An implementation in R for "Exact Soft Confidence-Weighted Learning" ( http://icml.cc/2012/papers/86.pdf )

scw.R

library("rjson")

scw <- function(D,eta,verbose=F) {
  phi <- qnorm(eta)
  psi <- 1+phi^2/2
  zeta <- 1+phi^2
  
  mu <- rep(0,D)
  sigma <- diag(D)
  
  predict <- function(x) {
    tmp <- sign(sum(mu*x))
    if (verbose) print(paste("y=",tmp))
    return(tmp)
  }
    
  loss <- function(m,v) {
    return(max(0,phi*sqrt(v)-m))
  }
  
  alpha <- function(m,v) {
    tmp <- -m*psi+sqrt(m^2*phi^4/4+v*phi^2*zeta)
    tmp <- tmp/(v*zeta)
    return(max(0,tmp))
  }
  
  calc_u <- function(a,v) {
    avphi <- a*v*phi
    tmp <- -avphi + sqrt(avphi^2+4*v)
    tmp <- tmp^2/4
    return(tmp)
  }
  
  beta <- function(a,u,v) {
    tmp <- a*phi/(sqrt(u)+v*a*phi) # vector
    tmp <- sum(tmp) # scalar
    return(tmp)
  }
  
  step <- function(x,y) {
    y1 <- predict(x)
    
    m <- y * sum(mu*x)
    v <- sum(t(x) %*% sigma %*% x)
    
    if (loss(m,v)>=0) {
      a <- alpha(m,v)
      u <- calc_u(a,v)
      b <- beta(a,u,v)
      x1 <- sigma %*% x
      
      mu <<- mu+a*y*x1
      sigma <<- sigma-b*(x1 %*% t(x1))
    }
    return(y1)
  }
  
  return(list(step=step,predict=predict))
}

test1 <- function(eta) {
  print("***test1***")
  
  f <- scw(2,eta,verbose=T)
  
  f$step(c(1,1),1)
  f$step(c(0.3,0.4),1)
  f$step(c(-1,-1),-1)
  f$step(c(-1,-1),-1)
  f$step(c(0.1,0.1),-1)
  
  f$predict(c(0.1,0.1))
}

test2 <- function(eta) {
  print("***test2***")
  
  # data set is here.
  # https://github.com/IshitaTakeshi/Hackathon/tree/master/MLAkiba2/Code
  
  f <- scw(64,eta)
  
  train <- fromJSON(paste(readLines("digits_train.json"), collapse=""))
  train.result <- c()
  for (i in 1:length(train$images)) {
    tmp <- f$step(train$images[[i]],train$labels[i])
    train.result <- c(train.result,tmp)
  }
  print(table(data.frame(train=train$labels,result=train.result)))
  
  test <- fromJSON(paste(readLines("digits_test.json"), collapse=""))
  test.result <- c()
  for (i in 1:length(test$images)) {
    tmp <- f$predict(test$images[[i]])
    test.result <- c(test.result,tmp)
  }
  print(table(data.frame(test=test$labels,result=test.result)))
}

test1(0.9)
test2(0.9)