train_features = data[train_indices]
时间: 2024-05-17 15:13:41 浏览: 35
这段代码中,`data` 是一个ndarray类型的数据集,`train_indices` 是一个索引列表,用于选择数据集中哪些数据用于训练模型。`train_features` 是一个新的ndarray类型的数组,包含了从 `data` 中选择的训练样本的特征。
具体来说,这行代码使用索引列表 `train_indices` 对 `data` 进行切片操作,选择其中的一部分数据作为训练数据,并将这些数据的特征保存在 `train_features` 中。这个切片操作相当于对 `data` 进行了过滤,只保留了 `train_indices` 中包含的样本。假设 `data` 中的每个样本都是一个长度为 `n` 的特征向量,那么 `train_features` 就是一个二维数组,它的形状为 `(len(train_indices), n)`,其中 `len(train_indices)` 表示训练样本的数量。
相关问题
for fea in numerical_fea: data_train = data_train[data_train[fea+'_outliers']=='正常值'] data_train = data_train.reset_index(drop=True)
This code snippet loops over the numerical features in a dataset and filters out any rows where the feature's outlier status is labeled as "正常值" (which likely means "normal value" in Chinese). The filtered dataset is then reset so that the row indices are sequential.
It's possible that the dataset has been preprocessed prior to this code being run, with a separate step that identifies and labels outliers for each numerical feature. Without knowing more about the dataset and the preprocessing steps, it's hard to say exactly what this code is doing or what the purpose of filtering out outliers might be.
clear; clc; addpath('libsvm-3.25/matlab'); load DATA_PCA_2445M_100.mat; num_DJI_Mavic_2_ZOOM=100; num_DJI_Phantom_4Pro=100; num_Parrot_ANAFI=100; num_XIRO=100; features=data_PCA; %特征 class=[ones(num_DJI_Mavic_2_ZOOM,1);ones(num_DJI_Phantom_4Pro,1)+1;ones(num_Parrot_ANAFI,1)+2;ones(num_XIRO,1)+3]; %类型 这是我准备在matlab中使用的SVM模型去给数据DATA_PCA_2445M_100.mat进行分类操作的,并且得到识别的准确度,请给出我完整的代码
你可以使用以下代码来使用SVM模型对数据进行分类并计算分类准确度:
```matlab
clear;
clc;
addpath('libsvm-3.25/matlab');
load DATA_PCA_2445M_100.mat;
num_DJI_Mavic_2_ZOOM = 100;
num_DJI_Phantom_4Pro = 100;
num_Parrot_ANAFI = 100;
num_XIRO = 100;
features = data_PCA; % 特征
class = [ones(num_DJI_Mavic_2_ZOOM,1); ones(num_DJI_Phantom_4Pro,1)+1; ones(num_Parrot_ANAFI,1)+2; ones(num_XIRO,1)+3]; % 类型
% 划分训练集和测试集
train_ratio = 0.7; % 训练集比例
[train_indices, test_indices] = crossvalind('Holdout', class, train_ratio);
train_features = features(train_indices,:);
train_class = class(train_indices);
test_features = features(test_indices,:);
test_class = class(test_indices);
% 训练SVM模型
model = svmtrain(train_class, train_features, '-s 0 -t 0');
% 预测测试集
[predicted_class, accuracy, decision_values] = svmpredict(test_class, test_features, model);
% 输出分类准确度
fprintf('分类准确度: %.2f%%\n', accuracy(1));
```
请确保你已经将libsvm的MATLAB接口添加到MATLAB搜索路径中,并且替换`DATA_PCA_2445M_100.mat`为你的数据文件路径。这段代码将会将数据分为训练集和测试集,并使用SVM模型对测试集进行预测,并输出分类的准确度。
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