Split a vector into multiple columns by another vector.
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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
2 个评论
the cyclist
2013-6-1
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
2013-6-1
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.
回答（2 个）
the cyclist
2013-6-1
编辑：the cyclist
2013-6-1
If you are guaranteed the same number of categorical variable, then
[~,idx] = sort(categoricalVariable);
measurementVariable = measurementVariable(idx);
measurementVariable = reshape(measurementVariable,[],5);
2 个评论
Leif
2013-6-1
Image Analyst
2013-6-1
编辑：Image Analyst
2013-6-1
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.
Andrei Bobrov
2013-6-1
mv = randi(120,150,1); %measurement values
cv = randi([1 5],150,1);%categorical identification variable
out = accumarray(cv,mv,[],@(x){x});
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