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Source code: Class Browse.scala part of factorie_2.11 version 1.2

/* Copyright (C) 2008-2016 University of Massachusetts Amherst.
   This file is part of "FACTORIE" (Factor graphs, Imperative, Extensible)
   http://factorie.cs.umass.edu, http://github.com/factorie
   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 cc.factorie.app.nlp.embedding
import cc.factorie.la._
import cc.factorie.maths

import scala.collection.mutable.LinkedHashMap

object Browse {
  
  // t1 is the anchor (with some zero dimensions ignored), t2 is the data (which may have fewer zeros)
  var zeroThreshold = 0.01
  def asymmetricDotSimilarity(t1:DenseTensor1, t2:DenseTensor1): Double = {
    val f1 = t1.map(x => if (math.abs(x) < zeroThreshold) 0.0 else x)
    //val f2 = t2.map(x => if (math.abs(x) < zeroThreshold) 0.0 else x)
    //new DenseTensor1(f1) cosineSimilarity new DenseTensor1(f2)
    new DenseTensor1(f1) cosineSimilarity new DenseTensor1(t2)
  }
  def asymmetricDotSimilarity2(t1:DenseTensor1, t2:DenseTensor1): Double = {
    val f1 = t1.map(x => if (math.abs(x) < zeroThreshold) 0.0 else x)
    val f2 = t2.map(x => if (math.abs(x) < zeroThreshold) 0.0 else x)
    new DenseTensor1(f1) cosineSimilarity new DenseTensor1(f2)
  }
  def main(args:Array[String]): Unit = {
    val embeddings = new LinkedHashMap[String,DenseTensor1]
    println("Reading embeddings...")
    var dim = -1
    var lineNum = 0
    for (line <- io.Source.fromFile(args(0)).getLines()) {
      val elts = line.split("\\s+")
      val t = new DenseTensor1(elts.drop(1).map(_.toDouble))
      // t.twoNormalize() // TODO Make this a command-line option
      embeddings(elts.head) = t
      if (dim > 0) { if (t.length != dim) println(s"At line $lineNum expected length $dim but got ${t.length}") } 
      else dim = t.length
      lineNum += 1
    }
    println(s"...Read $dim-dimensional embeddings for ${embeddings.size} words.")
    
    val prompt = "> "
    print(prompt); System.out.flush()
    val cosSimilarity: (DenseTensor1,DenseTensor1)=>Double = (t1,t2) => t1.cosineSimilarity(t2)   // cosine similarity
    val dotSimilarity: (DenseTensor1,DenseTensor1)=>Double = (t1,t2) => t1.dot(t2)                // dot product
    val sigSimilarity: (DenseTensor1,DenseTensor1)=>Double = (t1,t2) => maths.sigmoid(t1.dot(t2)) // sigmoid of dot product
    val maskedDotSimilarity: (DenseTensor1,DenseTensor1)=>Double = (t1,t2) => asymmetricDotSimilarity(t1, t2) //
    val maskedDotSimilarity2: (DenseTensor1,DenseTensor1)=>Double = (t1,t2) => asymmetricDotSimilarity2(t1, t2) //
    val euclideanSimilarity: (DenseTensor1,DenseTensor1)=>Double = (t1,t2) => 1.0 / t1.euclideanDistance(t2) // inverse of Euclidean distance
    var count = 10
    for (line <- io.Source.stdin.getLines()) {
      //val query = embeddings.getOrElse(line.stripLineEnd, null)
      //val query = line.split("\\s+").map(word => embeddings.getOrElse(word, null)).filter(_ eq null).foldLeft(new DenseTensor1(dim))((a,b) => {b += a; b})
      val query = new DenseTensor1(dim)
      val queryWords = line.split("\\s+")
      var similarity: (DenseTensor1,DenseTensor1)=>Double = (t1,t2) => {
        if (t1.length != t2.length) println(s"embedding.Browse t1=${t1.length} t2=${t2.length}")
        t1.cosineSimilarity(t2)
      }
      var operation = 1 // 1 for addition, -1 for subtraction
      for (word <- queryWords) {
        if (word.matches("\\d+")) count = word.toInt
        else if (word == "-") operation = -1
        else if (word == "+") operation = 1
        else if (word == "cos:") similarity = cosSimilarity 
        else if (word == "dot:") similarity = dotSimilarity 
        else if (word == "sig:") similarity = sigSimilarity
        else if (word == "asy:") similarity = asymmetricDotSimilarity
        else if (word == "asy2:") similarity = asymmetricDotSimilarity2
        else if (word == "euc:") similarity = euclideanSimilarity
        else if (word.matches("thresh=[\\d\\.]+")) zeroThreshold = word.split("=")(1).toDouble
        else if (word == "zero:") query.zero()
        else if (word.startsWith("[")) { // Expecting [2] for one-hot at dimension 2
          val oneHot = new DenseTensor1(dim)
          val i =  word.drop(1).dropRight(1).toInt
          oneHot(i) = 1.0
          if (operation == 1) query += oneHot else query -= oneHot
        } else {
          val embedding = embeddings.getOrElse(word, null)
          if (embedding eq null) println(s"'$word' is outside vocabulary.")
          else {
            if (operation == 1) query += embedding else query -= embedding
          }
        }
      }
      if (query.oneNorm != 0.0) {
        println("QUERY: "+line)
        val top = new cc.factorie.util.TopN[String](count)
        for (tuple <- embeddings) top += (0, similarity(query, tuple._2), tuple._1)
        for (entry <- top) 
          println(f"${entry.category}%-25s   ${entry.score}%3.8f    "+
              f"2norm=${embeddings(entry.category).twoNorm}%f3.5  "+
              f"min=${embeddings(entry.category).min}%f3.3  "+
              f"max=${embeddings(entry.category).max}%f3.3  "+
              f"absmin=${embeddings(entry.category).map(math.abs(_)).min}%f3.3  "+
              f"<${zeroThreshold}%1.1gcount=${embeddings(entry.category).filter(x => math.abs(x) < zeroThreshold).size}%d  "
              )
        println()
      }
      print(prompt); System.out.flush()
    }
  }
}




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