//[1,2,4,6,9,12,14,34]
func twofen1(_ arr:[Int],_ key:Int, _ low:Int, _ height:Int) -> Bool {
if low >= height{
if arr[low] == key{
return true
}
return false
}
let mid = (low + height)/2 // let mid = (height - low)/2 + low
if arr[mid] < key{
return self.twofen(arr, key, mid + 1, height)
}else if arr[mid] > key{
return self.twofen(arr, key, low, mid - 1)
}else{
print(mid)
return true
}
}
func twofen2(_ arr:[Int],_ key:Int, _ low:Int, _ height:Int) -> Bool {
if low >= height{
if arr[low] == key{
return true
}
return false
}
let mid = (height - low) * (key - arr[low]) / (arr[height] - arr[low]) + low //均匀分布的
if arr[mid] < key{
return self.twofen(arr, key, mid + 1, height)
}else if arr[mid] > key{
return self.twofen(arr, key, low, mid - 1)
}else{
print(mid)
return true
}
}
/*
与拆半查找一样,也是logn。不少博客说,在处理海量数据时,拆分查找的middle = (low + hight)/2,
除法可能会影响效率,而斐波那契的middle = low + F[k-1] -1,纯加减计算,速度要快一些。
我觉得是扯淡,因为完全可以用middle = (loe+hight)>>2来代替,要知道相比于加减乘除而言,
位运算的效率可是最高的哟。
*/
func makeFBArr(_ arrCount:Int) -> [Int] {
var i = 1
var arrFB :[Int] = [0]
while i < arrCount {
if i == 1{
arrFB.append( arrFB[0] + 1)
}else{
arrFB.append(arrFB[i - 1] + arrFB[i - 2])
}
i += 1
}
return arrFB
}
func twofen(_ arr1:[Int],_ key:Int, _ low:Int, _ height:Int) -> Bool {
var arr = arr1
var Low = low
// var Height = height
let fpArr = self.makeFBArr(height - low + 1)
// print(fpArr)
var k = 0
while arr.count > fpArr[k] {
k += 1
}
for _ in arr.count..<fpArr[k] {
arr.append(arr1[arr1.count - 1])
}
while Low <= height {
let mid = fpArr[k - 1] - 1 + Low
if arr[mid] > key{
k = k - 1
}else if arr[mid] < key{
Low = mid + 1
k = k - 2
}else{
return true
}
}
return false
}
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