如何在libsvm中创建用于训练和测试文件的源数据文件? [英] How to create source data file for training and testing file in libsvm?

问题描述

我想使用数据集来训练模型.数据集具有三种不同类型的生理数据.类型1，类型2和类型3.libSVM的格式如下: 标签index1:value1 index2:value2 ....

I want to use a dataset to train a model. The dataset has three different types of physiological data. type 1, type 2 and type 3. The format of the libSVM is as below: label index1:value1 index2:value2....

现在，我将类型1的标签设为1，将类型2的标签设为2，将类型3的标签设为3.其中，所有值都另存为1:(value).因此，我的培训和测试文件如下.

Now, I have taken label as 1 for type 1, 2 for type 2 and 3 for type 3. Where as all the values are save as 1:(value). So, my training and testing file is as below.

1 1:值\ n 1 1:值\ n 1 1:值\ n 1 1:值\ n 1 1:值\ n . . . 1 1:值\ n 2 1:值\ n 2 1:值\ n 2 1:值\ n 2 1:值\ n 2 1:值\ n . . . 2 1:值\ n 3 1:值\ n 3 1:值\ n 3 1:值\ n 3 1:值\ n . . . 3 1:值\ n

1 1:value \n 1 1:value \n 1 1:value \n 1 1:value \n 1 1:value \n . . . 1 1:value \n 2 1:value \n 2 1:value \n 2 1:value \n 2 1:value \n 2 1:value \n . . . 2 1:value \n 3 1:value \n 3 1:value \n 3 1:value \n 3 1:value \n . . . 3 1:value \n

因此，我正在用这种源文件训练svm并使用类似类型的源文件进行测试.我想确定我是否正确使用了SVM数据格式.谢谢

So, I am traing the svm with this kind of source file and testing with similar kind of source file. I want to make sure if I am using the SVM data format correctly. Thank you

推荐答案

因此，每个功能(或值)所需的都是自己的唯一标识符.

Thus, every feature (or value) needs is own unique identifier.

示例:

想象一下，您有三个不同的类标签1,2,3和一个由a(id=1),b(id=2),c=(id=3)组成的特征集，这些特征集是通过特征选择机制获得的.

Imagine you have three different class labels 1,2,3 and a feature set consisting of a(id=1),b(id=2),c=(id=3), which was obtained via feature selection mechanism.

因此，假设我们有三个数据点d1,d2,d3，我们想要在数据集中进行描述，例如:

So let's say, that we have three datapoints d1,d2,d3, we want to describe in our dataset, it would be for example:

2 1:0.5325 3:0.523

3 2:0.7853 3:0.6326

1 1:0.53265 2:0.5422

含义:

• d1包含功能a(id=1)和c(id=3)
• d2包含功能b(id=2)和c(id=3)
• d3包含功能a(id=1)和b(id=2)

• d1 contains feature a(id=1) and c(id=3)
• d2 contains feature b(id=2) and c(id=3)
• d3 contains feature a(id=1) and b(id=2)

请注意，没有必要为未包含在给定数据点中的要素提供feature_id1:feature_value1.

Note, that it is not necessary to provide feature_id1:feature_value1 for features, which are not contained in the given datapoint.

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