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So when I try to run the following code for arrays with more than 10k elements, it takes hours and I don't know how to make it in the most efficient way. Any ideas?

Thank you!

When I try to run the following code for arrays with more than 10k elements, it takes hours and I don't know how to make it in the most efficient way. 

Any ideas?

Link to example data:

The data file is a Python 2.7 pickle object. The format is as follows: One array "C" (an array of clusters, in this case len(C) = 2 = 2 clusters). Each cluster is an array of arrays (each cluster contains the vectorized representation of the observations).

Link to example data:

The data file is a xlsx. It has three columns: label (cluster label), feature_1 and feature_2.

The process to get C from the file and get the functions working should be something like this:

import pandas as pd
import numpy as np
df = pd.read_excel('example_data.xlsx')
c1 = np.asanyarray(df[df['labels'] == 0].apply(lambda row: ([row['feature_1'], row['feature_2']]), axis=1))
c2 = np.asanyarray(df[df['labels'] == 1].apply(lambda row: ([row['feature_1'], row['feature_2']]), axis=1))
C = [c1,c2]
H_score(C)

Link to example data:

The data file is a Python 2.7 pickle object. The format is as follows: One array "C" (an array of clusters, in this case len(C) = 2 = 2 clusters). Each cluster is an array of arrays (each cluster contains the vectorized representation of the observations).

Thank you!