analysis.k_means.centers = analysis.k_means.data[:num]
时间: 2024-04-26 18:21:51 浏览: 7
这段代码看起来像是在使用 K-Means 聚类算法对数据进行聚类。其中,`analysis.k_means.data` 是需要聚类的数据集,`num` 是指定的聚类中心数量。这一行代码的作用是将前 `num` 个数据点作为初始的聚类中心。简单来说,就是将数据集中的前 `num` 个点用作初始的聚类中心。
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import time import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import MiniBatchKMeans, KMeans from sklearn.metrics.pairwise import pairwise_distances_argmin from sklearn.datasets import make_blobs # Generate sample data np.random.seed(0) batch_size = 45 centers = [[1, 1], [-1, -1], [1, -1]] n_clusters = len(centers) X, labels_true = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7) # Compute clustering with Means k_means = KMeans(init='k-means++', n_clusters=3, n_init=10) t0 = time.time() k_means.fit(X) t_batch = time.time() - t0 # Compute clustering with MiniBatchKMeans mbk = MiniBatchKMeans(init='k-means++', n_clusters=3, batch_size=batch_size, n_init=10, max_no_improvement=10, verbose=0) t0 = time.time() mbk.fit(X) t_mini_batch = time.time() - t0 # Plot result fig = plt.figure(figsize=(8, 3)) fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9) colors = ['#4EACC5', '#FF9C34', '#4E9A06'] # We want to have the same colors for the same cluster from the # MiniBatchKMeans and the KMeans algorithm. Let's pair the cluster centers per # closest one. k_means_cluster_centers = k_means.cluster_centers_ order = pairwise_distances_argmin(k_means.cluster_centers_, mbk.cluster_centers_) mbk_means_cluster_centers = mbk.cluster_centers_[order] k_means_labels = pairwise_distances_argmin(X, k_means_cluster_centers) mbk_means_labels = pairwise_distances_argmin(X, mbk_means_cluster_centers) # KMeans for k, col in zip(range(n_clusters), colors): my_members = k_means_labels == k cluster_center = k_means_cluster_centers[k] plt.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.') plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=6) plt.title('KMeans') plt.xticks(()) plt.yticks(()) plt.show() 这段代码每一句在干什么
这段代码主要是使用Scikit-learn库中的KMeans和MiniBatchKMeans算法对生成的样本数据进行聚类,并通过可视化方式展示聚类结果。
具体每一句代码的作用如下:
1. 导入所需的库和模块
```
import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import MiniBatchKMeans, KMeans
from sklearn.metrics.pairwise import pairwise_distances_argmin
from sklearn.datasets import make_blobs
```
2. 生成样本数据
```
batch_size = 45
centers = [[1, 1], [-1, -1], [1, -1]]
n_clusters = len(centers)
# 生成样本数据
X, labels_true = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7)
```
3. 使用KMeans算法进行聚类
```
# 初始化KMeans模型
k_means = KMeans(init='k-means++', n_clusters=3, n_init=10)
# 训练KMeans模型
t0 = time.time()
k_means.fit(X)
t_batch = time.time() - t0
```
4. 使用MiniBatchKMeans算法进行聚类
```
# 初始化MiniBatchKMeans模型
mbk = MiniBatchKMeans(init='k-means++', n_clusters=3, batch_size=batch_size,
n_init=10, max_no_improvement=10, verbose=0)
# 训练MiniBatchKMeans模型
t0 = time.time()
mbk.fit(X)
t_mini_batch = time.time() - t0
```
5. 可视化聚类结果
```
# 可视化KMeans聚类结果
fig = plt.figure(figsize=(8, 3))
fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9)
colors = ['#4EACC5', '#FF9C34', '#4E9A06']
k_means_cluster_centers = k_means.cluster_centers_
order = pairwise_distances_argmin(k_means.cluster_centers_, mbk.cluster_centers_)
mbk_means_cluster_centers = mbk.cluster_centers_[order]
k_means_labels = pairwise_distances_argmin(X, k_means_cluster_centers)
mbk_means_labels = pairwise_distances_argmin(X, mbk_means_cluster_centers)
for k, col in zip(range(n_clusters), colors):
my_members = k_means_labels == k
cluster_center = k_means_cluster_centers[k]
plt.plot(X[my_members, 0], X[my_members, 1], 'w',
markerfacecolor=col, marker='.')
plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,
markeredgecolor='k', markersize=6)
plt.title('KMeans')
plt.xticks(())
plt.yticks(())
plt.show()
```
以上就是这段代码的每一句话的作用。
解释下面代码的意思from sklearn.preprocessing import StandardScaler sc_X = StandardScaler() data_means_stander = sc_X.fit_transform(data_means.iloc[:,[1,2,3]]) from sklearn.cluster import KMeans k = 5 kmeans_model = KMeans(n_clusters = k,n_jobs=4,random_state=123) fit_kmeans = kmeans_model.fit(data_means_stander) data_means['count']=data_means['income_risk']+data_means['economic_risk']+data_means.loc[:,'history_credit_risk'] sort_values=data_means.sort_values("count",inplace=False) kmeans_model.cluster_centers_ data_means['lable']=kmeans_model.labels_ r1 = pd.Series(kmeans_model.labels_).value_counts() data_means.to_csv("mean.csv",index=False,sep=',', encoding="utf_8_sig")
这段代码主要是对数据进行标准化和聚类分析。首先,导入了标准化工具StandardScaler,对数据集中的收入风险、经济风险和历史信用风险三个字段进行标准化处理。然后,导入了聚类分析工具KMeans,设置聚类数为5,使用kmeans_model.fit()对标准化后的数据进行聚类分析,得到聚类模型fit_kmeans。接下来,将聚类结果标签赋值给数据集中的lable字段。最后,使用pd.Series(kmeans_model.labels_).value_counts()函数统计每个聚类簇中数据的数量,将结果存储在r1中,并将数据集保存在文件mean.csv中。聚类分析的结果可以帮助我们对数据进行分类和分析,发现其中的规律和关联,为未来的数据处理和决策提供参考。