/*
 * 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 hivemall.knn.lsh;

import static hivemall.utils.hadoop.WritableUtils.val;
import hivemall.model.FeatureValue;
import hivemall.utils.hashing.MurmurHash3;

import java.math.BigInteger;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;

import org.apache.hadoop.hive.ql.exec.Description;
import org.apache.hadoop.hive.ql.exec.UDF;
import org.apache.hadoop.hive.ql.metadata.HiveException;
import org.apache.hadoop.hive.ql.udf.UDFType;
import org.apache.hadoop.io.Text;

@Description(name = "bbit_minhash", value = "_FUNC_(array<> features [, int numHashes])"
        + " - Returns a b-bits minhash value")
@UDFType(deterministic = true, stateful = false)
public final class bBitMinHashUDF extends UDF {

    private int[] _seeds = null;

    private int[] prepareSeeds(final int numHashes) {
        int[] seeds = this._seeds;
        if (seeds == null || seeds.length != numHashes) {
            seeds = new int[numHashes];
            final Random rand = new Random(31L);
            for (int i = 0; i < numHashes; i++) {
                seeds[i] = rand.nextInt();
            }
            this._seeds = seeds;
        }
        return seeds;
    }

    public Text evaluate(List<Integer> features) throws HiveException {
        return evaluate(features, 128);
    }

    public Text evaluate(List<Integer> features, int numHashes) throws HiveException {
        int[] seeds = prepareSeeds(numHashes);
        List<FeatureValue> featureList = parseFeatures(features);
        return val(computeSignatures(featureList, numHashes, seeds));
    }

    public Text evaluate(List<String> features, boolean noWeight) throws HiveException {
        return evaluate(features, 128, noWeight);
    }

    public Text evaluate(List<String> features, int numHashes, boolean noWeight)
            throws HiveException {
        int[] seeds = prepareSeeds(numHashes);
        List<FeatureValue> featureList = parseFeatures(features, noWeight);
        return val(computeSignatures(featureList, numHashes, seeds));
    }

    private static List<FeatureValue> parseFeatures(final List<Integer> features) {
        final List<FeatureValue> ftvec = new ArrayList<FeatureValue>(features.size());
        for (Integer f : features) {
            if (f != null) {
                FeatureValue fv = new FeatureValue(f, 1.f);
                ftvec.add(fv);
            }
        }
        return ftvec;
    }

    private static List<FeatureValue> parseFeatures(final List<String> features,
            final boolean noWeight) {
        final List<FeatureValue> ftvec = new ArrayList<FeatureValue>(features.size());
        for (String f : features) {
            if (f == null) {
                continue;
            }
            final FeatureValue fv;
            if (noWeight) {
                fv = new FeatureValue(f, 1.f);
            } else {
                fv = FeatureValue.parse(f);
            }
            ftvec.add(fv);
        }
        return ftvec;
    }

    private static String computeSignatures(final List<FeatureValue> features, final int numHashes,
            final int[] seeds) throws HiveException {
        if (numHashes <= 0 || numHashes > 512) {
            throw new HiveException("The number of hash function must be in range (0,512]: "
                    + numHashes);
        }
        final int[] hashes = new int[numHashes];
        // Compute N sets K minhash values
        for (int i = 0; i < numHashes; i++) {
            float weightedMinHashValues = Float.MAX_VALUE;
            for (FeatureValue fv : features) {
                Object f = fv.getFeature();
                assert (f != null);
                String fs = f.toString();
                int hashIndex = Math.abs(MurmurHash3.murmurhash3_x86_32(fs, seeds[i]));
                float w = fv.getValueAsFloat();
                float hashValue = calcWeightedHashValue(hashIndex, w);
                if (hashValue < weightedMinHashValues) {
                    weightedMinHashValues = hashValue;
                    hashes[i] = hashIndex;
                }
            }
        }
        BigInteger value = BigInteger.valueOf(0L);
        for (int i = 0; i < numHashes; i++) {
            if ((hashes[i] & 1) == 1) {
                value = value.setBit(i);
            }
        }
        return value.toString();
    }

    /**
     * For a larger w, hash value tends to be smaller and tends to be selected as minhash.
     */
    private static float calcWeightedHashValue(final int hashIndex, final float w)
            throws HiveException {
        if (w < 0.f) {
            throw new HiveException("Non-negative value is not accepted for a feature weight");
        }
        if (w == 0.f) {
            return Float.MAX_VALUE;
        } else {
            return hashIndex / w;
        }
    }
}