连续投影算法对红外光谱进行特征筛选的代码

j 0.0001+0.0003j 0.0001+0.0003j 0.0001+0.0002j 0.0001+0.0002j 0.0001+0.0002j 0以下是使用连续投影算法对红外光谱进行特征筛选的Python代码：

import numpy as.0001+0.0002j 0.0001+0.0002j 0.0001+0.000 np
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score

# 加载数据2j 0.0001+0.0002j 0.0001+0.0002j 0.0001
data = np.loadtxt("data.txt", delimiter=",")
X = data[:, :-1]  # 特征
y = data[:, -+0.0002j 0.0001+0.0002j 0.0001+0.0002j 1]  # 标签

# 特征筛选
threshold = 0.5  # 连续投影算法的阈0.0001+0.0002j 0.0001+0.0001j 0.0001+0.值
row_proj, _ = continuous_projection(X, threshold)
selected_features = np.where(row_proj > threshold)[0]
X_selected = X0001j 0.0001+0.0001j 0.0001+0.0001j 0.000[:, selected_features]

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split1+0.0001j 0.0001+0.0001j 0.0001+0.0001j(X_selected, y, test_size=0.2, random_state=0)

# 训练SVM分类器
clf = SVC(kernel; 
     0.0001+0.0005j 0.0001+0.0005j 0.000='linear', C=1)
clf.fit(X_train, y_train)

# 测试
y_pred = clf.predict(X_test)
acc = accuracy1+0.0004j 0.0001+0.0005j 0.0001+0.0009j_score(y_test, y_pred)
print("Accuracy:", acc)

其中，`data.txt`是包含特征和标签 0.0001+0.0005j 0.0001+0.0005j 0.0001+0的数据文件，`X`是所有特征，`y`是标签。首先使用连续投影算法对.0004j 0.0001+0.0004j 0.0001+0.0004j 0.特征进行筛选，然后选择投影值大于阈值的特征，得到`X_selected`。接着0001+0.0003j 0.0001+0.0003j 0.0001+0.0003，将`X_selected`和`y`划分为训练集和测试集，使用支持向量机（SVM）分类器进行训练和测试。最终输出测试集的准确率。