Javascript/Math: editing file name

Data-Structures/Heap/MinPriorityQueue.js

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+
+/* Minimum Priority Queue
+* It is a part of heap data structure
+* A heap is a specific tree based data structure
+* in which all the nodes of tree are in a specific order.
+* that is the children are arranged in some
+* respect of their parents, can either be greater
+* or less than the parent. This makes it a min priority queue
+* or max priority queue.
+*/
+
+// Functions: insert, delete, peek, isEmpty, print, heapSort, sink
+
+class MinPriorityQueue {
+  // calls the constructor and initializes the capacity
+  constructor (c) {
+    this.heap = []
+    this.capacity = c
+    this.size = 0
+  }
+
+  // inserts the key at the end and rearranges it
+  // so that the binary heap is in appropriate order
+  insert (key) {
+    if (this.isFull()) return
+    this.heap[this.size + 1] = key
+    let k = this.size + 1
+    while (k > 1) {
+      if (this.heap[k] < this.heap[Math.floor(k / 2)]) {
+        const temp = this.heap[k]
+        this.heap[k] = this.heap[Math.floor(k / 2)]
+        this.heap[Math.floor(k / 2)] = temp
+      }
+      k = Math.floor(k / 2)
+    }
+    this.size++
+  }
+
+  // returns the highest priority value
+  peek () {
+    return this.heap[1]
+  }
+
+  // returns boolean value whether the heap is empty or not
+  isEmpty () {
+    return this.size === 0
+  }
+
+  // returns boolean value whether the heap is full or not
+  isFull () {
+    if (this.size === this.capacity) return true
+    return false
+  }
+
+  // prints the heap
+  print () {
+    console.log(this.heap.slice(1))
+  }
+
+  // heap sorting can be done by performing
+  // delete function to the number of times of the size of the heap
+  // it returns reverse sort because it is a min priority queue
+  heapSort () {
+    for (let i = 1; i < this.capacity; i++) {
+      this.delete()
+    }
+  }
+
+  // this function reorders the heap after every delete function
+  sink () {
+    let k = 1
+    while (2 * k <= this.size || 2 * k + 1 <= this.size) {
+      let minIndex
+      if (this.heap[2 * k] >= this.heap[k]) {
+        if (2 * k + 1 <= this.size && this.heap[2 * k + 1] >= this.heap[k]) {
+          break
+        } else if (2 * k + 1 > this.size) {
+          break
+        }
+      }
+      if (2 * k + 1 > this.size) {
+        minIndex = this.heap[2 * k] < this.heap[k] ? 2 * k : k
+      } else {
+        if (
+          this.heap[k] > this.heap[2 * k] ||
+          this.heap[k] > this.heap[2 * k + 1]
+        ) {
+          minIndex =
+            this.heap[2 * k] < this.heap[2 * k + 1] ? 2 * k : 2 * k + 1
+        } else {
+          minIndex = k
+        }
+      }
+      const temp = this.heap[k]
+      this.heap[k] = this.heap[minIndex]
+      this.heap[minIndex] = temp
+      k = minIndex
+    }
+  }
+
+  // deletes the highest priority value from the heap
+  delete () {
+    const min = this.heap[1]
+    this.heap[1] = this.heap[this.size]
+    this.heap[this.size] = min
+    this.size--
+    this.sink()
+    return min
+  }
+}
+
+// testing
+const q = new MinPriorityQueue(8)
+
+q.insert(5)
+q.insert(2)
+q.insert(4)
+q.insert(1)
+q.insert(7)
+q.insert(6)
+q.insert(3)
+q.insert(8)
+q.print() // [ 1, 2, 3, 5, 7, 6, 4, 8 ]
+q.heapSort()
+q.print() // [ 8, 7, 6, 5, 4, 3, 2, 1 ]