expectation[oi] = 0; for (ai = 0; ai < contexts[ci].length; ai++) { vectorIndex = indexOf(oi, contexts[ci][ai]); predValue = values != null ? values[ci][ai] : 1.0; expectation[oi] += predValue * x[vectorIndex]; empirical = outcomeList[ci] == oi ? 1 : 0; for (ai = 0; ai < contexts[ci].length; ai++) { vectorIndex = indexOf(oi, contexts[ci][ai]); predValue = values != null ? values[ci][ai] : 1.0; gradient[vectorIndex] +=
/** * Negative log-likelihood */ public double valueAt(double[] x) { if (x.length != dimension) throw new IllegalArgumentException( "x is invalid, its dimension is not equal to domain dimension."); int ci, oi, ai, vectorIndex, outcome; double predValue, logSumOfExps; double negLogLikelihood = 0; for (ci = 0; ci < numContexts; ci++) { for (oi = 0; oi < numOutcomes; oi++) { tempSums[oi] = 0; for (ai = 0; ai < contexts[ci].length; ai++) { vectorIndex = indexOf(oi, contexts[ci][ai]); predValue = values != null ? values[ci][ai] : 1.0; tempSums[oi] += predValue * x[vectorIndex]; } } logSumOfExps = ArrayMath.logSumOfExps(tempSums); outcome = outcomeList[ci]; negLogLikelihood -= (tempSums[outcome] - logSumOfExps) * numTimesEventsSeen[ci]; } return negLogLikelihood; }
expectation[oi] = 0; for (ai = 0; ai < contexts[ci].length; ai++) { vectorIndex = indexOf(oi, contexts[ci][ai]); predValue = values != null ? values[ci][ai] : 1.0; expectation[oi] += predValue * x[vectorIndex]; empirical = outcomeList[ci] == oi ? 1 : 0; for (ai = 0; ai < contexts[ci].length; ai++) { vectorIndex = indexOf(oi, contexts[ci][ai]); predValue = values != null ? values[ci][ai] : 1.0; gradient[vectorIndex] +=
expectation[oi] = 0; for (ai = 0; ai < contexts[ci].length; ai++) { vectorIndex = indexOf(oi, contexts[ci][ai]); predValue = values != null ? values[ci][ai] : 1.0; expectation[oi] += predValue * x[vectorIndex]; empirical = outcomeList[ci] == oi ? 1 : 0; for (ai = 0; ai < contexts[ci].length; ai++) { vectorIndex = indexOf(oi, contexts[ci][ai]); predValue = values != null ? values[ci][ai] : 1.0; gradient[vectorIndex] +=
/** * Negative log-likelihood */ public double valueAt(double[] x) { if (x.length != dimension) throw new IllegalArgumentException( "x is invalid, its dimension is not equal to domain dimension."); int ci, oi, ai, vectorIndex, outcome; double predValue, logSumOfExps; double negLogLikelihood = 0; for (ci = 0; ci < numContexts; ci++) { for (oi = 0; oi < numOutcomes; oi++) { tempSums[oi] = 0; for (ai = 0; ai < contexts[ci].length; ai++) { vectorIndex = indexOf(oi, contexts[ci][ai]); predValue = values != null ? values[ci][ai] : 1.0; tempSums[oi] += predValue * x[vectorIndex]; } } logSumOfExps = ArrayMath.logSumOfExps(tempSums); outcome = outcomeList[ci]; negLogLikelihood -= (tempSums[outcome] - logSumOfExps) * numTimesEventsSeen[ci]; } return negLogLikelihood; }
/** * Negative log-likelihood */ public double valueAt(double[] x) { if (x.length != dimension) throw new IllegalArgumentException( "x is invalid, its dimension is not equal to domain dimension."); int ci, oi, ai, vectorIndex, outcome; double predValue, logSumOfExps; double negLogLikelihood = 0; for (ci = 0; ci < numContexts; ci++) { for (oi = 0; oi < numOutcomes; oi++) { tempSums[oi] = 0; for (ai = 0; ai < contexts[ci].length; ai++) { vectorIndex = indexOf(oi, contexts[ci][ai]); predValue = values != null ? values[ci][ai] : 1.0; tempSums[oi] += predValue * x[vectorIndex]; } } logSumOfExps = ArrayMath.logSumOfExps(tempSums); outcome = outcomeList[ci]; negLogLikelihood -= (tempSums[outcome] - logSumOfExps) * numTimesEventsSeen[ci]; } return negLogLikelihood; }