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/*
 *    Copyright 2022 The MITRE Corporation
 *
 *    Licensed 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.mitre.caasd.commons.math;

import static com.google.common.base.Preconditions.checkArgument;
import static java.util.stream.Collectors.toCollection;

import java.util.ArrayList;
import java.util.List;

import com.google.common.primitives.Ints;

/**
 * A VisvalingamSplitter partitions a Dataset by using the "simplified" Dataset provided by a
 * VisvalingamSimplifier.
 */
public class VisvalingamSplitter implements DataSplitter {

    private final VisvalingamSimplifier simplifier;

    private final double IMPORTANT_TRIANGLE_THRESHOLD;

    /**
     * Create a DataSplitter that uses the points identified by Visvalingam's Algorithm (via a
     * VisvalingamSimplifier) to partition the data.
     *
     * @param importantTriangleThreshold Choose the value based on the dimensions of the input X and
     *                                   Y data. The smaller this threshold is the finer the
     *                                   resulting splits are (i.e. the VisvalingamSimplifier
     *                                   retains more detail so more splits are found). If this
     *                                   value is set high enough no splits will be found.
     */
    public VisvalingamSplitter(double importantTriangleThreshold) {
        checkArgument(importantTriangleThreshold >= 0.0);
        this.simplifier = new VisvalingamSimplifier();
        this.IMPORTANT_TRIANGLE_THRESHOLD = importantTriangleThreshold;
    }

    @Override
    public int[] computeSplitsFor(List xData, List yData) {

        XyDataset data = new XyDataset(xData, yData);

        // identifies the most "visually important" points in the graph
        XyDataset keyPoints = simplifier.simplify(data, IMPORTANT_TRIANGLE_THRESHOLD);

        ArrayList indicesOfKeyPoints =
                keyPoints.xData().stream().map(xValue -> xData.indexOf(xValue)).collect(toCollection(ArrayList::new));

        /*
         * Increment the very last index value so you don't drop the very last piece of data.
         *
         * The goal is to support calls like "xData.subList(result[i], result[i+1])"
         */
        int n = indicesOfKeyPoints.size();
        int prior = indicesOfKeyPoints.get(n - 1);
        indicesOfKeyPoints.set(n - 1, prior + 1);

        return Ints.toArray(indicesOfKeyPoints);
    }
}