private SimpleMatrix GetAdjustment(ScalePoint point,
int level, int x, int y, out double dp)
{
dp = 0.0;
if (point.Level <= 0 || point.Level >= (spaces.Length - 1))
{
throw (new ArgumentException("point.Level is not within [bottom-1;top-1] range"));
}
ImageMap below = spaces[level - 1];
ImageMap current = spaces[level];
ImageMap above = spaces[level + 1];
SimpleMatrix H = new SimpleMatrix(3, 3);
H[0, 0] = below[x, y] - 2 * current[x, y] + above[x, y];
H[0, 1] = H[1, 0] = 0.25 * (above[x, y + 1] - above[x, y - 1] -
(below[x, y + 1] - below[x, y - 1]));
H[0, 2] = H[2, 0] = 0.25 * (above[x + 1, y] - above[x - 1, y] -
(below[x + 1, y] - below[x - 1, y]));
H[1, 1] = current[x, y - 1] - 2 * current[x, y] + current[x, y + 1];
H[1, 2] = H[2, 1] = 0.25 * (current[x + 1, y + 1] - current[x - 1, y + 1] -
(current[x + 1, y - 1] - current[x - 1, y - 1]));
H[2, 2] = current[x - 1, y] - 2 * current[x, y] + current[x + 1, y];
SimpleMatrix d = new SimpleMatrix(3, 1);
d[0, 0] = 0.5 * (above[x, y] - below[x, y]);
d[1, 0] = 0.5 * (current[x, y + 1] - current[x, y - 1]);
d[2, 0] = 0.5 * (current[x + 1, y] - current[x - 1, y]);
SimpleMatrix b = (SimpleMatrix)d.Clone();
b.Negate();
H.SolveLinear(b);
dp = b.Dot(d);
return(b);
}
private void ForwardPropagateTree(Tree tree, IList <string> words, IdentityHashMap <Tree, SimpleMatrix> nodeVectors, IdentityHashMap <Tree, double> scores)
{
if (tree.IsLeaf())
{
return;
}
if (tree.IsPreTerminal())
{
Tree wordNode = tree.Children()[0];
string word = wordNode.Label().Value();
SimpleMatrix wordVector = dvModel.GetWordVector(word);
wordVector = NeuralUtils.ElementwiseApplyTanh(wordVector);
nodeVectors[tree] = wordVector;
return;
}
foreach (Tree child in tree.Children())
{
ForwardPropagateTree(child, words, nodeVectors, scores);
}
// at this point, nodeVectors contains the vectors for all of
// the children of tree
SimpleMatrix childVec;
if (tree.Children().Length == 2)
{
childVec = NeuralUtils.ConcatenateWithBias(nodeVectors[tree.Children()[0]], nodeVectors[tree.Children()[1]]);
}
else
{
childVec = NeuralUtils.ConcatenateWithBias(nodeVectors[tree.Children()[0]]);
}
if (op.trainOptions.useContextWords)
{
childVec = ConcatenateContextWords(childVec, tree.GetSpan(), words);
}
SimpleMatrix W = dvModel.GetWForNode(tree);
if (W == null)
{
string error = "Could not find W for tree " + tree;
if (op.testOptions.verbose)
{
log.Info(error);
}
throw new NoSuchParseException(error);
}
SimpleMatrix currentVector = W.Mult(childVec);
currentVector = NeuralUtils.ElementwiseApplyTanh(currentVector);
nodeVectors[tree] = currentVector;
SimpleMatrix scoreW = dvModel.GetScoreWForNode(tree);
if (scoreW == null)
{
string error = "Could not find scoreW for tree " + tree;
if (op.testOptions.verbose)
{
log.Info(error);
}
throw new NoSuchParseException(error);
}
double score = scoreW.Dot(currentVector);
//score = NeuralUtils.sigmoid(score);
scores[tree] = score;
}
