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 * 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
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 * 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
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 *     http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.flink.ml.feature.lsh;

import org.apache.flink.ml.api.Estimator;
import org.apache.flink.ml.common.datastream.DataStreamUtils;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.param.Param;
import org.apache.flink.ml.util.ParamUtils;
import org.apache.flink.ml.util.ReadWriteUtils;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.table.api.internal.TableImpl;
import org.apache.flink.types.Row;
import org.apache.flink.util.Preconditions;

import java.io.IOException;
import java.util.HashMap;
import java.util.Map;

/**
 * Base class for estimators that support LSH (Locality-sensitive hashing) algorithm for different
 * metrics (e.g., Jaccard distance).
 *
 * 

The basic idea of LSH is to use a set of hash functions to map input vectors into different * buckets, where closer vectors are expected to be in the same bucket with higher probabilities. In * detail, each input vector is hashed by all functions. To decide whether two input vectors are * mapped into the same bucket, two mechanisms for assigning buckets are proposed as follows. * *

    *
  • AND-amplification: The two input vectors are defined to be in the same bucket as long as * ALL of the hash value matches. *
  • OR-amplification: The two input vectors are defined to be in the same bucket as long as ANY * of the hash value matches. *
* *

See: Locality-sensitive_hashing. * * @param class type of the Estimator implementation. * @param class type of the Model this Estimator produces. */ abstract class LSH, M extends LSHModel> implements Estimator, LSHParams { private final Map, Object> paramMap = new HashMap<>(); public LSH() { ParamUtils.initializeMapWithDefaultValues(paramMap, this); } @Override public M fit(Table... inputs) { Preconditions.checkArgument(inputs.length == 1); StreamTableEnvironment tEnv = (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment(); DataStream inputDim = getVectorSize(tEnv.toDataStream(inputs[0]), getInputCol()); return createModel(inputDim, tEnv); } protected abstract M createModel(DataStream inputDim, StreamTableEnvironment tEnv); private static DataStream getVectorSize(DataStream input, String vectorCol) { DataStream vectorSizes = input.map( d -> { Vector vec = d.getFieldAs(vectorCol); return vec.size(); }); return DataStreamUtils.reduce( vectorSizes, (s0, s1) -> { Preconditions.checkState( s0.equals(s1), "Vector sizes are not the same: %d %d.", s0, s1); return s0; }); } @Override public void save(String path) throws IOException { ReadWriteUtils.saveMetadata(this, path); } @Override public Map, Object> getParamMap() { return paramMap; } }





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