112 lines
3.0 KiB
R
112 lines
3.0 KiB
R
# compute the gaussian kernel between each row of X1 and each row of X2
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# should be done more efficiently
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gausskernel <-
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function(X1, X2, sigma2)
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{
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n1 <- dim(X1)[1]
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n2 <- dim(X2)[1]
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K <- matrix(nrow = n1, ncol = n2)
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for(i in 1:n1)
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for(j in 1:n2)
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K[i,j] <- sum(X1[i,] - X2[j,])^2
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K <- exp(-1*K/sigma2)
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}
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# Nystroem Approximation Kernel Ridge Regression
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nakr <-
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function(X, y, sigma2=NULL, lambda=1E-4, landmarks=NULL, nb.landmarks=NULL)
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{
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X <- as.matrix(X)
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n <- nrow(X)
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p <- ncol(X)
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if(is.null(sigma2)) { sigma2 <- p }
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if(is.null(landmarks)) {
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if(is.null(nb.landmarks)) { nb.landmarks <- round(sqrt(n)) }
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splidx <- sample(1:n, nb.landmarks, replace = FALSE)
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} else {
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splidx <- which(rownames(X) %in% as.character(landmarks))
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nb.landmarks <- length(splidx)
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}
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splidx <- sort(splidx)
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X <- scale(X)
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y <- scale(y)
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C <- gausskernel(X, as.matrix(X[splidx,]), sigma2)
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K11 <- C[splidx,]
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svdK11 <- svd(K11)
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# K11 will often be ill-formed, thus we drop the bottom singular values
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k <- which(svdK11$d < 1E-12)[1] - 1
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US <- svdK11$u[,1:k] %*% diag(1 / sqrt(svdK11$d[1:k]))
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L <- C %*% US
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Ginv <- t(L) %*% L
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diag(Ginv) <- diag(Ginv) + lambda
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Ginv <- chol2inv(chol(Ginv))
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Ginv <- L %*% Ginv %*% t(L)
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Ginv <- - Ginv / lambda
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diag(Ginv) <- diag(Ginv) + (1/lambda)
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coef <- Ginv %*% y
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K11inv <- svdK11$v[,1:k] %*% diag(1/svdK11$d[1:k]) %*% t(svdK11$u[,1:k])
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beta <- K11inv %*% t(C) %*% coef
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r <- list(X=X,
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y=y,
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sigma2=sigma2,
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lambda=lambda,
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splidx=splidx,
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coef=coef,
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beta=beta
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)
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class(r) <- "nakr"
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return(r)
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}
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predict.nakr <-
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function(o, newdata)
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{
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if(class(o) != "nakr") {
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warning("Object is not of class 'nakr'")
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UseMethod("predict")
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return(invisible(NULL))
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}
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newdata <- as.matrix(newdata)
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if(ncol(o$X)!=ncol(newdata)) {
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stop("Not the same number of variables btwn fitted nakr object and new data")
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}
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newdata <- scale(newdata,center=attr(o$X,"scaled:center"),
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scale=attr(o$X,"scaled:scale"))
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Ktest <- gausskernel(newdata, as.matrix(o$X[o$splidx,]), o$sigma2)
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yh <- Ktest %*% o$beta
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yh <- (yh * attr(o$y,"scaled:scale")) + attr(o$y,"scaled:center")
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}
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kfold.nakr <-
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function(X, y, K=5, lambdas=NULL, sigma2=NULL, landmarks=NULL, nb.landmarks=NULL)
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{
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if(is.null(lambdas)) { lambdas <- 10^seq(-8, 2, by=1) }
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N <- nrow(X)
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folds <- rep_len(1:K, N)
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folds <- sample(folds, N)
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maes <- matrix(data = NA, nrow = K, ncol = length(lambdas))
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colnames(maes) <- lambdas
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lambda_idx <- 1
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for(lambda in lambdas) {
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for(k in 1:K) {
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fold <- folds == k
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nakrm <- nakr(X[!fold,], y[!fold], sigma2, lambda, landmarks, nb.landmarks)
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pred <- predict(nakrm, X[fold,])
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maes[k,lambda_idx] <- mean(abs(pred - y[fold]))
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}
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lambda_idx <- lambda_idx + 1
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}
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mmaes <- colMeans(maes)
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minmmaes <- min(mmaes)
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bestlambda <- lambdas[which(mmaes == minmmaes)]
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nakrm <- nakr(X, y, sigma2, bestlambda, landmarks, nb.landmarks)
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} |