EuclideanDistance is a Distancer.

MinkowskiDistance ...

MinkowskiDistanceP ...

NewKDTree ...

NewKNeighborsClassifier returns an initialized *KNeighborsClassifier.

NewKNeighborsRegressor returns an initialized *KNeighborsRegressor Weights may be "uniform", "distance" or func(dist []float64) []float64.

NewNearestCentroid ..

NewNearestNeighbors returns an *NearestNeighbors.

NewRectangle ...

InnerNode ...

KDTree for quick nearest-neighbor lookup This class provides an index into a set of k-dimensional points which can be used to rapidly look up the nearest neighbors of any point.

KNeighborsClassifier is a Regression based on k-nearest neighbors.

KNeighborsRegressor is a Regression based on k-nearest neighbors.

LeafNode ...

NearestCentroid is a Regression based on k-nearest neighbors.

NearestNeighbors is the unsupervised alog implementing search of k nearest neighbors Algorithm is one of 'auto', 'ball_tree', 'kd_tree', 'brute' defaults to "auto" Metric = 'cityblock', 'cosine', 'euclidean', 'l1', 'l2', 'manhattan' defaults to euclidean (= minkowski with P=2) P is power for 'minkowski' NJobs: number of concurrent jobs.

Rectangle Hyperrectangle class.

Node ...

Distance has Distance(Vector,Vector)float64.