fuzzy_train_data, _ = cmeans_predict(train_data.iloc[:, :-1].values.T, cntr, 2, error=0.005, maxiter=1000)另一种写法
时间: 2023-06-14 11:02:36 浏览: 56
另一种写法可以使用 numpy 库中的 `fuzzy.cmeans_predict()` 方法,代码如下:
```python
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from scipy.spatial import distance
from scipy.cluster.vq import vq, kmeans, whiten
from scipy.cluster.vq import vq, kmeans2
from scipy.cluster.vq import whiten
from scipy.cluster.vq import kmeans, kmeans2, whiten
from scipy.cluster.vq import vq, kmeans, whiten
from scipy.cluster.vq import kmeans, whiten, vq
from scipy.cluster.vq import whiten, kmeans, vq
from scipy.cluster.vq import vq, whiten, kmeans2
from scipy.cluster.vq import whiten, vq, kmeans2
from scipy.cluster.vq import kmeans2, whiten, vq
from scipy.cluster.vq import kmeans2, vq, whiten
from scipy.cluster.vq import vq, kmeans, whiten, kmeans2
# 生成随机数据
np.random.seed(0)
train_data, _ = make_blobs(n_samples=150, centers=3, n_features=2, random_state=0)
# KMeans 聚类
kmeans = KMeans(n_clusters=3, random_state=0).fit(train_data)
# 计算每个数据点的隶属度
cntr, u, u0, d, jm, p, fpc = fuzz.cluster.cmeans(
train_data.T, 3, 2, error=0.005, maxiter=1000, init=None)
# 另一种写法
fuzzy_train_data, _ = fuzzy.cmeans_predict(train_data.T, cntr, 2, error=0.005, maxiter=1000)
```