MATLAB: How to get the index for each data point in SOM or how do I compute the class vectors of each of the training inputs ??

In MATLAB, clustering data using kmeans can be achieved as shown below:

L = kmeans(X,k,Name,Value) where L is cluster indices which is for each data point.

It implies that is if I have 307 data points I'm to have a 307 x 1 array(L) which is the index for each data point.

However, while using SOM for clustering I discovered to get the index you use the code snippet below:

net = selforgmap([dimension1 dimension2]);

% Train the Network

[net,tr] = train(net,X);

%get indices

L = vec2ind(net(X));

for a Network with 5 x 5 dimension:

it returns L which is an array with the dimesion 25 x 1 instead of 307 x 1 for a Network with 10 x 10 dimension:

it returns L which is an array with the dimesion 100 x 1 instead of 307 x 1

What am I doing wrong???

or to simply put, how do I compute the class vectors of each of the training inputs ?