public class MinimizerClassic extends java.lang.Object
|Constructor and Description|
Initializes the attributes.
|Modifier and Type||Method and Description|
Iteratively minimizes energy using the Barnes-Hut algorithm.
public MinimizerClassic(java.util.Collection<Node> nodes, java.util.Collection<Edge> attrEdges, double repuExponent, double attrExponent, double gravFactor, int nrDims)
nodes- nodes with weights specifying their repulsion strengths. More precisely, the repulsion between every pair of different nodes is proportional to the product of their weights. It is recommended to use so-called "edge-repulsion", which means to set the weight of each node to the sum of the weights of its attraction edges. Weights must not be negative.
attrEdges- attraction edges. Omit edges with weight 0.0 (i.e. non-edges). For unweighted graphs use weight 1.0 for all edges. Weights must not be negative. Weights must be symmetric, i.e. the weight from node
n2must be equal to the weight from node
repuExponent- exponent of the distance in the repulsion energy. Exception: The value 0.0 corresponds to logarithmic repulsion. Is 0.0 in both the LinLog and the Fruchterman-Reingold energy model. Negative values are permitted.
attrExponent- exponent of the distance in the attraction energy. Is 1.0 in the LinLog model (which is used for computing clusters, i.e. dense subgraphs), and 3.0 in standard energy model of Fruchterman and Reingold. Must be greater than
gravFactor- factor for the gravitation energy. Gravitation attracts each node to the barycenter of all nodes, to prevent distances between unconnected graph components from approaching infinity. Typical values are 0.0 if the graph is guaranteed to be connected, and positive values significantly smaller 1.0 (e.g. 0.05) otherwise.
nrDims- number of coordinates of each node.
public void minimizeEnergy(java.util.Map<Node,double> positions, int nrIterations)
positions, and stores the computed positions in
positions- position in
nrDims-dimensional space for each node. Is not copied and serves as input and output parameter. Each position must be a
double[nrDims]. If the input is two-dimensional (i.e. the third array element is 0.0 for all nodes), the output is also two-dimensional. Different nodes with nonzero weights must have different positions. Random initial positions are appropriate.
nrIterations- number of iterations. Choose appropriate values by observing the convergence of energy. A typical value is 100.