本文整理汇总了Java中weka.core.matrix.Matrix.transpose方法的典型用法代码示例。如果您正苦于以下问题：Java Matrix.transpose方法的具体用法？Java Matrix.transpose怎么用？Java Matrix.transpose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.transpose方法的1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;
}