#include <r.h>
void main(int mode, int model, int numSignals, void* Data)
{
if(!wait(0)) return 0;
// open an R script with the same name as the stratefy script
if(mode == NEURAL_INIT) {
if(!Rstart(strf("%s.r",Script),2)) return 0;
Rx("neural.init()");
return 1;
}
// export batch training samples and call the R training function
if(mode == NEURAL_TRAIN) {
string name = strf("Data\\signals%i.csv",Core);
file_write(name,Data,0);
Rx(strf("XY <- read.csv('%s%s',header = F)",slash(ZorroFolder),slash(name)));
Rset("AlgoVar",AlgoVar,8);
if(!Rx(strf("neural.train(%i,XY)",model+1),2))
return 0;
return 1;
}
// predict the target with the R predict function
if(mode == NEURAL_PREDICT) {
Rset("AlgoVar",AlgoVar,8);
Rset("X",(double*)Data,numSignals);
Rx(strf("Y <- neural.predict(%i,X)",model+1));
return Rd("Y[1]");
}
// save all trained models
if(mode == NEURAL_SAVE) {
print(TO_ANY,"\nStore %s",strrchr(Data,'\\')+1);
return Rx(strf("neural.save('%s')",slash(Data)),2);
}
// load all trained models
if(mode == NEURAL_LOAD) {
printf("\nLoad %s",strrchr(Data,'\\')+1);
return Rx(strf("load('%s')",slash(Data)),2);
}
return 1;
}

library('deepnet', quietly = T)
library('caret', quietly = T)

# called by Zorro for training
neural.train = function(model,XY)
{
XY <- as.matrix(XY)
X <- XY[,-ncol(XY)] # predictors
Y <- XY[,ncol(XY)] # target
Y <- ifelse(Y > 0,1,0) # convert -1..1 to 0..1
Models[[model]] <<- sae.dnn.train(X,Y,
hidden = c(20,20,20),
activationfun = "tanh",
learningrate = 0.5,
momentum = 0.5,
learningrate_scale = 1.0,
output = "sigm",
sae_output = "linear",
numepochs = 100,
batchsize = 100,
hidden_dropout = 0,
visible_dropout = 0)
}

# called by Zorro for prediction
neural.predict = function(model,X)
{
if(is.vector(X)) X <- t(X) # transpose horizontal vector
return(nn.predict(Models[[model]],X))
}

# called by Zorro for saving the models
neural.save = function(name)
{
save(Models,file=name) # save trained models
}

# called by Zorro for initialization
neural.init = function()
{
set.seed(365)
Models <<- vector("list")
}

# quick OOS test for experimenting with the settings
Test = function()
{
neural.init()
XY <<- read.csv('C:/Project/Zorro/Data/signals0.csv',header = F)
splits <- nrow(XY)*0.8
XY.tr <<- head(XY,splits) # training set
XY.ts <<- tail(XY,-splits) # test set
neural.train(1,XY.tr)
X <<- XY.ts[,-ncol(XY.ts)]
Y <<- XY.ts[,ncol(XY.ts)]
Y.ob <<- ifelse(Y > 0,1,0)
Y <<- neural.predict(1,X)
Y.pr <<- ifelse(Y > 0.5,1,0)
confusionMatrix(Y.pr,Y.ob) # display prediction accuracy
}