use of com.amazon.randomcutforest.config.Precision in project random-cut-forest-by-aws by aws.
the class RandomCutForestMapperTest method testRoundTripForEmptyForest.
@Test
public void testRoundTripForEmptyForest() {
Precision precision = Precision.FLOAT_64;
RandomCutForest forest = RandomCutForest.builder().compact(true).dimensions(dimensions).sampleSize(sampleSize).precision(precision).numberOfTrees(1).build();
mapper.setSaveTreeStateEnabled(true);
RandomCutForest forest2 = mapper.toModel(mapper.toState(forest));
assertCompactForestEquals(forest, forest2);
}
use of com.amazon.randomcutforest.config.Precision in project random-cut-forest-by-aws by aws.
the class ObjectStreamExample method run.
@Override
public void run() throws Exception {
// Create and populate a random cut forest
int dimensions = 10;
int numberOfTrees = 50;
int sampleSize = 256;
Precision precision = Precision.FLOAT_32;
RandomCutForest forest = RandomCutForest.builder().compact(true).dimensions(dimensions).numberOfTrees(numberOfTrees).sampleSize(sampleSize).precision(precision).build();
int dataSize = 1000 * sampleSize;
NormalMixtureTestData testData = new NormalMixtureTestData();
for (double[] point : testData.generateTestData(dataSize, dimensions)) {
forest.update(point);
}
// Convert to an array of bytes and print the size
RandomCutForestMapper mapper = new RandomCutForestMapper();
mapper.setSaveExecutorContextEnabled(true);
System.out.printf("dimensions = %d, numberOfTrees = %d, sampleSize = %d, precision = %s%n", dimensions, numberOfTrees, sampleSize, precision);
byte[] bytes = serialize(mapper.toState(forest));
System.out.printf("Object output stream size = %d bytes%n", bytes.length);
// Restore from object stream and compare anomaly scores produced by the two
// forests
RandomCutForestState state2 = (RandomCutForestState) deserialize(bytes);
RandomCutForest forest2 = mapper.toModel(state2);
int testSize = 100;
double delta = Math.log(sampleSize) / Math.log(2) * 0.05;
int differences = 0;
int anomalies = 0;
for (double[] point : testData.generateTestData(testSize, dimensions)) {
double score = forest.getAnomalyScore(point);
double score2 = forest2.getAnomalyScore(point);
// also scored as an anomaly by the other forest
if (score > 1 || score2 > 1) {
anomalies++;
if (Math.abs(score - score2) > delta) {
differences++;
}
}
forest.update(point);
forest2.update(point);
}
// first validate that this was a nontrivial test
if (anomalies == 0) {
throw new IllegalStateException("test data did not produce any anomalies");
}
// validate that the two forests agree on anomaly scores
if (differences >= 0.01 * testSize) {
throw new IllegalStateException("restored forest does not agree with original forest");
}
System.out.println("Looks good!");
}
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