diff --git a/zad3/generate_points.py b/zad3/generate_points.py
new file mode 100644
index 0000000..e2b8a23
--- /dev/null
+++ b/zad3/generate_points.py
@@ -0,0 +1,12 @@
+from math import pi, cos, sin, sqrt
+from random import random
+from typing import Tuple
+
+
+def get_random_point(center: Tuple[float, float], radius: float) -> Tuple[float, float]:
+    shift_x, shift_y = center
+
+    a = random() * 2 * pi
+    r = radius * sqrt(random())
+
+    return r * cos(a) + shift_x, r * sin(a) + shift_y
diff --git a/zad3/zad3.py b/zad3/zad3.py
new file mode 100644
index 0000000..154f2db
--- /dev/null
+++ b/zad3/zad3.py
@@ -0,0 +1,162 @@
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+from random import sample
+from generate_points import get_random_point
+import numpy as np
+
+
+def get_color(i):
+    return plt.get_cmap('tab20')(i)
+
+
+def get_data1():
+    data = []
+    for _ in range(200):
+        data.append(get_random_point((0, 0), 1))
+    return data
+
+
+def get_data2():
+    data = []
+    for i in range(2):
+        for _ in range(100):
+            data.append(get_random_point((3*((-1)**i), 0), 0.5))
+    return data
+
+
+def plot_data(data):
+    lst_x, lst_y = zip(*data)
+    lst_x = list(lst_x)
+    lst_y = list(lst_y)
+    plt.figure(1)
+    ax = plt.axes()
+    ax.scatter(lst_x, lst_y)
+    ax.set_xlabel('X')
+    ax.set_ylabel('Y')
+    plt.grid(True)
+    plt.show()
+
+
+def plot_kmeans(all_data, k):
+    fig, ax = plt.subplots()
+    ax.set_xlabel('X')
+    ax.set_ylabel('Y')
+    ax.set_title(f'k={k}')
+    time_text = ax.text(0.05, 0.95, 'iter=0', horizontalalignment='left',
+                        verticalalignment='top', transform=ax.transAxes)
+    plt.grid(True)
+    centroid_scatters = []
+    cluster_scatters = []
+    centroids, clusters = all_data[0]
+    for key in clusters:
+        lst_x, lst_y = zip(*clusters[key])
+        lst_x = list(lst_x)
+        lst_y = list(lst_y)
+        color = get_color(key/k)
+        cluster_scatters.append(ax.scatter(lst_x, lst_y, color=color))
+        centroid_scatters.append(ax.scatter([centroids[key][0]], [
+                                 centroids[key][1]], color=color, marker='X'))
+
+    def update_plot_kmeans(i):
+        centroids, clusters = all_data[i]
+        time_text.set_text(f'iter={i}')
+        for key in clusters:
+            centroid_scatters[key].set_offsets(centroids[key])
+            cluster_scatters[key].set_offsets(clusters[key])
+        return centroid_scatters+cluster_scatters+[time_text, ]
+    anim = FuncAnimation(fig, update_plot_kmeans,
+                         frames=len(all_data), blit=True)
+    # anim.save('animation.mp4')
+
+    plt.show()
+
+
+def calc_length(a, b):
+    # return ((b[0]-a[0])**2+(b[1]-a[1])**2)**0.5
+    # no need to calculate square root for comparison
+    return (b[0]-a[0])**2+(b[1]-a[1])**2
+
+
+def init_centroids(data, k, method='forgy'):
+    match method:
+        case 'forgy':
+            return sample(data, k)
+        case _:
+            raise NotImplementedError(
+                f'method {method} is not implemented yet')
+
+
+def calc_error(centroids, clusters, k):
+    squared_errors = []
+    for i in range(k):
+        cluster = np.array(clusters[i])
+        centroid = np.array([centroids[i] for _ in range(len(cluster))])
+        errors = centroid - cluster
+        squared_errors.append([e**2 for e in errors])
+    return sum([np.mean(err) for err in squared_errors])
+
+
+def plot_error_data(error_data):
+    fig, ax = plt.subplots()
+    ax.set_xlabel('k')
+    ax.set_ylabel('err')
+    ax.set_xlim(2, 20)
+    plt.title('Errors')
+    plt.grid(True)
+
+    lst_x, lst_y = zip(*error_data)
+    lst_x = list(lst_x)
+    lst_y = list(lst_y)
+    ax.plot(lst_x, lst_y, 'ro-')
+
+    plt.show()
+
+
+def main():
+    for get_data in [get_data1, get_data2]:
+        data = get_data()
+        plot_data(data)
+        kmeans_data = {}
+        for k in range(2, 21):
+            kmeans_with_err = []
+            for _ in range(10):
+                all_data = []
+                centroids = init_centroids(data, k)
+                clusters = {}
+                for i in range(k):
+                    clusters[i] = []
+                for point in data:
+                    lengths = [calc_length(c, point) for c in centroids]
+                    index_min = np.argmin(lengths)
+                    clusters[index_min].append(point)
+                all_data.append((list(centroids), clusters))
+                for _ in range(100):
+                    for key in clusters:
+                        if clusters[key]:
+                            centroids[key] = np.mean(clusters[key], axis=0)
+                    clusters = {}
+                    for i in range(k):
+                        clusters[i] = []
+                    for point in data:
+                        lengths = [calc_length(c, point) for c in centroids]
+                        index_min = np.argmin(lengths)
+                        clusters[index_min].append(point)
+                    all_data.append((list(centroids), clusters))
+                    if all([all(np.isclose(all_data[-1][0][i], all_data[-2][0][i])) for i in range(k)]):
+                        break
+                err = calc_error(centroids, clusters, k)
+                kmeans_with_err.append((all_data, err))
+            min_err = kmeans_with_err[0][1]
+            kmeans = kmeans_with_err[0][0]
+            for temp_kmeans, err in kmeans_with_err:
+                if err < min_err:
+                    min_err = err
+                    kmeans = temp_kmeans
+            kmeans_data[k] = (kmeans, min_err)
+            plot_kmeans(kmeans, k)
+        error_data = [[i, kmeans_data[i][1]] for i in range(2, 21, 2)]
+        plot_error_data(error_data)
+
+
+if __name__ == '__main__':
+    main()