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Zahra Rajabi
pymdptoolbox
Commits
ead52d78
Commit
ead52d78
authored
Feb 08, 2013
by
Steven Cordwell
Browse files
change check() to be more pythonic
parent
e26f2c78
Changes
1
Hide whitespace changes
Inline
Side-by-side
mdp.py
View file @
ead52d78
...
...
@@ -179,18 +179,69 @@ def check(P, R):
Raises an error if P and R do not define a MDP.
"""
# a small change to see what happens
# Check P
# Checking P
try
:
if
P
.
ndim
==
3
:
aP
,
sP0
,
sP1
=
P
.
shape
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
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.
if
type
(
P
)
!=
ndarray
:
raise
TypeError
(
mdperr
[
"P_type"
])
# also check R
if
type
(
R
)
!=
ndarray
:
raise
TypeError
(
mdperr
[
"R_type"
])
#
# 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
...
...
@@ -201,34 +252,16 @@ def check(P, R):
# 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.
if
P
.
dtype
==
object
:
if
P
.
ndim
>
1
:
raise
ValueError
(
mdperr
[
"obj_shape"
])
else
:
P_is_object
=
True
else
:
if
P
.
ndim
!=
3
:
raise
ValueError
(
mdperr
[
"P_shape"
])
else
:
P_is_object
=
False
#
# As above but for the reward array. A difference is that the reward
# array can have either two or 3 dimensions.
if
R
.
dtype
==
object
:
if
R
.
ndim
>
1
:
raise
ValueError
(
mdperr
[
"obj_shape"
])
else
:
R_is_object
=
True
else
:
if
R
.
ndim
not
in
(
2
,
3
):
raise
ValueError
(
mdperr
[
"R_shape"
])
else
:
R_is_object
=
False
#
# 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
P_is_object
:
#
# 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,
...
...
@@ -238,78 +271,55 @@ def check(P, R):
# 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.
aP
=
P
.
shape
[
0
]
#
# 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
sP0
,
sP1
=
P
[
0
].
shape
#
# Now we check to see that every element of the object array holds
# a matrix of the same shape, otherwise fail.
for
aa
in
range
(
1
,
aP
):
#
# 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.
sP0aa
,
sP1aa
=
P
[
aa
].
shape
if
(
sP0aa
!=
sP0
)
or
(
sP1aa
!=
sP1
):
raise
ValueError
(
mdperr
[
"obj_square"
])
else
:
#
# 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
aP
,
sP0
,
sP1
=
P
.
shape
#
# 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.
if
(
sP0
<
1
)
or
(
aP
<
1
)
or
(
sP0
!=
sP1
):
raise
ValueError
(
mdperr
[
"P_shape"
])
#
# 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
for
aa
in
range
(
aP
):
if
P_is_object
:
checkSquareStochastic
(
P
[
aa
])
else
:
checkSquareStochastic
(
P
[
aa
,
:,
:])
# aa = aa + 1 # why was this here?
if
R_is_object
:
#
# 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.
aR
=
R
.
shape
[
0
]
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"
])
elif
R
.
ndim
==
3
:
#
# 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.
aR
,
sR0
,
sR1
=
R
.
shape
else
:
#
# then the reward matrix is per state, so the first dimension is
# the states and the second dimension is the actions.
sR0
,
aR
=
R
.
shape
#
# this is added just so that the next check doesn't error out
# saying that sR1 doesn't exist
sR1
=
sR0
#
# the number of actions must be more than zero, the number of states
# must also be more than 0, and the states must agree
if
(
sR0
<
1
)
or
(
aR
<
1
)
or
(
sR0
!=
sR1
):
raise
ValueError
(
mdperr
[
"R_shape"
])
#
# 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
if
(
sP0
!=
sR0
)
or
(
aP
!=
aR
):
raise
ValueError
(
mdperr
[
"PR_incompat"
])
# 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
def
checkSquareStochastic
(
Z
):
"""Check if Z is a square stochastic matrix.
...
...
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