From b85ee9d64a536937912544c7bbd5b98b635b7e8d Mon Sep 17 00:00:00 2001 From: Christian C Date: Mon, 11 Nov 2024 12:29:32 -0800 Subject: Initial commit --- .../common/distribution/s_gaussian_distribution.py | 40 ++++++++++++++++++++++ 1 file changed, 40 insertions(+) create mode 100644 code/sunlab/common/distribution/s_gaussian_distribution.py (limited to 'code/sunlab/common/distribution/s_gaussian_distribution.py') diff --git a/code/sunlab/common/distribution/s_gaussian_distribution.py b/code/sunlab/common/distribution/s_gaussian_distribution.py new file mode 100644 index 0000000..cace57f --- /dev/null +++ b/code/sunlab/common/distribution/s_gaussian_distribution.py @@ -0,0 +1,40 @@ +from .adversarial_distribution import * + + +class SGaussianDistribution(AdversarialDistribution): + """# S Gaussian Distribution""" + + def __init__(self, N, scale=0): + """# S Gaussian Distribution Initialization + + Initializes the name and dimensions""" + super().__init__(N) + assert self.dims == 2, "This Distribution only Supports 2-Dimensions" + self.full_name = "2-Dimensional S-Gaussian Distribution" + self.name = "SG" + self.scale = scale + + def __call__(self, *args): + """# Magic method when calling the distribution + + This method is going to be called when you use xgauss(case_count)""" + import numpy as np + + assert len(args) == 1, "Only 1 argument supported" + N = args[0] + sample_base = np.zeros((4 * N, 2)) + scale = self.scale + sample_base[0 * N : (0 + 1) * N, :] = np.random.multivariate_normal( + mean=[1, 1], cov=[[1, scale], [scale, 1]], size=[N] + ) + sample_base[1 * N : (1 + 1) * N, :] = np.random.multivariate_normal( + mean=[-1, -1], cov=[[1, scale], [scale, 1]], size=[N] + ) + sample_base[2 * N : (2 + 1) * N, :] = np.random.multivariate_normal( + mean=[-1, 1], cov=[[1, -scale], [-scale, 1]], size=[N] + ) + sample_base[3 * N : (3 + 1) * N, :] = np.random.multivariate_normal( + mean=[1, -1], cov=[[1, -scale], [-scale, 1]], size=[N] + ) + np.random.shuffle(sample_base) + return sample_base[:N, :] -- cgit v1.2.1