View source: R/ordinalFunctions.R
predict.ordASDA | R Documentation |
Predicted values based on fit from the function ordASDA
. This
function is used to classify new observations based on their explanatory variables/features.
There is no need to normalize the data, the data is normalized based on the normalization
data from the ordASDA object.
## S3 method for class 'ordASDA' predict(object, newdata = NULL, ...)
object |
Object of class ordASDA. This object is returned from the function |
newdata |
A matrix of new observations to classify. |
... |
Arguments passed to |
A vector of predictions.
ordASDA
set.seed(123) # You can play around with these values to generate some 2D data to test one numClasses <- 5 sigma <- matrix(c(1,-0.2,-0.2,1),2,2) mu <- c(0,0) numObsPerClass <- 5 # Generate the data, can access with train$X and train$Y train <- accSDA::genDat(numClasses,numObsPerClass,mu,sigma) test <- accSDA::genDat(numClasses,numObsPerClass*2,mu,sigma) # Visualize it, only using the first variable gives very good separation plot(train$X[,1],train$X[,2],col = factor(train$Y),asp=1,main="Training Data") # Train the ordinal based model res <- accSDA::ordASDA(train$X,train$Y,s=2,h=1, gam=1e-6, lam=1e-3) vals <- predict(object = res,newdata = test$X) # Takes a while to run ~ 10 seconds sum(vals==test$Y)/length(vals) # Get accuracy on test set #plot(test$X[,1],test$X[,2],col = factor(test$Y),asp=1, # main="Test Data with correct labels") #plot(test$X[,1],test$X[,2],col = factor(vals),asp=1, # main="Test Data with predictions from ordinal classifier")
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