trplearn.regression module#

class trplearn.regression.MaxPlusRegression(gle=False)[source]#

Bases: object

Max-Plus regression.

Parameters:: gle (bool, default=False) – Whether to fit along GLE. (If False, fit alnog MMAE.)

coef_#

Estimated coefficients for the Max-Plus regression problem.

Type:: array

Examples

>>> reg = MaxPlusRegression().fit(x, y)

fit(x, y)[source]#

Fit Max-Plus model.

Parameters:

x (array) – Training data.
y (array) – Target values.

Returns:

self – Fitted Estimator.

Return type:

object

predict(x)[source]#

Predict using the Max-Plus model.

Parameters:: x (array) – Samples.
Returns:: y_pred – Returns predicted values.
Return type:: array

rms_score(x, y)[source]#

Calculate RMSE.

Parameters:

x (array) – Training data.
y (array) – Target values.

Returns:

C – RMSE.

Return type:

float

uniform_score(x, y)[source]#

Calculate uniform score.

Parameters:

x (array) – Training data.
y (array) – Target values.

Returns:

C – Uniform score.

Return type:

float

class trplearn.regression.MinPlusRegression(gue=False)[source]#

Bases: object

Min-Plus regression.

Parameters:: gue (bool, default=False) – Whether to fit along GUE. (If False, fit alnog MMAE.)

coef_#

Estimated coefficients for the Min-Plus regression problem.

Type:: array

Examples

>>> reg = MinPlusRegression().fit(x, y)

fit(x, y)[source]#

Fit Min-Plus model.

Parameters:

x (array) – Training data.
y (array) – Target values.

Returns:

self – Fitted Estimator.

Return type:

object

predict(x)[source]#

Predict using the Min-Plus model.

Parameters:: x (array) – Samples.
Returns:: y_pred – Returns predicted values.
Return type:: array

rms_score(x, y)[source]#

Calculate RMSE.

Parameters:

x (array) – Training data.
y (array) – Target values.

Returns:

C – RMSE.

Return type:

float

uniform_score(x, y)[source]#

Calculate uniform score.

Parameters:

x (array) – Training data.
y (array) – Target values.

Returns:

C – Uniform score.

Return type:

float