cma.transformations.AdaptiveDecoding

class documentation

class AdaptiveDecoding(object):

Known subclasses: cma.transformations.DiagonalDecoding

base class for adaptive decoding.

The adaptive decoding class is "dual" to the StasticalModel class, in that for linear transformations adapting either one or the other is equivalent.

TODO: this is a stump

Method	`__init__`	`len(scaling)` determines the dimension.
Method	`__mul__`	A linear transformation expressed by multiplication
Method	`norm`	return norm of `x` prior to the transformation
Method	`transform`	apply the transformation / decoding AKA geno-pheno tf
Method	`transform_inverse`	inverse transformation (encoding), might return None
Method	`update`	AKA update.
Method	`update_now`	update model here, if lazy update is implemented
Property	`condition_number`	return condition number of the squared transformation matrix
Property	`correlation_matrix`	return correlation matrix or None

def __init__(self, scaling): ¶

overridden in cma.transformations.DiagonalDecoding

len(scaling) determines the dimension.

The initial transformation is (typically) np.diag(scaling).

def __mul__(self, x): ¶

overridden in cma.transformations.DiagonalDecoding

A linear transformation expressed by multiplication

def norm(self, x): ¶

return norm of x prior to the transformation

def transform(self, x): ¶

overridden in cma.transformations.DiagonalDecoding

apply the transformation / decoding AKA geno-pheno tf

def transform_inverse(self, x): ¶

overridden in cma.transformations.DiagonalDecoding

inverse transformation (encoding), might return None

def update(self, vectors, weights): ¶

overridden in cma.transformations.DiagonalDecoding

AKA update.

vectors are "isotropic", e.g.:

sm = StatisticalModel...()
ad = AdaptiveDecoding...()
z = sm.sample(1)[0]
y = ad * z  # decoding applied
x = m + y  # candidate solution
ad.tell([sm.transform_inverse(z)], [0.1])
sm.update([y / ad], [0.01]) # remark that y / ad != z

where the symmetric transformation sm.transform_inverse(z) makes z isotropic.

TODO: what exactly does this mean, is this a generic construction, is this even the right construction?

Parameters
vectors	is a list of samples.
weights	define a learning rate for each vector.

def update_now(self, lazy_update_gap=None): ¶

update model here, if lazy update is implemented

@property
condition_number = ¶

overridden in cma.transformations.DiagonalDecoding

return condition number of the squared transformation matrix

@property
correlation_matrix = ¶

overridden in cma.transformations.DiagonalDecoding

return correlation matrix or None