estimator.nd.NoiseDistribution

class estimator.nd.NoiseDistribution(stddev: float, mean: float = 0, n: Optional[int] = None, bounds: tuple = (None, None), density: float = 1.0, tag: str = '')

All noise distributions are instances of this class.

__call__(**kwargs)

Call self as a function.

Methods

CenteredBinomial(eta[, n])	Sample a_1, …, a_η, b_1, …, b_η and return Σ(a_i - b_i).
DiscreteGaussian(stddev[, mean, n])	A discrete Gaussian distribution with standard deviation stddev per component.
DiscreteGaussianAlpha(alpha, q[, mean, n])	A discrete Gaussian distribution with standard deviation α⋅q/√(2π) per component.
SparseTernary(n, p[, m])	Distribution of vectors of length n with p entries of 1 and m entries of -1, rest 0.
Uniform(a, b[, n])	Uniform distribution ∈ [a,b], endpoints inclusive.
UniformMod(q[, n])	Uniform mod q, with balanced representation.
__init__(stddev[, mean, n, bounds, density, tag])
get_hamming_weight([n])
support_size([n, fraction])	Compute the size of the support covering the probability given as fraction.

Attributes

bounds
density
gaussian_tail_bound
gaussian_tail_prob
is_Gaussian_like
is_bounded
is_sparse	We consider a distribution "sparse" if its density is < 1/2.
mean
n
tag
stddev