Class BStat

src/com/winvector/variables/BObserver.java:62–125

	 * per-variable statistics
	 */
	public final class BStat {
		// def
		public final VariableMapping oldAdaption;
		// raw stats
		public double sumTotal = 0.0;
		public final double[] totalByCategory;
		public final Map<String,BLevelRow> levelStats = new HashMap<String,BLevelRow>(1000);

		public BStat(final VariableMapping oldAdaption) {
			this.oldAdaption = oldAdaption;
			totalByCategory = new double[noutcomes];
		}
		
		public BLevelRow newRow() {
			return new BLevelRow();
		}
		
		/**
		 * 
		 * @param outcome training outcome
		 * @param level level of variable
		 * @param pred current model predictions
		 * @param correct category
		 * @paream weight>0.0
		 */
		public void observe(final String outcome, final String level, final double[] pred, final int category, final double weight) {
			sumTotal += weight;
			totalByCategory[category] += weight;
			BLevelRow blevelRow = levelStats.get(level);
			if(null==blevelRow) {
				blevelRow = new BLevelRow();
				levelStats.put(level,blevelRow);
			}
			blevelRow.totalForLevel += weight;
			blevelRow.totalByCorrectCategory[category] += weight;
			for(int i=0;i<noutcomes;++i) {
				blevelRow.sumPFixedCategory[i] += weight*pred[i];
				if(category==i) {
					blevelRow.sumRunFixedCategory[i] += weight*1.0/Math.max(pred[i],smallValue);
				} else {
					blevelRow.sumRunFixedCategory[i] += weight*-1.0/Math.max(1.0-pred[i],smallValue);
				}
			}
			blevelRow.sumRunCorrectCategory[category] += weight*1.0/Math.max(pred[category],smallValue);
			blevelRow.sumPCorrectCategory[category] += weight*pred[category];
		}

		public void observe(final BStat o) {
			sumTotal += o.sumTotal;
			sumLeft(totalByCategory,o.totalByCategory);
			for(final Map.Entry<String,BLevelRow> op: o.levelStats.entrySet()) {
				final String key = op.getKey();
				final BLevelRow ov = op.getValue();
				BLevelRow val = levelStats.get(key);
				if(val==null) {
					val = new BLevelRow();
					levelStats.put(key,val);
				}