One other parameter is often a j x n array consisting of j unlabeled, n- dimensional observations. This purpose will output a 1-dimensional array consisting of j labels for the check array observations. It decides those labels in the next way: For each observation (row) from the examination array it will discover the closest observation during the schooling array and use its label to label the test observation.
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I've supplied function definitions from the attached percolation.py file. I've see provided a major function to test your code in a very independent file hw5A.py. Your code should do the job with the key functionality I have delivered.