n = ndK[1] ; d = ndK[2] ; K = ndK[3]
cs = n/K #cluster size
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)))
+ coefs = sapply(1:K, function(i) MASS::mvrnorm(cs, c(rep(0,(i-1)),5,rep(0,d-i)), Id))
indices_medoids1 = computeClusters1(coefs, K, verbose=TRUE)
indices_medoids2 = computeClusters2(dist(coefs), K, verbose=TRUE)
# Get coefs assignments (to medoids)
assignment1 = sapply(seq_len(n), function(i)
- which.min( rowSums( sweep(coefs[indices_medoids1,],2,coefs[i,],'-')^2 ) ) )
+ which.min( colSums( sweep(coefs[,indices_medoids1],1,coefs[,i],'-')^2 ) ) )
assignment2 = sapply(seq_len(n), function(i)
- which.min( rowSums( sweep(coefs[indices_medoids2,],2,coefs[i,],'-')^2 ) ) )
+ which.min( colSums( sweep(coefs[,indices_medoids2],1,coefs[,i],'-')^2 ) ) )
for (i in 1:K)
{
expect_equal(sum(assignment1==i), cs, tolerance=5)
#sum((s1-s3)^2) == 58
#sum((s2-s3)^2) == 38
s = list(s1, s2, s3)
- series = matrix(nrow=n, ncol=L)
+ series = matrix(nrow=L, ncol=n)
for (i in seq_len(n))
- series[i,] = s[[I(i,K)]] + rnorm(L,sd=0.01)
+ series[,i] = s[[I(i,K)]] + rnorm(L,sd=0.01)
getRefSeries = function(indices) {
indices = indices[indices <= n]
- if (length(indices)>0) series[indices,] else NULL
+ if (length(indices)>0) series[,indices] else NULL
}
- synchrones = computeSynchrones(bigmemory::as.big.matrix(rbind(s1,s2,s3)), getRefSeries,
+ synchrones = computeSynchrones(bigmemory::as.big.matrix(cbind(s1,s2,s3)), getRefSeries,
n, 100, verbose=TRUE, parll=FALSE)
- expect_equal(dim(synchrones), c(K,L))
+ expect_equal(dim(synchrones), c(L,K))
for (i in 1:K)
- expect_equal(synchrones[i,], s[[i]], tolerance=0.01)
+ expect_equal(synchrones[,i], s[[i]], tolerance=0.01)
})
# NOTE: medoids can be a big.matrix
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 = distortion + min( colSums( sweep(medoids,1,series[,i],'-')^2 ) / L )
distortion / n
}
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)
+ 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
+ if (length(indices)>0) series[,indices] else NULL
}
wf = "haar"
ctype = "absolute"
- getContribs = function(indices) curvesToContribs(series[indices,],wf,ctype)
+ 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_K1), c(K1,L))
+ expect_equal(dim(medoids_K1), c(L,K1))
# Not easy to evaluate result: at least we expect it to be better than random selection of
# medoids within initial series
distorGood = computeDistortion(series, medoids_K1)
for (i in 1:3)
- expect_lte( distorGood, computeDistortion(series,series[sample(1:n, K1),]) )
+ expect_lte( distorGood, computeDistortion(series,series[,sample(1:n, K1)]) )
})
test_that("clusteringTask2 behave as expected",
series[i,] = s[[I(i,K1)]] + rnorm(L,sd=0.01)
getRefSeries = function(indices) {
indices = indices[indices <= n]
- if (length(indices)>0) series[indices,] else NULL
+ if (length(indices)>0) series[,indices] else NULL
}
# 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_K1 = bigmemory::as.big.matrix( sapply( 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_K2), c(K2,L))
+ expect_equal(dim(medoids_K2), c(L,K2))
# 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),]) )
+ expect_lte( distorGood, computeDistortion(series,medoids_K1[,sample(1:K1, K2)]) )
})
#NOTE: rather redundant test