diff --git a/anomaly/gmm-change-detection/CMakeLists.txt b/anomaly/gmm-change-detection/CMakeLists.txt
new file mode 100644
index 00000000..012e5d71
--- /dev/null
+++ b/anomaly/gmm-change-detection/CMakeLists.txt
@@ -0,0 +1,30 @@
+# Copyright (c) 2021, United States Government, as represented by the
+# Administrator of the National Aeronautics and Space Administration.
+#
+# All rights reserved.
+#
+# The "ISAAC - Integrated System for Autonomous and Adaptive Caretaking
+# platform" software is licensed under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with the
+# License. You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations
+# under the License.
+
+cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
+project(gmm)
+
+## Compile as C++14, supported in ROS Kinetic and newer
+add_compile_options(-std=c++14)
+
+
+## Find catkin macros and libraries
+find_package(catkin REQUIRED COMPONENTS)
+
+catkin_package()
+
diff --git a/anomaly/gmm-change-detection/README.md b/anomaly/gmm-change-detection/README.md
new file mode 100644
index 00000000..b05a4cba
--- /dev/null
+++ b/anomaly/gmm-change-detection/README.md
@@ -0,0 +1,15 @@
+\page gmm GMM Change Detection
+
+# Overview
+
+This implementation of a GMM-based anomaly detection algorithm was created by Jamie Santos, for the purposes of a [Master thesis]().
+This algorithm is able to detect changes on environments such as the ISS using 3D point depth cloud data.
+
+# Requirements
+[To-Do]
+
+## Usage
+
+	rosrun gmm gmm_change_detection.py
+
+
diff --git a/anomaly/gmm-change-detection/package.xml b/anomaly/gmm-change-detection/package.xml
new file mode 100644
index 00000000..c49a2e9f
--- /dev/null
+++ b/anomaly/gmm-change-detection/package.xml
@@ -0,0 +1,21 @@
+<?xml version="1.0"?>
+<package format="2">
+  <name>gmm</name>
+  <version>0.0.0</version>
+  <description>GMM Change Detection package</description>
+  <license>
+    Apache License, Version 2.0
+  </license>
+  <author email="astrobee-fsw@nx.arc.nasa.gov">
+    ISAAC Flight Software
+  </author>
+  <maintainer email="astrobee-fsw@nx.arc.nasa.gov">
+    ISAAC Flight Software
+  </maintainer>
+
+  <buildtool_depend>catkin</buildtool_depend>
+  <build_depend>roscpp</build_depend>
+  <build_depend>cv_bridge</build_depend>
+  <build_export_depend>roscpp</build_export_depend>
+  <exec_depend>roscpp</exec_depend>
+</package>
diff --git a/anomaly/gmm-change-detection/scripts/__init__.py b/anomaly/gmm-change-detection/scripts/__init__.py
new file mode 100644
index 00000000..cb0030c1
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/__init__.py
@@ -0,0 +1 @@
+from .gmm_mml import GmmMml
diff --git a/anomaly/gmm-change-detection/scripts/artificial_data.py b/anomaly/gmm-change-detection/scripts/artificial_data.py
new file mode 100644
index 00000000..bce24197
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/artificial_data.py
@@ -0,0 +1,122 @@
+#!/usr/bin/env python
+# Copyright (c) 2017, United States Government, as represented by the
+# Administrator of the National Aeronautics and Space Administration.
+#
+# All rights reserved.
+#
+# The Astrobee platform is licensed under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with the
+# License. You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations
+# under the License.
+
+import copy
+
+import numpy as np
+
+
+def generate_data(n_start, n_disappearances, n_appearances):
+    N = 1000  # N points in each cluster
+    point_set_1 = []
+    point_set_2 = []
+
+    # Define first set of points
+    means_1 = np.random.uniform(-2, 2, (n_start, 3)).round(2)
+    covs_1 = np.zeros(shape=(n_start, 3, 3))
+    for i in range(n_start):
+        covs_1[i] = np.diag(np.random.uniform(-0.1, 0.1, (1, 3))[0].round(2))
+
+    # Remove old clusters in second set of points
+    means_2 = copy.deepcopy(means_1)
+    covs_2 = copy.deepcopy(covs_1)
+
+    for i in range(n_disappearances):
+        means_2 = np.delete(means_2, 0, 0)
+        covs_2 = np.delete(covs_2, 0, 0)
+
+    # Add new clusters in second set of points
+    means_appearances = np.random.uniform(-2, 2, (n_appearances, 3)).round(2)
+    covs_appearances = np.zeros(shape=(n_appearances, 3, 3))
+    for i in range(n_appearances):
+        covs_appearances[i] = np.diag(np.random.uniform(-0.1, 0.1, (1, 3))[0].round(2))
+    means_2 = np.vstack((means_2, means_appearances))
+    covs_2 = np.vstack((covs_2, covs_appearances))
+
+    # Concatenate clusters into point clouds
+    for i in range(len(means_1)):
+        x = np.random.multivariate_normal(means_1[i], covs_1[i], N)
+        point_set_1.append(x)
+    points_1 = np.concatenate(point_set_1)
+
+    for i in range(len(means_2)):
+        x = np.random.multivariate_normal(means_2[i], covs_2[i], N)
+        point_set_2.append(x)
+    points_2 = np.concatenate(point_set_2)
+
+    return points_1, points_2
+
+
+if __name__ == "__main__":
+    points_1, points_2 = generate_data(5, 3, 9)
+    print(points_1.shape, points_2.shape)
+
+# if fake_data:
+#    # Generate 3D data with 4 clusters
+#    # set Gaussian centers and covariances in 3D
+#    means = np.array([[1, 0.0, 0.0],
+#              [0.0, 0.0, 0.0],
+#                      [-0.5, -0.5, -0.5],
+#                      [-0.8, 0.3, 0.4]])
+#    covs = np.array([np.diag([0.01, 0.01, 0.03]),
+#                     np.diag([0.08, 0.01, 0.01]),
+#                     np.diag([0.01, 0.05, 0.01]),
+#                     np.diag([0.03, 0.07, 0.01])])
+#
+#    N = 1000 #Number of points to be generated for each cluster.
+#    points_a = []
+#    points_b = []
+#
+#    for i in range(len(means)):
+#        x = np.random.multivariate_normal(means[i], covs[i], N )
+#        points_a.append(x)
+#        points_b.append(x)
+#
+#    points1 = np.concatenate(points_a)
+#
+#    if appearance:
+#        # Add an extra Gaussian
+#        means2 = np.array([[1.5, 1.5, 1.5],
+#                          [0.2, 0.2, 0.2],
+#                          [0.8, -.03, -0.4]])
+#        covs2 = np.array([np.diag([0.01, 0.01, 0.01]),
+#                         np.diag([0.02, 0.01, 0.03]),
+#                         np.diag([0.03, 0.02, 0.01])])
+#
+#        for i in range(len(means2)):
+#            x = np.random.multivariate_normal(means2[i], covs2[i], N )
+#            points_b.append(x)
+#
+#        points2 = np.concatenate(points_b)
+#
+#    else:
+#        # Remove an extra Gaussian
+#        means2 = np.array([[1, 0.0, 0.0],
+#                          [0.0, 0.0, 0.0],
+#                          [-0.5, -0.5, -0.5]])
+#        covs2 = np.array([np.diag([0.01, 0.01, 0.03]),
+#                         np.diag([0.08, 0.01, 0.01]),
+#                         np.diag([0.01, 0.05, 0.01])])
+#        points_b = []
+#
+#        for i in range(len(means2)):
+#            x2 = np.random.multivariate_normal(means2[i], covs2[i], N )
+#            points_b.append(x2)
+#
+#        points2 = np.concatenate(points_b)
+#
diff --git a/anomaly/gmm-change-detection/scripts/emd_gmm.py b/anomaly/gmm-change-detection/scripts/emd_gmm.py
new file mode 100644
index 00000000..611112b7
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/emd_gmm.py
@@ -0,0 +1,84 @@
+# https://towardsdatascience.com/linear-programming-using-python-priyansh-22b5ee888fe0
+import numpy as np
+from pulp import *
+
+
+class EMDGMM:
+    def __init__(self, gmm1_weights, gmm2_weights):
+        self.warehouse_supply = gmm1_weights  # Supply Matrix
+        self.cust_demands = gmm2_weights  # Demand Matrix
+        self.n_warehouses = gmm1_weights.size
+        self.n_customers = gmm2_weights.size
+        self.weight_sum1 = np.sum(self.warehouse_supply)
+        self.weight_sum2 = np.sum(self.cust_demands)
+        self.distances = None
+        self.emd = None
+
+    def get_distance(self, means1, means2):
+        """Given two GMMs, generate a distance matrix between all cluster
+        representatives (means) of GMM1 and GMM2. Output: K1 x K2 matrix"""
+
+        distances = np.zeros((means1.shape[0], means2.shape[0]))
+        for i, row1 in enumerate(means1):
+            for j, row2 in enumerate(means2):
+                distances[i][j] = np.linalg.norm(row1 - row2)
+        self.distances = distances
+
+    def calculate_emd(self):
+        """Optimize the cost-distance (weight-distance) flow between the
+        two GMMs and use the optimized distance as the EMD distance metric."""
+
+        # Cost Matrix
+        cost_matrix = self.distances
+
+        # Initialize Model
+        model = LpProblem("Supply-Demand-Problem", LpMinimize)
+
+        # Define Variable Names
+        variable_names = [
+            str(i) + "_" + str(j)
+            for j in range(1, self.n_customers + 1)
+            for i in range(1, self.n_warehouses + 1)
+        ]
+        variable_names.sort()
+
+        # Decision Variables
+        DV_variables = LpVariable.matrix(
+            "X", variable_names, cat="Continuous", lowBound=0
+        )
+        allocation = np.array(DV_variables).reshape(self.n_warehouses, self.n_customers)
+
+        # Objective Function
+        obj_func = lpSum(allocation * cost_matrix)
+        model += obj_func
+
+        # Constraints
+        for i in range(self.n_warehouses):
+            # print(lpSum(allocation[i][j] for j in range(self.n_customers)) <= warehouse_supply[i])
+            model += lpSum(
+                allocation[i][j] for j in range(self.n_customers)
+            ) <= self.warehouse_supply[i], "Supply Constraints " + str(i)
+
+        for j in range(self.n_customers):
+            # print(lpSum(allocation[i][j] for i in range(self.n_warehouses)) >= cust_demands[j])
+            model += lpSum(
+                allocation[i][j] for i in range(self.n_warehouses)
+            ) >= self.cust_demands[j], "Demand Constraints " + str(j)
+
+        model.solve(GLPK_CMD(msg=0))
+        status = LpStatus[model.status]
+        # print(status)
+
+        # print("Total Cost:", model.objective.value())
+        # for v in model.variables():
+        #    try:
+        #        print(v.name, "=", v.value())
+        #    except:
+        #        print("error couldn't find value")
+
+        # for i in range(self.n_warehouses):
+        #    print("Warehouse ", str(i+1))
+        #    print(lpSum(allocation[i][j].value() for j in range(self.n_customers)))
+
+        total_flow = min(self.weight_sum1, self.weight_sum2)
+        self.emd = model.objective.value() / total_flow
diff --git a/anomaly/gmm-change-detection/scripts/example_code/emd/emd_gmm.py b/anomaly/gmm-change-detection/scripts/example_code/emd/emd_gmm.py
new file mode 100644
index 00000000..79576e58
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/emd/emd_gmm.py
@@ -0,0 +1,46 @@
+import numpy as np
+
+
+def earth_movers_distance(m1, m2):
+    """Compute the Earth Movers Distance between two Gaussian mixture models.
+
+    Parameters
+    ----------
+    m1 : np.ndarray
+        Array of means and covariances of the first Gaussian mixture model.
+    m2 : np.ndarray
+        Array of means and covariances of the second Gaussian mixture model.
+
+    Returns
+    -------
+    np.ndarray
+        Earth Movers Distance between the two Gaussian mixture models.
+    """
+    # Compute the mean and covariance of the combined Gaussian mixture model.
+    mean_combined = (m1.mean() + m2.mean()) / 2
+    covariance_combined = (m1.covariance() + m2.covariance()) / 2
+
+    # Compute the Earth Movers Distance between the two Gaussian mixture models.
+    earth_movers_distance = np.linalg.norm(mean_combined - m1.mean()) + np.linalg.norm(
+        mean_combined - m2.mean()
+    )
+
+    return earth_movers_distance
+
+
+def main():
+    m1 = np.random.multivariate_normal(mean=[0, 0, 0], covariance=np.eye(3))
+    m2 = np.random.multivariate_normal(mean=[1, 2, 3], covariance=np.eye(3))
+
+    # Compute the Earth Movers Distance between the two Gaussian mixture models.
+    earth_movers_distance = earth_movers_distance(m1, m2)
+
+    # Print the Earth Movers Distance.
+    print(
+        "The Earth Movers Distance between the two Gaussian mixture models is "
+        + earth_movers_distance
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/anomaly/gmm-change-detection/scripts/example_code/emd/emd_numpy.py b/anomaly/gmm-change-detection/scripts/example_code/emd/emd_numpy.py
new file mode 100644
index 00000000..3310f515
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/emd/emd_numpy.py
@@ -0,0 +1,45 @@
+import numpy as np
+
+
+def earth_movers_distance(m1, m2):
+    """Compute the Earth Movers Distance between two Gaussian mixture models.
+
+    Parameters
+    ----------
+    m1 : np.ndarray
+        Array of means and covariances of the first Gaussian mixture model.
+    m2 : np.ndarray
+        Array of means and covariances of the second Gaussian mixture model.
+
+    Returns
+    -------
+    np.ndarray
+        Earth Movers Distance between the two Gaussian mixture models.
+    """
+    # Compute the mean and covariance of the combined Gaussian mixture model.
+    mean_combined = (m1.mean() + m2.mean()) / 2
+    covariance_combined = (np.cov(m1) + np.cov(m2)) / 2
+
+    # Compute the Earth Movers Distance between the two Gaussian mixture models.
+    earth_movers_distance = np.linalg.norm(mean_combined - m1.mean()) + np.linalg.norm(
+        mean_combined - m2.mean()
+    )
+
+    return earth_movers_distance
+
+
+def main():
+    m1 = np.random.multivariate_normal((0, 0, 0), np.eye(3))
+    m2 = np.random.multivariate_normal((1, 2, 3), np.eye(3))
+    print(m1.shape)
+    print(m1)
+
+    # Compute the Earth Movers Distance between the two Gaussian mixture models.
+    emd = earth_movers_distance(m1, m2)
+
+    # Print the Earth Movers Distance.
+    print(emd)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/anomaly/gmm-change-detection/scripts/example_code/emd/emd_numpy_was.py b/anomaly/gmm-change-detection/scripts/example_code/emd/emd_numpy_was.py
new file mode 100644
index 00000000..6b485d70
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/emd/emd_numpy_was.py
@@ -0,0 +1,49 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def emd(m1, m2):
+    """Computes the Earth Movers Distance between two 3D Gaussian mixture models.
+
+    Args:
+        m1 (numpy.ndarray): The first 3D Gaussian mixture model.
+        m2 (numpy.ndarray): The second 3D Gaussian mixture model.
+
+    Returns:
+        numpy.ndarray: The Earth Movers Distance between the two models.
+    """
+    # Compute the ground distance matrix.
+    d = np.linalg.norm(m1 - m2, axis=2)
+
+    # Compute the Earth Movers Distance.
+    EMD = np.sum(np.min(d, d.T))
+
+    return EMD
+
+
+def wasserstein(m1, m2):
+    """Computes the Wasserstein Distance between two 3D Gaussian mixture models.
+
+    Args:
+        m1 (numpy.ndarray): The first 3D Gaussian mixture model.
+        m2 (numpy.ndarray): The second 3D Gaussian mixture model.
+
+    Returns:
+        numpy.ndarray: The Wasserstein Distance between the two models.
+    """
+    # Compute the optimal transport matrix.
+    T = np.argmin(np.sum(m1**2) + np.sum(m2**2) - 2 * np.dot(m1, m2))
+
+    # Compute the Wasserstein Distance.
+    Wasserstein = np.sum(np.dot(m1, T) * d)
+
+    return Wasserstein
+
+
+# Generate two 3D Gaussian mixture models.
+m1 = np.random.multivariate_normal(mean=np.array([0, 0, 0]), cov=np.eye(3), size=100)
+m2 = np.random.multivariate_normal(mean=np.array([1, 1, 1]), cov=np.eye(3), size=100)
+
+# Compute the Earth Movers Distance and Wasserstein Distance.
+# EMD = emd(m1, m2)
+Wasserstein = wasserstein(m1, m2)
diff --git a/anomaly/gmm-change-detection/scripts/example_code/emd/emd_test.py b/anomaly/gmm-change-detection/scripts/example_code/emd/emd_test.py
new file mode 100644
index 00000000..5f8e9655
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/emd/emd_test.py
@@ -0,0 +1,27 @@
+import numpy as np
+from sklearn.mixture import GaussianMixture
+
+# Define the first GMM
+gmm1 = GaussianMixture(n_components=2, covariance_type="full", random_state=42)
+X1 = np.random.randn(100, 2)
+gmm1.fit(X1)
+
+# Define the second GMM
+gmm2 = GaussianMixture(n_components=3, covariance_type="full", random_state=42)
+X2 = np.random.randn(100, 2)
+gmm2.fit(X2)
+
+from scipy.stats import wasserstein_distance
+
+# Extract the means and weights from the first GMM
+means1 = gmm1.means_
+weights1 = gmm1.weights_
+
+# Extract the means and weights from the second GMM
+means2 = gmm2.means_
+weights2 = gmm2.weights_
+
+emd = wasserstein_distance(
+    weights1, weights2, np.linalg.norm(means1[:, np.newaxis] - means2, axis=-1)
+)
+print(emd)
diff --git a/anomaly/gmm-change-detection/scripts/example_code/emd/emd_test_3d.py b/anomaly/gmm-change-detection/scripts/example_code/emd/emd_test_3d.py
new file mode 100644
index 00000000..76ec964e
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/emd/emd_test_3d.py
@@ -0,0 +1,40 @@
+import numpy as np
+from scipy.stats import wasserstein_distance
+from sklearn.mixture import GaussianMixture
+
+# Generate some data
+np.random.seed(0)
+n_samples = 1000
+X1 = np.concatenate(
+    [
+        np.random.normal(-1, 1, int(0.3 * n_samples)),
+        np.random.normal(5, 1, int(0.7 * n_samples)),
+    ]
+).reshape(-1, 1)
+X2 = np.concatenate(
+    [
+        np.random.normal(0, 2, int(0.4 * n_samples)),
+        np.random.normal(3, 1, int(0.6 * n_samples)),
+    ]
+).reshape(-1, 1)
+
+# Fit Gaussian mixture models to the data
+gm1 = GaussianMixture(n_components=2, random_state=0)
+gm1.fit(X1)
+gm2 = GaussianMixture(n_components=2, random_state=0)
+gm2.fit(X2)
+
+# Get the means and covariances of the components
+means1 = gm1.means_.ravel()
+covs1 = gm1.covariances_.ravel()
+weights1 = gm1.weights_
+
+means2 = gm2.means_.ravel()
+covs2 = gm2.covariances_.ravel()
+weights2 = gm2.weights_
+
+# Calculate the EMD between the GMMs
+emd = wasserstein_distance(
+    means1, means2, u_weights=weights1, v_weights=weights2, metric="euclidean"
+)
+print("The EMD between the two GMMs is: " + emd)
diff --git a/anomaly/gmm-change-detection/scripts/example_code/emd/emd_test_3d_pyemd.py b/anomaly/gmm-change-detection/scripts/example_code/emd/emd_test_3d_pyemd.py
new file mode 100644
index 00000000..b4c39ca1
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/emd/emd_test_3d_pyemd.py
@@ -0,0 +1,42 @@
+import numpy as np
+import pyemd
+from sklearn.mixture import GaussianMixture
+
+# Generate some random data for two Gaussian mixture models
+np.random.seed(0)
+n_samples = 1000
+X1 = np.concatenate(
+    [
+        np.random.normal(-1, 1, int(0.3 * n_samples)),
+        np.random.normal(5, 1, int(0.7 * n_samples)),
+    ]
+).reshape(-1, 1)
+X2 = np.concatenate(
+    [
+        np.random.normal(0, 2, int(0.4 * n_samples)),
+        np.random.normal(3, 1, int(0.6 * n_samples)),
+    ]
+).reshape(-1, 1)
+
+# Fit Gaussian mixture models to the data
+gm1 = GaussianMixture(n_components=2, random_state=0)
+gm1.fit(X1)
+gm2 = GaussianMixture(n_components=2, random_state=0)
+gm2.fit(X2)
+
+# Get the means and covariances of the components
+means1 = gm1.means_.ravel()
+covs1 = gm1.covariances_.ravel()
+means2 = gm2.means_.ravel()
+covs2 = gm2.covariances_.ravel()
+
+# Create the distance matrix between the means
+D = pyemd.euclidean_distances(means1.reshape(-1, 1), means2.reshape(-1, 1))
+
+# Create the flow matrix between the components
+flow = pyemd.emd(
+    gm1.weights_, gm2.weights_, D, covs1.reshape(-1, 1), covs2.reshape(-1, 1)
+)
+
+# Print the EMD between the two Gaussian mixture models
+print("EMD between the two Gaussian mixture models: {:.2f}".format(flow))
diff --git a/anomaly/gmm-change-detection/scripts/example_code/gmm/gmm_em.py b/anomaly/gmm-change-detection/scripts/example_code/gmm/gmm_em.py
new file mode 100644
index 00000000..694464f8
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/gmm/gmm_em.py
@@ -0,0 +1,247 @@
+# This code is taken from a tutorial from the following website:
+# https://python-course.eu/machine-learning/expectation-maximization-and-gaussian-mixture-models-gmm.php
+
+import matplotlib.pyplot as plt
+from matplotlib import style
+from mpl_toolkits import mplot3d
+
+style.use("fivethirtyeight")
+import numpy as np
+from scipy.stats import multivariate_normal
+from sklearn.datasets.samples_generator import make_blobs
+
+DEBUG = 0
+
+# 0. Create dataset
+X, Y = make_blobs(
+    cluster_std=1.5, n_features=3, random_state=20, n_samples=50, centers=3
+)
+
+# Stretch dataset to get ellipsoid data
+X = np.dot(X, np.random.RandomState(0).randn(3, 3))
+
+print(X.shape)
+
+
+class GMM:
+    def __init__(self, X, number_of_sources, iterations):
+        self.iterations = iterations
+        self.number_of_sources = number_of_sources
+        self.X = X
+        self.mu = None
+        self.pi = None
+        self.cov = None
+        self.XYZ = None
+
+    """Define a function which runs for iterations, iterations"""
+
+    def run(self):
+        self.reg_cov = 1e-6 * np.identity(len(self.X[0]))
+        x, y, z = np.meshgrid(
+            np.sort(self.X[:, 0]), np.sort(self.X[:, 1]), np.sort(self.X[:, 2])
+        )
+        self.XYZ = np.array([x.flatten(), y.flatten(), z.flatten()]).T
+
+        """ 1. Set the initial mu, covariance and pi values"""
+        self.mu = np.random.randint(
+            min(self.X[:, 0]),
+            max(self.X[:, 0]),
+            size=(self.number_of_sources, len(self.X[0])),
+        )  # This is a nxm matrix since we assume n sources (n Gaussians) where each has m dimensions
+        self.cov = np.zeros(
+            (self.number_of_sources, len(X[0]), len(X[0]))
+        )  # We need a nxmxm covariance matrix for each source since we have m features --> We create symmetric covariance matrices with ones on the digonal
+        for dim in range(len(self.cov)):
+            np.fill_diagonal(self.cov[dim], 5)
+
+        self.pi = (
+            np.ones(self.number_of_sources) / self.number_of_sources
+        )  # Are "Fractions"
+        log_likelihoods = (
+            []
+        )  # In this list we store the log likehoods per iteration and plot them in the end to check if
+        # if we have converged
+
+        if DEBUG:
+            #################################################
+            print("Debug enabled")
+            x = X[:, 0]
+            y = X[:, 1]
+            z = X[:, 2]
+
+            # Creating figures for the plot
+            fig = plt.figure(figsize=(10, 7))
+            ax = plt.axes(projection="3d")
+
+            # Creating a plot using the random datasets
+            ax.scatter3D(x, y, z, color="red")
+            plt.title("3D scatter plot")
+
+            # display the  plot
+            plt.show()
+        #################################################
+
+        """Plot the initial state"""
+        fig = plt.figure(figsize=(10, 10))
+        ax0 = plt.axes(projection="3d")
+        # ax0 = fig.add_subplot(111)
+        ax0.scatter3D(self.X[:, 0], self.X[:, 1], self.X[:, 2])
+        ax0.set_title("Initial state")
+        for m, c in zip(self.mu, self.cov):
+            c += self.reg_cov
+            multi_normal = multivariate_normal(mean=m, cov=c)
+            # ax0.contour(np.sort(self.X[:,0]),np.sort(self.X[:,1]),np.sort(self.X[:,2]),multi_normal.pdf(self.XYZ).reshape(len(self.X),len(self.X),len(self.X)),colors='black',alpha=0.3)
+            # ax0.contour3D(np.sort(self.X[:,0]),np.sort(self.X[:,1]),np.sort(self.X[:,2]).reshape((len(self.X[:,0]),len(self.X[:,1]))),multi_normal.pdf(self.XYZ).reshape(len(self.X),len(self.X),len(self.X)),colors='black',alpha=0.3)
+            ax0.scatter(m[0], m[1], c="grey", zorder=10, s=100)
+
+        for i in range(self.iterations):
+
+            """E Step"""
+            r_ic = np.zeros((len(self.X), len(self.cov)))
+
+            for m, co, p, r in zip(self.mu, self.cov, self.pi, range(len(r_ic[0]))):
+                co += self.reg_cov
+                mn = multivariate_normal(mean=m, cov=co)
+                r_ic[:, r] = (
+                    p
+                    * mn.pdf(self.X)
+                    / np.sum(
+                        [
+                            pi_c * multivariate_normal(mean=mu_c, cov=cov_c).pdf(X)
+                            for pi_c, mu_c, cov_c in zip(
+                                self.pi, self.mu, self.cov + self.reg_cov
+                            )
+                        ],
+                        axis=0,
+                    )
+                )
+
+            """
+            The above calculation of r_ic is not that obvious why I want to quickly derive what we have done above.
+            First of all the nominator:
+            We calculate for each source c which is defined by m,co and p for every instance x_i, the multivariate_normal.pdf() value.
+            For each loop this gives us a 100x1 matrix (This value divided by the denominator is then assigned to r_ic[:,r] which is in 
+            the end a 100x3 matrix).
+            Second the denominator:
+            What we do here is, we calculate the multivariate_normal.pdf() for every instance x_i for every source c which is defined by
+            pi_c, mu_c, and cov_c and write this into a list. This gives us a 3x100 matrix where we have 100 entrances per source c.
+            Now the formula wants us to add up the pdf() values given by the 3 sources for each x_i. Hence we sum up this list over axis=0.
+            This gives us then a list with 100 entries.
+            What we have now is FOR EACH LOOP a list with 100 entries in the nominator and a list with 100 entries in the denominator
+            where each element is the pdf per class c for each instance x_i (nominator) respectively the summed pdf's of classes c for each 
+            instance x_i. Consequently we can now divide the nominator by the denominator and have as result a list with 100 elements which we
+            can then assign to r_ic[:,r] --> One row r per source c. In the end after we have done this for all three sources (three loops)
+            and run from r==0 to r==2 we get a matrix with dimensionallity 100x3 which is exactly what we want.
+            If we check the entries of r_ic we see that there mostly one element which is much larger than the other two. This is because
+            every instance x_i is much closer to one of the three gaussians (that is, much more likely to come from this gaussian) than
+            it is to the other two. That is practically speaing, r_ic gives us the fraction of the probability that x_i belongs to class
+            c over the probability that x_i belonges to any of the classes c (Probability that x_i occurs given the 3 Gaussians).
+            """
+
+            """M Step"""
+
+            # Calculate the new mean vector and new covariance matrices, based on the probable membership of the single x_i to classes c --> r_ic
+            self.mu = []
+            self.cov = []
+            self.pi = []
+            log_likelihood = []
+
+            for c in range(len(r_ic[0])):
+                m_c = np.sum(r_ic[:, c], axis=0)
+                mu_c = (1 / m_c) * np.sum(
+                    self.X * r_ic[:, c].reshape(len(self.X), 1), axis=0
+                )
+                self.mu.append(mu_c)
+
+                # Calculate the covariance matrix per source based on the new mean
+                self.cov.append(
+                    (
+                        (1 / m_c)
+                        * np.dot(
+                            (
+                                np.array(r_ic[:, c]).reshape(len(self.X), 1)
+                                * (self.X - mu_c)
+                            ).T,
+                            (self.X - mu_c),
+                        )
+                    )
+                    + self.reg_cov
+                )
+                # Calculate pi_new which is the "fraction of points" respectively the fraction of the probability assigned to each source
+                self.pi.append(
+                    m_c / np.sum(r_ic)
+                )  # Here np.sum(r_ic) gives as result the number of instances. This is logical since we know
+                # that the columns of each row of r_ic adds up to 1. Since we add up all elements, we sum up all
+                # columns per row which gives 1 and then all rows which gives then the number of instances (rows)
+                # in X --> Since pi_new contains the fractions of datapoints, assigned to the sources c,
+                # The elements in pi_new must add up to 1
+
+            """Log likelihood"""
+            log_likelihoods.append(
+                np.log(
+                    np.sum(
+                        [
+                            k * multivariate_normal(self.mu[i], self.cov[j]).pdf(X)
+                            for k, i, j in zip(
+                                self.pi, range(len(self.mu)), range(len(self.cov))
+                            )
+                        ]
+                    )
+                )
+            )
+
+            """
+            This process of E step followed by a M step is now iterated a number of n times. In the second step for instance,
+            we use the calculated pi_new, mu_new and cov_new to calculate the new r_ic which are then used in the second M step
+            to calculat the mu_new2 and cov_new2 and so on....
+            """
+
+        fig2 = plt.figure(figsize=(10, 10))
+        ax1 = fig2.add_subplot(111)
+        ax1.set_title("Log-Likelihood")
+        ax1.plot(range(0, self.iterations, 1), log_likelihoods)
+        plt.show()
+
+    """Predict the membership of an unseen, new datapoint"""
+
+    def predict(self, Y):
+        # PLot the point onto the fittet gaussians
+        fig3 = plt.figure(figsize=(10, 10))
+        # ax2 = fig3.add_subplot(111)
+        ax2 = fig3.add_subplot(projection="3d")
+        ax2.scatter(self.X[:, 0], self.X[:, 1], self.X[:, 2])
+        for m, c in zip(self.mu, self.cov):
+            multi_normal = multivariate_normal(mean=m, cov=c)
+            ax2.contour(
+                np.sort(self.X[:, 0]),
+                np.sort(self.X[:, 1]),
+                np.sort(self.X[:, 2]),
+                multi_normal.pdf(self.XY).reshape(
+                    len(self.X), len(self.X), len(self.X)
+                ),
+                colors="black",
+                alpha=0.3,
+            )
+            ax2.scatter(m[0], m[1], c="grey", zorder=10, s=100)
+            ax2.set_title("Final state")
+            for y in Y:
+                ax2.scatter(y[0], y[1], c="orange", zorder=10, s=100)
+        prediction = []
+        for m, c in zip(self.mu, self.cov):
+            print(c)
+            prediction.append(
+                multivariate_normal(mean=m, cov=c).pdf(Y)
+                / np.sum(
+                    [
+                        multivariate_normal(mean=mean, cov=cov).pdf(Y)
+                        for mean, cov in zip(self.mu, self.cov)
+                    ]
+                )
+            )
+        plt.show()
+        return prediction
+
+
+GMM = GMM(X, 3, 100)
+GMM.run()
+GMM.predict([[0.5, 0.5]])
diff --git a/anomaly/gmm-change-detection/scripts/example_code/gmm/gmm_em_example.py b/anomaly/gmm-change-detection/scripts/example_code/gmm/gmm_em_example.py
new file mode 100644
index 00000000..c3101fa4
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/gmm/gmm_em_example.py
@@ -0,0 +1,219 @@
+# This code is taken from a tutorial from the following website:
+# https://python-course.eu/machine-learning/expectation-maximization-and-gaussian-mixture-models-gmm.php
+
+import matplotlib.pyplot as plt
+from matplotlib import style
+
+style.use("fivethirtyeight")
+import numpy as np
+from scipy.stats import multivariate_normal
+from sklearn.datasets.samples_generator import make_blobs
+
+# 0. Create dataset
+X, Y = make_blobs(cluster_std=1.5, random_state=20, n_samples=500, centers=3)
+
+# Stratch dataset to get ellipsoid data
+X = np.dot(X, np.random.RandomState(0).randn(2, 2))
+
+
+class GMM:
+    def __init__(self, X, number_of_sources, iterations):
+        self.iterations = iterations
+        self.number_of_sources = number_of_sources
+        self.X = X
+        self.mu = None
+        self.pi = None
+        self.cov = None
+        self.XY = None
+
+    """Define a function which runs for iterations, iterations"""
+
+    def run(self):
+        self.reg_cov = 1e-6 * np.identity(len(self.X[0]))
+        x, y = np.meshgrid(np.sort(self.X[:, 0]), np.sort(self.X[:, 1]))
+        self.XY = np.array([x.flatten(), y.flatten()]).T
+
+        """ 1. Set the initial mu, covariance and pi values"""
+        self.mu = np.random.randint(
+            min(self.X[:, 0]),
+            max(self.X[:, 0]),
+            size=(self.number_of_sources, len(self.X[0])),
+        )  # This is a nxm matrix since we assume n sources (n Gaussians) where each has m dimensions
+        self.cov = np.zeros(
+            (self.number_of_sources, len(X[0]), len(X[0]))
+        )  # We need a nxmxm covariance matrix for each source since we have m features --> We create symmetric covariance matrices with ones on the digonal
+        for dim in range(len(self.cov)):
+            np.fill_diagonal(self.cov[dim], 5)
+
+        self.pi = (
+            np.ones(self.number_of_sources) / self.number_of_sources
+        )  # Are "Fractions"
+        log_likelihoods = (
+            []
+        )  # In this list we store the log likehoods per iteration and plot them in the end to check if
+        # if we have converged
+
+        """Plot the initial state"""
+        fig = plt.figure(figsize=(10, 10))
+        ax0 = fig.add_subplot(111)
+        ax0.scatter(self.X[:, 0], self.X[:, 1])
+        ax0.set_title("Initial state")
+        for m, c in zip(self.mu, self.cov):
+            c += self.reg_cov
+            multi_normal = multivariate_normal(mean=m, cov=c)
+            ax0.contour(
+                np.sort(self.X[:, 0]),
+                np.sort(self.X[:, 1]),
+                multi_normal.pdf(self.XY).reshape(len(self.X), len(self.X)),
+                colors="black",
+                alpha=0.3,
+            )
+            ax0.scatter(m[0], m[1], c="grey", zorder=10, s=100)
+
+        for i in range(self.iterations):
+
+            """E Step"""
+            r_ic = np.zeros((len(self.X), len(self.cov)))
+
+            for m, co, p, r in zip(self.mu, self.cov, self.pi, range(len(r_ic[0]))):
+                co += self.reg_cov
+                mn = multivariate_normal(mean=m, cov=co)
+                r_ic[:, r] = (
+                    p
+                    * mn.pdf(self.X)
+                    / np.sum(
+                        [
+                            pi_c * multivariate_normal(mean=mu_c, cov=cov_c).pdf(X)
+                            for pi_c, mu_c, cov_c in zip(
+                                self.pi, self.mu, self.cov + self.reg_cov
+                            )
+                        ],
+                        axis=0,
+                    )
+                )
+
+            """
+            The above calculation of r_ic is not that obvious why I want to quickly derive what we have done above.
+            First of all the nominator:
+            We calculate for each source c which is defined by m,co and p for every instance x_i, the multivariate_normal.pdf() value.
+            For each loop this gives us a 100x1 matrix (This value divided by the denominator is then assigned to r_ic[:,r] which is in 
+            the end a 100x3 matrix).
+            Second the denominator:
+            What we do here is, we calculate the multivariate_normal.pdf() for every instance x_i for every source c which is defined by
+            pi_c, mu_c, and cov_c and write this into a list. This gives us a 3x100 matrix where we have 100 entrances per source c.
+            Now the formula wants us to add up the pdf() values given by the 3 sources for each x_i. Hence we sum up this list over axis=0.
+            This gives us then a list with 100 entries.
+            What we have now is FOR EACH LOOP a list with 100 entries in the nominator and a list with 100 entries in the denominator
+            where each element is the pdf per class c for each instance x_i (nominator) respectively the summed pdf's of classes c for each 
+            instance x_i. Consequently we can now divide the nominator by the denominator and have as result a list with 100 elements which we
+            can then assign to r_ic[:,r] --> One row r per source c. In the end after we have done this for all three sources (three loops)
+            and run from r==0 to r==2 we get a matrix with dimensionallity 100x3 which is exactly what we want.
+            If we check the entries of r_ic we see that there mostly one element which is much larger than the other two. This is because
+            every instance x_i is much closer to one of the three gaussians (that is, much more likely to come from this gaussian) than
+            it is to the other two. That is practically speaing, r_ic gives us the fraction of the probability that x_i belongs to class
+            c over the probability that x_i belonges to any of the classes c (Probability that x_i occurs given the 3 Gaussians).
+            """
+
+            """M Step"""
+
+            # Calculate the new mean vector and new covariance matrices, based on the probable membership of the single x_i to classes c --> r_ic
+            self.mu = []
+            self.cov = []
+            self.pi = []
+            log_likelihood = []
+
+            for c in range(len(r_ic[0])):
+                m_c = np.sum(r_ic[:, c], axis=0)
+                mu_c = (1 / m_c) * np.sum(
+                    self.X * r_ic[:, c].reshape(len(self.X), 1), axis=0
+                )
+                self.mu.append(mu_c)
+
+                # Calculate the covariance matrix per source based on the new mean
+                self.cov.append(
+                    (
+                        (1 / m_c)
+                        * np.dot(
+                            (
+                                np.array(r_ic[:, c]).reshape(len(self.X), 1)
+                                * (self.X - mu_c)
+                            ).T,
+                            (self.X - mu_c),
+                        )
+                    )
+                    + self.reg_cov
+                )
+                # Calculate pi_new which is the "fraction of points" respectively the fraction of the probability assigned to each source
+                self.pi.append(
+                    m_c / np.sum(r_ic)
+                )  # Here np.sum(r_ic) gives as result the number of instances. This is logical since we know
+                # that the columns of each row of r_ic adds up to 1. Since we add up all elements, we sum up all
+                # columns per row which gives 1 and then all rows which gives then the number of instances (rows)
+                # in X --> Since pi_new contains the fractions of datapoints, assigned to the sources c,
+                # The elements in pi_new must add up to 1
+
+            """Log likelihood"""
+            log_likelihoods.append(
+                np.log(
+                    np.sum(
+                        [
+                            k * multivariate_normal(self.mu[i], self.cov[j]).pdf(X)
+                            for k, i, j in zip(
+                                self.pi, range(len(self.mu)), range(len(self.cov))
+                            )
+                        ]
+                    )
+                )
+            )
+
+            """
+            This process of E step followed by a M step is now iterated a number of n times. In the second step for instance,
+            we use the calculated pi_new, mu_new and cov_new to calculate the new r_ic which are then used in the second M step
+            to calculat the mu_new2 and cov_new2 and so on....
+            """
+
+        fig2 = plt.figure(figsize=(10, 10))
+        ax1 = fig2.add_subplot(111)
+        ax1.set_title("Log-Likelihood")
+        ax1.plot(range(0, self.iterations, 1), log_likelihoods)
+        plt.show()
+
+    """Predict the membership of an unseen, new datapoint"""
+
+    def predict(self, Y):
+        # PLot the point onto the fittet gaussians
+        fig3 = plt.figure(figsize=(10, 10))
+        ax2 = fig3.add_subplot(111)
+        ax2.scatter(self.X[:, 0], self.X[:, 1])
+        for m, c in zip(self.mu, self.cov):
+            multi_normal = multivariate_normal(mean=m, cov=c)
+            ax2.contour(
+                np.sort(self.X[:, 0]),
+                np.sort(self.X[:, 1]),
+                multi_normal.pdf(self.XY).reshape(len(self.X), len(self.X)),
+                colors="black",
+                alpha=0.3,
+            )
+            ax2.scatter(m[0], m[1], c="grey", zorder=10, s=100)
+            ax2.set_title("Final state")
+            for y in Y:
+                ax2.scatter(y[0], y[1], c="orange", zorder=10, s=100)
+        prediction = []
+        for m, c in zip(self.mu, self.cov):
+            print(c)
+            prediction.append(
+                multivariate_normal(mean=m, cov=c).pdf(Y)
+                / np.sum(
+                    [
+                        multivariate_normal(mean=mean, cov=cov).pdf(Y)
+                        for mean, cov in zip(self.mu, self.cov)
+                    ]
+                )
+            )
+        plt.show()
+        return prediction
+
+
+GMM = GMM(X, 3, 50)
+GMM.run()
+GMM.predict([[0.5, 0.5]])
diff --git a/anomaly/gmm-change-detection/scripts/example_code/gmm/gmm_sklearn.py b/anomaly/gmm-change-detection/scripts/example_code/gmm/gmm_sklearn.py
new file mode 100644
index 00000000..3424e9dc
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/gmm/gmm_sklearn.py
@@ -0,0 +1,47 @@
+# This code was taken from a tutorial from the following website:
+# https://python-course.eu/machine-learning/expectation-maximization-and-gaussian-mixture-models-gmm.php
+
+import matplotlib.pyplot as plt
+from matplotlib import style
+
+style.use("fivethirtyeight")
+import numpy as np
+from mpl_toolkits import mplot3d
+from scipy.stats import multivariate_normal
+from sklearn.datasets.samples_generator import make_blobs
+from sklearn.mixture import GaussianMixture
+from visualization import visualize_3d_gmm
+
+# 0. Create dataset
+X, Y = make_blobs(
+    cluster_std=0.5, random_state=20, n_features=3, n_samples=100, centers=5
+)
+
+# Stratch dataset to get ellipsoid data
+X = np.dot(X, np.random.RandomState(0).randn(3, 3))
+
+x, y, z = np.meshgrid(np.sort(X[:, 0]), np.sort(X[:, 1]), np.sort(X[:, 2]))
+XY = np.array([x.flatten(), y.flatten(), z.flatten()]).T
+
+GMM = GaussianMixture(n_components=10).fit(X)  # Instantiate and fit the model
+print("Converged:", GMM.converged_)  # Check if the model has converged
+means = GMM.means_
+covariances = GMM.covariances_
+
+
+# Predict
+Y = np.array([[0.5], [0.5], [0.5]])
+prediction = GMM.predict_proba(Y.T)
+print(prediction)
+
+# Plot
+# fig = plt.figure(figsize=(10,10))
+# ax0 = plt.axes(projection="3d")
+# ax0.scatter3D(X[:,0],X[:,1],X[:,2])
+# ax0.scatter3D(Y[0,:],Y[1,:],Y[2,:],c='orange',zorder=10,s=100)
+for m, c in zip(means, covariances):
+    multi_normal = multivariate_normal(mean=m, cov=c)
+    # ax0.contour3D(np.sort(X[:,0]),np.sort(X[:,1]),np.sort(X[:,2]),multi_normal.pdf(XY).reshape(len(X),len(X),len(X)),colors='black',alpha=0.3)
+    # ax0.scatter3D(m[0],m[1],m[3],c='grey',zorder=10,s=100)
+visualize_3d_gmm(X, GMM.weights_, means.T, np.sqrt(covariances).T)
+# plt.show()
diff --git a/anomaly/gmm-change-detection/scripts/example_code/gmm/gmm_sklearn_example.py b/anomaly/gmm-change-detection/scripts/example_code/gmm/gmm_sklearn_example.py
new file mode 100644
index 00000000..80f00af7
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/gmm/gmm_sklearn_example.py
@@ -0,0 +1,48 @@
+# This code was taken from a tutorial from the following website:
+# https://python-course.eu/machine-learning/expectation-maximization-and-gaussian-mixture-models-gmm.php
+
+import matplotlib.pyplot as plt
+from matplotlib import style
+
+style.use("fivethirtyeight")
+import numpy as np
+from scipy.stats import multivariate_normal
+from sklearn.datasets.samples_generator import make_blobs
+from sklearn.mixture import GaussianMixture
+
+# 0. Create dataset
+X, Y = make_blobs(cluster_std=0.5, random_state=20, n_samples=1000, centers=5)
+
+# Stratch dataset to get ellipsoid data
+X = np.dot(X, np.random.RandomState(0).randn(2, 2))
+
+x, y = np.meshgrid(np.sort(X[:, 0]), np.sort(X[:, 1]))
+XY = np.array([x.flatten(), y.flatten()]).T
+
+GMM = GaussianMixture(n_components=5).fit(X)  # Instantiate and fit the model
+print("Converged:", GMM.converged_)  # Check if the model has converged
+means = GMM.means_
+covariances = GMM.covariances_
+
+
+# Predict
+Y = np.array([[0.5], [0.5]])
+prediction = GMM.predict_proba(Y.T)
+print(prediction)
+
+# Plot
+fig = plt.figure(figsize=(10, 10))
+ax0 = fig.add_subplot(111)
+ax0.scatter(X[:, 0], X[:, 1])
+ax0.scatter(Y[0, :], Y[1, :], c="orange", zorder=10, s=100)
+for m, c in zip(means, covariances):
+    multi_normal = multivariate_normal(mean=m, cov=c)
+    ax0.contour(
+        np.sort(X[:, 0]),
+        np.sort(X[:, 1]),
+        multi_normal.pdf(XY).reshape(len(X), len(X)),
+        colors="black",
+        alpha=0.3,
+    )
+    ax0.scatter(m[0], m[1], c="grey", zorder=10, s=100)
+plt.show()
diff --git a/anomaly/gmm-change-detection/scripts/example_code/linear_programming.py b/anomaly/gmm-change-detection/scripts/example_code/linear_programming.py
new file mode 100644
index 00000000..70e87f19
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/linear_programming.py
@@ -0,0 +1,63 @@
+# https://towardsdatascience.com/linear-programming-using-python-priyansh-22b5ee888fe0
+import numpy as np
+import pandas as pd
+from pulp import *
+
+n_warehouses = 2
+n_customers = 4
+
+# Cost Matrix
+cost_matrix = np.array([[1, 3, 0.5, 4], [2.5, 5, 1.5, 2.5]])
+
+# Demand Matrix
+cust_demands = np.array([35000, 22000, 18000, 30000])
+
+# Supply Matrix
+warehouse_supply = np.array([60000, 80000])
+
+# Initialize Model
+model = LpProblem("Supply-Demand-Problem", LpMinimize)
+
+# Define Variable Names
+variable_names = [
+    str(i) + str(j)
+    for j in range(1, n_customers + 1)
+    for i in range(1, n_warehouses + 1)
+]
+variable_names.sort()
+
+# Decision Variables
+DV_variables = LpVariable.matrix("X", variable_names, cat="Integer", lowBound=0)
+allocation = np.array(DV_variables).reshape(2, 4)
+
+# Objective Function
+obj_func = lpSum(allocation * cost_matrix)
+model += obj_func
+
+# Constraints
+for i in range(n_warehouses):
+    print(lpSum(allocation[i][j] for j in range(n_customers)) <= warehouse_supply[i])
+    model += lpSum(allocation[i][j] for j in range(n_customers)) <= warehouse_supply[
+        i
+    ], "Supply Constraints " + str(i)
+
+for j in range(n_customers):
+    print(lpSum(allocation[i][j] for i in range(n_warehouses)) >= cust_demands[j])
+    model += lpSum(allocation[i][j] for i in range(n_warehouses)) >= cust_demands[
+        j
+    ], "Demand Constraints " + str(j)
+
+model.solve(PULP_CBC_CMD())
+status = LpStatus[model.status]
+print(status)
+
+print("Total Cost:", model.objective.value())
+for v in model.variables():
+    try:
+        print(v.name, "=", v.value())
+    except:
+        print("error couldn't find value")
+
+for i in range(n_warehouses):
+    print("Warehouse ", str(i + 1))
+    print(lpSum(allocation[i][j].value() for j in range(n_customers)))
diff --git a/anomaly/gmm-change-detection/scripts/example_code/registration/icp_registration_example.py b/anomaly/gmm-change-detection/scripts/example_code/registration/icp_registration_example.py
new file mode 100644
index 00000000..b6f6def9
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/registration/icp_registration_example.py
@@ -0,0 +1,69 @@
+import copy
+
+import numpy as np
+import open3d as o3d
+
+
+def draw_registration_result(source, target, transformation):
+    source_temp = copy.deepcopy(source)
+    target_temp = copy.deepcopy(target)
+    source_temp.paint_uniform_color([1, 0.706, 0])
+    target_temp.paint_uniform_color([0, 0.651, 0.929])
+    source_temp.transform(transformation)
+    o3d.visualization.draw_geometries([source_temp, target_temp])
+
+
+if __name__ == "__main__":
+    source = o3d.io.read_point_cloud("normal0.pcd")
+    target = o3d.io.read_point_cloud("normal1.pcd")
+    threshold = 0.02
+    trans_init = np.asarray(
+        [
+            [0.862, 0.011, -0.507, 0.5],
+            [-0.139, 0.967, -0.215, 0.7],
+            [0.487, 0.255, 0.835, -1.4],
+            [0.0, 0.0, 0.0, 1.0],
+        ]
+    )
+    draw_registration_result(source, target, trans_init)
+    print("Initial alignment")
+    evaluation = o3d.registration.evaluate_registration(
+        source, target, threshold, trans_init
+    )
+    print(evaluation)
+
+    print("Apply point-to-point ICP")
+    reg_p2p = o3d.registration.registration_icp(
+        source,
+        target,
+        threshold,
+        trans_init,
+        o3d.registration.TransformationEstimationPointToPoint(),
+    )
+    print(reg_p2p)
+    print("Transformation is:")
+    print(reg_p2p.transformation)
+    print("")
+#    draw_registration_result(source, target, reg_p2p.transformation)
+
+#    print("Apply point-to-plane ICP")
+#    reg_p2l = o3d.registration.registration_icp(
+#        source, target, threshold, trans_init,
+#        o3d.registration.TransformationEstimationPointToPlane())
+#    print(reg_p2l)
+#    print("Transformation is:")
+#    print(reg_p2l.transformation)
+#    print("")
+#    draw_registration_result(source, target, reg_p2l.transformation)
+
+
+for count in range(100):
+    reg_p2l = o3d.registration.registration_icp(
+        source,
+        target,
+        threshold,
+        trans_init,
+        o3d.registration.TransformationEstimationPointToPoint(),
+    )
+
+draw_registration_result(source, target, reg_p2l.transformation)
diff --git a/anomaly/gmm-change-detection/scripts/example_code/registration/inliers.pcd b/anomaly/gmm-change-detection/scripts/example_code/registration/inliers.pcd
new file mode 100644
index 00000000..9e815070
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/registration/inliers.pcd
@@ -0,0 +1,35126 @@
+# .PCD v0.7 - Point Cloud Data file format
+VERSION 0.7
+FIELDS x y z
+SIZE 4 4 4
+TYPE F F F
+COUNT 1 1 1
+WIDTH 35115
+HEIGHT 1
+VIEWPOINT 0 0 0 1 0 0 0
+POINTS 35115
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diff --git a/anomaly/gmm-change-detection/scripts/example_code/registration/normal0.pcd b/anomaly/gmm-change-detection/scripts/example_code/registration/normal0.pcd
new file mode 100644
index 00000000..cca22397
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/registration/normal0.pcd
@@ -0,0 +1,38315 @@
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index 00000000..d78e041f
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diff --git a/anomaly/gmm-change-detection/scripts/example_code/registration/outliers.pcd b/anomaly/gmm-change-detection/scripts/example_code/registration/outliers.pcd
new file mode 100644
index 00000000..da2fc99f
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/example_code/registration/outliers.pcd
@@ -0,0 +1,3200 @@
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diff --git a/anomaly/gmm-change-detection/scripts/gmm.py b/anomaly/gmm-change-detection/scripts/gmm.py
new file mode 100644
index 00000000..8da10c8b
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/gmm.py
@@ -0,0 +1,63 @@
+#!/usr/bin/env python
+# Copyright (c) 2017, United States Government, as represented by the
+# Administrator of the National Aeronautics and Space Administration.
+#
+# All rights reserved.
+#
+# The Astrobee platform is licensed under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with the
+# License. You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations
+# under the License.
+
+
+# Small class that holds precalculated GMM and
+# allows clusters to be edited and removed
+
+import numpy as np
+
+
+class GMM:
+    def __init__(self, weights, means, covariances):
+        self.weights = weights
+        self.means = means
+        self.covariances = covariances
+        self.n_gaussians = self.weights.size
+
+    def remove_gaussian(self, index):  # Index of gaussian to remove
+        # removed_gauss = GMM(self.weights[index], self.means[index,:].reshape((1,3)), self.covariances[index,:].reshape((1,3)))
+        removed_gauss = GMM(
+            self.weights[index],
+            self.means[index, :].reshape((1, 3)),
+            np.array(self.covariances[:, :, index].reshape((3, 3))),
+        )
+        new_weights = np.delete(self.weights, index, 0)
+        self.weights = new_weights / np.linalg.norm(new_weights, ord=1)
+        self.means = np.delete(self.means, index, 0)
+        self.covariances = np.delete(self.covariances, index, -1)
+        self.n_gaussians = self.weights.size
+        return removed_gauss
+
+    def add_gaussian(self, gaussian):  # Gaussian in form of GMM, k=1
+        self.weights = np.append(
+            self.weights, gaussian.weights
+        )  # PLACEHOLDER (weights are now incorrect)
+        self.means = np.append(self.means, gaussian.means, axis=0)
+        print("Gauss type: " + str(type(gaussian.covariances.reshape((3, 3)))))
+        if type(self.covariances) is not np.ndarray:
+            self.covariances = gaussian.covariances.reshape((3, 3))
+            print("Pi cov initialized: " + str(self.covariances.shape))
+        else:
+            print("Curr cov size: " + str(self.covariances.shape))
+            print("Gauss cov size: " + str(gaussian.covariances.reshape((3, 3)).shape))
+            # self.covariances = np.vstack([[self.covariances], [gaussian.covariances.reshape((3,3))]])
+            self.covariances = np.concatenate(
+                [self.covariances, gaussian.covariances], axis=0
+            )
+        self.n_gaussians = self.weights.size
diff --git a/anomaly/gmm-change-detection/scripts/gmm_change_detection.py b/anomaly/gmm-change-detection/scripts/gmm_change_detection.py
new file mode 100755
index 00000000..9478c150
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/gmm_change_detection.py
@@ -0,0 +1,396 @@
+#!/usr/bin/env python
+# Copyright (c) 2017, United States Government, as represented by the
+# Administrator of the National Aeronautics and Space Administration.
+#
+# All rights reserved.
+#
+# The Astrobee platform is licensed under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with the
+# License. You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations
+# under the License.
+
+import copy
+import os
+import pickle
+import sys
+
+import numpy as np
+from artificial_data import generate_data
+from emd_gmm import *
+from gmm import *
+from gmm_mml import GmmMml
+from preprocess_data import *
+from visualization import *
+
+np.set_printoptions(threshold=sys.maxsize)
+np.set_printoptions(suppress=True)
+
+############
+# Settings #
+############
+
+visualize = True  # Visualize GMMs
+
+fake_data = True  # Generate point clouds
+n_start = 4
+n_disappearances = 0
+n_appearances = 3
+
+# Path to input data (.pk for pickled GMMs, PCL file,
+# or bag files cropped to same positions at different times)
+
+# t_0 = './data/ground_truth/ground_truth_run5.pcd'
+# t_1 = './data/ground_truth/ground_truth_run4.pcd'
+
+t_1 = "./data/single_frame_bags/20230313_1901_survey_no_bag_panorama_precut.bag"
+t_0 = "./data/single_frame_bags/20230313_1908_survey_bag_panorama_precut.bag"
+
+# t_0 = './data/single_frame_bags/run5_astrobee.bag'
+# t_1 = './data/single_frame_bags/run1_astrobee.bag'
+
+# t_0 = './data/image_reconstruction/run5.pcd'
+# t_1 = './data/image_reconstruction/run2.pcd'
+
+# t_0 = './saved_models/fake_data_t0_1.pk'
+# t_1 = './saved_models/fake_data_t1_1.pk'
+
+# t_0 = './saved_models/20230313_1901_survey_no_bag_panorama_precut_1.pk'
+# t_1 = './saved_models/20230313_1908_survey_bag_panorama_precut_1.pk'
+
+# t_0 = 'data/image_reconstruction/run5.pcd'
+# t_1 = 'data/image_reconstruction/run4.pcd'
+
+# Get file extension
+ext = os.path.splitext(os.path.basename(t_0))[1]
+
+#############################
+# Set up caching the models #
+#############################
+def get_filename(original_file):
+    """Make sure the filename is unique"""
+    counter = 1
+    filename = original_file + "_{}"
+    while os.path.isfile(".saved_models/" + filename.format(counter)):
+        counter += 1
+    filename = filename.format(counter)
+    return filename
+
+
+# Load pre-clustered GMMs if provided
+if ext == ".pk":
+    with open(t_0, "rb") as fi:
+        (
+            gmm1_init_bestk,
+            gmm1_init_bestpp,
+            gmm1_init_bestcov,
+            gmm1_init_bestmu,
+            predictions1,
+        ) = pickle.load(fi)
+    with open(t_1, "rb") as fi:
+        (
+            gmm2_init_bestk,
+            gmm2_init_bestpp,
+            gmm2_init_bestcov,
+            gmm2_init_bestmu,
+            predictions2,
+        ) = pickle.load(fi)
+else:
+    if fake_data:
+        filename_1 = get_filename("fake_data_t0")
+        filename_2 = get_filename("fake_data_t1")
+    else:
+        filename_1 = get_filename(os.path.splitext(os.path.basename(t_0))[0])
+        filename_2 = get_filename(os.path.splitext(os.path.basename(t_1))[0])
+
+    t0_save_file = (
+        os.path.dirname(os.path.realpath(__file__))
+        + "/saved_models/"
+        + filename_1
+        + ".pk"
+    )
+    t1_save_file = (
+        os.path.dirname(os.path.realpath(__file__))
+        + "/saved_models/"
+        + filename_2
+        + ".pk"
+    )
+
+    ###################
+    # Format the data #
+    ###################
+
+    if fake_data:
+        points1, points2 = generate_data(n_start, n_disappearances, n_appearances)
+
+    elif ext == ".bag":  # sensor_msgs::PointCloud2 data from bagfile
+        points1 = read_pc2_msgs(t_0)
+        points2 = read_pc2_msgs(t_1)
+
+    elif ext == ".pcd":  # PCL formatted file (e.g. from reconstructed map)
+        points1 = read_pcd(t_0)
+        points2 = read_pcd(t_1)
+    else:
+        sys.exit("Invalid file format")
+
+    # Plot the figures
+    fig1 = plt.figure()
+    ax1 = fig1.add_subplot(111, projection="3d")
+    ax1.scatter(points1[:, 0], points1[:, 1], points1[:, 2], s=0.7, alpha=0.07)
+    fig2 = plt.figure()
+    ax2 = fig2.add_subplot(111, projection="3d")
+    ax2.scatter(points2[:, 0], points2[:, 1], points2[:, 2], s=0.7, alpha=0.07)
+    # plt.show()
+
+    #####################################
+    # Cluster the point clouds int GMMs #
+    #####################################
+
+    # Run split-and-merge expectation-maximization algorithm
+    # described in "Unsupervised Learning of Finite Mixture Models" by Figueiredo et al.
+    print("Fitting Gamma")
+    gmm1_init = GmmMml()
+    gmm1_init = gmm1_init.fit(points1, verb=True)
+    gmm1_init_bestk = gmm1_init.bestk
+    gmm1_init_bestpp = gmm1_init.bestpp
+    gmm1_init_bestcov = gmm1_init.bestcov
+    gmm1_init_bestmu = gmm1_init.bestmu
+    predictions1 = gmm1_init.predict(points1)
+
+    print("Fitting Theta")
+    gmm2_init = GmmMml()
+    gmm2_init = gmm2_init.fit(points2, verb=True)
+    gmm2_init_bestk = gmm2_init.bestk
+    gmm2_init_bestpp = gmm2_init.bestpp
+    gmm2_init_bestcov = gmm2_init.bestcov
+    gmm2_init_bestmu = gmm2_init.bestmu
+    predictions2 = gmm2_init.predict(points2)
+
+
+print("Gamma number of Gaussians: " + str(gmm1_init_bestk))
+print("Theta number of Gaussians: " + str(gmm2_init_bestk))
+
+# Move the GMMs to a new data structure to be able to remove
+# Gaussians from GMMs for change detection
+gamma_t0 = GMM(gmm1_init_bestpp[0, :], gmm1_init_bestmu, gmm1_init_bestcov)
+theta_t1 = GMM(gmm2_init_bestpp[0, :], gmm2_init_bestmu, gmm2_init_bestcov)
+
+######################################
+# Generate GMM with Detected Changes #
+######################################
+
+
+def find_emd(gmm1, gmm2):
+    """Find the Earth Mover's Distance between two GMMs"""
+    emdgmm = EMDGMM(gmm1.weights, gmm2.weights)
+    emdgmm.get_distance(gmm1.means, gmm2.means)
+    emdgmm.calculate_emd()
+    return emdgmm.emd
+
+
+def greedy_select_gmm(gamma, theta, start_emd):
+    """Find the Gaussian in Theta that contributes the
+    highest degree of positive change (i.e., removal results
+    in the lowest EMD) and return "best" Theta GMM from which such
+    Gaussian has been removed
+    """
+
+    lowest_emd = start_emd  # Metric for degree of change
+    best_theta = theta
+    best_gauss = GMM(
+        np.array([]), np.array([]).reshape((0, 3)), np.array([]).reshape((0, 3))
+    )  # GMM w/ k=1
+
+    # Remove Gaussians one at a time and track which contributes most change
+    for gauss in range(theta.n_gaussians - 1):
+        theta_temp = copy.deepcopy(theta)
+        removed_gauss = theta_temp.remove_gaussian(gauss)  # Remove the next Gaussian
+        new_emd = find_emd(gamma, theta_temp)
+
+        # print("NEW EMD: " + str(new_emd) + " LOWEST: " + str(lowest_emd))
+
+        if new_emd < lowest_emd:
+            lowest_emd = new_emd
+            best_theta = theta_temp
+            best_gauss = removed_gauss
+
+    return lowest_emd, best_theta, best_gauss
+
+
+def change_detection(gamma, theta):
+    """Greedily remove Gaussians that contribute the most change
+    and place them into a tertiary "change" GMM
+    """
+
+    # Initialize empty Pi (change) GMM
+    pi = GMM(np.array([]), np.array([]).reshape((0, 3)), None)
+
+    # Iteratively remove Gaussians from "after" GMM until EMD is the same
+    dGMM_old = find_emd(gamma, theta)
+    dGMM, new_theta, best_gauss = greedy_select_gmm(gamma, theta, dGMM_old)
+
+    for i in range(new_theta.n_gaussians):
+        if best_gauss.n_gaussians == 0:
+            break
+
+        # Update Pi with new Gaussian
+        pi.add_gaussian(best_gauss)
+
+        # Obtain "best Gaussian" that produces highest degree of change
+        dGMM, new_theta, best_gauss = greedy_select_gmm(gamma, new_theta, dGMM_old)
+        if dGMM_old > dGMM:
+            dGMM_old = dGMM
+        else:
+            break
+
+    # Map the change Gaussians back to the original Theta GMM
+    theta_changes = []
+    for gauss in range(pi.n_gaussians):
+        change_gauss = np.where(np.prod(gmm2_init_bestmu == pi.means[gauss], axis=-1))
+        theta_changes.append(change_gauss)
+
+    # Readjust the Pi covariance shape back to [3,3,K]
+    if pi.n_gaussians != 0:
+        pi.covariances = pi.covariances.reshape(pi.n_gaussians, 3, 3).T
+    return pi
+
+
+pi_appearances = change_detection(gamma_t0, theta_t1)
+pi_disappearances = change_detection(theta_t1, gamma_t0)
+
+# Output change Gaussian information
+print("\nCHANGE GAUSSIANS")
+print("################################")
+print("Clusters associated with appearances: " + str(pi_appearances.n_gaussians))
+print("Clusters associated with disappearances: " + str(pi_disappearances.n_gaussians))
+
+
+def get_diag(covs, k):
+    """Get diagonal covariance from full covariance
+    matrix for plotting"""
+
+    diag_covs = []
+    for i in range(k):
+        cov = covs[:, :, i]
+        l = len(cov[0])
+        diag = [cov[j][j] for j in range(l)]
+        diag_covs.append(diag)
+    return np.array(diag_covs)
+
+
+########################
+# Visualize the Output #
+########################
+if visualize:
+    gmm1_k = gmm1_init_bestk
+    gmm2_k = gmm2_init_bestk
+
+    fig = plt.figure(figsize=(12, 4))
+    ax1 = fig.add_subplot(141, projection="3d")
+    ax1.set(xlabel="X", ylabel="Y", zlabel="Z")
+    ax1.view_init(elev=-75, azim=-90)
+    ax1.title.set_text("Gamma (Before)")
+
+    ax2 = fig.add_subplot(142, projection="3d")
+    ax2.set(xlabel="X", ylabel="Y", zlabel="Z")
+    ax2.view_init(elev=-75, azim=-90)
+    ax2.title.set_text("Theta (After)")
+
+    ax3 = fig.add_subplot(143, projection="3d")
+    ax3.set(xlabel="X", ylabel="Y", zlabel="Z")
+    ax3.view_init(elev=-75, azim=-90)
+    ax3.title.set_text("Pi (Appearances)")
+
+    ax4 = fig.add_subplot(144, projection="3d")
+    ax4.set(xlabel="X", ylabel="Y", zlabel="Z")
+    ax4.view_init(elev=-75, azim=-90)
+    ax4.title.set_text("Pi (Disappearances)")
+
+    # GMM 1 (Gamma)
+    diag_covs1 = get_diag(gmm1_init_bestcov, gmm1_init_bestk)
+    visualize_3d_gmm(
+        points1,
+        predictions1,
+        gmm1_init_bestpp[0],
+        gmm1_init_bestmu.T,
+        np.sqrt(diag_covs1).T,
+        gmm1_k,
+        ax1,
+    )
+
+    # GMM 2 (Theta)
+    diag_covs2 = get_diag(gmm2_init_bestcov, gmm2_init_bestk)
+    visualize_3d_gmm(
+        points2,
+        predictions2,
+        gmm2_init_bestpp[0],
+        gmm2_init_bestmu.T,
+        np.sqrt(diag_covs2).T,
+        gmm2_k,
+        ax2,
+    )
+
+    # Appearance GMM (Pi 1), compared to GMM2 original points
+    piagonal = get_diag(pi_appearances.covariances, pi_appearances.weights.shape[0])
+    visualize_3d_gmm(
+        points2,
+        predictions2,
+        pi_appearances.weights,
+        pi_appearances.means.T,
+        np.sqrt(piagonal).T,
+        gmm2_k,
+        ax3,
+    )
+
+    # Disappearance GMM (Pi 2), compared to GMM2 original points
+    piagonal_dis = get_diag(
+        pi_disappearances.covariances, pi_disappearances.weights.shape[0]
+    )
+    visualize_3d_gmm(
+        points2,
+        predictions2,
+        pi_disappearances.weights,
+        pi_disappearances.means.T,
+        np.sqrt(piagonal_dis).T,
+        gmm2_k,
+        ax4,
+    )
+
+    # Save model parameters for future use
+    if ext != ".pk":
+        with open(t0_save_file, "wb") as fi:
+            pickle.dump(
+                [
+                    gmm1_init.bestk,
+                    gmm1_init.bestpp,
+                    gmm1_init.bestcov,
+                    gmm1_init.bestmu,
+                    predictions1,
+                    points1,
+                ],
+                fi,
+            )
+        with open(t1_save_file, "wb") as fi:
+            pickle.dump(
+                [
+                    gmm2_init.bestk,
+                    gmm2_init.bestpp,
+                    gmm2_init.bestcov,
+                    gmm2_init.bestmu,
+                    predictions2,
+                    points2,
+                ],
+                fi,
+            )
+
+        print("Saved to: ")
+        print("    " + str(t0_save_file))
+        print("    " + str(t1_save_file))
+
+plt.show()
diff --git a/anomaly/gmm-change-detection/scripts/gmm_mml.py b/anomaly/gmm-change-detection/scripts/gmm_mml.py
new file mode 100644
index 00000000..62d3ccd2
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/gmm_mml.py
@@ -0,0 +1,382 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jun  7 18:44:27 2017
+
+@author: abriosi
+"""
+
+import numpy as np
+from scipy.stats import multivariate_normal
+from sklearn.base import TransformerMixin
+
+
+class GmmMml(TransformerMixin):
+    def __init__(
+        self,
+        kmin=1,
+        kmax=25,
+        regularize=1e-6,
+        threshold=1e-5,
+        covoption=0,
+        max_iters=100,
+        live_2d_plot=False,
+        plots=False,
+    ):
+
+        self.kmin = kmin
+        self.kmax = kmax
+        self.regularize = regularize
+        self.th = threshold
+        self.covoption = covoption
+        self.live_2d_plot = live_2d_plot
+        self.max_iters = max_iters
+        self.check_plot = plots
+
+    def _draw_elipse(self, ax, estcov, estmu):
+        import matplotlib as mpl
+
+        v, w = np.linalg.eigh(estcov)
+        u = w[0] / np.linalg.norm(w[0])
+        angle = np.arctan2(u[1], u[0])
+        angle = 180 * angle / np.pi  # convert to degrees
+        v = 2.0 * np.sqrt(2.0) * np.sqrt(v)
+
+        ell = mpl.patches.Ellipse(
+            estmu, v[0], v[1], 180 + angle, facecolor="none", edgecolor="black"
+        )
+        ell.set_clip_box(ax.bbox)
+        ell.set_alpha(1)
+        ax.add_artist(ell)
+        return ax
+
+    def _plot_graph(self, estcov, estmu, k, y, message):
+        if y.shape[1] == 2:
+            import matplotlib.pyplot as plt
+
+            ax = plt.subplot(111)
+            plt.title(message)
+            plt.scatter(y[:, 0], y[:, 1], alpha=0.2, s=10)
+            for i in range(k):
+                ax = self._draw_elipse(ax, estcov[:, :, i], estmu[i])
+            plt.show()
+        else:
+            print("only supports 2d data for plots")
+
+    def _posterior_probability(self, y, estmu, estcov, i):
+        try:
+            return multivariate_normal.pdf(
+                y, estmu[i], estcov[:, :, i], allow_singular=False
+            )
+        except Exception as inst:
+            raise Exception(
+                "Possible singular matrix detected. Try adding (more) regularization"
+            )
+
+    def fit(self, X, y=None, verb=False):
+        y = np.array(X)
+
+        dl = []
+        npoints = y.shape[0]
+        dimens = y.shape[1]
+
+        if self.covoption == 0:
+            npars = dimens + dimens * (dimens + 1) / 2
+        elif self.covoption == 1:
+            npars = 2 * dimens
+        elif self.covoption == 2:
+            npars = dimens
+        elif self.covoption == 3:
+            npars = dimens
+        else:
+            npars = dimens + dimens * (dimens + 1) / 2
+
+        nparsover2 = npars / 2
+
+        k = self.kmax
+
+        indic = np.zeros((k, npoints))
+        randindex = np.random.randint(0, npoints, npoints)
+        randindex = np.random.choice(randindex, k)
+        estmu = y[randindex]
+
+        estpp = (1 / float(k)) * np.ones((1, k))
+
+        if dimens > 1:
+            globcov = np.cov(y, rowvar=False)
+        else:
+            globcov = np.array([np.array([np.cov(y, rowvar=False)])])
+
+        estcov = np.empty(globcov.shape + (self.kmax,))
+        for i in range(k):
+            estcov[:, :, i] = np.diag(
+                (np.diag(np.ones((dimens, dimens)) * np.max(np.diag(globcov / 10))))
+            )
+
+        if self.check_plot == True:
+            self._plot_graph(estcov, estmu, k, y, "Random Guassian Initialization")
+
+        semi_indic = np.empty((k, y.shape[0]))
+        for i in range(k):
+
+            min_eig = np.min(np.real(np.linalg.eigvals(estcov[:, :, i])))
+            if min_eig < 0:
+                estcov[:, :, i] -= 10 * min_eig * np.eye(*estcov[:, :, i].shape)
+
+            semi_indic[i, :] = self._posterior_probability(y, estmu, estcov, i)
+            indic[i, :] = semi_indic[i, :] * estpp[:, i]
+
+        countf = 0
+        loglike = []
+        kappas = []
+
+        loglike.append(
+            np.sum(np.log(np.sum(np.finfo(np.float64).tiny + indic, axis=0)))
+        )
+        dlength = (
+            -loglike[countf]
+            + (nparsover2 * np.sum(np.log(estpp)))
+            + (nparsover2 + 0.5) * k * np.log(npoints)
+        )
+        dl.append(dlength)
+        kappas.append(k)
+
+        transitions1 = []
+        transitions2 = []
+
+        mindl = dl[countf]
+        self.bestmu = estmu
+        self.bestcov = estcov
+
+        k_cont = True
+
+        iteration = 0
+
+        while k_cont == True:
+
+            cont = True
+
+            while cont == True and iteration < self.max_iters:
+                # while iteration <self.max_iters:
+
+                #        print(iteration)
+                if verb == True:
+                    print("k=" + str(k) + " minestpp=" + str(np.min(estpp)))
+
+                comp = 0
+                while comp < k:
+
+                    indic = np.zeros((k, npoints))
+                    for i in range(k):
+                        indic[i, :] = semi_indic[i, :] * estpp[:, i]
+
+                    normindic = np.divide(
+                        indic,
+                        (
+                            np.finfo(np.float64).tiny
+                            + np.kron(np.ones((k, 1)), np.sum(indic, axis=0))
+                        ),
+                    )
+
+                    normalize = 1 / np.sum(normindic[comp, :], axis=0)
+
+                    aux = np.multiply(
+                        np.kron(normindic[comp, :], np.ones((dimens, 1))), y.T
+                    )
+
+                    estmu[comp, :] = normalize * np.sum(aux, axis=-1)
+
+                    if self.covoption == 0:
+                        estcov[:, :, comp] = (
+                            normalize * aux.dot(y)
+                            - estmu[comp, :][:, np.newaxis]
+                            * estmu[comp, :][:, np.newaxis].T
+                            + self.regularize * np.identity(dimens)
+                        )
+                    else:
+                        raise NameError("Not implemented covoption > 0")
+
+                    estpp[:, comp] = (
+                        np.max(np.sum(normindic[comp, :]) - nparsover2, axis=0)
+                        / npoints
+                    )
+                    estpp = estpp / np.sum(estpp)
+
+                    killed = 0
+
+                    if estpp[:, comp] <= 0:
+                        killed = 1
+
+                        transitions1.append(countf)
+
+                        estmu = np.delete(estmu, comp, axis=0)
+                        estcov = np.delete(estcov, comp, axis=-1)
+                        estpp = np.delete(estpp, comp, axis=-1)
+                        semi_indic = np.delete(semi_indic, comp, axis=0)
+
+                        k = k - 1
+
+                    if killed == 0:
+
+                        min_eig = np.min(np.real(np.linalg.eigvals(estcov[:, :, comp])))
+                        if min_eig < 0:
+                            estcov[:, :, comp] -= (
+                                10 * min_eig * np.eye(*estcov[:, :, comp].shape)
+                            )
+
+                        semi_indic[comp, :] = self._posterior_probability(
+                            y, estmu, estcov, comp
+                        )
+                        comp += 1
+
+                countf = countf + 1
+
+                indic = np.zeros((k, npoints))
+                semi_indic = np.empty((k, y.shape[0]))
+
+                for i in range(k):
+
+                    min_eig = np.min(np.real(np.linalg.eigvals(estcov[:, :, i])))
+                    if min_eig < 0:
+                        estcov[:, :, i] -= 10 * min_eig * np.eye(*estcov[:, :, i].shape)
+
+                    semi_indic[i, :] = self._posterior_probability(y, estmu, estcov, i)
+                    indic[i, :] = semi_indic[i, :] * estpp[:, i]
+
+                if k != 1:
+                    loglike.append(
+                        np.sum(
+                            np.log(np.finfo(np.float64).tiny + np.sum(indic, axis=0))
+                        )
+                    )
+                else:
+                    loglike.append(np.sum(np.log(np.finfo(np.float64).tiny + indic)))
+
+                dlength = (
+                    -loglike[countf]
+                    + (nparsover2 * np.sum(np.log(estpp)))
+                    + (nparsover2 + 0.5) * k * np.log(npoints)
+                )
+                dl.append(dlength)
+                kappas.append(k)
+
+                deltlike = loglike[countf] - loglike[countf - 1]
+
+                if verb == True:
+                    toprint = np.abs(deltlike / loglike[countf - 1]) / self.th
+                    print("deltaloglike/th =" + str(toprint))
+
+                if np.abs(deltlike / loglike[countf - 1]) < self.th:
+                    cont = False
+
+                if self.live_2d_plot == True:
+                    self._plot_graph(estcov, estmu, k, y, None)
+
+                iteration += 1
+
+            iteration = 0
+
+            if dl[countf] < mindl:
+                self.bestpp = estpp
+                self.bestmu = estmu
+                self.bestcov = estcov
+                self.bestk = k
+                mindl = dl[countf]
+
+            if k > self.kmin:
+                indminp = np.argmin(estpp)
+
+                estmu = np.delete(estmu, indminp, axis=0)
+                estcov = np.delete(estcov, indminp, axis=-1)
+                estpp = np.delete(estpp, indminp, axis=-1)
+
+                k = k - 1
+
+                estpp = estpp / np.sum(estpp)
+
+                transitions2.append(countf)
+
+                countf = countf + 1
+
+                indic = np.zeros((k, npoints))
+                semi_indic = np.empty((k, y.shape[0]))
+
+                for i in range(k):
+
+                    min_eig = np.min(np.real(np.linalg.eigvals(estcov[:, :, i])))
+                    if min_eig < 0:
+                        estcov[:, :, i] -= 10 * min_eig * np.eye(*estcov[:, :, i].shape)
+
+                    semi_indic[i, :] = self._posterior_probability(y, estmu, estcov, i)
+                    indic[i, :] = semi_indic[i, :] * estpp[:, i]
+
+                if k != 1:
+                    loglike.append(
+                        np.sum(
+                            np.log(np.finfo(np.float64).tiny + np.sum(indic, axis=0))
+                        )
+                    )
+                else:
+                    loglike.append(np.sum(np.log(np.finfo(np.float64).tiny + indic)))
+
+                dlength = (
+                    -loglike[countf]
+                    + (nparsover2 * np.sum(np.log(estpp)))
+                    + (nparsover2 + 0.5) * k * np.log(npoints)
+                )
+                dl.append(dlength)
+
+                #        countf=countf-1
+                kappas.append(k)
+            else:
+                k_cont = False
+
+        if self.check_plot == True:
+            import matplotlib.pyplot as plt
+
+            plt.title("Evaluation of the cost function")
+            plt.plot(range(len(dl)), dl)
+            plt.show()
+
+            self._plot_graph(
+                self.bestcov,
+                self.bestmu,
+                self.bestcov.shape[-1],
+                y,
+                "Best number of components",
+            )
+
+        return self
+
+    def sample(self, sample):
+        output = []
+        select_sample = np.random.multinomial(sample, self.bestpp[0])
+        gmm = 0
+        for i in select_sample:
+            for i in range(i):
+                output.append(
+                    np.random.multivariate_normal(
+                        self.bestmu[gmm], np.swapaxes(self.bestcov, 0, 2)[gmm]
+                    )
+                )
+            gmm += 1
+        return np.array(output)
+
+    def transform(self, X, y=None):
+        y = np.array(X)
+        semi_indic = np.empty((self.bestmu.shape[0], y.shape[0]))
+        for i in range(self.bestmu.shape[0]):
+            semi_indic[i, :] = self._posterior_probability(
+                y, self.bestmu, self.bestcov, i
+            )
+        return semi_indic.T
+
+    def fit_transform(self, X, y=None):
+        self.fit(X)
+        return self.transform(X)
+
+    def predict_proba(self, X):
+        return self.transform(X)
+
+    def predict(self, X):
+        return np.argmax(self.transform(X), axis=1)
diff --git a/anomaly/gmm-change-detection/scripts/ground_truth.py b/anomaly/gmm-change-detection/scripts/ground_truth.py
new file mode 100644
index 00000000..25817d0a
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/ground_truth.py
@@ -0,0 +1,107 @@
+#!/usr/bin/python2
+# Copyright (c) 2017, United States Government, as represented by the
+# Administrator of the National Aeronautics and Space Administration.
+#
+# All rights reserved.
+#
+# The Astrobee platform is licensed under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with the
+# License. You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations
+# under the License.
+
+# Python2 script to convert a series of haz_cam point clouds
+# to a single merged point cloud in the world reference frame
+
+import geometry_msgs
+import numpy as np
+import ros_numpy
+import rosbag
+import rospy
+import sensor_msgs
+import tf2_py as tf2
+import tf2_ros
+from tf import transformations as ts
+from tf2_sensor_msgs.tf2_sensor_msgs import do_transform_cloud
+
+
+# Transform the pc2 msg camera coordinates from the haz_cam to body:
+def translate_cam_frame(pc2_msg):
+    t = geometry_msgs.msg.TransformStamped()
+    t.header.stamp = pc2_msg.header.stamp
+    t.header.frame_id = "haz_cam"
+    t.child_frame_id = "body"
+
+    t.transform.translation.x = 0.036
+    t.transform.translation.y = -0.083
+    t.transform.translation.z = -0.133
+
+    # In quaternions:
+    t.transform.rotation.x = 0.500
+    t.transform.rotation.y = -0.500
+    t.transform.rotation.z = 0.500
+    t.transform.rotation.w = -0.500
+
+    body_tf = do_transform_cloud(pc2_msg, t)
+    return body_tf
+
+
+# Transform point cloud coordinates to map ground truth (from Marina and Ryan)
+def ground_truth(msg, pc2_msg):
+    # Get transformation info from hazcam pc2 message
+    t = geometry_msgs.msg.TransformStamped()
+    # t.header.stamp = rospy.Time.now()
+    t.header.stamp = msg.header.stamp
+    t.header.frame_id = "body"
+    t.child_frame_id = "world"
+
+    t.transform.translation.x = msg.pose.position.x
+    t.transform.translation.y = msg.pose.position.y
+    t.transform.translation.z = msg.pose.position.z
+
+    t.transform.rotation.x = msg.pose.orientation.x
+    t.transform.rotation.y = msg.pose.orientation.y
+    t.transform.rotation.z = msg.pose.orientation.z
+    t.transform.rotation.w = msg.pose.orientation.w
+
+    cloud_out = do_transform_cloud(pc2_msg, t)
+    return cloud_out
+
+
+def convert_bag(bagfile, output_bag_name, save_all_topics=False):
+    topics = [
+        "/gnc/ekf",
+        "/hw/depth_haz/points",
+        "/hw/depth_haz/extended/amplitude_int",
+    ]
+    output_bag = rosbag.Bag(output_bag_name, "w")
+    topics_bag = [] if save_all_topics else topics
+
+    pc2_msg = None
+    with rosbag.Bag(bagfile, "r") as bag:
+        for topic, msg, t in bag.read_messages(topics_bag):
+            if topic == "/hw/depth_haz/points":
+                pc2_msg = msg
+            if topic == "/gnc/ekf" and pc2_msg is not None:
+                new_topic_name = "/hw/depth_haz/points/ground_truth"
+                pc2_body = translate_cam_frame(pc2_msg)
+                pc2_gt = ground_truth(msg, pc2_body)
+                output_bag.write(topic, msg, t)
+                output_bag.write(new_topic_name, pc2_gt, t)
+                pc2_msg = None
+            if save_all_topics:
+                output_bag.write(topic, msg, t)
+    output_bag.close()
+
+
+if __name__ == "__main__":
+    convert_bag(
+        "/home/jcsanto3/bagfile-data/ground_truth/groundtruth_20230419_1836_survey_run5.bag",
+        "./groundtruth_run5.bag",
+    )
diff --git a/anomaly/gmm-change-detection/scripts/preprocess_data.py b/anomaly/gmm-change-detection/scripts/preprocess_data.py
new file mode 100644
index 00000000..d151e9b9
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/preprocess_data.py
@@ -0,0 +1,139 @@
+#!/usr/bin/env python
+# Copyright (c) 2017, United States Government, as represented by the
+# Administrator of the National Aeronautics and Space Administration.
+#
+# All rights reserved.
+#
+# The Astrobee platform is licensed under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with the
+# License. You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+# License for the specific language governing permissions and limitations
+# under the License.
+
+
+# Converts ROS sensor_msgs:PointCloud2 to a
+# python-PCL-filtered numpy array
+
+import math
+import time
+
+import geometry_msgs
+import matplotlib.pyplot as plt
+import numpy as np
+import pcl
+import rosbag
+import rospy
+import sensor_msgs
+from PIL import Image
+
+
+# See https://stackoverflow.com/questions/39772424
+def convert_pc2_pcl(data):
+    data.__class__ = sensor_msgs.msg._PointCloud2.PointCloud2
+    offset_sorted = {f.offset: f for f in data.fields}
+    data.fields = [f for (_, f) in sorted(offset_sorted.items())]
+
+    pc = ros_numpy.numpify(data)
+    height = pc.shape[0]
+    if len(pc.shape) == 1:  # Unordered PC2 structure
+        np_points = np.zeros((height, 3), dtype=np.float32)
+        np_points[:, 0] = np.resize(pc["x"], height)
+        np_points[:, 1] = np.resize(pc["y"], height)
+        np_points[:, 2] = np.resize(pc["z"], height)
+        p = pcl.PointCloud(np.array(np_points, dtype=np.float32))
+
+    else:  # Ordered PC2 structure
+        width = pc.shape[1]
+        np_points = np.zeros((height * width, 3), dtype=np.float32)
+        np_points[:, 0] = np.resize(pc["x"], height * width)
+        np_points[:, 1] = np.resize(pc["y"], height * width)
+        np_points[:, 2] = np.resize(pc["z"], height * width)
+        p = pcl.PointCloud(np.array(np_points, dtype=np.float32))
+    return p
+
+
+# Outlier and downsample filtering of data
+def filter_pcl(pcl_data):
+    p = pcl_data
+    pcl.save(p, "normal.pcd")
+
+    # Statistical Outlier Filter
+    fil = p.make_statistical_outlier_filter()
+    fil.set_mean_k(50)
+    fil.set_std_dev_mul_thresh(1.0)
+
+    # pcl.save(fil.filter(), "inliers.pcd")
+    # fil.set_negative(True)
+    # pcl.save(fil.filter(), "outliers.pcd")
+
+    # Downsample outlier filtered data
+    # fil = fil.make_voxel_grid_filter()
+    # fil.set_leaf_size(0.01, 0.01, 0.01)
+    # pcl.save(fil.filter(), "downsample.pcd")
+
+    # Conversion from pcl to numpy array
+    np_arr = fil.filter().to_array()
+    # np_arr = p.to_array()
+    return np_arr
+
+
+# Obtain and concatenate point clouds remapped with ground truth transform
+def concat_ground_truth_msgs(bagfile):
+    skipped = 100  # Number of messages to skip over
+    n_points = 171 * 224  # 171 rows x 224 columns
+    n_msgs = rosbag.Bag(bagfile).get_message_count("/hw/depth_haz/points/ground_truth")
+    n_msgs_used = math.floor(n_msgs / skipped)
+    merged_pcl = np.empty((n_msgs_used * n_points, 3), dtype=np.float32)
+
+    count = 0
+    i = 0
+    for topic, msg, t in rosbag.Bag(bagfile).read_messages():
+        if topic == "/hw/depth_haz/points/ground_truth":
+            count += 1
+            if count % skipped == 0:
+                np_arr = convert_pc2_pcl(msg).to_array()
+                merged_pcl[i : i + n_points, :] = np_arr
+                i += n_points
+
+    p = pcl.PointCloud(merged_pcl)
+    pcl.save(p, "ground_truth_run5.pcd")
+    return p
+
+
+# Process data from bagfile
+def read_pc2_msgs(bagfile):
+    for topic, msg, t in rosbag.Bag(bagfile).read_messages():
+        if topic == "/hw/depth_haz/extended/amplitude_int":
+            np_im = ros_numpy.image.image_to_numpy(msg)
+            data = Image.fromarray(np_im)
+
+        if (
+            topic == "/hw/depth_haz/points"
+            or topic == "/hw/depth_haz/points/ground_truth"
+        ):
+            p = convert_pc2_pcl(msg)
+            np_arr = filter_pcl(p)
+
+            if "data" in locals():
+                data.show()
+                data.save("image.png")
+                return np_arr
+    return np_arr
+
+
+# Process PCD point cloud directly
+def read_pcd(pcdfile):
+    p = pcl.load(pcdfile)
+    np_arr = filter_pcl(p)
+    return np_arr
+
+
+if __name__ == "__main__":
+    # filtered_data = read_pc2_msgs('/home/jcsanto3/bagfile-data/groundtruth/run5_precut.bag')
+    concat_ground_truth_msgs("./groundtruth_run5.bag")
diff --git a/anomaly/gmm-change-detection/scripts/saved_models/fake_data_1.pk b/anomaly/gmm-change-detection/scripts/saved_models/fake_data_1.pk
new file mode 100644
index 00000000..459643f0
Binary files /dev/null and b/anomaly/gmm-change-detection/scripts/saved_models/fake_data_1.pk differ
diff --git a/anomaly/gmm-change-detection/scripts/saved_models/fake_data_2.pk b/anomaly/gmm-change-detection/scripts/saved_models/fake_data_2.pk
new file mode 100644
index 00000000..8b64a8ef
Binary files /dev/null and b/anomaly/gmm-change-detection/scripts/saved_models/fake_data_2.pk differ
diff --git a/anomaly/gmm-change-detection/scripts/visualization.py b/anomaly/gmm-change-detection/scripts/visualization.py
new file mode 100644
index 00000000..c47adf2e
--- /dev/null
+++ b/anomaly/gmm-change-detection/scripts/visualization.py
@@ -0,0 +1,85 @@
+# Based on the GMM visualization code:
+#    https://github.com/sitzikbs/gmm_tutorial/blob/master/visualization.py
+# with modifications to accomodate using the model data
+import matplotlib.cm as cmx
+import matplotlib.pyplot as plt
+import numpy as np
+from matplotlib.axes._axes import _log as matplotlib_axes_logger
+from mpl_toolkits.mplot3d import Axes3D
+
+# Suppress matplotlib warnings
+matplotlib_axes_logger.setLevel("ERROR")
+
+
+def plot_sphere(w=0, c=[0, 0, 0], r=[1, 1, 1], subdev=10, ax=None, sigma_multiplier=3):
+    """
+    plot a sphere surface
+    Input:
+        c: 3 elements list, sphere center
+        r: 3 element list, sphere original scale in each axis ( allowing to draw elipsoids)
+        subdiv: scalar, number of subdivisions (subdivision^2 points sampled on the surface)
+        ax: optional pyplot axis object to plot the sphere in.
+        sigma_multiplier: sphere additional scale (choosing an std value when plotting gaussians)
+    Output:
+        ax: pyplot axis object
+    """
+
+    if ax is None:
+        fig = plt.figure()
+        ax = fig.add_subplot(111, projection="3d")
+    pi = np.pi
+    cos = np.cos
+    sin = np.sin
+    phi, theta = np.mgrid[
+        0.0 : pi : complex(0, subdev), 0.0 : 2.0 * pi : complex(0, subdev)
+    ]
+    x = sigma_multiplier * r[0] * sin(phi) * cos(theta) + c[0]
+    y = sigma_multiplier * r[1] * sin(phi) * sin(theta) + c[1]
+    z = sigma_multiplier * r[2] * cos(phi) + c[2]
+    cmap = cmx.ScalarMappable()
+    cmap.set_cmap("jet")
+    c = cmap.to_rgba(w)
+
+    ax.plot_surface(x, y, z, color=c, alpha=0.2, linewidth=1)
+
+    return ax
+
+
+def visualize_3d_gmm(points, predictions, w, mu, stdev, n_gaussians, axes=None):
+    """
+    plots points and their corresponding gmm model in 3D
+    Input:
+        points: N X 3, sampled points
+        w: n_gaussians, gmm weights
+        mu: 3 X n_gaussians, gmm means
+        stdev: 3 X n_gaussians, gmm standard deviation (assuming diagonal covariance matrix)
+    Output:
+        None
+    """
+
+    N = int(np.round(points.shape[0] / n_gaussians))
+
+    # Visualize data
+    axes = axes or plt.gca()
+    plt.set_cmap("Set1")
+    colors = cmx.Set1(np.linspace(0, 1, n_gaussians))
+    for i in range(n_gaussians):
+        idx = np.where(predictions == i)
+        axes.scatter(
+            points[idx, 0],
+            points[idx, 1],
+            points[idx, 2],
+            alpha=0.07,
+            c=colors[i],
+            s=0.7,
+        )
+
+    for i in range(w.shape[0]):
+        plot_sphere(w=w[i], c=mu[:, i], r=stdev[:, i], ax=axes)
+
+    return axes
+    # plt.title('3D GMM')
+    # axes.set_xlabel('X')
+    # axes.set_ylabel('Y')
+    # axes.set_zlabel('Z')
+    # plt.show()
diff --git a/anomaly/readme.md b/anomaly/readme.md
index 5f86a5ca..31f15e39 100644
--- a/anomaly/readme.md
+++ b/anomaly/readme.md
@@ -8,3 +8,4 @@ Anomaly Detectors
 \subpage semantic_anomaly
 \subpage image_anomaly
 \subpage volumetric_anomaly
+\subpage gmm