Examples of computeMarginals()

Examples of cc.mallet.grmm.inference.Inferencer.computeMarginals()

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  {
    FactorGraph fg = createBoltzmannChain (5);
    Assignment assn = fg.sampleContinuousVars (new Randoms (23423));
    FactorGraph sliced = (FactorGraph) fg.slice (assn);
    Inferencer bp = new TRP ();
    bp.computeMarginals (sliced);

    try {
      bp.lookupMarginal (new Variable (2));
      fail ("Expected exception");
    } catch (IllegalArgumentException e) {
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Examples of cc.mallet.grmm.inference.Inferencer.computeMarginals()

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    double total = 0;
    Inferencer inf = acrf.getInferencer ();
    for (int i = 0; i < trainingList.size (); i++) {
      Instance inst = trainingList.get (i);
      ACRF.UnrolledGraph unrolled = acrf.unroll (inst);
      inf.computeMarginals (unrolled);
      double lik = inf.lookupLogJoint (unrolled.getAssignment ());
      total += lik;
      logger.info ("...instance "+i+" likelihood = "+lik);
    }
    logger.info ("Unregularized joint likelihood = "+total);
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Examples of cc.mallet.grmm.inference.Inferencer.computeMarginals()

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          System.err.println ("WARNING: FactorGraph for instance " + instance.getName () + " : no factors.");
          continue;
        }

        Inferencer inf = acrf.getInferencer ();
        inf.computeMarginals (unrolled);

        Assignment target = unrolled.getAssignment ();
        for (Iterator it = unrolled.unrolledVarSetIterator (); it.hasNext ();) {
          ACRF.UnrolledVarSet vs = (ACRF.UnrolledVarSet) it.next ();
          Factor marg = inf.lookupMarginal (vs);
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Examples of cc.mallet.grmm.inference.Inferencer.computeMarginals()

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    }

    // STEP 2: Compute marginals

    Inferencer inf = new JunctionTreeInferencer ();
    inf.computeMarginals (mdl);

    // STEP 3: Collect the results
    //   We'll just print them out

    for (int varnum = 0; varnum < allVars.length; varnum++) {
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Examples of cc.mallet.grmm.inference.Inferencer.computeMarginals()

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    }

    // STEP 2: Compute marginals

    Inferencer inf = new JunctionTreeInferencer ();
    inf.computeMarginals (mdl);

    // STEP 3: Collect the results
    //   We'll just print them out

    for (int varnum = 0; varnum < allVars.length; varnum++) {
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Examples of cc.mallet.grmm.inference.Inferencer.computeMarginals()

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  {
    FactorGraph fg = createBoltzmannChain (5);
    Assignment assn = fg.sampleContinuousVars (new Randoms (23423));
    FactorGraph sliced = (FactorGraph) fg.slice (assn);
    Inferencer bp = new TRP ();
    bp.computeMarginals (sliced);

    try {
      bp.lookupMarginal (new Variable (2));
      fail ("Expected exception");
    } catch (IllegalArgumentException e) {
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Examples of cc.mallet.grmm.inference.JunctionTreeInferencer.computeMarginals()

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   *  If the model is intractable, then this method won't return a number anytime soon.
   */
  public static double entropy (FactorGraph mdl)
  {
    JunctionTreeInferencer inf = new JunctionTreeInferencer ();
    inf.computeMarginals (mdl);
    JunctionTree jt = inf.lookupJunctionTree ();
    return jt.entropy ();
  }

  /**
 
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Examples of cc.mallet.grmm.inference.JunctionTreeInferencer.computeMarginals()

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   * @return KL(mdl1||mdl2)
   */
  public static double KL (FactorGraph mdl1, FactorGraph mdl2)
  {
    JunctionTreeInferencer inf1 = new JunctionTreeInferencer ();
    inf1.computeMarginals (mdl1);
    JunctionTree jt1 = inf1.lookupJunctionTree ();

    JunctionTreeInferencer inf2 = new JunctionTreeInferencer ();
    inf2.computeMarginals (mdl2);
    JunctionTree jt2 = inf2.lookupJunctionTree ();
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Examples of cc.mallet.grmm.inference.JunctionTreeInferencer.computeMarginals()

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    JunctionTreeInferencer inf1 = new JunctionTreeInferencer ();
    inf1.computeMarginals (mdl1);
    JunctionTree jt1 = inf1.lookupJunctionTree ();

    JunctionTreeInferencer inf2 = new JunctionTreeInferencer ();
    inf2.computeMarginals (mdl2);
    JunctionTree jt2 = inf2.lookupJunctionTree ();

    double entropy = jt1.entropy ();
    double energy = 0;
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Examples of cc.mallet.grmm.inference.JunctionTreeInferencer.computeMarginals()

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    }

    // STEP 2: Compute marginals

    Inferencer inf = new JunctionTreeInferencer ();
    inf.computeMarginals (mdl);

    // STEP 3: Collect the results
    //   We'll just print them out

    for (int varnum = 0; varnum < allVars.length; varnum++) {
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