add: rtensornorm (sample tensor normal),
add: mcrossprod (mode crossprod), wip: simulations, add: notes on multi-array (tensor) normal sampling, wip: initial estimates for alpha, beta, fix: scaling in kpir_approx
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@ -66,6 +66,15 @@
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\newcommand{\pinv}[1]{{{#1}^{\dagger}}} % `Moore-Penrose pseudoinverse`
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\newcommand{\pinv}[1]{{{#1}^{\dagger}}} % `Moore-Penrose pseudoinverse`
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\newcommand{\todo}[1]{{\color{red}TODO: #1}}
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\newcommand{\todo}[1]{{\color{red}TODO: #1}}
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% \DeclareFontFamily{U}{mathx}{\hyphenchar\font45}
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% \DeclareFontShape{U}{mathx}{m}{n}{
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% <5> <6> <7> <8> <9> <10>
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% <10.95> <12> <14.4> <17.28> <20.74> <24.88>
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% mathx10
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% }{}
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% \DeclareSymbolFont{mathx}{U}{mathx}{m}{n}
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% \DeclareMathSymbol{\bigtimes}{1}{mathx}{"91}
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\begin{document}
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\begin{document}
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\maketitle
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\maketitle
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@ -73,6 +82,35 @@
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%% Introduction %%%
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%%% Introduction %%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\section{Notation}
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We start with a brief summary of the used notation.
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\todo{write this}
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Let $\ten{A}$ be a order (rank) $r$ tensor of dimensions $p_1\times ... \times p_r$ and the matrices $\mat{B}_i$ of dimensions $q_i\times p_i$ for $i = 1, ..., r$, then
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\begin{displaymath}
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\ten{A} \ttm[1] \mat{B}_1 \ttm[2] \ldots \ttm[r] \mat{B}_r
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= \ten{A}\times\{ \mat{B}_1, ..., \mat{B}_r \}
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= \ten{A}\times_{i\in[r]} \mat{B}_i
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= (\ten{A}\times_{i\in[r]\backslash j} \mat{B}_i)\ttm[j]\mat{B}_j
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\end{displaymath}
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As an alternative example consider
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\begin{displaymath}
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\ten{A}\times_2\mat{B}_2\times_3\mat{B}_3 = \ten{A}\times\{ \mat{I}, \mat{B}_2, \mat{B}_3 \} = \ten{A}\times_{i\in\{2, 3\}}\mat{B}_i
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\end{displaymath}
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Another example
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\begin{displaymath}
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\mat{B}\mat{A}\t{\mat{C}} = \mat{A}\times_1\mat{B}\times_2\mat{C}
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= \mat{A}\times\{\mat{B}, \mat{C}\}
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\end{displaymath}
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\begin{displaymath}
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(\ten{A}\ttm[i]\mat{B})_{(i)} = \mat{B}\ten{A}_{(i)}
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\end{displaymath}
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\todo{continue}
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\section{Introduction}
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\section{Introduction}
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We assume the model
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We assume the model
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\begin{displaymath}
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\begin{displaymath}
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@ -415,15 +453,45 @@ This leads to the following form of the differential of $\tilde{l}$ given by
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and therefore the gradients
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and therefore the gradients
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\begin{align*}
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\begin{align*}
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\nabla_{\mat{\alpha}}\tilde{l}(\mat{\alpha}, \mat{\beta}) &= \sum_{i = 1}^n \t{\mat{G}_i}\mat{\beta}\mat{f}_{y_i}
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\nabla_{\mat{\alpha}}\tilde{l}(\mat{\alpha}, \mat{\beta}) &= \sum_{i = 1}^n \t{\mat{G}_i}\mat{\beta}\mat{f}_{y_i}
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= \ten{G}_{(3)}\t{(\ten{F}\ttm[2]\beta)_{(3)}}, \\
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= \ten{G}_{(3)}\t{(\ten{F}\ttm[2]\mat{\beta})_{(3)}}, \\
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\nabla_{\mat{\beta}} \tilde{l}(\mat{\alpha}, \mat{\beta}) &= \sum_{i = 1}^n \mat{G}_i\mat{\alpha}\t{\mat{f}_{y_i}}
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\nabla_{\mat{\beta}} \tilde{l}(\mat{\alpha}, \mat{\beta}) &= \sum_{i = 1}^n \mat{G}_i\mat{\alpha}\t{\mat{f}_{y_i}}
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= \ten{G}_{(2)}\t{(\ten{F}\ttm[3]\beta)_{(2)}}.
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= \ten{G}_{(2)}\t{(\ten{F}\ttm[3]\mat{\alpha})_{(2)}}.
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\end{align*}
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\end{align*}
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\todo{check the tensor version of the gradient!!!}
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\todo{check the tensor version of the gradient!!!}
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\newpage
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\newpage
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\section{Thoughts on initial value estimation}
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\todo{This section uses an alternative notation as it already tries to generalize to general multi-dimensional arrays. Furthermore, one of the main differences is that the observation are indexed in the \emph{last} mode. The benefit of this is that the mode product and parameter matrix indices match not only in the population model but also in sample versions.}
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Let $\ten{X}, \ten{F}$ be order (rank) $r$ tensors of dimensions $p_1\times ... \times p_r$ and $q_1\times ... \times q_r$, respectively. Also denote the error tensor $\epsilon$ of the same order and dimensions as $\ten{X}$. The considered model for the $i$'th observation is
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\begin{displaymath}
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\ten{X}_i = \ten{\mu} + \ten{F}_i\times\{ \mat{\alpha}_1, ..., \mat{\alpha}_r \} + \ten{\epsilon}_i
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\end{displaymath}
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where we assume $\ten{\epsilon}_i$ to be i.i.d. mean zero tensor normal distributed $\ten{\epsilon}\sim\mathcal{TM}(0, \mat{\Delta}_1, ..., \mat{\Delta}_r)$ for $\mat{\Delta}_j\in\mathcal{S}^{p_j}_{++}$, $j = 1, ..., r$. Given $i = 1, ..., n$ observations the collected model containing all observations
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\begin{displaymath}
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\ten{X} = \ten{\mu} + \ten{F}\times\{ \mat{\alpha}_1, ..., \mat{\alpha}_r, \mat{I}_n \} + \ten{\epsilon}
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\end{displaymath}
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which is almost identical as the observations $\ten{X}_i, \ten{F}_i$ are stacked on an addition $r + 1$ mode leading to response, predictor and error tensors $\ten{X}, \ten{F}$ of order (rank) $r + 1$ and dimensions $p_1\times...\times p_r\times n$ for $\ten{X}, \ten{\epsilon}$ and $q_1\times...\times q_r\times n$ for $\ten{F}$.
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In the following we assume w.l.o.g that $\ten{\mu} = 0$, as if this is not true we simply replace $\ten{X}_i$ with $\ten{X}_i - \ten{\mu}$ for $i = 1, ..., n$ before collecting all the observations in the response tensor $\ten{X}$.
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The goal here is to find reasonable estimates for $\mat{\alpha}_i$, $i = 1, ..., n$ for the mean model
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\begin{displaymath}
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\E \ten{X}|\ten{F}, \mat{\alpha}_1, ..., \mat{\alpha}_r = \ten{F}\times\{\mat{\alpha}_1, ..., \mat{\alpha}_r, \mat{I}_n\}
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= \ten{F}\times_{i\in[r]}\mat{\alpha}_i.
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\end{displaymath}
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Under the mean model we have using the general mode product relation $(\ten{A}\times_j\mat{B})_{(j)} = \mat{B}\ten{A}_{(j)}$ we get
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\begin{align*}
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\ten{X}_{(j)}\t{\ten{X}_{(j)}} \overset{\text{SVD}}{=} \mat{U}_j\mat{D}_j\t{\mat{U}_j}
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= \mat{\alpha}_j(\ten{F}\times_{i\in[r]\backslash j}\mat{\alpha}_i)_{(j)}
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\t{(\ten{F}\times_{i\in[r]\backslash j}\mat{\alpha}_i)_{(j)}}\t{\mat{\alpha}_j}
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\end{align*}
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for the $j = 1, ..., r$ modes. Using this relation we construct an iterative estimation process by setting the initial estimates of $\hat{\mat{\alpha}}_j^{(0)} = \mat{U}_j[, 1:q_j]$ which are the first $q_j$ columns of $\mat{U}_j$.
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\todo{continue}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%% Numerical Examples %%%
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%%% Numerical Examples %%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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@ -596,6 +664,31 @@ if and only if $\vec\mat{X}\sim\mathcal{N}_{p q}(\vec\mat\mu, \mat\Delta_1\otime
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f(\mat{X}) = \frac{1}{(2\pi)^{p q / 2}|\mat\Delta_1|^{p / 2}|\mat\Delta_2|^{q / 2}}\exp\left(-\frac{1}{2}\tr(\mat\Delta_1^{-1}\t{(\mat X - \mat \mu)}\mat\Delta_2^{-1}(\mat X - \mat \mu))\right).
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f(\mat{X}) = \frac{1}{(2\pi)^{p q / 2}|\mat\Delta_1|^{p / 2}|\mat\Delta_2|^{q / 2}}\exp\left(-\frac{1}{2}\tr(\mat\Delta_1^{-1}\t{(\mat X - \mat \mu)}\mat\Delta_2^{-1}(\mat X - \mat \mu))\right).
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\end{displaymath}
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\end{displaymath}
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\section{Sampling form a Multi-Array Normal Distribution}
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Let $\ten{X}$ be an order (rank) $r$ Multi-Array random variable of dimensions $p_1\times...\times p_r$ following a Multi-Array (or Tensor) Normal distributed
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\begin{displaymath}
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\ten{X}\sim\mathcal{TN}(\mu, \mat{\Delta}_1, ..., \mat{\Delta}_r).
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\end{displaymath}
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Its density is given by
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\begin{displaymath}
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f(\ten{X}) = \Big( \prod_{i = 1}^r \sqrt{(2\pi)^{p_i}|\mat{\Delta}_i|^{q_i}} \Big)^{-1}
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\exp\!\left( -\frac{1}{2}\langle \ten{X} - \mu, (\ten{X} - \mu)\times\{\mat{\Delta}_1^{-1}, ..., \mat{\Delta}_r^{-1}\} \rangle \right)
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\end{displaymath}
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with $q_i = \prod_{j \neq i}p_j$. This is equivalent to the vectorized $\vec\ten{X}$ following a Multi-Variate Normal distribution
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\begin{displaymath}
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\vec{\ten{X}}\sim\mathcal{N}_{p}(\vec{\mu}, \mat{\Delta}_r\otimes...\otimes\mat{\Delta}_1)
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\end{displaymath}
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with $p = \prod_{i = 1}^r p_i$.
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When sampling from the Multi-Array Normal one way is to sample from the Multi-Variate Normal and then reshaping the result, but this is usually very inefficient because it requires to store the multi-variate covariance matrix which is very big. Instead, it is more efficient to sample $\ten{Z}$ as a tensor of the same shape as $\ten{X}$ with standard normal entries and then transform the $\ten{Z}$ to follow the Multi-Array Normal as follows
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\begin{displaymath}
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\ten{Z}\sim\mathcal{TN}(0, \mat{I}_{p_1}, ..., \mat{I}_{p_r})
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\quad\Rightarrow\quad
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\ten{X} = \ten{Z}\times\{\mat{\Delta}_1^{1/2}, ..., \mat{\Delta}_r^{1/2}\} + \mu\sim\mathcal{TN}(\mu, \mat{\Delta}_1, ..., \mat{\Delta}_r).
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\end{displaymath}
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where the sampling from the standard Multi-Array Normal is done by sampling all of the elements of $\ten{Z}$ from a standard Normal.
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\todo{Check this!!!}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%% Reference Summaries %%%
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%%% Reference Summaries %%%
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@ -2,6 +2,16 @@ library(tensorPredictors)
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library(dplyr)
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library(dplyr)
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library(ggplot2)
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library(ggplot2)
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## Logger callbacks
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log.prog <- function(max.iter) {
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function(iter, loss, alpha, beta, ...) {
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select <- as.integer(seq(1, max.iter, len = 30) <= iter)
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cat("\r[", paste(c(" ", "=")[1 + select], collapse = ""),
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"] ", iter, "/", max.iter, sep = "")
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}
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}
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### Exec all methods for a given data set and collect logs ###
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### Exec all methods for a given data set and collect logs ###
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sim <- function(X, Fy, shape, alpha.true, beta.true, max.iter = 500L) {
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sim <- function(X, Fy, shape, alpha.true, beta.true, max.iter = 500L) {
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@ -61,44 +71,40 @@ sim <- function(X, Fy, shape, alpha.true, beta.true, max.iter = 500L) {
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rbind(hist.base, hist.new, hist.momentum, hist.approx) #, hist.kron
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rbind(hist.base, hist.new, hist.momentum, hist.approx) #, hist.kron
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}
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}
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## Plot helper functions
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## Plot helper function
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plot.hist <- function(hist, response, ...) {
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plot.hist2 <- function(hist, response, type = "all", ...) {
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ggplot(hist, aes(x = iter, color = method, group = interaction(method, repetition))) +
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# Extract final results from history
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geom_line(aes_(y = as.name(response)), na.rm = TRUE) +
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sub <- na.omit(hist[c("iter", response, "method", "repetition")])
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geom_point(data = with(sub <- subset(hist, !is.na(as.symbol(response))),
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sub <- aggregate(sub, list(sub$method, sub$repetition), tail, 1)
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aggregate(sub, list(method, repetition), tail, 1)
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), aes_(y = as.name(response))) +
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# Setup ggplot
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labs(...) +
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p <- ggplot(hist, aes_(x = quote(iter),
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theme(legend.position = "bottom")
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y = as.name(response),
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}
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color = quote(method),
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plot.stats <- function(hist, response, ..., title = "Stats") {
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group = quote(interaction(method, repetition))))
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ggplot(hist, aes_(x = quote(iter), y = as.name(response),
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# Add requested layers
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color = quote(method), group = quote(method))) +
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if (type == "all") {
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geom_ribbon(aes(color = NULL, fill = method), alpha = 0.2,
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p <- p + geom_line(na.rm = TRUE)
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stat = "summary", fun.min = "min", fun.max = "max", na.rm = TRUE) +
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p <- p + geom_point(data = sub)
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geom_ribbon(aes(color = NULL, fill = method), alpha = 0.4,
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} else if (type == "mean") {
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stat = "summary", na.rm = TRUE,
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p <- p + geom_line(alpha = 0.5, na.rm = TRUE, linetype = "dotted")
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fun.min = function(y) quantile(y, 0.25),
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p <- p + geom_point(data = sub, alpha = 0.5)
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fun.max = function(y) quantile(y, 0.75)) +
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p <- p + geom_line(aes(group = method),
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geom_line(stat = "summary", fun = "mean", na.rm = TRUE) +
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stat = "summary", fun = "mean", na.rm = TRUE)
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labs(title = title, ...) +
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} else if (type == "median") {
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theme(legend.position = "bottom")
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p <- p + geom_line(alpha = 0.5, na.rm = TRUE, linetype = "dotted")
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}
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p <- p + geom_point(data = sub, alpha = 0.5)
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plot.mean <- function(hist, response, ..., title = "Mean") {
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p <- p + geom_line(aes(group = method),
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ggplot(hist, aes_(x = quote(iter), y = as.name(response),
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stat = "summary", fun = "median", na.rm = TRUE)
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color = quote(method), group = quote(method))) +
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}
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geom_line(stat = "summary", fun = "mean", na.rm = TRUE) +
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# return with theme and annotations
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labs(title = title, ...) +
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p + labs(...) + theme(legend.position = "bottom")
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theme(legend.position = "bottom")
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}
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plot.median <- function(hist, response, ..., title = "Median") {
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ggplot(hist, aes_(x = quote(iter), y = as.name(response),
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color = quote(method), group = quote(method))) +
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geom_line(stat = "summary", fun = "median", na.rm = TRUE) +
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labs(title = title, ...) +
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theme(legend.position = "bottom")
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}
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}
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################################################################################
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### Sim 1 / vec(X) has AR(0.5) Covariance ###
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################################################################################
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## Generate some test data / DEBUG
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## Generate some test data / DEBUG
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n <- 200 # Sample Size
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n <- 200 # Sample Size
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p <- sample(1:15, 1) # 11
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p <- sample(1:15, 1) # 11
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@ -137,39 +143,27 @@ saveRDS(hist, file = sprintf("AR_%s.rds", datetime))
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# for GGPlot2, as factors for grouping
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# for GGPlot2, as factors for grouping
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hist$repetition <- factor(hist$repetition)
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hist$repetition <- factor(hist$repetition)
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plot.hist(hist, "loss")
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dev.print(png, file = sprintf("sim01_loss_%s.png", datetime), width = 768, height = 768, res = 125)
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plot.stats(hist, "loss")
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dev.print(png, file = sprintf("sim01_loss_stats_%s.png", datetime), width = 768, height = 768, res = 125)
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plot.hist(hist, "dist")
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# Save simulation results
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dev.print(png, file = sprintf("sim01_dist_%s.png", datetime), width = 768, height = 768, res = 125)
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sim.name <- "sim01"
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plot.stats(hist, "dist")
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datetime <- format(Sys.time(), "%Y%m%dT%H%M")
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dev.print(png, file = sprintf("sim01_dist_stats_%s.png", datetime), width = 768, height = 768, res = 125)
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saveRDS(hist, file = sprintf("%s_%s.rds", sim.name, datetime))
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plot.hist(hist, "dist.alpha")
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dev.print(png, file = sprintf("sim01_dist_alpha_%s.png", datetime), width = 768, height = 768, res = 125)
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plot.stats(hist, "dist.alpha")
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dev.print(png, file = sprintf("sim01_dist_alpha_stats_%s.png", datetime), width = 768, height = 768, res = 125)
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plot.hist(hist, "dist.beta")
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dev.print(png, file = sprintf("sim01_dist_beta_%s.png", datetime), width = 768, height = 768, res = 125)
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plot.stats(hist, "dist.beta")
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dev.print(png, file = sprintf("sim01_dist_beta_stats_%s.png", datetime), width = 768, height = 768, res = 125)
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plot.hist(hist, "norm.alpha")
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dev.print(png, file = sprintf("sim01_norm_alpha_%s.png", datetime), width = 768, height = 768, res = 125)
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plot.stats(hist, "norm.alpha")
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dev.print(png, file = sprintf("sim01_norm_alpha_stats_%s.png", datetime), width = 768, height = 768, res = 125)
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plot.hist(hist, "norm.beta")
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dev.print(png, file = sprintf("sim01_norm_beta_%s.png", datetime), width = 768, height = 768, res = 125)
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plot.stats(hist, "norm.beta")
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dev.print(png, file = sprintf("sim01_norm_beta_stats_%s.png", datetime), width = 768, height = 768, res = 125)
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# for GGPlot2, as factors for grouping
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hist$repetition <- factor(hist$repetition)
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for (response in c("loss", "dist", "dist.alpha", "dist.beta")) {
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for (fun in c("all", "mean", "median")) {
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||||||
|
print(plot.hist2(hist, response, fun, title = fun) + coord_trans(x = "log1p"))
|
||||||
|
dev.print(png, file = sprintf("%s_%s_%s_%s.png", sim.name, datetime, response, fun),
|
||||||
|
width = 768, height = 768, res = 125)
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
################################################################################
|
||||||
|
### Sim 2 / X has AR(0.707) %x% AR(0.707) Covariance ###
|
||||||
|
################################################################################
|
||||||
|
|
||||||
n <- 200 # Sample Size
|
n <- 200 # Sample Size
|
||||||
p <- 11 # sample(1:15, 1)
|
p <- 11 # sample(1:15, 1)
|
||||||
|
|
@ -206,80 +200,90 @@ for (rep in 1:reps) {
|
||||||
|
|
||||||
|
|
||||||
# Save simulation results
|
# Save simulation results
|
||||||
|
sim.name <- "sim02"
|
||||||
datetime <- format(Sys.time(), "%Y%m%dT%H%M")
|
datetime <- format(Sys.time(), "%Y%m%dT%H%M")
|
||||||
saveRDS(hist, file = sprintf("sim02_%s.rds", datetime))
|
saveRDS(hist, file = sprintf("%s_%s.rds", sim.name, datetime))
|
||||||
|
|
||||||
# for GGPlot2, as factors for grouping
|
# for GGPlot2, as factors for grouping
|
||||||
hist$repetition <- factor(hist$repetition)
|
hist$repetition <- factor(hist$repetition)
|
||||||
|
|
||||||
plot.hist(hist, "loss")
|
for (response in c("loss", "dist", "dist.alpha", "dist.beta")) {
|
||||||
dev.print(png, file = sprintf("sim02_loss_%s.png", datetime), width = 768, height = 768, res = 125)
|
for (fun in c("all", "mean", "median")) {
|
||||||
plot.stats(hist, "loss")
|
print(plot.hist2(hist, response, fun, title = fun) + coord_trans(x = "log1p"))
|
||||||
dev.print(png, file = sprintf("sim02_loss_stats_%s.png", datetime), width = 768, height = 768, res = 125)
|
dev.print(png, file = sprintf("%s_%s_%s_%s.png", sim.name, datetime, response, fun),
|
||||||
|
width = 768, height = 768, res = 125)
|
||||||
plot.hist(hist, "dist")
|
|
||||||
dev.print(png, file = sprintf("sim02_dist_%s.png", datetime), width = 768, height = 768, res = 125)
|
|
||||||
plot.stats(hist, "dist")
|
|
||||||
dev.print(png, file = sprintf("sim02_dist_stats_%s.png", datetime), width = 768, height = 768, res = 125)
|
|
||||||
plot.mean(hist, "dist")
|
|
||||||
plot.median(hist, "dist")
|
|
||||||
|
|
||||||
plot.hist(hist, "dist.alpha")
|
|
||||||
dev.print(png, file = sprintf("sim02_dist_alpha_%s.png", datetime), width = 768, height = 768, res = 125)
|
|
||||||
plot.stats(hist, "dist.alpha")
|
|
||||||
dev.print(png, file = sprintf("sim02_dist_alpha_stats_%s.png", datetime), width = 768, height = 768, res = 125)
|
|
||||||
plot.mean(hist, "dist.alpha")
|
|
||||||
plot.median(hist, "dist.alpha")
|
|
||||||
|
|
||||||
plot.hist(hist, "dist.beta")
|
|
||||||
dev.print(png, file = sprintf("sim02_dist_beta_%s.png", datetime), width = 768, height = 768, res = 125)
|
|
||||||
plot.stats(hist, "dist.beta")
|
|
||||||
dev.print(png, file = sprintf("sim02_dist_beta_stats_%s.png", datetime), width = 768, height = 768, res = 125)
|
|
||||||
plot.mean(hist, "dist.beta")
|
|
||||||
plot.median(hist, "dist.beta")
|
|
||||||
|
|
||||||
plot.hist(hist, "norm.alpha")
|
|
||||||
dev.print(png, file = sprintf("sim02_norm_alpha_%s.png", datetime), width = 768, height = 768, res = 125)
|
|
||||||
plot.stats(hist, "norm.alpha")
|
|
||||||
dev.print(png, file = sprintf("sim02_norm_alpha_stats_%s.png", datetime), width = 768, height = 768, res = 125)
|
|
||||||
|
|
||||||
plot.hist(hist, "norm.beta")
|
|
||||||
dev.print(png, file = sprintf("sim02_norm_beta_%s.png", datetime), width = 768, height = 768, res = 125)
|
|
||||||
plot.stats(hist, "norm.beta")
|
|
||||||
dev.print(png, file = sprintf("sim02_norm_beta_stats_%s.png", datetime), width = 768, height = 768, res = 125)
|
|
||||||
|
|
||||||
plot.hist2 <- function(hist, response, type = "all", ...) {
|
|
||||||
# Extract final results from history
|
|
||||||
sub <- na.omit(hist[c("iter", response, "method", "repetition")])
|
|
||||||
sub <- aggregate(sub, list(sub$method, sub$repetition), tail, 1)
|
|
||||||
|
|
||||||
# Setup ggplot
|
|
||||||
p <- ggplot(hist, aes_(x = quote(iter),
|
|
||||||
y = as.name(response),
|
|
||||||
color = quote(method),
|
|
||||||
group = quote(interaction(method, repetition))))
|
|
||||||
# Add requested layers
|
|
||||||
if (type == "all") {
|
|
||||||
p <- p + geom_line(na.rm = TRUE)
|
|
||||||
p <- p + geom_point(data = sub)
|
|
||||||
} else if (type == "mean") {
|
|
||||||
p <- p + geom_line(alpha = 0.5, na.rm = TRUE, linetype = "dotted")
|
|
||||||
p <- p + geom_point(data = sub, alpha = 0.5)
|
|
||||||
p <- p + geom_line(aes(group = method),
|
|
||||||
stat = "summary", fun = "mean", na.rm = TRUE)
|
|
||||||
} else if (type == "median") {
|
|
||||||
p <- p + geom_line(alpha = 0.5, na.rm = TRUE, linetype = "dotted")
|
|
||||||
p <- p + geom_point(data = sub, alpha = 0.5)
|
|
||||||
p <- p + geom_line(aes(group = method),
|
|
||||||
stat = "summary", fun = "median", na.rm = TRUE)
|
|
||||||
}
|
}
|
||||||
# return with theme and annotations
|
|
||||||
p + labs(...) + theme(legend.position = "bottom")
|
|
||||||
}
|
}
|
||||||
|
|
||||||
plot.hist2(hist, "dist.alpha", "all", title = "all") + coord_trans(x = "log1p")
|
################################################################################
|
||||||
plot.hist2(hist, "dist.alpha", "mean", title = "mean") + coord_trans(x = "log1p")
|
### WIP ###
|
||||||
plot.hist2(hist, "dist.alpha", "median", title = "median") + coord_trans(x = "log1p")
|
################################################################################
|
||||||
|
n <- 200 # Sample Size
|
||||||
|
p <- 11 # sample(1:15, 1)
|
||||||
|
q <- 3 # sample(1:15, 1)
|
||||||
|
k <- 7 # sample(1:15, 1)
|
||||||
|
r <- 5 # sample(1:15, 1)
|
||||||
|
print(c(n, p, q, k, r))
|
||||||
|
|
||||||
|
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)
|
||||||
|
))
|
||||||
|
X <- tcrossprod(Fy, kronecker(alpha, beta)) + CVarE:::rmvnorm(n, sigma = Delta)
|
||||||
|
|
||||||
|
Delta.1 <- sqrt(0.5)^abs(outer(seq_len(q), seq_len(q), `-`))
|
||||||
|
Delta.2 <- sqrt(0.5)^abs(outer(seq_len(p), seq_len(p), `-`))
|
||||||
|
Delta <- kronecker(Delta.1, Delta.2)
|
||||||
|
|
||||||
|
shape <- c(p, q, k, r)
|
||||||
|
|
||||||
|
# Base (old)
|
||||||
|
Rprof(fit.base <- kpir.base(X, Fy, shape, max.iter = 500, logger = prog(500)))
|
||||||
|
|
||||||
|
# New (simple Gradient Descent)
|
||||||
|
Rprof(fit.new <- kpir.new(X, Fy, shape, max.iter = 500, logger = prog(500)))
|
||||||
|
|
||||||
|
# Gradient Descent with Nesterov Momentum
|
||||||
|
Rprof(fit.momentum <- kpir.momentum(X, Fy, shape, max.iter = 500, logger = prog(500)))
|
||||||
|
|
||||||
|
# # Residual Covariance Kronecker product assumpton version
|
||||||
|
# Rprof(fit.kron <- kpir.kron(X, Fy, shape, max.iter = 500, logger = prog(500)))
|
||||||
|
|
||||||
|
# Approximated MLE with Nesterov Momentum
|
||||||
|
Rprof("kpir.approx.Rprof")
|
||||||
|
fit.approx <- kpir.approx(X, Fy, shape, max.iter = 500, logger = prog(500))
|
||||||
|
summaryRprof("kpir.approx.Rprof")
|
||||||
|
|
||||||
|
par(mfrow = c(2, 2))
|
||||||
|
matrixImage(Delta, main = expression(Delta))
|
||||||
|
matrixImage(fit.base$Delta, main = expression(hat(Delta)), sub = "base")
|
||||||
|
matrixImage(fit.momentum$Delta, main = expression(hat(Delta)), sub = "momentum")
|
||||||
|
matrixImage(kronecker(fit.approx$Delta.1, fit.approx$Delta.2), main = expression(hat(Delta)), sub = "approx")
|
||||||
|
|
||||||
|
par(mfrow = c(2, 2))
|
||||||
|
matrixImage(Delta.1, main = expression(Delta[1]))
|
||||||
|
matrixImage(fit.approx$Delta.1, main = expression(hat(Delta)[1]), sub = "approx")
|
||||||
|
matrixImage(Delta.2, main = expression(Delta[2]))
|
||||||
|
matrixImage(fit.approx$Delta.2, main = expression(hat(Delta)[2]), sub = "approx")
|
||||||
|
|
||||||
|
par(mfrow = c(2, 2))
|
||||||
|
matrixImage(alpha.true, main = expression(alpha))
|
||||||
|
matrixImage(fit.base$alpha, main = expression(hat(alpha)), sub = "base")
|
||||||
|
matrixImage(fit.momentum$alpha, main = expression(hat(alpha)), sub = "momentum")
|
||||||
|
matrixImage(fit.approx$alpha, main = expression(hat(alpha)), sub = "approx")
|
||||||
|
|
||||||
|
par(mfrow = c(2, 2))
|
||||||
|
matrixImage(beta.true, main = expression(beta))
|
||||||
|
matrixImage(fit.base$beta, main = expression(hat(beta)), sub = "base")
|
||||||
|
matrixImage(fit.momentum$beta, main = expression(hat(beta)), sub = "momentum")
|
||||||
|
matrixImage(fit.approx$beta, main = expression(hat(beta)), sub = "approx")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
################################################################################
|
################################################################################
|
||||||
|
|
|
||||||
|
|
@ -78,17 +78,17 @@ kpir.approx <- function(X, Fy, shape = c(dim(X)[-1], dim(Fy[-1])),
|
||||||
R <- X - (Fy %x_3% alpha0 %x_2% beta0)
|
R <- X - (Fy %x_3% alpha0 %x_2% beta0)
|
||||||
|
|
||||||
# Covariance estimates and scaling factor
|
# Covariance estimates and scaling factor
|
||||||
Delta.1 <- tcrossprod(mat(R, 3))
|
Delta.1 <- tcrossprod(mat(R, 3)) / n
|
||||||
Delta.2 <- tcrossprod(mat(R, 2))
|
Delta.2 <- tcrossprod(mat(R, 2)) / n
|
||||||
s <- sum(diag(Delta.1))
|
s <- mean(diag(Delta.1))
|
||||||
|
|
||||||
# Inverse Covariances
|
# Inverse Covariances
|
||||||
Delta.1.inv <- solve(Delta.1)
|
Delta.1.inv <- solve(Delta.1)
|
||||||
Delta.2.inv <- solve(Delta.2)
|
Delta.2.inv <- solve(Delta.2)
|
||||||
|
|
||||||
# cross dependent covariance estimates
|
# cross dependent covariance estimates
|
||||||
S.1 <- n^-1 * tcrossprod(mat(R, 3), mat(R %x_2% Delta.2.inv, 3))
|
S.1 <- tcrossprod(mat(R, 3), mat(R %x_2% Delta.2.inv, 3)) / n
|
||||||
S.2 <- n^-1 * tcrossprod(mat(R, 2), mat(R %x_3% Delta.1.inv, 2))
|
S.2 <- tcrossprod(mat(R, 2), mat(R %x_3% Delta.1.inv, 2)) / n
|
||||||
|
|
||||||
# Evaluate negative log-likelihood (2 pi term dropped)
|
# Evaluate negative log-likelihood (2 pi term dropped)
|
||||||
loss <- -0.5 * (n * (p * q * log(s) - p * log(det(Delta.1)) -
|
loss <- -0.5 * (n * (p * q * log(s) - p * log(det(Delta.1)) -
|
||||||
|
|
@ -124,17 +124,17 @@ kpir.approx <- function(X, Fy, shape = c(dim(X)[-1], dim(Fy[-1])),
|
||||||
R <- X - (Fy %x_3% alpha.moment %x_2% beta.moment)
|
R <- X - (Fy %x_3% alpha.moment %x_2% beta.moment)
|
||||||
|
|
||||||
# Recompute Covariance Estimates and scaling factor
|
# Recompute Covariance Estimates and scaling factor
|
||||||
Delta.1 <- tcrossprod(mat(R, 3))
|
Delta.1 <- tcrossprod(mat(R, 3)) / n
|
||||||
Delta.2 <- tcrossprod(mat(R, 2))
|
Delta.2 <- tcrossprod(mat(R, 2)) / n
|
||||||
s <- sum(diag(Delta.1))
|
s <- mean(diag(Delta.1))
|
||||||
|
|
||||||
# Inverse Covariances
|
# Inverse Covariances
|
||||||
Delta.1.inv <- solve(Delta.1)
|
Delta.1.inv <- solve(Delta.1)
|
||||||
Delta.2.inv <- solve(Delta.2)
|
Delta.2.inv <- solve(Delta.2)
|
||||||
|
|
||||||
# cross dependent covariance estimates
|
# cross dependent covariance estimates
|
||||||
S.1 <- n^-1 * tcrossprod(mat(R, 3), mat(R %x_2% Delta.2.inv, 3))
|
S.1 <- tcrossprod(mat(R, 3), mat(R %x_2% Delta.2.inv, 3)) / n
|
||||||
S.2 <- n^-1 * tcrossprod(mat(R, 2), mat(R %x_3% Delta.1.inv, 2))
|
S.2 <- tcrossprod(mat(R, 2), mat(R %x_3% Delta.1.inv, 2)) / n
|
||||||
|
|
||||||
# Gradient "generating" tensor
|
# Gradient "generating" tensor
|
||||||
G <- (sum(S.1 * Delta.1.inv) - p * q / s) * R
|
G <- (sum(S.1 * Delta.1.inv) - p * q / s) * R
|
||||||
|
|
@ -161,12 +161,12 @@ kpir.approx <- function(X, Fy, shape = c(dim(X)[-1], dim(Fy[-1])),
|
||||||
|
|
||||||
# Update Residuals, Covariances, ...
|
# Update Residuals, Covariances, ...
|
||||||
R <- X - (Fy %x_3% alpha.temp %x_2% beta.temp)
|
R <- X - (Fy %x_3% alpha.temp %x_2% beta.temp)
|
||||||
Delta.1 <- tcrossprod(mat(R, 3))
|
Delta.1 <- tcrossprod(mat(R, 3)) / n
|
||||||
Delta.2 <- tcrossprod(mat(R, 2))
|
Delta.2 <- tcrossprod(mat(R, 2)) / n
|
||||||
s <- sum(diag(Delta.1))
|
s <- mean(diag(Delta.1))
|
||||||
Delta.1.inv <- solve(Delta.1)
|
Delta.1.inv <- solve(Delta.1)
|
||||||
Delta.2.inv <- solve(Delta.2)
|
Delta.2.inv <- solve(Delta.2)
|
||||||
S.1 <- n^-1 * tcrossprod(mat(R, 3), mat(R %x_2% Delta.2.inv, 3))
|
S.1 <- tcrossprod(mat(R, 3), mat(R %x_2% Delta.2.inv, 3)) / n
|
||||||
# S.2 not needed
|
# S.2 not needed
|
||||||
|
|
||||||
# Re-evaluate negative log-likelihood
|
# Re-evaluate negative log-likelihood
|
||||||
|
|
|
||||||
|
|
@ -0,0 +1,21 @@
|
||||||
|
#' Tensor Mode Crossproduct
|
||||||
|
#'
|
||||||
|
#' C = A_(m) t(A_(m))
|
||||||
|
#'
|
||||||
|
#' For a matrix `A`, the first mode is `mcrossprod(A, 1)` equivalent to
|
||||||
|
#' `A %*% t(A)` (`tcrossprod`). On the other hand for mode two `mcrossprod(A, 2)`
|
||||||
|
#' the equivalence is `t(A) %*% A` (`crossprod`).
|
||||||
|
#'
|
||||||
|
#' @param A multi-dimensional array
|
||||||
|
#' @param mode index (1-indexed)
|
||||||
|
#'
|
||||||
|
#' @returns matrix of dimensions \code{dim(A)[mode] by dim(A)[mode]}.
|
||||||
|
#'
|
||||||
|
#' @note equivalent to \code{tcrossprod(mat(A, mode))} with around the same
|
||||||
|
#' performance but only allocates the result matrix.
|
||||||
|
#'
|
||||||
|
#' @export
|
||||||
|
mcrossprod <- function(A, mode = length(dim(A))) {
|
||||||
|
storage.mode(A) <- "double"
|
||||||
|
.Call("C_mcrossprod", A, as.integer(mode))
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,49 @@
|
||||||
|
#' Random sampling from a tensor (multi-array) normal distribution
|
||||||
|
#'
|
||||||
|
#' @examples
|
||||||
|
#' n <- 1000
|
||||||
|
#' Sigma.1 <- 0.5^abs(outer(1:5, 1:5, "-"))
|
||||||
|
#' Sigma.2 <- diag(1:4)
|
||||||
|
#' X <- rtensornorm(n, 0, Sigma.1, Sigma.2)
|
||||||
|
#'
|
||||||
|
#' @export
|
||||||
|
rtensornorm <- function(n, mean, ..., sample.mode) {
|
||||||
|
# get covariance matrices
|
||||||
|
cov <- list(...)
|
||||||
|
|
||||||
|
# get sample shape (dimensions of an observation)
|
||||||
|
shape <- unlist(Map(nrow, cov))
|
||||||
|
|
||||||
|
# result tensor dimensions
|
||||||
|
dims <- c(shape, n)
|
||||||
|
|
||||||
|
# validate mean dimensions
|
||||||
|
if (!missing(mean)) {
|
||||||
|
stopifnot(is.numeric(mean))
|
||||||
|
stopifnot(is.array(mean) || length(mean) == 1)
|
||||||
|
stopifnot(length(shape) == length(cov))
|
||||||
|
stopifnot(all(shape == dim(mean)))
|
||||||
|
}
|
||||||
|
|
||||||
|
# sample i.i.d. normal entries
|
||||||
|
X <- array(rnorm(prod(dims)), dim = dims)
|
||||||
|
|
||||||
|
# transform from standard normal to tensor normal with given covariances
|
||||||
|
for (i in seq_along(cov)) {
|
||||||
|
X <- ttm(X, matpow(cov[[i]], 1 / 2), i)
|
||||||
|
}
|
||||||
|
|
||||||
|
# add mean (using recycling, observations on last mode)
|
||||||
|
X <- X + mean
|
||||||
|
|
||||||
|
# permute axis for indeing observations on sample mode (permute first axis
|
||||||
|
# with sample mode axis)
|
||||||
|
if (!missing(sample.mode)) {
|
||||||
|
axis <- seq_len(length(dims) - 1)
|
||||||
|
start <- seq_len(sample.mode - 1)
|
||||||
|
end <- seq_len(length(dims) - sample.mode) + sample.mode - 1
|
||||||
|
X <- aperm(X, c(axis[start], length(dims), axis[end]))
|
||||||
|
}
|
||||||
|
|
||||||
|
X
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,88 @@
|
||||||
|
// The need for `USE_FC_LEN_T` and `FCONE` is due to a Fortran character string
|
||||||
|
// to C incompatibility. See: Writing R Extentions: 6.6.1 Fortran character strings
|
||||||
|
#define USE_FC_LEN_T
|
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#include <R.h>
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#include <Rinternals.h>
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#include <R_ext/BLAS.h>
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#ifndef FCONE
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#define FCONE
|
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#endif
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|
/**
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|
* Tensor Mode Crossproduct
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*
|
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|
* C = A_(m) t(A_(m))
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||||||
|
*
|
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|
* For a matrix `A`, the first mode is `mcrossprod(A, 1)` equivalent to
|
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|
* `A %*% t(A)` (`tcrossprod`). On the other hand for mode two `mcrossprod(A, 2)`
|
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|
* the equivalence is `t(A) %*% A` (`crossprod`).
|
||||||
|
*
|
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|
* @param A multi-dimensional array
|
||||||
|
* @param m mode index (1-indexed)
|
||||||
|
*/
|
||||||
|
extern SEXP mcrossprod(SEXP A, SEXP m) {
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|
// get zero indexed mode
|
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|
int mode = asInteger(m) - 1;
|
||||||
|
|
||||||
|
// get dimension attribute of A
|
||||||
|
SEXP dim = getAttrib(A, R_DimSymbol);
|
||||||
|
|
||||||
|
// validate mode (0-indexed, must be smaller than the tensor order)
|
||||||
|
if (mode < 0 || length(dim) <= mode) {
|
||||||
|
error("Illegal mode");
|
||||||
|
}
|
||||||
|
|
||||||
|
// the strides
|
||||||
|
// `stride[0] <- prod(dim(X)[seq_len(mode - 1)])`
|
||||||
|
// `stride[1] <- dim(X)[mode]`
|
||||||
|
// `stride[2] <- prod(dim(X)[-seq_len(mode)])`
|
||||||
|
int stride[3] = {1, INTEGER(dim)[mode], 1};
|
||||||
|
for (int i = 0; i < length(dim); ++i) {
|
||||||
|
int size = INTEGER(dim)[i];
|
||||||
|
stride[0] *= (i < mode) ? size : 1;
|
||||||
|
stride[2] *= (i > mode) ? size : 1;
|
||||||
|
}
|
||||||
|
|
||||||
|
// create response matrix C
|
||||||
|
SEXP C = PROTECT(allocMatrix(REALSXP, stride[1], stride[1]));
|
||||||
|
|
||||||
|
// raw data access pointers
|
||||||
|
double* a = REAL(A);
|
||||||
|
double* c = REAL(C);
|
||||||
|
|
||||||
|
// employ BLAS dsyrk (Double SYmmeric Rank K) operation
|
||||||
|
// (C = alpha A A^T + beta C or C = alpha A^T A + beta C)
|
||||||
|
const double zero = 0.0;
|
||||||
|
const double one = 1.0;
|
||||||
|
if (mode == 0) {
|
||||||
|
// mode 1: special case C = A_(1) A_(1)^T
|
||||||
|
// C = 1 A A^T + 0 C
|
||||||
|
F77_CALL(dsyrk)("U", "N", &stride[1], &stride[2],
|
||||||
|
&one, a, &stride[1], &zero, c, &stride[1] FCONE FCONE);
|
||||||
|
} else {
|
||||||
|
// Other modes writen as accumulated sum of matrix products
|
||||||
|
// initialize C to zero
|
||||||
|
memset(c, 0, stride[1] * stride[1] * sizeof(double));
|
||||||
|
|
||||||
|
// Sum over all modes > mode
|
||||||
|
for (int i2 = 0; i2 < stride[2]; ++i2) {
|
||||||
|
// C = 1 A^T A + 1 C
|
||||||
|
F77_CALL(dsyrk)("U", "T", &stride[1], &stride[0],
|
||||||
|
&one, &a[i2 * stride[0] * stride[1]], &stride[0],
|
||||||
|
&one, c, &stride[1] FCONE FCONE);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// Symmetric matrix result is stored in upper triangular part only
|
||||||
|
// Copy upper triangular part to lower
|
||||||
|
for (int j = 0; j + 1 < stride[1]; j++) {
|
||||||
|
for (int i = j + 1; i < stride[1]; ++i) {
|
||||||
|
c[i + j * stride[1]] = c[j + i * stride[1]];
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// release C to grabage collector
|
||||||
|
UNPROTECT(1);
|
||||||
|
|
||||||
|
return C;
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue