R Under development (unstable) (2024-01-25 r85826 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(MixAll)
Loading required package: rtkore
Loading required package: Rcpp

Attaching package: 'rtkore'

The following object is masked from 'package:Rcpp':

    LdFlags

> 
> testPredict<-function(nbTrain , nbTest)
+ {
+   ## test categorical predictions
+   train1 <- matrix( c( sample(1:3,size=nbTrain,replace=TRUE, prob = c(0.05,0.05,0.9))
+                      , sample(1:3,size=nbTrain,replace=TRUE, prob = c(0.9,0.05,0.05))
+                      , sample(1:3,size=nbTrain,replace=TRUE, prob = c(0.05,0.05,0.9))
+                      , sample(1:3,size=nbTrain,replace=TRUE, prob = c(0.9,0.05,0.05))
+                      )
+                   , ncol =2
+                   )
+   model <- clusterCategorical(train1,2,models = "categorical_p_pjk")
+   pred  <- clusterPredict(train1,model)
+   # more than 5 classification errors is abnormal
+   if ( sum(pred@zi - model@zi) != 0)
+   { print("Predict Categorical failed");return(FALSE)}
+   
+   ##------------------------------------------------------------------------------
+   ## test Poisson predictions
+   train2 <- matrix( c( rpois(nbTrain,lambda = 1), rpois(nbTrain,lambda = 10)
+                      , rpois(nbTrain,lambda = 1), rpois(nbTrain,lambda = 10)
+                      )
+                   , ncol =2
+                   )
+   model <- clusterPoisson(train2,2,models = "poisson_p_lk")
+   pred  <- clusterPredict(train2,model)
+   # Predictions should be the same
+   if ( sum(pred@zi -model@zi) != 0)
+   { print("Predict Poisson failed");return(FALSE)}
+   
+   ##------------------------------------------------------------------------------
+   ## test Gaussian predictions
+   train3 <- matrix( c( rnorm(nbTrain, mean = 1, sd=1), rnorm(nbTrain,mean = 10, sd=1)
+                      , rnorm(nbTrain, mean = 1, sd=1), rnorm(nbTrain,mean = 10, sd=1)
+                      )
+                   , ncol =2
+                   )
+   model <- clusterDiagGaussian(train3, 2, models = "gaussian_p_s")
+   pred  <- clusterPredict(train3, model)
+   # Predictions should be the same
+   if ( sum(pred@zi -model@zi) != 0)
+   { print("Predict Gaussian failed");return(FALSE)}
+   
+   ##------------------------------------------------------------------------------
+   ## test gamma predictions
+ 	train4 <- matrix( c( rgamma(nbTrain, shape = 1, scale=1), rgamma(nbTrain,shape = 10, scale=1)
+ 	                   , rgamma(nbTrain, shape = 1, scale=1), rgamma(nbTrain,shape = 10, scale=1)
+ 	                   )
+ 	                , ncol =2
+ 	                )
+   ## model <- clusterGamma(train4, 2, models = "gamma_p_ak_b")
+   ## pred  <- clusterPredict(train4,model)
+   ## # more than 5 classification errors is abnormal
+   ## if ( sum(pred@zi -model@zi) != 0)
+   ## { print("Predict gamma failed");return(FALSE)}
+   
+   ##------------------------------------------------------------------------------
+   ## test mixed data predictions
+   train <- list(train1, train2, train3, train4)
+   models <- c("categorical_p_pjk", "poisson_p_lk",  "gaussian_p_s","gamma_p_ak_b")
+   
+   model <- clusterMixedData(train, models, 2)
+   pred  <- clusterPredict(train, model)
+   # more than 5 classification errors is abnormal
+   if ( sum(pred@zi -model@zi) != 0)
+   { print("Predict mixed failed");return(FALSE)}
+   
+   ##------------------------------------------------------------------------------
+   return(TRUE)  
+ }
> 
> testPredict(1000, 20)
[1] TRUE
> 
> proc.time()
   user  system elapsed 
   5.48    0.12    5.59