/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.math.hadoop.stochasticsvd.qr;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.Vector;
import org.apache.mahout.math.function.DoubleFunction;
/**
* Gram Schmidt quick helper.
*
*
*/
public class GramSchmidt {
private GramSchmidt() {
}
public static void orthonormalizeColumns(Matrix mx) {
int n = mx.numCols();
for (int c = 0; c < n; c++) {
Vector col = mx.viewColumn(c);
for (int c1 = 0; c1 < c; c1++) {
Vector viewC1 = mx.viewColumn(c1);
col.assign(col.minus(viewC1.times(viewC1.dot(col))));
}
final double norm2 = col.norm(2);
col.assign(new DoubleFunction() {
@Override
public double apply(double x) {
return x / norm2;
}
});
}
}
}