Matlab to Numpy translation - matrix + scalar differences

I'm trying to translate a Matlab script into Numpy. This is part of the Matlab code:

function [idx,D]=knnsearch(varargin)
[N,M] = size(Q);
L=size(R,1);
idx = zeros(N,K);
D = idx;
for k=1:N
    d=zeros(L,1);
    for t=1:M
        d=d+(R(:,t)-Q(k,t)).^2;
    end
    d(k)=inf;
    [D(k),idx(k)]=min(d);
end

where Q and R are matrices that can be considered e.g. as eye(5); you can consider K = 1. An example function call could be:

Q = eye(5);
R = eye(5);

[idx,D] = knnsearch(Q,R,1);

which returns:

idx:      
 2
 1
 1
 1
 1
D:
 2
 2
 2
 2
 2

This is the Numpy code:

import numpy as np
def knnsearch(Q, R, K):
    (N,M) = Q.shape
    L = len(R[:,1])    
    idx = np.zeros((N,K), dtype=int)
    D = np.copy(idx)
    for k in range(0, N):
        d = np.zeros((L, 1))
        for t in range(0, M):
            d = d + (R[:,t] - Q[k,t])**2
        d[k] = np.inf
        idx[k] = np.argmin(d)
        D[k] = np.amin(d)
    return (idx, D)

where

Q0 = np.identity(5)
R0 = np.identity(5)

idxout, Dout = knnsearch(Q0, R0, 1)

This returns different from Matlab:

idx: 
[[5]
 [1]
 [2]
 [3]
 [4]]
D: 
[[0]
 [0]
 [0]
 [0]
 [0]]

There is a problem with the row number 9. The second part of the row, the scalar ((R(:,t)-Q(k,t)).^2), returns the same values for both Matlab and Numpy. Instead, the addition (d + scalar) returns different values. So, the matrix d contains different values in Matlab and Numpy.

Thanks in advance.