/**
 * 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.decomposer;

import org.apache.hadoop.mapred.JobConf;
import org.apache.mahout.math.DenseMatrix;
import org.apache.mahout.math.Matrix;
import org.apache.mahout.math.decomposer.SolverTest;
import org.apache.mahout.math.hadoop.DistributedRowMatrix;
import org.apache.mahout.math.hadoop.TestDistributedRowMatrix;
import org.junit.Test;

import java.io.File;
import java.io.IOException;
import java.util.ArrayList;
import java.util.List;

public final class TestDistributedLanczosSolver extends SolverTest {

  private void doTestDistributedLanczosSolver(boolean symmetric) throws IOException {
    File testData = getTestTempDir("testdata");
    DistributedRowMatrix corpus = new TestDistributedRowMatrix().randomDistributedMatrix(500,
        450, 400, 10, 10.0, symmetric, testData.getAbsolutePath());
    corpus.configure(new JobConf());
    DistributedLanczosSolver solver = new DistributedLanczosSolver();
    int desiredRank = 30;
    Matrix eigenVectors = new DenseMatrix(desiredRank, corpus.numCols());
    List<Double> eigenValues = new ArrayList<Double>();
    solver.solve(corpus, desiredRank, eigenVectors, eigenValues, symmetric);
    assertOrthonormal(eigenVectors);
    assertEigen(eigenVectors, corpus, eigenVectors.numRows() / 2, 0.01, symmetric);
  }

  @Test
  public void testDistributedLanczosSolver() throws Exception {
    doTestDistributedLanczosSolver(true);
  }

}
