lab11/lab11.py

@@ -1,104 +0,0 @@
-import math
-import random
-import numpy as np
-import matplotlib.pyplot as plt
-
-
-def get_distance(first: np.ndarray, second: np.ndarray) -> float:
-    return math.sqrt(sum([(first[i] - second[i]) ** 2 for i in range(first.shape[0])])) + 1e-5
-
-
-def affiliation_calculation(data: np.ndarray, centers: np.ndarray, k: int, m: int) -> np.ndarray:
-    data_len = data.shape[0]
-    u = np.zeros((data_len, k))
-    for i in range(data_len):
-        for j in range(k):
-            total = 0
-            distance = get_distance(data[i], centers[j])
-            for c in range(k):
-                total += (distance / get_distance(data[i], centers[c])) ** (2 / (m - 1))
-            u[i, j] = 1 / total
-    return u
-
-
-def variance_calculation(data: np.ndarray, centers: np.ndarray, u: np.ndarray) -> float:
-    value = 0
-    for j in range(k):
-        for i in range(data.shape[0]):
-            value += get_distance(data[i], centers[j]) ** 2 * u[i, j]
-    return value
-
-
-def center_update(data: np.ndarray, u: np.ndarray, k: int, m: int) -> np.ndarray:
-    centers = np.zeros((k, data.shape[1]))
-    for j in range(k):
-        total = 0
-        for i in range(data.shape[0]):
-            total += u[i, j] ** m * data[i]
-        centers[j] = total / np.sum(u[:, j] ** m)
-    return centers
-
-
-def fuzzy_c_means(data: np.ndarray, k: int, m: int, max_iter: int = 100, tol: float = 1e-5) -> (
-        np.ndarray, np.ndarray, float):
-    centers = np.array([[random.randint(data.min(), data.max()) for i in range(data.shape[1])] for j in range(k)])
-    u = None
-
-    value = 0
-
-    for _ in range(max_iter):
-        u = affiliation_calculation(data, centers, k, m)
-
-        new_value = variance_calculation(data, centers, u)
-        if abs(new_value - value) <= tol:
-            return centers, u, value
-
-        value = new_value
-
-        centers = center_update(data, u, k, m)
-
-    return centers, u, value
-
-
-def visualise_resout(centers: np.ndarray, u: np.ndarray):
-    center_colors = [[random.random(), random.random(), random.random()] for i in range(k)]
-    point_colors = []
-    for i in u:
-        tmp_color = [0, 0, 0]
-        for j in range(k):
-            tmp_color[0] += center_colors[j][0] * i[j]
-            tmp_color[1] += center_colors[j][1] * i[j]
-            tmp_color[2] += center_colors[j][1] * i[j]
-        point_colors.append(tmp_color)
-
-    plt.title("Нечёткая кластеризация")
-    plt.xlabel("Размер зарплаты")
-
-    if data.shape[1] == 1:
-        plt.scatter(data[:, 0], [0] * data.shape[0], c=point_colors)
-        plt.scatter(centers[:, 0], [0] * centers.shape[0], marker='*', edgecolor='black', s=100, c=center_colors)
-        plt.gca().axes.get_yaxis().set_visible(False)
-    else:
-        plt.scatter(data[:, 0], data[:, 1], c=point_colors)
-        plt.scatter(centers[:, 0], centers[:, 1], marker='*', edgecolor='black', s=100, c=center_colors)
-    plt.show()
-
-
-if __name__ == '__main__':
-    data: np.ndarray = np.array(
-        [
-            [
-                random.randint(0, 500)
-            ]
-            for i in range(random.randint(40, 100))
-        ])
-    k = 3
-    m = 2
-
-    centers, u, value = fuzzy_c_means(data, k, m)
-    print(f"Значение функции отклонений: {value}")
-    print("Степени принадлежности первых 10 точек:")
-    print(*u[:10], sep="\n")
-    print("Центры всех кластеров:")
-    print(*centers, sep="\n")
-    visualise_resout(centers, u)