用Birch算法得到score、mse、rmse结果的代码

以下是使用Birch算法得到score、mse、rmse结果的Python代码示例：

from sklearn.cluster import Birch
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import numpy as np

# Generate sample data
X = np.random.rand(100, 10)

# Initialize Birch clustering algorithm
brc = Birch(threshold=0.1, n_clusters=3)

# Fit the model to the data
brc.fit(X)

# Predict the cluster labels for the data
labels = brc.predict(X)

# Calculate the score
score = brc.score(X)

# Calculate the mean squared error (MSE)
mse = mean_squared_error(labels, brc.labels_)

# Calculate the root mean squared error (RMSE)
rmse = np.sqrt(mse)

# Print the results
print("Score:", score)
print("MSE:", mse)
print("RMSE:", rmse)

在此示例中，我们首先生成了一个大小为100x10的随机数据矩阵X。然后，我们初始化了Birch算法，并将其拟合到数据上。接下来，我们根据拟合的模型预测了数据的聚类标签，并计算了score、mse和rmse。最后，我们打印了计算结果。