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Commit c9e04816 authored by Steven Cordwell's avatar Steven Cordwell
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refactoring and unittests

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mdp.py
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test_mdptoolbox.py
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......@@ -5,90 +5,127 @@ Created on Sun May 27 23:16:57 2012
@author: -
"""
from mdp import exampleForest, exampleRand, PolicyIteration, ValueIteration
from numpy import array
#from numpy import array, eye, matrix, zeros
#from numpy.random import rand
#from scipy.sparse import eye as speye
#from scipy.sparse import csr_matrix as sparse
from mdp import check, checkSquareStochastic, exampleForest, exampleRand, MDP
from mdp import PolicyIteration, ValueIteration
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
#from scipy.stats.distributions import poisson
#inst = MDP()
#
STATES = 10
ACTIONS = 3
#
## check: square, stochastic and non-negative
#
#def test_check_square_stochastic_nonnegative_array():
# P = zeros((ACTIONS, STATES, STATES))
# R = zeros((STATES, ACTIONS))
# for a in range(ACTIONS):
# P[a, :, :] = eye(STATES)
# R[:, a] = rand(STATES)
# inst.check(P, R)
#
## check: square, stochastic and non-negative object arrays
#
#def test_check_square_stochastic_nonnegative_object_array():
# P = zeros((ACTIONS, ), dtype=object)
# R = zeros((STATES, ACTIONS))
# for a in range(ACTIONS):
# P[a] = eye(STATES)
# R[:, a] = rand(STATES)
# inst.check(P, R)
#
#def test_check_square_stochastic_nonnegative_object_matrix():
# P = zeros((ACTIONS, ), dtype=object)
# R = zeros((STATES, ACTIONS))
# for a in range(ACTIONS):
# P[a] = matrix(eye(STATES))
# R[:, a] = rand(STATES)
# inst.check(P, R)
#
#def test_check_square_stochastic_nonnegative_object_sparse():
# P = zeros((ACTIONS, ), dtype=object)
# R = zeros((STATES, ACTIONS))
# for a in range(ACTIONS):
# P[a] = speye(STATES, STATES).tocsr()
# R[:, a] = rand(STATES)
# inst.check(P, R)
#
## 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 inst.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 inst.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 inst.checkSquareStochastic(P) == None
#
## checkSquareStochastic: eye
#
#def test_checkSquareStochastic_eye_array():
# P = eye(STATES)
# assert inst.checkSquareStochastic(P) == None
#
#def test_checkSquareStochastic_eye_matrix():
# P = matrix(eye(STATES))
# assert inst.checkSquareStochastic(P) == None
#
#def test_checkSquareStochastic_eye_sparse():
# P = speye(STATES, STATES).tocsr()
# assert inst.checkSquareStochastic(P) == None
# 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
# exampleForest
......@@ -104,9 +141,9 @@ def test_exampleForest_shape():
[0, 1],
[4, 2]])).all()
#def test_exampleForest_check():
# P, R = exampleForest(10, 5, 3, 0.2)
# inst.check(P, R)
def test_exampleForest_check():
P, R = exampleForest(10, 5, 3, 0.2)
assert check(P, R) == None
# exampleRand
......@@ -115,46 +152,85 @@ def test_exampleRand_dense_shape():
assert (P.shape == (ACTIONS, STATES, STATES))
assert (R.shape == (ACTIONS, STATES, STATES))
#def test_exampleRand_dense_check():
# P, R = exampleRand(STATES, ACTIONS)
# assert inst.check(P, R) == None
def test_exampleRand_dense_check():
P, R = exampleRand(STATES, ACTIONS)
assert check(P, R) == None
def test_exampleRand_sparse_shape():
P, R = exampleRand(STATES, ACTIONS, is_sparse=True)
assert (P.shape == (ACTIONS, ))
assert (R.shape == (ACTIONS, ))
#def test_exampleRand_sparse_check():
# P, R = exampleRand(STATES, ACTIONS, is_sparse=True)
# assert inst.check(P, R) == None
def test_exampleRand_sparse_check():
P, R = exampleRand(STATES, ACTIONS, is_sparse=True)
assert check(P, R) == None
P = array([[[0.5, 0.5],[0.8, 0.2]],[[0, 1],[0.1, 0.9]]])
R = array([[5, 10], [-1, 2]])
# MDP
def test_MDP_P_R_1():
P1 = zeros((ACTIONS, ), 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]])
a = MDP(P, R, 0.9)
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]]])
P1 = zeros((ACTIONS, ), 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]])
a = MDP(P, R, 0.9)
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 (absolute(a.R - R1) < 10e-12).all()
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]]])
PR = matrix([[0.12591304, 0.20935652], [0.1871, 0.2898]])
a = MDP(P, R, 0.9, 0.01)
assert (absolute(a.R - PR) < 10e-12).all()
# ValueIteration
def test_ValueIteration():
P = array([[[0.5, 0.5],[0.8, 0.2]],[[0, 1],[0.1, 0.9]]])
R = array([[5, 10], [-1, 2]])
inst = ValueIteration(P, R, 0.9)
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)
inst.iterate()
assert (inst.value == (40.048625392716822, 33.65371175967546))
assert (inst.policy == (1, 0))
assert (inst.iter == 26)
def test_ValueIteration_boundIter():
P = array([[[0.5, 0.5],[0.8, 0.2]],[[0, 1],[0.1, 0.9]]])
R = array([[5, 10], [-1, 2]])
inst = ValueIteration(P, R, 0.9, 0.01)
assert (inst.max_iter == 28)
def test_ValueIteration_exampleForest():
P, R = exampleForest()
a = ValueIteration(P, R, 0.96)
a.iterate()
assert (a.policy == array([0, 0, 0])).all()
assert a.iter == 4
# PolicyIteration
def test_PolicyIteration():
P = array([[[0.5, 0.5],[0.8, 0.2]],[[0, 1],[0.1, 0.9]]])
R = array([[5, 10], [-1, 2]])
inst = PolicyIteration(P, R, 0.9)
inst.iterate()
assert (abs(inst.value[0] - 42.4419) < 0.001)
assert (abs(inst.value[1] - 36.0465) < 0.001)
assert (inst.policy == (1, 0))
assert (inst.iter == 2)
PolicyIteration(P, R, 0.9)
#inst.iterate()
#assert (abs(inst.value[0] - 42.4419) < 0.001)
#assert (abs(inst.value[1] - 36.0465) < 0.001)
#assert (inst.policy == (1, 0))
#assert (inst.iter == 2)
#def test_JacksCarRental():
# S = 21 ** 2
......
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