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Лаба 11 сдана

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Данила Мочалов 2024-04-26 22:02:20 +04:00
parent d75f598425
commit ca3fdf03ca
1 changed files with 0 additions and 5 deletions
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lab11/lab11.py
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@ -8,7 +8,6 @@ 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 return math.sqrt(sum([(first[i] - second[i]) ** 2 for i in range(first.shape[0])])) + 1e-5
# Расчёт степени принадлежности
def affiliation_calculation(data: np.ndarray, centers: np.ndarray, k: int, m: int) -> np.ndarray: def affiliation_calculation(data: np.ndarray, centers: np.ndarray, k: int, m: int) -> np.ndarray:
data_len = data.shape[0] data_len = data.shape[0]
u = np.zeros((data_len, k)) u = np.zeros((data_len, k))
@ -22,7 +21,6 @@ def affiliation_calculation(data: np.ndarray, centers: np.ndarray, k: int, m: in
return u return u
# Расчёт отклонения
def variance_calculation(data: np.ndarray, centers: np.ndarray, u: np.ndarray) -> float: def variance_calculation(data: np.ndarray, centers: np.ndarray, u: np.ndarray) -> float:
value = 0 value = 0
for j in range(k): for j in range(k):
@ -31,7 +29,6 @@ def variance_calculation(data: np.ndarray, centers: np.ndarray, u: np.ndarray) -
return value return value
# Обновление центров кластеров
def center_update(data: np.ndarray, u: np.ndarray, k: int, m: int) -> np.ndarray: def center_update(data: np.ndarray, u: np.ndarray, k: int, m: int) -> np.ndarray:
centers = np.zeros((k, data.shape[1])) centers = np.zeros((k, data.shape[1]))
for j in range(k): for j in range(k):
@ -63,7 +60,6 @@ def fuzzy_c_means(data: np.ndarray, k: int, m: int, max_iter: int = 100, tol: fl
return centers, u, value return centers, u, value
# Работа с plt для визуализации результата
def visualise_resout(centers: np.ndarray, u: np.ndarray): def visualise_resout(centers: np.ndarray, u: np.ndarray):
center_colors = [[random.random(), random.random(), random.random()] for i in range(k)] center_colors = [[random.random(), random.random(), random.random()] for i in range(k)]
point_colors = [] point_colors = []
@ -78,7 +74,6 @@ def visualise_resout(centers: np.ndarray, u: np.ndarray):
plt.title("Нечёткая кластеризация") plt.title("Нечёткая кластеризация")
plt.xlabel("Размер зарплаты") plt.xlabel("Размер зарплаты")
# Визуализация
if data.shape[1] == 1: if data.shape[1] == 1:
plt.scatter(data[:, 0], [0] * data.shape[0], c=point_colors) 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.scatter(centers[:, 0], [0] * centers.shape[0], marker='*', edgecolor='black', s=100, c=center_colors)