void DrawingItemGroup::updatePoints()
{
QRectF rect = boundingRect();
DrawingItemPoint* itemPoint;
DrawingItemPoint* leftPoint;
DrawingItemPoint* rightPoint;
itemPoint = cornerPoint(Qt::TopLeftCorner);
if (itemPoint) itemPoint->setPos(rect.topLeft());
itemPoint = cornerPoint(Qt::BottomRightCorner);
if (itemPoint) itemPoint->setPos(rect.bottomRight());
itemPoint = cornerPoint(Qt::TopRightCorner);
if (itemPoint) itemPoint->setPos(rect.topRight());
itemPoint = cornerPoint(Qt::BottomLeftCorner);
if (itemPoint) itemPoint->setPos(rect.bottomLeft());
itemPoint = point(4); leftPoint = point(0); rightPoint = point(2);
if (itemPoint && leftPoint && rightPoint) itemPoint->setPos((leftPoint->pos() + rightPoint->pos()) / 2);
itemPoint = point(5); leftPoint = point(1);
if (itemPoint && leftPoint && rightPoint) itemPoint->setPos((leftPoint->pos() + rightPoint->pos()) / 2);
itemPoint = point(6); rightPoint = point(3);
if (itemPoint && leftPoint && rightPoint) itemPoint->setPos((leftPoint->pos() + rightPoint->pos()) / 2);
itemPoint = point(7); leftPoint = point(0);
if (itemPoint && leftPoint && rightPoint) itemPoint->setPos((leftPoint->pos() + rightPoint->pos()) / 2);
}
QPainterPath DArrowRectangle::getBottomCornerPath()
{
qreal delta = shadowBlurRadius() + shadowDistance();
QRect rect = this->rect().marginsRemoved(QMargins(delta, delta, delta, delta));
QPoint cornerPoint(rect.x() + (m_arrowX > 0 ? m_arrowX : rect.width() / 2), rect.y() + rect.height());
QPoint topLeft(rect.x(), rect.y());
QPoint topRight(rect.x() + rect.width(), rect.y());
QPoint bottomRight(rect.x() + rect.width(), rect.y() + rect.height() - m_arrowHeight);
QPoint bottomLeft(rect.x(), rect.y() + rect.height() - m_arrowHeight);
int radius = this->m_radius > (rect.height() / 2 - m_arrowHeight) ? rect.height() / 2 -m_arrowHeight : this->m_radius;
QPainterPath border;
border.moveTo(topLeft.x() + radius, topLeft.y());
border.lineTo(topRight.x() - radius, topRight.y());
border.arcTo(topRight.x() - 2 * radius, topRight.y(), 2 * radius, 2 * radius, 90, -90);
border.lineTo(bottomRight.x(), bottomRight.y() - radius);
border.arcTo(bottomRight.x() - 2 * radius, bottomRight.y() - 2 * radius, 2 * radius, 2 * radius, 0, -90);
border.lineTo(cornerPoint.x() + m_arrowWidth / 2, cornerPoint.y() - m_arrowHeight);
border.lineTo(cornerPoint);
border.lineTo(cornerPoint.x() - m_arrowWidth / 2, cornerPoint.y() - m_arrowHeight);
border.lineTo(bottomLeft.x() + radius, bottomLeft.y());
border.arcTo(bottomLeft.x(), bottomLeft.y() - 2 * radius, 2 * radius, 2 * radius, -90, -90);
border.lineTo(topLeft.x(), topLeft.y() + radius);
border.arcTo(topLeft.x(), topLeft.y(), 2 * radius, 2 * radius, 180, -90);
return border;
}
//鼠标移动,箭头实时显示
void FlowWidget::moveArrowShow(int startNum, int dx, int dy)
{
if (startNum != -1)
{
//设置开始点为选择模块的中心点
QPoint startPoint(flowIcon[startNum]->pos().x() + flowIcon_sizeHalf, flowIcon[startNum]->pos().y() + flowIcon_sizeHalf);
QPoint cornerPoint(startPoint.x() + dx, startPoint.y());//直角拐弯点
QPoint endPoint(startPoint.x() + dx, startPoint.y()+dy);
//设置横线
lineShow[0].setP1(startPoint);
lineShow[0].setP2(cornerPoint);
//竖线
lineShow[1].setP1(cornerPoint);
lineShow[1].setP2(endPoint);
//箭头
lines[numLine][2].setP1(endPoint);
lines[numLine][2].setP2(QPoint(endPoint.x() - 5, endPoint.y() - 5));
lines[numLine][3].setP1(endPoint);
lines[numLine][3].setP2(QPoint(endPoint.x() + 5, endPoint.y() - 5));
}
}
double PellegMooreKMeansRules<MetricType, TreeType>::Score(
const size_t /* queryIndex */,
TreeType& referenceNode)
{
// Obtain the parent's blacklist. If this is the root node, we'll start with
// an empty blacklist. This means that after each iteration, we don't need to
// reset any statistics.
if (referenceNode.Parent() == NULL ||
referenceNode.Parent()->Stat().Blacklist().n_elem == 0)
referenceNode.Stat().Blacklist().zeros(centroids.n_cols);
else
referenceNode.Stat().Blacklist() =
referenceNode.Parent()->Stat().Blacklist();
// The query index is a fake index that we won't use, and the reference node
// holds all of the points in the dataset. Our goal is to determine whether
// or not this node is dominated by a single cluster.
const size_t whitelisted = centroids.n_cols -
arma::accu(referenceNode.Stat().Blacklist());
distanceCalculations += whitelisted;
// Which cluster has minimum distance to the node?
size_t closestCluster = centroids.n_cols;
double minMinDistance = DBL_MAX;
for (size_t i = 0; i < centroids.n_cols; ++i)
{
if (referenceNode.Stat().Blacklist()[i] == 0)
{
const double minDistance = referenceNode.MinDistance(centroids.col(i));
if (minDistance < minMinDistance)
{
minMinDistance = minDistance;
closestCluster = i;
}
}
}
// Now, for every other whitelisted cluster, determine if the closest cluster
// owns the point. This calculation is specific to hyperrectangle trees (but,
// this implementation is specific to kd-trees, so that's okay). For
// circular-bound trees, the condition should be simpler and can probably be
// expressed as a comparison between minimum and maximum distances.
size_t newBlacklisted = 0;
for (size_t c = 0; c < centroids.n_cols; ++c)
{
if (referenceNode.Stat().Blacklist()[c] == 1 || c == closestCluster)
continue;
// This algorithm comes from the proof of Lemma 4 in the extended version
// of the Pelleg-Moore paper (the CMU tech report, that is). It has been
// adapted for speed.
arma::vec cornerPoint(centroids.n_rows);
for (size_t d = 0; d < referenceNode.Bound().Dim(); ++d)
{
if (centroids(d, c) > centroids(d, closestCluster))
cornerPoint(d) = referenceNode.Bound()[d].Hi();
else
cornerPoint(d) = referenceNode.Bound()[d].Lo();
}
const double closestDist = metric.Evaluate(cornerPoint,
centroids.col(closestCluster));
const double otherDist = metric.Evaluate(cornerPoint, centroids.col(c));
distanceCalculations += 3; // One for cornerPoint, then two distances.
if (closestDist < otherDist)
{
// The closest cluster dominates the node with respect to the cluster c.
// So we can blacklist c.
referenceNode.Stat().Blacklist()[c] = 1;
++newBlacklisted;
}
}
if (whitelisted - newBlacklisted == 1)
{
// This node is dominated by the closest cluster.
counts[closestCluster] += referenceNode.NumDescendants();
newCentroids.col(closestCluster) += referenceNode.NumDescendants() *
referenceNode.Stat().Centroid();
return DBL_MAX;
}
// Perform the base case here.
for (size_t i = 0; i < referenceNode.NumPoints(); ++i)
{
size_t bestCluster = centroids.n_cols;
double bestDistance = DBL_MAX;
for (size_t c = 0; c < centroids.n_cols; ++c)
{
if (referenceNode.Stat().Blacklist()[c] == 1)
continue;
++distanceCalculations;
// The reference index is the index of the data point.
const double distance = metric.Evaluate(centroids.col(c),
dataset.col(referenceNode.Point(i)));
if (distance < bestDistance)
{
bestDistance = distance;
bestCluster = c;
}
}
// Add to resulting centroid.
newCentroids.col(bestCluster) += dataset.col(referenceNode.Point(i));
++counts(bestCluster);
}
// Otherwise, we're not sure, so we can't prune. Recursion order doesn't make
// a difference, so we'll just return a score of 0.
return 0.0;
}