(Adjust existing code) Vector Search and Clustering on the MNIST Dataset using C/C++ in Linux -- 2
Budget: $30 – $250 USD
Description:
We are seeking an experienced C/C++ developer with a strong background in algorithms, particularly in the domains of vector search and clustering. The primary task involves implementing the LSH algorithm for d-dimensional vectors and vector clustering techniques on the MNIST dataset of handwritten numeric digits.
-ANALYTICAL REQUIREMENTS DOCX WILL BE PROVIDED IN CHAT
-A code is available but needs to read and output different things and that changes many .c files.
Key Deliverables:
Implement the LSH algorithm based on the Euclidean (L2) metric.
Implement the random projection algorithm on the hypercube for the L2 metric.
Implement vector clustering techniques using the k-Means++ initialization and MacQueen method for updating.
Parse and handle input from binary files containing the MNIST dataset.
Generate output files detailing search and clustering results.
Ensure modular and documented code with a comprehensive README.
Provide a Makefile for easy compilation.
Requirements:
Proficiency in C/C++ programming.
Strong understanding of hashing, hash tables, and the LSH algorithm.
Experience with vector spaces, nearest neighbor search, and clustering algorithms.
Ability to read and handle binary file formats.
Knowledge of the MNIST dataset format is a plus.
Familiarity with Git for version control.
Project Timeline: We expect the project to be completed within 4 weeks of commencement.
Budget: $65 USD
Deadline: 18/10/2023
Please include relevant samples or references of past work in your proposal. Additionally, a brief overview of how you plan to approach this project will be greatly appreciated.
We are seeking an experienced C/C++ developer with a strong background in algorithms, particularly in the domains of vector search and clustering. The primary task involves implementing the LSH algorithm for d-dimensional vectors and vector clustering techniques on the MNIST dataset of handwritten numeric digits.
-ANALYTICAL REQUIREMENTS DOCX WILL BE PROVIDED IN CHAT
-A code is available but needs to read and output different things and that changes many .c files.
Key Deliverables:
Implement the LSH algorithm based on the Euclidean (L2) metric.
Implement the random projection algorithm on the hypercube for the L2 metric.
Implement vector clustering techniques using the k-Means++ initialization and MacQueen method for updating.
Parse and handle input from binary files containing the MNIST dataset.
Generate output files detailing search and clustering results.
Ensure modular and documented code with a comprehensive README.
Provide a Makefile for easy compilation.
Requirements:
Proficiency in C/C++ programming.
Strong understanding of hashing, hash tables, and the LSH algorithm.
Experience with vector spaces, nearest neighbor search, and clustering algorithms.
Ability to read and handle binary file formats.
Knowledge of the MNIST dataset format is a plus.
Familiarity with Git for version control.
Project Timeline: We expect the project to be completed within 4 weeks of commencement.
Budget: $65 USD
Deadline: 18/10/2023
Please include relevant samples or references of past work in your proposal. Additionally, a brief overview of how you plan to approach this project will be greatly appreciated.