"Algorithm Design and Analysis Basics" 17, heap

Storing heap data in arrays

Package Cn.xf.algorithm.ch06changerule;import Java.util.arraylist;import Java.util.list;import org.junit.Test;/** * * Function: Construction of heap * 1, the heap can be defined as a binary tree, the node of the tree contains the key, and satisfies the condition * 1) The shape requirements of the tree: This binary tree is a basic complete (full binary tree), each layer of the tree is full, except the last layer of the rightmost element may be absent * 2) parental advantage, heap characteristics, Each node has a key greater than or equal to the key of his or her child (for any leaf we think it is automatically satisfied) * for the heap: * There is only one N-node complete binary tree his height: the logarithm of N of the log2 of the removed bounds * The root of the heap always contains the largest element of the heap * A node of the heap and the child of that node Sun is a heap. * You can use arrays to implement heaps by recording the elements of the heap from top to bottom, left to right.  * @author Xiaofeng * @date July 9, 2017 * @fileName Heap.java * */public class Heap {/** * heap data storage structure */private List<double> heap;/** * Build a heap from the bottom up */private list<double> createheaddowntoup (list<double> heap) {if ( Heap = = NULL | | Heap.size () <= 0) return heap;//number of data int nums = Heap.size ();//bar The whole array is moved back one bit to facilitate the calculation of data, since 0, then 2*0 or 0, does not reflect the n is the basic setting for the left child of N heap.add (0, 0d);//Build a heap, starting at the middle of the array, because twice times the mid-seat mid is almost the end of the tree, and in the vicinity of this 2*mid is the child node for the mid node for (int i = NUMS/2 + 1; i > 0; i) {//Get the address of the datum node int baseindex = i;//Get the value of this node double vbasevalue = Heap.get (BaseIndex); Boolean isheap = FalSe This is used to determine whether the current traversal of these three numbers satisfies the concept of a heap//heap transformation, Exchange tree node and child node values, so that the current tree satisfies the concept of the heap//2 * BaseIndex <= nums This tree is used to judge the subtree also satisfies the definition of the heap while (!). Isheap && 2 * baseindex <= nums) {//Gets the position of the left child node of the currently traversed data int maxchildindex = 2 * baseindex;//get the large position from the two child nodes if (m Axchildindex < Nums) {//If the left child's position is smaller than the total length, due to the full binary tree attribute, then there must be a right child node//judge the child node's data is relatively large, so that Max is the big if (Heap.get ( Maxchildindex) < Heap.get (Maxchildindex + 1)) {//If the right child is larger Maxchildindex + + 1;}} Again, the value of the current node is not greater than the value of the child node, if so, the current subtree is the property that satisfies the heap//maxchildindex = = Nums That's still a lie, can be compared to the left sub-tree if (Maxchildindex > Nums | | Vbasevalue >= Heap.get (maxchildindex)) {isheap = true;} else {//if not, then swap, the big data is exchanged to the node, the data on the node is switched to the child node Heap.set ( BaseIndex, Heap.get (Maxchildindex)); BaseIndex = Maxchildindex;heap.set (BaseIndex, Vbasevalue);}}}    Remove the first 0 and then return to Heap.remove (0); return heap;}                private void Shifheaddowntoup (int i) {if (heap = = NULL | | heap.size () <= 0) return;        Number of data int nums = Heap.size (); The whole array is moved back one bit to facilitate the calculation of the data, because starting from 0, then 2*0 or 0, does not reflect then is the basic setting of the left child of N heap.add (0, 0d);        Boolean isheap = false;        int baseindex = i;        Double vbasevalue = Heap.get (i);  while (!isheap && 2 * baseindex <= nums) {//Gets the position of the left child node of the currently traversed data int maxchildindex = 2 *            BaseIndex;                Get the big one from the two child nodes if (Maxchildindex < nums) {//If the left child's position is smaller than the total length, there must be a right child node due to the full binary tree attribute                    Judge the child's node data to be large, so that Max is the big if (Heap.get (Maxchildindex) < Heap.get (Maxchildindex + 1)) {                If the right child compares larger maxchildindex + = 1; }}//Again, the value of the current node is not greater than the value of the child node, if so, the current subtree is the property that satisfies the heap//Maxchildindex = = Nums that or Hide the condition, you can make a comparison of the left subtree if (Maxchildindex > Nums | | vbasevalue >= heap.get (maxchildindex)) {Ishea            P = true; } else {//if dissatisfied, then swap, the big data is exchanged on the node, the data of the node is swapped to the child node Heap.set (BaseIndex, Heap.get (Maxchildindex) );                BaseIndex = Maxchildindex;            Heap.set (BaseIndex, Vbasevalue);    }}//Remove the first 0 and return to Heap.remove (0);    }//Create heap public heap () {heap = new arraylist<double> ();    Createheaddowntoup (heap); }public Heap (list<double> data) {if (data = = NULL | | data.size () <= 0) {data = new arraylist<double    > ();        } heap = data; Createheaddowntoup (heap);} @Overridepublic String toString () {return heap.tostring ();}    public void Add (Double value) {if (value = = null) return;    Heap.add (value);//int insertinedx = Heap.size ();    Bottom-up build heap for (int i = Heap.size ()/2; I >= 0; i.) {Shifheaddowntoup (i + 1); }}/** * Delete an element, get the index position of this element to delete * 1, the root of the key "and" the last key of the heap K do exchange * 2, the scale of the heap minus one * 3, strictly according to the bottom-up of the algorithm of the practice, bar k down filtering, heap data heap * @param index */publi    c void Delete (int index) {//This is a bottom-up heap of data//bar The last data is populated with the data to be deleted Double Lastvalue = Heap.get (Heap.size ()-1); Remove the last element, the last element to replace theAn element Heap.set (index, lastvalue) that needs to be deleted;    Heap.remove (Heap.size ()-1); Start from bottom up to heap for (int i = index; I >= 0; i) shifheaddowntoup (i + 1);}}

　　

"Algorithm Design and Analysis Basics" 17, heap