print('round features to {} digits'.format(self.round_digits))
时间: 2024-04-24 19:26:20 浏览: 15
您好!要将特征四舍五入到指定的小数位数,可以使用以下代码:
```python
print('round features to {} digits'.format(self.round_digits))
```
这行代码会将`self.round_digits`的值插入到字符串中,然后打印出来。假设`self.round_digits`是一个整数变量,它将决定要将特征值四舍五入到的小数位数。
相关问题
import pandas as pd import numpy as np from sklearn.datasets import load_digits from sklearn.metrics import * from sklearn.cluster import * digits = load_digits() digits_X = digits.data digits_y = digits.target n_clusters = 10 clf =(n_clusters=n_clusters, random_state=0) clf.load_digits() result = homogeneity_completeness_v_measure(digits_y, clf.labels_)
在你提供的代码中,有一些问题需要修改才能正确运行。
首先,你在定义 `clf` 时缺少了 `KMeans` 这个类的名字,应该改为:
```python
clf = KMeans(n_clusters=n_clusters, random_state=0)
```
接着,你需要用 `clf.fit()` 方法来拟合模型,而不是 `clf.load_digits()`。
最后,你可以使用 `homogeneity_completeness_v_measure()` 方法来计算聚类结果的完整性、同质性和V-measure值。需要注意的是,这个方法需要传入真实标签和预测标签两个参数,因此我们需要用 `clf.predict(digits_X)` 来获取预测标签。修改后的代码如下:
```python
import pandas as pd
import numpy as np
from sklearn.datasets import load_digits
from sklearn.metrics import *
from sklearn.cluster import *
digits = load_digits()
digits_X = digits.data
digits_y = digits.target
n_clusters = 10
clf = KMeans(n_clusters=n_clusters, random_state=0)
clf.fit(digits_X)
result = homogeneity_completeness_v_measure(digits_y, clf.predict(digits_X))
print("Homogeneity: {:.3f}, Completeness: {:.3f}, V-measure: {:.3f}".format(result[0], result[1], result[2]))
```
希望这个回答对你有所帮助!
from sklearn.datasets import ? from sklearn.model selection import train_test_split from sklearn.neural network import MLPClassifier import numpy as np digits = load_digits() X=digits.data y=digits.target X train X test,y_train,y_test = ? (X,y,random_state=0) mip = ? (solver ='lbfgs',hidden_layer_sizes=[100,100],activation='relu',random_state=62) mlp.fit(X_train,y_train) print(X_train.shape,y_train.shape,X_test.shape,y_test.shape) print("训练得分: {:.2f}".format(mlp.score(X_train.y_train))) print("测试得分: {:.2f}".format(mlp.score(X_test y_test)))
这段代码存在一些问题,可能会导致运行错误,我来帮你修改一下:
```
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
import numpy as np
digits = load_digits()
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
mlp = MLPClassifier(solver ='lbfgs', hidden_layer_sizes=[100,100], activation='relu', random_state=62)
mlp.fit(X_train, y_train)
print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
print("训练得分: {:.2f}".format(mlp.score(X_train, y_train)))
print("测试得分: {:.2f}".format(mlp.score(X_test, y_test)))
```
修改了以下几个地方:
1. `load_digits` 的拼写错误
2. `train_test_split` 的调用方式错误,应该为 `train_test_split(X, y, random_state=0)`
3. `MLPClassifier` 的调用方式错误,应该为 `MLPClassifier(solver='lbfgs', hidden_layer_sizes=[100,100], activation='relu', random_state=62)`
4. 打印得分时,应该为 `mlp.score(X_train, y_train)` 和 `mlp.score(X_test, y_test)`