/// <summary>
///
/// </summary>
/// <param name="maximumTreeDepth">The maximal tree depth before a leaf is generated</param>
/// <param name="featuresPrSplit">The number of features to be selected between at each split.
/// 0 means use all availible features</param>
/// <param name="minimumInformationGain">The minimum improvement in information gain before a split is made</param>
/// <param name="seed">Seed for feature selection if number of features pr split is not equal
/// to the total amount of features in observations. The features will be selected at random for each split</param>
/// <param name="splitSearcher">The type of searcher used for finding the best features splits when learning the tree</param>
/// <param name="impurityCalculator">Impurity calculator used to decide which split is optimal</param>
public DepthFirstTreeBuilder(int maximumTreeDepth, int featuresPrSplit, double minimumInformationGain, int seed,
ISplitSearcher splitSearcher, IImpurityCalculator impurityCalculator)
{
if (splitSearcher == null)
{
throw new ArgumentException("splitSearcher");
}
if (maximumTreeDepth <= 0)
{
throw new ArgumentException("maximum tree depth must be larger than 0");
}
if (minimumInformationGain <= 0)
{
throw new ArgumentException("minimum information gain must be larger than 0");
}
if (featuresPrSplit < 0)
{
throw new ArgumentException("features pr split must be at least 0");
}
if (impurityCalculator == null)
{
throw new ArgumentException("impurityCalculator");
}
m_maximumTreeDepth = maximumTreeDepth;
m_featuresPrSplit = featuresPrSplit;
m_splitSearcher = splitSearcher;
m_impurityCalculator = impurityCalculator;
m_minimumInformationGain = minimumInformationGain;
m_random = new Random(seed);
}
/// <summary>
///
/// </summary>
/// <param name="maximumTreeDepth">The maximal tree depth before a leaf is generated</param>
/// <param name="maximumLeafCount">The maximal allowed leaf nodes in the tree</param>
/// <param name="featuresPrSplit">The number of features to be selected between at each split.
/// 0 means use all available features</param>
/// <param name="minimumInformationGain">The minimum improvement in information gain before a split is made</param>
/// <param name="seed">Seed for feature selection if number of features pr split is not equal
/// to the total amount of features in observations. The features will be selected at random for each split</param>
/// <param name="splitSearcher">The type of searcher used for finding the best features splits when learning the tree</param>
/// <param name="impurityCalculator">Impurity calculator used to decide which split is optimal</param>
public BestFirstTreeBuilder(int maximumTreeDepth,
int maximumLeafCount,
int featuresPrSplit,
double minimumInformationGain,
int seed,
ISplitSearcher splitSearcher,
IImpurityCalculator impurityCalculator)
{
if (maximumTreeDepth <= 0)
{
throw new ArgumentException("maximum tree depth must be larger than 0");
}
if (maximumLeafCount <= 1)
{
throw new ArgumentException("maximum leaf count must be larger than 1");
}
if (minimumInformationGain <= 0)
{
throw new ArgumentException("minimum information gain must be larger than 0");
}
if (featuresPrSplit < 0)
{
throw new ArgumentException("features pr split must be at least 0");
}
m_splitSearcher = splitSearcher ?? throw new ArgumentException(nameof(splitSearcher));
m_impurityCalculator = impurityCalculator ?? throw new ArgumentException(nameof(impurityCalculator));
m_maximumTreeDepth = maximumTreeDepth;
m_maximumLeafCount = maximumLeafCount;
m_featuresPrSplit = featuresPrSplit;
m_minimumInformationGain = minimumInformationGain;
m_random = new Random(seed);
}
