Package gov.nih.mipav.model.algorithms

Class StochasticForests.Forest

java.lang.Object
gov.nih.mipav.model.algorithms.StochasticForests.Forest
Direct Known Subclasses:
StochasticForests.ForestClassification, StochasticForests.ForestProbability, StochasticForests.ForestRegression, StochasticForests.ForestSurvival
Enclosing class:
StochasticForests
private abstract class StochasticForests.Forest extends Object

  • Field Details

    • random

      protected Random random

    • verbose_out

      protected boolean verbose_out

    • num_trees

      protected int num_trees

    • mtry

      protected int mtry

    • min_node_size

      protected int min_node_size

    • num_variables

      protected int num_variables

    • num_independent_variables

      protected int num_independent_variables

    • seed

      protected long seed

    • dependent_varID

      protected int dependent_varID

    • num_samples

      protected int num_samples

    • prediction_mode

      protected boolean prediction_mode

    • memory_mode

      protected StochasticForests.MemoryMode memory_mode

    • sample_with_replacement

      protected boolean sample_with_replacement

    • memory_saving_splitting

      protected boolean memory_saving_splitting

    • splitrule

      protected StochasticForests.SplitRule splitrule

    • predict_all

      protected boolean predict_all

    • keep_inbag

      protected boolean keep_inbag

    • sample_fraction

      protected Vector<Double> sample_fraction

    • holdout

      protected boolean holdout

    • prediction_type

      protected StochasticForests.PredictionType prediction_type

    • num_random_splits

      protected int num_random_splits

    • alpha

      protected double alpha

    • minprop

      protected double minprop

    • num_threads

      protected int num_threads

    • thread_ranges

      protected Vector<Integer> thread_ranges

    • trees

      protected Vector<StochasticForests.Tree> trees

    • data

      protected StochasticForests.Data data

    • predictions

      protected Vector<Vector<Vector<Double>>> predictions

    • overall_prediction_error

      protected double overall_prediction_error

    • deterministic_varIDs

      protected Vector<Integer> deterministic_varIDs

    • split_select_varIDs

      protected Vector<Integer> split_select_varIDs

    • split_select_weights

      protected Vector<Vector<Double>> split_select_weights

    • case_weights

      protected Vector<Double> case_weights

    • output_prefix

      protected String output_prefix

    • importance_mode

      protected StochasticForests.ImportanceMode importance_mode

    • variable_importance

      protected Vector<Double> variable_importance

    • progress

      protected int progress

    • mutex

      protected final Lock mutex

    • condition_variable

      private final Condition condition_variable

  • Constructor Details

    • Forest

      public Forest()

  • Method Details

    • growInternal

      protected abstract void growInternal()

    • allocatePredictMemory

      protected abstract void allocatePredictMemory()

    • predictInternal

      protected abstract void predictInternal(int sample_idx)

    • computePredictionErrorInternal

      protected abstract void computePredictionErrorInternal()

    • loadFromFileInternal

      protected abstract void loadFromFileInternal(BufferedReader br)

    • initInternal

      public abstract void initInternal(String status_variable_name)

    • writeOutputInternal

      public abstract void writeOutputInternal()

    • writeConfusionFile

      public abstract void writeConfusionFile()

    • writePredictionFile

      public abstract void writePredictionFile()

    • saveToFileInternal

      public abstract void saveToFileInternal(BufferedWriter bw)

    • getChildNodeIDs

      public Vector<Vector<Vector<Integer>>> getChildNodeIDs()

    • getSplitVarIDs

      public Vector<Vector<Integer>> getSplitVarIDs()

    • getSplitValues

      public Vector<Vector<Double>> getSplitValues()

    • getVariableImportance

      public Vector<Double> getVariableImportance()

    • getOverallPredictionError

      public double getOverallPredictionError()

    • getPredictions

      public Vector<Vector<Vector<Double>>> getPredictions()

    • getDependentVarId

      public int getDependentVarId()

    • getNumTrees

      public int getNumTrees()

    • getMtry

      public int getMtry()

    • getMinNodeSize

      public int getMinNodeSize()

    • getNumIndependentVariables

      public int getNumIndependentVariables()

    • getIsOrderedVariable

      public Vector<Boolean> getIsOrderedVariable()

    • getInbagCounts

      public Vector<Vector<Integer>> getInbagCounts()

    • dispose

      public void dispose()

    • initCpp

      public void initCpp(String dependent_variable_name, StochasticForests.MemoryMode memory_mode, String input_file, int mtry, String output_prefix, int num_trees, boolean verbose_out, long seed, int num_threads, String load_forest_filename, StochasticForests.ImportanceMode importance_mode, int min_node_size, String split_select_weights_file, Vector<String> always_split_variable_names, String status_variable_name, boolean sample_with_replacement, Vector<String> unordered_variable_names, boolean memory_saving_splitting, StochasticForests.SplitRule splitrule, String case_weights_file, boolean predict_all, double sample_fraction, double alpha, double minprop, boolean holdout, StochasticForests.PredictionType prediction_type, int num_random_splits)

    • initR

      public void initR(String dependent_variable_name, StochasticForests.Data input_data, int mtry, int num_trees, boolean verbose_out, long seed, int num_threads, StochasticForests.ImportanceMode importance_mode, int min_node_size, Vector<Vector<Double>> split_select_weights, Vector<String> always_split_variable_names, String status_variable_name, boolean prediction_mode, boolean sample_with_replacement, Vector<String> unordered_variable_names, boolean memory_saving_splitting, StochasticForests.SplitRule splitrule, Vector<Double> case_weights, boolean predict_all, boolean keep_inbag, Vector<Double> sample_fraction, double alpha, double minprop, boolean holdout, StochasticForests.PredictionType prediction_type, int num_random_splits)

    • init

      public void init(String dependent_variable_name, StochasticForests.MemoryMode memory_mode, StochasticForests.Data input_data, int mtry, String output_prefix, int num_trees, long seed, int num_threads, StochasticForests.ImportanceMode importance_mode, int min_node_size, String status_variable_name, boolean prediction_mode, boolean sample_with_replacement, Vector<String> unordered_variable_names, boolean memory_saving_splitting, StochasticForests.SplitRule splitrule, boolean predict_all, Vector<Double> sample_fraction, double alpha, double minprop, boolean holdout, StochasticForests.PredictionType prediction_type, int num_random_splits)

    • run

      public void run(boolean verbose)

    • writeOutput

      public void writeOutput()

    • writeImportanceFile

      public void writeImportanceFile()

    • saveToFile

      public void saveToFile()

    • grow

      public void grow()

    • predict

      public void predict()

    • computePredictionError

      public void computePredictionError()

    • computePermutationImportance

      public void computePermutationImportance()

    • loadFromFile

      public void loadFromFile(String filename)

    • setSplitWeightVector

      public void setSplitWeightVector(Vector<Vector<Double>> split_select_weights)

    • setAlwaysSplitVariables

      public void setAlwaysSplitVariables(Vector<String> always_split_variable_names)