1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
|
from .adversarial_distribution import *
class SwissRollDistribution(AdversarialDistribution):
"""# Swiss Roll Distribution"""
def __init__(self, N, scaling_factor=0.25, noise_level=0.15):
"""# Swiss Roll Distribution Initialization
Initializes the name and dimensions"""
super().__init__(N)
assert (self.dims == 2) or (
self.dims == 3
), "This Distribution only Supports 2,3-Dimensions"
self.full_name = f"{self.dims}-Dimensional Swiss Roll Distribution Distribution"
self.name = f"SR{self.dims}"
self.noise_level = noise_level
self.scale = scaling_factor
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]
noise = self.noise_level
scaling_factor = self.scale
t = 3 * np.pi / 2 * (1 + 2 * np.random.rand(1, N))
h = 21 * np.random.rand(1, N)
RANDOM = np.random.randn(3, N) * noise
data = (
np.concatenate(
(scaling_factor * t * np.cos(t), h, scaling_factor * t * np.sin(t))
)
+ RANDOM
)
if self.dims == 2:
return data.T[:, [0, 2]]
return data.T[:, [0, 2, 1]]
|
