[pymvpa] Sanity check

[Raúl Hernández]

Hi, I’m trying to evaluate on trial by trial basis how well a region can
predict the stimulus being presented to compare it with the participant’s
judgment of the stimulus. So I’m training the classifier with data from all
the trials on all the runs except by the one that I want to predict.


I’m getting really good classifications better than when I was predicting
one run using all the others. Supposedly it should be a little better as
I’m training with a little more data but I’m worried I’m doing something
wrong.


Could anyone let me know if I’m making some sort of mistake?


I know that there should be a more efficient way to do it but I wanted
something easy, this is my code:


predictions = [] #this is a vector that will contain the predictions of the
classifier

for i,dsTest in enumerate(ds): #go through all the trials on ds and
separate one to test

    clf = LinearCSVMC()

    fclf = FeatureSelectionClassifier(clf, fsel)

    dsTrain = []

    dsTrain.append(ds[0:i]) #separates the training data

    dsTrain.append(ds[i:-1])

    dsTrain = vstack(dsTrain) #stacks it

    fclf.train(dsTrain)

    predicted = fclf.predict(dsTest) #stores the prediction

    predictions.append(dsTest.targets == predicted) #checks whether the
prediction was correct

print np.mean(predictions) #checks the mean -accuracy of all predictions



I would really appreciate any feedback, thanks!
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