estimator.nd

Description

Classes

CenteredBinomial(eta[, n])	Sample a_1, …, a_η, b_1, …, b_η uniformly from {0, 1}, and return Σ(a_i - b_i).
DiscreteGaussian(stddev[, mean, n])	A discrete Gaussian distribution with standard deviation stddev per component.
NoiseDistribution(n, mean, stddev, bounds, ...)	All noise distributions are instances of this class.
SparseTernary(p[, m, n])	Distribution of vectors of length n with p entries of 1 and m entries of -1, rest 0.
TUniform(b[, n])	TUniform distribution ∈ ZZ ∩ [-2**b, 2**b], endpoints inclusive.
Uniform(a, b[, n])	Uniform distribution ∈ ZZ ∩ [a, b], endpoints inclusive.

Functions

DiscreteGaussianAlpha(alpha, q[, mean, n])	A discrete Gaussian distribution with standard deviation α⋅q/√(2π) per component.
SparseBinary(hw[, n])	Sparse binary noise distribution having hw coefficients equal to 1, and the rest zero.
UniformMod(q[, n])	Uniform mod q, with balanced representation, i.e. values in ZZ ∩ [-q/2, q/2).
sigmaf(stddev)	Standard deviation → Gaussian width parameter σ.
stddevf(sigma)	Gaussian width parameter σ → standard deviation.