void SOM::operation(std::vector< std::vector<double> >& inputData, std::vector<int>& target)
{
int xWinner, yWinner;
int hits, errors;
hits = 0;
errors = 0;
for (unsigned int example = 0; example < inputData.size(); example++)
{
shortestDistance(inputData[example]);
xWinner = winnerNeuron / dimension;
yWinner = winnerNeuron % dimension;
if(neurons[xWinner][yWinner].label == target[example])
hits++;
else
errors++;
cout << "Test " << example << ": "
<< "(Actual --> " << neurons[xWinner][yWinner].label << ") | "
<< "(Expected --> " << target[example] << ")" << endl;
}
cout << endl << "Hits: " << hits << endl;
cout << "Errors: " << errors << endl;
}
int main () {
std::cout << "Graph example program "
<< " - find shortest path in a directed graph."
<< std::endl;
static const char pendleton[] = "Pendleton";
static const char pensacola[] = "Pensacola";
static const char peoria[] = "Peoria";
static const char phoenix[] = "Phoenix";
static const char pierre[] = "Pierre";
static const char pittsburgh[] = "Pittsburgh";
static const char princeton[] = "Princeton";
static const char pueblo[] = "Pueblo";
Cities cityMap;
cityMap[pendleton][phoenix] = 4;
cityMap[pendleton][pueblo] = 8;
cityMap[pensacola][phoenix] = 5;
cityMap[peoria][pittsburgh] = 5;
cityMap[peoria][pueblo] = 3;
cityMap[phoenix][peoria] = 4;
cityMap[phoenix][pittsburgh] = 10;
cityMap[phoenix][pueblo] = 3;
cityMap[pierre][pendleton] = 2;
cityMap[pittsburgh][pensacola] = 4;
cityMap[princeton][pittsburgh] = 2;
cityMap[pueblo][pierre] = 3;
Distances dist;
shortestDistance (cityMap, pierre, dist);
Distances::iterator where;
std::cout << "Find the shortest path from : "
<< pierre << '\n';
for (where = dist.begin (); where != dist.end (); ++where)
std::cout << " Distance to: " << (*where).first << ":"
<< (*where).second << '\n';
std::cout << "End of graph example program" << '\n';
return 0;
}
void SOM::training(std::vector< std::vector<double> >& inputData)
{
for (int epoch = 0; epoch < numEpochs; epoch++)
{
for (unsigned int example = 0; example < inputData.size(); example++)
{
shortestDistance(inputData[example]);
int index = ( epoch * inputData.size() ) + example;
adjustWeights(index, inputData[example]);
}
lastSeason++;
if(distance < acceptableError)
break;
}
}
int sdistance(char* Maze, int lrud, int dest)
{
Maze[2650] = '$';
char store = Maze[dest];
Maze[dest] = 'R';
int i;
int visit[52*52];
for(i=0;i<52*52;i++)
{
visit[i]=0;
}
Queue* p;
p=queue_new();
p=queue_push(p,lrud);
p=queue_push(p,-1); // '#' to -5 changed
int dis=shortestDistance(Maze,visit,p,0,52);
Maze[dest] = store;
Maze[2650] = 'R';
return dis;
}
void SOM::clustering(std::vector< std::vector<double> >& inputData, std::vector<int>& target)
{
int xWinner, yWinner;
for (unsigned int example = 0; example < inputData.size(); example++)
{
shortestDistance(inputData[example]);
xWinner = winnerNeuron / dimension;
yWinner = winnerNeuron % dimension;
neurons[xWinner][yWinner].label = target[example];
}
for (unsigned int x = 0; x < neurons.size(); x++)
{
for (unsigned int y = 0; y < neurons[x].size(); y++)
{
cout << neurons[x][y].label << " ";
}
cout << endl;
}
cout << endl;
}
void HClustering<Pt>::cluster(Pt *data, unsigned nElm)
{
unsigned i, j, k, n;
typedef std::pair<unsigned,float> shortestDistanceRow;
std::vector<shortestDistanceRow>
shortestDistance(nElm, shortestDistanceRow(nElm, INF));
// Initially there are nElm clusters, so have these many
// TreeNodes and link them to the input data.
clusters.resize(nElm);
for (i = 0; i < nElm; i++) {
clusters[i].id = i;
clusters[i].rep = i;
}
cacheDistances(data, nElm);
// compute the shortest distance array.
for (i = 0; i < nElm; i++) {
for (j = 0; j < nElm; j++) {
float linkageIJ;
// No need to check for cluster rep here since
// at this point everybody is his own rep.
assert(IS_REP(i));
if (i == j)
continue;
linkageIJ = computeLinkage(i, j);
if (shortestDistance[i].second == INF ||
linkageIJ < shortestDistance[i].second)
{
shortestDistance[i].first = j;
shortestDistance[i].second = linkageIJ;
}
}
}
// Run the clustering loop till there are only numClusters remaining.
for (n = nElm; n > numClusters; n--) {
float shortestDistanceSeen = INF;
unsigned shortestDistanceFrom = nElm;
// TODO: Keep a list of elements that represent a cluster.
// TODO: Maybe some kind of sorted list? I don't know
for (i = 0; i < nElm; i++) {
// If this element is not the rep for the cluster it is in:
if (!IS_REP(i))
continue;
if (shortestDistanceSeen == INF ||
shortestDistanceSeen > shortestDistance[i].second)
{
shortestDistanceSeen = shortestDistance[i].second;
shortestDistanceFrom = i;
}
}
assert (shortestDistanceSeen != INF && shortestDistanceFrom < nElm);
i = shortestDistanceFrom;
j = shortestDistance[i].first;
assert(i != j);
// i and j must both represent their clusters already
// (since we only look at reps during during each iteration).
assert(IS_REP(i) && IS_REP(j));
// If the shortest distance was from i to j, then j to i
// must also be the shortest distance.
assert(shortestDistance[j].second == shortestDistanceSeen);
if (clusters[i].members.size() < clusters[j].members.size()) {
// let 'j' represent the smaller cluster.
unsigned temp = i;
i = j;
j = temp;
}
// We now need to combine the clusters i and j,
// So make 'j' belong to 'i' as it is smaller.
clusters[i].members.push_back(j);
clusters[i].members.insert(clusters[i].members.end(),
clusters[j].members.begin(),
clusters[j].members.end());
clusters[j].rep = i;
{
// Just to free up memory used for the members array.
std::vector<unsigned> tmp;
clusters[j].members.swap(tmp);
}
// The shortest distance from i needs recomputation.
shortestDistance[i].first = nElm;
shortestDistance[i].second = INF;
// compute linkage between clusters (w.r.t i).
for (k = 0; k < nElm; k++) {
float distance;
// ignore non rep data. also ignore i itself.
if (!IS_REP(k) || k == i)
continue;
distance = computeLinkage(i, k);
assert(distance != INF);
// Now update the shortestDistance array.
if ((shortestDistance[i].second == INF ||
shortestDistance[i].second > distance))
{
shortestDistance[i].second = distance;
shortestDistance[i].first = k;
}
// for 'k', the shortestDistance array may be
// pointing to a non-rep now, or the distance
// to cluster 'i' may change (as cluster 'i' has
// more members now - not always LINK_MINIMUM).
// TODO: Maybe this can be done on-demand, i.e.
// when we see (i, j) distance with j being non-rep.
if (!IS_REP(shortestDistance[k].first) ||
shortestDistance[k].first == i)
{
assert(clusters[shortestDistance[k].first].rep ==
clusters[i].id);
if (linkageType == LINK_MINIMUM) {
// In case of LINK_MINIMUM the nearest
// cluster would now be 'i' (since 'j'
// now belongs to 'i').
assert(shortestDistance[k].second == distance);
shortestDistance[k].first = i;
} else {
// computeLinkage(i, k) cannot be smaller than
// the known shortest distance for node k
assert(distance >= shortestDistance[k].second);
// reset shortestDistance for 'k' since its now invalid.
shortestDistance[k].second = INF;
shortestDistance[k].first = nElm;
// Will now need to look through ALL
// clusters for a new nearest linkage.
for (j = 0; j < nElm; j++) {
float linkageKJ;
if (j == k || !IS_REP(j))
continue;
linkageKJ = computeLinkage(k, j);
if (shortestDistance[k].second == INF ||
linkageKJ < shortestDistance[k].second)
{
shortestDistance[k].first = j;
shortestDistance[k].second = linkageKJ;
}
}
}
// At this point, the shortest distance from k must be to a rep.
assert(IS_REP(shortestDistance[k].first));
}
}
}
}
