)
else if (y[1] > y[2])
y.null <- sqrt(r^2 - x.null^2)
else y.null <- 0
c(x.null, y.null)
}
draw.text <-
function (label, x, y, xy.null = c(0, 0), color, alignment = c("left",
"bottom"), ...)
{
x.label <- x[1] + xy.null[1]
y.label <- y[1] - xy.null[2]
x.delta <- x[2] - x[1]
y.delta <- y[2] - y[1]
angle = atan(y.delta/x.delta) * (180/pi)
if (angle < 0)
angle <- angle + 0
else if (angle > 0)
angle <- angle - 0
if (is.numeric(label))
label <- round(label, 5)
vp <- grid::viewport(x = x.label, y = y.label, width = 0, height = ,
angle = angle, name = "vp1", just = alignment)
grid::grid.text(label, x = 0, y = grid::unit(0.75, "mm"), just = alignment,
gp = grid::gpar(col = color, ...), vp = vp)
}
```
## Plot
```{r}
x1 <- rnorm(1000, mean = 0, sd = 1)
x2 <- rnorm(1000, mean = 0, sd = 1)
e <- rnorm(1000, mean = 0, sd = 1)
y <- x1 * 2 - x2 * 3 + e
y <- (y - min(y)) / (max(y) - min(y))
model <- neuralnet(y ~ x1 + x2, data = cbind(y, x1, x2))
plot(model)
```