Initial commit

9 files changed, 265 insertions, 0 deletions

diff --git a/code/sunlab/common/distribution/__init__.py b/code/sunlab/common/distribution/__init__.py
new file mode 100644
index 0000000..a23cb0c
--- /dev/null
+++ b/code/sunlab/common/distribution/__init__.py
@@ -0,0 +1,7 @@
+from .gaussian_distribution import *
+from .x_gaussian_distribution import *
+from .o_gaussian_distribution import *
+from .s_gaussian_distribution import *
+from .uniform_distribution import *
+from .symmetric_uniform_distribution import *
+from .swiss_roll_distribution import *
diff --git a/code/sunlab/common/distribution/adversarial_distribution.py b/code/sunlab/common/distribution/adversarial_distribution.py
new file mode 100644
index 0000000..675c00e
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+++ b/code/sunlab/common/distribution/adversarial_distribution.py
@@ -0,0 +1,35 @@
+class AdversarialDistribution:
+    """# Distribution Class to use in Adversarial Autoencoder
+
+    For any distribution to be implemented, make sure to ensure each of the
+    methods are implemented"""
+
+    def __init__(self, N):
+        """# Initialize the distribution for N-dimensions"""
+        self.dims = N
+        return
+
+    def get_full_name(self):
+        """# Return a human-readable name of the distribution"""
+        return self.full_name
+
+    def get_name(self):
+        """# Return a shortened name of the distribution
+
+        Preferrably, the name should include characters that can be included in
+        a file name"""
+        return self.name
+
+    def __str__(self):
+        """# Returns the short name"""
+        return self.get_name()
+
+    def __repr__(self):
+        """# Returns the short name"""
+        return self.get_name()
+
+    def __call__(self, *args):
+        """# Magic method when calling the distribution
+
+        This method is going to be called when you use `dist(...)`"""
+        raise NotImplementedError("This distribution has not been implemented yet")
diff --git a/code/sunlab/common/distribution/gaussian_distribution.py b/code/sunlab/common/distribution/gaussian_distribution.py
new file mode 100644
index 0000000..e478ab6
--- /dev/null
+++ b/code/sunlab/common/distribution/gaussian_distribution.py
@@ -0,0 +1,23 @@
+from .adversarial_distribution import *
+
+
+class GaussianDistribution(AdversarialDistribution):
+    """# Gaussian Distribution"""
+
+    def __init__(self, N):
+        """# Gaussian Distribution Initialization
+
+        Initializes the name and dimensions"""
+        super().__init__(N)
+        self.full_name = f"{N}-Dimensional Gaussian Distribution"
+        self.name = "G"
+
+    def __call__(self, *args):
+        """# Magic method when calling the distribution
+
+        This method is going to be called when you use gauss(N1,...,Nm)"""
+        import numpy as np
+
+        return np.random.multivariate_normal(
+            mean=np.zeros(self.dims), cov=np.eye(self.dims), size=[*args]
+        )
diff --git a/code/sunlab/common/distribution/o_gaussian_distribution.py b/code/sunlab/common/distribution/o_gaussian_distribution.py
new file mode 100644
index 0000000..1222ca1
--- /dev/null
+++ b/code/sunlab/common/distribution/o_gaussian_distribution.py
@@ -0,0 +1,38 @@
+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, :]
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, :]
diff --git a/code/sunlab/common/distribution/swiss_roll_distribution.py b/code/sunlab/common/distribution/swiss_roll_distribution.py
new file mode 100644
index 0000000..613bfc5
--- /dev/null
+++ b/code/sunlab/common/distribution/swiss_roll_distribution.py
@@ -0,0 +1,42 @@
+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]]
diff --git a/code/sunlab/common/distribution/symmetric_uniform_distribution.py b/code/sunlab/common/distribution/symmetric_uniform_distribution.py
new file mode 100644
index 0000000..c3a4db0
--- /dev/null
+++ b/code/sunlab/common/distribution/symmetric_uniform_distribution.py
@@ -0,0 +1,21 @@
+from .adversarial_distribution import *
+
+
+class SymmetricUniformDistribution(AdversarialDistribution):
+    """# Symmetric Uniform Distribution on [-1, 1)"""
+
+    def __init__(self, N):
+        """# Symmetric Uniform Distribution Initialization
+
+        Initializes the name and dimensions"""
+        super().__init__(N)
+        self.full_name = f"{N}-Dimensional Symmetric Uniform Distribution"
+        self.name = "SU"
+
+    def __call__(self, *args):
+        """# Magic method when calling the distribution
+
+        This method is going to be called when you use suniform(N1,...,Nm)"""
+        import numpy as np
+
+        return np.random.rand(*args, self.dims) * 2.0 - 1.0
diff --git a/code/sunlab/common/distribution/uniform_distribution.py b/code/sunlab/common/distribution/uniform_distribution.py
new file mode 100644
index 0000000..3e23e67
--- /dev/null
+++ b/code/sunlab/common/distribution/uniform_distribution.py
@@ -0,0 +1,21 @@
+from .adversarial_distribution import *
+
+
+class UniformDistribution(AdversarialDistribution):
+    """# Uniform Distribution on [0, 1)"""
+
+    def __init__(self, N):
+        """# Uniform Distribution Initialization
+
+        Initializes the name and dimensions"""
+        super().__init__(N)
+        self.full_name = f"{N}-Dimensional Uniform Distribution"
+        self.name = "U"
+
+    def __call__(self, *args):
+        """# Magic method when calling the distribution
+
+        This method is going to be called when you use uniform(N1,...,Nm)"""
+        import numpy as np
+
+        return np.random.rand(*args, self.dims)
diff --git a/code/sunlab/common/distribution/x_gaussian_distribution.py b/code/sunlab/common/distribution/x_gaussian_distribution.py
new file mode 100644
index 0000000..b4330aa
--- /dev/null
+++ b/code/sunlab/common/distribution/x_gaussian_distribution.py
@@ -0,0 +1,38 @@
+from .adversarial_distribution import *
+
+
+class XGaussianDistribution(AdversarialDistribution):
+    """# X Gaussian Distribution"""
+
+    def __init__(self, N):
+        """# X 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 X-Gaussian Distribution"
+        self.name = "XG"
+
+    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, 0.7], [0.7, 1]], size=[N]
+        )
+        sample_base[1 * N : (1 + 1) * N, :] = np.random.multivariate_normal(
+            mean=[-1, -1], cov=[[1, 0.7], [0.7, 1]], size=[N]
+        )
+        sample_base[2 * N : (2 + 1) * N, :] = np.random.multivariate_normal(
+            mean=[-1, 1], cov=[[1, -0.7], [-0.7, 1]], size=[N]
+        )
+        sample_base[3 * N : (3 + 1) * N, :] = np.random.multivariate_normal(
+            mean=[1, -1], cov=[[1, -0.7], [-0.7, 1]], size=[N]
+        )
+        np.random.shuffle(sample_base)
+        return sample_base[:N, :]