public void ConstructorTest()
{
double[][] inputs =
{
new double[] { 10, 42 },
new double[] { 162, 96 },
new double[] { 125, 20 },
new double[] { 96, 6 },
new double[] { 2, 73 },
new double[] { 52, 51 },
new double[] { 71, 49 },
};
int[] outputs =
{
-1, -1, +1, +1, -1, -1, +1
};
var classifier = new Boost <DecisionStump>();
var teacher = new AdaBoost <DecisionStump>(classifier)
{
Creation = (weights) =>
{
var stump = new DecisionStump(2);
stump.Learn(inputs, outputs, weights);
return(stump);
},
Iterations = 5,
Tolerance = 1e-3
};
double error = teacher.Run(inputs, outputs);
Assert.AreEqual(0, error);
Assert.AreEqual(5, classifier.Models.Count);
Assert.AreEqual(0.16684734250395147, classifier.Models[0].Weight);
Assert.AreEqual(0.22329026900109736, classifier.Models[1].Weight);
Assert.AreEqual(0.28350372170582383, classifier.Models[2].Weight);
Assert.AreEqual(0.16684734250395139, classifier.Models[3].Weight);
Assert.AreEqual(0.15951132428517592, classifier.Models[4].Weight);
int[] actual = new int[outputs.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = classifier.Compute(inputs[i]);
}
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(outputs[i], actual[i]);
}
}
public void learn_stump_classifier()
{
#region doc_learn
// Let's say we want to classify the following 2-dimensional
// data samples into 2 possible classes, either true or false:
double[][] inputs =
{
new double[] { 10, 42 },
new double[] { 162, 96 },
new double[] { 125, 20 },
new double[] { 96, 6 },
new double[] { 2, 73 },
new double[] { 52, 51 },
new double[] { 71, 49 },
};
// And those are their associated class labels
bool[] outputs =
{
false, false, true, true, false, false, true
};
// We create a learning algorithm as:
var teacher = new ThresholdLearning();
// Now, we can use the Learn method to learn a classifier:
DecisionStump classifier = teacher.Learn(inputs, outputs);
// Now, we can check how good it is using a confusion matrix:
var cm = ConfusionMatrix.Estimate(classifier, inputs, outputs);
double error = cm.Error; // should be ~0.14
// We can also compute the model outputs for new samples using
bool y = classifier.Decide(new double[] { 71, 48 }); // should be false
#endregion
Assert.AreEqual(false, y);
Assert.AreEqual(0.14285714285714285, error, 1e-10);
}
public void ConstructorTest()
{
// Let's say we want to classify the following 2-dimensional
// data samples into 2 possible classes, either true or false:
double[][] inputs =
{
new double[] { 10, 42 },
new double[] { 162, 96 },
new double[] { 125, 20 },
new double[] { 96, 6 },
new double[] { 2, 73 },
new double[] { 52, 51 },
new double[] { 71, 49 },
};
// And those are their associated class labels
int[] outputs =
{
-1, -1, +1, +1, -1, -1, +1
};
// First, we create a classsifier using:
var classifier = new Boost <DecisionStump>();
// Now, we can create a AdaBoost learning algorithm as:
var teacher = new AdaBoost <DecisionStump>(classifier)
{
Creation = (weights) =>
{
var stump = new DecisionStump(2);
stump.Learn(inputs, outputs, weights);
return(stump);
},
// Train until:
MaxIterations = 5,
Tolerance = 1e-3
};
// Now, we can use the Run method to learn:
double error = teacher.Run(inputs, outputs); // error should be zero.
// Now, we can compute the model outputs for new samples using
int y = classifier.Compute(new double[] { 71, 48 }); // should be 1
Assert.AreEqual(1, y);
Assert.AreEqual(0, error);
Assert.AreEqual(5, classifier.Models.Count);
Assert.AreEqual(0.16684734250395147, classifier.Models[0].Weight);
Assert.AreEqual(0.22329026900109736, classifier.Models[1].Weight);
Assert.AreEqual(0.28350372170582383, classifier.Models[2].Weight);
Assert.AreEqual(0.16684734250395139, classifier.Models[3].Weight);
Assert.AreEqual(0.15951132428517592, classifier.Models[4].Weight);
int[] actual = new int[outputs.Length];
for (int i = 0; i < actual.Length; i++)
{
actual[i] = classifier.Compute(inputs[i]);
}
for (int i = 0; i < actual.Length; i++)
{
Assert.AreEqual(outputs[i], actual[i]);
}
}
