Hi
I built a GMM classifier with the scikit learn package and would like to score
my model.
Training and test data consist of an array of quaternions, each quaternion
representing a motion frame, structured like this:
[ [1,2,3,4], [5,6,7,8], [5,4,3,2] ]
When I use the score function, it looks at each individual frame and calculates
the log probability. So if I have an array with 100 rows, I get 100
probabilities. But instead of having, let's say, 100 separate log
probabilities, I would like to look at 100 test samples and get a single log
probability for the whole array being generated by my GMM.
I have tried to flatten my arrays so that training and test samples are
represented by an array with a single row. But apparently the training data
array has to have as many columns as the mean_ array and I'm getting errors.
Can my problem be solved within the scikit-learn package?
Kind regards,
Adelina Grant
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