package extratrees
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Type Members
- case class ClassificationLeaf(targetDistribution: Seq[Double]) extends ClassificationTree with Product with Serializable
- case class ClassificationNonLeaf(left: ClassificationTree, right: ClassificationTree, splitFeature: Int, cutpoint: Double) extends ClassificationTree with Product with Serializable
- sealed trait ClassificationTree extends AnyRef
- case class RegressionLeaf(targetMean: Double) extends RegressionTree with Product with Serializable
- case class RegressionNonLeaf(left: RegressionTree, right: RegressionTree, splitFeature: Int, cutpoint: Double) extends RegressionTree with Product with Serializable
- sealed trait RegressionTree extends AnyRef
Value Members
- def buildForestClassification(data: Mat[Double], target: Vec[Int], sampleWeights: Option[Vec[Double]], numClasses: Int, nMin: Int, k: Int, m: Int, parallelism: Int, seed: Long = java.time.Instant.now.toEpochMilli): Seq[ClassificationTree]
- def buildForestRegression(data: Mat[Double], target: Vec[Double], nMin: Int, k: Int, m: Int, parallelism: Int, seed: Long = java.time.Instant.now.toEpochMilli): Seq[RegressionTree]
- def buildTreeClassification(data: Mat[Double], subset: Vec[Int], target: Vec[Int], sampleWeights: Option[Vec[Double]], nMin: Int, k: Int, rng: Generator, numClasses: Int, attributes: Array[Int], numConstant: Int): ClassificationTree
- def buildTreeRegression(data: Mat[Double], subset: Vec[Int], target: Vec[Double], nMin: Int, k: Int, rng: Generator, attributes: Array[Int], numConstant: Int): RegressionTree
- def col(data: Mat[Double], col: Int): Vec[Double]
- def computeVarianceReduction(target: Vec[Double], samplesInSplit: Vec[Boolean], varianceNoSplit: Double): Double
- def distribution(v: Vec[Int], sampleWeights: Option[Vec[Double]], numClasses: Int): Vec[Double]
- def giniImpurity(target: Vec[Int], weights: Option[Vec[Double]], numClasses: Int): Double
- def giniImpurityFromDistribution(distribution: Array[Double]): Double
- def giniScore(target: Vec[Int], sampleWeights: Option[Vec[Double]], samplesInSplit: Vec[Boolean], giniImpurityNoSplit: Double, numClasses: Int, buf1: Array[Double], buf2: Array[Double]): Double
- def minmax(self: Vec[Double]): (Double, Double)
- def partition[T](vec: Vec[T])(pred: Array[Boolean])(implicit arg0: ClassTag[T]): (Vec[T], Vec[T])
- def predictClassification(trees: Seq[ClassificationTree], samples: Mat[Double]): Mat[Double]
- def predictClassification(root: ClassificationTree, sample: Vec[Double]): Vec[Double]
- def predictRegression(trees: Seq[RegressionTree], samples: Mat[Double]): Vec[Double]
- def predictRegression(root: RegressionTree, sample: Vec[Double]): Double
- def splitClassification(data: Mat[Double], subset: Vec[Int], attributes: Array[Int], numConstant: Int, k: Int, targetAtSubset: Vec[Int], weightsAtSubset: Option[Vec[Double]], rng: Generator, numClasses: Int): (Int, Double, Int)
- def splitRegression(data: Mat[Double], subset: Vec[Int], attributes: Array[Int], numConstant: Int, k: Int, targetAtSubset: Vec[Double], rng: Generator): (Int, Double, Int)
- def takeCol(data: Mat[Double], rows: Vec[Int], col: Int): Vec[Double]
- object ClassificationLeaf extends Serializable
- object ClassificationNonLeaf extends Serializable
- object ClassificationTree
- object RegressionLeaf extends Serializable
- object RegressionNonLeaf extends Serializable
- object RegressionTree