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## kernel based on-line learning algorithms for classification, novelty detection and regression.
##
## created 15.09.04 alexandros
## updated
setGeneric("onlearn",function(obj, x, y = NULL, nu = 0.2, lambda = 1e-4) standardGeneric("onlearn"))
setMethod("onlearn", signature(obj = "onlearn"),
function(obj , x, y = NULL, nu = 0.2, lambda = 1e-4)
{
if(onstart(obj) == 1 && onstop(obj) < buffer(obj))
buffernotfull <- TRUE
else
buffernotfull <- FALSE
if(is.vector(x))
x <- matrix(x,,length(x))
d <- dim(x)[2]
for (i in 1:dim(x)[1])
{
xt <- x[i,,drop=FALSE]
yt <- y[i]
if(type(obj)=="novelty")
{
phi <- fit(obj)
if(phi < 0)
{
alpha(obj) <- (1-lambda) * alpha(obj)
if(buffernotfull)
onstop(obj) <- onstop(obj) + 1
else{
onstop(obj) <- onstop(obj)%%buffer(obj) + 1
onstart(obj) <- onstart(obj)%%buffer(obj) +1
}
alpha(obj)[onstop(obj)] <- lambda
xmatrix(obj)[onstop(obj),] <- xt
rho(obj) <- rho(obj) + lambda*(nu-1)
}
else
rho(obj) <- rho(obj) + lambda*nu
rho(obj) <- max(rho(obj), 0)
if(onstart(obj) == 1 && onstop(obj) < buffer(obj))
fit(obj) <- drop(kernelMult(kernelf(obj), xt, matrix(xmatrix(obj)[1:onstop(obj),],ncol=d), matrix(alpha(obj)[1:onstop(obj)],ncol=1)) - rho(obj))
else
fit(obj) <- drop(kernelMult(kernelf(obj), xt, xmatrix(obj), matrix(alpha(obj),ncol=1)) - rho(obj))
}
if(type(obj)=="classification")
{
if(is.null(pattern(obj)) && is.factor(y))
pattern(obj) <- yt
if(!is.null(pattern(obj)))
if(pattern(obj) == yt)
yt <- 1
else yt <- -1
phi <- fit(obj)
alpha(obj) <- (1-lambda) * alpha(obj)
if(yt*phi < rho(obj))
{
if(buffernotfull)
onstop(obj) <- onstop(obj) + 1
else{
onstop(obj) <- onstop(obj)%%buffer(obj) + 1
onstart(obj) <- onstart(obj)%%buffer(obj) +1
}
alpha(obj)[onstop(obj)] <- lambda*yt
b(obj) <- b(obj) + lambda*yt
xmatrix(obj)[onstop(obj),] <- xt
rho(obj) <- rho(obj) + lambda*(nu-1) ## (1-nu) ??
}
else
rho(obj) <- rho(obj) + lambda*nu
rho(obj) <- max(rho(obj), 0)
if(onstart(obj) == 1 && onstop(obj) < buffer(obj))
fit(obj) <- drop(kernelMult(kernelf(obj), xt, xmatrix(obj)[1:onstop(obj),,drop=FALSE], matrix(alpha(obj)[1:onstop(obj)],ncol=1)) + b(obj))
else
fit(obj) <-drop(kernelMult(kernelf(obj), xt, xmatrix(obj), matrix(alpha(obj),ncol=1)) + b(obj))
}
if(type(obj)=="regression")
{
alpha(obj) <- (1-lambda) * alpha(obj)
phi <- fit(obj)
if(abs(-phi) < rho(obj))
{
if(buffernotfull)
onstop(obj) <- onstop(obj) + 1
else{
onstop(obj) <- onstop(obj)%%buffer(obj) + 1
onstart(obj) <- onstart(obj)%% buffer(obj) +1
}
alpha(obj)[onstop(obj)] <- sign(yt-phi)*lambda
xmatrix(obj)[onstop(obj),] <- xt
rho(obj) <- rho(obj) + lambda*(1-nu) ## (1-nu) ??
}
else{
rho(obj) <- rho(obj) - lambda*nu
alpha(obj)[onstop(obj)] <- sign(yt-phi)/rho(obj)
}
if(onstart(obj) == 1 && onstop(obj) < buffer(obj))
fit(obj) <- drop(kernelMult(kernelf(obj), xt, matrix(xmatrix(obj)[1:onstop(obj),],ncol=d), matrix(alpha(obj)[1:onstop(obj)],ncol=1)) + b(obj))
else
fit(obj) <- drop(kernelMult(kernelf(obj), xt, xmatrix(obj), matrix(alpha(obj),ncol=1)) + b(obj))
}
}
return(obj)
})
setGeneric("inlearn",function(d, kernel = "rbfdot", kpar = list(sigma=0.1), type = "novelty", buffersize = 1000) standardGeneric("inlearn"))
setMethod("inlearn", signature(d = "numeric"),
function(d ,kernel = "rbfdot", kpar = list(sigma=0.1), type = "novelty", buffersize = 1000)
{
obj <- new("onlearn")
if(!is(kernel,"kernel"))
{
if(is(kernel,"function")) kernel <- deparse(substitute(kernel))
kernel <- do.call(kernel, kpar)
}
if(!is(kernel,"kernel")) stop("kernel must inherit from class `kernel'")
type(obj) <- match.arg(type,c("novelty","classification","regression"))
xmatrix(obj) <- matrix(0,buffersize,d)
kernelf(obj) <- kernel
onstart(obj) <- 1
onstop(obj) <- 1
fit(obj) <- 0
b(obj) <- 0
alpha(obj) <- rep(0, buffersize)
rho(obj) <- 0
buffer(obj) <- buffersize
return(obj)
})
setMethod("show","onlearn",
function(object){
cat("On-line learning object of class \"onlearn\"","\n")
cat("\n")
cat(paste("Learning problem :", type(object), "\n"))
cat
cat(paste("Data dimensions :", dim(xmatrix(object))[2], "\n"))
cat(paste("Buffersize :", buffer(object), "\n"))
cat("\n")
show(kernelf(object))
})
setMethod("predict",signature(object="onlearn"),
function(object, x)
{
if(is.vector(x))
x<- matrix(x,1)
d <- dim(xmatrix(object))[2]
if(type(object)=="novelty")
{
if(onstart(object) == 1 && onstop(object) < buffer(object))
res <- drop(kernelMult(kernelf(object), x, matrix(xmatrix(object)[1:onstop(object),],ncol= d), matrix(alpha(object)[1:onstop(object)],ncol=1)) - rho(object))
else
res <- drop(kernelMult(kernelf(object), x, matrix(xmatrix(object),ncol=d), matrix(alpha(object),ncol=1)) - rho(object))
}
if(type(object)=="classification")
{
if(onstart(object) == 1 && onstop(object) < buffer(object))
res <- drop(kernelMult(kernelf(object), x, matrix(xmatrix(object)[1:onstop(object),],ncol=d), matrix(alpha(object)[1:onstop(object)],ncol=1)) + b(object))
else
res <- drop(kernelMult(kernelf(object), x, matrix(xmatrix(object),ncol=d), matrix(alpha(object),ncol=1)) + b(object))
}
if(type(object)=="regression")
{
if(onstart(object) == 1 && onstop(object) < buffer(object))
res <- drop(kernelMult(kernelf(object), x, matrix(xmatrix(object)[1:onstop(object),],ncol=d), matrix(alpha(object)[1:onstop(object)],ncol=1)) + b(object))
else
res <- drop(kernelMult(kernelf(object), x, matrix(xmatrix(object),ncol=d), matrix(alpha(object),ncol=1)) + b(object))
}
return(res)
})
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