encode a 0-1 matrix from an integer matrix numpy
Question:
So I have an n*K
integer matrix [Note: its a representation of the number of samples drawn from K-distributions
(K-columns)]
a =[[0,1,0,0,2,0],
[0,0,1,0,0,0],
[3,0,0,0,0,0],
]
[Note: in the application context this matrix basically means that for the i
row (sim instance) we drew 1 element from the "distribution 1" (1 in [0,..K]
) (a[0,1] = 1
) and 2 from the distribution 4(a[0,4] = 2
)].
What I need is to generate a 0-1 matrix that represents the same integer matrix but with ones(1). In this case, is a 3D matrix of n*a.max()*K
that has a 1 for each sample that is drawn from the distributions. [Note: we need this matrix so we can multiply by our K-distribution sample matrix]
Output
b = [[[0,1,0,0,1,0], # we don't care if they samples are stack
[0,0,0,0,1,0],
[0,0,0,0,0,0]], # this is the first row representation
[[0,0,1,0,0,0],
[0,0,0,0,0,0],
[0,0,0,0,0,0]], # this is the second row representation
[[1,0,0,0,0,0],
[1,0,0,0,0,0],
[1,0,0,0,0,0]], # this is the third row representation
]
how to do that in NumPy ?
Thanks !
Answers:
from @michael-szczesny comment
a = np.array([[0,1,0,0,2,0],
[0,0,1,0,0,0],
[3,0,0,0,0,0],
])
b = (np.arange(1, a.max()+1)[:,None] <= a[:,None]).astype('uint8')
print(b)
array([[[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0]],
[[0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]],
[[1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0]]], dtype=uint8)
So I have an n*K
integer matrix [Note: its a representation of the number of samples drawn from K-distributions
(K-columns)]
a =[[0,1,0,0,2,0],
[0,0,1,0,0,0],
[3,0,0,0,0,0],
]
[Note: in the application context this matrix basically means that for the i
row (sim instance) we drew 1 element from the "distribution 1" (1 in [0,..K]
) (a[0,1] = 1
) and 2 from the distribution 4(a[0,4] = 2
)].
What I need is to generate a 0-1 matrix that represents the same integer matrix but with ones(1). In this case, is a 3D matrix of n*a.max()*K
that has a 1 for each sample that is drawn from the distributions. [Note: we need this matrix so we can multiply by our K-distribution sample matrix]
Output
b = [[[0,1,0,0,1,0], # we don't care if they samples are stack
[0,0,0,0,1,0],
[0,0,0,0,0,0]], # this is the first row representation
[[0,0,1,0,0,0],
[0,0,0,0,0,0],
[0,0,0,0,0,0]], # this is the second row representation
[[1,0,0,0,0,0],
[1,0,0,0,0,0],
[1,0,0,0,0,0]], # this is the third row representation
]
how to do that in NumPy ?
Thanks !
from @michael-szczesny comment
a = np.array([[0,1,0,0,2,0],
[0,0,1,0,0,0],
[3,0,0,0,0,0],
])
b = (np.arange(1, a.max()+1)[:,None] <= a[:,None]).astype('uint8')
print(b)
array([[[0, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0]],
[[0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]],
[[1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0]]], dtype=uint8)