source: cpp/frams/model/similarity/measure-hungarian.cpp @ 1193

// This file is a part of Framsticks SDK.  http://www.framsticks.com/
// Copyright (C) 1999-2020  Maciej Komosinski and Szymon Ulatowski.
// See LICENSE.txt for details.

#include "measure-hungarian.h"

const int SimilMeasureHungarian::iNOFactors = 4;

#define FIELDSTRUCT SimilMeasureHungarian

static ParamEntry simil_hungarian_paramtab[] = {
                { "Creature: Similarity: Graph optimal", 1, 7, "SimilMeasureHungarian", "Evaluates morphological dissimilarity using the measure. More information:\nhttp://www.framsticks.com/bib/Komosinski-et-al-2001\nhttp://www.framsticks.com/bib/Komosinski-and-Kubiak-2011\nhttp://www.framsticks.com/bib/Komosinski-2016\nhttps://www.framsticks.com/bib/Komosinski-and-Mensfelt-2019", },
                { "simil_parts", 0, 0, "Weight of parts count", "f 0 100 0", FIELD(m_adFactors[0]), "Differing number of parts is also handled by the 'part degree' similarity component.", },
                { "simil_partdeg", 0, 0, "Weight of parts' degree", "f 0 100 1", FIELD(m_adFactors[1]), "", },
                { "simil_neuro", 0, 0, "Weight of neurons count", "f 0 100 0.1", FIELD(m_adFactors[2]), "", },
                { "simil_partgeom", 0, 0, "Weight of parts' geometric distances", "f 0 100 0", FIELD(m_adFactors[3]), "", },
                { "simil_fixedZaxis", 0, 0, "Fix 'z' (vertical) axis?", "d 0 1 0", FIELD(fixedZaxis), "", },
                { "simil_weightedMDS", 0, 0, "Should weighted MDS be used?", "d 0 1 0", FIELD(wMDS), "If activated, weighted MDS with vertex (i.e., Part) degrees as weights is used for 3D alignment of body structure.", },
                { "evaluateDistance", 0, PARAM_DONTSAVE | PARAM_USERHIDDEN, "Evaluate model dissimilarity", "p f(oGeno,oGeno)", PROCEDURE(p_evaldistance), "Calculates dissimilarity between two models created from Geno objects.", },
                { 0, },
};

#undef FIELDSTRUCT

SimilMeasureHungarian::SimilMeasureHungarian() : localpar(simil_hungarian_paramtab, this)
{
        localpar.setDefault();
        
        nSmaller = 0;
        nBigger = 0;
        
        for (int i = 0; i < 2; i++)
        {
                degrees[i] = nullptr;
                neurons[i] = nullptr;
                on_joint[i] = 0;
                anywhere[i] = 0;        
        }
        
        assignment = nullptr;
        parts_distances = nullptr;
        temp_parts_distances = nullptr;
        
        save_matching = false;
}

void SimilMeasureHungarian::prepareData()
{
        m_iSmaller = models[0]->getPartCount() <= models[1]->getPartCount() ? 0 : 1;
        nSmaller = models[m_iSmaller]->getPartCount();
        nBigger = models[1 - m_iSmaller]->getPartCount();

        for (int i = 0; i < 2; i++)
        {
                int size = models[i]->getPartCount();
                degrees[i] = new int[size]();
                neurons[i] = new int[size]();
        }
        
        countDegrees();
        countNeurons();
        
        parts_distances = new double[nBigger*nBigger]();
        fillPartsDistances(parts_distances, nBigger, nSmaller, false);
        assignment = new int[nBigger]();
        
        if (save_matching)
                for (int i = 0; i < nBigger; i++)
                        min_assignment.push_back(0);
        
        if (m_adFactors[3] == 0)
            with_alignment = false;
}

void SimilMeasureHungarian::beforeTransformation()
{
        temp_parts_distances = new double[nBigger*nBigger]();
        std::copy(parts_distances, parts_distances + nBigger * nBigger, temp_parts_distances);
}

double SimilMeasureHungarian::distanceForTransformation()
{
        fillPartsDistances(temp_parts_distances, nBigger, nSmaller, true);
        std::fill_n(assignment, nBigger, 0);
        double distance = hungarian.Solve(temp_parts_distances, assignment, nBigger, nBigger);

        delete[] temp_parts_distances;
        return addNeuronsPartsDiff(distance);
}

double SimilMeasureHungarian::distanceWithoutAlignment()
{
        double distance = hungarian.Solve(parts_distances, assignment, nBigger, nBigger);
        if (save_matching)
                copyMatching();
        return addNeuronsPartsDiff(distance);
}

double SimilMeasureHungarian::addNeuronsPartsDiff(double dist)
{
        //add difference in anywhere and onJoint neurons
        dist += m_adFactors[2] * (abs(on_joint[0] - on_joint[1]) + abs(anywhere[0] - anywhere[1]));
        //add difference in part numbers
        dist += (nBigger - nSmaller) * m_adFactors[0];
        return dist;
}

void SimilMeasureHungarian::copyMatching()
{
        min_assignment.clear();
        min_assignment.insert(min_assignment.begin(), assignment, assignment + nBigger);
}

void SimilMeasureHungarian::cleanData()
{
        for (int i = 0; i < 2; i++)
        {
                // delete degree and position arrays
                SAFEDELETEARRAY(degrees[i]);
                SAFEDELETEARRAY(neurons[i]);    

                on_joint[i] = 0;
                anywhere[i] = 0;        
        }

        delete[] assignment;
        delete[] parts_distances;
        
        if (save_matching)
                min_assignment.clear();
        
        with_alignment = true; //restore default value
}

void SimilMeasureHungarian::countDegrees()
{
        Part *P1, *P2;
        int i, j, i1, i2;

        for (i = 0; i < 2; i++)
        {
                for (j = 0; j < models[i]->getJointCount(); j++)
                {
                        Joint *J = models[i]->getJoint(j);

                        P1 = J->part1;
                        P2 = J->part2;

                        i1 = models[i]->findPart(P1);
                        i2 = models[i]->findPart(P2);

                        degrees[i][i1]++;
                        degrees[i][i2]++;
                }
        }
}

void SimilMeasureHungarian::countNeurons()
{
        Part *P1;
        Joint *J1;
        int i, j, i2;

        for (i = 0; i < 2; i++)
        {
                for (j = 0; j < models[i]->getNeuroCount(); j++)
                {
                        Neuro *N = models[i]->getNeuro(j);
                        // count parts attached to neurons
                        P1 = N->getPart();
                        if (P1)
                        {
                                i2 = models[i]->findPart(P1);
                                neurons[i][i2]++;
                        }
                        else
                        // count unattached neurons
                        {
                                J1 = N->getJoint();
                                if (J1)
                                        on_joint[i]++;
                                else
                                        anywhere[i]++;
                        }
                }
        }
}

void SimilMeasureHungarian::fillPartsDistances(double*& dist, int bigger, int smaller, bool geo)
{
        for (int i = 0; i < bigger; i++)
        {
                for (int j = 0; j < bigger; j++)
                {
                        // assign penalty for unassignment for vertex from bigger model
                        if (j >= smaller)
                        {
                                if (geo)
                                        dist[i*bigger + j] += m_adFactors[3] * coordinates[1 - m_iSmaller][i].length();
                                else
                                        dist[i*bigger + j] = m_adFactors[1] * degrees[1 - m_iSmaller][i] + m_adFactors[2] * neurons[1 - m_iSmaller][i];
                        }
                        // compute distance between parts
                        else
                        {
                                if (geo){
                                        dist[i*bigger + j] += m_adFactors[3] * coordinates[1 - m_iSmaller][i].distanceTo(coordinates[m_iSmaller][j]);
        }
                                else
                                        dist[i*bigger + j] = m_adFactors[1] * abs(degrees[1 - m_iSmaller][i] - degrees[m_iSmaller][j])
                                        + m_adFactors[2] * abs(neurons[1 - m_iSmaller][i] - neurons[m_iSmaller][j]);
                        }
                }
        }
}

/** Returns number of factors involved in final distance computation.
                These factors include differences in numbers of parts, degrees,
                number of neurons.
                */
int SimilMeasureHungarian::getNOFactors()
{
        return SimilMeasureHungarian::iNOFactors;
}

int SimilMeasureHungarian::setParams(std::vector<double> params)
{
        int i = 0;
        for (i = 0; i < SimilMeasureHungarian::iNOFactors; i++)
                m_adFactors[i] = params.at(i);
        fixedZaxis = params.at(i);
        return 0;
}