I’m working on a clustering model, and to compute cluster weights I have to compare the cohesions of the current cluster k to the one of cluster k plus unit j (respectively old (o) and new (n) in the code). To do so I thought about using views for the current cluster k, but I haven’ figured out a way to avoid the copy in order to also add the unit j’s information.

Is there a way to define also the new variables (`s1n`

, `s2n`

, etc) in terms of views? since I guess all the copies are affecting a bit the performance, since also I am doing this computations thousands of times during the fit. But maybe it’s impossible since those new variables are obtained merging the information of two different arrays (eg `sp1_red`

and `sp1`

for `s1n`

).

```
## stuff for minimal working example
Si_red = rand((-2:2),10)
j = 1; k=1
sp1_red = rand(10); sp2_red = rand(10)
sp1 = rand(10); sp2 = rand(10)
spatial_cohesion(s1,s2) = sum(s1)+sum(s2)
covariate_similarity(X) = sum(X)
sPPM = true
cl_xPPM = true
p_cl = 2
t=1
Xcl_covariates_red = rand(10,2)
Xcl_covariates = rand(10,2,10)
aux_idxs = findall(Si_red .== k)
if sPPM
s1o = sp1_red[aux_idxs]
s2o = sp2_red[aux_idxs]
s1n = copy(s1o); push!(s1n, sp1[j])
s2n = copy(s2o); push!(s2n, sp2[j])
lCo = spatial_cohesion(s1o, s2o)
lCn = spatial_cohesion(s1n, s2n)
end
if cl_xPPM
lSo = 0.; lSn = 0.
for p in 1:p_cl
Xo = Xcl_covariates_red[aux_idxs,p]
Xn = copy(Xo); push!(Xn,Xcl_covariates[j,p,t])
lSo += covariate_similarity(Xo)
lSn += covariate_similarity(Xn)
end
end
```