cleaned up

Author: leokim-l

boqa-core/src/main/java/org/p2gx/boqa/core/algorithm/AlgorithmParameters.java

@@ -5,6 +5,8 @@
  * Alpha and beta are fixed (across all diseases and patients) for a single run.
  *
  * {@code ALPHA} represents the probability of a false positive, {@code BETA} that of a false negative.
+ * {@code TEMPERATURE} (or T) can be used to temper the posterior distribution. The posterior is effectively raised
+ * to the 1/T-th power, or, equivalently, this can be seen as taking the T-th root.
  */
 public final class AlgorithmParameters {
     private static final double DEFAULT_ALPHA = 1.0 / 19077;
@@ -43,8 +45,9 @@ public static AlgorithmParameters create() {
      *
      * @param alpha the alpha parameter (false positive probability), or null for default
      * @param beta the beta parameter (false negative probability), or null for default
+     * @param temperature the inverse of the exponent for the posterior, or null for default
      * @return AlgorithmParameters instance
-     * @throws IllegalArgumentException if alpha or beta is not in the range (0, 1)
+     * @throws IllegalArgumentException if alpha or beta is not in the range (0, 1) and temperature is less than 1
      */
     public static AlgorithmParameters create(Double alpha, Double beta, Double temperature) {
         double a = (alpha != null) ? alpha : DEFAULT_ALPHA;

boqa-core/src/main/java/org/p2gx/boqa/core/analysis/BoqaPatientAnalyzer.java

@@ -99,6 +99,7 @@ public static BoqaAnalysisResult computeBoqaResults(
                     .mapToDouble(r -> Math.exp(r.boqaScore() - maxLogP))
                     .sum();
         } else {
+            // Alternative to Standard BOQA
             // Non-unit temperature normalizes trivially by largest score, which is already present below
             normalizationFactor = 1.0;
         }
@@ -118,11 +119,7 @@ public static BoqaAnalysisResult computeBoqaResults(
      * <p>
      * log(P) = [fp × log(α) + fn × log(β) + tn × log(1-α)  + tp × log(1-β)] / T
      * </p>
-     * <p>
-     * P = α<sup>fpBoqaCount</sup> × β<sup>fpBoqaCount</sup> ×
-     * (1-α)<sup>fnBoqaCount</sup> × (1-β)<sup>tpBoqaCount</sup>
-     *  </pre>
-     * @param params  alpha, beta, log(alpha), log(beta) etc.
+     * @param params  alpha, beta, log(alpha), log(beta), termperature T etc.
      * @param counts The {@link BoqaCounts} for a query and a disease.
      * @return The un-normalized BOQA log probability score.
      */