# Python code - kronecker product

## Kronecker product - numpy

{% embed url="<https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.kron.html>" %}

`numpy.kron`(*a*, *b*)[\[source\]](http://github.com/numpy/numpy/blob/v1.13.0/numpy/lib/shape_base.py#L727-L823)

a, b에는 kronecker product를 구하고자 하는 행렬이 각각 파라미터로 들어가고, kronecker product의 결과값을 리턴합니다.&#x20;

## Kronecker product MLE 구현하기

```python
import numpy as np
G = [[1,0,1,1],[0,1,0,1],[1,0,1,1],[1,1,1,1]]
KM = [[0.5,0.2],[0.1,0.3]]
KM = np.kron(KM, KM)
P = 1
for i in range(KM.shape[0]):
  for j in range(KM.shape[1]):
    # G에 속할 때.
    if G[i][j]==1:
      P = P*KM[i][j]
    # G에 속하지 않을 때.
    else:
      P = P*(1-KM[i][j])
# P는 kronecker 로부터 생성된 그래프가 실제 G일 확률을 나타냄.
print(P)
```


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