public void sample_ratio_less_than_1()
{
// https://github.com/accord-net/framework/issues/576
string localPath = Path.Combine(NUnit.Framework.TestContext.CurrentContext.TestDirectory, "gh576");
Accord.Math.Random.Generator.Seed = 1;
var nursery = new DataSets.Nursery(localPath);
int[][] inputs = nursery.Instances;
int[] outputs = nursery.ClassLabels;
var teacher = new RandomForestLearning(nursery.VariableNames)
{
NumberOfTrees = 1,
SampleRatio = 0.5
};
teacher.ParallelOptions.MaxDegreeOfParallelism = 1;
var forest = teacher.Learn(inputs, outputs);
forest.ParallelOptions.MaxDegreeOfParallelism = 1;
int[] predicted = forest.Decide(inputs);
double error = new ZeroOneLoss(outputs).Loss(forest.Decide(inputs));
Assert.AreEqual(0.0023148148148148147d, error, 1e-10);
}
public void LargeRunTest()
{
string localPath = Path.Combine(NUnit.Framework.TestContext.CurrentContext.TestDirectory, "rf");
#region doc_nursery
// Fix random seed for reproducibility
Accord.Math.Random.Generator.Seed = 1;
// This example uses the Nursery Database available from the University of
// California Irvine repository of machine learning databases, available at
//
// http://archive.ics.uci.edu/ml/machine-learning-databases/nursery/nursery.names
//
// The description paragraph is listed as follows.
//
// Nursery Database was derived from a hierarchical decision model
// originally developed to rank applications for nursery schools. It
// was used during several years in 1980's when there was excessive
// enrollment to these schools in Ljubljana, Slovenia, and the
// rejected applications frequently needed an objective
// explanation. The final decision depended on three subproblems:
// occupation of parents and child's nursery, family structure and
// financial standing, and social and health picture of the family.
// The model was developed within expert system shell for decision
// making DEX (M. Bohanec, V. Rajkovic: Expert system for decision
// making. Sistemica 1(1), pp. 145-157, 1990.).
//
// Let's begin by loading the raw data. This string variable contains
// the contents of the nursery.data file as a single, continuous text.
//
var nursery = new DataSets.Nursery(path: localPath);
int[][] inputs = nursery.Instances;
int[] outputs = nursery.ClassLabels;
// Now, let's create the forest learning algorithm
var teacher = new RandomForestLearning(nursery.VariableNames)
{
NumberOfTrees = 1,
SampleRatio = 1.0
};
// Finally, learn a random forest from data
var forest = teacher.Learn(inputs, outputs);
// We can estimate class labels using
int[] predicted = forest.Decide(inputs);
// And the classification error (0) can be computed as
double error = new ZeroOneLoss(outputs).Loss(forest.Decide(inputs));
#endregion
Assert.AreEqual(0, error, 1e-10);
Assert.IsTrue(outputs.IsEqual(predicted));
Assert.AreEqual(0, error);
for (int i = 0; i < inputs.Length; i++)
{
int expected = outputs[i];
int actual = forest.Compute(inputs[i].ToDouble());
Assert.AreEqual(expected, actual);
}
}
