diff --git a/datasets/data1.csv b/datasets/data1.csv
new file mode 100644
index 0000000..fc64cdb
--- /dev/null
+++ b/datasets/data1.csv
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diff --git a/datasets/data2.csv b/datasets/data2.csv
new file mode 100644
index 0000000..fd89ff2
--- /dev/null
+++ b/datasets/data2.csv
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diff --git a/datasets/data3.csv b/datasets/data3.csv
new file mode 100644
index 0000000..146fe3f
--- /dev/null
+++ b/datasets/data3.csv
@@ -0,0 +1,1001 @@
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diff --git a/datasets/data4.csv b/datasets/data4.csv
new file mode 100644
index 0000000..97da6fa
--- /dev/null
+++ b/datasets/data4.csv
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diff --git a/datasets/data5.csv b/datasets/data5.csv
new file mode 100644
index 0000000..adacbe0
--- /dev/null
+++ b/datasets/data5.csv
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diff --git a/example.py b/example.py
new file mode 100644
index 0000000..ce8ba90
--- /dev/null
+++ b/example.py
@@ -0,0 +1,32 @@
+from mean_shift.mean_shift import MeanShift
+import numpy as np
+
+
+def save_input(file_name):
+    with open(file_name, "r") as f:
+        input_string = f.read()
+    return input_string.splitlines()[1:]
+
+
+def main():
+    # Read from input file and convert to numpy.ndarray
+    file_name = "datasets/data1.csv"
+    input_data = save_input(file_name)
+    dataset = np.array([list(map(float, line.split(","))) for line in input_data])
+
+    # To apply mean shift cluster to given dataset
+    mean_shifter = MeanShift(kernel="flat", kernel_parameters=[3], data_type="circular-linear")
+    mean_shifter.fit(dataset)
+
+    # To get cluster centers and labels for all samples in dataset
+    cluster_centers, labels = mean_shifter.cluster_centers_, mean_shifter.labels_
+
+    # To get cluster(mode) information
+    cluster_info = mean_shifter.get_cluster_info(cluster_centers[0])
+
+    # To predict the clusters of a dataset
+    cluster_res = mean_shifter.predict(dataset)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/mean_shift/__init__.py b/mean_shift/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mean_shift/exceptions.py b/mean_shift/exceptions.py
new file mode 100644
index 0000000..0dcbbba
--- /dev/null
+++ b/mean_shift/exceptions.py
@@ -0,0 +1,8 @@
+class NotFittedError(Exception):
+    # Exception class to raise if mean-shift is used before fitting.
+    pass
+
+
+class KernelInputError(ValueError):
+    # Exception class to raise if given kernel info is not correct.
+    pass
diff --git a/mean_shift/mean_shift.py b/mean_shift/mean_shift.py
new file mode 100644
index 0000000..647c1f3
--- /dev/null
+++ b/mean_shift/mean_shift.py
@@ -0,0 +1,223 @@
+import math
+from typing import List, Tuple, Any
+
+import numpy as np
+
+from joblib import Parallel  # type: ignore
+from sklearn.utils.fixes import delayed  # type: ignore
+
+from mean_shift.validate import validate_fitted_instance, validate_data, validate_kernel_inputs
+from mean_shift import utils
+
+
+class MeanShift:
+    dataset_: np.ndarray
+    cluster_centers_: np.ndarray
+    labels_: np.ndarray
+    n_iter_: int
+
+    def __init__(
+            self,
+            *,
+            kernel: str = "flat",
+            kernel_parameters: List[float] = None,
+            max_iter: int = 300,
+            data_type: str = "circular_linear",
+            cluster_all: bool = True,
+    ) -> None:
+        self.kernel = kernel
+        self.kernel_parameters = kernel_parameters if kernel_parameters else []
+        self.max_iter = max_iter
+        self.data_type = data_type
+        self.cluster_all = cluster_all
+
+    def fit(self, dataset: np.ndarray) -> None:
+        # Perform clustering.
+        #
+        # Parameters
+        # ----------
+        # dataset : array-like of shape (n_samples, n_features)
+        #     Samples to cluster.
+        #
+        # Returns
+        # -------
+        # None
+        validate_kernel_inputs(self.kernel, self.kernel_parameters)
+        self.dataset_ = validate_data(dataset, self.data_type)
+
+        # Execute iterations on all seeds.
+        seeds = self._generate_seeds()
+
+        # all_res = [self._mean_shift_single_seed(seed) for seed in seeds]
+        all_res = Parallel()(delayed(self._mean_shift_single_seed)(seed) for seed in seeds)
+
+        # copy results in a dictionary
+        center_intensity_dict = {}
+        for i in range(len(seeds)):
+            if all_res[i][1]:  # i.e. len(points_within) > 0
+                center_intensity_dict[all_res[i][0]] = all_res[i][1]
+
+        self.n_iter_ = max([x[2] for x in all_res])
+
+        if not center_intensity_dict:
+            # nothing near seeds
+            raise ValueError(
+                "No point was within bandwidth=%f of any seed. Try a different seeding"
+                " strategy or increase the bandwidth." % self.kernel_parameters[0]
+            )
+
+        # POST PROCESSING: remove near duplicate points
+        # If the distance between two kernels is less than the bandwidth,
+        # then we have to remove one because it is a duplicate. Remove the
+        # one with fewer points.
+        sorted_by_intensity = sorted(
+            center_intensity_dict.items(),
+            key=lambda tup: (tup[1], tup[0]),
+            reverse=True,
+        )
+        sorted_centers = np.array([tup[0] for tup in sorted_by_intensity])
+        cluster_centers_list = [sorted_centers[0]]
+
+        if self.kernel in ["flat", "truncated_gaussian"]:
+            bandwidth = self.kernel_parameters[0]
+        else:
+            bandwidth = math.sqrt(1 / (2 * self.kernel_parameters[0]))
+        for sorted_center in sorted_centers[1:]:
+            if any(utils.get_euclidean_distance(x, sorted_center) < bandwidth
+                   for x in cluster_centers_list):
+                continue
+            cluster_centers_list.append(sorted_center)
+        cluster_centers = np.round(np.array(cluster_centers_list), 2)
+
+        # ASSIGN LABELS: a point belongs to the cluster that it is closest to
+        n_samples = dataset.shape[0]
+        labels = np.zeros(n_samples, dtype=int)
+        distances, indices = self._get_nearest_cluster_center_and_distance(cluster_centers)
+        if self.cluster_all:
+            labels = indices.flatten()
+        else:
+            labels.fill(-1)
+            bool_selector = distances.flatten() <= self.kernel_parameters[0]
+            labels[bool_selector] = indices.flatten()[bool_selector]
+
+        self.cluster_centers_, self.labels_ = cluster_centers, labels
+
+    def predict(self, dataset: np.ndarray) -> np.ndarray:
+        # Predict the closest cluster each sample in X belongs to.
+        #
+        # Parameters
+        # ----------
+        # dataset : array-like of shape (n_samples, n_features)
+        #     New data to predict.
+        #
+        # Returns
+        # -------
+        # labels : ndarray of shape (n_samples,)
+        #     Index of the cluster each sample belongs to.
+        validate_fitted_instance(self)
+        dataset = validate_data(dataset, self.data_type)
+        indices = np.array(
+            [np.argmin([utils.get_euclidean_distance(sample, cluster_center)  # type: ignore
+                        for cluster_center in self.cluster_centers_]) for sample in dataset])
+
+        return indices
+
+    def get_cluster_info(
+            self, cluster_center: np.ndarray
+    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        # In the given dataset, get information of one cluster
+        #
+        # Parameters
+        # ----------
+        # cluster_center : ndarray of shape
+        #     Position of cluster center
+        #
+        # Returns
+        # -------
+        # cluster_info : tuple
+        #     (mean, variance, covariance)
+        validate_fitted_instance(self)
+        index = np.where(np.all(self.cluster_centers_ == cluster_center, axis=1))[0]
+        if not len(index):
+            raise ValueError("The given cluster center does not exist.")
+        data = self.dataset_[self.labels_ == index]
+
+        mean = np.mean(data, axis=0)
+        variance = np.var(data, axis=0)
+        cov = np.cov(data)
+
+        return mean, variance, cov
+
+    def _generate_seeds(self) -> np.ndarray:
+        seeds = self.dataset_
+        return seeds
+
+    def _mean_shift_single_seed(self, seed: np.ndarray) -> Tuple[Any, int, int]:
+        # A separate function for each seed's iterative loop
+        # For each seed, climb gradient until convergence or max_iter
+        # convergence_threshold = 1e-3 * self.kernel_parameters[0]  # when mean has converged
+        convergence_threshold = 1e-6
+        # params initialization
+        completed_iterations = 0
+        current_mean = seed
+        mean_shift = np.inf
+
+        while mean_shift > convergence_threshold and completed_iterations < self.max_iter:
+            previous_mean = current_mean
+            current_mean = self._get_mean_value(current_mean)
+            mean_shift = np.linalg.norm(current_mean - previous_mean)
+            completed_iterations += 1
+
+        neighbor_count = self._get_nearest_neighbor_by_radius(current_mean)
+
+        return tuple(current_mean), neighbor_count, completed_iterations
+
+    def _get_mean_value(self, cluster_center: np.ndarray) -> np.ndarray:
+        kernel_res = self._apply_kernel(cluster_center)
+        mean = np.dot(kernel_res, self.dataset_) / sum(kernel_res)
+
+        return mean
+
+    def _apply_kernel(self, cluster_center: np.ndarray) -> np.ndarray:
+        # Get weighted points based on kernel options
+        if self.kernel == "flat":
+            return utils.get_flat_kernel(cluster_center, self.dataset_, self.kernel_parameters)
+        elif self.kernel == "gaussian":
+            return utils.get_gaussian_kernel(cluster_center, self.dataset_, self.kernel_parameters)
+        else:  # self.kernel == "truncated_gaussian":
+            return utils.get_truncated_gaussian_kernel(
+                cluster_center, self.dataset_, self.kernel_parameters
+            )
+
+    def _get_nearest_neighbor_by_radius(self, cluster_center: np.ndarray) -> int:
+        # Return numbers of neighbors near cluster center.
+        # For flat and truncated Gaussian kernels, bandwidth(lambda) is used as radius
+        # For Gaussian kernel, sigma = sqrt(1/2*beta), is used as radius
+        if self.kernel in ["flat", "truncated_gaussian"]:
+            bandwidth = self.kernel_parameters[0]
+        else:
+            bandwidth = math.sqrt(1 / (2 * self.kernel_parameters[0]))
+        neighbor_count = sum(map(lambda x:
+                                 utils.get_euclidean_distance(x, cluster_center) <
+                                 bandwidth,
+                                 self.dataset_)
+                             )
+
+        return int(neighbor_count)
+
+    def _get_nearest_cluster_center_and_distance(
+            self, cluster_centers: np.ndarray
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        # Assign samples to the nearest cluster
+        distances, indices = [], []
+
+        for sample in self.dataset_:
+            distances = [
+                utils.get_euclidean_distance(sample, cluster_center)
+                for cluster_center in cluster_centers
+            ]
+            min_index = np.argmin(distances)
+            distances.append(distances[min_index])
+            indices.append(min_index)
+
+        return np.array(distances), np.array(indices)
diff --git a/mean_shift/utils.py b/mean_shift/utils.py
new file mode 100644
index 0000000..a33df57
--- /dev/null
+++ b/mean_shift/utils.py
@@ -0,0 +1,39 @@
+from typing import List
+
+import numpy as np
+
+
+def get_euclidean_distance(sample1: np.ndarray, sample2: np.ndarray) -> float:
+    if len(sample1) != len(sample2):
+        raise ValueError("Feature numbers of given samples are not equal.")
+
+    return np.linalg.norm(sample1 - sample2)
+
+
+def get_flat_kernel(
+        cluster_center: np.ndarray, dataset: np.ndarray, kernel_parameters: List
+) -> np.ndarray:
+    diff_to_center, bandwidth = cluster_center - dataset, kernel_parameters[0]
+
+    return np.array(
+        [1. if num <= bandwidth else 0. for num in np.sqrt(np.power(diff_to_center, 2).sum(axis=1))]
+    )
+
+
+def get_gaussian_kernel(
+        cluster_center: np.ndarray, dataset: np.ndarray, kernel_parameters: List
+) -> np.ndarray:
+    diff_to_center, beta = cluster_center - dataset, kernel_parameters[0]
+
+    return np.exp(-1. * beta * np.power(diff_to_center, 2).sum(axis=1))
+
+
+def get_truncated_gaussian_kernel(
+        cluster_center: np.ndarray, dataset: np.ndarray, kernel_parameters: List
+) -> np.ndarray:
+    diff_to_center, bandwidth, beta = \
+        cluster_center - dataset, kernel_parameters[0], kernel_parameters[1]
+    norm_square = np.power(diff_to_center, 2).sum(axis=1)
+    gaussian, bool_selector = np.exp(-1. * beta * norm_square), np.sqrt(norm_square) <= bandwidth
+
+    return np.multiply(gaussian, bool_selector)
diff --git a/mean_shift/validate.py b/mean_shift/validate.py
new file mode 100644
index 0000000..af6863d
--- /dev/null
+++ b/mean_shift/validate.py
@@ -0,0 +1,77 @@
+from typing import List, Type
+
+import numpy as np
+from mean_shift.exceptions import NotFittedError, KernelInputError
+
+
+def validate_data(dataset: np.ndarray, data_type: str) -> np.ndarray:
+    # Validate input circular-linear data and convert from polar to cartesian coordinates.
+    #
+    # Parameters
+    # ----------
+    # dataset : array-like of shape (n_samples, n_features)
+    #     The input samples.
+    #
+    # Returns
+    # -------
+    # out : (n_samples, n_features)
+    #     The validated input. The output data is in cartesian coordinates.
+    if not isinstance(dataset, np.ndarray) or dataset.shape[1] != 2:
+        raise TypeError(
+            "Could not find input dataset or dataset type is not correct. "
+            "Input dataset type should be numpy.ndarray, and with two features: theta, r"
+        )
+
+    if data_type == "linear":
+        return dataset
+
+    if any(theta <= 0 or theta >= 2 * np.pi for theta in dataset[:, 0]):
+        raise ValueError(
+            "In circular-linear dataset, theta value should be within range [0, 2*pi]"
+        )
+
+    return np.array(
+        [[sample[1] * np.cos(sample[0]), sample[1] * np.sin(sample[0])] for sample in dataset]
+    )
+
+
+def validate_fitted_instance(mean_shift_instance) -> None:
+    if (mean_shift_instance.cluster_centers_ is None) or (mean_shift_instance.labels_ is None):
+        raise NotFittedError(
+            "This instance is not fitted yet. Call 'fit' with "
+            "appropriate arguments before using mean_shift tool."
+        )
+
+
+def validate_kernel_inputs(kernel: str, kernel_parameters: List) -> None:
+    if kernel_parameters is None or not isinstance(kernel_parameters, list):
+        raise KernelInputError(
+            "Could not find Kernel parameter, or input format is wrong. "
+            "Examples: "
+            "kernel='flat', kernel_parameters=[1]"
+            "kernel='gaussian', kernel_parameters=[0.1]"
+            "kernel='truncated_gaussian', kernel_parameters=[1, 0.1]"
+        )
+
+    if kernel in ["flat", "gaussian"] and (
+            not len(kernel_parameters) == 1 or
+            kernel_parameters[0] <= 0):
+        raise KernelInputError(
+            "For flat or Gaussian kernel, parameter needs to be float and greater than zero."
+        )
+
+    if kernel == "truncated_gaussian" and (
+            not len(kernel_parameters) == 2 or
+            any(parameter <= 0 for parameter in kernel_parameters)
+    ):
+        raise KernelInputError(
+            "Given kernel parameter is wrong. For truncated Gaussian kernel, "
+            "two parameters are required: beta and bandwidth."
+            "And parameters needs to be greater than zero, "
+        )
+
+    if kernel not in ["flat", "gaussian", "truncated_gaussian"]:
+        raise KernelInputError(
+            "Given kernel name is not supported. "
+            "Available options are: flat, gaussian and truncated_gaussian"
+        )
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..cc17f43
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,873 @@
+[[package]]
+name = "astroid"
+version = "2.9.0"
+description = "An abstract syntax tree for Python with inference support."
+category = "dev"
+optional = false
+python-versions = "~=3.6"
+
+[package.dependencies]
+lazy-object-proxy = ">=1.4.0"
+typing-extensions = {version = ">=3.10", markers = "python_version < \"3.10\""}
+wrapt = ">=1.11,<1.14"
+
+[[package]]
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+version = "1.4.0"
+description = "Atomic file writes."
+category = "dev"
+optional = false
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+
+[[package]]
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+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
+[package.extras]
+dev = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit"]
+docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
+tests = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface"]
+tests_no_zope = ["cloudpickle", "coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins"]
+
+[[package]]
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+version = "8.0.1"
+description = "Composable command line interface toolkit"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
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+[[package]]
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+
+[[package]]
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+description = "Composable style cycles"
+category = "main"
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+python-versions = ">=3.6"
+
+[[package]]
+name = "fonttools"
+version = "4.28.5"
+description = "Tools to manipulate font files"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=13.0.0)", "xattr"]
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+
+[[package]]
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+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "isort"
+version = "5.10.1"
+description = "A Python utility / library to sort Python imports."
+category = "dev"
+optional = false
+python-versions = ">=3.6.1,<4.0"
+
+[package.extras]
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diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..858e4cd
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,32 @@
+[tool.poetry]
+name = "mean-shift"
+version = "0.1.0"
+description = "Tool used for mean shift clustering"
+authors = ["Yufei Zhu <yufeizhu@kth.se>"]
+
+[tool.poetry.dependencies]
+python = "3.8"
+PyYAML = "5.4.1"
+click = "8.0.1"
+numpy = "1.21.5"
+matplotlib = "3.5.1"
+scikit-learn = "1.0.1"
+joblib = "1.1.0"
+
+[tool.poetry.dev-dependencies]
+mypy = "0.930"
+pytest = "6.2.5"
+pylint = "2.12.2"
+
+[tool.pylint.master]
+max-line-length = 100
+
+[tool.pylint.messages_control]
+disable = "too-many-arguments, missing-docstring"
+
+[tool.mypy]
+check_untyped_defs = true
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
diff --git a/reporter.py b/reporter.py
new file mode 100644
index 0000000..cd3128c
--- /dev/null
+++ b/reporter.py
@@ -0,0 +1,67 @@
+import csv
+
+from mean_shift.mean_shift import MeanShift
+import numpy as np
+import matplotlib.pyplot as plt  # type: ignore
+import matplotlib.colors as mcolors  # type: ignore
+import time
+import random
+
+
+def generate_circular_shape_dataset():
+    with open("datasets/data5.csv", "w", newline="") as csvfile:
+        fieldnames = ['theta', 'r']
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+        writer.writeheader()
+        for i in range(300):
+            writer.writerow({'theta': 2 * np.pi * random.random(), 'r': 8 + 2 * random.random()})
+        for i in range(300):
+            writer.writerow({'theta': 2 * np.pi * random.random(), 'r': 4 + 2 * random.random()})
+
+
+def save_input(file_name):
+    with open(file_name, "r") as f:
+        input_string = f.read()
+    return input_string.splitlines()[1:]
+
+
+def convert_mean(mean):
+    x, y = mean[0], mean[1]
+    return [np.arctan2(y, x), np.sqrt(x ** 2 + y ** 2)]
+
+
+def main():
+    colors = list(mcolors.TABLEAU_COLORS) + list(mcolors.CSS4_COLORS)
+    file_name = "datasets/data1.csv"
+    input_data = save_input(file_name)
+    dataset = np.array([list(map(float, line.split(","))) for line in input_data])
+
+    fig = plt.figure()
+
+    start_time = time.time()
+    mean_shifter = MeanShift(kernel="flat", kernel_parameters=[3], data_type="circular-linear")
+    mean_shifter.fit(dataset)
+    print("--- %s seconds ---" % (time.time() - start_time))
+    centers = mean_shifter.cluster_centers_
+    label = mean_shifter.labels_
+    means = [convert_mean(mean_shifter.get_cluster_info(np.array(center))[0]) for center in centers]
+    n_sample, _ = dataset.shape
+
+    ax = fig.add_subplot(1, 2, 1)
+    polar_ax = fig.add_subplot(1, 2, 2, projection="polar")
+    ax.set_xlim([0, 2*np.pi])
+    ax.set_xticks([0, np.pi, 2*np.pi])
+    ax.set_xticklabels([r'$0$', r'$\pi$', r'$2\pi$'])
+    for i in range(n_sample):
+        ax.plot(dataset[i][0], dataset[i][1], "o", color=colors[label[i]], markersize=3)
+    for i in range(n_sample):
+        polar_ax.plot(dataset[i][0], dataset[i][1], "o", color=colors[label[i]], markersize=3)
+    for mean in means:
+        # ax.plot(mean[0], mean[1], "o", color="r", markersize=4)
+        polar_ax.plot(mean[0], mean[1], "o", color="r", markersize=4)
+
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tests/__pycache__/test_mean_shift.cpython-38-pytest-6.2.5.pyc b/tests/__pycache__/test_mean_shift.cpython-38-pytest-6.2.5.pyc
new file mode 100644
index 0000000..af1bedd
Binary files /dev/null and b/tests/__pycache__/test_mean_shift.cpython-38-pytest-6.2.5.pyc differ
diff --git a/tests/__pycache__/test_utils.cpython-38-pytest-6.2.5.pyc b/tests/__pycache__/test_utils.cpython-38-pytest-6.2.5.pyc
new file mode 100644
index 0000000..e0e9607
Binary files /dev/null and b/tests/__pycache__/test_utils.cpython-38-pytest-6.2.5.pyc differ
diff --git a/tests/__pycache__/test_validate.cpython-38-pytest-6.2.5.pyc b/tests/__pycache__/test_validate.cpython-38-pytest-6.2.5.pyc
new file mode 100644
index 0000000..0575df9
Binary files /dev/null and b/tests/__pycache__/test_validate.cpython-38-pytest-6.2.5.pyc differ
diff --git a/tests/test_mean_shift.py b/tests/test_mean_shift.py
new file mode 100644
index 0000000..068c6cc
--- /dev/null
+++ b/tests/test_mean_shift.py
@@ -0,0 +1,45 @@
+import numpy as np
+
+from mean_shift.mean_shift import MeanShift
+
+
+DATASET = np.array([[1, 1], [2, 1], [1, 0], [4, 7], [3, 5], [3, 6]])
+TEST_DATASET = np.array([[0, 0], [2, 2]])
+
+
+def test_flat_kernel():
+    mean_shift_cluster = MeanShift(kernel="flat", kernel_parameters=[2], data_type="linear")
+    mean_shift_cluster.fit(DATASET)
+    result = [mean_shift_cluster.cluster_centers_.tolist(), mean_shift_cluster.labels_.tolist()]
+    expected = [[[3.33, 6.], [1.33, 0.67]], [1, 1, 1, 0, 0, 0]]
+
+    assert result == expected
+
+
+def test_gaussian_kernel():
+    mean_shift_cluster = MeanShift(kernel="gaussian", kernel_parameters=[0.25], data_type="linear")
+    mean_shift_cluster.fit(DATASET)
+    result = [mean_shift_cluster.cluster_centers_.tolist(), mean_shift_cluster.labels_.tolist()]
+    expected = [[[3.27, 5.95], [1.33, 0.69]], [1, 1, 1, 0, 0, 0]]
+
+    assert result == expected
+
+
+def test_truncated_gaussian_kernel():
+    mean_shift_cluster = MeanShift(
+        kernel="truncated_gaussian", kernel_parameters=[1, 0.25], data_type="linear"
+    )
+    mean_shift_cluster.fit(DATASET)
+    result = [mean_shift_cluster.cluster_centers_.tolist(), mean_shift_cluster.labels_.tolist()]
+    expected = [[[1.32, 0.68], [3., 5.5], [4., 7.]], [0, 0, 0, 2, 1, 1]]
+
+    assert result == expected
+
+
+def test_mean_shift_cluster_predict():
+    mean_shift_cluster = MeanShift(kernel="flat", kernel_parameters=[2], data_type="linear")
+    mean_shift_cluster.fit(DATASET)
+    result = mean_shift_cluster.predict(TEST_DATASET).tolist()
+    expected = [1, 1]
+
+    assert result == expected
diff --git a/tests/test_utils.py b/tests/test_utils.py
new file mode 100644
index 0000000..d33a63e
--- /dev/null
+++ b/tests/test_utils.py
@@ -0,0 +1,34 @@
+import numpy as np
+
+from mean_shift import utils
+
+
+DATASET = np.array([[1, 1], [2, 1], [1, 0], [4, 7], [3, 5], [3, 6]])
+
+
+def test_get_euclidean_distance():
+    result = np.round(utils.get_euclidean_distance(np.array([1,1]), np.array([2,2])), 2)
+    expected = 1.41
+
+    assert result == expected
+
+
+def test_get_flat_kernel():
+    result = utils.get_flat_kernel(np.array([1, 1]), DATASET, [2]).tolist()
+    expected = [1., 1., 1., 0., 0., 0.]
+
+    assert result == expected
+
+
+def test_get_gaussian_kernel():
+    result = np.round(utils.get_gaussian_kernel(np.array([1, 1]), DATASET, [0.25]), 2).tolist()
+    expected = [1., 0.78, 0.78, 0., 0.01, 0.]
+
+    assert result == expected
+
+
+def test_get_truncated_gaussian_kernel():
+    result = np.round(utils.get_truncated_gaussian_kernel(np.array([3, 6]), DATASET, [2, 0.25]), 2).tolist()
+    expected = [0., 0., 0., 0.61, 0.78, 1.]
+
+    assert result == expected
diff --git a/tests/test_validate.py b/tests/test_validate.py
new file mode 100644
index 0000000..c5def0a
--- /dev/null
+++ b/tests/test_validate.py
@@ -0,0 +1,13 @@
+import numpy as np
+
+from mean_shift.validate import validate_data
+
+
+DATASET = np.array([[0, 1], [np.pi, 1], [-1.2, 4]])
+
+
+def test_validate_data():
+    result = np.round(validate_data(DATASET, data_type="linear"), 2).tolist()
+    expected = [[0., 1.], [3.14, 1.], [-1.2, 4.]]
+
+    assert result == expected