All Java Code I Know

Saturday, 29 February 2020

File Compression and Decompression using GZIPCompression in java, String Compression and Decompression using GZIPCompression in java

Here is a simple java GZIP example program showing how can we compress a string into GZIP format and then decompress the GZIP formatted string to original string.

Traffic Flow Dataset

This dataset contains traffic flow details. It has 16 attributes.

(1) Current_Location

(2) Destination

(3) Route

(4) Distance

(5) Average_Speed

(6) Time_to_Reach_Destination

(7) Date_&_Time

(8) Two_Wheeler

(9) Truck_Lorry

(10) Car

(11) Bus

(12) Non_Motorised_Vehicles

(13) Other_Motorised_Vehicles

(14) Three_Wheeler

(15) SUVOrStation_Wagon

(16) Jeep

AES Encryption and Decryption Java Code

AES algorithm is a well-known cryptography algorithm. It is a symmetric key cryptography algorithm. Symmetric means same key used for both encryption and decryption. This algorithm encrypts a text to ciphertext and decrypt ciphertext to original text based on AES key.

Create a bar chart in JSP using JFreeChart

This Example Code demonstrates you how to generate a bar chart in JSP with JFreeChart. Source Code given below generates a bar chart of fruit prices of two days in a fruit shop.

Two jar file is necessary
==================

1) jcommon-1.0.16.jar
2) jfreechart.jar

jcommon-1.0.16.jar Link -->

https://repo1.maven.org/maven2/jfree/jcommon/1.0.16/jcommon-1.0.16.jar

jfreechart.jar Link -->

http://www.java2s.com/Code/JarDownload/jfreechart/jfreechart.jar.zip

(Extract jfreechart.jar.zip file and and use jfreechart.jar)

Output:

Wednesday, 30 May 2018

SVM Classification using WEKA Java Code

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. The vectors (cases) that define the hyperplane are the support vectors. SVM uses a technique called the kernel trick to transform your data and then based on these transformations it finds an optimal boundary between the possible outputs.

Requirements:

==========

2 Jar Files

--> weka-3.7.1-beta.jar

--> weka-3.7.3.jar

2 Datasets

--> ClevelandHeartDiseaseTrainingDataset.arff

--> ClevelandHeartDiseaseTestingDataset.arff

How to Implement:

==============

ClevelandHeartDiseaseTrainingDataset.arff contains lot of patients Health Records. It has 5 attributes and 1 class attribute.

1) sex: patient sex (1 = male, 0 = female),

2) cp: chest pain type (1 = typical angina, 2 = atypical angina, 3 = non-anginal pain, 4 = asymptomatic),

3) slope: the slope of the peak exercise ST segment (1 = upsloping, 2 = flat, 3 = downsloping)

4) ca: number of major vessels (0-3) colored by flourosopy

5) thal: (3 = normal, 6 = fixed defect, 7 = reversable defect)

6) class: (0 = no heart disease, 1 = presence of heart disease)

This Classification Algorithm classify this training dataset. After Classification, it generate some classification rules. Followed by, this algorithm load ClevelandHeartDiseaseTestingDataset.arff. This testing dataset contains 5 attributes with one class attribute. This class attribute contains (?) question mark. Because we predict each testing record is possible to presence of heart disease or not. Then this classification algorithm predicts each records class attribute value based on classification rules (It is generated after Training Process).

How to Run this Code in Command Prompt:

===================================

>set classpath=%classpath%;weka-3.7.1-beta.jar;

>set classpath=%classpath%;weka-3.7.3.jar;

>javac SVMClassification.java

>java SVMClassification

Output: SVMOutput.txt

SVM Classification Output

@relation ClevelandHeartDiseaseTestingDataset

@attribute sex {0,1}

@attribute cp {1,2,3,4}

@attribute slope {1,2,3}

@attribute ca {0,1,2,3}

@attribute thal {3,6,7}

@attribute class {0,1}

@data

1,1,2,0,3,1

0,1,1,0,3,1

1,2,2,0,3,1

1,3,1,2,7,0

Naive Bayes Classification using WEKA Java Code

Naive Bayes is a simple technique for constructing classifiers: models that assign class labels to problem instances, represented as vectors of feature values, where the class labels are drawn from some finite set. It is not a single algorithm for training such classifiers, but a family of algorithms based on a common principle: all naive Bayes classifiers assume that the value of a particular feature is independent of the value of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 10 cm in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of any possible correlations between the color, roundness, and diameter features.