pytuq.utils.maps

Module for various mapping functions.

Classes

`XMap`	Base class for a map.
`Expon`	Exponential map.
`Logar`	Logarithmic map.
`ComposeMap`	Composition of two maps.
`LinearScaler`	Linear scaler map.
`Standardizer`	Standardizer map, linearly scaling data to zero mean and unit variance.
`Normalizer`	Normalizer map, linearly scaling data to [0,1].
`Domainizer`	Domainizer map, linearly scaling data (assumed to be in [0,1]) to a given domain.
`Affine`	Affine map.

Functions

`scale01ToDom`(xx, dom)	Scaling an array to a given domain, assuming the inputs are in [0,1]^d.
`scaleDomTo01`(xx, dom)	Scaling an array from a given domain to [0,1]^d.
`scaleTo01`(xx)	Scale an array to [0,1], using dimension-wise min and max.
`standardize`(xx)	Normalize an array, i.e. map it to zero mean and unit variance.

Module Contents

pytuq.utils.maps.scale01ToDom(xx, dom)[source]

Scaling an array to a given domain, assuming the inputs are in [0,1]^d.

Parameters:

xx (np.ndarray) – Nxd input array.
dom (np.ndarray) – dx2 domain.

Returns:

Nxd scaled array.

Return type:

np.ndarray

Note

If input is outside [0,1]^d, a warning is given, but the scaling will happen nevertheless.

pytuq.utils.maps.scaleDomTo01(xx, dom)[source]

Scaling an array from a given domain to [0,1]^d.

Parameters:

xx (np.ndarray) – Nxd input array.
dom (np.ndarray) – dx2 domain.

Returns:

Nxd scaled array.

Return type:

np.ndarray

Note

If input is outside domain, a warning is given, but the scaling will happen nevertheless.

pytuq.utils.maps.scaleTo01(xx)[source]

Scale an array to [0,1], using dimension-wise min and max.

Parameters:: xx (np.ndarray) – Initial 2d array
Returns:: Scaled array.
Return type:: np.ndarray

pytuq.utils.maps.standardize(xx)[source]

Normalize an array, i.e. map it to zero mean and unit variance.

Parameters:: xx (np.ndarray) – Initial 2d array
Returns:: Normalized array.
Return type:: np.ndarray

class pytuq.utils.maps.XMap[source]

Base class for a map.

forw(x)[source]

Forward map.

Parameters:: x (np.ndarray) – 2d numpy input array.
Returns:: 2d numpy output array.
Return type:: np.ndarray

abstract inv(xs)[source]

Inverse of the map.

Parameters:: xs (np.ndarray) – 2d numpy array.
Returns:: if implemented, 2d numpy array.
Return type:: np.ndarray

class pytuq.utils.maps.Expon[source]

Bases: XMap

Exponential map.

inv(xs)[source]

Inverse of the map.

Parameters:: xs (np.ndarray) – 2d numpy array.
Returns:: if implemented, 2d numpy array.
Return type:: np.ndarray

class pytuq.utils.maps.Logar[source]

Bases: XMap

Logarithmic map.

inv(xs)[source]

Inverse of the map.

Parameters:: xs (np.ndarray) – 2d numpy array.
Returns:: if implemented, 2d numpy array.
Return type:: np.ndarray

class pytuq.utils.maps.ComposeMap(map1, map2)[source]

Bases: XMap

Composition of two maps.

map1[source]

map2[source]

inv(xs)[source]

Inverse of the map.

Parameters:: xs (np.ndarray) – 2d numpy array.
Returns:: if implemented, 2d numpy array.
Return type:: np.ndarray

class pytuq.utils.maps.LinearScaler(shift=None, scale=None)[source]

Bases: XMap

Linear scaler map.

shift = None[source]

scale = None[source]

inv(xs)[source]

Inverse of the map.

Parameters:: xs (np.ndarray) – 2d numpy array.
Returns:: if implemented, 2d numpy array.
Return type:: np.ndarray

class pytuq.utils.maps.Standardizer(x)[source]

Bases: LinearScaler

Standardizer map, linearly scaling data to zero mean and unit variance.

class pytuq.utils.maps.Normalizer(x, nugget=0.0)[source]

Bases: LinearScaler

Normalizer map, linearly scaling data to [0,1].

class pytuq.utils.maps.Domainizer(dom)[source]

Bases: LinearScaler

Domainizer map, linearly scaling data (assumed to be in [0,1]) to a given domain.

Note

This also can be accomplished by functions scaleDomTo01 and its inverse scale01ToDom.

class pytuq.utils.maps.Affine(weight=None, bias=None)[source]

Bases: XMap

Affine map.

weight = None[source]

bias = None[source]

inv(xs)[source]

Inverse of the map.

Parameters:: xs (np.ndarray) – 2d numpy array.
Returns:: if implemented, 2d numpy array.
Return type:: np.ndarray