Third Week

What was done ?

1) Both Standard and Randomised SVD has been converted into PBblas format. Code can be found on github.

2) Current Runnign times are :

    Test.ecl - 12 sec

    Performance.ecl - 20 x 10 matrix - 49 sec

    Performance.ecl - 200 x 100 matrix - 1 min 31 sec

What for next week?

1) From here on, main concern would be to reduce running time as low as possible. As such, I will spend next week researching better algorithms that can be easily parallelizable and implemented.

2) Run some tests on semantic similarity and document clustering based on current implementation.

Second Week

What was done ?

1) Completed Algorithm for Randomised SVD.

2) Implemented a rudimentary SVD algorithm for dense matrix svd using PB blas based on Cholesky factorisation. Can be found at : https://github.com/successar/ecl-ml/tree/lsa/ML/LSA/DenseSVD 

For next week?

1) Convert existing Dense SVD Implementation into PBblas format completely.

1) Improve running time for Dense Matrix SVD by implementing better QR algorithm using PBblas.

First Week

1) Completed a basic interface for LSA.

The files can be found at : https://github.com/successar/ecl-ml/tree/lsa/ML/LSA

Interface include functions :

  i) ComputeSVD - Computes Eigenvectors for V and Eigenvalues for S.
                            Calculates Eigenvectors for U = A(VS^-1)

  ii) ComputeQueryVector

  iii) GetDocVectors

  iv) CosineSim

The interface is valid for small test dataset and results are included.

For next week ?

1) Do performance testing with bigger matrices.

2) More Validation tests for various sizes and shapes of matrices.

3) Begin Randomized Truncated SVD implementation for faster results.