----- Mail original -----
> De: "Adelina Grant" <adelina.gr...@yahoo.co.uk>
> À: scikit-learn-general@lists.sourceforge.net
> Envoyé: Mardi 14 Janvier 2014 17:34:22
> Objet: [Scikit-learn-general] Scoring multiple test samples together
> rather than separately
> 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.
Adelina, this single log proba is the sum of the samples log proba (the joint
probability is the product, assuming i.i.d. samples, hence the joint log proba
is the sum of the log-probabilities).
Bertrand
> 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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