Commit 6ad0c1a6 authored by Steven Cordwell's avatar Steven Cordwell

[util] Make util module more modular

Seperate out functionality into more modular and single purpose
functions. Move exception classes to own module for this purpose. Add
new user facing functions for checking MDPs.
parent 5af7d2a5
# -*- coding: utf-8 -*-
"""Markov Decision Process (MDP) Toolbox: ``error`` module
=======================================================
The ``error`` module provides exception classes that can be raised by
the toolbox.
Available classes
-----------------
Error
Base exception class derived from ``Exception``
InvalidError
Exception for invalid definitions of an MDP
NonNegativeError
Exception for transition matrices that have negative elements
SquareError
Exception for transition matrices that are not square
StochasticError
Exception for transition matrices that are not stochastic
"""
# Copyright (c) 2015 Steven A. W. Cordwell
# Copyright (c) 2009 INRA
#
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# * Neither the name of the <ORGANIZATION> nor the names of its contributors
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
class Error(Exception):
"""Base class for exceptions in this module."""
def __init__(self):
Exception.__init__(self)
self.message = "PyMDPToolbox - "
def __str__(self):
return repr(self.message)
class InvalidError(Error):
"""Class for invalid definitions of a MDP."""
def __init__(self, msg):
Error.__init__(self)
self.message += msg
self.args = tuple(msg)
class NonNegativeError(Error):
"""Class for transition matrix stochastic errors"""
default_msg = "The transition probability matrix is negative."
def __init__(self, msg=None):
if msg is None:
msg = self.default_msg
Error.__init__(self)
self.message += msg
self.args = tuple(msg)
class SquareError(Error):
"""Class for transition matrix square errors"""
default_msg = "The transition probability matrix is not square."
def __init__(self, msg=None):
if msg is None:
msg = self.default_msg
Error.__init__(self)
self.message += msg
self.args = tuple(msg)
class StochasticError(Error):
"""Class for transition matrix stochastic errors"""
default_msg = "The transition probability matrix is not stochastic."
def __init__(self, msg=None):
if msg is None:
msg = self.default_msg
Error.__init__(self)
self.message += msg
self.args = tuple(msg)
This diff is collapsed.
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
"""
Created on Sat Aug 24 14:52:17 2013
@author: steve
"""
import numpy as np import numpy as np
import scipy as sp import scipy as sp
...@@ -174,19 +169,66 @@ def test_check_vector_R(): ...@@ -174,19 +169,66 @@ def test_check_vector_R():
P = [np.matrix(np.eye(STATES))] * 3 P = [np.matrix(np.eye(STATES))] * 3
assert mdptoolbox.util.check(P, R) is None assert mdptoolbox.util.check(P, R) is None
def test_check_vector_R_error():
R = np.random.rand(STATES+1)
P = [np.matrix(np.eye(STATES))] * 3
assert_raises(mdptoolbox.error.InvalidError,
mdptoolbox.util.check, P=P, R=R)
# Exception tests # Exception tests
def test_check_P_shape_error_1(): def test_check_P_shape_error_1():
P = np.eye(STATES)[:STATES - 1, :STATES] P = np.eye(STATES)[:STATES - 1, :STATES]
assert_raises(mdptoolbox.util.InvalidMDPError, mdptoolbox.util.check, assert_raises(mdptoolbox.error.InvalidError, mdptoolbox.util.check,
P=P, R=np.random.rand(10, 3)) P=P, R=np.random.rand(STATES, ACTIONS))
def test_check_P_shape_error_2(): def test_check_P_shape_error_2():
P = (np.random.rand(9, 9), np.random.rand(9, 9), np.random.rand(9, 5)) P = (np.random.rand(9, 9), np.random.rand(9, 9), np.random.rand(9, 5))
assert_raises(mdptoolbox.util.InvalidMDPError, mdptoolbox.util.check, assert_raises(mdptoolbox.error.InvalidError, mdptoolbox.util.check,
P=P, R=np.random.rand(9)) P=P, R=np.random.rand(9))
def test_check_R_shape_error_1(): def test_check_R_shape_error_1():
R = (np.random.rand(9, 9), np.random.rand(9, 9), np.random.rand(9, 5)) R = (np.random.rand(9, 9), np.random.rand(9, 9), np.random.rand(9, 5))
P = np.random.rand(3, 10, 10) P = np.random.rand(3, 10, 10)
assert_raises(mdptoolbox.util.InvalidMDPError, mdptoolbox.util.check, assert_raises(mdptoolbox.error.InvalidError, mdptoolbox.util.check,
P=P, R=R) P=P, R=R)
def test_isSqaure_tuple():
P = ((1, 0, 0), (0, 1, 0), (0, 0, 1))
assert mdptoolbox.util.isSquare(P)
def test_isSqaure_string():
P = "a string, the wrong type"
assert not mdptoolbox.util.isSquare(P)
def test_isStochastic_tuple():
P = ((1, 0, 0), (0, 1, 0), (0, 0, 1))
assert mdptoolbox.util.isStochastic(P)
def test_isStochastic_string():
P = "a string, the wrong type"
assert_raises(TypeError, mdptoolbox.util.isStochastic, matrix=P)
def test_isNonNegative_tuple():
P = ((1, 0, 0), (0, 1, 0), (0, 0, 1))
assert mdptoolbox.util.isStochastic(P)
def test_isNonNegative_string():
P = "a string, the wrong type"
assert_raises(TypeError, mdptoolbox.util.isStochastic, matrix=P)
def test_checkSquareStochastic_SquareError():
P = np.eye(STATES)[:STATES - 1, :STATES]
assert_raises(mdptoolbox.error.SquareError,
mdptoolbox.util.checkSquareStochastic, matrix=P)
def test_checkSquareStochastic_StochasticError():
P = np.random.rand(STATES, STATES)
assert_raises(mdptoolbox.error.StochasticError,
mdptoolbox.util.checkSquareStochastic, matrix=P)
def test_checkSquareStochastic_NonNegativeError():
P = np.eye(STATES)
P[0, 0] = -0.5
P[0, 1] = 1.5
assert_raises(mdptoolbox.error.NonNegativeError,
mdptoolbox.util.checkSquareStochastic, matrix=P)
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