103 lines
2.7 KiB
Go
103 lines
2.7 KiB
Go
// Package clusters provides abstract definitions of clusterers as well as
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// their implementations.
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package clusters
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import (
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"math"
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)
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// DistFunc represents a function for measuring distance
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// between n-dimensional vectors.
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type DistFunc func([]float64, []float64) float64
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// Online represents parameters important for online learning in
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// clustering algorithms.
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type Online struct {
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Alpha float64
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Dimension int
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}
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// HCEvent represents the intermediate result of computation of hard clustering algorithm
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// and are transmitted periodically to the caller during online learning
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type HCEvent struct {
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Cluster int
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Observation []float64
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}
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// Clusterer defines the operation of learning
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// common for all algorithms
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type Clusterer interface {
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Learn([][]float64) error
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}
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// HardClusterer defines a set of operations for hard clustering algorithms
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type HardClusterer interface {
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// Sizes returns sizes of respective clusters
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Sizes() []int
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// Guesses returns mapping from data point indices to cluster numbers. Clusters' numbering begins at 1.
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Guesses() []int
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// Predict returns number of cluster to which the observation would be assigned
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Predict(observation []float64) int
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// IsOnline tells the algorithm supports online learning
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IsOnline() bool
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// WithOnline configures the algorithms for online learning with given parameters
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WithOnline(Online) HardClusterer
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// Online begins the process of online training of an algorithm. Observations are sent on the observations channel,
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// once no more are expected an empty struct needs to be sent on done channel. Caller receives intermediate results of computation via
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// the returned channel.
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Online(observations chan []float64, done chan struct{}) chan *HCEvent
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// Clusterer implements common operation
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Clusterer
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}
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// Estimator defines a computation used to determine an optimal number of clusters in the dataset
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type Estimator interface {
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// Estimate provides an expected number of clusters in the dataset
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Estimate([][]float64) (int, error)
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}
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// Importer defines an operation of importing the dataset from an external file
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type Importer interface {
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// Import fetches the data from a file, start and end arguments allow user
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// to specify the span of data columns to be imported (inclusively)
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Import(file string, start, end int) ([][]float64, error)
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}
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var (
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// EuclideanDist is one of the common distance measurement
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EuclideanDist = func(a, b []float64) float64 {
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var (
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s, t float64
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)
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for i := range a {
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t = a[i] - b[i]
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s += t * t
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}
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return math.Sqrt(s)
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}
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// EuclideanDistSquared is one of the common distance measurement
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EuclideanDistSquared = func(a, b []float64) float64 {
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var (
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s, t float64
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)
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for i := range a {
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t = a[i] - b[i]
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s += t * t
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}
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return s
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}
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)
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