R/computationTime.R

In adriansidor/antminer:

time<-function(size) {
  trainingSet<-mushroom[-1]
  trainingSet[length(trainingSet)+1]<-mushroom[1]
  terms<-getTerms(mushroom[-1])
  class<-"edibility"
  nr_of_class<-length(unique(trainingSet[,class]))
  entropies<-computeEntropy(terms, trainingSet, class)
  pheromones <- initPheromone(terms)
  rule<-build_rule(trainingSet, terms, 10, class, nr_of_class, entropies, pheromones)
  #system.time( replicate(size, build_rule(trainingSet, terms, 10, class, nr_of_class, entropies, pheromones) ) )
  #system.time( replicate(size, prune(rule, trainingSet, class) ) )
  #system.time( replicate(size, quality(rule, trainingSet, class) ) )
  system.time( replicate(size, as.data.table(trainingSet) ) )
  #system.time( replicate(size, TN(rule, trainingSet, class)))
  #system.time( replicate(size, TP(rule, trainingSet, class)))
  #system.time( replicate(size, TP2(rule, trainingSet, class)))
  #system.time( replicate(size, TP3(rule, trainingSet, class)))
  #TP4(rule, trainingSet, class)
}

time<-function(size) {

  trainingSet<-mushroom[-1]
  trainingSet[length(trainingSet)+1]<-mushroom[1]
  trainingSet<-as.data.table(trainingSet)
  terms<-getTerms(mushroom[-1])
  class<-"edibility"
  nr_of_class<-length(unique(trainingSet[,class, with=FALSE]))
  entropies<-computeEntropy(terms, trainingSet, class)
  pheromones <- initPheromone(terms)
  rule<-build_rule(trainingSet, terms, 10, class, nr_of_class, entropies, pheromones)
  #system.time( replicate(size, build_rule(trainingSet, terms, 10, class, nr_of_class, entropies, pheromones) ) )
  #system.time( replicate(size, prune(rule, trainingSet, class) ) )
  #system.time( replicate(size, quality(rule, trainingSet, class) ) )
  #system.time( replicate(size, as.data.table(trainingSet) ) )
  #system.time( replicate(size, TN(rule, trainingSet, class)))
  #system.time( replicate(size, TP(rule, trainingSet, class)))
  #system.time( replicate(size, TP2(rule, trainingSet, class)))
  #system.time( replicate(size, TP3(rule, trainingSet, class)))
  #TP4(rule, trainingSet, class)
}

time<-function(size) {
  trainingSet<-kredyt
  system.time( replicate(size, antminer4(trainingSet, "ryzyko", 10, 10, 10, 10) ) )
}

time<-function(size) {
  trainingSet<-kredyt
  #terms<-getTerms(trainingSet[-5])
  #class<-"ryzyko"
  #nr_of_class<-length

  #b<-as.data.table(trainingSet)
  #entropies<-computeEntropy(terms, trainingSet, class)
  #entropies<-computeEntropy(terms, b, class)
  #pheromones <- initPheromone(terms)
  #rule<-build_rule(trainingSet, terms, 2, class, nr_of_class, entropies, pheromones)
  #print(rule)
  #print(FN2(rule, trainingSet, class))
  #print(FN(rule, trainingSet, class))
  #system.time( replicate(size, quality2(rule, trainingSet, class) ) )
  #system.time( replicate(size, prune(rule, trainingSet, class) ) )
  #system.time( replicate(size, TP2(rule[[1]],rule[[2]], trainingSet, class) ) )
  #system.time( replicate(size, TP(rule, trainingSet, class) ) )
  #system.time( replicate(size, quality(rule, trainingSet, class)))
  #system.time( replicate(size, TP2(rule[[1]],rule[[2]], trainingSet, class)))
  #system.time( replicate(size, TP3(rule, trainingSet, class)))
  #FP(rule, trainingSet, class)
  #system.time( replicate(size, antminer3(trainingSet, "ryzyko", 2, 100, 2, 2) ) )
  model <- antminer5(trainingSet, "ryzyko", 2, 100, 2, 2)
  predict(model, subset(trainingSet, select=-ryzyko))
}

time<-function(size) {
  model <- antminer4(kredyt, "ryzyko", 2, 100, 10, 2)
  #system.time( replicate(size, prune(rule, trainingSet, class) ) )
  #system.time( replicate(size, TP2(rule[[1]],rule[[2]], trainingSet, class) ) )
  #system.time( replicate(size, TP(rule, trainingSet, class) ) )
  #system.time( replicate(size, quality(rule, trainingSet, class)))
  #system.time( replicate(size, TP2(rule[[1]],rule[[2]], trainingSet, class)))
  #system.time( replicate(size, TP3(rule, trainingSet, class)))
  #FP(rule, trainingSet, class)
}

time2<-function() {
  trainingSet<-mushroom[-1]
  trainingSet[length(trainingSet)+1]<-mushroom[1]
  terms<-getTerms(mushroom[-1])
  class<-"edibility"
  entropy<-computeEntropy(terms, trainingSet, class)
  pheromone <- initPheromone(terms)
  k<- nrow(unique(trainingSet[class]));
  #number of attributes
  n<- ncol(mushroom[-1]);
  system.time( replicate(1, build.rule(trainingSet, terms, 10, class, k, n, entropy, pheromone) ) )

}

time3<-function() {
  system.time(replicate(100, mushroom[which(mushroom[1]=="p"),]))
  system.time(replicate(100,dt[edibility=="p"]))
}

time4<-function() {
  system.time(replicate(100,dt[edibility=="p"]))
}

runalgorytm<-function() {
  cars <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data"), header = FALSE)
  names(cars) <- c("buying", "maint", "doors", "persons", "lug_boot", "safety", "evaluation")
  sam<-sample(2,nrow(cars),replace = TRUE, prob=c(0.7,0.3))
  trainset_cars<-cars[sam==1,]
  testset_cars<-cars[sam==2,]
  model <- antminer5(trainset_cars, "evaluation", 10, 1000, 10, 10)
  result <- predict.antminer5(model, subset(testset_cars, select=-evaluation))
  tab<-conf_matrix_table(result$class, testset_cars$evaluation)
  conf <- confusionMatrix(tab)
  res<-c("antminer", conf$overall['Accuracy'])
}

cars <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data"), header = FALSE)
names(cars) <- c("buying", "maint", "doors", "persons", "lug_boot", "safety", "evaluation")
sam<-sample(2,nrow(cars),replace = TRUE, prob=c(0.7,0.3))
trainset_cars<-cars[sam==1,]
testset_cars<-cars[sam==2,]
model <- antminer5(trainset_cars, "evaluation", 10, 1000, 10, 10)
result <- predict.antminer5(model, subset(testset_cars, select=-evaluation))
tab<-conf_matrix_table(result$class, testset_cars$evaluation)
conf <- confusionMatrix(tab)
res<-c("antminer", conf$overall['Accuracy'])

nursery <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/nursery/nursery.data"), header = FALSE)
names(nursery) <- c("parents", "has_nurs", "form", "children", "housing", "finance", "social", "health", "application")
sam<-sample(2,nrow(nursery),replace = TRUE, prob=c(0.7,0.3))
trainset_nursery<-nursery[sam==1,]
testset_nursery<-nursery[sam==2,]
model_nursery <- antminer5(trainset_nursery, "application", 10, 1000, 10, 10)
result_nursery <- predict.antminer5(model_nursery, subset(testset_nursery, select=-application))
tab_nursery<-conf_matrix_table(result_nursery$class, testset_nursery$application)
conf_nursery <- confusionMatrix(tab_nursery)
res_nursery<-c("antminer", conf_nursery$overall['Accuracy'])