104 lines
3.4 KiB
R
104 lines
3.4 KiB
R
#' Tensor Sliced Inverse Regression
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#'
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#' @export
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TSIR <- function(X, y, d, sample.axis = 1L,
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nr.slices = 10L, # default slices, ignored if y is a factor or integer
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max.iter = 50L,
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eps = sqrt(.Machine$double.eps)
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) {
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if (!(is.factor(y) || is.integer(y))) {
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y <- cut(y, nr.slices)
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}
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stopifnot(exprs = {
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dim(X)[sample.axis] == length(y)
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length(d) + 1L == length(dim(X))
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})
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# rearrange `X`, `Fy` such that the last axis enumerates observations
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axis.perm <- c(seq_along(dim(X))[-sample.axis], sample.axis)
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X <- aperm(X, axis.perm)
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# get dimensions
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n <- tail(dim(X), 1)
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p <- head(dim(X), -1)
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modes <- seq_along(p)
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# reinterpretation of `y` as factor index as number of different slices
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y <- as.factor(y)
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nr.slices <- length(levels(y))
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slice.sizes <- table(y)
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y <- as.integer(y)
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# center `X`
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X <- X - c(rowMeans(X, dim = length(modes)))
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# Slice `X` into slices governed by `y`
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slices <- Map(function(s) {
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X_s <- X[rep(s == y, each = prod(p))]
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dim(X_s) <- c(p, slice.sizes[s])
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X_s
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}, seq_len(nr.slices))
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# For each slice we get the slice means
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slice.means <- Map(rowMeans, slices, MoreArgs = list(dim = length(modes)))
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# Initial `Gamma_k` estimates as dominent eigenvalues of `Cov_c(E[X_(k) | Y])`
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Sigmas <- Map(function(k) {
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Reduce(`+`, Map(function(X_s, mu_s, n_s) {
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mcrossprod(rowMeans(X_s - c(mu_s), dims = length(modes)), mode = k)
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}, slices, slice.means, slice.sizes)) / nr.slices
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}, modes)
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Gammas <- Map(`[[`, Map(La.svd, Sigmas, nu = d, nv = 0), list("u"))
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# setup projections as `Proj_k = Gamma_k Gamma_k'`
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projections <- Map(tcrossprod, Gammas)
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# compute initial loss for the break condition
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loss.last <- n^-1 * Reduce(`+`, Map(function(X_s, n_s) {
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n_s * sum((X_s - mlm(X_s, projections))^2)
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}, slice.means, slice.sizes))
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# Iterate till convergence
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for (iter in seq_len(max.iter)) {
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# For each mode assume mode reduction `Gamma`s fixed and update
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# current mode reduction `Gamma_k`
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for (k in modes) {
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# Other mode projection of slice means
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slice.proj <- Map(mlm, slice.means, MoreArgs = list(
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Gammas[-k], modes[-k], transposed = TRUE
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))
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# Update mode `k` slice mean covariances
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Sigmas[[k]] <- n^-1 * Reduce(`+`, Map(function(PX_s, n_s) {
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n_s * mcrossprod(PX_s, mode = k)
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}, slice.proj, slice.sizes))
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# Recompute mode `k` basis `Gamma_k`
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Gammas[[k]] <- La.svd(Sigmas[[k]], nu = d[k], nv = 0)$u
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# update mode `k` projection matrix onto the new span of `Gamma_k`
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projections[[k]] <- tcrossprod(Gammas[[k]])
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# compute loss for break condition
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loss <- n^-1 * Reduce(`+`, Map(function(X_s, n_s) {
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n_s * sum((X_s - mlm(X_s, projections))^2)
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}, slice.means, slice.sizes))
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}
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# check break condition
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if (abs(loss - loss.last) < eps * loss) {
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break
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}
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loss.last <- loss
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}
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# mode sample covariance matrices
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Omegas <- Map(function(k) n^-1 * mcrossprod(X, mode = k), modes)
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# reductions matrices `Omega_k^-1 Gamma_k` where there (reverse) kronecker
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# product spans the central tensor subspace (CTS) estimate
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Map(solve, Omegas, Gammas)
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}
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