Are there alternative or better ways to convert a numpy matrix to a python array than this?
>>> import numpy
>>> import array
>>> b = numpy.matrix("1.0 2.0 3.0; 4.0 5.0 6.0", dtype="float16")
>>> print(b)
[[ 1. 2. 3.]
[ 4. 5. 6.]]
>>> a = array.array("f")
>>> a.fromlist((b.flatten().tolist())[0])
>>> print(a)
array('f', [1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
You could convert to a NumPy array and generate its flattened version with .ravel() or .flatten(). This could also be achieved by simply using the function np.ravel itself as it does both these takes under the hood. Finally, use array.array() on it, like so -
a = array.array('f',np.ravel(b))
Sample run -
In [107]: b
Out[107]:
matrix([[ 1., 2., 3.],
[ 4., 5., 6.]], dtype=float16)
In [108]: array.array('f',np.ravel(b))
Out[108]: array('f', [1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
here is an example :
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.tolist()
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]
