import numpy as np

def criterion(D_0, D_1, S, beta, size) -> bool:
    EPS = 10**(-5)
    nul = np.array([0]*size)
    eden = np.array([1] * size)
    print(EPS)
    D = D_0 + D_1
    D = np.matrix.transpose(D)
    D[0] = [1] * size
    nul[0] = 1
    # print(D)
    Teta = np.linalg.solve(D, nul)
    print(Teta)
    lamb = np.matmul(Teta, np.matmul(D_1, eden))
    print(lamb, " = Lambda")
    print("Inverse matrix S")
    print(np.linalg.inv(S))
    print(np.linalg.inv(S)*np.matrix.transpose(S))
    nym = np.matmul(np.dot(beta, np.linalg.inv(S)), eden)
    nym = (-1)/nym
    print(nym, " = Ny")
    print(lamb/nym, " = r")
    return (lamb/nym + EPS) < 1


D_0 = np.array([[-8, 1], [1, -11]])
D_1 = np.array([[2, 5], [4, 6]])
S = np.array([[-30., 30.], [0, -30.]])
beta = np.array([1,0])
size = 2
print(criterion(D_0, D_1, S, beta, size))