桥梁裂缝数据集提取特征后训练分类器代码
时间: 2023-06-25 19:05:44 浏览: 50
这里是一个使用Python和Scikit-learn库来训练桥梁裂缝分类器的示例代码:
1. 导入必要的库和数据集
```
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# 导入数据集
data = pd.read_csv('bridge_cracks.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
```
2. 数据预处理
```
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# 特征缩放
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
```
3. 训练分类器
```
# SVM分类器
classifier = SVC(kernel='rbf', random_state=0)
classifier.fit(X_train, y_train)
```
4. 预测并评估分类器性能
```
# 预测测试集结果
y_pred = classifier.predict(X_test)
# 计算分类器准确率
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: {:.2f}%".format(accuracy * 100))
```
完整代码如下:
```
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
# 导入数据集
data = pd.read_csv('bridge_cracks.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# 特征缩放
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# SVM分类器
classifier = SVC(kernel='rbf', random_state=0)
classifier.fit(X_train, y_train)
# 预测测试集结果
y_pred = classifier.predict(X_test)
# 计算分类器准确率
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: {:.2f}%".format(accuracy * 100))
```