I = function(i, base)
(i-1) %% base + 1
-test_that("computeClusters1 behave as expected",
+test_that("computeClusters1&2 behave as expected",
{
require("MASS", quietly=TRUE)
if (!require("clue", quietly=TRUE))
Id = diag(d)
coefs = do.call(rbind,
lapply(1:K, function(i) MASS::mvrnorm(cs, c(rep(0,(i-1)),5,rep(0,d-i)), Id)))
- indices_medoids = computeClusters1(coefs, K)
+ indices_medoids1 = computeClusters1(coefs, K, verbose=TRUE)
+ indices_medoids2 = computeClusters2(dist(coefs), K, verbose=TRUE)
# Get coefs assignments (to medoids)
- assignment = sapply(seq_len(n), function(i)
- which.min( rowSums( sweep(coefs[indices_medoids,],2,coefs[i,],'-')^2 ) ) )
+ assignment1 = sapply(seq_len(n), function(i)
+ which.min( rowSums( sweep(coefs[indices_medoids1,],2,coefs[i,],'-')^2 ) ) )
+ assignment2 = sapply(seq_len(n), function(i)
+ which.min( rowSums( sweep(coefs[indices_medoids2,],2,coefs[i,],'-')^2 ) ) )
for (i in 1:K)
- expect_equal(sum(assignment==i), cs, tolerance=5)
+ {
+ expect_equal(sum(assignment1==i), cs, tolerance=5)
+ expect_equal(sum(assignment2==i), cs, tolerance=5)
+ }
- costs_matrix = matrix(nrow=K,ncol=K)
+ costs_matrix1 = matrix(nrow=K,ncol=K)
+ costs_matrix2 = matrix(nrow=K,ncol=K)
for (i in 1:K)
{
for (j in 1:K)
{
# assign i (in result) to j (order 1,2,3)
- costs_matrix[i,j] = abs( mean(assignment[((i-1)*cs+1):(i*cs)]) - j )
+ costs_matrix1[i,j] = abs( mean(assignment1[((i-1)*cs+1):(i*cs)]) - j )
+ costs_matrix2[i,j] = abs( mean(assignment2[((i-1)*cs+1):(i*cs)]) - j )
}
}
- permutation = as.integer( clue::solve_LSAP(costs_matrix) )
+ permutation1 = as.integer( clue::solve_LSAP(costs_matrix1) )
+ permutation2 = as.integer( clue::solve_LSAP(costs_matrix2) )
for (i in 1:K)
{
expect_equal(
- mean(assignment[((i-1)*cs+1):(i*cs)]), permutation[i], tolerance=0.05)
+ mean(assignment1[((i-1)*cs+1):(i*cs)]), permutation1[i], tolerance=0.05)
+ expect_equal(
+ mean(assignment2[((i-1)*cs+1):(i*cs)]), permutation2[i], tolerance=0.05)
}
}
})
indices = indices[indices <= n]
if (length(indices)>0) series[indices,] else NULL
}
- synchrones = computeSynchrones(rbind(s1,s2,s3), getRefSeries, n, 100,
- verbose=TRUE, parll=FALSE)
+ synchrones = computeSynchrones(bigmemory::as.big.matrix(rbind(s1,s2,s3)), getRefSeries,
+ n, 100, verbose=TRUE, parll=FALSE)
expect_equal(dim(synchrones), c(K,L))
for (i in 1:K)
expect_equal(synchrones[i,], s[[i]], tolerance=0.01)
})
+# NOTE: medoids can be a big.matrix
computeDistortion = function(series, medoids)
{
n = nrow(series) ; L = ncol(series)
distortion = 0.
+ if (bigmemory::is.big.matrix(medoids))
+ medoids = medoids[,]
for (i in seq_len(n))
distortion = distortion + min( rowSums( sweep(medoids,2,series[i,],'-')^2 ) / L )
distortion / n
}
-test_that("computeClusters2 behave as expected",
+test_that("clusteringTask1 behave as expected",
{
n = 900
x = seq(0,9.5,0.1)
L = length(x) #96 1/4h
K1 = 60
- K2 = 3
- #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
series = matrix(nrow=n, ncol=L)
for (i in seq_len(n))
series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
- getRefSeries = function(indices) {
+ getSeries = function(indices) {
indices = indices[indices <= n]
if (length(indices)>0) series[indices,] else NULL
}
- # Artificially simulate 60 medoids - perfect situation, all equal to one of the refs
- medoids_K1 = do.call(rbind, lapply( 1:K1, function(i) s[[I(i,K1)]] ) )
- medoids_K2 = computeClusters2(medoids_K1, K2, getRefSeries, n, 75,
- verbose=TRUE, parll=FALSE)
+ wf = "haar"
+ ctype = "absolute"
+ getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype)
+ indices1 = clusteringTask1(1:n, getContribs, K1, 75, verbose=TRUE, parll=FALSE)
+ medoids_K1 = getSeries(indices1)
- expect_equal(dim(medoids_K2), c(K2,L))
+ expect_equal(dim(medoids_K1), c(K1,L))
# Not easy to evaluate result: at least we expect it to be better than random selection of
- # medoids within 1...K1 (among references)
- distorGood = computeDistortion(series, medoids_K2)
+ # medoids within initial series
+ distorGood = computeDistortion(series, medoids_K1)
for (i in 1:3)
- expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) )
+ expect_lte( distorGood, computeDistortion(series,series[sample(1:n, K1),]) )
})
-test_that("clusteringTask1 + computeClusters2 behave as expected",
+test_that("clusteringTask2 behave as expected",
{
n = 900
x = seq(0,9.5,0.1)
L = length(x) #96 1/4h
K1 = 60
K2 = 3
+ #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
series = matrix(nrow=n, ncol=L)
for (i in seq_len(n))
series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
- getSeries = function(indices) {
+ getRefSeries = function(indices) {
indices = indices[indices <= n]
if (length(indices)>0) series[indices,] else NULL
}
- wf = "haar"
- ctype = "absolute"
- getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype)
- medoids_K1 = getSeries( clusteringTask1(1:n, getContribs, K1, 75,
- verbose=TRUE, parll=FALSE) )
- medoids_K2 = computeClusters2(medoids_K1, K2, getSeries, n, 120,
- verbose=TRUE, parll=FALSE)
+ # Artificially simulate 60 medoids - perfect situation, all equal to one of the refs
+ medoids_K1 = bigmemory::as.big.matrix(
+ do.call(rbind, lapply( 1:K1, function(i) s[[I(i,K1)]] ) ) )
+ medoids_K2 = clusteringTask2(medoids_K1, K2, getRefSeries, n, 75, verbose=TRUE, parll=FALSE)
- expect_equal(dim(medoids_K1), c(K1,L))
expect_equal(dim(medoids_K2), c(K2,L))
# Not easy to evaluate result: at least we expect it to be better than random selection of
# medoids within 1...K1 (among references)
for (i in 1:3)
expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) )
})
+
+#NOTE: rather redundant test
+#test_that("clusteringTask1 + clusteringTask2 behave as expected",
+#{
+# n = 900
+# x = seq(0,9.5,0.1)
+# L = length(x) #96 1/4h
+# K1 = 60
+# K2 = 3
+# s = lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
+# series = matrix(nrow=n, ncol=L)
+# for (i in seq_len(n))
+# series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
+# getSeries = function(indices) {
+# indices = indices[indices <= n]
+# if (length(indices)>0) series[indices,] else NULL
+# }
+# wf = "haar"
+# ctype = "absolute"
+# getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype)
+# require("bigmemory", quietly=TRUE)
+# indices1 = clusteringTask1(1:n, getContribs, K1, 75, verbose=TRUE, parll=FALSE)
+# medoids_K1 = bigmemory::as.big.matrix( getSeries(indices1) )
+# medoids_K2 = clusteringTask2(medoids_K1, K2, getSeries, n, 120, verbose=TRUE, parll=FALSE)
+#
+# expect_equal(dim(medoids_K1), c(K1,L))
+# expect_equal(dim(medoids_K2), c(K2,L))
+# # Not easy to evaluate result: at least we expect it to be better than random selection of
+# # medoids within 1...K1 (among references)
+# distorGood = computeDistortion(series, medoids_K2)
+# for (i in 1:3)
+# expect_lte( distorGood, computeDistortion(series,medoids_K1[sample(1:K1, K2),]) )
+#})
projects / epclust.git / blobdiff