[Java] Hopfield Network Implementation

Why Java?

Well, actually I have a deep love with this language, I enjoy my time writing a Java code. Also, it is good for me to write some OOP projects to not forget its concepts. Getting used to Python and other scripted languages may effect badly to my designing and OOP skills.

So, from time to time, I like to code some Java code. And here we go!

This is a light simple Java implementation of Hopfield Recurrent Neural Network.

Supported Features (cont. updated)

  • Data Normalization
  • Network Visualization using GraphStream

An example of a hopfield network visualization with pattern of size 5 (recognizing pattern of 5 elements)

– Here you can see the repository of implementation.

– I am working on a brief tutorial that explains the hopfield network on the README file

 

[Python] U-V Decomposition using Swarm Optimization

Recently, I have implemented U-V decomposition technique for Recommendation Systems using Particle Swarm Optimization.

U-V decomposition is an optimization problem for a matrix. Here, our matrix elements represent some users review about movies.

M

We have 5 users and 5 movies. Each row represents a user’s review for each movie. There are some missing values from the matrix which means that the user hasn’t put his/her review on that movie.

You can check the code here https://github.com/AhmedHani/CS624-ML/tree/master/Implementations/U-V%20Decomposition . Later, I will write another post to explain my code.

Input: Matrix 5×5 with unknown reviews

Intermediate output: Matrix U and V which multiplication gives a new matrix without unknown reviews

Output: Matrix 5×5 without unknown values.

 

References 

– The matrix example is taken from the book http://infolab.stanford.edu/~ullman/mmds/book.pdf at chapter 9

 

[Python] 3 Ways of Multi-threaded Matrix Multiplication

Recently, I have implemented 3 different ways of multi-threaded matrix multiplication. There are 3 ways of thinking when writing a parallel program: –

  • Input Decomposition
  • Output Decomposition
  • Intermediate Decomposition

We want to create matrix multiplication (3 x 3) program in multi-threaded way.

Input: Matrix A, B and each one of them is 3×3 size.
Output: Matrix C which is the resultant of matrix A * B

Input Decomposition

There are several way when multiply 2 matrices, one of them is Block Matrix, on which you divide the matrix to sub-matrices (under some constraints), then multiplying the sub-matrices and finally sum them up in matrix C.

Here, the matrices are 3×3, so, we can divide them to A = [A1, A2, A3] transpose, B = [B1, B2, B3], where A1, A2, A3 are of size 3×1 and B1, B2, B3 are of size 1×3. Each thread calculates A1*B1, A2*B2, A3*B3. The result of these multiplications is 3×3 matrix.

The only thing remaining now is add the 3 3×3 matrices together to get a matrix C.

Output Decomposition

We know that the elements of each cell in the matrix C rows depends on a row of matrix A and the matrix B columns. So, we make each thread calculates the row values of matrix C. And the input of each thread is a row from matrix A and the whole matrix B.

od

Intermediate Decomposition

It is similar to Input Decomposition, the only difference here is that every thread will return a unique matrix C (intermediate matrix). In other words, every thread will return an intermediate matrix with size 3×3. So, we will have 3 temp matrix of size 3×3, then the matrix C = (thread_1_matrix_C + thread_2_matrix_C, thread_3_matrix_C)

inter

Here https://github.com/AhmedHani/CS617-HPC/tree/master/Assignments/parallel_matrix_multiplication/multi-threaded_matrix_multiplication is the source code that implements the 3 techniques

The input is: A = [[1, 2, 3], [4, 5, 6], [7, 8, 9]], B = [[9, 8, 7], [6, 5, 4], [3, 2, 1]]

The output: C = A * B


References

– Algorithms and Parallel Computing by Fayez Gebali
– Introduction to Parallel Computing, Second Edition by  Ananth Grama, Anshul Gupta, George Karypis, Vipin Kumar

 

[Java] Hidden Markov Model Implementation

This is my last post this year. So, I have decided to finish the HMM implementation before the end of the year.

I tried to make the project clean as far as possible to enable those who want to use it understandable and easy to use. However, this isn’t the final version, there are many things that I want to add to the API and other enhancements. Any updates will be added to the README of the repository.

– Here you can see the repository of implementation.

– To see the features, check the README file

– To check the javadoc of the whole project click here

The test model is taken from my own created HMM model using creately.com app.

HMM1

Posts related:

I will add the other 2 parts after finishing them.

That’s all. See you next year! 🙂