Searched refs:sumProb (Results 1 – 12 of 12) sorted by relevance
17 public double sumProb; field in MultivariateGaussian164 sumProb = 1E-10; in maximizeGaussian()172 sumProb += prob; in maximizeGaussian()175 divideEqualsMu( sumProb ); in maximizeGaussian()177 final double shrinkageFactor = (SHRINKAGE * sumProb) / (SHRINKAGE + sumProb); in maximizeGaussian()202 mu[iii] = (sumProb * mu[iii] + SHRINKAGE * empiricalMu[iii]) / (sumProb + SHRINKAGE); in maximizeGaussian()206 hyperParameter_b = sumProb + SHRINKAGE; in maximizeGaussian()213 sumProb = 0.0; in evaluateFinalModelParameters()220 sumProb += prob; in evaluateFinalModelParameters()223 divideEqualsMu( sumProb ); in evaluateFinalModelParameters()[all …]
76 gaussian.sumProb = 1.0 / ((double) gaussians.size()); in initializeRandomModel()154 final double sumPK = gaussians.stream().mapToDouble(g -> g.sumProb).sum(); in normalizePMixtureLog10()157 …final double[] pGaussianLog10 = gaussians.stream().mapToDouble(g -> Math.log10(g.sumProb) - log10S… in normalizePMixtureLog10()
149 double sumProb = 0; in Train() local156 sumProb += probabilities[i]; in Train()159 if (sumProb == 0) in Train()169 if (sumProb > 0) in Train()170 mean /= sumProb; in Train()180 if (sumProb > 0) in Train()181 covariance /= sumProb; in Train()
32 double sumProb = 0; in Random() local36 if ((sumProb += probabilities[d][obs]) >= randObs) in Random()43 if (sumProb > 1.0) in Random()
109 double sumProb = 0; in Random() local112 sumProb += weights(g); in Random()113 if (gaussRand <= sumProb) in Random()
111 double sumProb = 0; in Random() local114 sumProb += weights(g); in Random()115 if (gaussRand <= sumProb) in Random()
274 float sumProb = 0; in yinProb() local296 sumProb += peakProb[tau]; in yinProb()309 if (sumProb > 0) { in yinProb()312 peakProb[i] = peakProb[i] / sumProb * peakProb[minInd]; in yinProb()
367 float sumProb = 0; in getRemainingFeatures() local376 sumProb += prob; in getRemainingFeatures()382 tempPitchProb[iFrame][iProb].second /= sumProb; in getRemainingFeatures()
183 double sumProb = 0; in generateExpectedAlignment() local200 sumProb += (double)patFre*log((double)aln->at(patID).frequency/(double)nsite); in generateExpectedAlignment()206 prob = fac - sumFac + sumProb; in generateExpectedAlignment()
4864 double sumProb = 0; in multinomialProb() local4876 prob = fac - sumFac + sumProb; in multinomialProb()4935 double sumProb = 0; in multinomialProb() local4940 sumProb += (double)patFre*log((double)at(patID).frequency/(double)alignLen); in multinomialProb()4942 prob = fac - sumFac + sumProb; in multinomialProb()5001 double sumProb = 0; in multinomialProb() local5006 sumProb += (double)patFre*log((double)at(patID).frequency/(double)alignLen); in multinomialProb()5008 prob = fac - sumFac + sumProb; in multinomialProb()5024 double sumProb = 0; in multinomialProb() local5029 sumProb += (double)patFre*log((double)at(patID).frequency/(double)alignLen); in multinomialProb()[all …]
291 float sumProb = 0.0; in mapProfile() local293 sumProb += profile[l * PROFILE_AA_SIZE + aa]; in mapProfile()295 if(sumProb > 0.9){ in mapProfile()
363 double sumProb = 0; variable366 sumProb += transition(st, states[n][t - 1]);367 if (sumProb >= state)