def sufficientStats( cls, x, constParams=None ):
# Compute T( x ). This is for when we're treating this class as P( x, y | Ѳ )
if( cls.dataN( x ) > 1 ):
t = [ 0, 0, 0, 0, 0, 0, 0, 0 ]
for _x, _ys in zip( *x ):
s = cls.sufficientStats( ( _x, _ys ), constParams=constParams )
for i in range( 8 ):
t[ i ] += s[ i ]
return tuple( t )
( x, ys ) = x
u = constParams
xIn = x[ :-1 ]
xOut = x[ 1: ] - u[ :-1 ]
t1, t2, t3 = Regression.sufficientStats( x=( xIn, xOut ), constParams=constParams )
t4, t5, t6 = Regression.sufficientStats( x=( x, ys ), constParams=constParams )
t7, t8 = Normal.sufficientStats( x=x[ 0 ], constParams=constParams )
return t1, t2, t3, t4, t5, t6, t7, t8
def initialStats( cls, x, constParams=None ):
# Assumes that only a single element is passed in
assert x.ndim == 1
return Normal.sufficientStats( x=x, constParams=constParams )