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import java.lang.reflect.Array;
import java.lang.Class;
public class SortingAlgos {
/* ************************************************************* */
// Name: Selection Sort
// Basis: Comparison
// Complexity: - Number of comparisons O(n^2)
// - Number of swaps O(n)
// Space: Constant (in place)
// Stability: Not Stable
// Informal Description:
// 1. Find the minimum value in the list
// 2. Swap it with the value in the first position
// 3. Repeat the above steps for the rest of the list,
// starting at the second, third, etc. position each
// time
// Notes:
// Don't confuse this with insertion sort. This algoritm
// isn't used much since its pretty inefficient and
// it's not stable.
/* ************************************************************* */
public static <T extends Comparable<T>> T[] selectionSort(T[] A) {
int min;
T temp = A[0];
for (int i = 0; i < A.length - 1; i++) {
min = i;
for (int j = i + 1; j < A.length; j++) {
if (A[j].compareTo(A[min]) < 0) {
min = j;
}
}
if (i != min) {
temp = A[i];
A[i] = A[min];
}
A[min] = temp;
}
return A;
}
/* ************************************************************* */
// Name: Bubble Sort
// Basis: Comparison
// Complexity: - Number of comparisons O(n^2)
// - Number of swaps O(n^2)
// Space: Constant (in place)
// Stability: Stable
// Informal Description:
// 1. Scan the array, comparing pairs
// 2. If pairs are the wrong way round, swap them
// 3. Repeat until the array is sorted
// Notes:
// This algorithm isn't great. No-one uses it. The only
// advantage I can think of is that it's simple to
// understand. At least it's stable and constant.
/* ************************************************************* */
public static <T extends Comparable<T>> T[] bubbleSort(T[] A) {
T temp;
for (int i = A.length - 1; i > 0; i--) {
for (int j = 0; j < i; j++) {
if (A[j].compareTo(A[j + 1]) > 0) {
temp = A[j];
A[j] = A[j + 1];
A[j + 1] = temp;
}
}
}
return A;
}
/* ************************************************************* */
// Name: Insertion Sort
// Basis: Comparison
// Complexity: - Number of comparisons O(n^2)
// - Number of swaps O(n^2)
// - Best case O(n)
// Space: Constant (in place)
// Stability: Stable
// Informal Description:
// 1. Insert the first element into a new list
// (A list with only one value in it is already sorted)
// 2. Insert the second element into the already sorted
// list
// 3. For the third element to the nth, put in the correct
// place in the new list
// Notes:
// Even though this algorithm is worst case O(n^2), its
// best case is O(n). This is because it doesn't touch
// elements which are already sorted. In some ways, this
// makes it better than some algoritms like mergesort
// who's best and worst case are the same since it always
// deals with every element. This algorithm is actually
// used in real life, for example parts of it are used in
// timsort. It's also uses constant space, and is stable,
// which is poggers.
/* ************************************************************* */
public static <T extends Comparable<T>> T[] insertionSort(T[] A) {
T temp;
int j;
for (int i = 1; i < A.length; i++) {
temp = A[i];
j = i - 1;
while (j >= 0 && temp.compareTo(A[j]) < 0) {
A[j + 1] = A[j];
j--;
}
A[j + 1] = temp;
}
return A;
}
/* ************************************************************* */
// Name: Merge Sort
// Basis: Comparison
// Complexity: - O(nlogn) in all cases
// Space: O(n) memory usage (cannot sort in place)
// Stability: Stable
// Informal Description:
// Merge sort is a recursive algorithm. It works under
// the principal that an array with 1 item in it is sorted.
// In the divide stage, the array is split up into many
// arrays with single items using recursion. Then in the
// conquer stage, elements are recursively merged together
// linearlly.
// Base Case:
// A single element, return it
// Divide Stage:
// Split the array into two halves, left and right
// Recursively call mergeSort on these halves
// Conquer Stage:
// Combine the two sorted arrays into one sorted
// array, using the merge() algorithm
// Notes:
// Invented by von Neumann in 1945.
// The operation is O(nlogn) which is pretty much as good
// as you can get for general-purpose sorting algorithms.
// It has the same complexity in every case, since it
// fiddles with elements even if they're already sorted.
// Consequently insertionSort will be faster if lots of the
// elements are already sorted. Timsort merges the two
// algorithms for the best preformance.
/* ************************************************************* */
public static <T extends Comparable<T>> T[] mergeSort(T[] A) {
throw new UnsupportedOperationException("Not supported yet.");
// return mergeSortRecurse(A, 0, A.length - 1);
}
public static <T> void printArray(T[] A) {
System.out.print("[");
for (T i : A) {
System.out.print(i + ", ");
}
System.out.println("]");
}
public static void main(String[] args) {
Integer myArray[] = {5, 6, 42, 12, 62, 9, 0, 21, 19};
// printArray(selectionSort(myArray));
// printArray(bubbleSort(myArray));
// printArray(insertionSort(myArray));
}
}
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