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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, :]
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