synchronized void recordTiming(long timeNanos) { meanTimeNanos.increment(timeNanos); stdevTimeNanos.increment(timeNanos); }
/** * Mean/sigma linking coefficients are computed from the mean and standard deviation of item difficulty. * The summary statistics are computed in a storeless manner. This method allows for the incremental * update to item difficulty summary statistics by combining them with other summary statistics. * * @param mean item difficulty mean. * @param sd item difficulty standard deviation. */ public void incrementMeanSigma(Mean mean, StandardDeviation sd){//TODO check for correctness mean.increment(difficulty); sd.increment(difficulty); }
public void incrementMeanSigma(Mean mean, StandardDeviation sd){ for(int i=0;i<ncatM1;i++){ mean.increment(difficulty-threshold[i]); sd.increment(difficulty-threshold[i]); } }
public void incrementMeanSigma(Mean mean, StandardDeviation sd){ for(int i=0;i<ncatM1;i++){ mean.increment(difficulty-threshold[i]); sd.increment(difficulty-threshold[i]); } }
public void incrementMeanSigma(Mean mean, StandardDeviation sd){ for(int i=0;i<maxCategory;i++){ mean.increment(step[i]); sd.increment(step[i]); } }
public void increment(double score){ stats.addValue(score); stdDev.increment(score); }
public void increment(double score){ stats.addValue(score); stdDev.increment(score); }
public void incrementMeanSigma(Mean mean, StandardDeviation sd){ for(int i=1;i<ncat;i++){//Start at 1 because first step is fixed to zero. Do not count it here. mean.increment(step[i]); sd.increment(step[i]); } }
/** * Update the summary statistics with a new value. * * @param x a data value. */ public void increment(double x){ min.increment(x); max.increment(x); sd.increment(x); n++; }
public void increment(double score, int response, double freqWeight){ Double d = Double.valueOf(score); NumericItemResponseSummary irs = summaryTreeMap.get(d); if(null==irs){ irs = new NumericItemResponseSummary(variableName); summaryTreeMap.put(d, irs); } irs.increment(response, freqWeight); mean.increment(score); sd.increment(score); pearsonCorrelation.increment(score, irs.getScoreAt(Double.valueOf(response).toString())); }
public void increment(double score, double response, double freqWeight){ Double d = Double.valueOf(score); NumericItemResponseSummary irs = summaryTreeMap.get(d); if(null==irs){ irs = new NumericItemResponseSummary(variableName); summaryTreeMap.put(d, irs); } irs.increment(response, freqWeight); mean.increment(score); sd.increment(score); pearsonCorrelation.increment(score, irs.getScoreAt(Double.valueOf(response).toString())); }
public void increment(double score, double response, double freqWeight){ Double d = Double.valueOf(score); TextItemResponseSummary irs = summaryTreeMap.get(d); if(null==irs){ irs = new TextItemResponseSummary(variableName); summaryTreeMap.put(d, irs); } irs.increment(response, freqWeight); mean.increment(score); sd.increment(score); pearsonCorrelation.increment(score, irs.getScoreAt(Double.valueOf(response).toString())); }
public void increment(double score, int response, double freqWeight){ Double d = Double.valueOf(score); TextItemResponseSummary irs = summaryTreeMap.get(d); if(null==irs){ irs = new TextItemResponseSummary(variableName); summaryTreeMap.put(d, irs); } irs.increment(response, freqWeight); mean.increment(score); sd.increment(score); pearsonCorrelation.increment(score, irs.getScoreAt(Double.valueOf(response).toString())); }
public void increment(double score, String response, double freqWeight){ Double d = Double.valueOf(score); TextItemResponseSummary irs = summaryTreeMap.get(d); if(null==irs){ irs = new TextItemResponseSummary(variableName); summaryTreeMap.put(d, irs); } irs.increment(response, freqWeight); mean.increment(score); sd.increment(score); pearsonCorrelation.increment(score, irs.getScoreAt(response)); }
public void increment(double score, String response, double freqWeight){ Double d = Double.valueOf(score); NumericItemResponseSummary irs = summaryTreeMap.get(d); if(null==irs){ irs = new NumericItemResponseSummary(variableName); summaryTreeMap.put(d, irs); } irs.increment(response, freqWeight); mean.increment(score); sd.increment(score); pearsonCorrelation.increment(score, irs.getScoreAt(response)); }
static private Double evaluate(Collection<?> values, boolean biasCorrected){ StandardDeviation statistic = new StandardDeviation(); statistic.setBiasCorrected(biasCorrected); for(Object value : values){ Number number = (Number)TypeUtil.parseOrCast(DataType.DOUBLE, value); statistic.increment(number.doubleValue()); } return statistic.getResult(); } }
static private Double evaluate(Collection<?> values, boolean biasCorrected){ StandardDeviation statistic = new StandardDeviation(); statistic.setBiasCorrected(biasCorrected); for(Object value : values){ Number number = (Number)TypeUtil.parseOrCast(DataType.DOUBLE, value); statistic.increment(number.doubleValue()); } return statistic.getResult(); } }
static private Double evaluate(Collection<?> values, boolean biasCorrected){ StandardDeviation statistic = new StandardDeviation(); statistic.setBiasCorrected(biasCorrected); for(Object value : values){ Double doubleValue = (Double)TypeUtil.parseOrCast(DataType.DOUBLE, value); statistic.increment(doubleValue); } return statistic.getResult(); } }
public void increment(double x){ min.increment(x); max.increment(x); m.increment(x); sd.increment(x); skew.increment(x); kurt.increment(x); }