distance
¶
Functions for measuring distances.

class
pyphi.distance.
MeasureRegistry
¶ Storage for measures registered with PyPhi.
Users can define custom measures:
Examples
>>> @measures.register('ALWAYS_ZERO') ... def always_zero(a, b): ... return 0
And use them by setting
config.MEASURE = 'ALWAYS_ZERO'
.
desc
= 'measures'¶

register
(name, asymmetric=False)¶ Decorator for registering a measure with PyPhi.
Parameters: name (string) – The name of the measure. Keyword Arguments: asymmetric (boolean) – True
if the measure is asymmetric.

asymmetric
()¶ Return a list of asymmetric measures.


class
pyphi.distance.
np_suppress
¶ Decorator to suppress NumPy warnings about dividebyzero and multiplication of
NaN
.Note
This should only be used in cases where you are sure that these warnings are not indicative of deeper issues in your code.

pyphi.distance.
hamming_emd
(d1, d2)¶ Return the Earth Mover’s Distance between two distributions (indexed by state, one dimension per node) using the Hamming distance between states as the transportation cost function.
Singleton dimensions are sqeezed out.

pyphi.distance.
effect_emd
(d1, d2)¶ Compute the EMD between two effect repertoires.
Because the nodes are independent, the EMD between effect repertoires is equal to the sum of the EMDs between the marginal distributions of each node, and the EMD between marginal distribution for a node is the absolute difference in the probabilities that the node is OFF.
Parameters:  d1 (np.ndarray) – The first repertoire.
 d2 (np.ndarray) – The second repertoire.
Returns: The EMD between
d1
andd2
.Return type: float

pyphi.distance.
l1
(d1, d2)¶ Return the L1 distance between two distributions.
Parameters:  d1 (np.ndarray) – The first distribution.
 d2 (np.ndarray) – The second distribution.
Returns: The sum of absolute differences of
d1
andd2
.Return type: float

pyphi.distance.
kld
(d1, d2)¶ Return the KullbackLeibler Divergence (KLD) between two distributions.
Parameters:  d1 (np.ndarray) – The first distribution.
 d2 (np.ndarray) – The second distribution.
Returns: The KLD of
d1
fromd2
.Return type: float

pyphi.distance.
entropy_difference
(d1, d2)¶ Return the difference in entropy between two distributions.

pyphi.distance.
psq2
(d1, d2)¶ Compute the PSQ2 measure.
Parameters:  d1 (np.ndarray) – The first distribution.
 d2 (np.ndarray) – The second distribution.

pyphi.distance.
mp2q
(p, q)¶ Compute the MP2Q measure.
Parameters:  p (np.ndarray) – The unpartitioned repertoire
 q (np.ndarray) – The partitioned repertoire

pyphi.distance.
bld
(p, q)¶ Compute the Buzz Lightyear (BillyLeo) Divergence.

pyphi.distance.
directional_emd
(direction, d1, d2)¶ Compute the EMD between two repertoires for a given direction.
The full EMD computation is used for cause repertoires. A fast analytic solution is used for effect repertoires.
Parameters: Returns: The EMD between
d1
andd2
, rounded toPRECISION
.Return type: float
Raises: ValueError
– Ifdirection
is invalid.

pyphi.distance.
repertoire_distance
(direction, r1, r2)¶ Compute the distance between two repertoires for the given direction.
Parameters: Returns: The distance between
d1
andd2
, rounded toPRECISION
.Return type: float

pyphi.distance.
system_repertoire_distance
(r1, r2)¶ Compute the distance between two repertoires of a system.
Parameters:  r1 (np.ndarray) – The first repertoire.
 r2 (np.ndarray) – The second repertoire.
Returns: The distance between
r1
andr2
.Return type: float