Thanks Nick! Thanks Matt! On Wed, Sep 17, 2014 at 10:48 AM, Nicholas Mitri <[email protected]> wrote:
> Hey David, > > It was a silly mistake on my part where I was comparing the prediction > with input from the previous time step. > The classifier is performing great now (log below). > > Nick > > Training over sequence ['B', 'A', 'B', 'D', 'D', 'D'] > Training over sequence ['C', 'A', 'B', 'D', 'E', 'D'] > > Testing over sequence ['B', 'A', 'B', 'D', 'D', 'D'] > Actual value / Multiple Predictions / Best Prediction = B / none / none > Actual value / Multiple Predictions / Best Prediction = A / A / A > Actual value / Multiple Predictions / Best Prediction = B / B / B > Actual value / Multiple Predictions / Best Prediction = D / D / D > Actual value / Multiple Predictions / Best Prediction = D / D / D > Actual value / Multiple Predictions / Best Prediction = D / D / D > > Testing over sequence ['C', 'A', 'B', 'D', 'E', 'D'] > Actual value / Multiple Predictions / Best Prediction = C / none / none > Actual value / Multiple Predictions / Best Prediction = A / A / A > Actual value / Multiple Predictions / Best Prediction = B / B / B > Actual value / Multiple Predictions / Best Prediction = D / D / D > Actual value / Multiple Predictions / Best Prediction = E / E / E > Actual value / Multiple Predictions / Best Prediction = D / D / D > > > RESULTS: > ------- > Prediction Accuracy from metric 1 = 100.00% with an average score of > 5.00/5.00 > Prediction Accuracy from metric 2 = 100.00% with an average score of > 5.00/5.00 > > > > On Sep 17, 2014, at 5:57 PM, cogmission1 . <[email protected]> > wrote: > > Hi Nicholas, > > Can you please describe the "shifting" you did with more detail? I was > curiously watching this too and would find it helpful. > > Thanks, > David > > On Wed, Sep 17, 2014 at 8:16 AM, Nicholas Mitri <[email protected]> > wrote: > >> Problem resolved! >> Shifting the predictions aligned them perfectly with those of the TP. >> >> On Sep 17, 2014, at 3:28 PM, Nicholas Mitri <[email protected]> wrote: >> >> > Hey all, >> > >> > Picking up on the questions in the ‘Understanding Hello_tp.py’ thread, >> I’m curious to know where in the code base I can find the cla classifier >> class being used. >> > Even in the “tp_likelihood_test.py” file, the classifier which is >> responsible for outputting likelihoods is completely ignored. >> > >> > My attempts at using the classifier class have been (terribly) >> unsuccessful with odd, seemingly haphazard predictions. Is the classifier >> still being used or has it been deprecated? >> > If it’s still being used, Can anyone point me to some code where the >> class is utilized so I can use that as a template? >> > >> > thank you, >> > Nick >> >> >> > > > -- > *We find it hard to hear what another is saying because of how loudly "who > one is", speaks...* > > > -- *We find it hard to hear what another is saying because of how loudly "who one is", speaks...*
