cleanup of the standalone fps method

fps.py

-import numpy as np
-from itertools import combinations
-
-
-def greedy(k, dist_matrix):
-    # initialize some arrays
-    indices = np.arange(len(dist_matrix))
-    mask = np.ones(len(dist_matrix),dtype='bool')
-    
-    # select two indices with largest distance
-    select = np.array(np.unravel_index(np.argmax(dist_matrix), dist_matrix.shape))
-
-    while len(select)<k:
-        # mask selected indices
-        mask[select] = False
-
-        # find the point with the largest minimum distance to already selected points
-        maxmin = np.argmax(np.amin(dist_matrix[select][:,mask], axis=0))
-        index_maxmin = indices[mask][maxmin]
-
-        # add the index to the selection
-        select = np.append(select, index_maxmin)
-
-    # return resulting selection (approximate solution)
-    return select
-
-
-def brute_force(k, dist_matrix):
-    # initialize indices and selection
-    indices = np.arange(len(dist_matrix))
-    select = np.arange(k)
-    mask = np.logical_not(np.eye(k,dtype='bool')) # ignore diagonal elements
-    mindist_select = np.amin(dist_matrix[select][:,select][mask])
-
-    # iterate over all possible combinations
-    for combo in combinations(indices, k):
-        # convert to array
-        combo = np.array(combo)
-        
-        # calculate the minimal distance between points
-        mindist_combo = np.amin(dist_matrix[combo][:,combo][mask])
-
-        # replace the selected combination if mindist is larger
-        if mindist_combo>mindist_select:
-            select = combo
-            mindist_select = mindist_combo
-
-    # return selection with maximum mindist (exact solution)
-    return select