dataset1[3][0] = 0.55; MultivariateEstimator mv1 = new MultivariateGaussianEstimator(); mv1.estimate(dataset1, new double[]{0.7, 0.2, 0.05, 0.05}); dataset[3][2] = 0.54; MultivariateEstimator mv = new MultivariateGaussianEstimator(); mv.estimate(dataset, new double[]{2, 0.2, 0.05, 0.05}); dataset3[3][2] = 0.54; MultivariateEstimator mv3 = new MultivariateGaussianEstimator(); mv3.estimate(dataset3, new double[]{1, 0.2, 0.05, 0.05, 1}); weights[1] = new double[] {2, 1, 1}; MultivariateGaussianEstimator mv4 = new MultivariateGaussianEstimator(); mv4.estimatePooled(dataset4, weights); weights2[1] = new double[] {1, 1, 1, 1}; MultivariateGaussianEstimator mv5 = new MultivariateGaussianEstimator(); mv5.estimatePooled(dataset5, weights2);
dataset1[3][0] = 0.55; MultivariateEstimator mv1 = new MultivariateGaussianEstimator(); mv1.estimate(dataset1, new double[]{0.7, 0.2, 0.05, 0.05}); dataset[3][2] = 0.54; MultivariateEstimator mv = new MultivariateGaussianEstimator(); mv.estimate(dataset, new double[]{2, 0.2, 0.05, 0.05}); dataset3[3][2] = 0.54; MultivariateEstimator mv3 = new MultivariateGaussianEstimator(); mv3.estimate(dataset3, new double[]{1, 0.2, 0.05, 0.05, 1}); weights[1] = new double[] {2, 1, 1}; MultivariateGaussianEstimator mv4 = new MultivariateGaussianEstimator(); mv4.estimatePooled(dataset4, weights); weights2[1] = new double[] {1, 1, 1, 1}; MultivariateGaussianEstimator mv5 = new MultivariateGaussianEstimator(); mv5.estimatePooled(dataset5, weights2);
for (int i = 0; i < insts.numClasses(); i++) { if (sumOfWeightsPerClass[i] > 0) { m_Estimators[i] = new MultivariateGaussianEstimator(); m_Estimators[i].setRidge(getRidge()); m_Estimators[i].estimate(data[i], weights[i]);
m_Estimator = new MultivariateGaussianEstimator(); m_Estimator.setRidge(getRidge()); m_Means = m_Estimator.estimatePooled(data, weights);