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clustered

Clustered

Bases: Designs

Super class for clustered designs.

Attributes:

Name Type Description
k int

The number of factors.
seed int

The seed for the random number generator.
Source code in spotpython/design/clustered.py 
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class Clustered(Designs):
    """
    Super class for clustered designs.

    Attributes:
        k (int): The number of factors.
        seed (int): The seed for the random number generator.
    """

    def __init__(self, k: int = 2, seed: int = 123) -> None:
        """
        Initializes a clustered design object.

        Args:
            k (int): The number of factors. Defaults to 2.
            seed (int): The seed for the random number generator. Defaults to 123.
        """
        super().__init__(k, seed)
        self.k = k
        self.seed = seed

    def generate_clustered_design(self, n_points: int, n_clusters: int, seed: Optional[int] = None) -> np.ndarray:
        """Generates a clustered design.

        Args:
            n_points (int): The number of points to generate.
            n_clusters (int): The number of clusters.
            seed (Optional[int]): Optional seed for reproducibility.

        Returns:
            numpy.ndarray: A 2D array of shape (n_points, n_dim) with clustered points.

        Examples:
            >>> from spotpython.design.clustered import Clustered
            >>> clustered_design = Clustered(k=3)
            >>> clustered_design.generate_clustered_design(n_points=100, n_clusters=5, seed=42)
            array([[0.12, 0.34, 0.56],
                   [0.23, 0.45, 0.67],
                   ...])
        """
        X, _ = make_blobs(n_samples=n_points, n_features=self.k, centers=n_clusters, cluster_std=0.05, random_state=seed, center_box=(0.1, 0.9))
        X_min = X.min(axis=0)
        X_max = X.max(axis=0)
        if np.any(X_min < 0) or np.any(X_max > 1):
            X = (X - X_min) / (X_max - X_min + 1e-6)
        return X

__init__(k=2, seed=123)

Initializes a clustered design object.

Parameters:

Name Type Description Default
k int

The number of factors. Defaults to 2.

 2
seed int

The seed for the random number generator. Defaults to 123.

 123
Source code in spotpython/design/clustered.py 
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def __init__(self, k: int = 2, seed: int = 123) -> None:
    """
    Initializes a clustered design object.

    Args:
        k (int): The number of factors. Defaults to 2.
        seed (int): The seed for the random number generator. Defaults to 123.
    """
    super().__init__(k, seed)
    self.k = k
    self.seed = seed

generate_clustered_design(n_points, n_clusters, seed=None)

Generates a clustered design.

Parameters:

Name Type Description Default
n_points int

The number of points to generate.

 required
n_clusters int

The number of clusters.

 required
seed Optional[int]

Optional seed for reproducibility.

 None

Returns:

Type Description
ndarray

numpy.ndarray: A 2D array of shape (n_points, n_dim) with clustered points.

Examples:

>>> from spotpython.design.clustered import Clustered
>>> clustered_design = Clustered(k=3)
>>> clustered_design.generate_clustered_design(n_points=100, n_clusters=5, seed=42)
array([[0.12, 0.34, 0.56],
       [0.23, 0.45, 0.67],
       ...])
Source code in spotpython/design/clustered.py 
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def generate_clustered_design(self, n_points: int, n_clusters: int, seed: Optional[int] = None) -> np.ndarray:
    """Generates a clustered design.

    Args:
        n_points (int): The number of points to generate.
        n_clusters (int): The number of clusters.
        seed (Optional[int]): Optional seed for reproducibility.

    Returns:
        numpy.ndarray: A 2D array of shape (n_points, n_dim) with clustered points.

    Examples:
        >>> from spotpython.design.clustered import Clustered
        >>> clustered_design = Clustered(k=3)
        >>> clustered_design.generate_clustered_design(n_points=100, n_clusters=5, seed=42)
        array([[0.12, 0.34, 0.56],
               [0.23, 0.45, 0.67],
               ...])
    """
    X, _ = make_blobs(n_samples=n_points, n_features=self.k, centers=n_clusters, cluster_std=0.05, random_state=seed, center_box=(0.1, 0.9))
    X_min = X.min(axis=0)
    X_max = X.max(axis=0)
    if np.any(X_min < 0) or np.any(X_max > 1):
        X = (X - X_min) / (X_max - X_min + 1e-6)
    return X