1

我想获得由 java 中的两个数组表示的 xy 对的加权线性回归的系数。我已经对 weka 进行了归零，但它在“LinearRegression”类中询问“Instances”类对象。要创建“Instances”类文件，需要一个包含数据的 ARFF 文件。我遇到了使用 FastVector 类的解决方案，但现在在最新的 weka 版本中已弃用。如何为 xy 对和相应的权重创建一个 ARFF 文件，这些权重都由 java 中的数组表示？

这是我基于 Baz 回答的代码。它在最后一行 "lr.buildClassifier(newDataset)" - Thread [main] (Suspended (exception UnassignedClassException))
Capabilities.testWithFail(Instances) line: 1302 上给出了一个异常。这是代码 -

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);             
}
4

2 回答 2

5

我认为这可能会对您有所帮助：

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是你的数据集。

注意：Weka 的当前版本（3.6.8）没有抱怨，即使我使用了FastVector.

但是，对于Developer版本 (3.7.7)，请使用以下命令：

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);
于 2012-10-18T12:22:56.530 回答
0

You want to construct an Instances object, that class overrides toString() to output in ARFF format. If FastVector is deprecated you could just use Vector.

于 2012-10-18T12:12:40.620 回答