108 lines
1.7 KiB
Go
108 lines
1.7 KiB
Go
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package clusters
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import (
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"container/heap"
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"math/rand"
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"sync"
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)
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// struct denoting start and end indices of database portion to be scanned for nearest neighbours by workers in DBSCAN and OPTICS
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type rangeJob struct {
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a, b int
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}
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// priority queue
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type pItem struct {
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v int
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p float64
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i int
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}
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type priorityQueue []*pItem
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func newPriorityQueue(size int) priorityQueue {
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q := make(priorityQueue, 0, size)
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heap.Init(&q)
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return q
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}
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func (pq priorityQueue) Len() int { return len(pq) }
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func (pq priorityQueue) Less(i, j int) bool {
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return pq[i].p > pq[j].p
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}
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func (pq priorityQueue) Swap(i, j int) {
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pq[i], pq[j] = pq[j], pq[i]
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pq[i].i = i
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pq[j].i = j
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}
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func (pq *priorityQueue) Push(x interface{}) {
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n := len(*pq)
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item := x.(*pItem)
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item.i = n
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*pq = append(*pq, item)
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heap.Fix(pq, item.i)
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}
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func (pq *priorityQueue) Pop() interface{} {
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old := *pq
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n := len(old)
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item := old[n-1]
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item.i = -1
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*pq = old[0 : n-1]
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return item
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}
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func (pq *priorityQueue) NotEmpty() bool {
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return len(*pq) > 0
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}
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func (pq *priorityQueue) Update(item *pItem, value int, priority float64) {
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item.v = value
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item.p = priority
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heap.Fix(pq, item.i)
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}
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func bounds(data [][]float64) []*[2]float64 {
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var (
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wg sync.WaitGroup
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l = len(data[0])
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r = make([]*[2]float64, l)
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)
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for i := 0; i < l; i++ {
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r[i] = &[2]float64{
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data[0][i],
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data[0][i],
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}
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}
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wg.Add(l)
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for i := 0; i < l; i++ {
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go func(n int) {
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defer wg.Done()
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for j := 0; j < len(data); j++ {
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if data[j][n] < r[n][0] {
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r[n][0] = data[j][n]
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} else if data[j][n] > r[n][1] {
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r[n][1] = data[j][n]
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}
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}
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}(i)
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}
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wg.Wait()
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return r
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}
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func uniform(data *[2]float64) float64 {
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return rand.Float64()*(data[1]-data[0]) + data[0]
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}
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