Nothing
library(MixAll)
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
)
test1 <- matrix( c( sample(1:3,size=nbTest,replace=TRUE, prob = c(0.05,0.05,0.9))
, sample(1:3,size=nbTest,replace=TRUE, prob = c(0.9,0.05,0.05))
, sample(1:3,size=nbTest,replace=TRUE, prob = c(0.05,0.05,0.9))
, sample(1:3,size=nbTest,replace=TRUE, prob = c(0.9,0.05,0.05))
)
, ncol =2
)
model <- clusterCategorical(train1,2,models = "categorical_p_pjk")
pred <- clusterPredict(test1,model)
# more than 5 classification errors is abnormal
if (abs(sum(pred@zi) - nbTest)>5)
{ 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
)
test2 <- matrix( c( rpois(nbTest,lambda = 1), rpois(nbTest,lambda = 10)
, rpois(nbTest,lambda = 1), rpois(nbTest,lambda = 10))
, ncol =2
)
model <- clusterPoisson(train2,2,models = "poisson_p_lk")
pred <- clusterPredict(test2,model)
# more than 5 classification errors is abnormal
if (abs(sum(pred@zi) - nbTest)>5)
{ 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
)
test3 <- matrix( c( rnorm(nbTest,mean = 1, sd=1), rnorm(nbTest,mean = 10, sd=1)
, rnorm(nbTest,mean = 1, sd=1), rnorm(nbTest,mean = 10, sd=1))
, ncol =2
)
model <- clusterDiagGaussian(train3,2,models = "gaussian_p_s")
pred <- clusterPredict(test3,model)
# more than 5 classification errors is abnormal
if (abs(sum(pred@zi) - nbTest)>5)
{ 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
)
test4 <- matrix( c( rgamma(nbTest,shape = 1, scale=1), rgamma(nbTest,shape = 10, scale=1)
, rgamma(nbTest,shape = 1, scale=1), rgamma(nbTest,shape = 10, scale=1))
, ncol =2
)
model <- clusterGamma(train4, 2, models = "gamma_p_ak_b")
pred <- clusterPredict(test4,model)
# more than 5 classification errors is abnormal
if (abs(sum(pred@zi) - nbTest)>5)
{ print("Predict gamma failed");return(FALSE)}
##------------------------------------------------------------------------------
## test mixed data predictions
train <- list(train1, train2, train3, train4)
test <- list(test1, test2, test3, test4)
models <- c("categorical_p_pjk", "poisson_p_lk", "gaussian_p_s","gamma_p_ak_b")
model <- clusterMixedData(train, models, 2)
pred <- clusterPredict(test,model)
# more than 5 classification errors is abnormal
if (abs(sum(pred@zi) - nbTest)>5)
{ print("Predict mixed failed");return(FALSE)}
##------------------------------------------------------------------------------
return(TRUE)
}
testPredict(1000, 20)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.