please run:
-
question_1.py to test the first problem
- For question_1.py the unknown data-points with their class can also be visualized by "un-commenting" the
render_graph(dataSet_red, dataSet_blue, dataset_unknown, nearest_dataset)line. After running the program by giving input from console by setting dimension (2, 4 or 8; although the program runs for any dimension of data) output files will be generated as output_2D.txt, output_4D.txt & output_8D.txt.
- For question_1.py the unknown data-points with their class can also be visualized by "un-commenting" the
-
question_2.py to test the second problem
- For question_2.py change the parameter of
dataset = io.read_multi_dim_data("datasets/K-means/data_4c_2d.txt")to change the dataset for clustering andinitial_points = [(0.02, 0.82), (-0.004, 4.3), (3.8955, 2.392), (0.1, 2.1)]to change the points according to the files dimensions and number of centers. Initial points takes input as a list of tuples.
- For question_2.py change the parameter of
###1. Nearest Neighbour Classifier
| id | file name | count |
|---|---|---|
| 1 | red_2d.txt | 90 samples |
| 2 | blue_2d.txt | 90 samples |
| 3 | unknown_2d.txt | 20 samples |
| 4 | red_4d.txt | 65 samples |
| 5 | blue_4d.txt | 65 samples |
| 6 | unknown_4d.txt | 20 samples |
| 7 | red_8d.txt | 40 samples |
| 8 | blue_8d.txt | 40 samples |
| 9 | unknown_8d.txt | 20 samples |
###2. K-means Clustering
| id | file name | description |
|---|---|---|
| 1 | data_2c_2d.txt | 200 samples, 2 clusters, 2 dimensions |
| 2 | data_4c_2d.txt | 200 samples, 4 clusters, 2 dimensions |
| 3 | data_2c_4d.txt | 400 samples, 2 clusters, 4 dimensions |
| 4 | data_4c_4d.txt | 400 samples, 4 clusters, 4 dimensions |