本文整理汇总了Java中weka.classifiers.functions.LinearRegression.coefficients方法的典型用法代码示例。如果您正苦于以下问题：Java LinearRegression.coefficients方法的具体用法？Java LinearRegression.coefficients怎么用？Java LinearRegression.coefficients使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类weka.classifiers.functions.LinearRegression的用法示例。
在下文中一共展示了LinearRegression.coefficients方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: buildLinearModel
import weka.classifiers.functions.LinearRegression; //导入方法依赖的package包/类
/**
* Build a linear model for this node using those attributes specified in
* indices.
*
* @param indices an array of attribute indices to include in the linear model
* @throws Exception if something goes wrong
*/
private void buildLinearModel(int[] indices) throws Exception {
// copy the training instances and remove all but the tested
// attributes
Instances reducedInst = new Instances(m_instances);
Remove attributeFilter = new Remove();
attributeFilter.setInvertSelection(true);
attributeFilter.setAttributeIndicesArray(indices);
attributeFilter.setInputFormat(reducedInst);
reducedInst = Filter.useFilter(reducedInst, attributeFilter);
// build a linear regression for the training data using the
// tested attributes
LinearRegression temp = new LinearRegression();
temp.buildClassifier(reducedInst);
double[] lmCoeffs = temp.coefficients();
double[] coeffs = new double[m_instances.numAttributes()];
for (int i = 0; i < lmCoeffs.length - 1; i++) {
if (indices[i] != m_classIndex) {
coeffs[indices[i]] = lmCoeffs[i];
}
}
m_nodeModel = new PreConstructedLinearModel(coeffs,
lmCoeffs[lmCoeffs.length - 1]);
m_nodeModel.buildClassifier(m_instances);
}
示例2: buildLinearModel
import weka.classifiers.functions.LinearRegression; //导入方法依赖的package包/类
/**
* Build a linear model for this node using those attributes
* specified in indices.
*
* @param indices an array of attribute indices to include in the linear
* model
* @throws Exception if something goes wrong
*/
private void buildLinearModel(int [] indices) throws Exception {
// copy the training instances and remove all but the tested
// attributes
Instances reducedInst = new Instances(m_instances);
Remove attributeFilter = new Remove();
attributeFilter.setInvertSelection(true);
attributeFilter.setAttributeIndicesArray(indices);
attributeFilter.setInputFormat(reducedInst);
reducedInst = Filter.useFilter(reducedInst, attributeFilter);
// build a linear regression for the training data using the
// tested attributes
LinearRegression temp = new LinearRegression();
temp.buildClassifier(reducedInst);
double [] lmCoeffs = temp.coefficients();
double [] coeffs = new double [m_instances.numAttributes()];
for (int i = 0; i < lmCoeffs.length - 1; i++) {
if (indices[i] != m_classIndex) {
coeffs[indices[i]] = lmCoeffs[i];
}
}
m_nodeModel = new PreConstructedLinearModel(coeffs, lmCoeffs[lmCoeffs.length - 1]);
m_nodeModel.buildClassifier(m_instances);
}