MA500/CS4102: Geometric Foundations of Data Analysis - Euclidean Metric - IT/Computer Science Assignment Help

Assignment Task:

Homework solutions should be submitted as a single .pdf document with an accompanying .py file. 

The .pdf document should contain a main part in which you present your answers to the questions, and in which you provide a description of the mathematical methods used to obtain your answers. This main part should contain no Python code. an appendix listing any Python code used. The .txt file should be a machine readable version of the appendix code which, when run, reproduces your answers. 

The homework will be graded according to a scheme in which content (=correctness of your answers, choice of methods, python code) is weighted at 70% and presentation (=manner in which you present your answers, methods and code) is weighted at 30%. 

3 Third Homework 

Please submit as two files: 

MA500 Third Homework firstname familyname.pdf MA500 Third Homework first name familyname.py 

1. Implement an algorithm that applies single-linkage hierarchical clustering to an n×n matrix of distances (or dissimilarities) and returns the corresponding barcode. 

2. Create a sample S of n points in R2 that are clearly partitioned into several distinct ‘clusters’. Plot the points S. 

3. For the Euclidean metric, and then the taxicab metric, construct the two n × n distance matrices for your set S of points. 

4. Apply your implementation to the two matrices in (3) and display the resulting barcodes.

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