コード例 #1
0
ファイル: BucketTree.java プロジェクト: cofinoa/javabayes
  /*
   * Recover the maximizing variables going back through the maximizing
   * bucket_tree; the variables are returned as an array of markers
   * (non-explanation variables get INVALID_INDEX).
   */
  private int[] backward_maximization() {
    int i, j;
    int bi = bucket_tree.length - 1;
    DiscreteFunction back_df;
    Bucket b = bucket_tree[bi];

    // If there are no explanation variables in the BayesNet, return null
    if (b.backward_pointers == null) return (null);

    // Initialize the markers for backward pointers with INVALID_INDEX
    int backward_markers[] = new int[bn.number_variables()];
    for (i = 0; i < backward_markers.length; i++) backward_markers[i] = BayesNet.INVALID_INDEX;

    // Initialize the marker for the last bucket
    backward_markers[b.variable.get_index()] = (int) (b.backward_pointers.get_value(0) + 0.5);

    // Go backwards through the bucket_tree
    for (i = (bi - 1); i >= 0; i--) {
      if (!bucket_tree[i].is_explanation()) break;
      back_df = bucket_tree[i].backward_pointers;
      // Skip null pointers (caused by evidence)
      if (back_df == null) continue;
      // Special treatment for bucket with only one value,
      // since it can be a bucket with only the bucket variable left
      if (back_df.number_values() == 1) {
        backward_markers[bucket_tree[i].variable.get_index()] = (int) (back_df.get_value(0) + 0.5);
        continue;
      }
      // Process the bucket
      j = back_df.get_position_from_indexes(bn.get_probability_variables(), backward_markers);
      backward_markers[bucket_tree[i].variable.get_index()] = (int) (back_df.get_value(j) + 0.5);
    }

    return (backward_markers);
  }
コード例 #2
0
ファイル: BucketTree.java プロジェクト: cofinoa/javabayes
  /*
   * Eliminates all variables defined as evidence. The order of the variables
   * that are not eliminated is the same order in the original function.
   */
  private ProbabilityFunction check_evidence(ProbabilityFunction pf) {
    int i, j, k, v, aux_i;
    boolean markers[] = new boolean[bn.number_variables()];
    int n = build_evidence_markers(pf, markers);

    // Handle special cases
    if (n == 0) return (null); // No variable remains
    if (n == pf.number_variables()) return (pf); // No relevant evidence

    // Calculate necessary quantities in such a
    // way that the order of variables in the original
    // function is not altered.
    int joined_indexes[] = new int[n];
    for (i = 0, j = 0, v = 1; i < pf.number_variables(); i++) {
      aux_i = pf.get_variable(i).get_index();
      if (markers[aux_i] == true) {
        joined_indexes[j] = aux_i;
        j++;
        v *= bn.get_probability_variable(aux_i).number_values();
      }
    }

    // Create new function to be filled with joined variables
    ProbabilityFunction new_pf = new ProbabilityFunction(bn, n, v, null);
    for (i = 0; i < n; i++) new_pf.set_variable(i, bn.get_probability_variable(joined_indexes[i]));

    // Loop through the values
    check_evidence_loop(new_pf, pf);

    return (new_pf);
  }
コード例 #3
0
ファイル: BucketTree.java プロジェクト: cofinoa/javabayes
 /*
  * Build an array of markers. The marker for a variable is true only if the
  * variable is present in the input ProbabilityFunction pf and is not
  * observed. Even explanatory variables can be observed and taken as
  * evidence.
  */
 private int build_evidence_markers(ProbabilityFunction pf, boolean markers[]) {
   int i, n;
   // Initialize the markers
   for (i = 0; i < markers.length; i++) markers[i] = false;
   // Insert the variables of the ProbabilityFunction
   for (i = 0; i < pf.number_variables(); i++) markers[pf.get_index(i)] = true;
   // Take the evidence out
   for (i = 0; i < bn.number_variables(); i++) {
     if (bn.get_probability_variable(i).is_observed()) markers[i] = false;
   }
   // Count how many variables remain
   n = 0;
   for (i = 0; i < markers.length; i++) {
     if (markers[i] == true) n++;
   }
   return (n);
 }
コード例 #4
0
ファイル: BucketTree.java プロジェクト: cofinoa/javabayes
  /*
   * Obtain the values for the evidence plus function.
   */
  private void check_evidence_loop(ProbabilityFunction new_pf, ProbabilityFunction pf) {
    int i, j, k, l, m, p, last, current;
    int indexes[] = new int[bn.number_variables()];
    int value_lengths[] = new int[bn.number_variables()];

    for (i = 0; i < bn.number_variables(); i++) {
      indexes[i] = 0;
      value_lengths[i] = bn.get_probability_variable(i).number_values();
    }
    for (i = 0; i < bn.number_variables(); i++) {
      if (bn.get_probability_variable(i).is_observed()) {
        indexes[i] = bn.get_probability_variable(i).get_observed_index();
      }
    }
    last = new_pf.number_variables() - 1;
    for (i = 0; i < new_pf.number_values(); i++) {
      p = new_pf.get_position_from_indexes(indexes);
      new_pf.set_value(p, pf.evaluate(indexes));

      indexes[new_pf.get_index(last)]++;
      for (j = last; j > 0; j--) {
        current = new_pf.get_index(j);
        if (indexes[current] >= value_lengths[current]) {
          indexes[current] = 0;
          indexes[new_pf.get_index(j - 1)]++;
        } else break;
      }
    }
  }
コード例 #5
0
ファイル: BucketTree.java プロジェクト: cofinoa/javabayes
  /**
   * Constructor for BucketTree. Does the whole initialization; it should be the only method that
   * deals with symbolic names for variables.
   */
  public BucketTree(Ordering ord, boolean dpc) {
    int i, j, markers[];
    ProbabilityFunction pf;
    ProbabilityVariable pv;
    DiscreteVariable aux_pv;
    DiscreteFunction ut;
    String order[];

    do_produce_clusters = dpc;
    ordering = ord;

    // Collect information from the Ordering object.
    bn = ord.bn;
    explanation_status = ord.explanation_status;
    order = ord.order;

    // Indicate the first bucket to process
    active_bucket = 0;

    // Check the possibility that the query has an observed variable
    i = bn.index_of_variable(order[order.length - 1]);
    pv = bn.get_probability_variable(i);
    if (pv.is_observed() == true) {
      pf = transform_to_probability_function(bn, pv);
      bucket_tree = new Bucket[1];
      bucket_tree[0] = new Bucket(this, pv, do_produce_clusters);
      insert(pf);
    } else {
      // Initialize the bucket objects
      bucket_tree = new Bucket[order.length];
      for (i = 0; i < order.length; i++) {
        j = bn.index_of_variable(order[i]);
        bucket_tree[i] = new Bucket(this, bn.get_probability_variable(j), do_produce_clusters);
      }
      // Insert the probability functions into the bucket_tree;
      // first mark all functions that are actually going
      // into the bucket_tree.
      markers = new int[bn.number_variables()];
      for (i = 0; i < order.length; i++) markers[bn.index_of_variable(order[i])] = 1;
      // Now insert functions that are marked and non-null.
      for (i = 0; i < bn.number_probability_functions(); i++) {
        if (markers[bn.get_probability_function(i).get_index(0)] == 1) {
          pf = check_evidence(bn.get_probability_function(i));
          if (pf != null) {
            aux_pv = (bn.get_probability_function(i)).get_variable(0);
            insert(pf, !pf.memberOf(aux_pv.get_index()));
          }
        }
      }
      // Insert the utility_function.
      ut = bn.get_utility_function();
      if (ut != null) insert(ut);
    }
  }
コード例 #6
0
ファイル: BucketTree.java プロジェクト: cofinoa/javabayes
  /**
   * Distribute evidence in the BucketTree.
   *
   * @return true if successful; false if not.
   */
  public boolean distribute() {
    int i, j;
    boolean mark_non_conditioning[] = new boolean[bn.number_variables()];

    // First make sure the BucketTree has been reduced.
    if (unnormalized_result == null) reduce();
    // Second make sure there is more than one Bucket in the BucketTree.
    int last = bucket_tree.length - 1;
    if (last < 1) return (true);
    // Third, this method is used only if do_produce_clusters is true.
    if (do_produce_clusters == false) return (false);
    // Fourth, this method is use only if no explanatory variable was max'ed
    // out.
    if (backward_pointers != null) return (false);

    // Go through the Bucket objects, from bottom to top,
    // to compute the new separator and cluster for each bucket.
    for (i = (last - 1); i >= 0; i--) { // Start from (last-1); last does
      // not have child.
      // Check whether the Bucket has any valid content.
      if (bucket_tree[i].cluster == null) break;
      // Take the non-conditioning variables in a boolean array.
      for (j = 0; j < mark_non_conditioning.length; j++) mark_non_conditioning[j] = true;
      // OBS: The following piece of code will actually be less efficient
      // than
      // necessary. It will count as "conditioning" any variable in the
      // cluster
      // except the bucket variable. This will imply that some variables
      // in the
      // separator will be normalized over without need, and the separator
      // will
      // be larger than necessary.
      // OBS: this code was contributed by Wei Zhou ([email protected]),
      // who also detected the problem with the original code.
      // if (bucket_tree[i].cluster.number_variables() >
      // bucket_tree[i].non_conditioning_variables.size())
      for (j = 1; j < bucket_tree[i].cluster.number_variables(); j++) {
        mark_non_conditioning[(bucket_tree[i].cluster.get_variables())[j].get_index()] = false;
      }

      // The following piece of code does the right thing (compared to the
      // piece of code above): it selects the
      // minimum number of non-conditioning variables. To use this piece
      // of code, it will be necessary to create a "normalize" method that
      // normalizes with respect to a number of variables at at time.
      /*
       * for (j=0; j<bucket_tree[i].cluster.number_variables(); j++) {
       * mark_non_conditioning[
       * (bucket_tree[i].cluster.get_variables())[j].get_index() ] =
       * false; } for (Enumeration e =
       * bucket_tree[i].non_conditioning_variables.elements();
       * e.hasMoreElements(); ) { ProbabilityVariable pv =
       * (ProbabilityVariable)(e.nextElement());
       * mark_non_conditioning[pv.get_index() ] = true; }
       */

      // Update the separator.
      bucket_tree[i].separator =
          bucket_tree[i].child.cluster.sum_out(
              bn.get_probability_variables(), mark_non_conditioning);

      // Compute cluster using new separator (note that if separator
      // is null, the cluster had all variables already processed).
      if (bucket_tree[i].separator != null) {
        // OBS: the method here should normalize with respect to more
        // than one variable, to allow this algorithm to be more
        // efficient!
        bucket_tree[i].cluster.normalize_first();
        // Now combine the cluster and the separator.
        bucket_tree[i].cluster =
            bucket_tree[i].cluster.multiply(
                bn.get_probability_variables(), bucket_tree[i].separator);
      }

      // Mark the Bucket as DISTRIBUTED.
      bucket_tree[i].bucket_status = Bucket.DISTRIBUTED;
    }
    // Indicate success.
    return (true);
  }