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The S-Space Package is a collection of algorithms for building Semantic Spaces as well as a highly-scalable library for designing new distributional semantics algorithms. Distributional algorithms process text corpora and represent the semantic for words as high dimensional feature vectors. This package also includes matrices, vectors, and numerous clustering algorithms. These approaches are known by many names, such as word spaces, semantic spaces, or distributed semantics and rest upon the Distributional Hypothesis: words that appear in similar contexts have similar meanings.

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/*
 * Copyright 2010 Keith Stevens 
 *
 * This file is part of the S-Space package and is covered under the terms and
 * conditions therein.
 *
 * The S-Space package is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License version 2 as published
 * by the Free Software Foundation and distributed hereunder to you.
 *
 * THIS SOFTWARE IS PROVIDED "AS IS" AND NO REPRESENTATIONS OR WARRANTIES,
 * EXPRESS OR IMPLIED ARE MADE.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, WE MAKE
 * NO REPRESENTATIONS OR WARRANTIES OF MERCHANT- ABILITY OR FITNESS FOR ANY
 * PARTICULAR PURPOSE OR THAT THE USE OF THE LICENSED SOFTWARE OR DOCUMENTATION
 * WILL NOT INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER
 * RIGHTS.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program. If not, see .
 */

package edu.ucla.sspace.evaluation;

import edu.ucla.sspace.common.SemanticSpace;
import edu.ucla.sspace.common.Similarity;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOError;
import java.io.IOException;

import java.util.HashSet;
import java.util.Set;


/**
 * @author Keith Stevens
 */
public class OneNormedQuestionPerLineTest 
        extends AbstractNormedWordPrimingTest { 

    /**
     * Creates a new {@link OneNormedQuestionPerLineTest} from a string
     * designated a file name.
     */
    public OneNormedQuestionPerLineTest(String primingFile) {
        this(new File(primingFile));
    }

    /**
     * Creates a new {@link OneNormedQuestionPerLineTest} from a {@link File}.
     */
    public OneNormedQuestionPerLineTest(File primingFile) {
        super(prepareQuestionSet(primingFile));
    }

    /**
     * Returns a set of {@link NormedPrimingQuestion}s that are extracted from a
     * text file.
     */
    private static Set prepareQuestionSet(
            File primingFile) {
        Set questions =
            new HashSet();
        try {
            BufferedReader br = new BufferedReader(new FileReader(primingFile));
            // Read each line as if it were a new question.  Words in each
            // question are separated by pipes ("|").  The first word of each
            // line is the cue.  For each target, there is is the target string
            // and the target association.
            for (String line = null; (line = br.readLine()) != null;) {
                String[] cueAndTargets = line.split("\\|");
                String[] targets = new String[cueAndTargets.length - 1];
                double[] strengths = new double[cueAndTargets.length - 1];

                // Targets and their associational strength are separated by
                // commas.
                for (int i = 1; i < cueAndTargets.length; ++i) {
                    String[] targetAndStrength = cueAndTargets[i].split(",");
                    targets[i-1] = targetAndStrength[0].trim();
                    strengths[i-1] = Double.parseDouble(targetAndStrength[1]);
                }

                // Add the new question.
                questions.add(new SimpleNormedPrimingQuestion(
                            cueAndTargets[0].trim(), targets, strengths));
            }
        } catch (IOException ioe) {
            throw new IOError(ioe);
        }
        return questions;
    }

    /**
     * {@inheritDoc}
     */
    public double computeStrength(SemanticSpace sspace,
                                  String word1,
                                  String word2) {
        return Similarity.cosineSimilarity(sspace.getVector(word1),
                                           sspace.getVector(word2));
    }
}