heap sort python

Sort Time Limit: 6000/1000 MS (Java/others) memory limit: 65536/32768 K (Java/Others)Total submission (s): 27771 accepted submission (s): 8400 Problem description gives you n integers. Output The first m in the ascending order. There are two rows of test data in each input group. The first row has two numbers n, m (0 5 33 -35 92 213 -644Sample output 213 92 3 knowledge points: heap, STL#include

#include Heap sort (Keng Gen)

This is a created article in which the information may have evolved or changed. "' Gopackage mainimport (" FMT ") func main () {array2: = []int{3, 2, 1}heapsort (array2) fmt. Println ("Heapmy", Array2)}func heapsort (array []int) {EP: = (len (array)-1) >> 1fmt. Println (EP) for I: = EP; I >= 0; i--{heapt (array, I, Len (array)-1)}for I: = Len (array)-1; i > 0; i--{array[0], array[i] = Array[i], array[0]heapt (array, 0, i-1)}}func heapt (array []int, start int, end int) {le: = start * * + 1re:

) { returnPriorityqueue (N1.merge (N1.getroot (), N2.getroot ()), n1.size () +n2.size ()); } node* Merge (node* N1, node* n2)//whether continue or direction? (minimum leftist heaps) { if(N1 = =NULL)returnN2; if(N2 = =NULL)returnN1; if(N1->data data) {mergeoperate (n1, N2); returnN1;// } Else{mergeoperate (N2, N1); returnN2;//}} node* Mergeoperate (node* N1, node*n2) { if(N1->left = =NULL) {N1->left =N2; returnN1; } Else{N1->right = Merge (n

JavaScript sorting algorithm--heap sort

#include using namespacestd;voidHeapadjust (intBintStartintN) { intmax=start; intLchild = start*2+1; intRchild = start*2+2; if(Start 1)/2){ if(Lchild A[max]) {Max=Lchild; } if(Rchild A[max]) {Max=Rchild; } if(max!=start) {Swap (A[start], A[max]); Heapadjust (A, max, n); } }}voidBuildheap (intBintN) {//N: [0,n] for(inti = (n1)/2; I >=0; i--) {Heapadjust (A, I, N); }}voidHeapsort (int*a,intN) {buildheap (a,n); for(inti=n;i>=0; i--) {Swap (a[0],a

#include 　　Heap sort C + + implementation

1#include 2 using namespacestd;3 4 intPARENT (Const inti) {5 return(i>>2)-1;6 }7 8 intLeft (Const inti) {9 return(i1)+1;Ten } One A intRight (Const inti) { - return(i1) +2; - } the - voidMax_heapify (int*ConstAConst inti,Const intheap_size) { - intL =Left (i); - intR =Right (i); + intlargest=i; - if(L A[i]) { +largest =l; A } at Else{ -largest =i; - } - - if(RA[largest]) { -largest =R; in } - if(Largest! =i) { to intKey =A[i]; +A[i]

than base from the front - $ if(I >= J) Break;//Note that the equals sign $ - swap (Arry[i], arry[j]); - } the -Swap (Arry[lo], arry[j]);//determine the location of JWuyi the returnJ; - } Wu - voidQsort_3way (vectorint> arry,intLointhi) About { $ if(Lo >= hi)return; - - intLt=lo, i=lo+1, gt=Hi; - intPivot =Arry[lo]; A + while(I GT) the { - if(arry[i]pivot) $Swap (arry[lt++], arry[i++]);//i++, should be Lt->pivot, after Exchange lt++, pivot, i+

The process of bubbling sorting is to first compare the keywords of the first record with the keywords of the second record, and, in reverse order, Exchange two records and then compare the keywords for the second record and the third record. And so on, until the n-1 record and the nth record's keywords have been compared. The above process is called the first bubbling sort, then the second trip to the previous n-1 the same operation, ...The quick

] forIinchRange (len (data_set)):#Big Circle determines the number of rounds that we chooseMinindex =I forJinchRange (I+1,len (data_set)):#a small circle is the number of times we compare each time we choose #//If the subsequent element is smaller than the element I chose, swap the position ifDATA_SET[J] Data_set[minindex]: Minindex=J Temp=Data_set[i] Data_set[i]=Data_set[minindex] Data_set[minindex]=TempPrint(Data_set)" "The basic idea of inserting

The example in this paper describes how Python implements heap ordering. Share to everyone for your reference, as follows: Heap sorting is one of the basic sorting methods, similar to a merge sort rather than an insert sort, which runs at O (Nlogn), like an insert

interchange Total-= 1 if total = = 2 and Array[total] >= Array[total-1]: Break Heap_adjust (Total,1,a Rray) return Arrayprint_tree (sort (total,origin))Five, algorithm analysis1, the use of a heap nature of a selection of sorting, at the top of the heap to select the maximum or minimum value2. Complexity of TimeThe time complexity of

Heap: One of the priority queues that use the priority queue to increase the object in any order, and to find (or possibly remove) the smallest element at any time (possibly at the same time as the object is incremented), which is more efficient than the method used for min in the list.Python does not have a separate heap type, only a module that includes some heap