from bussilab import potts
import numpy as np
import matplotlib.pyplot as plt

m=potts.Model(6)

h=np.random.uniform(size=6)
J=np.reshape(np.random.uniform(size=36),(6,6))
J=m.fixJ(J) # make symmetric and cancel diagonal
h,J

(array([0.88896261, 0.09934222, 0.45486963, 0.22375098, 0.9102452 ,
        0.46952976]),
 array([[0.        , 0.44811401, 0.44179955, 0.60276147, 0.53811439,
         0.59348674],
        [0.44811401, 0.        , 0.7390881 , 0.67438462, 0.62920129,
         0.39100428],
        [0.44179955, 0.7390881 , 0.        , 0.51378989, 0.03647317,
         0.28402245],
        [0.60276147, 0.67438462, 0.51378989, 0.        , 0.70031567,
         0.89372253],
        [0.53811439, 0.62920129, 0.03647317, 0.70031567, 0.        ,
         0.69047674],
        [0.59348674, 0.39100428, 0.28402245, 0.89372253, 0.69047674,
         0.        ]]))

result=m.compute(h,J)

infer=m.infer(result[1])
h_inferred,J_inferred=infer.h,infer.J

h_inferred,J_inferred

(array([0.88896262, 0.09934222, 0.45486963, 0.22375098, 0.91024519,
        0.46952976]),
 array([[0.        , 0.44811401, 0.44179955, 0.60276146, 0.5381144 ,
         0.59348673],
        [0.44811401, 0.        , 0.73908811, 0.67438462, 0.62920129,
         0.39100428],
        [0.44179955, 0.73908811, 0.        , 0.51378989, 0.03647317,
         0.28402245],
        [0.60276146, 0.67438462, 0.51378989, 0.        , 0.70031569,
         0.89372252],
        [0.5381144 , 0.62920129, 0.03647317, 0.70031569, 0.        ,
         0.69047674],
        [0.59348673, 0.39100428, 0.28402245, 0.89372252, 0.69047674,
         0.        ]]))

plt.plot(h,h_inferred,"x")
plt.show()
np.sum((h-h_inferred)**2)

3.0177189225292775e-16

plt.plot(J.flatten(),J_inferred.flatten(),"x")
plt.show()
np.sum((J-J_inferred)**2)

2.195972799014477e-15