本文整理汇总了Java中weka.core.matrix.Matrix.times方法的典型用法代码示例。如果您正苦于以下问题:Java Matrix.times方法的具体用法?Java Matrix.times怎么用?Java Matrix.times使用的例子?那么恭喜您, 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.core.matrix.Matrix
的用法示例。
在下文中一共展示了Matrix.times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: calculateStdErrorOfCoef
import weka.core.matrix.Matrix; //导入方法依赖的package包/类
/**
* Returns the standard errors of slope and intercept for a simple linear
* regression model: y = a + bx. The first element is the standard error of
* slope, the second element is standard error of intercept.
*
* @param data (the data set)
* @param chosen (chosen x-attribute)
* @param slope (slope determined by simple linear regression model)
* @param intercept (intercept determined by simple linear regression model)
* @param df (number of instances - 2)
*
* @return array of standard errors of slope and intercept
* @throws Exception if there is a missing class value in data
*/
public static double[] calculateStdErrorOfCoef(Instances data,
Attribute chosen, double slope, double intercept, int df) throws Exception {
// calculate sum of squared residuals, mean squared error
double ssr = calculateSSR(data, chosen, slope, intercept);
double mse = ssr / df;
/*
* put data into 2-D array with 2 columns first column is value of chosen
* attribute second column is constant (1's)
*/
double[][] array = new double[data.numInstances()][2];
for (int i = 0; i < data.numInstances(); i++) {
array[i][0] = data.instance(i).value(chosen);
array[i][1] = 1.0;
}
/*
* linear algebra calculation: covariance matrix = mse * (XtX)^-1 diagonal
* of covariance matrix is square of standard error of coefficients
*/
Matrix X = new Matrix(array);
Matrix Xt = X.transpose();
Matrix XtX = Xt.times(X);
Matrix inverse = XtX.inverse();
Matrix cov = inverse.times(mse);
double[] result = new double[2];
for (int i = 0; i < 2; i++) {
result[i] = Math.sqrt(cov.get(i, i));
}
return result;
}
示例2: convertInstance
import weka.core.matrix.Matrix; //导入方法依赖的package包/类
/**
* Transform an instance in original (unnormalized) format
* @param instance an instance in the original (unnormalized) format
* @return a transformed instance
* @throws Exception if instance can't be transformed
*/
public Instance convertInstance(Instance instance) throws Exception {
if (m_s == null) {
throw new Exception("convertInstance: Latent Semantic Analysis not " +
"performed yet.");
}
// array to hold new attribute values
double [] newValues = new double[m_outputNumAttributes];
// apply filters so new instance is in same format as training instances
Instance tempInstance = (Instance)instance.copy();
if (!instance.dataset().equalHeaders(m_trainHeader)) {
throw new Exception("Can't convert instance: headers don't match: " +
"LatentSemanticAnalysis");
}
// replace missing values
m_replaceMissingFilter.input(tempInstance);
m_replaceMissingFilter.batchFinished();
tempInstance = m_replaceMissingFilter.output();
// normalize
if (m_normalize) {
m_normalizeFilter.input(tempInstance);
m_normalizeFilter.batchFinished();
tempInstance = m_normalizeFilter.output();
}
// convert nominal attributes to binary
m_nominalToBinaryFilter.input(tempInstance);
m_nominalToBinaryFilter.batchFinished();
tempInstance = m_nominalToBinaryFilter.output();
// remove class/other attributes
if (m_attributeFilter != null) {
m_attributeFilter.input(tempInstance);
m_attributeFilter.batchFinished();
tempInstance = m_attributeFilter.output();
}
// record new attribute values
if (m_hasClass) { // copy class value
newValues[m_outputNumAttributes - 1] = instance.classValue();
}
double [][] oldInstanceValues = new double[1][m_numAttributes];
oldInstanceValues[0] = tempInstance.toDoubleArray();
Matrix instanceVector = new Matrix(oldInstanceValues); // old attribute values
instanceVector = instanceVector.times(m_transformationMatrix); // new attribute values
for (int i = 0; i < m_actualRank; i++) {
newValues[i] = instanceVector.get(0, i);
}
// return newly transformed instance
if (instance instanceof SparseInstance) {
return new SparseInstance(instance.weight(), newValues);
} else {
return new Instance(instance.weight(), newValues);
}
}