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Algorithms - Radix Sort





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Radix Sort

We may say a radix is a position in a number.


In the decimal system, a radix is just a digit in a decimal number. For example, the number 42 has two digits, or two radices, which are 4 and 2. In hexadecimal, the radix is 8 bits wide. For example, the hexadecimal number 0xAB has two radices, A and B. The Radix Sort gets its name from those radices, because the method first sorts the input values according to their first radix, then according to the second one, and so on. The Radix Sort is then a multipass sort, and the number of passes equals the number of radices in the input values.

  1. Least significant digit (LSD)
    Short keys come before longer keys, and keys of the same length are sorted lexicographically.
    1, 2, 3, 4, 5, 6, 7, 8, 9, 10
  2. Most significant digit (MSD)
    MSD radix sorts use lexicographic order, which is suitable for sorting strings, such as words, or fixed-length integer representations.
    "b, c, d, e, f, g, h, i, j, ba" would be lexicographically sorted as "b, ba, c, d, e, f, g, h, i, j"
    Numbers from 1 to 10 would be output as 1, 10, 2, 3, 4, 5, 6, 7, 8, 9
Radix Sort - LSD

Each key is first figuratively dropped into one level of buckets corresponding to the value of the rightmost digit. Each bucket preserves the original order of the keys as the keys are dropped into. There is a one-to-one correspondence between the number of buckets and the number of values that can be represented by a digit. Then, the process repeats with the next neighboring digit until there are no more digits to process. In other words:

  1. Take the least significant digit (or group of bits, both being examples of radices) of each key.
  2. Group the keys based on that digit, but otherwise keep the original order of keys. (This is what makes the LSD radix sort a stable sort).
  3. Repeat the grouping process with each more significant digit.

The sort in step 2 is usually done using bucket sort or counting sort, which are efficient in this case since there are usually only a small number of digits.

-from wiki

C++ Code:


#include <iostream>

using namespace std;
const int MAX = 10;

void print(int *a,int sz) {               
	for(int i = 0; i < sz; i++)
		cout << a[i] << " ";
	cout << endl;
}

void RadixSortLSD(int *a, int arraySize)
{
	int i, bucket[MAX], maxVal = 0, digitPosition =1 ;
	for(i = 0; i < arraySize; i++) {
		if(a[i] > maxVal) maxVal = a[i];
	}

	int pass = 1;  // used to show the progress
	/* maxVal: this variable decide the while-loop count 
	           if maxVal is 3 digits, then we loop through 3 times */
	while(maxVal/digitPosition > 0) {
		/* reset counter */
		int digitCount[10] = {0};

		/* count pos-th digits (keys) */
		for(i = 0; i < arraySize; i++)
			digitCount[a[i]/digitPosition%10]++;

		/* accumulated count */
		for(i = 1; i < 10; i++)
			digitCount[i] += digitCount[i-1];

		/* To keep the order, start from back side */
		for(i = arraySize - 1; i >= 0; i--)
			bucket[--digitCount[a[i]/digitPosition%10]] = a[i];

		/* rearrange the original array using elements in the bucket */
		for(i = 0; i < arraySize; i++)
			a[i] = bucket[i];

		/* at this point, a array is sorted by digitPosition-th digit */
		cout << "pass #" << pass++ << ": ";
		print(a,arraySize);

		/* move up the digit position */
		digitPosition *= 10;
	}   
 }
 
int main()
{
	int a[] = {170, 45, 75, 90, 2, 24, 802, 66};
	const size_t sz = sizeof(a)/sizeof(a[0]);

	cout << "pass #0: ";
	print(a,sz);
	RadixSortLSD(&a;[0],sz);
	return 0;
}

Output from the run is:

pass #0: 170 45 75 90 2 24 802 66 
pass #1: 170 90 2 802 24 45 75 66 
pass #2: 2 802 24 45 66 170 75 90 
pass #3: 2 24 45 66 75 90 170 802 


Here is a diagram for the first pass of the code above.

radixsort_diagram








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LIST OF ALGORITHMS



Algorithms - Introduction

Bubble Sort

Bucket Sort

Counting Sort

Heap Sort

Insertion Sort

Merge Sort

Quick Sort

Radix Sort - LSD

Selection Sort

Shell Sort



Queue/Priority Queue - Using linked list & Heap

Stack Data Structure

Trie Data Structure

Binary Tree Data Structure - BST

Hash Map/Hash Table

Linked List Data Structure

Closest Pair of Points

Spatial Data Structure and Physics Engines



Recursive Algorithms

Dynamic Programming

Knapsack Problems - Discrete Optimization

(Batch) Gradient Descent in python and scikit



Uniform Sampling on the Surface of a Sphere.

Bayes' Rule

Monty Hall Paradox

Compression Algorithm - Huffman Codes

Shannon Entropy

Path Finding Algorithm - A*

Dijkstra's Shortest Path

Prim's spanning tree algorithm in Python

Bellman-Ford Shortest Path

Encryption/Cryptography Algorithms

minHash

tf-idf weight

Natural Language Processing (NLP): Sentiment Analysis I (IMDb & bag-of-words)

Natural Language Processing (NLP): Sentiment Analysis II (tokenization, stemming, and stop words)

Natural Language Processing (NLP): Sentiment Analysis III (training & cross validation)

Natural Language Processing (NLP): Sentiment Analysis IV (out-of-core)

Locality-Sensitive Hashing (LSH) using Cosine Distance (Cosine Similarity)



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Thank you.

- K Hong







Machine Learning with scikit-learn



scikit-learn installation

scikit-learn : Features and feature extraction - iris dataset

scikit-learn : Machine Learning Quick Preview

scikit-learn : Data Preprocessing I - Missing / Categorical data

scikit-learn : Data Preprocessing II - Partitioning a dataset / Feature scaling / Feature Selection / Regularization

scikit-learn : Data Preprocessing III - Dimensionality reduction vis Sequential feature selection / Assessing feature importance via random forests

Data Compression via Dimensionality Reduction I - Principal component analysis (PCA)

scikit-learn : Data Compression via Dimensionality Reduction II - Linear Discriminant Analysis (LDA)

scikit-learn : Data Compression via Dimensionality Reduction III - Nonlinear mappings via kernel principal component (KPCA) analysis

scikit-learn : Logistic Regression, Overfitting & regularization

scikit-learn : Supervised Learning & Unsupervised Learning - e.g. Unsupervised PCA dimensionality reduction with iris dataset

scikit-learn : Unsupervised_Learning - KMeans clustering with iris dataset

scikit-learn : Linearly Separable Data - Linear Model & (Gaussian) radial basis function kernel (RBF kernel)

scikit-learn : Decision Tree Learning I - Entropy, Gini, and Information Gain

scikit-learn : Decision Tree Learning II - Constructing the Decision Tree

scikit-learn : Random Decision Forests Classification

scikit-learn : Support Vector Machines (SVM)

scikit-learn : Support Vector Machines (SVM) II

Flask with Embedded Machine Learning I : Serializing with pickle and DB setup

Flask with Embedded Machine Learning II : Basic Flask App

Flask with Embedded Machine Learning III : Embedding Classifier

Flask with Embedded Machine Learning IV : Deploy

Flask with Embedded Machine Learning V : Updating the classifier

scikit-learn : Sample of a spam comment filter using SVM - classifying a good one or a bad one




Machine learning algorithms and concepts

Batch gradient descent algorithm

Single Layer Neural Network - Perceptron model on the Iris dataset using Heaviside step activation function

Batch gradient descent versus stochastic gradient descent

Single Layer Neural Network - Adaptive Linear Neuron using linear (identity) activation function with batch gradient descent method

Single Layer Neural Network : Adaptive Linear Neuron using linear (identity) activation function with stochastic gradient descent (SGD)

Logistic Regression

VC (Vapnik-Chervonenkis) Dimension and Shatter

Bias-variance tradeoff

Maximum Likelihood Estimation (MLE)

Neural Networks with backpropagation for XOR using one hidden layer

minHash

tf-idf weight

Natural Language Processing (NLP): Sentiment Analysis I (IMDb & bag-of-words)

Natural Language Processing (NLP): Sentiment Analysis II (tokenization, stemming, and stop words)

Natural Language Processing (NLP): Sentiment Analysis III (training & cross validation)

Natural Language Processing (NLP): Sentiment Analysis IV (out-of-core)

Locality-Sensitive Hashing (LSH) using Cosine Distance (Cosine Similarity)




Artificial Neural Networks (ANN)

[Note] Sources are available at Github - Jupyter notebook files

1. Introduction

2. Forward Propagation

3. Gradient Descent

4. Backpropagation of Errors

5. Checking gradient

6. Training via BFGS

7. Overfitting & Regularization

8. Deep Learning I : Image Recognition (Image uploading)

9. Deep Learning II : Image Recognition (Image classification)

10 - Deep Learning III : Deep Learning III : Theano, TensorFlow, and Keras




C++ Tutorials

C++ Home

Algorithms & Data Structures in C++ ...

Application (UI) - using Windows Forms (Visual Studio 2013/2012)

auto_ptr

Binary Tree Example Code

Blackjack with Qt

Boost - shared_ptr, weak_ptr, mpl, lambda, etc.

Boost.Asio (Socket Programming - Asynchronous TCP/IP)...

Classes and Structs

Constructor

C++11(C++0x): rvalue references, move constructor, and lambda, etc.

C++ API Testing

C++ Keywords - const, volatile, etc.

Debugging Crash & Memory Leak

Design Patterns in C++ ...

Dynamic Cast Operator

Eclipse CDT / JNI (Java Native Interface) / MinGW

Embedded Systems Programming I - Introduction

Embedded Systems Programming II - gcc ARM Toolchain and Simple Code on Ubuntu and Fedora

Embedded Systems Programming III - Eclipse CDT Plugin for gcc ARM Toolchain

Exceptions

Friend Functions and Friend Classes

fstream: input & output

Function Overloading

Functors (Function Objects) I - Introduction

Functors (Function Objects) II - Converting function to functor

Functors (Function Objects) - General



Git and GitHub Express...

GTest (Google Unit Test) with Visual Studio 2012

Inheritance & Virtual Inheritance (multiple inheritance)

Libraries - Static, Shared (Dynamic)

Linked List Basics

Linked List Examples

make & CMake

make (gnu)

Memory Allocation

Multi-Threaded Programming - Terminology - Semaphore, Mutex, Priority Inversion etc.

Multi-Threaded Programming II - Native Thread for Win32 (A)

Multi-Threaded Programming II - Native Thread for Win32 (B)

Multi-Threaded Programming II - Native Thread for Win32 (C)

Multi-Threaded Programming II - C++ Thread for Win32

Multi-Threaded Programming III - C/C++ Class Thread for Pthreads

MultiThreading/Parallel Programming - IPC

Multi-Threaded Programming with C++11 Part A (start, join(), detach(), and ownership)

Multi-Threaded Programming with C++11 Part B (Sharing Data - mutex, and race conditions, and deadlock)

Multithread Debugging

Object Returning

Object Slicing and Virtual Table

OpenCV with C++

Operator Overloading I

Operator Overloading II - self assignment

Pass by Value vs. Pass by Reference

Pointers

Pointers II - void pointers & arrays

Pointers III - pointer to function & multi-dimensional arrays

Preprocessor - Macro

Private Inheritance

Python & C++ with SIP

(Pseudo)-random numbers in C++

References for Built-in Types

Socket - Server & Client

Socket - Server & Client 2

Socket - Server & Client 3

Socket - Server & Client with Qt (Asynchronous / Multithreading / ThreadPool etc.)

Stack Unwinding

Standard Template Library (STL) I - Vector & List

Standard Template Library (STL) II - Maps

Standard Template Library (STL) II - unordered_map

Standard Template Library (STL) II - Sets

Standard Template Library (STL) III - Iterators

Standard Template Library (STL) IV - Algorithms

Standard Template Library (STL) V - Function Objects

Static Variables and Static Class Members

String

String II - sstream etc.

Taste of Assembly

Templates

Template Specialization

Template Specialization - Traits

Template Implementation & Compiler (.h or .cpp?)

The this Pointer

Type Cast Operators

Upcasting and Downcasting

Virtual Destructor & boost::shared_ptr

Virtual Functions



Programming Questions and Solutions ↓

Strings and Arrays

Linked List

Recursion

Bit Manipulation

Small Programs (string, memory functions etc.)

Math & Probability

Multithreading

140 Questions by Google



Qt 5 EXPRESS...

Win32 DLL ...

Articles On C++

What's new in C++11...

C++11 Threads EXPRESS...

Go Tutorial

OpenCV...


List of Design Patterns



Introduction

Abstract Factory Pattern

Adapter Pattern

Bridge Pattern

Chain of Responsibility

Command Pattern

Composite Pattern

Decorator Pattern

Delegation

Dependency Injection(DI) and Inversion of Control(IoC)

Façade Pattern

Factory Method

Model View Controller (MVC) Pattern

Observer Pattern

Prototype Pattern

Proxy Pattern

Singleton Pattern

Strategy Pattern

Template Method Pattern








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