Nahom Senay
Project OwnerProject Manager and meTTa and python programmer
Our approach to develop the meTTa based clustering library has the following layers or components. There is the frontend, the preprocessing layer, the core layer, the visualization layer. This will be tackled using an agile-like methodology where planning is coupled with coding. This project is expected to be completed within 3 months duration containing the above components.
The goal is to implement clustering algorithms in MeTTa and demonstrate interesting functionality on simple but meaningful test problems. This serves as a working prototype providing guidance for development of scalable tooling providing similar functionality, suitable for serving as part of a Hyperon-based AGI system following the PRIMUS cognitive architecture.
Conduct an in-depth study of algorithmic functioning at an implementation level, focusing on leveraging non-deterministic approaches for integration into meTTa.
- Study the core algorithms' functional and implementational aspects. - Having a pseudocode for the algorithms.
$500 USD
- A clear understanding fo the algorithms
Develop and implement a K-Means Clustering algorithm with a variable number of clusters in the meTTa programming language, along with three evaluation metrics: Rand Index, Mutual Information, and Purity Measures.
- Implementation of the K-Means algorithm in meTTa with support for a variable number of clusters. - Rand Index: Implementation of the Rand Index to measure similarity between two clusterings. Mutual Information Metrics: Implementation of metrics to quantify the amount of shared information between cluster assignments and ground truth. Purity Measures: Implementation to compute purity for evaluating clustering quality against labeled data.
$3,000 USD
Functional implementation of K-Means in meTTa, supporting: Dynamic adjustment of the number of clusters. Accurate computation of Rand Index, Mutual Information, and Purity Measures for evaluating clustering results. Comprehensive tests passing for both clustering and metric evaluation. Clear and actionable documentation for future usage and maintenance.
Develop and implement Hierarchical Clustering in the meTTa programming language, enabling robust cluster formation and analysis.
Implement hierarchical clustering algorithm as a function with the meTTa DSL
$1,000 USD
- Having a successful working version of hierarchical clustering algorithm.
Develop and implement Spectral Clustering in the meTTa programming language, enabling robust cluster formation and analysis.
Implement spectral clustering algorithm as a function with the meTTa DSL
$1,000 USD
- Having a working version of spectral clustering algorithm.
Develop and implement Gaussian Mixture Models in the meTTa programming language, enabling robust cluster formation and analysis.
Implement GMM as a function with the meTTa DSL
$1,000 USD
- Having a working version of GMM in the meTTa DSL.
Enhance the usability and functionality of the clustering and modeling algorithms in the meTTa programming language by implementing utility functions, data format adapters, and a visualization tool.
Develop intuitive functions for easier access and execution of the following algorithms: Hierarchical Clustering Spectral Clustering Gaussian Mixture Models (GMM) K-Means Clustering Create an adapter to convert common data structures like dataframes or series into meTTa atoms. Support seamless conversion for different input formats, ensuring compatibility with meTTa’s internal representations. Implement a visualization tool using matplotlib to: Plot cluster results for algorithms (e.g., 2D scatter plots, dendrograms). Support customization options (e.g., colors, markers, titles). Provide hooks to save visualizations in standard formats (e.g., PNG, SVG).
$1,500 USD
All algorithms (Hierarchical Clustering, Spectral Clustering, GMM, and K-Means) are easily accessible through user-friendly functions. Adapter reliably transforms data into meTTa-compatible formats without data loss or inconsistencies.
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