desdeo_emo.utilities.ReferenceVectors
Module Contents
Classes
Class object for reference vectors. 
Functions

Normalize a set of vectors. 

Shear a set of vectors lying on the plane z=0 towards the zaxis, such that the 

Calculate the rotation matrix that rotates the initial_vector to the 

Return reflection matrix via householder transformation. 

Rotate other_vectors (with the centre at initial_vector) towards final_vector 
 desdeo_emo.utilities.ReferenceVectors.normalize(vectors)[source]
Normalize a set of vectors.
The length of the returned vectors will be unity.
 Parameters:
vectors (np.ndarray) – Set of vectors of any length, except zero.
 desdeo_emo.utilities.ReferenceVectors.shear(vectors, degrees: float = 5)[source]
Shear a set of vectors lying on the plane z=0 towards the zaxis, such that the resulting vectors ‘degrees’ angle away from the z axis.
z is the last element of the vector, and has to be equal to zero.
 Parameters:
vectors (numpy.ndarray) – The final element of each vector should be zero.
degrees (float, optional) – The angle that the resultant vectors make with the z axis. Unit is radians. (the default is 5)
 desdeo_emo.utilities.ReferenceVectors.rotate(initial_vector, rotated_vector, other_vectors)[source]
Calculate the rotation matrix that rotates the initial_vector to the rotated_vector. Apply that rotation on other_vectors and return. Uses Householder reflections twice to achieve this.
 desdeo_emo.utilities.ReferenceVectors.householder(vector)[source]
Return reflection matrix via householder transformation.
 desdeo_emo.utilities.ReferenceVectors.rotate_toward(initial_vector, final_vector, other_vectors, degrees: float = 5)[source]
Rotate other_vectors (with the centre at initial_vector) towards final_vector by an angle degrees.
 Parameters:
initial_vector (np.ndarray) – Centre of the vectors to be rotated.
final_vector (np.ndarray) – The final position of the center of other_vectors.
other_vectors (np.ndarray) – The array of vectors to be rotated
degrees (float, optional) – The amount of rotation (the default is 5)
 Returns:
rotated_vectors (np.ndarray) – The rotated vectors
reached (bool) – True if final_vector has been reached
 class desdeo_emo.utilities.ReferenceVectors.ReferenceVectors(lattice_resolution: int = None, number_of_vectors: int = None, number_of_objectives: int = None, creation_type: str = 'Uniform', vector_type: str = 'Spherical', ref_point: list = None)[source]
Class object for reference vectors.
 _create(creation_type: str = 'Uniform')[source]
Create the reference vectors.
 Parameters:
creation_type (str, optional) – ‘Uniform’ creates the reference vectors uniformly using simplex lattice design. ‘Focused’ creates reference vectors symmetrically around a central reference vector. By default ‘Uniform’.
 adapt(fitness: numpy.ndarray)[source]
Adapt reference vectors. Then normalize.
 Parameters:
fitness (np.ndarray) –
 interactive_adapt_1(z: numpy.ndarray, n_solutions: int, translation_param: float = 0.2) None [source]
Adapt reference vectors using the information about prefererred solution(s) selected by the Decision maker.
 Parameters:
z (np.ndarray) – Preferred solution(s).
n_solutions (int) – Number of solutions in total.
translation_param (float) – Parameter determining how close the reference vectors are to the central vector
solution (**v** defined by using the selected) –
Returns:
 interactive_adapt_2(z: numpy.ndarray, n_solutions: int, predefined_distance: float = 0.2) None [source]
Adapt reference vectors by using the information about nonpreferred solution(s) selected by the Decision maker. After the Decision maker has specified nonpreferred solution(s), Euclidian distance between normalized solution vector(s) and each of the reference vectors are calculated. Those reference vectors that are closer than a predefined distance are either removed or repositioned somewhere else.
Note
At the moment, only the removal of reference vectors is supported. Repositioning of the reference vectors is not supported.
Note
In case the Decision maker specifies multiple nonpreferred solutions, the reference vector(s) for which the distance to any of the nonpreferred solutions is less than predefined distance are removed.
Note
Future developer should implement a way for a user to say: “Remove some percentage of objecive space/reference vectors” rather than giving a predefined distance value.
 Parameters:
z (np.ndarray) – Nonpreferred solution(s).
n_solutions (int) – Number of solutions in total.
predefined_distance (float) – The reference vectors that are closer than this distance are either removed or
else. (repositioned somewhere) –
value (Default) – 0.2
Returns:
 iteractive_adapt_3(ref_point, translation_param=0.2)[source]
Adapt reference vectors linearly towards a reference point. Then normalize.
The details can be found in the following paper: Hakanen, Jussi & Chugh, Tinkle & Sindhya, Karthik & Jin, Yaochu & Miettinen, Kaisa. (2016). Connections of Reference Vectors and Different Types of Preference Information in Interactive Multiobjective Evolutionary Algorithms.
 Parameters:
ref_point –
translation_param – (Default value = 0.2)
 interactive_adapt_4(preferred_ranges: numpy.ndarray) None [source]
Adapt reference vectors by using the information about the Decision maker’s preferred range for each of the objective. Using these ranges, Latin hypercube sampling is applied to generate m number of samples between within these ranges, where m is the number of reference vectors. Normalized vectors constructed of these samples are then set as new reference vectors.
 Parameters:
preferred_ranges (np.ndarray) – Preferred lower and upper bound for each of the objective function values.
Returns:
 slow_interactive_adapt(ref_point)[source]
Basically a wrapper around rotate_toward. Slowly rotate ref vectors toward ref_point. Return a boolean value to tell if the ref_point has been reached.
 Parameters:
ref_point (list or np.ndarray) – The reference vectors will slowly move towards the ref_point.
 Returns:
True if ref_point has been reached. False otherwise.
 Return type:
boolean