bug: matrix.adjuadge return a error value when row and col are greater than 2;

[mmooyyii]

A matrix dot by it's inverse matrix will be a cell matrix.
There are two example of numpy and trueskill.mathematics

>> from trueskill.trueskill.mathematics import Matrix
>> import numpy as np
>> d = [[1, 2, 3], [6, 5, 10], [7, 8, 9]]
>> m = Matrix(d[:])
>> m.inverse() * m
Matrix([[4.2222222222222205, 3.1666666666666656, 4.777777777777777], [-0.6666666666666659, 4.440892098500626e-16, -1.3333333333333321], [0.11111111111111138, -0.16666666666666652, 0.8888888888888893]])
>> m = np.array(d[:])
>> np.linalg.inv(m) @ m
array([[ 1.00000000e+00,  1.11022302e-15,  1.85962357e-15],
       [-5.27355937e-16,  1.00000000e+00, -3.60822483e-16],
       [-2.22044605e-16, -2.22044605e-16,  1.00000000e+00]])

This reason for this bug is that adjugate matrix is not transposed.

### trueskill\trueskill\mathematics.py

def adjugate(self):
        height, width = self.height, self.width
        if height != width:
            raise ValueError('Only square matrix can be adjugated')
        if height == 2:
            a, b = self[0][0], self[0][1]
            c, d = self[1][0], self[1][1]
            return type(self)([[d, -b], [-c, a]])
        src = {}
        for r in range(height):
            for c in range(width):
                sign = -1 if (r + c) % 2 else 1
                src[r, c] = self.minor(r, c).determinant() * sign
---     return type(self)(src, height, width)
+++     return type(self)(src, height, width).transpose()