# -*- coding: utf-8 -*-
"""
Created on Sun Aug 18 14:30:09 2013
@author: steve
"""
from numpy import absolute, ones
def check(P, R):
"""Check if P and R define a Markov Decision Process.
Let S = number of states, A = number of actions.
Parameters
---------
P : array
The transition matrices. It can be a three dimensional array with
a shape of (A, S, S). It can also be a one dimensional arraye with
a shape of (A, ), where each element contains a matrix of shape (S, S)
which can possibly be sparse.
R : array
The reward matrix. It can be a three dimensional array with a
shape of (S, A, A). It can also be a one dimensional array with a
shape of (A, ), where each element contains matrix with a shape of
(S, S) which can possibly be sparse. It can also be an array with
a shape of (S, A) which can possibly be sparse.
Notes
-----
Raises an error if P and R do not define a MDP.
"""
# Checking P
try:
if P.ndim == 3:
aP, sP0, sP1 = P.shape
elif P.ndim == 1:
# A hack so that we can go into the next try-except statement and
# continue checking from there
raise AttributeError
else:
raise ValueError(mdperr["P_shape"])
except AttributeError:
try:
aP = len(P)
sP0, sP1 = P[0].shape
for aa in xrange(1, aP):
sP0aa, sP1aa = P[aa].shape
if (sP0aa != sP0) or (sP1aa != sP1):
raise ValueError(mdperr["obj_square"])
except AttributeError:
raise TypeError(mdperr["P_shape"])
except:
raise
# Checking R
try:
if R.ndim == 2:
sR0, aR = R.shape
sR1 = sR0
elif R.ndim == 3:
aR, sR0, sR1 = R.shape
elif R.ndim == 1:
# A hack so that we can go into the next try-except statement
raise AttributeError
else:
raise ValueError(mdperr["R_shape"])
except AttributeError:
try:
aR = len(R)
sR0, sR1 = R[0].shape
for aa in range(1, aR):
sR0aa, sR1aa = R[aa].shape
if ((sR0aa != sR0) or (sR1aa != sR1)):
raise ValueError(mdperr["obj_square"])
except AttributeError:
raise ValueError(mdperr["R_shape"])
except:
raise
# Checking dimensions
if (sP0 < 1) or (aP < 1) or (sP0 != sP1):
raise ValueError(mdperr["P_shape"])
if (sR0 < 1) or (aR < 1) or (sR0 != sR1):
raise ValueError(mdperr["R_shape"])
if (sP0 != sR0) or (aP != aR):
raise ValueError(mdperr["PR_incompat"])
# Check that the P's are square and stochastic
for aa in xrange(aP):
checkSquareStochastic(P[aa])
#checkSquareStochastic(P[aa, :, :])
# We are at the end of the checks, so if no exceptions have been raised
# then that means there are (hopefullly) no errors and we return None
return None
# These are the old code comments, which need to be converted to
# information in the docstring:
#
# tranitions must be a numpy array either an AxSxS ndarray (with any
# dtype other than "object"); or, a 1xA ndarray with a "object" dtype,
# and each element containing an SxS array. An AxSxS array will be
# be converted to an object array. A numpy object array is similar to a
# MATLAB cell array.
#
# NumPy has an array type of 'object', which is roughly equivalent to
# the MATLAB cell array. These are most useful for storing sparse
# matrices as these can only have two dimensions whereas we want to be
# able to store a transition matrix for each action. If the dytpe of
# the transition probability array is object then we store this as
# P_is_object = True.
# If it is an object array, then it should only have one dimension
# otherwise fail with a message expalining why.
# If it is a normal array then the number of dimensions must be exactly
# three, otherwise fail with a message explaining why.
#
# As above but for the reward array. A difference is that the reward
# array can have either two or 3 dimensions.
#
# We want to make sure that the transition probability array and the
# reward array are in agreement. This means that both should show that
# there are the same number of actions and the same number of states.
# Furthermore the probability of transition matrices must be SxS in
# shape, so we check for that also.
#
# If the user has put their transition matrices into a numpy array
# with dtype of 'object', then it is possible that they have made a
# mistake and not all of the matrices are of the same shape. So,
# here we record the number of actions and states that the first
# matrix in element zero of the object array says it has. After
# that we check that every other matrix also reports the same
# number of actions and states, otherwise fail with an error.
# aP: the number of actions in the transition array. This
# corresponds to the number of elements in the object array.
#
# sP0: the number of states as reported by the number of rows of
# the transition matrix
# sP1: the number of states as reported by the number of columns of
# the transition matrix
#
# Now we check to see that every element of the object array holds
# a matrix of the same shape, otherwise fail.
#
# sp0aa and sp1aa represents the number of states in each
# subsequent element of the object array. If it doesn't match
# what was found in the first element, then we need to fail
# telling the user what needs to be fixed.
#
# if we are using a normal array for this, then the first
# dimension should be the number of actions, and the second and
# third should be the number of states
#
# the first dimension of the transition matrix must report the same
# number of states as the second dimension. If not then we are not
# dealing with a square matrix and it is not a valid transition
# probability. Also, if the number of actions is less than one, or the
# number of states is less than one, then it also is not a valid
# transition probability.
#
# now we check that each transition matrix is square-stochastic. For
# object arrays this is the matrix held in each element, but for
# normal arrays this is a matrix formed by taking a slice of the array
#
# if the rewarad array has an object dtype, then we check that
# each element contains a matrix of the same shape as we did
# above with the transition array.
#
# This indicates that the reward matrices are constructed per
# transition, so that the first dimension is the actions and
# the second two dimensions are the states.
#
# then the reward matrix is per state, so the first dimension is
# the states and the second dimension is the actions.
#
# this is added just so that the next check doesn't error out
# saying that sR1 doesn't exist
#
# the number of actions must be more than zero, the number of states
# must also be more than 0, and the states must agree
#
# now we check to see that what the transition array is reporting and
# what the reward arrar is reporting agree as to the number of actions
# and states. If not then fail explaining the situation
def checkSquareStochastic(Z):
"""Check if Z is a square stochastic matrix.
Let S = number of states.
Parameters
----------
Z : matrix
This should be a two dimensional array with a shape of (S, S). It can
possibly be sparse.
Notes
----------
Returns None if no error has been detected, else it raises an error.
"""
# try to get the shape of the matrix
try:
s1, s2 = Z.shape
except AttributeError:
raise TypeError("Matrix should be a numpy type.")
except ValueError:
raise ValueError(mdperr["mat_square"])
# check that the matrix is square, and that each row sums to one
if s1 != s2:
raise ValueError(mdperr["mat_square"])
elif (absolute(Z.sum(axis=1) - ones(s2))).max() > 10e-12:
raise ValueError(mdperr["mat_stoch"])
# make sure that there are no values less than zero
try:
if (Z < 0).any():
raise ValueError(mdperr["mat_nonneg"])
except AttributeError:
try:
if (Z.data < 0).any():
raise ValueError(mdperr["mat_nonneg"])
except AttributeError:
raise TypeError("Matrix should be a numpy type.")
except:
raise
return(None)
def getSpan(W):
"""Return the span of W
sp(W) = max W(s) - min W(s)
"""
return (W.max() - W.min())