aub_htp.random.BaseSpectralMeasureSampler

class aub_htp.random.BaseSpectralMeasureSampler

Bases: ABC

Spectral Measure Sampler is an interface to define the sampling algorithm for a spectral measure. The underlying mathematical spectral measure \(\Lambda\) has to uphold the following property:

\[\int_{\mathbb{S}^{d-1}}s\Lambda(ds)=0\]

To create a custom spectral measure sampler, you need to inherit from this class and implement the following methods:

• sample()

• dimensions()

• mass()

Example:

class CustomSpectralMeasureSampler(BaseSpectralMeasureSampler):
    def sample(self, number_of_samples: int, random_state: None | int | np.random.RandomState | np.random.Generator = None) -> np.ndarray:
        random_state = get_random_state_generator(random_state)
        return random_state.rand(number_of_samples, self.dimensions())
    def dimensions(self) -> int:
        return 2
    def mass(self) -> float:
        return 1.0

__init__()

Methods

__init__()
dimensions()	Number of dimensions of the vector space of the spectral measure.
mass()	Mass of the spectral measure.
sample(number_of_samples[, random_state])	Sampling algorithm for the spectral measure sampler.

abstractmethod dimensions() → int

Number of dimensions of the vector space of the spectral measure.

Returns

dimensions : int

abstractmethod mass() → float

Mass of the spectral measure.

Returns

mass : float

abstractmethod sample(number_of_samples: int, random_state: None | int | RandomState | Generator = None) → ndarray

Sampling algorithm for the spectral measure sampler.

Parameters

number_of_samplesint

The number of samples to draw from the spectral measure.

random_stateNone | int | np.random.RandomState | np.random.Generator, optional

The random state to use for the sampling. You are encouraged to use aub_htp.random.get_random_state_generator() to get a random state generator as such: random_state = get_random_state_generator(random_state)

Returns

samplesnp.ndarray

The samples from the spectral measure.