treble_tsdk.collections.distributions¶

Classes

`Bimodal`
`DistributionBase`
`Exponential`
`Gaussian`
`LogNormal`
`Uniform`

class treble_tsdk.collections.distributions.Bimodal¶

__init__(mean1: float, std1: float, mean2: float, std2: float, weight1: float = 0.5, seed: int | None = None)¶

Initialize the distribution with an optional seed.

Parameters:: seed – Random seed for reproducibility. If None, non-deterministic.

reset_seed(seed: int | None = None) → None¶: Reset the random number generator with a new seed.

sample(n: int) → numpy.ndarray¶: Sample from bimodal distribution by mixing samples from both distributions.

class treble_tsdk.collections.distributions.DistributionBase¶

__init__(seed: int | None = None)¶

Initialize the distribution with an optional seed.

Parameters:: seed – Random seed for reproducibility. If None, non-deterministic.

reset_seed(seed: int | None = None) → None¶: Reset the random number generator with a new seed.

abstract sample(n: int) → numpy.ndarray¶

class treble_tsdk.collections.distributions.Exponential¶

__init__(scale: float, seed: int | None = None)¶

Initialize the distribution with an optional seed.

Parameters:: seed – Random seed for reproducibility. If None, non-deterministic.

reset_seed(seed: int | None = None) → None¶: Reset the random number generator with a new seed.

sample(n: int) → numpy.ndarray¶

class treble_tsdk.collections.distributions.Gaussian¶

__init__(mean: float, std: float, seed: int | None = None)¶

Initialize the distribution with an optional seed.

Parameters:: seed – Random seed for reproducibility. If None, non-deterministic.

reset_seed(seed: int | None = None) → None¶: Reset the random number generator with a new seed.

sample(n: int) → numpy.ndarray¶

class treble_tsdk.collections.distributions.LogNormal¶

__init__(mean: float, sigma: float, seed: int | None = None)¶

Initialize the distribution with an optional seed.

Parameters:: seed – Random seed for reproducibility. If None, non-deterministic.

reset_seed(seed: int | None = None) → None¶: Reset the random number generator with a new seed.

sample(n: int) → numpy.ndarray¶

class treble_tsdk.collections.distributions.Uniform¶

__init__(low: float, high: float, seed: int | None = None)¶

Initialize the distribution with an optional seed.

Parameters:: seed – Random seed for reproducibility. If None, non-deterministic.

reset_seed(seed: int | None = None) → None¶: Reset the random number generator with a new seed.

sample(n: int) → numpy.ndarray¶