如何在Java中从数组创建ARFF文件？ [英] How to create an ARFF file from an array in java?

问题描述

我想获取由Java中的两个数组表示的x-y对的加权线性回归的系数。我已经开始关注weka了，但它是在 LinearRegression类中询问 Instances类对象。要创建实例类文件，需要一个包含数据的ARFF文件。我遇到过使用FastVector类的解决方案，但现在在最新的weka版本中已弃用。如何为x-y对及其所有权重创建一个ARFF文件，它们均由java中的数组表示？

I want to get the coefficients of a weighted linear regression of an x-y pair represented by two arrays in java. I have zeroed in on weka, but it is asking an 'Instances' class object in the 'LinearRegression' class. To create an 'Instances' class file, an ARFF file is needed which contains the data. I have come across solutions that use the FastVector class but that has now been deprecated in the latest weka version. How do I create an ARFF file for the x-y pair and the corresponding weights all represented by arrays in java?

这是基于Baz的答案的代码。它在最后一行 lr.buildClassifier（newDataset）处给出了例外-线程[main]（已暂停（UnassignedClassException异常））

Capabilities.testWithFail（Instances）行：1302。这是代码-

Here's my code based on Baz's answer. It's giving an exception on the last line "lr.buildClassifier(newDataset)" - Thread [main] (Suspended (exception UnassignedClassException))
Capabilities.testWithFail(Instances) line: 1302 . Here's the code -

public static void test() throws Exception
{
    double[][] data = {{4058.0, 4059.0, 4060.0, 214.0, 1710.0, 2452.0, 2473.0, 2474.0, 2475.0, 2476.0, 2477.0, 2478.0, 2688.0, 2905.0, 2906.0, 2907.0, 2908.0, 2909.0, 2950.0, 2969.0, 2970.0, 3202.0, 3342.0, 3900.0, 4007.0, 4052.0, 4058.0, 4059.0, 4060.0}, {19.0, 20.0, 21.0, 31.0, 103.0, 136.0, 141.0, 142.0, 143.0, 144.0, 145.0, 146.0, 212.0, 243.0, 244.0, 245.0, 246.0, 247.0, 261.0, 270.0, 271.0, 294.0, 302.0, 340.0, 343.0, 354.0, 356.0, 357.0, 358.0}};

    int numInstances = data[0].length;

    ArrayList<Attribute> atts = new ArrayList<Attribute>();
    List<Instance> instances = new ArrayList<Instance>();
    for(int dim = 0; dim < 2; dim++)
    {
        Attribute current = new Attribute("Attribute" + dim, dim);

        if(dim == 0)
        {
            for(int obj = 0; obj < numInstances; obj++)
            {
                instances.add(new SparseInstance(numInstances));
            }
        }

        for(int obj = 0; obj < numInstances; obj++)
        {
            instances.get(obj).setValue(current, data[dim][obj]);
            //instances.get(obj).setWeight(weights[obj]);
        }
        atts.add(current);
    }

    Instances newDataset = new Instances("Dataset", atts, instances.size());

    for(Instance inst : instances)
        newDataset.add(inst);

    LinearRegression lr = new LinearRegression();

    lr.buildClassifier(newDataset);             
}

推荐答案

我认为这可能对您有所帮助：

I think this might help you:

FastVector atts = new FastVector();
List<Instance> instances = new ArrayList<Instance>();
for(int dim = 0; dim < numDimensions; dim++)
{
    // Create new attribute / dimension
    Attribute current = new Attribute("Attribute" + dim, dim);
    // Create an instance for each data object
    if(dim == 0)
    {
        for(int obj = 0; obj < numInstances; obj++)
        {
            instances.add(new SparseInstance(numDimensions));
        }
    }

    // Fill the value of dimension "dim" into each object
    for(int obj = 0; obj < numInstances; obj++)
    {
        instances.get(obj).setValue(current, data[dim][obj]);
    }

    // Add attribute to total attributes
    atts.addElement(current);
}

// Create new dataset
Instances newDataset = new Instances("Dataset", atts, instances.size());

// Fill in data objects
for(Instance inst : instances)
    newDataset.add(inst);

之后 Instances 是您的数据集。

注意：即使我使用了<$ c，Weka的当前版本（3.6.8）都没有抱怨。 $ c> FastVector 。

Note: The current version (3.6.8) of Weka did not complain, even though I used FastVector.

但是，对于 Developer 版本（3.7.7），请使用以下命令：

However, for the Developer version (3.7.7), use this:

ArrayList<Attribute> atts = new ArrayList<Attribute>();
List<Instance> instances = new ArrayList<Instance>();
for(int dim = 0; dim < numDimensions; dim++)
{
    Attribute current = new Attribute("Attribute" + dim, dim);
    if(dim == 0)
    {
        for(int obj = 0; obj < numInstances; obj++)
        {
            instances.add(new SparseInstance(numDimensions));
        }
    }

    for(int obj = 0; obj < numInstances; obj++)
    {
        instances.get(obj).setValue(current, data[dim][obj]);
    }

    atts.add(current);
}

Instances newDataset = new Instances("Dataset", atts, instances.size());

for(Instance inst : instances)
    newDataset.add(inst);

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