public void WeightedRandomSampler_Sample_Weight_10()
{
var sut = new WeightedRandomSampler();
var indices = Enumerable.Range(0, 10).ToArray();
var weights = new double[] { 1, 1, 1, 1, 1, 10, 10, 10, 10, 10 };
var actual = new int[indices.Length];
sut.Sample(indices, weights, actual);
var expected = new int[] { 2, 5, 6, 7, 7, 8, 8, 8, 9, 9 };
CollectionAssert.AreEqual(expected, actual);
}
bool Boost(F64Matrix observations, double[] targets, int[] indices, int iteration)
{
m_sampler.Sample(indices, m_sampleWeights, m_sampleIndices);
var model = m_modelLearner.Learn(observations, targets,
m_sampleIndices); // weighted sampling is used instead of weights in training
var predictions = model.Predict(observations, indices);
for (int i = 0; i < predictions.Length; i++)
{
var index = indices[i];
m_workErrors[index] = Math.Abs(m_indexedTargets[i] - predictions[i]);
}
var maxError = m_workErrors.Max();
for (int i = 0; i < m_workErrors.Length; i++)
{
var error = m_workErrors[i];
if (maxError != 0.0)
{
error = error / maxError;
}
switch (m_loss)
{
case AdaBoostRegressionLoss.Linear:
break;
case AdaBoostRegressionLoss.Squared:
error = error * error;
break;
case AdaBoostRegressionLoss.Exponential:
error = 1.0 - Math.Exp(-error);
break;
default:
throw new ArgumentException("Unsupported loss type");
}
m_workErrors[i] = error;
}
var modelError = m_workErrors.WeightedMean(m_sampleWeights, indices);
if (modelError <= 0.0)
{
m_modelErrors.Add(0.0);
m_modelWeights.Add(1.0);
m_models.Add(model);
return(true);
}
else if (modelError >= 0.5)
{
return(false);
}
var beta = modelError / (1.0 - modelError);
var modelWeight = m_learningRate * Math.Log(1.0 / beta);
// Only boost if not last iteration
if (iteration != m_iterations - 1)
{
for (int i = 0; i < indices.Length; i++)
{
var index = indices[i];
var sampleWeight = m_sampleWeights[index];
var error = m_workErrors[index];
m_sampleWeights[index] = sampleWeight * Math.Pow(beta, (1.0 - error) * m_learningRate);
}
}
m_modelErrors.Add(modelError);
m_modelWeights.Add(modelWeight);
m_models.Add(model);
return(true);
}
