ca14ae065b90a5aae6f282d84b16af60a465c115,examples/01_Ising/quantum_ising_chain.py,TransverseFieldIsingChain,Randomize,#TransverseFieldIsingChain#Any#,47

Before Change

        v=np.zeros((self.nc,N))
        for i in range(v.shape[0]):
            for j in range(N):
                if (random.random()>0.5):
                    v[i,j]=1.0
                else:
                    v[i,j]=0.0

After Change

        s[:, i] *= -1.0

    def Randomize(self, N):
        p = torch.ones(self.nc, N) * 0.5
        return torch.bernoulli(p)

    def Run(self, nn_state, n_eq=100, show_convergence=False):
        v = self.Randomize(nn_state.num_visible).to(dtype=torch.double,

Instances