/// <summary>
/// Trains a linear binary classifier using the averaged perceptron.
/// <a href='https://en.wikipedia.org/wiki/Perceptron'>Wikipedia entry for Perceptron</a>
/// </summary>
/// <param name="env">The local instance of the <see cref="IHostEnvironment"/></param>
/// <param name="lossFunction">The classification loss function. </param>
/// <param name="label">The name of the label column. </param>
/// <param name="features">The name of the feature column.</param>
/// <param name="weights">The optional name of the weights column.</param>
/// <param name="learningRate">The learning rate. </param>
/// <param name="decreaseLearningRate">Wheather to decrease learning rate as iterations progress.</param>
/// <param name="l2RegularizerWeight">L2 Regularization Weight.</param>
/// <param name="numIterations">The number of training iteraitons.</param>
/// <param name="advancedSettings">A delegate to supply more advanced arguments to the algorithm.</param>
public AveragedPerceptronTrainer(IHostEnvironment env,
string label,
string features,
string weights = null,
ISupportClassificationLossFactory lossFunction = null,
float learningRate = Arguments.AveragedDefaultArgs.LearningRate,
bool decreaseLearningRate = Arguments.AveragedDefaultArgs.DecreaseLearningRate,
float l2RegularizerWeight = Arguments.AveragedDefaultArgs.L2RegularizerWeight,
int numIterations = Arguments.AveragedDefaultArgs.NumIterations,
Action <Arguments> advancedSettings = null)
: this(env, new Arguments
{
LabelColumn = label,
FeatureColumn = features,
InitialWeights = weights,
LearningRate = learningRate,
DecreaseLearningRate = decreaseLearningRate,
L2RegularizerWeight = l2RegularizerWeight,
NumIterations = numIterations
})
{
if (lossFunction == null)
{
lossFunction = new HingeLoss.Arguments();
}
LossFunction = lossFunction.CreateComponent(env);
if (advancedSettings != null)
{
advancedSettings.Invoke(_args);
}
}
public void LossHinge()
{
HingeLoss.Arguments args = new HingeLoss.Arguments();
HingeLoss loss = new HingeLoss(args);
// Positive examples.
TestHelper(loss, 1, 2, 0, 0);
TestHelper(loss, 1, 1, 0, 0, false);
TestHelper(loss, 1, 0.99, 0.01, 1, false);
TestHelper(loss, 1, 0.5, 0.5, 1);
// Negative examples.
TestHelper(loss, 0, 0.5, 1.5, -1);
TestHelper(loss, 0, -0.5, 0.5, -1);
TestHelper(loss, 0, -1, 0, 0, false);
TestHelper(loss, 0, -2, 0, 0);
}
