/*
 * 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.ignite.ml.math.distances;

import java.util.Arrays;
import java.util.Collection;
import org.apache.ignite.ml.math.primitives.vector.Vector;
import org.apache.ignite.ml.math.primitives.vector.impl.DenseVector;
import org.junit.Test;
import org.junit.runner.RunWith;
import org.junit.runners.Parameterized;

import static org.junit.Assert.assertEquals;

/**
 * Evaluate WeightedMinkowski in multiple test datasets
 */
@RunWith(Parameterized.class)
public class WeightedMinkowskiDistanceTest {
  /** Precision. */
  private static final double PRECISION = 0.01;

  /** */
  @Parameterized.Parameters(name = "{0}")
  public static Collection<TestData> data() {
    return Arrays.asList(
        new TestData(
            new double[] {1.0, 0.0, 0.0},
            new double[] {0.0, 1.0, 0.0},
            1,
            new double[] {2.0, 3.0, 4.0},
            5.0
        ),
        new TestData(
            new double[] {1.0, 0.0, 0.0},
            new double[] {0.0, 1.0, 0.0},
            2,
            new double[] {2.0, 3.0, 4.0},
            3.60
        ),
        new TestData(
            new double[] {1.0, 0.0, 0.0},
            new double[] {0.0, 1.0, 0.0},
            3,
            new double[] {2.0, 3.0, 4.0},
            3.27
        )
    );
  }

  private final TestData testData;

  /** */
  public WeightedMinkowskiDistanceTest(TestData testData) {
    this.testData = testData;
  }

  /** */
  @Test
  public void testWeightedMinkowski() {
    DistanceMeasure distanceMeasure = new WeightedMinkowskiDistance(testData.p, testData.weights);

    assertEquals(testData.expRes,
        distanceMeasure.compute(testData.vectorA, testData.vectorB), PRECISION);
    assertEquals(testData.expRes,
        distanceMeasure.compute(testData.vectorA, testData.vectorB), PRECISION);
  }

  private static class TestData {
    public final Vector vectorA;

    public final Vector vectorB;

    public final Integer p;

    public final double[] weights;

    public final Double expRes;

    private TestData(double[] vectorA, double[] vectorB, Integer p, double[] weights, double expRes) {
      this.vectorA = new DenseVector(vectorA);
      this.vectorB = new DenseVector(vectorB);
      this.p = p;
      this.weights = weights;
      this.expRes = expRes;
    }

    @Override public String toString() {
      return String.format("d(%s,%s;%s,%s) = %s",
          Arrays.toString(vectorA.asArray()),
          Arrays.toString(vectorB.asArray()),
          p,
          Arrays.toString(weights),
          expRes
      );
    }
  }
}
