Examples with NoEdgesException org.deeplearning4j.graph.exception.NoEdgesException used on opensource projects

Example 1 with NoEdgesException

use of org.deeplearning4j.graph.exception.NoEdgesException in project deeplearning4j by deeplearning4j.

the class RandomWalkIterator method next.

@Override
public IVertexSequence<V> next() {
    if (!hasNext())
        throw new NoSuchElementException();
    //Generate a random walk starting at vertex order[current]
    int currVertexIdx = order[position++];
    int[] indices = new int[walkLength + 1];
    indices[0] = currVertexIdx;
    if (walkLength == 0)
        return new VertexSequence<>(graph, indices);
    Vertex<V> next;
    try {
        next = graph.getRandomConnectedVertex(currVertexIdx, rng);
    } catch (NoEdgesException e) {
        switch(mode) {
            case SELF_LOOP_ON_DISCONNECTED:
                for (int i = 1; i < walkLength; i++) indices[i] = currVertexIdx;
                return new VertexSequence<>(graph, indices);
            case EXCEPTION_ON_DISCONNECTED:
                throw e;
            default:
                throw new RuntimeException("Unknown/not implemented NoEdgeHandling mode: " + mode);
        }
    }
    indices[1] = next.vertexID();
    currVertexIdx = indices[1];
    for (int i = 2; i <= walkLength; i++) {
        //<= walk length: i.e., if walk length = 2, it contains 3 vertices etc
        next = graph.getRandomConnectedVertex(currVertexIdx, rng);
        currVertexIdx = next.vertexID();
        indices[i] = currVertexIdx;
    }
    return new VertexSequence<>(graph, indices);
}

Example 2 with NoEdgesException

use of org.deeplearning4j.graph.exception.NoEdgesException in project deeplearning4j by deeplearning4j.

the class Graph method getRandomConnectedVertex.

@Override
public Vertex<V> getRandomConnectedVertex(int vertex, Random rng) throws NoEdgesException {
    if (vertex < 0 || vertex >= vertices.size())
        throw new IllegalArgumentException("Invalid vertex index: " + vertex);
    if (edges[vertex] == null || edges[vertex].isEmpty())
        throw new NoEdgesException("Cannot generate random connected vertex: vertex " + vertex + " has no outgoing/undirected edges");
    int connectedVertexNum = rng.nextInt(edges[vertex].size());
    Edge<E> edge = edges[vertex].get(connectedVertexNum);
    if (edge.getFrom() == vertex)
        //directed or undirected, vertex -> x
        return vertices.get(edge.getTo());
    else
        //Undirected edge, x -> vertex
        return vertices.get(edge.getFrom());
}

Example 3 with NoEdgesException

use of org.deeplearning4j.graph.exception.NoEdgesException in project deeplearning4j by deeplearning4j.

the class WeightedRandomWalkIterator method next.

@Override
public IVertexSequence<V> next() {
    if (!hasNext())
        throw new NoSuchElementException();
    //Generate a weighted random walk starting at vertex order[current]
    int currVertexIdx = order[position++];
    int[] indices = new int[walkLength + 1];
    indices[0] = currVertexIdx;
    if (walkLength == 0)
        return new VertexSequence<>(graph, indices);
    for (int i = 1; i <= walkLength; i++) {
        List<? extends Edge<? extends Number>> edgeList = graph.getEdgesOut(currVertexIdx);
        //First: check if there are any outgoing edges from this vertex. If not: handle the situation
        if (edgeList == null || edgeList.isEmpty()) {
            switch(mode) {
                case SELF_LOOP_ON_DISCONNECTED:
                    for (int j = i; j < walkLength; j++) indices[j] = currVertexIdx;
                    return new VertexSequence<>(graph, indices);
                case EXCEPTION_ON_DISCONNECTED:
                    throw new NoEdgesException("Cannot conduct random walk: vertex " + currVertexIdx + " has no outgoing edges. " + " Set NoEdgeHandling mode to NoEdgeHandlingMode.SELF_LOOP_ON_DISCONNECTED to self loop instead of " + "throwing an exception in this situation.");
                default:
                    throw new RuntimeException("Unknown/not implemented NoEdgeHandling mode: " + mode);
            }
        }
        //To do a weighted random walk: we need to know total weight of all outgoing edges
        double totalWeight = 0.0;
        for (Edge<? extends Number> edge : edgeList) {
            totalWeight += edge.getValue().doubleValue();
        }
        double d = rng.nextDouble();
        double threshold = d * totalWeight;
        double sumWeight = 0.0;
        for (Edge<? extends Number> edge : edgeList) {
            sumWeight += edge.getValue().doubleValue();
            if (sumWeight >= threshold) {
                if (edge.isDirected()) {
                    currVertexIdx = edge.getTo();
                } else {
                    if (edge.getFrom() == currVertexIdx) {
                        currVertexIdx = edge.getTo();
                    } else {
                        //Undirected edge: might be next--currVertexIdx instead of currVertexIdx--next
                        currVertexIdx = edge.getFrom();
                    }
                }
                indices[i] = currVertexIdx;
                break;
            }
        }
    }
    return new VertexSequence<>(graph, indices);
}