prefered_vect = array([ int(x) for x in bytearray.fromhex(prefered_color)[0::1] ] )
max_size = norm( prefered_vect ) * norm( rgb_vect )
return max_size > 0 and (
1.0 * sum( prefered_vect * rgb_vect) / ( max_size )
) or -1
criterion_ponderation = VectorDict(float, dict(
eye = 1 ,
fitness = 5,
bankable = 4,
extra = .5,
not_too_expensive = .1
) )
norm_of_choice = criterion_ponderation.dot(criterion_ponderation)
for candidate,ldot in map (
lambda x : [
VectorDict(VectorDict,x) , criterion_ponderation.dot(
criterion_ponderation * matrix( x )
)
] ,
data
) :
if ldot > .5:
candidate.pprint()
print "fitting with a projection of %f" % ldot
criterion_ponderation = VectorDict(float, dict(
