Split a vector into multiple columns by another vector.

I have two large vectors. The first is a list of measurement values and the second is a categorical identification variable (5 different variables). I am trying to create a matrix of 5 columns (one for each categorical identifier) with all of the measurement values as the different rows?

Thanks

[the cyclist]

Are you guaranteed that the categorical identification variables appear the same number of times, so that your columns have equal length? Or might you need to pad them?

[Image Analyst]

How is the second vector "large" when it has only 5 categorical identifications, like {'Class1', 'Class2', 'Class3', 'Class4', 'Class5'}? Or is the second vector really a N by 5 array of numbers??? How large is "large"? 50 million? 300 million? If you're talking about something less than a few million, that's not large. If only the first vector is measurements, how are you going to get 5 more columns of measurements for the output array? Where are those extra measurements going to come from or how are they to be calculated? I think you'd best give a small example, maybe with 5 or 10 rows or something.

Answer 1

[the cyclist]

If you are guaranteed the same number of categorical variable, then

[~,idx] = sort(categoricalVariable);
measurementVariable = measurementVariable(idx);
measurementVariable = reshape(measurementVariable,[],5);

[Leif]

Unfortunately no I do not have the same number of categorical variables. The numbers are the results of a particle tracking model so the variability in the number of values is very high.

[Image Analyst]

What are the categories? Do you have multiple types of particles, so you'd have something category #1 = "particle of type #1", and category #2 = "particle of type #2", and so on? Hopefully you're using standard terminology as outlined in http://en.wikipedia.org/wiki/Categorical_variable, because I don't really see why the number of categories should change, even if the number of particles changes. A category could always have a count of zero if no particles of that category existed in that movie frame. Posting an image or figure or diagram would probably help me to understand.

Answer 2

[Andrei Bobrov]

mv = randi(120,150,1);  %measurement values
cv = randi([1 5],150,1);%categorical identification variable

out = accumarray(cv,mv,[],@(x){x});