关于使用Python进行Kmeans聚类,我有两个问题。
我有一个名为Mystery.npy的自动生成的数据,其形状为(30309,784)。我正在尝试在其上应用KMeans群集,但是出现以下错误:
valueerror: the truth value of an array with more than one element is ambiguous. use a.any() or a.all()
您是否知道如何克服此错误,或者如何使用KMeans方法将此类数据聚类?
第二个问题,是否有某些代码可以知道我拥有的数据类型?
非常感谢您的协助。 谢谢,
答案 0 :(得分:3)
@Nael Alsaleh,您可以通过以下方式运行K-Means:
from sklearn.cluster import KMeans
import numpy as np
import matplotlib.pyplot as plt
X=np.load('Mistery.npy')
wx = []
for i in range(1, 11):
kmeans = KMeans(n_clusters = i, random_state = 0)
kmeans.fit(X)
wx.append(kmeans.inertia_)
plt.plot(range(1, 11), wx)
plt.xlabel('Number of clusters')
plt.ylabel('Variance Explained')
plt.show()
请注意,X
是一个numpy数组。该代码将创建肘部曲线,您可以在其中选择理想的簇数,在这种情况下为5-6。
如果使用numpy,将有一个数组:
array([0.86992608, 0.11252552, 0.25573737, ..., 0.32652233, 0.14927118,
0.1662449 ])
您也可能正在使用列表
[0.86992608, 0.11252552, 0.25573737, ..., 0.32652233, 0.14927118,
0.1662449 ]
您将需要转换为array
:np.array(X)
,甚至是Pandas
数据框:
您可以通过执行以下操作来检查Pandas
数据框中的列类型:
import pandas as pd
pd.DataFrame(X).dtypes
在numpy
中,x.dtype
将数据转换为数组后,运行:
n=5
kmeans=KMeans(n_clusters=n, random_state=20).fit(X)
labels_of_clusters = kmeans.fit_predict(X)
这将为您提供每个示例所属的群集类的编号。
array([1, 4, 0, 0, 4, 1, 4, 0, 2, 0, 0, 4, 3, 1, 4, 2, 2, 3, 0, 1, 1, 0,
4, 4, 2, 0, 3, 0, 3, 1, 1, 2, 1, 0, 2, 4, 0, 3, 2, 1, 1, 2, 2, 2,
2, 0, 0, 4, 1, 3, 1, 0, 1, 4, 1, 0, 0, 0, 2, 0, 1, 2, 2, 1, 2, 2,
0, 4, 4, 4, 4, 3, 1, 2, 1, 2, 2, 1, 1, 3, 4, 3, 3, 1, 0, 1, 2, 2,
1, 2, 3, 1, 3, 3, 4, 2, 2, 0, 2, 1, 3, 4, 2, 0, 2, 1, 3, 3, 3, 4,
3, 1, 4, 4, 4, 2, 0, 3, 2, 0, 1, 2, 2, 0, 3, 1, 1, 1, 4, 0, 2, 2,
0, 0, 1, 1, 0, 3, 0, 2, 2, 1, 2, 2, 4, 0, 1, 0, 3, 1, 4, 4, 0, 4,
1, 2, 0, 2, 4, 0, 1, 2, 3, 1, 1, 0, 3, 2, 4, 0, 1, 3, 1, 2, 4, 3,
1, 1, 2, 0, 0, 2, 3, 1, 3, 4, 1, 2, 2, 0, 2, 1, 4, 3, 1, 0, 3, 2,
4, 1, 4, 1, 4, 4, 0, 4, 4, 3, 1, 3, 4, 0, 4, 2, 1, 1, 3, 4, 0, 4,
4, 4, 4, 2, 4, 2, 3, 4, 3, 3, 1, 1, 4, 2, 3, 0, 2, 4])
可视化:
from sklearn.datasets.samples_generator import make_blobs
X, y_true = make_blobs(n_samples=200, centers=4,
cluster_std=0.60, random_state=0)
kmeans = KMeans(n_clusters=4, random_state=0).fit(X)
cc=kmeans.fit_predict(X)
plt.scatter(X[:, 0], X[:, 1], c=cc, s=50, cmap='viridis')
答案 1 :(得分:1)
您可以使用 scikit-learns KMeans模块完成您想做的事情,这是一个使用数据的有效示例:
import numpy as np
from sklearn.cluster import KMeans
# loading your data from .npy-file
mystery = np.load('mystery.npy')
# n_clusters is a hyperparameter set by you
kmeans = KMeans(n_clusters=42, n_jobs=-1).fit(mystery[:1000])
pred = kmeans.predict(mystery[1000:1200])
print(pred)
array([36, 16, 21, 15, 15, 0, 5, 7, 31, 33, 10, 14, 1, 36, 30, 22, 12,
1, 35, 12, 16, 12, 28, 14, 13, 15, 2, 21, 36, 7, 7, 4, 39, 4,
4, 18, 5, 31, 17, 2, 2, 26, 38, 34, 34, 36, 13, 13, 26, 1, 26,
8, 38, 0, 38, 34, 0, 21, 36, 12, 16, 38, 23, 15, 0, 6, 34, 0,
19, 7, 8, 21, 16, 36, 24, 0, 4, 22, 33, 21, 12, 12, 2, 10, 23,
2, 3, 0, 12, 0, 24, 21, 12, 33, 4, 14, 34, 10, 21, 0, 33, 26,
36, 2, 12, 34, 29, 27, 33, 3, 12, 12, 15, 39, 34, 26, 26, 16, 8,
2, 12, 0, 21, 15, 40, 16, 38, 22, 26, 36, 17, 3, 12, 3, 23, 39,
34, 36, 33, 38, 15, 21, 7, 34, 23, 33, 34, 33, 26, 34, 26, 30, 16,
2, 3, 0, 33, 34, 39, 12, 5, 34, 26, 33, 30, 39, 12, 2, 15, 29,
12, 38, 36, 10, 36, 28, 1, 19, 12, 17, 32, 35, 11, 16, 28, 18, 14,
15, 31, 34, 19, 0, 17, 12, 11, 39, 18, 26, 31, 0], dtype=int32)
如果要使用完整的数据集,kmeans.fit(mystery)
可能需要一些时间,出于测试目的,我仅使用了前1000个实例,并预测了200个实例。