Return index of minimum value 'IndexError: arrays used as indices must be of integer (or boolea-CodePudding

I'm trying to build a function that returns the index of the shortest distance between a data point and a centroid. However I'm getting an error IndexError: arrays used as indices must be of integer (or boolean) type.

import numpy as np

b = np.ndarray((3, 2, 1, 4))
DEFAULT_CENTROIDS = np.array([[5.664705882352942, 3.0352941176470587, 3.3352941176470585, 1.0176470588235293],
                              [5.446153846153847, 3.2538461538461543, 2.9538461538461536, 0.8846153846153846],
                              [5.906666666666667, 2.933333333333333, 4.1000000000000005, 1.3866666666666667],
                              [5.992307692307692, 3.0230769230769234, 4.076923076923077, 1.3461538461538463],
                              [5.747619047619048, 3.0714285714285716, 3.6238095238095243, 1.1380952380952383],
                              [6.161538461538462, 3.030769230769231, 4.484615384615385, 1.5307692307692309],
                              [6.294117647058823, 2.9764705882352938, 4.494117647058823, 1.4],
                              [5.853846153846154, 3.215384615384615, 3.730769230769231, 1.2076923076923078],
                              [5.52857142857143, 3.142857142857143, 3.107142857142857, 1.007142857142857],
                              [5.828571428571429, 2.9357142857142855, 3.664285714285714, 1.1]])

def get_closest(data_point: np.ndarray, centroids: np.ndarray):
    """
    Takes a data_point and a nd.array of multiple centroids and returns the index of the centroid closest to data_point
    by computing the euclidean distance for each centroid and picking the closest.
    """
    N = centroids.shape[0]
    dist = np.empty(N)
    for i in centroids:
        dist[i] = np.linalg.norm(centroids[i]-data_point)
    index_min = np.argmin(dist)
    return index_min # the index of the centroid closest to the datapoint

get_closest(b,DEFAULT_CENTROIDS)

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-52-7c41e5374a5e> in <module>
----> 1 get_closest(b,DEFAULT_CENTROIDS)

<ipython-input-51-8e1cd568d0df> in get_closest(data_point, centroids)
     11     dist = np.empty(N)
     12     for i in centroids:
---> 13         dist[i] = np.linalg.norm(centroids[i]-data_point)
     14     index_min = np.argmin(dist)
     15     return index_min # the index of the centroid closest to the datapoint

IndexError: arrays used as indices must be of integer (or boolean) type

I don't quite understand the error or why my code is wrong. Suggestions? Thanks in advance.

CodePudding user response：

for i in centroids: -> centroids[i].

i is an element of centroids, not the indices of it. So when you try to index by an array of floats it doesn't work.

For what you seem to want, I think enumerate would be your best bet.

def get_closest(data_point: np.ndarray, centroids: np.ndarray):
    """
    Takes a data_point and a nd.array of multiple centroids and returns the index of the centroid closest to data_point
    by computing the euclidean distance for each centroid and picking the closest.
    """
    N = centroids.shape[0]
    dist = np.empty(N)
    for i, c in enumerate(centroids): 
        dist[i] = np.linalg.norm(c - data_point)
    index_min = np.argmin(dist)
    return index_min # the index of the centroid closest to the datapoint

That said, you'd probably in this case be better off using a KDTree if centroids is large or you're aiming for speed.

from scipy.spatial import KDTree
centroid_KDTree = KDTree(centroids)
dist, index_min = centroid_KDTree.query(data_point)