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 ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type KNNClassifier ¶
type KNNClassifier struct {
base.BaseEstimator
TrainingData *base.Instances
DistanceFunc string
NearestNeighbours int
}
A KNNClassifier 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
NewKnnClassifier returns a new classifier
func (*KNNClassifier) Fit ¶
func (KNN *KNNClassifier) Fit(trainingData *base.Instances)
Fit stores the training data for later
func (*KNNClassifier) Predict ¶
func (KNN *KNNClassifier) Predict(what *base.Instances) *base.Instances
func (*KNNClassifier) PredictOne ¶
func (KNN *KNNClassifier) PredictOne(vector []float64) string
PredictOne 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 KNNRegressor consists of a data matrix, associated result variables in the same order as the matrix, and a name.
func NewKnnRegressor ¶
func NewKnnRegressor(distfunc string) *KNNRegressor
NewKnnRegressor mints a new classifier.
Source Files