Bucket Sort

Bucket sort, or bin sort, is a sorting algorithm that works by partitioning an array into a number of buckets. Each bucket is then sorted individually, either using a different sorting algorithm, or by recursively applying the bucket sorting algorithm. (Wikipedia)

Worst case performance - O(n^2)

Bucket Sort

C++

void bucket_sort(int array[], int length)
{
    int k = max_value(array,length);
    int bucket[k+1];
    for(int i=0; i<=k; i++)
    {
        bucket[i] = 0;
    }
    for(int j=0; j<length; j++)
    {
        bucket[array[j]]++;
    }
    int index = 0;
    for (int i=0; i<=k; i++)
    {
        for (int j=0; j<bucket[i]; j++)
        {
            array[index++]=i;
        }
    }
}

int max_value(int array[], int length)
{
    int max = 0;
    for (int i = 0; i < length; i++)
        if (max < array[i])
            max = array[i];
    return max;
}

Java

public int[] bucket_sort(int[] array) {
    int k = max_value(array);
    int[] bucket = new int[k+1];
    for (int j = 0; j < array.length; j++) {
        bucket[array[j]]++;
    }
    int index = 0;
    for (int i = 0; i < bucket.length; i++) {
        for (int j = 0; j < bucket[i]; j++) {
            array[index++] = i;
        }
    }
    return array;
}

public int max_value(int[] array) {
    int max = 0;
    for (int i = 0; i < array.length; i++) {
        if (max < array[i]) {
            max = array[i];
        }
    }
    return max;
}

Matlab

function array = bucket_sort(array)
    k = max(array);
    bucket = zeros(1,k+1);
    B = zeros(1,numel(array));
    for j = 1:numel(array)
        bucket(array(j)) = bucket(array(j)) + 1;
    end
    index = 1;
    for i = 1:k+1
        for j = 1:bucket(i)
            array(index) = i;
            index = index + 1;
        end
    end
end

Python

def bucket_sort(array):
    k = max(array)
    bucket = [0]* (k+1)
    for j in range(len(array)):
        bucket[array[j]] = bucket[array[j]] + 1
    index = 0
    for i in range(k+1):
        for j in range(bucket[i]):
            array[index] = i
            index = index + 1

Shell Sort

Shell sort is an in-place comparison sort. It can be seen as either a generalization of sorting by exchange (bubble sort) or sorting by insertion (insertion sort). Shell sort starts by sorting pairs of elements far apart from each other, then progressively reducing the gap between elements to be compared. (Wikipedia)

Worst case performance - O(n^2)

Shell sort

C++

void shell_sort(int array[], int length)
{
    for (int gap = length/2; gap > 0; gap /= 2)
    {
        for (int i = gap; i < length; i += 1)
        {
            int temp = array[i];
            int j;
            for (j = i; j >= gap && array[j - gap] > temp; j -= gap)
                array[j] = array[j - gap];
            array[j] = temp;
        }
    }
}

Java

     public int[] shell_sort(int[] array) {
        for (int gap = array.length / 2; gap > 0; gap /= 2) {
            for (int i = gap; i < array.length; i += 1) {
                int temp = array[i];
                int j;
                for (j = i; j >= gap && array[j - gap] > temp; j -= gap) {
                    array[j] = array[j - gap];
                }
                array[j] = temp;
            }
        }
        return array;
    } 

Matlab

function array = shell_sort(array)
    length = numel(array);
    gap = floor(length/2);
    while gap > 0
        for i = gap+1:length
            temp = array(i);
            j = i;
            while (j >= gap+1) && (array(j-gap) > temp)
                array(j) = array(j-gap);
                j = j - gap;
            end
            array(j) = temp;
        end
        gap = floor(gap/2);
    end
end

Python

import math

def shell_sort(array):
    length = len(array)
    gap = math.floor(length/2)
    while gap > 0:
        for i in range(gap,length):
            temp = array[i]
            j = i
            while (j >= gap) and (array[j - gap] > temp):
                array[j] = array[j-gap]
                j = j - gap
            array[j] = temp
        gap = math.floor(gap/2) 

Comb Sort

Comb sort is a comparison sorting algorithm improves on Bubble sort. The basic idea is to eliminate turtles, or small values near the end of the list, since in a bubble sort these slow the sorting down tremendously. (Wikipedia)

Comb Sort

Worst case performance - O(n^2)

C++

void comb_sort(int array[], int length)
{
    int gap = length;
    bool swapped = true;
    while((gap > 1) || (swapped == true))
    {
        gap /= 1.25;
        if (gap < 1)
        {
            gap = 1;
        }
        int i = 0;
        swapped = false;
        while (i + gap < length)
        {
            if (array[i] > array[i + gap])
            {
                int temp = array[i];
                array[i] = array[i + gap];
                array[i + gap] = temp;
                swapped = true;
            }
            i++;
        }
    }
} 

Java

      public int[] comb_sort(int[] array) {
        int gap = array.length;
        boolean swapped = true;
        while ((gap > 1) || (swapped == true)) {
            gap /= 1.25;
            if (gap < 1) {
                gap = 1;
            }
            int i = 0;
            swapped = false;
            while (i + gap < array.length) {
                if (array[i] > array[i + gap]) {
                    int temp = array[i];
                    array[i] = array[i + gap];
                    array[i + gap] = temp;
                    swapped = true;
                }
                i++;
            }
        }
        return array;
    }

Matlab

function array = comb_sort(array)
    gap = numel(array);
    swapped = true;
    while (gap > 1) || swapped
        gap = floor(gap/1.25);
        if gap < 1
            gap = 1;
        end
        i = 1;
        swapped = false;
        while (i+gap-1) < numel(array)
            if array(i) > array(i+gap)
                array([i i+gap]) = array([i+gap i]);
                swapped = true;
            end
            i = i + 1;
        end
    end
end

Python

import math

def comb_sort(array):
    gap = len(array)
    swapped = True
    while gap > 1 or swapped :
        gap = math.floor(gap/1.25)
        if gap < 1 :
            gap = 1
        i = 0
        swapped = False
        while (i+gap)< len(array):
            if array[i] > array[i + gap]:
                temp = array[i]
                array[i] = array[i + gap]
                array[i + gap] = temp
                swapped = True
            i = i + 1

Radix Sort

The idea of explained Radix Sort is to do digit by digit sort starting from least significant digit to most significant digit.

Worst case performance - O(n.k/d)

Radix sort

 C++

void radix_sort(int array[], int length)
{
    int k = max_value(array,length);
    for (int base = 1; k/base > 0; base *= 10)
        counting_sort(array,length,base);
}

void counting_sort(int array[], int length, int base)
{
    int C[10] = {0};
    int B[length];
    for (int j = 0; j < length; j++)
        C[(array[j]/base)%10]++;
    for (int i = 1; i < 10; i++)
        C[i] = C[i] + C[i - 1];
    for (int j = length - 1; j >= 0; j--)
    {
        B[C[(array[j]/base)%10] - 1] = array[j];
        C[(array[j]/base)%10]--;
    }
    for (int i = 0; i < length; i++)
        array[i] = B[i];
}

int max_value(int array[], int length)
{
    int max = 0;
    for (int i = 0; i < length; i++)
        if (max < array[i])
            max = array[i];
    return max;
} 

Java

public int[] radix_sort(int[] array) {
    int k = max_value(array);
    for (int base = 1; k / base > 0; base *= 10) {
        array = counting_sort(array, base);
    }
    return array;
}
public int[] counting_sort(int[] array, int base) {
    int[] C = new int[10];
    int[] B = new int[array.length];
    for (int j = 0; j < array.length; j++) {
        C[(array[j]/base)%10]++;
    }
    for (int i = 1; i < 10; i++) {
        C[i] = C[i] + C[i - 1];
    }
    for (int j = array.length - 1; j >= 0; j--) {
        B[C[(array[j]/base)%10] - 1] = array[j];
        C[(array[j]/base)%10]--;
    }
    return B;
}
public int max_value(int[] array) {
    int max = 0;
    for (int i = 0; i < array.length; i++) {
        if (max < array[i]) {
            max = array[i];
        }
    }
    return max;
} 

Matlab

function array = radix_sort(array)
k = max(array);
base = 1;
while k/base > 0
    array = counting_sort(array,base);
    base = base * 10;
end

    function B = counting_sort(array,base)
        C = zeros(1,11);
        B = zeros(1,numel(array));
        for j = 1:numel(array)
            C(rem(floor(array(j)/base),10)+1) = C(rem(floor(array(j)/base),10)+1) + 1;
        end
        for i = 2:11
            C(i) = C(i) + C(i-1);
        end
        for j = numel(array):-1:1
            B(C(rem(floor(array(j)/base),10)+1)) = array(j);
            C(rem(floor(array(j)/base),10)+1) = C(rem(floor(array(j)/base),10)+1) - 1;
        end
    end
end 

Python

import math

def counting_sort(array,base):
    C = [0]* (10)
    B = [0]* (len(array))
    for j in range(len(array)):
        C[math.floor(array[j]/base)%10] = C[math.floor(array[j]/base)%10] + 1
    for i in range(1,10):
        C[i] = C[i] + C[i-1]
    for j in range(len(array)-1,-1,-1):
        B[C[math.floor(array[j]/base)%10] - 1] = array[j]
        C[math.floor(array[j]/base)%10] = C[math.floor(array[j]/base)%10] - 1
    for i in range(len(array)):
        array[i] = B[i]

def radix_sort(array):
    k = max(array)
    base = 1
    while(k/base > 0):
        counting_sort(array,base)
        base = base * 10 

Counting Sort

Counting sort determines, for each input element x, the number of elements less than x. It uses this information to place element x directly into its position in the output array. (Introduction to Algorithms)

Worst case performance - O(n + k)

 

Counting sort

 C++

void counting_sort(int array[], int length)
{
    int k = max_value(array,length);
    int C[k+1];
    int B[length];
    memset(C, 0, sizeof(C));
    for (int j = 0; j < length; j++)
        C[array[j]]++;
    for (int i = 1; i <= k; i++)
        C[i] = C[i] + C[i - 1];
    for (int j = length - 1; j >= 0; j--)
    {
        B[C[array[j]] - 1] = array[j];
        C[array[j]]--;
    }
    print_array(B,length);
}

int max_value(int array[], int length)
{
    int max = 0;
    for (int i = 0; i < length; i++)
        if (max < array[i])
            max = array[i];
    return max;
}

void print_array(int array[], int length)
{
    for(int i=0; i<length; i++)
        cout <<array[i]<<" ";
    cout << endl;
}

 Java

public int[] counting_sort(int[] array) {
    int k = max_value(array);
    int[] C = new int[k + 1];
    int[] B = new int[array.length];
    for (int j = 0; j < array.length; j++) {
        C[array[j]]++;
    }
    for (int i = 1; i <= k; i++) {
        C[i] = C[i] + C[i - 1];
    }
    for (int j = array.length - 1; j >= 0; j--) {
        B[C[array[j]] - 1] = array[j];
        C[array[j]]--;
    }
    return B;
}

public int max_value(int[] array) {
    int max = 0;
    for (int i = 0; i < array.length; i++) {
        if (max < array[i]) {
            max = array[i];
        }
    }
    return max;
}

 Matlab

function B = counting_sort(array)
    k = max(array);
    C = zeros(1,k+1);
    B = zeros(1,numel(array));
    for j = 1:numel(array)
        C(array(j)) = C(array(j)) + 1;
    end
    for i = 2:k+1
        C(i) = C(i) + C(i-1);
    end
    for j = numel(array):-1:1
        B(C(array(j))) = array(j);
        C(array(j)) = C(array(j)) - 1;
    end
end

 Python

def counting_sort(array):
    k = max(array)
    C = [0]* (k+1)
    B = [0]* (len(array))
    for j in range(len(array)):
        C[array[j]] = C[array[j]] + 1
    for i in range(1,k+1):
        C[i] = C[i] + C[i-1]
    for j in range(len(array)-1,-1,-1):
        B[C[array[j]] - 1] = array[j]
        C[array[j]] = C[array[j]] - 1
    print(B)

Heap Sort

Heap sort divides its input into a sorted and an unsorted region, and it iteratively shrinks the unsorted region by extracting the largest element and moving that to the sorted region. The improvement consists of the use of a heap data structure rather than a linear-time search to find the maximum.(Wikipedia)

Worst case performance - O(n log n)

Heap sort

C++

void heap_sort(int array[],int heap_size){
    build_max_heap(array,heap_size);
    for (int i = heap_size; i > 0; i--) {
        swap(array,0, i);
        heap_size = heap_size - 1;
        max_heapify(array, 0,heap_size);
    }
}

void build_max_heap(int array[], int heap_size){
    for (int i = heap_size / 2; i >= 0; i--){
        max_heapify(array, i,heap_size);
    }
}

void max_heapify(int array[], int i,int heap_size){
    int l = left(i);
    int r = right(i);
    int largest;
    if (l <= heap_size && array[l] > array[i]){
        largest = l;
    }
    else{
        largest = i;
    }
    if (r <= heap_size && array[r] > array[largest]){
        largest = r;
    }
    if (largest != i){
        swap(array,i, largest);
        max_heapify(array, largest,heap_size);
    }
}

int left(int i){
    return 2 * i;
}

int right(int i){
    return 2 * i + 1;
}

void swap(int array[], int i, int j)
{
    int temp = array[i];
    array[i] = array[j];
    array[j] = temp;
}

Java

    public void heap_sort(int[] array) {
        build_max_heap(array);
        for (int i = heap_size; i > 0; i--) {
            swap(0, i);
            heap_size = heap_size - 1;
            max_heapify(array, 0);
        }
    }
   
    public void build_max_heap(int[] array) {
        heap_size = array.length - 1;
        for (int i = heap_size / 2; i >= 0; i--) {
            max_heapify(array, i);
        }
    }
   
    public void max_heapify(int[] array, int i) {
        int l = left(i);
        int r = right(i);
        int largest;
        if (l <= heap_size && array[l] > array[i]) {
            largest = l;
        } else {
            largest = i;
        }
        if (r <= heap_size && array[r] > array[largest]) {
            largest = r;
        }
        if (largest != i) {
            swap(i, largest);
            max_heapify(array, largest);
        }
    }
   
    public int left(int i) {
        return 2 * i;
    }
   
    public int right(int i) {
        return 2 * i + 1;
    }
   
    public void swap(int i, int j) {
        int temp = array[i];
        array[i] = array[j];
        array[j] = temp;
    }

Matlab

function array = heap_sort(array)
    heap_size = numel(array);
    array = build_max_heap(array,heap_size);
    for i = numel(array):-1:2
        array([i 1]) = array([1 i]);
        heap_size = heap_size - 1;
        array = max_heapify(array,heap_size,1);
    end
end

function array = build_max_heap(array,heap_size)
    for i = floor(heap_size/2):-1:1
        array = max_heapify(array,heap_size,i);
    end
end

function array = max_heapify(array,heap_size,i)
    l = left(i);
    r = right(i);
    if l <= heap_size && array(l) > array(i)
        largest = l;
    else
        largest = i;
    end
    if r <= heap_size && array(r) > array(largest)
        largest = r;
    end
    if largest ~= i
        array([largest i]) = array([i largest]);
        array = max_heapify(array,heap_size,largest);
    end
end

function l = left(i)
    l = 2 * i;
end

function r = right(i)
    r = 2 * i + 1;
end

Python

import math

def left(i):
    return 2*i

def right(i):
    return 2*i+1

def max_heapify(array,heap_size,i):
    l = left(i)
    r = right(i)
    if l <= heap_size and array[l] > array[i]:
        largest = l
    else:
        largest = i
    if r <= heap_size and array[r] > array[largest]:
        largest = r
    if largest != i:
        array[largest],array[i] = array[i],array[largest]
        max_heapify(array,heap_size,largest)

def build_max_heap(array,heap_size):
    for i in range(math.floor(heap_size/2),-1,-1):
        max_heapify(array,heap_size,i)

def heap_sort(array):
    heap_size = len(array)- 1
    build_max_heap(array,heap_size)
    for i in range(heap_size,0,-1):
        array[i],array[0] = array[0],array[i]
        heap_size = heap_size - 1
        max_heapify(array,heap_size,0)

Quick Sort

Quicksort is a divide and conquer algorithm. Quicksort first divides a large array into two smaller sub-arrays, the low elements and the high elements. Quicksort can then recursively sort the sub-arrays. (Wikipedia)

Worst case performance - O(n^2)

Source - Wikipedia

 C++

void quick_sort(int array[], int low, int high)
{
    if (low < high)
    {
        int q = partition(array, low, high);
        quick_sort(array, low, q - 1);
        quick_sort(array, q + 1, high);
    }
}

int partition(int array[], int low, int high)
{
    int pivot = array[high];
    int i = low - 1;
    for (int j = low; j < high; j++)
    {
        if (array[j] <= pivot)
        {
            i++;
            swap(array, i, j);
        }
    }
    swap(array, i + 1, high);
    return i + 1;
}

void swap(int array[], int i, int j)
{
    int temp = array[i];
    array[i] = array[j];
    array[j] = temp;
}

Java

public void quick_sort(int[] array, int low, int high) {
    if (low < high) {
        int q = partition(array, low, high);
        quick_sort(array, low, q - 1);
        quick_sort(array, q + 1, high);
    }
}

public int partition(int[] array, int low, int high) {
    int pivot = array[high];
    int i = low - 1;
    for (int j = low; j < high; j++) {
        if (array[j] <= pivot) {
            i++;
            swap(array, i, j);
        }
    }
    swap(array, i + 1, high);
    return i + 1;
}

public void swap(int[] array, int i, int j) {
    int temp = array[i];
    array[i] = array[j];
    array[j] = temp;
}

Matlab

function array = quick_sort(array,low,high)
    if(low < high)
        [array,q] = partition(array, low, high);
        array = quick_sort(array, low, q - 1);
        array = quick_sort(array, q + 1, high);
    end
end

function [array,q] = partition(array,low,high)
    pivot = array(high);
    i = low - 1;
    for j= low : high - 1
        if (array(j) <= pivot)
            i = i + 1;
            array([j i]) = array([i j]);
        end
    end
    array([high i+1]) = array([i+1 high]);
    q = i + 1;
end

Python

def partition(array,low,high):
    pivot = array[high]
    i = low - 1
    for j in range(low,high):
        if array[j] <= pivot:
            i = i + 1
            array[i],array[j] = array[j],array[i]
    array[i+1],array[high] = array[high],array[i+1]
    return i+1

def quick_sort(array,low,high):
    if low < high:
        q = partition(array,low,high)
        quick_sort(array,low,q - 1)
        quick_sort(array,q + 1,high)

Tracking the Trajectory of a mass using linear Kalman filter

The Kalman filter is an algorithm that estimates the state of a process from measured data. The word filter comes because of the estimations given for unknown variables, are more precise than noisy, inaccurate measurements taken from the environment. Kalman filter algorithm is a two stage process. The first stage (prediction) predicts the state of the system and the second stage (observation and update) updates the predicted state using the observed noisy measurements. In this post I explain an application of tracking the trajectory of a mass using linear Kalman filter. First let's have an idea about the kinematic model of a trajectory. 

Initial conditions

Now, let's look at the discrete linear Kalman filter equations.

Matlab code

 clear all; close all; clc;  
 N = 145; % no of iterations  
   
 %% parameters  
 v = 100; % initial velocity  
 T = 14.42096; % flight duration  
 deltaT = 0.1; % time slice  
 g = 9.80665;   
 theta = pi/4; % angle from the ground  
 F = [1 deltaT 0 0; 0 1 0 0; 0 0 1 deltaT; 0 0 0 1]; % state transition model  
 B = [0 0 0 0; 0 0 0 0; 0 0 1 0; 0 0 0 1]; % control input matrix  
 u = [0; 0; 0.5*(-g)*(deltaT^2); (-g)*deltaT]; % control vector  
 H = [1 0 0 0; 0 0 1 0]; % observation matrix  
 phat = eye(4); % initial predicted estimate covariance  
 Q = zeros(4); % process covariance  
 R = 0.2*eye(2); % measurement error covariance  
 xhat = [0; v*cos(theta); 200; v*sin(theta)]; % initial estimate  
 x_estimate = xhat;  
   
 %% system model  
 t = 0:deltaT:T;  
 x_sys = zeros(2,N);  
 x_sys(1,:) = v*cos(theta)*t ;  
 x_sys(2,:) = v*(sin(theta)*t)-(0.5*g*(t.^2));  
   
 %% noisy measurements  
 sigma = 25;  
 z = x_sys + sigma*randn(2,N);  
   
 %% kalman filter  
 for i=1:N  
   % prediction  
   x_pred = F*xhat + B*u; % project the state ahead  
   p_pred = F*phat*F' + Q; % project the error covariance  
     
   % observation and update  
   kalman_gain = (p_pred*H')/(H*p_pred*H'+R); % compute the Kalman gain  
   xhat = x_pred + kalman_gain*(z(:,i)-H*x_pred); % update estimate with measurement z  
   phat = (eye(4)-kalman_gain*H)*p_pred; % update error covariance   
   x_estimate = [x_estimate xhat]; %#ok[agrow] (to ignore array grow in loop warning)  
 end  
   
 %% plot results  
 figure; hold on;  
 plot(x_sys(1,:),x_sys(2,:),'k');  
 plot(z(1,:),z(2,:),'.b');  
 plot(x_estimate(1,:),x_estimate(3,:),'--r');  
   
 xlabel('X (meters)');  
 ylabel('Y (meters)');  
 title('Trajectory with linear Kalman filter');  
 legend('System model', 'Measured', 'Estimated');  

Simulation Results

 * You can download the matlab file from here.

How to add an echo effect to an audio signal using Matlab

In this post I explain how to add an echo to an audio signal using Matlab. If you closely look at the below code, you can understand, what kind of a process is there. Initially the original signal x is delayed by 0.5 seconds and then multiplied by the attenuation constant alpha(0.65) to reduce the amplitude of the echo signal. Finally the delayed and attenuated signal is added back to the original signal to get the echo effect of the audio signal. You can visualize this process using the below mentioned Matlab simulink model.

Matlab code

 clear all;  
 %% Hallelujah Chorus  
 [x,Fs] = audioread('Hallelujah.wav');  
 sound(x,Fs);  
 pause(10);  
 delay = 0.5; % 0.5s delay  
 alpha = 0.65; % echo strength  
 D = delay*Fs;  
 y = zeros(size(x));  
 y(1:D) = x(1:D);  
   
 for i=D+1:length(x)  
   y(i) = x(i) + alpha*x(i-D);  
 end  
   
 %% using filter method.  
 % b = [1,zeros(1,D),alpha];  
 % y = filter(b,1,x);  
   
 %% echoed Hallelujah Chorus  
 sound(y,Fs);  

 * You can download Hallelujah.wav from here or else you can find more .wav files from WavSource.com. Varying the value of alpha would change the echo strength of the audio signal.

Original and echoed audio signals in Audacity

You can create the Matlab simulink model for echo generation as follows.

Matlab simulink model for echo generation

Model parameters

• From Multimedia File 

samples per audio channel - 8192
Audio output sampling mode - Frame based
Audio output data type - double

• Gain

 Gain - 0.65

• Delay

Delay length - 4096
Input Processing - Columns as channels (frame based)

* If you use any other .wav file other than Hallelujah.wav, then you should change the above parameters accordingly.

Merge Sort

The basic idea of the algorithm is to split the collection into smaller groups by halving it until the groups only have one element or no elements (which are both entirely sorted groups). Then merge the groups back together so that their elements are in order (Rosetta code). Merge sort follows divide-and-conquer approach.

Worst case performance - O(n log n)

Source - Wikipedia

 C++

 void merge_sort(int array[], int low, int high)  
 {  
   if (low < high)  
   {  
     int middle = (low + high) / 2;  
     merge_sort(array,low, middle);  
     merge_sort(array,middle + 1, high);  
     merge(array, low, middle, high);  
   }  
 }  
   
 void merge(int array[], int low, int middle, int high)  
 {  
   int size1 = middle - low + 1;  
   int size2 = high - middle;  
   int left[size1 + 1];  
   int right[size2 + 1];  
   for (int i = 0; i < size1; i++)  
   {  
     left[i] = array[low + i];  
   }  
   for (int j = 0; j < size2; j++)  
   {  
     right[j] = array[middle + j + 1];  
   }  
   left[size1] = numeric_limits<int>::max();;  
   right[size2] = numeric_limits<int>::max();;  
   int i = 0;  
   int j = 0;  
   for (int k = low; k <= high; k++)  
   {  
     if (left[i] <= right[j])  
     {  
       array[k] = left[i];  
       i++;  
     }  
     else  
     {  
       array[k] = right[j];  
       j++;  
     }  
   }  
 }   

Java

   static int[] array = {5, 4, 8, 3, 1, 2, 9, 6, 7, 10};  
   
   static void merge_sort(int[] array, int low, int high) {  
       if (low < high) {  
         int middle = (low + high) / 2;  
         merge_sort(array, low, middle);  
         merge_sort(array, middle + 1, high);  
         merge(array, low, middle, high);  
       }  
    }  
   
   static void merge(int[] array, int low, int middle, int high) {  
       int size1 = middle - low + 1;  
       int size2 = high - middle;  
       int[] left = new int[size1 + 1];  
       int[] right = new int[size2 + 1];  
       for (int i = 0; i < size1; i++) {  
         left[i] = array[low + i];  
       }  
       for (int j = 0; j < size2; j++) {  
         right[j] = array[middle + j + 1];  
       }  
       left[size1] = Integer.MAX_VALUE;  
       right[size2] = Integer.MAX_VALUE;  
       int i = 0;  
       int j = 0;  
       for (int k = low; k <= high; k++) {  
         if (left[i] <= right[j]) {  
           array[k] = left[i];  
           i++;  
         } else {  
           array[k] = right[j];  
           j++;  
         }  
       }  
    }  

Matlab

   function array = merge_sort(array,low,high)  
     if(low < high)  
       middle = floor((low + high)/2);  
       array = merge_sort(array,low, middle);  
       array = merge_sort(array,middle + 1, high);  
       array = merge(array, low, middle, high);  
     end  
   end  
   
   function array = merge(array,low,middle,high)  
     size1 = middle - low + 1;  
     size2 = high - middle;  
     left = zeros(1,size1+1);  
     right = zeros(1,size2+1);  
     for i=1:size1  
       left(i) = array(low+i-1);  
     end  
     for j=1:size2  
       right(j) = array(middle+j);  
     end  
     left(size1+1) = inf;  
     right(size2+1) = inf;  
     i = 1;  
     j = 1;  
     for k=low:high  
       if left(i)<= right(j)  
         array(k) = left(i);  
         i = i + 1;  
       else  
         array(k) = right(j);  
         j = j + 1;  
       end  
     end  
   end   

Python 

import math

def merge(array,low,middle,high):
    size1 = middle - low + 1
    size2 = high - middle
    left = [0]* (size1+1)
    right = [0]* (size2+1)
    for i in range(size1):
        left[i] = array[low+i]
    for j in range(size2):
        right[j] = array[middle+j+1]
    left[size1] = float("inf")
    right[size2] = float("inf")
    i = 0
    j = 0
    for k in range(low,high+1):
        if left[i]<= right[j]:
            array[k] = left[i]
            i = i + 1
        else:
            array[k] = right[j]
            j = j + 1
   
def merge_sort(array,low,high):
    if low<high:
        middle = int(math.floor((low+high)/2))
        merge_sort(array,low,middle)
        merge_sort(array,middle+1,high)
        merge(array,low,middle,high)

Bubble Sort

Bubble sort is a simple sorting algorithm that repeatedly steps through the list to be sorted, compares each pair of adjacent items and swaps them if they are in the wrong order. The pass through the list is repeated until no swaps are needed, which indicates that the list is sorted. (Wikipedia)

Worst case performance - O(n^2)

Source - Wikipedia

C++

void bubble_sort(int array[], int length)
{
    bool swapped;
    do
    {
        swapped = false;
        for (int i = 1; i < length; i++)
        {
            if (array[i - 1] > array[i])
            {
                int temp = array[i];
                array[i] = array[i - 1];
                array[i - 1] = temp;
                swapped = true;
            }
        }

    }
    while (swapped);
}

Java

public int[] bubble_sort(int[] array) {
    boolean swapped;
    do {
        swapped = false;
        for (int i = 1; i < array.length; i++) {
            if (array[i - 1] > array[i]) {
                int temp = array[i];
                array[i] = array[i - 1];
                array[i - 1] = temp;
                swapped = true;
            }
        }

    } while (swapped);
    return array;
}

Matlab

function array = bubble_sort(array)
    length = numel(array);
    swapped = true;
    while swapped
        swapped = false;
        for i=2:length
            if array(i-1)>array(i)
                array([(i-1) i]) = array([i (i-1)]);
                swapped = true;
            end
        end
    end
end

Python

def bubble_sort(array):
  swapped = True
 
  while swapped:
    swapped = False
   
    for i in range(1,len(array)):
      if array[i-1]>array[i]:
        temp = array[i]
        array[i] = array[i - 1]
        array[i - 1] = temp
        swapped = True
      

More generally, it can happen that more than one element is placed in their final position on a single pass. In particular, after every pass, all elements after the last swap are sorted, and do not need to be checked again. (Wikipedia). Therefore we can further optimize the algorithm. Here is the Java implementation of the optimized algorithm.

 public int[] bubble_sort(int[] array) {
    int n = array.length;
    int newLimit = 0;
    boolean swapped;
    do {
        swapped = false;
        for (int i = 1; i < n; i++) {
            if (array[i - 1] > array[i]) {
                int temp = array[i];
                array[i] = array[i - 1];
                array[i - 1] = temp;
                swapped = true;
                newLimit = i;
            }
        }
        n = newLimit;

    } while (swapped);
    return array;
}

Selection Sort

The main idea of the algorithm is quite simple as follows. The input data array is divided to two imaginary parts, sorted and unsorted. Initially the sorted part is empty. The algorithm searches for the smallest element of the unsorted part and adds it to the end of the sorted part. When the unsorted part becomes empty algorithm stops. However Selection sort is inefficient in sorting large sets.

Worst case performance - O(n^2)

Source - Quazoo

C++

void selection_sort(int array[], int length)
{
    for (int j = 0; j < length - 1; j++)
    {
        int min = j;
        for (int i = j + 1; i < length; i++)
        {
            if (array[i] < array[min])
            {
                min = i;
            }
        }
        if (min != j)
        {
            int temp = array[j];
            array[j] = array[min];
            array[min] = temp;
        }
    }
}

 Java

public int[] selection_sort(int[] array) {
    for (int j = 0; j < array.length - 1; j++) {
        int min = j;
        for (int i = j + 1; i < array.length; i++) {
            if (array[i] < array[min]) {
                min = i;
            }
        }
        if (min != j) {
            int temp = array[j];
            array[j] = array[min];
            array[min] = temp;
        }
    }
    return array;
}

Matlab

function array = selection_sort(array)
    length = numel(array);
    for i = (1:length-1)
        min = i;
        for j = (i+1:length)
            if array(j) <= array(min)
                min = j;
            end
        end
        if i ~= min
            array([min i]) = array([i min]);
        end
    end
end

Python

def selection_sort(array):
  for j in range(len(array)-1):
 
    min = j
    
    for i in range(j+1,len(array)):
      if array[i] < array[min]:
        min = i
        
    if min != j:
      temp = array[j]
      array[j] = array[min]
      array[min] = temp