| public static createNurseryExample ( double &inputs, int &outputs, int first ) : Accord.MachineLearning.DecisionTrees.DecisionTree | ||
| inputs | double | |
| outputs | int | |
| first | int | |
| return | Accord.MachineLearning.DecisionTrees.DecisionTree | |
public void RunTest3()
{
Accord.Math.Random.Generator.Seed = 0;
double[][] inputs;
int[] outputs;
int training = 6000;
DecisionTree tree = ReducedErrorPruningTest.createNurseryExample(out inputs, out outputs, training);
double[] actual = new double[10];
for (int i = 0; i < actual.Length; i++)
{
int nodeCount2;
repeat(inputs, outputs, tree, training, i * 0.1, out nodeCount2);
actual[i] = nodeCount2;
}
double[] expected = { 447, 424, 410, 402, 376, 362, 354, 348, 336, 322 };
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i]);
}
}
public void RunTest3()
{
double[][] inputs;
int[] outputs;
int training = 6000;
DecisionTree tree = ReducedErrorPruningTest.createNurseryExample(out inputs, out outputs, training);
double[] actual = new double[10];
for (int i = 0; i < actual.Length; i++)
{
int nodeCount2;
repeat(inputs, outputs, tree, training, i * 0.1, out nodeCount2);
actual[i] = nodeCount2;
}
double[] expected = { 447, 193, 145, 140, 124, 117, 109, 103, 95, 87 };
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(expected[i], actual[i]);
}
}
public void RunTest()
{
Accord.Math.Random.Generator.Seed = 0;
double[][] inputs;
int[] outputs;
int trainingSamplesCount = 6000;
DecisionTree tree = ReducedErrorPruningTest.createNurseryExample(out inputs, out outputs, trainingSamplesCount);
int nodeCount = 0;
foreach (var node in tree)
{
nodeCount++;
}
var pruningInputs = inputs.Submatrix(trainingSamplesCount, inputs.Length - 1);
var pruningOutputs = outputs.Submatrix(trainingSamplesCount, 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(0.28922413793103446, error, 5e-4);
Assert.AreEqual(447, nodeCount);
Assert.AreEqual(424, nodeCount2);
}
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);
}
