本文整理汇总了Java中org.ujmp.core.Matrix.mean方法的典型用法代码示例。如果您正苦于以下问题：Java Matrix.mean方法的具体用法？Java Matrix.mean怎么用？Java Matrix.mean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org.ujmp.core.Matrix的用法示例。
在下文中一共展示了Matrix.mean方法的4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: calculateObjects
import org.ujmp.core.Matrix; //导入方法依赖的package包/类
public Map<String, Object> calculateObjects(Map<String, Object> input) {
int dimension = defaultDimension;
boolean ignoreNaN = defaultIgnoreNaN;
Map<String, Object> result = new HashMap<String, Object>();
Matrix source = MathUtil.getMatrix(input.get(SOURCE));
Object o2 = input.get(DIMENSION);
if (o2 != null) {
dimension = MathUtil.getInt(o2);
}
Object o3 = input.get(IGNORENAN);
if (o3 != null) {
ignoreNaN = MathUtil.getBoolean(o3);
}
Matrix target = source.mean(Ret.NEW, dimension, ignoreNaN);
result.put(TARGET, target);
return result;
}
示例2: trainAll
import org.ujmp.core.Matrix; //导入方法依赖的package包/类
public void trainAll(ListDataSet dataSet) {
featureCount = getFeatureCount(dataSet);
classCount = getClassCount(dataSet);
dimensions = featureCount + classCount;
Matrix x = Matrix.Factory.zeros(dataSet.size(), dimensions);
int i = 0;
for (Sample s : dataSet) {
Matrix input = s.getAsMatrix(getInputLabel()).toColumnVector(Ret.LINK);
for (int c = 0; c < featureCount; c++) {
x.setAsDouble(input.getAsDouble(0, c), i, c);
}
Matrix target = s.getAsMatrix(getTargetLabel()).toColumnVector(Ret.LINK);
for (int c = 0; c < classCount; c++) {
x.setAsDouble(target.getAsDouble(0, c), i, c + featureCount);
}
i++;
}
meanMatrix = x.mean(Ret.NEW, Matrix.ROW, true);
covarianceMatrix = x.cov(Ret.NEW, true, true);
try {
inverse = covarianceMatrix.inv();
factor = 1.0 / Math.sqrt(covarianceMatrix.det() * Math.pow(2.0 * Math.PI, dimensions));
} catch (Exception e) {
inverse = covarianceMatrix.pinv();
factor = 1.0;
}
}
示例3: predictOne
import org.ujmp.core.Matrix; //导入方法依赖的package包/类
public Matrix predictOne(Matrix input) {
List<Matrix> results = new FastArrayList<Matrix>();
for (Regressor learningAlgorithm : learningAlgorithms) {
Matrix result = learningAlgorithm.predictOne(input);
results.add(result);
}
Matrix all = Matrix.Factory.vertCat(results);
Matrix mean = all.mean(Ret.NEW, Matrix.ROW, true);
return mean;
}
示例4: train
import org.ujmp.core.Matrix; //导入方法依赖的package包/类
public void train(ListDataSet dataSet) {
System.out.println("training started");
Matrix x = Matrix.Factory.zeros(dataSet.size(), getFeatureCount(dataSet));
int i = 0;
for (Sample s : dataSet) {
Matrix input = s.getAsMatrix(getInputLabel()).toColumnVector(Ret.LINK);
for (int c = 0; c < input.getColumnCount(); c++) {
x.setAsDouble(input.getAsDouble(0, c), i, c);
}
i++;
}
System.out.println("data loaded");
mean = x.mean(Ret.NEW, ROW, true);
for (int r = 0; r < x.getRowCount(); r++) {
for (int c = 0; c < x.getColumnCount(); c++) {
x.setAsDouble(x.getAsDouble(r, c) - mean.getAsDouble(0, c), r, c);
}
}
std = x.std(Ret.NEW, ROW, true, true);
for (int r = 0; r < x.getRowCount(); r++) {
for (int c = 0; c < x.getColumnCount(); c++) {
x.setAsDouble(x.getAsDouble(r, c) / std.getAsDouble(0, c), r, c);
}
}
Matrix xtx = x.transpose().mtimes(x);
Matrix[] svd;
if (numberOfPrincipalComponents == 0) {
svd = xtx.svd();
} else {
svd = xtx.svd(numberOfPrincipalComponents);
}
u = svd[0];
System.out.println("training finished");
}