test_mdptoolbox.py 13.7 KB
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# -*- coding: utf-8 -*-
"""
Created on Sun May 27 23:16:57 2012

@author: -
"""

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from mdp import check, checkSquareStochastic, exampleForest, exampleRand, MDP
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from mdp import PolicyIteration, RelativeValueIteration, ValueIteration
from mdp import ValueIterationGS
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from numpy import absolute, array, eye, matrix, zeros
from numpy.random import rand
from scipy.sparse import eye as speye
from scipy.sparse import csr_matrix as sparse
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#from scipy.stats.distributions import poisson
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STATES = 10
ACTIONS = 3
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SMALLNUM = 10e-12
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# Arrays
P = array([[[0.5, 0.5],[0.8, 0.2]],[[0, 1],[0.1, 0.9]]])
R = array([[5, 10], [-1, 2]])
Pf, Rf = exampleForest()
Pr, Rr = exampleRand(STATES, ACTIONS)
Prs, Rrs = exampleRand(STATES, ACTIONS, is_sparse=True)

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# check: square, stochastic and non-negative ndarrays

def test_check_square_stochastic_nonnegative_array_1():
    P = zeros((ACTIONS, STATES, STATES))
    R = zeros((STATES, ACTIONS))
    for a in range(ACTIONS):
        P[a, :, :] = eye(STATES)
        R[:, a] = rand(STATES)
    assert (check(P, R) == None)

def test_check_square_stochastic_nonnegative_array_2():
    P = zeros((ACTIONS, STATES, STATES))
    R = rand(ACTIONS, STATES, STATES)
    for a in range(ACTIONS):
        P[a, :, :] = eye(STATES)
    assert (check(P, R) == None)

# check: P - square, stochastic and non-negative object arrays

def test_check_P_square_stochastic_nonnegative_object_array():
    P = zeros((ACTIONS, ), dtype=object)
    R = rand(STATES, ACTIONS)
    for a in range(ACTIONS):
        P[a] = eye(STATES)
    assert (check(P, R) == None)

def test_check_P_square_stochastic_nonnegative_object_matrix():
    P = zeros((ACTIONS, ), dtype=object)
    R = rand(STATES, ACTIONS)
    for a in range(ACTIONS):
        P[a] = matrix(eye(STATES))
    assert (check(P, R) == None)

def test_check_P_square_stochastic_nonnegative_object_sparse():
    P = zeros((ACTIONS, ), dtype=object)
    R = rand(STATES, ACTIONS)
    for a in range(ACTIONS):
        P[a] = speye(STATES, STATES).tocsr()
    assert (check(P, R) == None)

# check: R - square stochastic and non-negative sparse

def test_check_R_square_stochastic_nonnegative_sparse():
    P = zeros((ACTIONS, STATES, STATES))
    R = sparse(rand(STATES, ACTIONS))
    for a in range(ACTIONS):
        P[a, :, :] = eye(STATES)
    assert (check(P, R) == None)

# check: R - square, stochastic and non-negative object arrays

def test_check_R_square_stochastic_nonnegative_object_array():
    P = zeros((ACTIONS, STATES, STATES))
    R = zeros((ACTIONS, ), dtype=object)
    for a in range(ACTIONS):
        P[a, :, :] = eye(STATES)
        R[a] = rand(STATES, STATES)
    assert (check(P, R) == None)

def test_check_R_square_stochastic_nonnegative_object_matrix():
    P = zeros((ACTIONS, STATES, STATES))
    R = zeros((ACTIONS, ), dtype=object)
    for a in range(ACTIONS):
        P[a, :, :] = eye(STATES)
        R[a] = matrix(rand(STATES, STATES))
    assert (check(P, R) == None)

def test_check_R_square_stochastic_nonnegative_object_sparse():
    P = zeros((ACTIONS, STATES, STATES))
    R = zeros((ACTIONS, ), dtype=object)
    for a in range(ACTIONS):
        P[a, :, :] = eye(STATES)
        R[a] = sparse(rand(STATES, STATES))
    assert (check(P, R) == None)

# checkSquareStochastic: square, stochastic and non-negative

def test_checkSquareStochastic_square_stochastic_nonnegative_array():
    P = rand(STATES, STATES)
    for s in range(STATES):
        P[s, :] = P[s, :] / P[s, :].sum()
    assert checkSquareStochastic(P) == None

def test_checkSquareStochastic_square_stochastic_nonnegative_matrix():
    P = rand(STATES, STATES)
    for s in range(STATES):
        P[s, :] = P[s, :] / P[s, :].sum()
    P = matrix(P)
    assert checkSquareStochastic(P) == None

def test_checkSquareStochastic_square_stochastic_nonnegative_sparse():
    P = rand(STATES, STATES)
    for s in range(STATES):
        P[s, :] = P[s, :] / P[s, :].sum()
    P = sparse(P)
    assert checkSquareStochastic(P) == None

# checkSquareStochastic: eye

def test_checkSquareStochastic_eye_array():
    P = eye(STATES)
    assert checkSquareStochastic(P) == None

def test_checkSquareStochastic_eye_matrix():
    P = matrix(eye(STATES))
    assert checkSquareStochastic(P) == None

def test_checkSquareStochastic_eye_sparse():
    P = speye(STATES, STATES).tocsr()
    assert checkSquareStochastic(P) == None
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# exampleForest

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def test_exampleForest_P_shape():
    assert (Pf == array([[[0.1, 0.9, 0.0],
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                         [0.1, 0.0, 0.9],
                         [0.1, 0.0, 0.9]],
                        [[1, 0, 0],
                         [1, 0, 0],
                         [1, 0, 0]]])).all()
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def test_exampleForest_R_shape():
    assert (Rf == array([[0, 0],
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                        [0, 1],
                        [4, 2]])).all()

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def test_exampleForest_check():
    P, R = exampleForest(10, 5, 3, 0.2)
    assert check(P, R) == None
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# exampleRand
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def test_exampleRand_dense_P_shape():
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    assert (Pr.shape == (ACTIONS, STATES, STATES))
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def test_exampleRand_dense_R_shape():
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    assert (Rr.shape == (ACTIONS, STATES, STATES))
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def test_exampleRand_dense_check():
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    assert check(Pr, Rr) == None
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def test_exampleRand_sparse_P_shape():
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    assert (Prs.shape == (ACTIONS, ))
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def test_exampleRand_sparse_R_shape():
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    assert (Rrs.shape == (ACTIONS, ))
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def test_exampleRand_sparse_check():
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    assert check(Prs, Rrs) == None
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# MDP

def test_MDP_P_R_1():
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    P1 = zeros((2, ), dtype=object)
    P1[0] = matrix('0.5 0.5; 0.8 0.2')
    P1[1] = matrix('0 1; 0.1 0.9')
    R1 = matrix('5 10; -1 2')
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    a = MDP(P, R, 0.9, 0.01)
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    assert a.P.dtype == P1.dtype
    assert a.R.dtype == R1.dtype
    for kk in range(2):
        assert (a.P[kk] == P1[kk]).all()
    assert (a.R == R1).all()

def test_MDP_P_R_2():
    R = array([[[5, 10], [-1, 2]], [[1, 2], [3, 4]]])
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    P1 = zeros((2, ), dtype=object)
    P1[0] = matrix('0.5 0.5; 0.8 0.2')
    P1[1] = matrix('0 1; 0.1 0.9')
    R1 = matrix('7.5 2; -0.4 3.9')
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    a = MDP(P, R, 0.9, 0.01)
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    assert type(a.P) == type(P1)
    assert type(a.R) == type(R1)
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    assert a.P.dtype == P1.dtype
    assert a.R.dtype == R1.dtype
    for kk in range(2):
        assert (a.P[kk] == P1[kk]).all()
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    assert (absolute(a.R - R1) < SMALLNUM).all()
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def test_MDP_P_R_3():
    P = array([[[0.6116, 0.3884],[0, 1]],[[0.6674, 0.3326],[0, 1]]])
    R = array([[[-0.2433, 0.7073],[0, 0.1871]],[[-0.0069, 0.6433],[0, 0.2898]]])
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    PR = matrix('0.12591304 0.20935652; 0.1871 0.2898')
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    a = MDP(P, R, 0.9, 0.01)
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    assert (absolute(a.R - PR) < SMALLNUM).all()
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# ValueIteration

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def test_ValueIteration_boundIter():
    inst = ValueIteration(P, R, 0.9, 0.01)
    assert (inst.max_iter == 28)

def test_ValueIteration_iterate():
    inst = ValueIteration(P, R, 0.9, 0.01)
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    inst.iterate()
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    assert (inst.V == (40.048625392716822,  33.65371175967546))
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    assert (inst.policy == (1, 0))
    assert (inst.iter == 26)

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def test_ValueIteration_exampleForest():
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    a = ValueIteration(Pf, Rf, 0.96)
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    a.iterate()
    assert (a.policy == array([0, 0, 0])).all()
    assert a.iter == 4
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# PolicyIteration
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def test_PolicyIteration_init_policy0():
    a = PolicyIteration(P, R, 0.9)
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    p = matrix('1; 1')
    assert (a.policy == p).all()

def test_PolicyIteration_init_policy0_exampleForest():
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    a = PolicyIteration(Pf, Rf, 0.9)
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    p = matrix('0; 1; 0')
    assert (a.policy == p).all()

def test_PolicyIteration_computePpolicyPRpolicy_exampleForest():
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    a = PolicyIteration(Pf, Rf, 0.9)
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    P1 = matrix('0.1 0.9 0; 1 0 0; 0.1 0 0.9')
    R1 = matrix('0; 1; 4')
    Ppolicy, Rpolicy = a.computePpolicyPRpolicy()
    assert (absolute(Ppolicy - P1) < SMALLNUM).all()
    assert (absolute(Rpolicy - R1) < SMALLNUM).all()

def test_PolicyIteration_evalPolicyIterative_exampleForest():
    v0 = matrix('0; 0; 0')
    v1 = matrix('4.47504640074458; 5.02753258879703; 23.17234211944304')
    p = matrix('0; 1; 0')
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    a = PolicyIteration(Pf, Rf, 0.9)
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    assert (absolute(a.V - v0) < SMALLNUM).all()
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    a.evalPolicyIterative()
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    assert (absolute(a.V - v1) < SMALLNUM).all()
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    assert (a.policy == p).all()

def test_PolicyIteration_evalPolicyIterative_bellmanOperator_exampleForest():
    v = matrix('4.47504640074458; 5.02753258879703; 23.17234211944304')
    p = matrix('0; 0; 0')
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    a = PolicyIteration(Pf, Rf, 0.9)
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    a.evalPolicyIterative()
    policy, value = a.bellmanOperator()
    assert (policy == p).all()
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    assert (absolute(a.V - v) < SMALLNUM).all()
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def test_PolicyIteration_iterative_exampleForest():
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    a = PolicyIteration(Pf, Rf, 0.9, eval_type=1)
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    v = matrix('26.2439058351861 29.4839058351861 33.4839058351861')
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    p = matrix('0 0 0')
    itr = 2
    a.iterate()
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    assert (absolute(array(a.V) - v) < SMALLNUM).all()
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    assert (array(a.policy) == p).all()
    assert a.iter == itr

def test_PolicyIteration_evalPolicyMatrix_exampleForest():
    v_pol = matrix('4.47513812154696; 5.02762430939227; 23.17243384704857')
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    a = PolicyIteration(Pf, Rf, 0.9)
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    a.evalPolicyMatrix()
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    assert (absolute(a.V - v_pol) < SMALLNUM).all()
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def test_PolicyIteration_matrix_exampleForest():
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    a = PolicyIteration(Pf, Rf, 0.9)
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    v = matrix('26.2440000000000 29.4840000000000 33.4840000000000')
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    p = matrix('0 0 0')
    itr = 2
    a.iterate()
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    assert (absolute(array(a.V) - v) < SMALLNUM).all()
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    assert (array(a.policy) == p).all()
    assert a.iter == itr
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# ValueIterationGS

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def test_ValueIterationGS_boundIter_exampleForest():
    a = ValueIterationGS(Pf, Rf, 0.9)
    itr = 39
    assert (a.max_iter == itr)

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def test_ValueIterationGS_exampleForest():
    a = ValueIterationGS(Pf, Rf, 0.9)
    p = matrix('0 0 0')
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    v = matrix('25.5833879767579 28.8306546355469 32.8306546355469')
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    itr = 33
    a.iterate()
    assert (array(a.policy) == p).all()
    assert a.iter == itr
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    assert (absolute(array(a.V) - v) < SMALLNUM).all()
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# RelativeValueIteration

def test_RelativeValueIteration_exampleForest():
    a = RelativeValueIteration(Pf, Rf)
    itr = 4
    p = matrix('0 0 0')
    v = matrix('-4.360000000000000 -0.760000000000000 3.240000000000000')
    a.iterate()
    assert (array(a.policy) == p).all()
    assert a.iter == itr
    assert (absolute(array(a.V) - v) < SMALLNUM).all()

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#def test_JacksCarRental():
#    S = 21 ** 2
#    A = 11
#    P = zeros((A, S, S))
#    R = zeros((A, S, S))
#    for a in range(A):
#        for s in range(21):
#            for s1 in range(21):
#                c1s = int(s / 21)
#                c2s = s - c1s * 21
#                c1s1 = int(s1 / 21)
#                c2s1 = s - c1s * 21
#                cs = c1s + c2s
#                cs1 = c1s1 + c2s1
#                netmove = 5 - a
#                if (s1 < s):
#                    pass
#                else:
#                    pass
#                P[a, s, s1] = 1
#                R[a, s, s1] = 10 * (cs - cs1) - 2 * abs(a)
#    
#    inst = PolicyIteration(P, R, 0.9)
#    inst.iterate()
#    #assert (inst.policy == )
#
#def test_JacksCarRental2():
#    pass
#
#def test_GamblersProblem():
#    inst = ValueIteration()
#    inst.iterate()
#    #assert (inst.policy == )
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# checkSquareStochastic: not square, stochastic and non-negative

#@raises(ValueError(mdperr["mat_square"]))
#def test_checkSquareStochastic_notsquare_stochastic_nonnegative_array():
#    P = eye(STATES, STATES + 1)
#    inst.checkSquareStochastic(P)
#
#@raises(ValueError(mdperr["mat_square"]))
#def test_checkSquareStochastic_notsquare_stochastic_nonnegative_matrix():
#    P = matrix(eye(STATES, STATES + 1))
#    inst.checkSquareStochastic(P)
#
#@raises(ValueError(mdperr["mat_square"]))
#def test_checkSquareStochastic_notsquare_stochastic_nonnegative_sparse():
#    P = speye(STATES, STATES + 1).tocsr()
#    inst.checkSquareStochastic(P)

# checkSquareStochastic: square, not stochastic and non-negative
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#def test_checkSquareStochastic_square_notstochastic_nonnegative_array():
#    P = eye(STATES)
#    i = randint(STATES)
#    j = randint(STATES)
#    P[i, j] = P[i, j] + 1
#    try:
#        inst.checkSquareStochastic(P)
#    except ValueError(mdperr["mat_stoch"]):
#        pass
#
#def test_checkSquareStochastic_square_notstochastic_nonnegative_matrix():
#    P = matrix(eye(STATES))
#    i = randint(STATES)
#    j = randint(STATES)
#    P[i, j] = P[i, j] + 1
#    try:
#        inst.checkSquareStochastic(P)
#    except ValueError(mdperr["mat_stoch"]):
#        pass
#
#def test_checkSquareStochastic_square_notstochastic_nonnegative_sparse():
#    P = speye(STATES, STATES).tolil()
#    i = randint(STATES)
#    j = randint(STATES)
#    P[i, j] = P[i, j] + 1
#    P = P.tocsr()
#    try:
#        inst.checkSquareStochastic(P)
#    except ValueError(mdperr["mat_stoch"]):
#        pass

# checkSquareStochastic: square, stochastic and negative

#def test_checkSquareStochastic_square_stochastic_negative_array():
#    P = eye(STATES, STATES)
#    i = randint(STATES)
#    j = randint(STATES)
#    while j == i:
#        j = randint(STATES)
#    P[i, i] = -1
#    P[i, j] = 1
#    try:
#        inst.checkSquareStochastic(P)
#    except ValueError(mdperr["mat_nonneg"]):
#        pass
#
#def test_checkSquareStochastic_square_stochastic_negative_matrix():
#    P = matrix(eye(STATES, STATES))
#    i = randint(STATES)
#    j = randint(STATES)
#    while j == i:
#        j = randint(STATES)
#    P[i, i] = -1
#    P[i, j] = 1
#    try:
#        inst.checkSquareStochastic(P)
#    except ValueError(mdperr["mat_nonneg"]):
#        pass
#
#def test_checkSquareStochastic_square_stochastic_negative_sparse():
#    P = speye(STATES, STATES)
#    i = randint(STATES)
#    j = randint(STATES)
#    while j == i:
#        j = randint(STATES)
#    P[i, i] = -1
#    P[i, j] = 1
#    try:
#        inst.checkSquareStochastic(P)
#    except ValueError(mdperr["mat_nonneg"]):
#        pass

#def test_check_square_stochastic_array_Rtranspose():
#    P = array([eye(STATES), eye(STATES)])
#    R = array([ones(STATES), ones(STATES)])
#    assert inst.check(P, R) == (True, "R is wrong way")