How do I convert a float matrix into integer matrix?-CodePudding

I have a 25 by 25 matrix looks like

solution = array([[ 3, 14, 12,  6, 25, 19,  7, 21, 18, 16,  5, 24,  9, 10,  1, 13,
                 23,  4, 20,  8, 22, 11, 17, 15,  2],
                  [ 2,  9, 19,  8, 13, 12, 20,  3, 10, 11, 17,  7, 23, 15, 14, 22,
                 25, 18,  5, 16,  4, 21,  6, 24,  1],
                  [21, 18, 15,  7,  5,  4,  6, 22, 17,  1, 13, 20,  3, 11,  2, 24,
                 10, 14, 12,  9, 16,  8, 25, 19, 23],
                 ...
                 ...
                  [14, 13, 21,  1,  3, 17,  5, 12, 16, 15,  6, 19, 22,  4, 23, 10,
                 8, 24, 25,  2,  9, 20, 18,  7, 11]])

I want to convert all elements into string letters. For example, now solution[0][2]=12, and in my new solution it should be solution_new[0][2]='L'.

I've tried the following code but didn't work.

for i in rows:
        for j in cols:
            for k in vals:
                solution[i][j] = chr(k   64)

Is there any other function? Thanks!

CodePudding user response：

list(map((lambda sol: [chr(k 64) for k in sol]), solution))

CodePudding user response：

Try the following:

import numpy as np
x = np.array([[1.,2.,3.],[4.,5.,6.]])
np.vectorize(lambda x: chr(64 x))(x.astype(int))
>>> array([['A', 'B', 'C'],
   ['D', 'E', 'F']], dtype='<U1')

Its a quick elegant solution that does exactly what you need.

Further Explanation: np.vectorize(function, object): It applies the function per element in object. In this case it applies our lambda x: chr(64 x) function to each element. This is the equivalent of writing:

def f(x):
    return char(64 x)

for each element of the object. x.astype(int) simply converts all elements of x to the same type int like you want.

Hope this helps and happy coding!

CodePudding user response：

(Note: you may also want to look at this SO answer).

Edit: Oops: I didn't see the 64. Amended.

How about:

out = np.apply_along_axis(np.vectorize(chr), 0, 64   x.astype(int))

Example:

x = np.array([
    [3, 14, 12,  6, 25, 19,  7, 21, 18, 16,  5, 24,
     9, 10,  1, 13, 23,  4, 20,  8, 22, 11, 17, 15,  2],
    [ 2,  9, 19,  8, 13, 12, 20,  3, 10, 11, 17,  7, 23,
     15, 14, 22, 25, 18,  5, 16,  4, 21,  6, 24,  1],
    [21, 18, 15,  7,  5,  4,  6, 22, 17,  1, 13, 20,
     3, 11,  2, 24, 10, 14, 12,  9, 16,  8, 25, 19, 23],
    [14, 13, 21,  1,  3, 17,  5, 12, 16, 15,  6, 19, 22,
     4, 23, 10, 8, 24, 25,  2,  9, 20, 18,  7, 11]
], dtype=float)

>>> x.dtype
dtype('float64')

>>> np.apply_along_axis(np.vectorize(chr), 1, 64   x.astype(int))
array([['C', 'N', 'L', 'F', 'Y', 'S', 'G', 'U', 'R', 'P', 'E', 'X', 'I',
        'J', 'A', 'M', 'W', 'D', 'T', 'H', 'V', 'K', 'Q', 'O', 'B'],
       ['B', 'I', 'S', 'H', 'M', 'L', 'T', 'C', 'J', 'K', 'Q', 'G', 'W',
        'O', 'N', 'V', 'Y', 'R', 'E', 'P', 'D', 'U', 'F', 'X', 'A'],
       ['U', 'R', 'O', 'G', 'E', 'D', 'F', 'V', 'Q', 'A', 'M', 'T', 'C',
        'K', 'B', 'X', 'J', 'N', 'L', 'I', 'P', 'H', 'Y', 'S', 'W'],
       ['N', 'M', 'U', 'A', 'C', 'Q', 'E', 'L', 'P', 'O', 'F', 'S', 'V',
        'D', 'W', 'J', 'H', 'X', 'Y', 'B', 'I', 'T', 'R', 'G', 'K']],
      dtype='<U1')

Timings

n, m = 1000, 1000
x = np.random.randint(0, 26, size=(n, m))

%timeit np.apply_along_axis(np.vectorize(chr), 1, 64   x.astype(int))
177 ms ± 190 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit list(map((lambda sol: [chr(k   64) for k in sol]), x))
233 ms ± 1.65 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

%timeit np.vectorize(lambda x: chr(64 x))(x.astype(int))
268 ms ± 280 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)