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Package v.1.0 ready to be sent to CRAN
[morpheus.git]
/
pkg
/
tests
/
testthat
/
test-alignMatrices.R
diff --git
a/pkg/tests/testthat/test-alignMatrices.R
b/pkg/tests/testthat/test-alignMatrices.R
index
8ce7abf
..
47625b1
100644
(file)
--- a/
pkg/tests/testthat/test-alignMatrices.R
+++ b/
pkg/tests/testthat/test-alignMatrices.R
@@
-4,7
+4,7
@@
context("alignMatrices")
.generateMatrices = function(d, K, N, noise)
{
matrices = list( matrix(runif(d*K, min=-1, max=1),ncol=K) ) #reference
.generateMatrices = function(d, K, N, noise)
{
matrices = list( matrix(runif(d*K, min=-1, max=1),ncol=K) ) #reference
- for (i in 2:
N
)
+ for (i in 2:
(N+1)
)
{
matrices[[i]] <- matrices[[1]][,sample(1:K)]
if (noise)
{
matrices[[i]] <- matrices[[1]][,sample(1:K)]
if (noise)
@@
-15,55
+15,50
@@
context("alignMatrices")
test_that("labelSwitchingAlign correctly aligns de-noised parameters",
{
test_that("labelSwitchingAlign correctly aligns de-noised parameters",
{
- N
=
30 #number of matrices
- d_K_list
=
list(c(2,2), c(5,3))
+ N
<-
30 #number of matrices
+ d_K_list
<-
list(c(2,2), c(5,3))
for (i in 1:2)
{
for (i in 1:2)
{
- d
=
d_K_list[[i]][1]
- K
=
d_K_list[[i]][2]
+ d
<-
d_K_list[[i]][1]
+ K
<-
d_K_list[[i]][2]
# 1] Generate matrix series
# 1] Generate matrix series
-
matrices_permut = .generateMatrices(d,K,N,
noise=FALSE)
+
Ms <- .generateMatrices(d, K, N,
noise=FALSE)
# 2] Call align function with mode=approx1
# 2] Call align function with mode=approx1
- matrices_aligned =
- alignMatrices(matrices_permut[2:N], ref=matrices_permut[[1]], ls_mode="approx1")
+ aligned <- alignMatrices(Ms[2:(N+1)], ref=Ms[[1]], ls_mode="approx1")
# 3] Check alignment
# 3] Check alignment
- for (j in
2
:N)
- expect_equal(
matrices_aligned[[j-1]], matrices_permut
[[1]])
+ for (j in
1
:N)
+ expect_equal(
aligned[[j]], Ms
[[1]])
# 2bis] Call align function with mode=approx2
# 2bis] Call align function with mode=approx2
- matrices_aligned =
- alignMatrices(matrices_permut[2:N], ref=matrices_permut[[1]], ls_mode="approx2")
+ aligned <- alignMatrices(Ms[2:(N+1)], ref=Ms[[1]], ls_mode="approx2")
# 3bis] Check alignment
# 3bis] Check alignment
- for (j in
2
:N)
- expect_equal(
matrices_aligned[[j-1]], matrices_permut
[[1]])
+ for (j in
1
:N)
+ expect_equal(
aligned[[j]], Ms
[[1]])
}
})
test_that("labelSwitchingAlign correctly aligns noisy parameters",
{
}
})
test_that("labelSwitchingAlign correctly aligns noisy parameters",
{
- N
=
30 #number of matrices
- d_K_list
=
list(c(2,2), c(5,3))
+ N
<-
30 #number of matrices
+ d_K_list
<-
list(c(2,2), c(5,3))
for (i in 1:2)
{
for (i in 1:2)
{
- d
=
d_K_list[[i]][1]
- K
=
d_K_list[[i]][2]
- max_error
=
d * 0.2 #TODO: what value to choose ?
+ d
<-
d_K_list[[i]][1]
+ K
<-
d_K_list[[i]][2]
+ max_error
<-
d * 0.2 #TODO: what value to choose ?
# 1] Generate matrix series
# 1] Generate matrix series
-
matrices_permut = .generateMatrices(d,K,N,
noise=TRUE)
+
Ms <- .generateMatrices(d, K, N,
noise=TRUE)
# 2] Call align function
# 2] Call align function
-
matrices_aligned = alignMatrices(matrices_permut, ref="mean"
, ls_mode="exact")
+
aligned <- alignMatrices(Ms[2:(N+1)], ref=Ms[[1]]
, ls_mode="exact")
# 3] Check alignment
for (j in 2:N)
# 3] Check alignment
for (j in 2:N)
- {
- expect_that( norm(matrices_aligned[[j]] - matrices_permut[[1]]),
- is_less_than(max_error) )
- }
+ expect_that( norm(aligned[[j]] - Ms[[1]]), is_less_than(max_error) )
}
})
}
})