Documentation
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Overview ¶
Package KNN implements a K Nearest Neighbors object, capable of both classification and regression. It accepts data in the form of a slice of float64s, which are then reshaped into a X by Y matrix.
Index ¶
Constants ¶
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Variables ¶
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Functions ¶
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Types ¶
type KNNClassifier ¶
type KNNClassifier struct {
base.BaseEstimator
TrainingData *base.Instances
DistanceFunc string
NearestNeighbours int
}
A KNN Classifier. Consists of a data matrix, associated labels in the same order as the matrix, and a distance function. The accepted distance functions at this time are 'euclidean' and 'manhattan'.
func NewKnnClassifier ¶
func NewKnnClassifier(distfunc string, neighbours int) *KNNClassifier
Returns a new classifier
func (*KNNClassifier) Fit ¶
func (KNN *KNNClassifier) Fit(trainingData *base.Instances)
Train stores the training data for llater
func (*KNNClassifier) Predict ¶
func (KNN *KNNClassifier) Predict(what *base.Instances) *base.Instances
func (*KNNClassifier) PredictOne ¶
func (KNN *KNNClassifier) PredictOne(vector []float64) string
Returns a classification for the vector, based on a vector input, using the KNN algorithm. See http://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm.
type KNNRegressor ¶
type KNNRegressor struct {
base.BaseEstimator
Values []float64
DistanceFunc string
}
A KNN Regressor. Consists of a data matrix, associated result variables in the same order as the matrix, and a name.
Source Files