ML_modules.NeuralPosteriorEstimator.generate_samples#

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Summary#

Generate samples from of mode amplitude ratios (e.g., R33=A33/A22) as a function of the input calpha.

Signature#

def generate_samples(self, calpha, num_samples = 2048, set_seed = False)

Input variables#
Name	Type	Default	Description
`calpha`			np.array The input calpha to generate samples for.
`num_samples`		2048	int The number of samples to generate.
`set_seed`		False	bool Whether to set the seed for reproducibility.

Output variables#

Return annotation	Docstring type	Description
`None`		[R33_samples, R44_samples, weight_samples] the weights correspond to the sensitive volume of the samples

Docstring#

Generate samples from of mode amplitude ratios (e.g., R33=A33/A22)
as a function of the input calpha.
:param calpha: np.array
    The input calpha to generate samples for.
:param num_samples: int
    The number of samples to generate.
:param set_seed: bool
    Whether to set the seed for reproducibility.
:return: [R33_samples, R44_samples, weight_samples]
        the weights correspond to the sensitive volume of the samples