ezmsg.learn.util

Functions

get_regressor(regressor_type, regressor_name)

Parameters:

• regressor_type (RegressorType | str)

• regressor_name (AdaptiveLinearRegressor | StaticLinearRegressor | str)

with_fingerprint(axis)

Compute axis’s fingerprint now, and return the axis.

Every stateful consumer reads the fingerprint of the coordinate axes that describe a stream’s configuration, and the value is cached on the instance and pickled with it. Computing it where the axis is built pays the checksum once, for everybody:

• In this process the axis object is reused for the life of the stream, so one call covers every message and every consumer downstream of it.

• Across a process boundary it is better than that. Unpickling hands out a new axis object per message, so a cold axis is re-checksummed by the first consumer in every receiving process, on every message.

Apply it to axes that describe the stream – channel labels, class labels, lag labels – not to per-message coordinates along the stream dimension, whose fingerprint no consumer reads.

Return type:

CoordinateAxis

Parameters:

axis (CoordinateAxis)

Classes

class AdaptiveLinearRegressor(*values)

Bases: str, Enum

LINEAR = 'linear'

LOGISTIC = 'logistic'

SGD = 'sgd'

PAR = 'par'

class ClassifierMessage(data, dims, axes=<factory>, attrs=<factory>, key='', stream_dim=None, labels=<factory>)

Bases: AxisArray

Parameters:

• data (NDArray)

• dims (list[str])

• axes (dict[str, AxisBase])

• attrs (dict[str, Any])

• key (str)

• stream_dim (str | None)

• labels (list[str])

labels: list[str]

__init__(data, dims, axes=<factory>, attrs=<factory>, key='', stream_dim=None, labels=<factory>)

Parameters:

• data (NDArray)

• dims (list[str])

• axes (dict[str, AxisBase])

• attrs (dict[str, Any])

• key (str)

• stream_dim (str | None)

• labels (list[str])

Return type:

None

class RegressorType(*values)

Bases: str, Enum

ADAPTIVE = 'adaptive'

STATIC = 'static'

class StaticLinearRegressor(*values)

Bases: str, Enum

LINEAR = 'linear'

RIDGE = 'ridge'