R/network_classify.R

In xinying17/FeatureCalc:

# main function
network_classify <- function(data_train,data_test,feature_type,p,corr,nf,powerS,nc,L,classifier,kern){
  if(missing(p)) p=0;
  if(missing(corr)) corr=0;

  if(missing(nf)){
    nf=0
  }

  if(missing(powerS)){
    powerS=1
  }

  if(missing(nc)){
    nc=1
  }

  if(missing(classifier)){
    classifier = "SVM"
  }

  if(missing(kern)){
    kern = "linear"
  }

  nc = round(max(1,min(nc,nf/5)))

  newdata <- network_features(data_train,data_test,feature_type,p,corr,nf,powerS,nc,L)

  # test
  if(classifier=="SVM")
  {
    require('e1071', quietly = TRUE)
    library(e1071)

    wts <- nrow(newdata$new_train) / table(newdata$train_label)
    model1 <- svm(newdata$new_train,newdata$train_label,type="C-classification",class.weights = wts,kernel = kern)
    prediction <- predict(model1, newdata$new_test)
  }

  if(classifier=="RF")
  {
    require('randomForest', quietly = TRUE)
    library(randomForest)
    model_rf <- randomForest(newdata$new_train,newdata$train_label,importance=TRUE, proximity=TRUE)
    prediction <- predict(model_rf, newdata$new_test)
  }

  return(list(pred = prediction, acc = sum(prediction==newdata$test_label)/nrow(data_test)))
}