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authorChristian C <cc@localhost>2024-11-11 12:29:32 -0800
committerChristian C <cc@localhost>2024-11-11 12:29:32 -0800
commitb85ee9d64a536937912544c7bbd5b98b635b7e8d (patch)
treecef7bc17d7b29f40fc6b1867d0ce0a742d5583d0 /code/sunlab/common/distribution/o_gaussian_distribution.py
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diff --git a/code/sunlab/common/distribution/o_gaussian_distribution.py b/code/sunlab/common/distribution/o_gaussian_distribution.py
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+++ b/code/sunlab/common/distribution/o_gaussian_distribution.py
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+from .adversarial_distribution import *
+
+
+class OGaussianDistribution(AdversarialDistribution):
+ """# O Gaussian Distribution"""
+
+ def __init__(self, N):
+ """# O 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 O-Gaussian Distribution"
+ self.name = "OG"
+
+ 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))
+ sample_base[0 * N : (0 + 1) * N, :] = np.random.multivariate_normal(
+ mean=[1, 1], cov=[[1, 0], [0, 1]], size=[N]
+ )
+ sample_base[1 * N : (1 + 1) * N, :] = np.random.multivariate_normal(
+ mean=[-1, -1], cov=[[1, 0], [0, 1]], size=[N]
+ )
+ sample_base[2 * N : (2 + 1) * N, :] = np.random.multivariate_normal(
+ mean=[-1, 1], cov=[[1, 0], [0, 1]], size=[N]
+ )
+ sample_base[3 * N : (3 + 1) * N, :] = np.random.multivariate_normal(
+ mean=[1, -1], cov=[[1, 0], [0, 1]], size=[N]
+ )
+ np.random.shuffle(sample_base)
+ return sample_base[:N, :]