Value Members

1.  def classification(values: Vec[Int], indices: Mat[Int], numClasses: Int, log: Boolean): Mat[Double]
2.  def jaccardDistance(v1: STen, v2: STen)(implicit scope: Scope): STen
3.  def knn(d: STen, query: STen, k: Int, distanceMatrix: DistanceFunction)(implicit scope: Scope): STen
4.  def knnClassification(features: Mat[Double], values: Vec[Int], query: Mat[Double], k: Int, distance: DistanceFunction, device: Device, precision: FloatingPointPrecision, minibatchSize: Int, log: Boolean): Mat[Double]
5.  def knnMinibatched(d: STen, query: STen, k: Int, distanceMatrix: DistanceFunction, minibatchSize: Int)(implicit scope: Scope): STen
6.  def knnRegression(features: Mat[Double], values: Vec[Double], query: Mat[Double], k: Int, distance: DistanceFunction, device: Device = CPU, precision: FloatingPointPrecision = DoublePrecision, minibatchSize: Int = Int.MaxValue): Vec[Double]
7.  def knnSearch(features: Mat[Double], query: Mat[Double], k: Int, distance: DistanceFunction, device: Device, precision: FloatingPointPrecision, minibatchSize: Int): Mat[Int]
8.  def regression(values: Vec[Double], indices: Mat[Int]): Vec[Double]
9.  def squaredEuclideanDistance(v1: STen, v2: STen)(implicit scope: Scope): STen
10.  object JaccardDistance extends DistanceFunction
11.  object SquaredEuclideanDistance extends DistanceFunction