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Author SHA1 Message Date
ca3fdf03ca Лаба 11 сдана 2024-04-26 22:02:20 +04:00
d75f598425 Лаба 11 сдана 2024-04-26 22:01:48 +04:00

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lab11/lab11.py Normal file
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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)