python - Get N maximum values and indices along an axis in a NumPy array -

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i think easy question experienced numpy users.

i have score matrix. raw index corresponds samples , column index corresponds items. example,

score_matrix =    [[ 1. ,  0.3,  0.4],    [ 0.2,  0.6,  0.8],    [ 0.1,  0.3,  0.5]]

i want top-m indices of items each samples. want top-m scores. example,

top2_ind =    [[0, 2],    [2, 1],    [2, 1]]  top2_score =    [[1. , 0.4],    [0,8, 0.6],    [0.5, 0.3]]

what best way using numpy?

i'd use argsort():

top2_ind = score_matrix.argsort()[:,::-1][:,:2]

that is, produce array contains indices sort score_matrix:

array([[1, 2, 0],        [0, 1, 2],        [0, 1, 2]])

then reverse columns ::-1, take first 2 columns :2:

array([[0, 2],        [2, 1],        [2, 1]])

then similar regular np.sort() values:

top2_score = np.sort(score_matrix)[:,::-1][:,:2]

which following same mechanics above, gives you:

array([[ 1. ,  0.4],        [ 0.8,  0.6],        [ 0.5,  0.3]])

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