Finished proper implementation of etsiParas
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2 changed files with 46 additions and 5 deletions
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@ -1,9 +1,25 @@
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etsiParas <- function = (osuus, osuusTaulu, omaFreqs, logml) {
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ready <- 0;
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#' @export
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#' @title Etsi Paras
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#' @description Search for the best?
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#' @param osuus Percentages?
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#' @param omaFreqs own Freqs?
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#' @param osuusTaulu Percentage table?
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#' @param logml log maximum likelihood
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etsiParas <- function (osuus, osuusTaulu, omaFreqs, logml) {
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ready <- 0
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while (ready != 1) {
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muutokset <- laskeMuutokset4(osuus, osuusTaulu, omaFreqs, logml)
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[maxMuutos, indeksi] = max(muutokset[1:end]) # TODO: how does this work on Matlab?
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if (maxMuutos > 0) {
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# Work around R's max() limitation on complex numbers
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if (any(sapply(muutokset, class) == "complex")) {
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maxRe <- max(Re(as.vector(muutokset)))
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maxIm <- max(Im(as.vector(muutokset)))
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maxMuutos <- complex(real = maxRe, imaginary = maxIm)
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} else {
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maxMuutos <- max(as.vector(muutokset))
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}
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indeksi <- which(muutokset == maxMuutos)
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if (Re(maxMuutos) > 0) {
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osuusTaulu <- suoritaMuutos(osuusTaulu, osuus, indeksi)
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logml <- logml + maxMuutos
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} else {
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@ -1,6 +1,5 @@
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context("Admixture analysis")
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test_that("learn*partition behaves like on Matlab", {
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# Test data
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p1 <- c(0, .5, 1, 1.5)
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@ -146,4 +145,30 @@ test_that("suoritaMuutos works like on Matlab", {
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expect_equal(suoritaMuutos(mx2, 0, 5), mx2)
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expect_equal(suoritaMuutos(mx2, 0, 5), mx2)
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expect_equal(suoritaMuutos(mx2, -3, 6), matrix(c(13, 9, 5, 8, 8, -10), 2))
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})
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test_that("laskeMuutokset4 works like on Matlab", {
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# TODO: build these tests based on problems found in etsiParas
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mx1 <- t(c(.4, 7))
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expect_equivalent(
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object = laskeMuutokset4(2, mx1, c(8, 2), 3),
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expected = t(c(0, .3742)),
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tol = .0001
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)
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})
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test_that("etsiParas works like on Matlab", {
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mx1 <- t(c(.4, 7))
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expect_equal(etsiParas(2, mx1, c(8, 1), 8), c(.4, 7, 8))
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expect_equivalent(etsiParas(2, mx1, c(8, 1), 1), c(-1.6, 9, 3.1864), .0001)
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expect_equivalent(
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object = etsiParas(5, mx1, c(8, 1), -pi),
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expected = c(-4.6, 12, 3.8111),
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tol = .001
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)
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expect_equivalent(
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object = etsiParas(-.5, mx1, c(-1, 0), -10),
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expected = c(7.4, 0, complex(real = 1.8563, imaginary = 3.1416)),
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tol = .0001
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)
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})
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