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lint: Fix relint imports
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‎src/sage/stats/distributions/discrete_gaussian_lattice.py

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@@ -18,7 +18,7 @@
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: D = DGL(ZZ^10, 3.0)
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sage: D(), D(), D() # random
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((3, 0, -5, 0, -1, -3, 3, 3, -7, 2), (4, 0, 1, -2, -4, -4, 4, 0, 1, -4), (-3, 0, 4, 5, 0, 1, 3, 2, 0, -1))
@@ -120,7 +120,7 @@ class DiscreteGaussianDistributionLatticeSampler(SageObject):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: D = DGL(ZZ^10, 3.0); D
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Discrete Gaussian sampler with Gaussian parameter σ = 3.00000000000000, c=(0, 0, 0, 0, 0, 0, 0, 0, 0, 0) over lattice with basis
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<BLANKLINE>
@@ -138,7 +138,7 @@ class DiscreteGaussianDistributionLatticeSampler(SageObject):
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We plot a histogram::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: import warnings
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sage: warnings.simplefilter('ignore', UserWarning)
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sage: D = DGL(identity_matrix(2), 3.0)
@@ -167,7 +167,7 @@ def compute_precision(precision, sigma):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: DGL.compute_precision(100, RR(3))
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100
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sage: DGL.compute_precision(100, RealField(200)(3))
@@ -208,7 +208,7 @@ def _normalisation_factor_zz(self, tau=None, prec=None):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: n = 3; sigma = 1.0
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sage: D = DGL(ZZ^n, sigma)
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sage: f = D.f
@@ -349,7 +349,7 @@ def _maximal_r(self):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: n = 3
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sage: Sigma = Matrix(ZZ, [[5, -2, 4], [-2, 10, -5], [4, -5, 5]])
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sage: c = vector(ZZ, [7, 2, 5])
@@ -420,7 +420,7 @@ def __init__(self, B, sigma=1, c=0, r=None, precision=None, sigma_basis=False):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: n = 2; sigma = 3.0
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sage: D = DGL(ZZ^n, sigma)
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sage: f = D.f
@@ -485,7 +485,7 @@ def __init__(self, B, sigma=1, c=0, r=None, precision=None, sigma_basis=False):
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We can also initialise with matrix-like objects:
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: qf = QuadraticForm(matrix(3, [2, 1, 1, 1, 2, 1, 1, 1, 2]))
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sage: D = DGL(qf, 3.0); D # needs sage.symbolic
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Discrete Gaussian sampler with Gaussian parameter σ = 3.00000000000000, c=(0, 0, 0) over lattice with basis
@@ -597,7 +597,7 @@ def __call__(self):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: D = DGL(ZZ^3, 3.0, c=(1,0,0))
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sage: L = [D() for _ in range(2^12)]
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sage: mean_L = sum(L) / len(L)
@@ -649,7 +649,7 @@ def sigma(self):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: D = DGL(ZZ^3, 3.0, c=(1,0,0))
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sage: D.sigma
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3.00000000000000
@@ -663,7 +663,7 @@ def sigma(self, sigma):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: D = DGL(ZZ^3, 3.0, c=(1,0,0))
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sage: D.c = (2, 0, 0)
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sage: D
@@ -684,7 +684,7 @@ def c(self):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: D = DGL(ZZ^3, 3.0, c=(1,0,0)); D
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Discrete Gaussian sampler with Gaussian parameter σ = 3.00000000000000, c=(1, 0, 0) over lattice with basis
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<BLANKLINE>
@@ -704,7 +704,7 @@ def c(self, c):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: D = DGL(ZZ^3, 3.0, c=(1,0,0))
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sage: D.c = (2, 0, 0)
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sage: D
@@ -739,7 +739,7 @@ def __repr__(self):
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r"""
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: D = DGL(ZZ^3, 3.0, c=(1,0,0)); D
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Discrete Gaussian sampler with Gaussian parameter σ = 3.00000000000000, c=(1, 0, 0) over lattice with basis
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<BLANKLINE>
@@ -770,7 +770,7 @@ def _call_in_lattice(self):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: D = DGL(ZZ^3, 3.0, c=(1,0,0))
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sage: L = [D._call_in_lattice() for _ in range(2^12)]
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sage: mean_L = sum(L) / len(L)
@@ -790,7 +790,7 @@ def _call(self):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: D = DGL(ZZ^3, 3.0, c=(1/2,0,0))
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sage: L = [D._call() for _ in range(2^12)]
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sage: mean_L = sum(L) / len(L)
@@ -822,7 +822,7 @@ def add_offline_samples(self, cnt=1):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: Sigma = Matrix([[5, -2, 4], [-2, 10, -5], [4, -5, 5]])
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sage: D = DGL(ZZ^3, Sigma)
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sage: assert not D.is_spherical
@@ -843,7 +843,7 @@ def _call_non_spherical(self):
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EXAMPLES::
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sage: from sage.stats.all import DGL
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sage: from sage.stats.distributions.discrete_gaussian_lattice import DGL
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sage: Sigma = Matrix([[5, -2, 4], [-2, 10, -5], [4, -5, 5]])
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sage: D = DGL(ZZ^3, Sigma, c=(1/2,0,0))
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sage: L = [D._call_non_spherical() for _ in range(2^12)]

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