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Algorithms - Shell Sort





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Shell Sort

Shell sort is a sorting algorithm, devised by Donald Shell in 1959, that is a generalization of insertion sort, which exploits the fact that insertion sort works efficiently on input that is already almost sorted.


It improves on insertion sort by allowing the comparison and exchange of elements that are far apart. In other words, it improves upon bubble sort and insertion sort by moving out of order elements more than one position at a time. The last step of Shell sort is a plain insertion sort, but by then, the array of data is guaranteed to be almost sorted.


C++ code

#include <iostream>
#include <iomanip>
using namespace std;

void print(int ar[], int sz, int step)
{	
	for(int i = 0; i < sz; ++i) { 
		if(((i + 1) % step) != 0)
			cout << setw(3) << ar[i]; 
		else
			cout << setw(3) << ar[i] << endl;

	}
	cout << endl;
}

void ShellSort(int a[], int sz)
{
	int i, j;
	int step, temp;
	step = sz / 2 ;
	while(step) {
		print(a, sz, step);
		cout << "==>" << endl;
		for (i = step; i < sz; i++) {
			temp = a[i];
			j = i;
			while (j >= step && a[j - step] > temp) {
				a[j] = a[j - step];
				j = j - step;
			}
			a[j] = temp;
		}
		print(a, sz, step);
		cout << "current array" << endl;
		print(a, sz, sz);
		cout << "----------------" << endl;

		step = step / 2.2;
	}
}

int main(void)
{	
	int a[] = {13, 14, 94, 33, 82, 25, 59, 94, 65, 23, 45, 27, 73, 25, 39, 10};
	const size_t sz = sizeof(a)/sizeof(a[0]);

	cout << "Initial array" << endl;
	print(a,sz,sz);
	cout << "-------------" << endl;

	ShellSort(a,sz); 

	cout << "Sorted array"  << endl;
	print(a,sz,sz); 
	return 0;
}


The implementation can be described as arranging the data sequence in a two-dimensional array and then sorting the columns of the array using insertion sort.


Output is:

Initial array
 13 14 94 33 82 25 59 94 65 23 45 27 73 25 39 10

-------------
 13 14 94 33 82 25 59 94
 65 23 45 27 73 25 39 10

==>
 13 14 45 27 73 25 39 10
 65 23 94 33 82 25 59 94

current array
 13 14 45 27 73 25 39 10 65 23 94 33 82 25 59 94

----------------
 13 14 45
 27 73 25
 39 10 65
 23 94 33
 82 25 59
 94
==>
 13 10 25
 23 14 33
 27 25 45
 39 73 59
 82 94 65
 94
current array
 13 10 25 23 14 33 27 25 45 39 73 59 82 94 65 94

----------------
 13
 10
 25
 23
 14
 33
 27
 25
 45
 39
 73
 59
 82
 94
 65
 94

==>
 10
 13
 14
 23
 25
 25
 27
 33
 39
 45
 59
 65
 73
 82
 94
 94

current array
 10 13 14 23 25 25 27 33 39 45 59 65 73 82 94 94

----------------
Sorted array
 10 13 14 23 25 25 27 33 39 45 59 65 73 82 94 94

Shell sort groups with step elements together.
So, if step = 8 as in this example, we have two rows of data with 8 columns.

Initial array
 13 14 94 33 82 25 59 94 65 23 45 27 73 25 39 10

 13 14 94 33 82 25 59 94
 65 23 45 27 73 25 39 10

Then, sort each column.

 13 14 45 27 73 25 39 10
 65 23 94 33 82 25 59 94

After that, it gathers the elements starting from the top row.
Then, we have the following array.

13 14 45 27 73 25 39 10 65 23 94 33 82 25 59 94

Repeat this process with different steps. The step sequence is a geometric sequence in which every term is roughly 2.2 times smaller than the previous one.

Although this method is inefficient for large data sets, it is one of the fastest algorithms for sorting small numbers of elements (sets with less than 1000 or so elements). Another advantage of this algorithm is that it requires relatively small amounts of memory.








Ph.D. / Golden Gate Ave, San Francisco / Seoul National Univ / Carnegie Mellon / UC Berkeley / DevOps / Deep Learning / Visualization

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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)



Sponsor Open Source development activities and free contents for everyone.

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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