void compute() {
if (ll != null) {
return;
}
Filter filter = new Filter();
filter.setSsf(ssf);
DiffuseFilteringResults dfr = new DiffuseFilteringResults();
filter.process(ssfdata, dfr);
DiffuseConcentratedLikelihood dll = new DiffuseConcentratedLikelihood();
LikelihoodEvaluation.evaluate(dfr, dll);
ll = new DefaultLikelihoodEvaluation<>(dll);
}
/*
* def test_truncatewords
* assert_equal 'one two three', @filters.truncatewords('one two three', 4)
* assert_equal 'one two...', @filters.truncatewords('one two three', 2)
* assert_equal 'one two three', @filters.truncatewords('one two three')
* assert_equal 'Two small (13” x 5.5” x 10” high) baskets fit inside one large basket (13”...',
* @filters.truncatewords('Two small (13” x 5.5” x 10” high) baskets fit inside one large basket (13” x 16” x 10.5” high) with cover.', 15)
* end
*/
@Test
public void applyOriginalTest() {
final Filter filter = Filter.getFilter("truncatewords");
assertThat(filter.apply("one two three", 4), is((Object) "one two three"));
assertThat(filter.apply("one two three", 2), is((Object) "one two..."));
assertThat(filter.apply("one two three", 3), is((Object) "one two three"));
assertThat(
filter.apply(
"Two small (13” x 5.5” x 10” high) baskets fit inside one large basket (13” x 16” x 10.5” high) with cover.",
15),
is(
(Object)
"Two small (13” x 5.5” x 10” high) baskets fit inside one large basket (13”..."));
}
@Test
public void mustBeAbleToFilterAFuture() throws Throwable {
final CountDownLatch latch = new CountDownLatch(1);
Promise<String> cf = Futures.promise();
Future<String> f = cf.future();
Future<String> r =
f.filter(
Filter.filterOf(
new Function<String, Boolean>() {
public Boolean apply(String r) {
latch.countDown();
return r.equals("foo");
}
}),
system.dispatcher());
cf.success("foo");
assertTrue(latch.await(5000, TimeUnit.MILLISECONDS));
assertEquals(Await.result(f, timeout), "foo");
assertEquals(Await.result(r, timeout), "foo");
}
@Test
public void testSODA() {
// should say fieldsTypes, maybe with object/component prefix
Map<String, Object> eventTypes = new HashMap<>();
eventTypes.put(LITERAL_SYMBOL, String.class);
eventTypes.put(LITERAL_PRICE, Integer.class);
EPStatementObjectModel model = new EPStatementObjectModel();
model.setInsertInto(InsertIntoClause.create(LITERAL_RETURN_OBJ));
model.setSelectClause(
SelectClause.create().add(Expressions.avg(LITERAL_PRICE), LITERAL_AVG).add(LITERAL_PRICE));
Filter filter = Filter.create("quotes_default", Expressions.eq(LITERAL_SYMBOL, "A"));
model.setFromClause(
FromClause.create(
FilterStream.create(filter).addView("win", "length", Expressions.constant(2))));
model.setHavingClause(
Expressions.gt(Expressions.avg(LITERAL_PRICE), Expressions.constant(60.0)));
TopologyBuilder builder = new TopologyBuilder();
builder.setSpout(LITERAL_QUOTES, new RandomSentenceSpout());
builder
.setBolt(
LITERAL_ESPER,
(new EsperBolt())
.addEventTypes(eventTypes)
.addOutputTypes(
Collections.singletonMap(
LITERAL_RETURN_OBJ, Arrays.asList(LITERAL_AVG, LITERAL_PRICE)))
.addObjectStatemens(Collections.singleton(model)))
.shuffleGrouping(LITERAL_QUOTES);
builder.setBolt("print", new PrinterBolt()).shuffleGrouping(LITERAL_ESPER, LITERAL_RETURN_OBJ);
Config conf = new Config();
LocalCluster cluster = new LocalCluster();
cluster.submitTopology("test", conf, builder.createTopology());
Utils.sleep(10000);
cluster.shutdown();
assertEquals(resultSODA.get(100), new Double(75.0));
assertEquals(resultSODA.get(50), new Double(75.0));
}
// time-varying trading days
// @Test
public void demoTD() {
TsData s = Data.X;
CompositeResults rslts = TramoSeatsProcessingFactory.process(s, TramoSeatsSpecification.RSA5);
PreprocessingModel regarima = rslts.get("preprocessing", PreprocessingModel.class);
SeatsResults seats = rslts.get("decomposition", SeatsResults.class);
assertTrue(seats != null && regarima != null);
if (regarima.isMultiplicative()) {
s = s.log();
}
int[] calPos =
regarima.description.getRegressionVariablePositions(ComponentType.CalendarEffect);
UcarimaModel ucm = seats.getUcarimaModel();
// compute the full decomposition...
SsfUcarima stoch = new SsfUcarima(ucm);
ExtendedSsfData xdata = new ExtendedSsfData(new SsfData(s, null));
xdata.setForecastsCount(s.getFrequency().intValue());
Matrix x =
regarima
.description
.buildRegressionVariables()
.all()
.matrix(new TsDomain(s.getStart(), xdata.getCount()));
RegSsf xssf = new RegSsf(stoch, x.subMatrix());
Filter filter = new Filter();
filter.setInitializer(new DiffuseSquareRootInitializer());
filter.setSsf(xssf);
DiffuseFilteringResults fr = new DiffuseFilteringResults(true);
fr.getVarianceFilter().setSavingP(true);
fr.getFilteredData().setSavingA(true);
filter.process(xdata, fr);
Smoother smoother = new Smoother();
smoother.setSsf(xssf);
smoother.setCalcVar(true);
SmoothingResults sm = new SmoothingResults();
smoother.process(xdata, fr, sm);
Smoother lsmoother = new Smoother();
lsmoother.setSsf(stoch);
lsmoother.setCalcVar(true);
SmoothingResults lsm = new SmoothingResults();
ExtendedSsfData xldata =
new ExtendedSsfData(new SsfData(regarima.linearizedSeries(false), null));
xldata.setForecastsCount(s.getFrequency().intValue());
lsmoother.process(xldata, lsm);
int spos = stoch.cmpPos(1);
DataBlock Z = new DataBlock(xssf.getStateDim());
double[] v = new double[xdata.getCount()];
double[] c = new double[xdata.getCount()];
double[] svar = sm.componentVar(spos);
double[] slvar = lsm.componentVar(spos);
int start = regarima.description.getRegressionVariablesStartingPosition();
for (int i = 0; i < v.length; ++i) {
Z.set(spos, 1);
for (int j = 0; j < calPos.length; ++j) {
Z.set(stoch.getStateDim() + calPos[j], x.get(i, calPos[j]));
}
v[i] = sm.zvariance(i, Z);
Z.set(spos, 0);
c[i] = sm.zvariance(i, Z);
System.out.print(svar[i]);
System.out.print('\t');
System.out.print(slvar[i]);
System.out.print('\t');
System.out.print(c[i]);
System.out.print('\t');
System.out.println(v[i]);
}
System.out.println(sm.P(50));
System.out.println(sm.P(svar.length - 1));
System.out.println(regarima.estimation.getLikelihood().getBVar());
}
