如何将 numpy 数组的每一行向量与其自身和每个元素进行比较

Question

I have a numpy array which contains vectorised data. I need to compare each of these vectors (a row in the array) euclidean distances to itself and every other row.

The vectors are of the form

[[0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 ...
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]
 [0 0 0 ... 0 0 0]]

I know I need two loops, here is what I have so far

def euclidean_distance_loop(termdoc):
    i = 0
    j = 0
    matrix = np.array([])
    while( j < (len(termdoc-1))):
        matrix = np.append(matrix,[euclidean_distance(termdoc[i],termdoc[j])])
        j = j + 1
        
    return np.array([matrix])

euclidean_distance_loop(termdoc)

I know this is an index problem and I need another index or an incremented index in another loop but not sure how to construct it

Answer 1

You don't need loops.

def self_distance(x):
    return np.linalg.norm(x[:,np.newaxis] - x, axis=-1)

See also:

• Numpy. Compare all vector row in one array with every other one in the same array
• How can the Euclidean distance be calculated with NumPy?