utils.py 8.98 KB
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# -*- 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())