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diff --git a/code/sunlab/common/distribution/swiss_roll_distribution.py b/code/sunlab/common/distribution/swiss_roll_distribution.py
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+++ b/code/sunlab/common/distribution/swiss_roll_distribution.py
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+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]]