Merge branch 'hmc-clock' of https://github.com/beast-dev/beast-mcmc i…

…nto hmc-clock

src/dr/evomodel/speciation/EfficientSpeciationLikelihood.java

@@ -237,9 +237,11 @@ public void addTrait(TreeTrait trait) {
 
     public String getReport() {
         String message = super.getReport();
-        message += "\n";
-        // add likelihood calculation time
-        message += "Likelihood calculation time is " + likelihoodTime / likelihoodCounts + " nanoseconds.\n";
+        if (MEASURE_RUN_TIME) {
+            message += "\n";
+            // add likelihood calculation time
+            message += "Likelihood calculation time is " + likelihoodTime / likelihoodCounts + " nanoseconds.\n";
+        }
         return message;
     }
 }

src/dr/evomodel/speciation/EfficientSpeciationLikelihoodGradient.java

@@ -35,6 +35,8 @@
 import dr.util.Timer;
 import dr.xml.Reportable;
 
+import static dr.evomodel.speciation.CachedGradientDelegate.MEASURE_RUN_TIME;
+
 /**
  * @author Andy Magee
  * @author Yucai Shao
@@ -136,7 +138,7 @@ public LogColumn[] getColumns() {
     @Override
     public String getReport() {
         String message = GradientWrtParameterProvider.getReportAndCheckForError(this, 0.0, Double.POSITIVE_INFINITY, 1E-3);
-        if (gradientProvider instanceof CachedGradientDelegate) {
+        if (gradientProvider instanceof CachedGradientDelegate && MEASURE_RUN_TIME) {
             message += "\n";
             message += "Gradient calculation time is " + ((CachedGradientDelegate) gradientProvider).getGradientTime() + " nanoseconds.\n";
         }

src/dr/evomodel/speciation/MasBirthDeathSerialSamplingModel.java

@@ -0,0 +1,181 @@
+/*
+ * NewBirthDeathSerialSamplingModel.java
+ *
+ * Copyright (c) 2002-2015 Alexei Drummond, Andrew Rambaut and Marc Suchard
+ *
+ * This file is part of BEAST.
+ * See the NOTICE file distributed with this work for additional
+ * information regarding copyright ownership and licensing.
+ *
+ * BEAST is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU Lesser General Public License as
+ * published by the Free Software Foundation; either version 2
+ * of the License, or (at your option) any later version.
+ *
+ *  BEAST is distributed in the hope that it will be useful,
+ *  but WITHOUT ANY WARRANTY; without even the implied warranty of
+ *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ *  GNU Lesser General Public License for more details.
+ *
+ * You should have received a copy of the GNU Lesser General Public
+ * License along with BEAST; if not, write to the
+ * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
+ * Boston, MA  02110-1301  USA
+ */
+
+package dr.evomodel.speciation;
+
+import dr.inference.model.Parameter;
+
+public class MasBirthDeathSerialSamplingModel extends NewBirthDeathSerialSamplingModel {
+
+    public MasBirthDeathSerialSamplingModel(Parameter birthRate, Parameter deathRate, Parameter serialSamplingRate, Parameter treatmentProbability, Parameter samplingProbability, Parameter originTime, boolean condition, int numIntervals, double gridEnd, Type units) {
+        super(birthRate, deathRate, serialSamplingRate, treatmentProbability, samplingProbability, originTime, condition, numIntervals, gridEnd, units);
+    }
+
+    @Override
+    public final double processModelSegmentBreakPoint(int model, double intervalStart, double intervalEnd, int nLineages) {
+//        double lnL = nLineages * (logQ(model, intervalEnd) - logQ(model, intervalStart));
+        double lnL = nLineages * Math.log(Q(model, intervalEnd) / Q(model, intervalStart));
+        if ( samplingProbability.getValue(model + 1) > 0.0 && samplingProbability.getValue(model + 1) < 1.0) {
+            // Add in probability of un-sampled lineages
+            // We don't need this at t=0 because all lineages in the tree are sampled
+            // TODO: check if we're right about how many lineages are actually alive at this time. Are we inadvertently over-counting or under-counting due to samples added at this _exact_ time?
+            lnL += nLineages * Math.log(1.0 - samplingProbability.getValue(model + 1));
+        }
+        this.savedLogQ = Double.NaN;
+        return lnL;
+    }
+
+    final void accumulateGradientForInterval(final double[] gradient, final int currentModelSegment, final int nLineages,
+                                             final double[] partialQ_all_old, final double Q_Old,
+                                             final double[] partialQ_all_young, final double Q_young) {
+
+        for (int k = 0; k <= currentModelSegment; k++) {
+            for (int p = 0; p < 4; ++p) {
+                gradient[k * 5 + p] += nLineages * (partialQ_all_old[k * 4 + p] / Q_Old
+                        - partialQ_all_young[k * 4 + p] / Q_young);
+            }
+        }
+    }
+
+    final void accumulateGradientForSampling(double[] gradient, int currentModelSegment, double term1,
+                                             double[] intermediate) {
+
+        for (int k = 0; k <= currentModelSegment; k++) {
+            for (int p = 0; p < 4; ++p) {
+                gradient[k * 5 + p] += term1 * intermediate[k * 4 + p];
+            }
+        }
+
+    }
+
+    final void dPCompute(int model, double t, double intervalStart, double eAt, double[] dP, double[] dG2) {
+
+        double G1 = g1(eAt);
+
+        double term1 = -A / lambda * ((1 - B) * (eAt - 1) + G1) / (G1 * G1);
+
+        for (int k = 0; k  < model; k ++) {
+            for (int p = 0; p < 4; p++) {
+                dP[k * 4 + p] = term1 * dB[k * 4 + p];
+            }
+        }
+
+        for (int p = 0; p < 3; ++p) {
+            double term2 = eAt * (1 + B) * dA[p] * (t - intervalStart) + (eAt - 1) * dB[model * 4 + p];
+            dG2[p] = dA[p] - 2 * (G1 * (dA[p] * (1 - B) - dB[model * 4 + p] * A) - (1 - B) * term2 * A) / (G1 * G1);
+        }
+
+        double G2 = g2(G1);
+
+        dP[model * 4 + 0] = (-mu - psi - lambda * dG2[0] + G2) / (2 * lambda * lambda);
+        dP[model * 4 + 1] = (1 - dG2[1]) / (2 * lambda);
+        dP[model * 4 + 2] = (1 - dG2[2]) / (2 * lambda);
+        dP[model * 4 + 3] = -A / lambda * ((1 - B) * (eAt - 1) + G1) * dB[model * 4 + 3] / (G1 * G1);
+    }
+
+
+    final void dQCompute(int model, double t, double[] dQ, double eAt) {
+
+        double dwell = t - modelStartTimes[model];
+        double G1 = g1(eAt);
+
+        double term1 = 8 * eAt;
+        double term2 = G1 / 2 - eAt * (1 + B);
+        double term3 = eAt - 1;
+        double term4 = G1 * G1 * G1;
+        double term5 = -term1 * term3 / term4;
+
+        for (int k = 0; k < model; ++k) {
+            dQ[k * 4 + 0] = term5 * dB[k * 4 + 0];
+            dQ[k * 4 + 1] = term5 * dB[k * 4 + 1];
+            dQ[k * 4 + 2] = term5 * dB[k * 4 + 2];
+            dQ[k * 4 + 3] = term5 * dB[k * 4 + 3];
+        }
+
+        double term6 = term1 / term4;
+        double term7 = dwell * term2;
+
+        dQ[model * 4 + 0] = term6 * (dA[0] * term7 - dB[model * 4 + 0] * term3);
+        dQ[model * 4 + 1] = term6 * (dA[1] * term7 - dB[model * 4 + 1] * term3);
+        dQ[model * 4 + 2] = term6 * (dA[2] * term7 - dB[model * 4 + 2] * term3);
+        dQ[model * 4 + 3] = term5 * dB[model * 4 + 3];
+    }
+
+
+    final double Q(int model, double time) {
+        double At = A * (time - modelStartTimes[model]);
+        double eAt = Math.exp(At);
+        double sqrtDenominator = g1(eAt);
+        return eAt / (sqrtDenominator * sqrtDenominator);
+    }
+
+    final double logQ(int model, double time) {
+        double At = A * (time - modelStartTimes[model]);
+        double eAt = Math.exp(At);
+        double sqrtDenominator = g1(eAt);
+        return At - 2 * Math.log(sqrtDenominator); // TODO log4 (additive constant) is not needed since we always see logQ(a) - logQ(b)
+    }
+
+    @Override
+    public double processInterval(int model, double tYoung, double tOld, int nLineages) {
+        double logQ_young;
+        double logQ_old = Q(model, tOld);
+        if (!Double.isNaN(this.savedLogQ)) {
+            logQ_young = this.savedLogQ;
+        } else {
+            logQ_young = Q(model, tYoung);
+        }
+        this.savedLogQ = logQ_old;
+        return nLineages * Math.log(logQ_old / logQ_young);
+    }
+
+    @Override
+    public double processSampling(int model, double tOld) {
+
+        double logSampProb;
+
+        boolean sampleIsAtEventTime = tOld == modelStartTimes[model];
+        boolean samplesTakenAtEventTime = rho > 0;
+
+        if (sampleIsAtEventTime && samplesTakenAtEventTime) {
+            logSampProb = Math.log(rho);
+            if (model > 0) {
+                logSampProb += Math.log(r + ((1.0 - r) * previousP));
+            }
+        } else {
+            double logPsi = Math.log(psi);
+            logSampProb = logPsi + Math.log(r + (1.0 - r) * p(model,tOld));
+        }
+
+        return logSampProb;
+    }
+}
+
+/*
+ * Notes on inlining:
+ *  https://www.baeldung.com/jvm-method-inlining#:~:text=Essentially%2C%20the%20JIT%20compiler%20tries,times%20we%20invoke%20the%20method.
+ *  https://miuv.blog/2018/02/25/jit-optimizations-method-inlining/
+ * static, private, final 
+ */