コード例 #1
0
        // Implementation for cached computation of weighted output
        // (used by smoothing)
        public double GetWeightedOutput()
        {
            if (!double.IsNaN(_weightedOutput))
            {
                return(_weightedOutput);
            }
            if (NodeIndex < 0)
            {
                return(Tree.GetOutput(~NodeIndex));
            }

            int lteDocCount = LteNode.GetDocumentCount();
            int gtCount     = GtNode.GetDocumentCount();

            _weightedOutput = (lteDocCount * LteNode.GetWeightedOutput() + gtCount * GtNode.GetWeightedOutput())
                              / (lteDocCount + gtCount);
            return(_weightedOutput);
        }
コード例 #2
0
        /// <summary>
        /// Regularize a regression tree with smoothing paramter alpha.
        /// </summary>
        protected virtual void SmoothTree(InternalRegressionTree tree, double smoothing)
        {
            if (smoothing == 0.0)
            {
                return;
            }

            //Create recursive structure of the tree starting from root node
            var regularizer = new RecursiveRegressionTree(tree, TreeLearner.Partitioning, 0);

            //Perform bottom-up computation of weighted interior node output
            double rootNodeOutput = regularizer.GetWeightedOutput();

            //followed by top-down propagation of parent's output value
            regularizer.SmoothLeafOutputs(rootNodeOutput, smoothing);
        }