wip: writing next method kpir_approx

LaTeX/main.tex

@@ -306,10 +306,11 @@ because with $\|\mat{R}_i\|_F^2 = \tr \mat{R}_i\t{\mat{R}_i} = \tr \t{\mat{R}_i}
 \todo{ prove they are consistent, especially $\widetilde{\mat\Delta} = \tilde{s}^{-1}(\widetilde{\mat\Delta}_1\otimes\widetilde{\mat\Delta}_2)$!}
 
 The hoped for a benefit is that these covariance estimates are in a closed form which means there is no need for an additional iterative estimations step. Before we start with the derivation of the gradients define the following two quantities
-\begin{displaymath}
+\begin{align*}
-    \mat{S}_1 = \frac{1}{n}\sum_{i = 1}^n \t{\mat{R}_i}\widetilde{\mat{\Delta}}_2^{-1}\mat{R}_i\quad{\color{gray}(q\times q)}, \qquad
+    \mat{S}_1 = \frac{1}{n}\sum_{i = 1}^n \t{\mat{R}_i}\widetilde{\mat{\Delta}}_2^{-1}\mat{R}_i = \frac{1}{n}\ten{R}_{(3)}\t{(\ten{R}\ttm[2]\widetilde{\mat{\Delta}}_2^{-1})_{(3)}}\quad{\color{gray}(q\times q)}, \\
-    \mat{S}_2 = \frac{1}{n}\sum_{i = 1}^n \mat{R}_i\widetilde{\mat{\Delta}}_1^{-1}\t{\mat{R}_i}\quad{\color{gray}(p\times p)}.
+    \mat{S}_2 = \frac{1}{n}\sum_{i = 1}^n \mat{R}_i\widetilde{\mat{\Delta}}_1^{-1}\t{\mat{R}_i} = \frac{1}{n}\ten{R}_{(2)}\t{(\ten{R}\ttm[3]\widetilde{\mat{\Delta}}_1^{-1})_{(2)}}\quad{\color{gray}(p\times p)}.
-\end{displaymath}
+\end{align*}
+\todo{Check tensor form!}
 
 Now, the matrix normal with the covariance matrix of the vectorized quantities of the form $\mat{\Delta} = s^{-1}(\mat{\Delta}_1\otimes\mat{\Delta}_2)$ has the form
 \begin{align*}

simulations/kpir_sim.R

@@ -0,0 +1,985 @@
+library(tensorPredictors)
+library(dplyr)
+library(ggplot2)
+
+###          Exec all methods for a given data set and collect logs          ###
+sim <- function(X, Fy, shape, alpha.true, beta.true, max.iter = 500L) {
+
+    # Logger creator
+    logger <- function(name) {
+        eval(substitute(function(iter, loss, alpha, beta, ...) {
+            hist[iter + 1L, ] <<- c(
+                iter = iter,
+                loss = loss,
+                dist = (dist <- dist.subspace(c(kronecker(alpha.true, beta.true)),
+                                              c(kronecker(alpha, beta)))),
+                dist.alpha = (dist.alpha <- dist.subspace(c(alpha.true), c(alpha))),
+                dist.beta  = (dist.beta  <- dist.subspace(c( beta.true), c(beta ))),
+                norm.alpha = norm(alpha, "F"),
+                norm.beta  = norm(beta, "F")
+            )
+
+           cat(sprintf(
+                "%3d | l = %-12.4f - dist = %-.4e - alpha(%d, %d) = %-.4e - beta(%d, %d) = %-.4e\n",
+                iter, loss,
+                dist,
+                nrow(alpha), ncol(alpha), dist.alpha,
+                nrow(beta),  ncol(beta),  dist.beta
+            ))
+        }, list(hist = as.symbol(paste0("hist.", name)))))
+    }
+
+    # Initialize logger history targets
+    hist.base <- hist.new <- hist.momentum <- # hist.kron <-
+        data.frame(iter = seq(0L, max.iter),
+            loss = NA, dist = NA, dist.alpha = NA, dist.beta = NA,
+            norm.alpha = NA, norm.beta = NA
+        )
+    hist.kron <- NULL   # TODO: fit kron version
+
+    # Base (old)
+    kpir.base(X, Fy, shape, max.iter = max.iter, logger = logger("base"))
+
+    # New (simple Gradient Descent)
+    kpir.new(X, Fy, shape, max.iter = max.iter, logger = logger("new"))
+
+    # Gradient Descent with Nesterov Momentum
+    kpir.momentum(X, Fy, shape, max.iter = max.iter, logger = logger("momentum"))
+
+    # # Residual Covariance Kronecker product assumpton version
+    # kpir.kron(X, Fy, shape, max.iter = max.iter, logger = logger("kron"))
+
+    # Add method tags
+    hist.base$type     <- factor("base")
+    hist.new$type      <- factor("new")
+    hist.momentum$type <- factor("momentum")
+    # hist.kron$type     <- factor("kron")
+
+    # Combine results and return
+    rbind(hist.base, hist.new, hist.momentum, hist.kron)
+}
+
+## Generate some test data / DEBUG
+n <- 200    # Sample Size
+p <- sample(1:15, 1) # 11
+q <- sample(1:15, 1) # 3
+k <- sample(1:15, 1) # 7
+r <- sample(1:15, 1) # 5
+print(c(n, p, q, k, r))
+
+hist <- NULL
+for (rep in 1:20) {
+    alpha.true <- alpha <- matrix(rnorm(q * r), q, r)
+    beta.true <- beta <- matrix(rnorm(p * k), p, k)
+    y <- rnorm(n)
+    Fy <- do.call(cbind, Map(function(slope, offset) {
+            sin(slope * y + offset)
+        },
+        head(rep(seq(1, ceiling(0.5 * k * r)), each = 2), k * r),
+        head(rep(c(0, pi / 2), ceiling(0.5 * k * r)), k * r)
+    ))
+    Delta <- 0.5^abs(outer(seq_len(p * q), seq_len(p * q), `-`))
+    X <- tcrossprod(Fy, kronecker(alpha, beta)) + CVarE:::rmvnorm(n, sigma = Delta)
+
+    hist.sim <- sim(X, Fy, shape = c(p, q, k, r), alpha.true, beta.true)
+    hist.sim$repetition <- rep
+
+    hist <- rbind(hist, hist.sim)
+}
+
+
+saveRDS(hist, file = "AR.rds")
+hist$repetition <- factor(hist$repetition)
+
+ggplot(hist, aes(x = iter, color = type, group = interaction(type, repetition))) +
+    geom_line(aes(y = loss)) +
+    geom_point(data = with(sub <- subset(hist, !is.na(loss)),
+        aggregate(sub, list(type, repetition), tail, 1)
+    ), aes(y = loss)) +
+    labs(
+        title = bquote(paste("Optimization Objective: negative log-likelihood ",
+            l(hat(alpha), hat(beta)))),
+        subtitle = bquote(paste(Delta[i][j] == 0.25, " * ", 0.5^abs(i - j), ", ",
+            "20 repetitions, ", n == .(n), ", ",
+            p == .(p), ", ", q == .(q), ", ", k == .(k), ", ", r == .(r))),
+        x = "nr. of iterations",
+        y = expression(l(hat(alpha), hat(beta))),
+        color = "method"
+    ) +
+    theme(legend.position = "bottom")
+
+dev.print(png, file = "sim01_loss.png", width = 768, height = 768, res = 125)
+
+
+ggplot(hist, aes(x = iter, color = type, group = interaction(type, repetition))) +
+    geom_line(aes(y = dist)) +
+    geom_point(data = with(sub <- subset(hist, !is.na(dist)),
+        aggregate(sub, list(type, repetition), tail, 1)
+    ), aes(y = dist)) +
+    labs(
+        title = bquote(paste("Distance of estimate ", hat(B), " to true ", B == alpha %*% beta)),
+        subtitle = bquote(paste(Delta[i][j] == 0.25, " * ", 0.5^abs(i - j), ", ",
+            "20 repetitions, ", n == .(n), ", ",
+            p == .(p), ", ", q == .(q), ", ", k == .(k), ", ", r == .(r))),
+        x = "nr. of iterations",
+        y = expression(abs(B * B^T - hat(B) * hat(B)^T)),
+        color = "method"
+    ) +
+    theme(legend.position = "bottom")
+
+dev.print(png, file = "sim01_dist.png", width = 768, height = 768, res = 125)
+
+
+ggplot(hist, aes(x = iter, color = type, group = interaction(type, repetition))) +
+    geom_line(aes(y = dist.alpha)) +
+    geom_point(data = with(sub <- subset(hist, !is.na(dist.alpha)),
+        aggregate(sub, list(type, repetition), tail, 1)
+    ), aes(y = dist.alpha)) +
+    labs(
+        title = bquote(paste("Distance of estimate ", hat(alpha), " to true ", alpha)),
+        subtitle = bquote(paste(Delta[i][j] == 0.25, " * ", 0.5^abs(i - j), ", ",
+            "20 repetitions, ", n == .(n), ", ",
+            p == .(p), ", ", q == .(q), ", ", k == .(k), ", ", r == .(r))),
+        x = "nr. of iterations",
+        y = expression(abs(alpha * alpha^T - hat(alpha) * hat(alpha)^T)),
+        color = "method"
+    ) +
+    theme(legend.position = "bottom")
+
+dev.print(png, file = "sim01_dist_alpha.png", width = 768, height = 768, res = 125)
+
+
+ggplot(hist, aes(x = iter, color = type, group = interaction(type, repetition))) +
+    geom_line(aes(y = dist.beta)) +
+    geom_point(data = with(sub <- subset(hist, !is.na(dist.beta)),
+        aggregate(sub, list(type, repetition), tail, 1)
+    ), aes(y = dist.beta)) +
+    labs(
+        title = bquote(paste("Distance of estimate ", hat(beta), " to true ", beta)),
+        subtitle = bquote(paste(Delta[i][j] == 0.25, " * ", 0.5^abs(i - j), ", ",
+            "20 repetitions, ", n == .(n), ", ",
+            p == .(p), ", ", q == .(q), ", ", k == .(k), ", ", r == .(r))),
+        x = "nr. of iterations",
+        y = expression(abs(beta * beta^T - hat(beta) * hat(beta)^T)),
+        color = "method"
+    ) +
+    theme(legend.position = "bottom")
+
+dev.print(png, file = "sim01_dist_beta.png", width = 768, height = 768, res = 125)
+
+
+ggplot(hist, aes(x = iter, color = type, group = interaction(type, repetition))) +
+    geom_line(aes(y = norm.alpha)) +
+    geom_point(data = with(sub <- subset(hist, !is.na(norm.alpha)),
+        aggregate(sub, list(type, repetition), tail, 1)
+    ), aes(y = norm.alpha)) +
+    labs(
+        title = expression(paste("Norm of ", hat(alpha))),
+        subtitle = bquote(paste(Delta[i][j] == 0.25, " * ", 0.5^abs(i - j), ", ",
+            "20 repetitions, ", n == .(n), ", ",
+            p == .(p), ", ", q == .(q), ", ", k == .(k), ", ", r == .(r))),
+        x = "nr. of iterations",
+        y = expression(abs(hat(alpha))[F]),
+        color = "method"
+    ) +
+    theme(legend.position = "bottom")
+
+dev.print(png, file = "sim01_norm_alpha.png", width = 768, height = 768, res = 125)
+
+ggplot(hist, aes(x = iter, color = type, group = interaction(type, repetition))) +
+    geom_line(aes(y = norm.beta)) +
+    geom_point(data = with(sub <- subset(hist, !is.na(norm.beta)),
+        aggregate(sub, list(type, repetition), tail, 1)
+    ), aes(y = norm.beta)) +
+    labs(
+        title = expression(paste("Norm of ", hat(beta))),
+        subtitle = bquote(paste(Delta[i][j] == 0.25, " * ", 0.5^abs(i - j), ", ",
+            "20 repetitions, ", n == .(n), ", ",
+            p == .(p), ", ", q == .(q), ", ", k == .(k), ", ", r == .(r))),
+        x = "nr. of iterations",
+        y = expression(abs(hat(beta))[F]),
+        color = "method"
+    ) +
+    theme(legend.position = "bottom")
+
+dev.print(png, file = "sim01_norm_beta.png", width = 768, height = 768, res = 125)
+
+
+
+# local({
+#     par(mfrow = c(2, 3), oma = c(2, 1, 1, 1), mar = c(3.1, 2.1, 2.1, 1.1), lwd = 2)
+#     plot(c(1, max.iter), range(c(hist.base$loss, hist.new$loss, hist.momentum$loss, hist.kron$loss), na.rm = TRUE),
+#         type = "n", log = "x", main = "loss")
+#     lines(    hist.base$loss, col = 2)
+#     lines(     hist.new$loss, col = 3)
+#     lines(hist.momentum$loss, col = 4)
+#     lines(    hist.kron$loss, col = 5)
+#     yrange <- range(c(hist.base$step.size, hist.new$step.size, hist.momentum$step.size, hist.kron$step.size),
+#                     na.rm = TRUE)
+#     plot(c(1, max.iter), yrange,
+#         type = "n", log = "x", main = "step.size")
+#     lines(    hist.base$step.size, col = 2)
+#     lines(     hist.new$step.size, col = 3)
+#     lines(hist.momentum$step.size, col = 4)
+#     lines(    hist.kron$step.size, col = 5)
+#     # lines(    hist.base$step.size, col = 4) # there is no step.size
+#     plot(0, 0, type = "l", bty = "n", xaxt = "n", yaxt = "n")
+#     legend("topleft", legend = c("Base", "GD", "GD + Momentum", "Kron + GD + Momentum"), col = 2:5,
+#         lwd = par("lwd"), xpd = TRUE, horiz = FALSE, cex = 1.2, bty = "n",
+#         x.intersp = 1, y.intersp = 1.5)
+#         # xpd = TRUE makes the legend plot to the figure
+#     plot(c(1, max.iter), range(c(hist.base$dist, hist.new$dist, hist.momentum$dist, hist.kron$dist), na.rm = TRUE),
+#         type = "n", log = "x", main = "dist")
+#     lines(    hist.base$dist, col = 2)
+#     lines(     hist.new$dist, col = 3)
+#     lines(hist.momentum$dist, col = 4)
+#     lines(    hist.kron$dist, col = 5)
+#     plot(c(1, max.iter), range(c(hist.base$dist.alpha, hist.new$dist.alpha, hist.momentum$dist.alpha, hist.kron$dist.alpha), na.rm = TRUE),
+#         type = "n", log = "x", main = "dist.alpha")
+#     lines(    hist.base$dist.alpha, col = 2)
+#     lines(     hist.new$dist.alpha, col = 3)
+#     lines(hist.momentum$dist.alpha, col = 4)
+#     lines(    hist.kron$dist.alpha, col = 5)
+#     plot(c(1, max.iter), range(c(hist.base$dist.beta, hist.new$dist.beta, hist.momentum$dist.beta, hist.kron$dist.beta), na.rm = TRUE),
+#         type = "n", log = "x", main = "dist.beta")
+#     lines(    hist.base$dist.beta, col = 2)
+#     lines(     hist.new$dist.beta, col = 3)
+#     lines(hist.momentum$dist.beta, col = 4)
+#     lines(    hist.kron$dist.beta, col = 5)
+#     # par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0), mar = c(0, 0, 0, 0), new = TRUE)
+#     # plot(0, 0, type = 'l', bty = 'n', xaxt = 'n', yaxt = 'n')
+#     # legend('bottom', legend = c('GD', 'GD + Nesterov Momentum', 'Alternating'), col = 2:4,
+#     #     lwd = 5, xpd = TRUE, horiz = TRUE, cex = 1, seg.len = 1, bty = 'n')
+#     #     # xpd = TRUE makes the legend plot to the figure
+# })
+# dev.print(png, file = "loss.png", width = 768, height = 768, res = 125)
+
+# with(list(a1 = alpha, a2 = fit.base$alpha, a3 = fit.new$alpha, a4 = fit.momentum$alpha,
+#           b1 = beta,  b2 = fit.base$beta,  b3 = fit.new$beta,  b4 = fit.momentum$beta), {
+#     par(mfrow = c(2, 4))
+#     a2 <- sign(sum(sign(a1 * a2))) * a2
+#     a3 <- sign(sum(sign(a1 * a3))) * a3
+#     a4 <- sign(sum(sign(a1 * a4))) * a4
+#     b2 <- sign(sum(sign(b1 * b2))) * b2
+#     b3 <- sign(sum(sign(b1 * b3))) * b3
+#     b4 <- sign(sum(sign(b1 * b4))) * b4
+
+#     matrixImage(a1, main = expression(alpha))
+#     matrixImage(a2, main = expression(paste(hat(alpha)["Base"])))
+#     matrixImage(a3, main = expression(paste(hat(alpha)["GD"])))
+#     matrixImage(a4, main = expression(paste(hat(alpha)["GD+Nest"])))
+#     matrixImage(b1, main = expression(beta))
+#     matrixImage(b2, main = expression(paste(hat(beta)["Base"])))
+#     matrixImage(b3, main = expression(paste(hat(beta)["GD"])))
+#     matrixImage(b4, main = expression(paste(hat(beta)["GD+Nest"])))
+# })
+# dev.print(png, file = "estimates.png", width = 768, height = 768, res = 125)
+
+
+# with(list(d1 = Delta, d2 = fit.base$Delta, d3 = fit.new$Delta, d4 = fit.momentum$Delta), {
+#     par(mfrow = c(2, 2))
+
+#     matrixImage(d1, main = expression(Delta))
+#     matrixImage(d2, main = expression(hat(Delta)["Base"]))
+#     matrixImage(d3, main = expression(hat(Delta)["GD"]))
+#     matrixImage(d4, main = expression(hat(Delta)["GD+Nest"]))
+# })
+# dev.print(png, file = "Delta.png", width = 768, height = 768, res = 125)
+
+# with(list(a1 = alpha, a2 = fit.base$alpha, a3 = fit.new$alpha, a4 = fit.momentum$alpha,
+#           b1 = beta,  b2 = fit.base$beta,  b3 = fit.new$beta,  b4 = fit.momentum$beta), {
+#     par(mfrow = c(2, 2))
+
+#     matrixImage(kronecker(a1, b1), main = expression(B))
+#     matrixImage(kronecker(a2, b2), main = expression(hat(B)["Base"]))
+#     matrixImage(kronecker(a3, b3), main = expression(hat(B)["GD"]))
+#     matrixImage(kronecker(a4, b4), main = expression(hat(B)["GD+Nest"]))
+# })
+# dev.print(png, file = "B.png", width = 768, height = 768, res = 125)
+
+# with(list(a1 = alpha, a2 = fit.base$alpha, a3 = fit.new$alpha, a4 = fit.momentum$alpha,
+#           b1 = beta,  b2 = fit.base$beta,  b3 = fit.new$beta,  b4 = fit.momentum$beta), {
+#     par(mfrow = c(3, 1), lwd = 1)
+
+#     d2 <- kronecker(a1, b1) - kronecker(a2, b2)
+#     d3 <- kronecker(a1, b1) - kronecker(a3, b3)
+#     d4 <- kronecker(a1, b1) - kronecker(a4, b4)
+#     xlim <- c(-1, 1) * max(abs(c(d2, d3, d4)))
+#     breaks <- seq(xlim[1], xlim[2], len = 41)
+
+#     hist(d2, main = expression(paste(base,      (B - hat(B))[i])),
+#          breaks = breaks, xlim = xlim, freq = FALSE)
+#     lines(density(d2), col = 2)
+#     abline(v = range(d2), lty = 2)
+#     hist(d3, main = expression(paste(GD,        (B - hat(B))[i])),
+#          breaks = breaks, xlim = xlim, freq = FALSE)
+#     lines(density(d3), col = 3)
+#     abline(v = range(d3), lty = 2)
+#     hist(d4, main = expression(paste(GD + Nest, (B - hat(B))[i])),
+#          breaks = breaks, xlim = xlim, freq = FALSE)
+#     lines(density(d4), col = 4)
+#     abline(v = range(d4), lty = 2)
+# })
+# dev.print(png, file = "hist.png", width = 768, height = 768, res = 125)
+
+
+
+
+
+
+
+
+# options(width = 300)
+# print(pr <- prof.tree::prof.tree("./Rprof.out"), limit = NULL
+#     , pruneFun = function(x) x$percent > 0.01)
+
+# par(mfrow = c(2, 2))
+# matrixImage(alpha, main = "alpha")
+# matrixImage(fit$alpha, main = "fit$alpha")
+# matrixImage(beta, main = "beta")
+# matrixImage(fit$beta, main = "fit$beta")
+
+# if (diff(dim(alpha) * dim(beta)) > 0) {
+#     par(mfrow = c(2, 1))
+# } else {
+#     par(mfrow = c(1, 2))
+# }
+# matrixImage(kronecker(alpha, beta), main = "kronecker(alpha, beta)")
+# matrixImage(kronecker(fit$alpha, fit$beta), main = "kronecker(fit$alpha, fit$beta)")
+
+# matrixImage(Delta, main = "Delta")
+# matrixImage(fit$Delta, main = "fit$Delta")
+
+
+# local({
+#     a <- (-1 * (sum(sign(fit$alpha) * sign(alpha)) < 0)) * fit$alpha / mean(fit$alpha^2)
+#     b <- alpha / mean(alpha^2)
+
+#     norm(a - b, "F")
+# })
+# local({
+#     a <- (-1 * (sum(sign(fit$beta) * sign(beta)) < 0)) * fit$beta / mean(fit$beta^2)
+#     b <- beta / mean(beta^2)
+
+#     norm(a - b, "F")
+# })
+
+
+# # Which Sequence?
+# x <- y <- 1
+# replicate(40, x <<- (y <<- x + y) - x)
+
+
+# # Face-Splitting Product
+# n <- 100
+# p <- 3
+# q <- 500
+# A <- matrix(rnorm(n * p), n)
+# B <- matrix(rnorm(n * q), n)
+
+# faceSplit <- function(A, B) {
+#     C <- vapply(seq_len(ncol(A)), function(i) A[, i] * B, B)
+#     dim(C) <- c(nrow(A), ncol(A) * ncol(B))
+#     C
+# }
+
+# all.equal(
+#     tkhatriRao(A, B),
+#     faceSplit(A, B)
+# )
+# microbenchmark::microbenchmark(
+#     tkhatriRao(A, B),
+#     faceSplit(A, B)
+# )
+
+
+
+
+
+
+# dist.kron <- function(a0, b0, a1, b1) {
+#     sqrt(sum(a0^2) * sum(b0^2) -
+#          2 * sum(diag(crossprod(a0, a1))) * sum(diag(crossprod(b0, b1))) +
+#          sum(a1^2) * sum(b1^2))
+# }
+
+
+# alpha <- matrix(rnorm(q * r), q)
+# beta  <- matrix(rnorm(p * k), p)
+# alpha.true <- matrix(rnorm(q * r), q)
+# beta.true  <- matrix(rnorm(p * k), p)
+# all.equal(
+#     dist.kron(alpha, beta, alpha.true, beta.true),
+#     norm(kronecker(alpha, beta) - kronecker(alpha.true, beta.true), "F")
+# )
+
+# A <- matrix(rnorm(p^2), p)
+# B <- matrix(rnorm(p^2), p)
+
+# tr <- function(A) sum(diag(A))
+# tr(crossprod(A, B))
+# tr(tcrossprod(B, A))
+
+# tr(crossprod(A, A))
+# tr(tcrossprod(A, A))
+# sum(A^2)
+
+# alpha <- matrix(rnorm(q * r), q)
+# beta  <- matrix(rnorm(p * k), p)
+
+# norm(kronecker(alpha, beta), "F")^2
+# norm(alpha, "F")^2 * norm(beta, "F")^2
+# tr(crossprod(kronecker(alpha, beta)))
+# tr(tcrossprod(kronecker(alpha, beta)))
+# tr(crossprod(kronecker(t(alpha), t(beta))))
+# tr(crossprod(alpha)) * tr(crossprod(beta))
+# tr(tcrossprod(alpha)) * tr(tcrossprod(beta))
+# tr(crossprod(alpha)) * tr(tcrossprod(beta))
+# sum(alpha^2) * sum(beta^2)
+
+# alpha <- matrix(rnorm(q * r), q)
+# beta  <- matrix(rnorm(p * k), p)
+# alpha.true <- matrix(rnorm(q * r), q)
+# beta.true  <- matrix(rnorm(p * k), p)
+# microbenchmark::microbenchmark(
+#     norm(kronecker(alpha, beta), "F")^2,
+#     norm(alpha, "F")^2 * norm(beta, "F")^2,
+#     tr(crossprod(kronecker(alpha, beta))),
+#     tr(tcrossprod(kronecker(alpha, beta))),
+#     tr(crossprod(kronecker(t(alpha), t(beta)))),
+#     tr(crossprod(alpha)) * tr(crossprod(beta)),
+#     tr(tcrossprod(alpha)) * tr(tcrossprod(beta)),
+#     tr(crossprod(alpha)) * tr(tcrossprod(beta)),
+#     sum(alpha^2) * sum(beta^2),
+
+#     setup = {
+#         p <- sample(1:10, 1)
+#         q <- sample(1:10, 1)
+#         k <- sample(1:10, 1)
+#         r <- sample(1:10, 1)
+#         assign("alpha", matrix(rnorm(q * r), q), .GlobalEnv)
+#         assign("beta", matrix(rnorm(p * k), p), .GlobalEnv)
+#         assign("alpha.true", matrix(rnorm(q * r), q), .GlobalEnv)
+#         assign("beta.true", matrix(rnorm(p * k), p), .GlobalEnv)
+#     }
+# )
+
+
+
+# p <- sample(1:15, 1) # 11
+# q <- sample(1:15, 1) # 3
+# k <- sample(1:15, 1) # 7
+# r <- sample(1:15, 1) # 5
+# A <- matrix(rnorm(q * r), q)
+# B <- matrix(rnorm(p * k), p)
+# a <- matrix(rnorm(q * r), q)
+# b <- matrix(rnorm(p * k), p)
+
+# all.equal(
+#     kronecker(A + a, B + b),
+#     kronecker(A, B) + kronecker(A, b) + kronecker(a, B) + kronecker(a, b)
+# )
+
+
+# p <- 200L
+# n <- 100L
+# R <- matrix(rnorm(n * p), n)
+# A <- matrix(rnorm(p^2), p)              # Base Matrix
+# B <- A + 0.01 * matrix(rnorm(p^2), p)   # Distortion / Update of A
+# A.inv <- solve(A)
+
+# microbenchmark::microbenchmark(
+#     solve = R %*% solve(B),
+#     neumann.raw = R %*% (A.inv + A.inv %*% (A - B) %*% A.inv + A.inv %*% (A - B) %*% A.inv %*% (A - B) %*% A.inv),
+#     neumann.fun = {
+#         AD <- A.inv %*% (A - B)
+#         res <- A.inv + AD %*% A.inv
+#         res <- A.inv + AD %*% res
+#         R %*% res
+#     }
+# )
+
+# all.equal(
+#     A.inv + A.inv %*% (A - B) %*% A.inv + A.inv %*% (A - B) %*% A.inv %*% (A - B) %*% A.inv,
+#     {
+#         DA <- (A - B) %*% A.inv
+#         res <- A.inv + A.inv %*% DA
+#         res <- A.inv + res %*% DA
+#         res
+#     }
+# )
+
+# all.equal(
+#     A.inv + A.inv %*% (A - B) %*% A.inv + A.inv %*% (A - B) %*% A.inv %*% (A - B) %*% A.inv,
+#     {
+#         AD <- A.inv %*% (A - B)
+#         res <- A.inv + AD %*% A.inv
+#         res <- A.inv + AD %*% res
+#         res
+#     }
+# )
+
+# #####
+# sym <- function(A) A + t(A)
+# n <- 101
+# p <- 7
+# q <- 11
+# r <- 3
+# k <- 5
+# R <- array(rnorm(n * p * q), dim = c(n = n, p = p, q = q))
+# F <- array(rnorm(n * k * r), dim = c(n = n, k = k, r = r))
+# alpha <- array(rnorm(q * r), dim = c(q = q, r = r))
+# beta  <- array(rnorm(p * k), dim = c(p = p, k = k))
+# Delta.1 <- sym(matrix(rnorm(q * q), q, q))
+# dim(Delta.1) <- c(q = q, q = q)
+# Delta.2 <- sym(matrix(rnorm(p * p), p, p))
+# dim(Delta.2) <- c(p = p, p = p)
+
+# Delta <- kronecker(Delta.1, Delta.2)
+
+# grad.alpha.1 <- local({
+#     Di.1 <- solve(Delta.1)
+#     Di.2 <- solve(Delta.2)
+#     .R <- sapply(seq_len(n), function(i) tcrossprod(Di.1, R[i, , ]) %*% Di.2)
+#     dim(.R) <- c(q, p, n)
+#     .F <- sapply(seq_len(n), function(i) beta %*% F[i, , ])
+#     dim(.F) <- c(p, r, n)
+
+#     .C <- sapply(seq_len(n), function(i) .R[i, , ] %*% .F[i, , ])
+#     dim(.C) <- c(n, q, r)
+#     colSums(.C)
+# })
+# grad.alpha.2 <- local({
+#     # Delta.1^-1 R' Delta.2^-1
+#     .R <- aperm(R, c(2, 1, 3))
+#     dim(.R) <- c(q, n * p)
+#     .R <- solve(Delta.1) %*% .R
+#     dim(.R) <- c(q, n, p)
+#     .R <- aperm(.R, c(3, 2, 1))
+#     dim(.R) <- c(p, n * q)
+#     .R <- solve(Delta.2) %*% .R
+#     dim(.R) <- c(p, n, q)
+#     .R <- aperm(.R, c(2, 3, 1)) # n x q x p
+
+#     # beta F
+#     .F <- aperm(F, c(2, 1, 3))
+#     dim(.F) <- c(k, n * r)
+#     .F <- beta %*% .F
+#     dim(.F) <- c(p, n, r)
+#     .F <- aperm(.F, c(2, 1, 3)) # n x p x r
+
+#     # (Delta.1^-1 R' Delta.2^-1) (beta F)
+#     .R <- aperm(.R, c(1, 3, 2))
+#     dim(.R) <- c(n * p, q)
+#     dim(.F) <- c(n * p, r)
+#     crossprod(.R, .F)
+# })
+
+# all.equal(
+#     grad.alpha.1,
+#     grad.alpha.2
+# )
+
+# all.equal({
+#     .R <- matrix(0, q, p)
+#     Di.1 <- solve(Delta.1)
+#     Di.2 <- solve(Delta.2)
+#     for (i in 1:n) {
+#         .R <- .R + Di.1 %*% t(R[i, , ]) %*% Di.2
+#     }
+#     .R
+# }, {
+#     .R <- R
+#     Di.1 <- solve(Delta.1)
+#     Di.2 <- solve(Delta.2)
+#     .R <- sapply(seq_len(n), function(i) tcrossprod(Di.1, R[i, , ]) %*% Di.2)
+#     dim(.R) <- c(q, p, n)
+#     .R <- aperm(.R, c(3, 1, 2))
+#     colSums(.R)
+# })
+# all.equal({
+#     Di.1 <- solve(Delta.1)
+#     Di.2 <- solve(Delta.2)
+#     .R <- sapply(seq_len(n), function(i) tcrossprod(Di.1, R[i, , ]) %*% Di.2)
+#     dim(.R) <- c(q, p, n)
+#     .R <- aperm(.R, c(3, 1, 2))
+#     .R
+# }, {
+#     .R <- R
+#     dim(.R) <- c(n * p, q)
+#     .R <- .R %*% solve(Delta.1)
+#     dim(.R) <- c(n, p, q)
+#     .R <- aperm(.R, c(1, 3, 2))
+#     dim(.R) <- c(n * q, p)
+#     .R <- .R %*% solve(Delta.2)
+#     dim(.R) <- c(n, q, p)
+#     .R
+# })
+# all.equal({
+#     .F <- matrix(0, p, r)
+#     for (i in 1:n) {
+#         .F <- .F + beta %*% F[i, , ]
+#     }
+#     .F
+# }, {
+#     .F <- apply(F, 1, function(Fi) beta %*% Fi)
+#     dim(.F) <- c(p, r, n)
+#     .F <- aperm(.F, c(3, 1, 2))
+#     colSums(.F)
+# })
+# all.equal({
+#     .F <- apply(F, 1, function(Fi) beta %*% Fi)
+#     dim(.F) <- c(p, r, n)
+#     .F <- aperm(.F, c(3, 1, 2))
+#     colSums(.F)
+# }, {
+#     .F <- aperm(F, c(1, 3, 2))
+#     dim(.F) <- c(n * r, k)
+#     .F <- tcrossprod(.F, beta)
+#     dim(.F) <- c(n, r, p)
+#     t(colSums(.F))
+# })
+
+# all.equal({
+#     Di.1 <- solve(Delta.1)
+#     Di.2 <- solve(Delta.2)
+#     grad.alpha <- 0
+#     grad.beta  <- 0
+#     dim(R) <- c(n, p, q)
+#     dim(F) <- c(n, k, r)
+#     for (i in 1:n) {
+#         grad.alpha <- grad.alpha + (
+#             Di.1 %*% t(R[i, , ]) %*% Di.2 %*% beta %*% F[i, , ]
+#         )
+#         grad.beta <- grad.beta + (
+#             Di.2 %*% R[i, , ] %*% Di.1 %*% alpha %*% t(F[i, , ])
+#         )
+#     }
+
+#     g1 <- c(dim(grad.alpha), dim(grad.beta), grad.alpha, grad.beta)
+# }, {
+#     # Note that the order is important since for grad.beta the residuals do NOT
+#     # need to be transposes.
+
+#     # left/right standardized residuals Delta_1^-1 R_i' Delta_2^-1 for i in 1:n
+#     dim(R) <- c(n * p, q)
+#     .R <- R %*% solve(Delta.1)
+#     dim(.R) <- c(n, p, q)
+#     .R <- aperm(.R, c(1, 3, 2))
+#     dim(.R) <- c(n * q, p)
+#     .R <- .R %*% solve(Delta.2)
+#     dim(.R) <- c(n, q, p)
+
+#     # gradient with respect to beta
+#     # Responces times beta (alpha f_i')
+#     dim(F) <- c(n * k, r)
+#     .F <- tcrossprod(F, alpha)
+#     dim(.F) <- c(n, k, q)
+#     .F <- aperm(.F, c(1, 3, 2))
+#     # Matricize
+#     dim(.R) <- c(n * q, p)
+#     dim(.F) <- c(n * q, k)
+#     grad.beta <- crossprod(.R, .F)
+
+#     # gradient with respect to beta
+#     # Responces times alpha
+#     dim(F) <- c(n, k, r)
+#     .F <- aperm(F, c(1, 3, 2))
+#     dim(.F) <- c(n * r, k)
+#     .F <- tcrossprod(.F, beta)
+#     dim(.F) <- c(n, r, p)
+#     .F <- aperm(.F, c(1, 3, 2))
+#     # Transpose stand. residuals
+#     dim(.R) <- c(n, q, p)
+#     .R <- aperm(.R, c(1, 3, 2))
+#     # Matricize
+#     dim(.R) <- c(n * p, q)
+#     dim(.F) <- c(n * p, r)
+#     grad.alpha <- crossprod(.R, .F)
+
+#     g2 <- c(dim(grad.alpha), dim(grad.beta), grad.alpha, grad.beta)
+# })
+
+
+# microbenchmark::microbenchmark(R1 = {
+#     Di.1 <- solve(Delta.1)
+#     Di.2 <- solve(Delta.2)
+#     grad.alpha <- 0 # matrix(0, q, r)
+#     grad.beta  <- 0 # matrix(0, p, k)
+#     dim(R) <- c(n, p, q)
+#     dim(F) <- c(n, k, r)
+#     for (i in 1:n) {
+#         grad.alpha <- grad.alpha + (
+#             Di.1 %*% t(R[i, , ]) %*% Di.2 %*% beta %*% F[i, , ]
+#         )
+#         grad.beta <- grad.beta + (
+#             Di.2 %*% R[i, , ] %*% Di.1 %*% alpha %*% t(F[i, , ])
+#         )
+#     }
+
+#     g1 <- c(dim(grad.alpha), dim(grad.beta), grad.alpha, grad.beta)
+# }, R3 = {
+#     # Note that the order is important since for grad.beta the residuals do NOT
+#     # need to be transposes.
+
+#     # left/right standardized residuals Delta_1^-1 R_i' Delta_2^-1 for i in 1:n
+#     dim(R) <- c(n * p, q)
+#     .R <- R %*% solve(Delta.1)
+#     dim(.R) <- c(n, p, q)
+#     .R <- aperm(.R, c(1, 3, 2))
+#     dim(.R) <- c(n * q, p)
+#     .R <- .R %*% solve(Delta.2)
+#     dim(.R) <- c(n, q, p)
+
+#     # gradient with respect to beta
+#     # Responces times beta (alpha f_i')
+#     dim(F) <- c(n * k, r)
+#     .F <- tcrossprod(F, alpha)
+#     dim(.F) <- c(n, k, q)
+#     .F <- aperm(.F, c(1, 3, 2))
+#     # Matricize
+#     dim(.R) <- c(n * q, p)
+#     dim(.F) <- c(n * q, k)
+#     grad.beta <- crossprod(.R, .F)
+
+#     # gradient with respect to beta
+#     # Responces times alpha
+#     dim(F) <- c(n, k, r)
+#     .F <- aperm(F, c(1, 3, 2))
+#     dim(.F) <- c(n * r, k)
+#     .F <- tcrossprod(.F, beta)
+#     dim(.F) <- c(n, r, p)
+#     .F <- aperm(.F, c(1, 3, 2))
+#     # Transpose stand. residuals
+#     dim(.R) <- c(n, q, p)
+#     .R <- aperm(.R, c(1, 3, 2))
+#     # Matricize
+#     dim(.R) <- c(n * p, q)
+#     dim(.F) <- c(n * p, r)
+#     grad.alpha <- crossprod(.R, .F)
+
+#     g2 <- c(dim(grad.alpha), dim(grad.beta), grad.alpha, grad.beta)
+# })
+
+
+# n <- 100
+# p <- 7
+# q <- 11
+# k <- 3
+# r <- 5
+
+# X <- array(rnorm(n * p * q), dim = c(n = n, p = p, q = q))
+# F <- array(rnorm(n * k * r), dim = c(n = n, k = k, r = r))
+# alpha <- array(rnorm(q * r), dim = c(q = q, r = r))
+# beta  <- array(rnorm(p * k), dim = c(p = p, k = k))
+
+# all.equal({
+#     R <- array(NA, dim = c(n, p, q))
+#     for (i in 1:n) {
+#         R[i, , ] <- X[i, , ] - beta %*% F[i, , ] %*% t(alpha)
+#     }
+#     R
+# }, {
+#     X - (F %x_3% alpha %x_2% beta)
+# }, check.attributes = FALSE)
+
+# microbenchmark::microbenchmark(base = {
+#     R <- array(NA, dim = c(n, p, q))
+#     for (i in 1:n) {
+#         R[i, , ] <- X[i, , ] - beta %*% F[i, , ] %*% t(alpha)
+#     }
+#     R
+# }, ttm = {
+#     X - (F %x_3% alpha %x_2% beta)
+# })
+
+
+
+# n <- 100; p <- 7; q <- 11; k <- 3; r <- 5
+# sym <- function(x) t(x) + x
+# Di.1 <- sym(matrix(rnorm(q^2), q, q))
+# Di.2 <- sym(matrix(rnorm(p^2), p, p))
+# R <- array(rnorm(n, p, q), dim = c(n, p, q))
+# F <- array(rnorm(n, k, r), dim = c(n, k, r))
+# alpha <- matrix(rnorm(q * r), q, r)
+# beta <- matrix(rnorm(p * k), p, k)
+
+# all.equal({
+#     .R <- array(NA, dim = c(n, p, q))
+#     for (i in 1:n) {
+#         .R[i, , ] <- Di.2 %*% R[i, , ] %*% Di.1
+#     }
+#     .R
+# }, {
+#     R %x_3% Di.1 %x_2% Di.2
+# })
+# all.equal({
+#     .Rt <- array(NA, dim = c(n, q, p))
+#     for (i in 1:n) {
+#         .Rt[i, , ] <- Di.1 %*% t(R[i, , ]) %*% Di.2
+#     }
+#     .Rt
+# }, {
+#     .Rt <- R %x_3% Di.1 %x_2% Di.2
+#     aperm(.Rt, c(1, 3, 2))
+# })
+# all.equal({
+#     .Fa <- array(NA, dim = c(n, q, k))
+#     for (i in 1:n) {
+#         .Fa[i, , ] <- alpha %*% t(F[i, , ])
+#     }
+#     .Fa
+# }, {
+#     aperm(F %x_3% alpha, c(1, 3, 2))
+# })
+# all.equal({
+#     .Fb <- array(NA, dim = c(n, p, r))
+#     for (i in 1:n) {
+#         .Fb[i, , ] <- beta %*% F[i, , ]
+#     }
+#     .Fb
+# }, {
+#     F %x_2% beta
+# })
+# all.equal({
+#     .F <- array(NA, dim = c(n, p, q))
+#     for (i in 1:n) {
+#         .F[i, , ] <- beta %*% F[i, , ] %*% t(alpha)
+#     }
+#     .F
+# }, {
+#     F %x_3% alpha %x_2% beta
+# })
+# all.equal({
+#     .Ga <- 0
+#     for (i in 1:n) {
+#         .Ga <- .Ga + Di.1 %*% t(R[i, , ]) %*% Di.2 %*% beta %*% F[i, , ]
+#     }
+#     .Ga
+# }, {
+#     .R <- R %x_3% Di.1 %x_2% Di.2
+#     dim(.R) <- c(n * p, q)
+#     .Fb <- F %x_2% beta
+#     dim(.Fb) <- c(n * p, r)
+#     crossprod(.R, .Fb)
+# })
+# all.equal({
+#     .Gb <- 0
+#     for (i in 1:n) {
+#         .Gb <- .Gb + Di.2 %*% R[i, , ] %*% Di.1 %*% alpha %*% t(F[i, , ])
+#     }
+#     .Gb
+# }, {
+#     .Rt <- aperm(R %x_3% Di.1 %x_2% Di.2, c(1, 3, 2))
+#     dim(.Rt) <- c(n * q, p)
+#     .Fa <- aperm(F %x_3% alpha, c(1, 3, 2))
+#     dim(.Fa) <- c(n * q, k)
+#     crossprod(.Rt, .Fa)
+# })
+# all.equal({
+#     .Ga <- 0
+#     for (i in 1:n) {
+#         .Ga <- .Ga + Di.1 %*% t(R[i, , ]) %*% Di.2 %*% beta %*% F[i, , ]
+#     }
+#     .Gb <- 0
+#     for (i in 1:n) {
+#         .Gb <- .Gb + Di.2 %*% R[i, , ] %*% Di.1 %*% alpha %*% t(F[i, , ])
+#     }
+#     c(.Ga, .Gb)
+# }, {
+#     .R <- R %x_3% Di.1 %x_2% Di.2
+#     .Fb <- F %x_2% beta
+#     .Fa <- aperm(F %x_3% alpha, c(1, 3, 2))
+
+#     dim(.R)  <- c(n * p, q)
+#     dim(.Fb) <- c(n * p, r)
+#     .Ga <- crossprod(.R, .Fb)
+
+#     dim(.R) <- c(n, p, q)
+#     .R <- aperm(.R, c(1, 3, 2))
+#     dim(.R)  <- c(n * q, p)
+#     dim(.Fa) <- c(n * q, k)
+#     .Gb <- crossprod(.R, .Fa)
+
+#     c(.Ga, .Gb)
+# })
+# all.equal({
+#     .Ga <- 0
+#     for (i in 1:n) {
+#         .Ga <- .Ga + Di.1 %*% t(R[i, , ]) %*% Di.2 %*% beta %*% F[i, , ]
+#     }
+#     .Gb <- 0
+#     for (i in 1:n) {
+#         .Gb <- .Gb + Di.2 %*% R[i, , ] %*% Di.1 %*% alpha %*% t(F[i, , ])
+#     }
+#     c(.Ga, .Gb)
+# }, {
+#     .R <- R %x_3% Di.1 %x_2% Di.2
+
+#     .Ga <- tcrossprod(mat(.R, 3), mat(F %x_2% beta, 3))
+#     .Gb <- tcrossprod(mat(.R, 2), mat(F %x_3% alpha, 2))
+
+#     c(.Ga, .Gb)
+# })
+
+
+
+
+
+# n <- 101; p <- 5; q <- 7
+
+# sym <- function(x) crossprod(x)
+# D1 <- sym(matrix(rnorm(q^2), q, q))
+# D2 <- sym(matrix(rnorm(p^2), p, p))
+
+# X <- tensorPredictors:::rmvnorm(n, sigma = kronecker(D1, D2))
+# dim(X) <- c(n, p, q)
+
+# D1.hat <- tcrossprod(mat(X, 3)) / n
+# D2.hat <- tcrossprod(mat(X, 2)) / n
+
+# local({
+#     par(mfrow = c(2, 2))
+#     matrixImage(D1,     main = "D1")
+#     matrixImage(D1.hat, main = "D1.hat")
+#     matrixImage(D2,     main = "D2")
+#     matrixImage(D2.hat, main = "D2.hat")
+# })
+
+# sum(X^2) / n
+# sum(diag(D1.hat))
+# sum(diag(D2.hat))
+# sum(diag(kronecker(D1, D2)))
+# sum(diag(kronecker(D1.hat / sqrt(sum(diag(D1.hat))),
+#                    D2.hat / sqrt(sum(diag(D1.hat))))))
+
+# all.equal({
+#     mat(X, 1) %*% kronecker(D1.hat, D2.hat)
+# }, {
+#     mat(X %x_3% D1.hat %x_2% D2.hat, 1)
+# })
+# all.equal({
+#     C <- mat(X, 1) %*% kronecker(D1.hat, D2.hat) * (n / sum(X^2))
+#     dim(C) <- c(n, p, q)
+#     C
+# }, {
+#     (X %x_3% D1.hat %x_2% D2.hat) / sum(diag(D1.hat))
+# })
+
+
+
+
+# D.1 <- tcrossprod(mat(X, 3))
+# D.2 <- tcrossprod(mat(X, 2))
+# tr <- sum(diag(D.1))
+# D.1 <- D.1 / sqrt(n * tr)
+# D.2 <- D.2 / sqrt(n * tr)
+
+# sum(diag(kronecker(D1, D2)))
+# sum(diag(kronecker(D.1, D.2)))
+# det(kronecker(D1, D2))
+# det(kronecker(D.1, D.2))
+# det(D.1)^p * det(D.2)^q
+
+# log(det(kronecker(D.1, D.2)))
+# p * log(det(D.1)) + q * log(det(D.2))

tensorPredictors/R/kpir_approx.R

@@ -0,0 +1,136 @@
+#' Using unbiased (but not MLE) estimates for the Kronecker decomposed
+#' covariance matrices Delta_1, Delta_2 for approximating the log-likelihood
+#' giving a closed form solution for the gradient.
+#'
+#'      Delta_1 = n^-1 sum_i R_i' R_i,
+#'      Delta_2 = n^-1 sum_i R_i R_i'.
+#' 
+#' @export
+kpir.approx <- function(X, Fy, shape = c(dim(X)[-1], dim(Fy[-1])),
+    max.iter = 500L, max.line.iter = 50L, step.size = 1e-3,
+    nesterov.scaling = function(a, t) 0.5 * (1 + sqrt(1 + (2 * a)^2)),
+    eps = .Machine$double.eps,
+    logger = NULL
+) {
+
+    # Check if X and Fy have same number of observations
+    stopifnot(nrow(X) == NROW(Fy))
+    n <- nrow(X)                        # Number of observations
+
+    # Get and check predictor dimensions
+    if (length(dim(X)) == 2L) {
+        stopifnot(!missing(shape))
+        stopifnot(ncol(X) == prod(shape[1:2]))
+        p <- as.integer(shape[1])       # Predictor "height"
+        q <- as.integer(shape[2])       # Predictor "width"
+    } else if (length(dim(X)) == 3L) {
+        p <- dim(X)[2]
+        q <- dim(X)[3]
+        dim(X) <- c(n, p * q)
+    } else {
+        stop("'X' must be a matrix or 3-tensor")
+    }
+
+    # Get and check response dimensions
+    if (!is.array(Fy)) {
+        Fy <- as.array(Fy)
+    }
+    if (length(dim(Fy)) == 1L) {
+        k <- r <- 1L
+        dim(Fy) <- c(n, 1L)
+    } else if (length(dim(Fy)) == 2L) {
+        stopifnot(!missing(shape))
+        stopifnot(ncol(Fy) == prod(shape[3:4]))
+        k <- as.integer(shape[3])       # Response functional "height"
+        r <- as.integer(shape[4])       # Response functional "width"
+    } else if (length(dim(Fy)) == 3L) {
+        k <- dim(Fy)[2]
+        r <- dim(Fy)[3]
+        dim(Fy) <- c(n, k * r)
+    } else {
+        stop("'Fy' must be a vector, matrix or 3-tensor")
+    }
+
+
+    ### Step 1: (Approx) Least Squares solution for `X = Fy B' + epsilon`
+    # Vectorize
+    dim(Fy) <- c(n, k * r)
+    dim(X)  <- c(n, p * q)
+    # Solve
+    cpFy <- crossprod(Fy)               # TODO: Check/Test and/or replace
+    if (n <= k * r || qr(cpFy)$rank < k * r) {
+        # In case of under-determined system replace the inverse in the normal
+        # equation by the Moore-Penrose Pseudo Inverse
+        B <- t(matpow(cpFy, -1) %*% crossprod(Fy, X))
+    } else {
+        # Compute OLS estimate by the Normal Equation
+        B <- t(solve(cpFy, crossprod(Fy, X)))
+    }
+
+    # De-Vectroize (from now on tensor arithmetics)
+    dim(Fy) <- c(n, k, r)
+    dim(X)  <- c(n, p, q)
+
+    # Decompose `B = alpha x beta` into `alpha` and `beta`
+    c(alpha0, beta0) %<-% approx.kronecker(B, c(q, r), c(p, k))
+
+    # Compute residuals
+    R <- X - (Fy %x_3% alpha0 %x_2% beta0)
+
+    # Covariance estimates and scaling factor
+    Delta.1 <- tcrossprod(mat(R, 3))
+    Delta.2 <- tcrossprod(mat(R, 2))
+    s <- sum(diag(Delta.1))
+
+    # Inverse Covariances
+    Delta.1.inv <- solve(Delta.1)
+    Delta.2.inv <- solve(Delta.2)
+
+    # cross dependent covariance estimates
+    S.1 <- n^-1 * tcrossprod(mat(R, 3), mat(R %x_2% Delta.2.inv, 3))
+    S.2 <- n^-1 * tcrossprod(mat(R, 2), mat(R %x_3% Delta.1.inv, 2))
+
+    # Evaluate negative log-likelihood (2 pi term dropped)
+    loss <- -0.5 * n * (p * q * log(s) - p * log(det(Delta.1)) -
+        q * log(det(Delta.2)) - s * sum(S.1 * Delta.1.inv))
+
+    # Gradient "generating" tensor
+    G <- (sum(S.1 * Delta.1.inv) - p * q / s) * R
+    G <- G + R %x_2% ((diag(q, p, p) - s * (Delta.2.inv %*% S.2)) %*% Delta.2.inv)
+    G <- G + R %x_3% ((diag(p, q, q) - s * (Delta.1.inv %*% S.1)) %*% Delta.1.inv)
+    G <- G + s * (R %x_2% Delta.2.inv %x_3% Delta.1.inv)
+
+    # Call history callback (logger) before the first iteration
+    if (is.function(logger)) {
+        logger(0L, loss, alpha0, beta0, Delta.1, Delta.2, NA)
+    }
+
+
+    ### Step 2: MLE estimate with LS solution as starting value
+    a0 <- 0
+    a1 <- 1
+    alpha1 <- alpha0
+    beta1 <- beta0
+
+    # main descent loop
+    no.nesterov <- TRUE
+    break.reason <- NA
+    for (iter in seq_len(max.iter)) {
+        if (no.nesterov) {
+            # without extrapolation as fallback
+            alpha.moment <- alpha1
+            beta.moment  <- beta1
+        } else {
+            # extrapolation using previous direction
+            alpha.moment <- alpha1 + ((a0 - 1) / a1) * (alpha1 - alpha0)
+            beta.moment  <-  beta1 + ((a0 - 1) / a1) * ( beta1 -  beta0)
+        }
+    }
+
+    # Extrapolated residuals
+    R <- X - (Fy %x_3% alpha.moment %x_2% beta.moment)
+
+    
+
+    list(loss = loss, alpha = alpha1, beta = beta1, Delta = Delta)
+}

tensorPredictors/R/kpir_base.R

@@ -1,12 +1,50 @@
 #' (Slightly altered) old implementation
 #'
 #' @export
-kpir.base <- function(X, Fy, p, t, k = 1L, r = 1L, d1 = 1L, d2 = 1L,
+kpir.base <- function(X, Fy, shape = c(dim(X)[-1], dim(Fy[-1])),
     method = c("mle", "ls"),
     eps1 = 1e-10, eps2 = 1e-10, max.iter = 500L,
     logger = NULL
 ) {
 
+    # Check if X and Fy have same number of observations
+    stopifnot(nrow(X) == NROW(Fy))
+    n <- nrow(X)                        # Number of observations
+
+    # Get and check predictor dimensions
+    if (length(dim(X)) == 2L) {
+        stopifnot(!missing(shape))
+        stopifnot(ncol(X) == prod(shape[1:2]))
+        p <- as.integer(shape[1])       # Predictor "height"
+        q <- as.integer(shape[2])       # Predictor "width"
+    } else if (length(dim(X)) == 3L) {
+        p <- dim(X)[2]
+        q <- dim(X)[3]
+        dim(X) <- c(n, p * q)
+    } else {
+        stop("'X' must be a matrix or 3-tensor")
+    }
+
+    # Get and check response dimensions
+    if (!is.array(Fy)) {
+        Fy <- as.array(Fy)
+    }
+    if (length(dim(Fy)) == 1L) {
+        k <- r <- 1L
+        dim(Fy) <- c(n, 1L)
+    } else if (length(dim(Fy)) == 2L) {
+        stopifnot(!missing(shape))
+        stopifnot(ncol(Fy) == prod(shape[3:4]))
+        k <- as.integer(shape[3])       # Response functional "height"
+        r <- as.integer(shape[4])       # Response functional "width"
+    } else if (length(dim(Fy)) == 3L) {
+        k <- dim(Fy)[2]
+        r <- dim(Fy)[3]
+        dim(Fy) <- c(n, k * r)
+    } else {
+        stop("'Fy' must be a vector, matrix or 3-tensor")
+    }
+
     log.likelihood <- function(par, X, Fy, Delta.inv, da, db) {
         alpha <- matrix(par[1:prod(da)], da[1L])
         beta <- matrix(par[(prod(da) + 1):length(par)], db[1L])
@@ -17,10 +55,6 @@ kpir.base <- function(X, Fy, p, t, k = 1L, r = 1L, d1 = 1L, d2 = 1L,
     # Validate method using unexact matching.
     method <- match.arg(method)
 
-    # ## Step 1:
-    # # OLS estimate of the model `X = F_y B + epsilon`.
-    # B <- t(solve(crossprod(Fy), crossprod(Fy, X)))
-
     ### Step 1: (Approx) Least Squares solution for `X = Fy B' + epsilon`
     cpFy <- crossprod(Fy)
     if (n <= k * r || qr(cpFy)$rank < k * r) {
@@ -33,7 +67,7 @@ kpir.base <- function(X, Fy, p, t, k = 1L, r = 1L, d1 = 1L, d2 = 1L,
     }
 
     # Estimate alpha, beta as nearest kronecker approximation.
-    c(alpha, beta) %<-% approx.kronecker(B, c(t, r), c(p, k))
+    c(alpha, beta) %<-% approx.kronecker(B, c(q, r), c(p, k))
 
     if (method == "ls") {
         # Estimate Delta.
@@ -63,13 +97,13 @@ kpir.base <- function(X, Fy, p, t, k = 1L, r = 1L, d1 = 1L, d2 = 1L,
         for (iter in 1:max.iter) {
             # Optimize log-likelihood for alpha, beta with fixed Delta.
             opt <- optim(c(alpha, beta), log.likelihood, gr = NULL,
-                         X, Fy, matpow(Delta, -1), c(t, r), c(p, k))
+                         X, Fy, matpow(Delta, -1), c(q, r), c(p, k))
             # Store previous alpha, beta and Delta (for break consition).
             Delta.last <- Delta
             B.last     <- B
             # Extract optimized alpha, beta.
-            alpha <- matrix(opt$par[1:(t * r)], t, r)
+            alpha <- matrix(opt$par[1:(q * r)], q, r)
-            beta <- matrix(opt$par[(t * r + 1):length(opt$par)], p, k)
+            beta <- matrix(opt$par[(q * r + 1):length(opt$par)], p, k)
             # Calc new Delta with likelihood optimized alpha, beta.
             B <- kronecker(alpha, beta)
             resid <- X - tcrossprod(Fy, B)
@@ -85,9 +119,9 @@ kpir.base <- function(X, Fy, p, t, k = 1L, r = 1L, d1 = 1L, d2 = 1L,
             }
 
             # Check break condition 1.
-            if (norm(Delta - Delta.last, 'F') < eps1 * norm(Delta, 'F')) {
+            if (norm(Delta - Delta.last, "F") < eps1 * norm(Delta, "F")) {
                 # Check break condition 2.
-                if (norm(B - B.last, 'F') < eps2 * norm(B, 'F')) {
+                if (norm(B - B.last, "F") < eps2 * norm(B, "F")) {
                     break
                 }
             }

tensorPredictors/R/kpir_kron.R

@@ -65,7 +65,7 @@ kpir.kron <- function(X, Fy, shape = c(dim(X)[-1], dim(Fy[-1])),
     # De-Vectroize (from now on tensor arithmetics)
     dim(Fy) <- c(n, k, r)
     dim(X)  <- c(n, p, q)
-    
+
     # Decompose `B = alpha x beta` into `alpha` and `beta`
     c(alpha0, beta0) %<-% approx.kronecker(B, c(q, r), c(p, k))
 

tensorPredictors/R/kpir_momentum.R

@@ -1,10 +1,10 @@
-#' Gradient Descent Bases Tensor Predictors method with Nesterov Accelerated
+#' Gradient Descent based Tensor Predictors method with Nesterov Accelerated
 #' Momentum
 #'
 #' @export
 kpir.momentum <- function(X, Fy, shape = c(dim(X)[-1], dim(Fy[-1])),
     max.iter = 500L, max.line.iter = 50L, step.size = 1e-3,
-    nesterov.scaling = function(a, t) { 0.5 * (1 + sqrt(1 + (2 * a)^2)) },
+    nesterov.scaling = function(a, t) 0.5 * (1 + sqrt(1 + (2 * a)^2)),
     eps = .Machine$double.eps,
     logger = NULL
 ) {