//NewRaffle creates a new instance of Raffle
func NewRaffle(options *RaffleOptions) (raffle *Raffle) {
raffle = &Raffle{}
raffle.contenders = lang.NewArrayList()
raffle.winners = lang.NewArrayList()
raffle.finished = false
raffle.started = time.Now()
raffle.options = options
return
}
//ArrayToMentions returns an array of users with mention tags
func ArrayToMentions(users *lang.ArrayList) (mentions *lang.ArrayList) {
mentions = lang.NewArrayList()
for i := 0; i < users.Len(); i++ {
mentions.Add(ToMention(users.Get(i).(*discordgo.User)))
}
return
}
func NewSimpleKMeans(numberOfClusters int) *SimpleKMeans {
self := &SimpleKMeans{}
self.points = lang.NewArrayList()
self.numberOfClusters = numberOfClusters
self.delta = 0.001 // default delta
return self
}
func NewKMeansPoint(items []float64) *KMeansPoint {
self := &KMeansPoint{}
self.items = lang.NewArrayList()
for i := 0; i < len(items); i++ {
self.items.Add(items[i])
}
return self
}
func main() {
fmt.Println("*** Queue ***")
queue := lang.NewQueue()
queue.Push("Hello")
queue.Push(4)
queue.Push(5)
queue.Push(8)
queue.Push(6)
queue.Push("World")
fmt.Println("Total items before poll: ", queue.Len())
fmt.Println("peek before poll:", queue.Peek())
fmt.Println(queue.Poll(), queue.Poll(), queue.Poll(), queue.Poll(), queue.Poll(), queue.Poll(), queue.Poll())
fmt.Println("Total items after poll: ", queue.Len())
fmt.Println("peek: ", queue.Peek())
fmt.Println("\n")
fmt.Println("*** Stack ***")
stack := lang.NewStack()
stack.Push("World")
stack.Push(4)
stack.Push(5)
stack.Push(8)
stack.Push(6)
stack.Push("Hello")
fmt.Println("Total items before pop: ", stack.Len())
fmt.Println("peek before popping:", stack.Peek())
fmt.Println(stack.Pop(), stack.Pop(), stack.Pop(), stack.Pop(), stack.Pop(), stack.Pop(), stack.Pop())
fmt.Println("Total items after pop: ", stack.Len())
fmt.Println("peek: ", stack.Peek())
fmt.Println("\n")
fmt.Println("*** ArrayList ***")
array := lang.NewArrayList()
fmt.Println("random item in empty array:", array.Sample())
array.Add("hello")
array.Add("world")
array.Add("Hicham")
fmt.Println("total items in array:", array.Len())
fmt.Println("array:", array)
fmt.Println("array[1]:", array.Get(1))
fmt.Println("array.indexOf(world):", array.IndexOf("world"))
fmt.Println("array.Contains(world):", array.Contains("world"), array.Contains("random"))
fmt.Println("random item in array:", array.Sample())
fmt.Println("random item in array:", array.Sample())
for i := 0; i < array.Len(); i++ {
fmt.Println("item(", i, "):", array.Get(i))
}
fmt.Println("array.Remove(world):", array.Remove("world"), array)
fmt.Println("array.Remove(Hicham):", array.Remove("Hicham"), array)
fmt.Println("array.IsEmpty():", array.IsEmpty(), array.Remove("hello"), array.IsEmpty(), array.Len())
for i := 0; i < 30; i++ {
array.Add(i)
}
fmt.Println("array.indexOf(5):", array.IndexOf(5), array.Len())
array.Clear()
array.Add("one", "two", "three")
array2 := lang.NewArrayList()
array2.Add("four", "five", "six")
array.AddFromArrayList(array2)
fmt.Println("array:", array, array.Len())
fmt.Println("array.First/Last:", array.First(), array.Last())
fmt.Println("array.ToSlice:", array.ToSlice())
fmt.Println("\n")
fmt.Println("*** HashSet ***")
set := lang.NewHashSet()
set.Add("hello", "world", "hello")
fmt.Println("total items in set:", set.Len(), set)
setToSlice := set.ToSlice()
fmt.Println("set.ToSlice:", setToSlice, len(setToSlice))
fmt.Println("\n")
fmt.Println("*** KMeansSimpleCluster ***")
kMeansCluster := ml.NewSimpleKMeans(4)
kMeansCluster.AddPointAsSlice([]float64{48.2641334571, 86.4516903905})
kMeansCluster.AddPointAsSlice([]float64{0.114004262656, 35.8368597414})
kMeansCluster.AddPointAsSlice([]float64{97.4319168245, 92.8009240744})
kMeansCluster.AddPointAsSlice([]float64{24.4614031388, 18.3292584382})
kMeansCluster.AddPointAsSlice([]float64{36.2367675367, 32.8294024271})
kMeansCluster.AddPointAsSlice([]float64{75.5836860736, 68.30729977})
kMeansCluster.AddPointAsSlice([]float64{38.6577034445, 25.7701728584})
kMeansCluster.AddPointAsSlice([]float64{28.2607136287, 64.4493377817})
kMeansCluster.AddPointAsSlice([]float64{61.5358486771, 61.2195232194})
kMeansCluster.AddPointAsSlice([]float64{1.52352224798, 38.5083779618})
kMeansCluster.AddPointAsSlice([]float64{11.6392182793, 68.2369021579})
kMeansCluster.AddPointAsSlice([]float64{53.9486870607, 53.9136556533})
kMeansCluster.AddPointAsSlice([]float64{14.6671651772, 26.0132534731})
kMeansCluster.AddPointAsSlice([]float64{65.9506725878, 82.5639317581})
kMeansCluster.AddPointAsSlice([]float64{58.3682872339, 51.6414580337})
kMeansCluster.AddPointAsSlice([]float64{12.6918921252, 2.28888447759})
kMeansCluster.AddPointAsSlice([]float64{31.7587852231, 18.1368234166})
kMeansCluster.AddPointAsSlice([]float64{63.6631115204, 24.933301389})
kMeansCluster.AddPointAsSlice([]float64{29.1652289905, 34.456759171})
kMeansCluster.AddPointAsSlice([]float64{44.3830953085, 70.4813875779})
kMeansCluster.AddPointAsSlice([]float64{47.0571691145, 65.3507625811})
kMeansCluster.AddPointAsSlice([]float64{74.0584537502, 98.2271944247})
kMeansCluster.AddPointAsSlice([]float64{55.8929146157, 86.6196265477})
kMeansCluster.AddPointAsSlice([]float64{20.4744253473, 12.0025149302})
kMeansCluster.AddPointAsSlice([]float64{14.2867767281, 40.2850440995})
kMeansCluster.AddPointAsSlice([]float64{40.43551369, 94.5410407116})
kMeansCluster.AddPointAsSlice([]float64{87.6178871195, 12.4700151639})
kMeansCluster.AddPointAsSlice([]float64{47.2703048197, 93.0636237124})
kMeansCluster.AddPointAsSlice([]float64{59.7895104175, 69.2621288413})
kMeansCluster.AddPointAsSlice([]float64{80.8612333922, 42.9183411179})
kMeansCluster.AddPointAsSlice([]float64{31.1271795535, 55.6669044656})
kMeansCluster.AddPointAsSlice([]float64{78.9671049353, 65.833739365})
kMeansCluster.AddPointAsSlice([]float64{39.8324533414, 63.0343115139})
kMeansCluster.AddPointAsSlice([]float64{79.126343548, 14.9128874133})
kMeansCluster.AddPointAsSlice([]float64{65.8152400306, 77.5202358013})
kMeansCluster.AddPointAsSlice([]float64{75.2762752704, 42.4858435609})
kMeansCluster.AddPointAsSlice([]float64{29.6475948493, 61.2068411763})
kMeansCluster.AddPointAsSlice([]float64{67.421857106, 54.8955604259})
kMeansCluster.AddPointAsSlice([]float64{10.4652931501, 29.7954139372})
kMeansCluster.AddPointAsSlice([]float64{32.0272462745, 99.5422900971})
kMeansCluster.AddPointAsSlice([]float64{80.1520927001, 84.2710379142})
kMeansCluster.AddPointAsSlice([]float64{2.27240208403, 41.2138854089})
kMeansCluster.AddPointAsSlice([]float64{44.4601509555, 1.72563901513})
kMeansCluster.AddPointAsSlice([]float64{16.8676021068, 35.3415636277})
kMeansCluster.AddPointAsSlice([]float64{58.1977544121, 29.2752085455})
kMeansCluster.AddPointAsSlice([]float64{24.6119080085, 39.9440735137})
kMeansCluster.AddPointAsSlice([]float64{63.0759798755, 60.9841014448})
kMeansCluster.AddPointAsSlice([]float64{30.9289119657, 95.0173219502})
kMeansCluster.AddPointAsSlice([]float64{8.54972950047, 41.7384441737})
kMeansCluster.AddPointAsSlice([]float64{61.2606910793, 4.06738902059})
kMeansCluster.AddPointAsSlice([]float64{83.2302091964, 11.6373312879})
kMeansCluster.AddPointAsSlice([]float64{89.4443065362, 42.5694882801})
kMeansCluster.AddPointAsSlice([]float64{24.5619318152, 97.7947977804})
kMeansCluster.AddPointAsSlice([]float64{50.3134024475, 40.6429336223})
kMeansCluster.AddPointAsSlice([]float64{58.1422402033, 36.1112632557})
kMeansCluster.AddPointAsSlice([]float64{32.0668520827, 29.9924151435})
kMeansCluster.AddPointAsSlice([]float64{89.6057447137, 84.9532177777})
kMeansCluster.AddPointAsSlice([]float64{9.8876440816, 18.2540486261})
kMeansCluster.AddPointAsSlice([]float64{17.9670383961, 47.596032257})
kMeansCluster.AddPointAsSlice([]float64{50.2977668282, 93.6851189223})
kMeansCluster.AddPointAsSlice([]float64{98.0700386253, 86.5816924579})
kMeansCluster.AddPointAsSlice([]float64{10.8175290981, 26.4344732252})
kMeansCluster.AddPointAsSlice([]float64{34.7463851288, 24.4154447141})
kMeansCluster.AddPointAsSlice([]float64{92.5470100593, 17.3595513748})
kMeansCluster.AddPointAsSlice([]float64{79.0426629356, 4.59850018907})
kMeansCluster.AddPointAsSlice([]float64{89.9791366918, 29.523946842})
kMeansCluster.AddPointAsSlice([]float64{3.89920214563, 91.3650215111})
kMeansCluster.AddPointAsSlice([]float64{35.4669861576, 62.1865368798})
kMeansCluster.AddPointAsSlice([]float64{2.78150918086, 24.5280230552})
kMeansCluster.AddPointAsSlice([]float64{50.0390951889, 57.0414421682})
kMeansCluster.AddPointAsSlice([]float64{64.4521660758, 48.4962172448})
kMeansCluster.AddPointAsSlice([]float64{94.4915452316, 56.6508179406})
kMeansCluster.AddPointAsSlice([]float64{47.1655534769, 15.8292055671})
kMeansCluster.AddPointAsSlice([]float64{94.2027011374, 45.6802385454})
kMeansCluster.AddPointAsSlice([]float64{30.5846324871, 54.783635876})
kMeansCluster.AddPointAsSlice([]float64{57.7043252948, 0.286661610381})
kMeansCluster.AddPointAsSlice([]float64{41.7908674949, 14.7206014023})
kMeansCluster.AddPointAsSlice([]float64{59.6689465934, 64.8849831965})
kMeansCluster.AddPointAsSlice([]float64{92.2553335495, 55.9096460272})
kMeansCluster.AddPointAsSlice([]float64{48.493467262, 69.4766837809})
kMeansCluster.AddPointAsSlice([]float64{23.1837859581, 71.4406867443})
kMeansCluster.AddPointAsSlice([]float64{29.0737623652, 66.9391416961})
kMeansCluster.AddPointAsSlice([]float64{95.7442323112, 89.4677505059})
kMeansCluster.AddPointAsSlice([]float64{68.7707275828, 40.9900140055})
kMeansCluster.AddPointAsSlice([]float64{84.5445737133, 32.1707309618})
kMeansCluster.AddPointAsSlice([]float64{67.4126251988, 56.6710579117})
kMeansCluster.AddPointAsSlice([]float64{10.688352016, 28.1745892928})
kMeansCluster.AddPointAsSlice([]float64{56.7620324155, 18.3034334207})
kMeansCluster.AddPointAsSlice([]float64{50.6751320678, 86.6916908032})
kMeansCluster.AddPointAsSlice([]float64{74.6185482896, 34.022483532})
kMeansCluster.AddPointAsSlice([]float64{20.7011996002, 32.855295357})
kMeansCluster.AddPointAsSlice([]float64{11.479054664, 1.59204297586})
kMeansCluster.AddPointAsSlice([]float64{51.6805387648, 25.4063026358})
kMeansCluster.AddPointAsSlice([]float64{84.4109522357, 47.237632645})
kMeansCluster.AddPointAsSlice([]float64{90.6395051745, 57.7917166935})
kMeansCluster.AddPointAsSlice([]float64{58.6159601042, 84.1226173848})
kMeansCluster.AddPointAsSlice([]float64{46.2184509277, 28.559934585})
kMeansCluster.AddPointAsSlice([]float64{97.0302485783, 41.3135022812})
kMeansCluster.AddPointAsSlice([]float64{31.3144587058, 87.2459910122})
kMeansCluster.AddPointAsSlice([]float64{5.93357833962, 95.6812831872})
clusters := kMeansCluster.Cluster()
for i := 0; i < clusters.Len(); i++ {
cluster := clusters.Get(i).(*support.KMeansCluster)
fmt.Println("cluster:", cluster.Center().Items().ToSlice())
for j := 0; j < cluster.Points().Len(); j++ {
point := cluster.Points().Get(j).(*support.KMeansPoint)
fmt.Println("--", point.Items().ToSlice())
}
}
}
func (self *SimpleKMeans) Cluster() *lang.ArrayList {
if self.numberOfClusters == 1 {
panic("please specify more than one cluster")
}
clusters := lang.NewArrayList()
uniqueCenters := lang.NewHashSet()
for i := 0; i < self.numberOfClusters; i++ {
randomCenter := self.points.Sample().(*support.KMeansPoint)
for uniqueCenters.Contains(randomCenter) {
randomCenter = self.points.Sample().(*support.KMeansPoint)
}
uniqueCenters.Add(randomCenter)
cluster := support.NewKMeansCluster(randomCenter)
clusters.Add(cluster)
}
for {
// find nearest cluster and assign point to cluster
for i := 0; i < self.points.Len(); i++ {
smallestDistance := math.MaxFloat64
var nearestCluster *support.KMeansCluster
point := self.points.Get(i).(*support.KMeansPoint)
for j := 0; j < clusters.Len(); j++ {
cluster := clusters.Get(j).(*support.KMeansCluster)
distanceBetweenCenterAndPoint := point.DistanceFromPoint(cluster.Center())
if distanceBetweenCenterAndPoint < smallestDistance {
smallestDistance = distanceBetweenCenterAndPoint
nearestCluster = cluster
}
}
nearestCluster.Points().Add(point)
}
// recalculate new center in cluster and check if delta was satisfied
biggestDeltaDistance := -math.MaxFloat64
newDeltaDistance := self.delta
for i := 0; i < clusters.Len(); i++ {
cluster := clusters.Get(i).(*support.KMeansCluster)
newDeltaDistance = cluster.Recenter()
if newDeltaDistance > biggestDeltaDistance {
biggestDeltaDistance = newDeltaDistance
}
}
// quit if delta was satisfied
if newDeltaDistance < self.delta {
break
} else {
// otherwise clear cluster and try again
for i := 0; i < clusters.Len(); i++ {
cluster := clusters.Get(i).(*support.KMeansCluster)
cluster.Points().Clear()
}
}
}
return clusters
}
func NewKMeansCluster(center *KMeansPoint) *KMeansCluster {
self := &KMeansCluster{}
self.center = center
self.points = lang.NewArrayList()
return self
}
