I would really appreciate if I can get some help on this.

Write a class called SourceModel with the following constructors and methods:

A single constructor with two String parameters, where the first parameter is the name of the source model and the second is the file name of the corpus file for the model. The constructor should create a letter-letter transition matrix using this recommended algorithm sketch:

Initialize a 26x26 matrix for character counts

Print Training {name} model

Read the corpus file one character at a time, converting all characters to lower case and ignoring any non-alphabetic character.

For each character, increment the corresponding (row, col) in your counts matrix. The row is the for the previous character, the col is for the current character. (You could also think of this in terms of bigrams.)

After you read the entire corpus file, youll have a matrix of counts.

From the matrix of counts, create a matrix of probabilities each row of the transition matrix is a probability distribution.

A probabilities in a distribution must sum to 1. To turn counts into probabilities, divide each count by the sum of all the counts in a row.

Print done. followed by a newline character.

A getName method with no parameters which returns the name of the SourceModel.

A toString method which returns a String representation of the model like the one shown below under Running Your Program in jshell.

A probability method which takes a String and returns a double which indicates the probability that the test string was generated by the source model, using the transition probability matrix created in the constructor. Heres a recommended algorithm:

Initialize the probability to 1.0

For each two-character sequences of characters in the test string test, cici and ci+1ci+1 for i=0i=0 to test.length()1test.length()1, multiply the probability by the entry in the transition probability matrix for the c1c1 to c2c2 transition, which should be found in row cici an column ci+1ci+1 in the matrix. (You could also think of the indices as ci1,cici1,ci for i=1i=1 to test.length()1test.length()1.)

A main method that makes SourceModel runnable from the command line. You program should take 1 or more corpus file names as command line arguments followed by a quoted string as the last argument. The program should create models for all the corpora and test the string with all the corpora. Heres an algorithm sketch:

The first n-1 arguments to the program are corpus file names to use to train models. Corpus files are of the form .corpus

The last argument to the program is a quoted string to test.

Create a SourceModel object for each corpus

Use the models to compute the probability that the test text was produced by the model

Probabilities will be very small. Normalize the probabilities of all the model predictions to a probability distribution (so they sum to 1) (closed-world assumption we only state probabilities relative to models we have).

Print results of analysis

Running Your Program

Sample runs from the command line:

$ java SourceModel *.corpus "If you got a gun up in your waist please don't shoot up the place (why?)" Training english model ... done. Training french model ... done. Training hiphop model ... done. Training lisp model ... done. Training spanish model ... done. Analyzing: If you got a gun up in your waist please don't shoot up the place (why?) Probability that test string is english: 0.00 Probability that test string is french: 0.00 Probability that test string is hiphop: 1.00 Probability that test string is lisp: 0.00 Probability that test string is spanish: 0.00 Test string is most likely hiphop. $ java SourceModel *.corpus "Ou va le monde?" Training english model ... done. Training french model ... done. Training hiphop model ... done. Training lisp model ... done. Training spanish model ... done. Analyzing: Ou va le monde? Probability that test string is english: 0.02 Probability that test string is french: 0.85 Probability that test string is hiphop: 0.01 Probability that test string is lisp: 0.10 Probability that test string is spanish: 0.01 Test string is most likely french. $ java SourceModel *.corpus "My other car is a cdr." Training english model ... done. Training french model ... done. Training hiphop model ... done. Training lisp model ... done. Training spanish model ... done. Analyzing: My other car is a cdr. Probability that test string is english: 0.39 Probability that test string is french: 0.00 Probability that test string is hiphop: 0.61 Probability that test string is lisp: 0.00 Probability that test string is spanish: 0.00 Test string is most likely hiphop. $ java SourceModel *.corpus "defun Let there be rock" Training english model ... done. Training french model ... done. Training hiphop model ... done. Training lisp model ... done. Training spanish model ... done. Analyzing: defun Let there be rock Probability that test string is english: 0.01 Probability that test string is french: 0.00 Probability that test string is hiphop: 0.42 Probability that test string is lisp: 0.57 Probability that test string is spanish: 0.00 Test string is most likely lisp. 

Sample runs from jshell:

$ jshell | Welcome to JShell -- Version 10.0.2 | For an introduction type: /help intro jshell> /open SourceModel.java jshell> var french = new SourceModel("french", "french.corpus") Training french model ... done. french ==> Model: french a b c d e f ... 1.00 0.01 0.01 0.01 0.01 jshell> System.out.println(french) // implicitly calls french.toString() Model: french a b c d e f g h i j k l m n o p q r s t u v w x y z a 0.01 0.03 0.03 0.02 0.01 0.01 0.03 0.01 0.26 0.01 0.01 0.07 0.07 0.13 0.01 0.06 0.01 0.09 0.06 0.04 0.06 0.05 0.01 0.01 0.01 0.01 b 0.07 0.01 0.01 0.03 0.14 0.01 0.01 0.01 0.07 0.01 0.01 0.21 0.01 0.01 0.14 0.01 0.01 0.24 0.01 0.03 0.07 0.01 0.01 0.01 0.01 0.01 c 0.04 0.02 0.02 0.01 0.26 0.01 0.01 0.19 0.06 0.01 0.01 0.08 0.02 0.01 0.15 0.01 0.01 0.11 0.01 0.01 0.06 0.01 0.01 0.01 0.01 0.01 d 0.14 0.01 0.01 0.01 0.39 0.01 0.01 0.01 0.13 0.01 0.01 0.03 0.01 0.01 0.11 0.01 0.01 0.07 0.03 0.01 0.07 0.01 0.01 0.01 0.01 0.01 e 0.04 0.01 0.04 0.05 0.07 0.01 0.01 0.01 0.01 0.04 0.00 0.07 0.05 0.13 0.01 0.04 0.01 0.07 0.15 0.14 0.06 0.00 0.00 0.01 0.01 0.00 f 0.15 0.01 0.01 0.01 0.23 0.01 0.01 0.01 0.08 0.01 0.01 0.08 0.01 0.01 0.23 0.01 0.01 0.15 0.08 0.01 0.01 0.01 0.01 0.01 0.01 0.01 g 0.01 0.01 0.01 0.01 0.27 0.01 0.01 0.01 0.09 0.01 0.01 0.18 0.05 0.09 0.05 0.01 0.01 0.23 0.01 0.01 0.05 0.01 0.01 0.01 0.01 0.01 h 0.43 0.01 0.01 0.07 0.14 0.01 0.01 0.01 0.07 0.01 0.01 0.07 0.01 0.01 0.21 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 i 0.03 0.02 0.04 0.04 0.16 0.01 0.04 0.01 0.01 0.01 0.01 0.11 0.06 0.09 0.03 0.02 0.01 0.03 0.15 0.14 0.01 0.01 0.01 0.01 0.01 0.01 j 0.24 0.01 0.01 0.01 0.53 0.01 0.01 0.01 0.03 0.01 0.01 0.01 0.01 0.01 0.06 0.01 0.01 0.01 0.01 0.01 0.15 0.01 0.01 0.01 0.01 0.01 k 0.50 0.01 0.01 0.01 0.50 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 l 0.20 0.01 0.01 0.01 0.46 0.01 0.01 0.01 0.07 0.01 0.01 0.11 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.06 0.01 0.01 0.01 0.01 0.01 m 0.22 0.16 0.01 0.01 0.26 0.01 0.01 0.01 0.10 0.01 0.01 0.01 0.06 0.01 0.12 0.04 0.01 0.01 0.01 0.01 0.03 0.01 0.01 0.01 0.01 0.01 n 0.06 0.01 0.03 0.13 0.16 0.04 0.01 0.01 0.05 0.03 0.01 0.02 0.01 0.04 0.03 0.01 0.04 0.01 0.08 0.22 0.02 0.01 0.01 0.01 0.01 0.01 o 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.09 0.01 0.01 0.03 0.06 0.24 0.01 0.02 0.01 0.18 0.04 0.01 0.28 0.01 0.02 0.01 0.01 0.01 p 0.25 0.01 0.01 0.02 0.11 0.01 0.01 0.02 0.02 0.01 0.01 0.13 0.01 0.01 0.20 0.05 0.01 0.13 0.05 0.01 0.04 0.01 0.01 0.01 0.01 0.01 q 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 1.00 0.01 0.01 0.01 0.01 0.01 r 0.20 0.01 0.03 0.02 0.30 0.01 0.01 0.01 0.08 0.01 0.01 0.06 0.01 0.01 0.05 0.01 0.01 0.03 0.05 0.12 0.02 0.01 0.01 0.01 0.01 0.01 s 0.07 0.02 0.05 0.04 0.15 0.01 0.01 0.01 0.10 0.03 0.01 0.06 0.01 0.01 0.09 0.06 0.03 0.01 0.05 0.09 0.10 0.03 0.01 0.01 0.01 0.01 t 0.13 0.01 0.01 0.04 0.19 0.01 0.01 0.01 0.05 0.04 0.01 0.08 0.03 0.01 0.13 0.01 0.02 0.08 0.01 0.03 0.12 0.01 0.01 0.01 0.01 0.01 u 0.04 0.01 0.02 0.01 0.10 0.01 0.01 0.01 0.07 0.01 0.01 0.05 0.02 0.20 0.01 0.02 0.01 0.24 0.12 0.05 0.02 0.01 0.01 0.01 0.01 0.01 v 0.26 0.01 0.01 0.01 0.37 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.26 0.01 0.01 0.11 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 w 0.01 0.01 0.01 0.67 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.33 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 x 0.01 0.01 0.14 0.01 0.14 0.01 0.01 0.01 0.29 0.01 0.01 0.01 0.01 0.14 0.01 0.14 0.01 0.01 0.01 0.01 0.14 0.01 0.01 0.01 0.01 0.01 y 0.50 0.01 0.25 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.25 0.01 0.01 0.01 0.01 0.01 0.01 0.01 z 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 1.00 0.01 0.01 0.01 0.01 jshell> french.probability("Il y a tout ce que vous voulez aux Champs-Elysees") $8 ==> 3.966845096265183E-43

Refer to Oracles tutorial on reading a file one character at a time: https://docs.oracle.com/javase/tutorial/essential/io/charstreams.html

FileReaders read method returns int. Youll probably want to cast these to chars. Thats fine. As the documentation says, the lower 16 bits are the Unicode code point for a character.

If you use String.split to get corpus names from file names, remember that . is a special regex character. Use a character class to match a literal . character. For example "foo.fighters".split("[.]") is ["foo", "fighters"].

char is an integral type, so you can easily find a chars offset from 'a' with an expression like ch - 'a', where ch is a charvariable.

The Character class has many static utility methods you will find useful, like isAlphabetic, toLowerCase.

Corpus files:

https://drive.google.com/drive/folders/18Xa784tmnQFqz_yGeRlfjgA-u0BHCz-z?usp=sharing