生成随机数据,200个点,分成4类,返回样本及标签 data, labels = make_blobs( )
时间: 2024-02-15 13:31:57 浏览: 21
好的,您的问题已经记录下来了。关于您的第一个问题,生成随机数据200个点,分成4类,可以使用scikit-learn库中的make_blobs函数来实现。下面是相应的代码:
```
from sklearn.datasets import make_blobs
# 生成随机数据,200个点,分成4类
data, labels = make_blobs(n_samples=200, centers=4, random_state=0)
print("随机数据:\n", data)
print("标签:\n", labels)
```
注意这里的参数解释:
- n_samples: 生成的样本数,这里设为200;
- centers: 生成的类别数,这里设为4;
- random_state: 随机种子,设为0以保证结果可重复。
运行以上代码,即可得到生成的随机数据及其对应的标签。
希望我的回答对您有所帮助。若有其他问题,请随时提出。
相关问题
生成随机数据,200个点,分成4类,返回样本及标签 data, labels = make_blobs( ) 完成上述填空的代码,并输出data,labels
好的,我理解了你的要求。下面是填空后的代码:
```python
from sklearn.datasets import make_blobs
data, labels = make_blobs(n_samples=200, centers=4, random_state=0)
print(data)
print(labels)
```
这段代码会生成200个随机数据点,分为4类,并返回这些点和它们的标签。运行后,会打印出data和labels的值。请注意,这里的make_blobs函数是来自sklearn.datasets模块,用于产生随机数据,你可以在你的代码中通过导入该模块来使用它。
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。