Accord.MachineLearning.DecisionTrees.Pruning.ErrorBasedPruning.Run C# (CSharp) Method

        public double Run()
        {
            // Traverse the tree in bottom-up (post-order) order
            foreach (DecisionNode node in tree.Traverse(DecisionTreeTraversal.PostOrder))
            {
                if (node.IsLeaf)
                    continue;

                if (compute(node))
                    break;
            }

            return computeError();
        }

        public void RunTest()
        {
            double[][] inputs;
            int[] outputs;

            int training = 6000;
            DecisionTree tree = ReducedErrorPruningTest.createNurseryExample(out inputs, out outputs, training);

            int nodeCount = 0;
            foreach (var node in tree)
                nodeCount++;

            var pruningInputs = inputs.Submatrix(training, inputs.Length - 1);
            var pruningOutputs = outputs.Submatrix(training, inputs.Length - 1);
            ErrorBasedPruning prune = new ErrorBasedPruning(tree, pruningInputs, pruningOutputs);

            prune.Threshold = 0.1;

            double lastError, error = Double.PositiveInfinity;
            do
            {
                lastError = error;
                error = prune.Run();
            } while (error < lastError);

            int nodeCount2 = 0;
            foreach (var node in tree)
                nodeCount2++;

            Assert.AreEqual(0.25459770114942532, error);
            Assert.AreEqual(447, nodeCount);
            Assert.AreEqual(193, nodeCount2);
        }