public void finish() {
for (PointValue value : values) {
value.finish();
}
}
@Test
public void testRealData() throws Exception {
// Define projection for the test case
TiledMap map = new TiledMap(492, 690, new LatLng(2.293492496, 30.538372993), 9);
// Read data
BufferedReader in =
new BufferedReader(
new InputStreamReader(
GraphTest.class.getResourceAsStream("/distribscolaire-points.csv")));
double originalSum = 0;
List<PointValue> points = new ArrayList<PointValue>();
while (in.ready()) {
String line = in.readLine();
String[] columns = line.split(",");
double lat = Double.parseDouble(columns[0]);
double lng = Double.parseDouble(columns[1]);
PointValue pv = new PointValue();
pv.px = map.fromLatLngToPixel(new LatLng(lat, lng));
pv.value = Double.parseDouble(columns[2]);
pv.symbol = new MapSymbol();
originalSum += pv.value;
points.add(pv);
}
// Now build the graph
MarkerGraph graph =
new MarkerGraph(points, new BubbleLayerGenerator.IntersectionCalculator(15));
// make sure nothing was lost in the merging of coincident points
double nodeSum = 0;
for (MarkerGraph.Node node : graph.getNodes()) {
nodeSum += node.getPointValue().value;
}
Assert.assertEquals("values after construction of graph", originalSum, nodeSum, 0.001);
saveGraphImage("clusterTest2", graph, 15);
GeneticSolver solver = new GeneticSolver();
List<Cluster> clusters =
solver.solve(
graph,
new GsLogCalculator(5, 15),
new CircleFitnessFunctor(),
UpperBoundsCalculator.calculate(graph, new FixedRadiiCalculator(5)));
// check to make sure all values were included
double sumAfterClustering = 0;
for (Cluster cluster : clusters) {
sumAfterClustering += cluster.sumValues();
}
Assert.assertEquals(originalSum, sumAfterClustering, 0.001);
Assert.assertEquals(16, clusters.size());
saveClusters(graph, "clusterTest-solution", clusters);
}
public void update(float scale) {
for (PointValue value : values) {
value.update(scale);
}
}