estimator.nd.NoiseDistribution
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 = '')[source]#
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
withp
entries of 1 andm
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
We consider a distribution "sparse" if its density is < 1/2.