public void xtestNullNaNHandling() throws Exception {
// create a feature collection
SimpleFeatureType ft =
DataUtilities.createType("classification.nullnan", "id:0,foo:int,bar:double");
Integer iVal[] =
new Integer[] {
new Integer(0),
new Integer(0),
new Integer(0),
new Integer(13),
new Integer(13),
new Integer(13),
null,
null,
null
};
Double dVal[] =
new Double[] {
new Double(0.0),
new Double(50.01),
null,
new Double(0.0),
new Double(50.01),
null,
new Double(0.0),
new Double(50.01),
null
};
SimpleFeature[] testFeatures = new SimpleFeature[iVal.length];
for (int i = 0; i < iVal.length; i++) {
testFeatures[i] =
SimpleFeatureBuilder.build(
ft,
new Object[] {
new Integer(i + 1), iVal[i], dVal[i],
},
"nantest.t" + (i + 1));
}
MemoryDataStore store = new MemoryDataStore();
store.createSchema(ft);
store.addFeatures(testFeatures);
FeatureCollection<SimpleFeatureType, SimpleFeature> thisFC =
store.getFeatureSource("nullnan").getFeatures();
// create the expression
Divide divide = ff.divide(ff.property("foo"), ff.property("bar"));
QuantileFunction qf = (QuantileFunction) ff.function("Quantile", divide, ff.literal(3));
RangedClassifier range = (RangedClassifier) qf.evaluate(thisFC);
assertEquals(2, range.getSize()); // 2 or 3?
assertEquals("0..0", range.getTitle(0));
assertEquals("0..0.25995", range.getTitle(1));
}
public void testEvaluateWithExpressions() throws Exception {
Literal classes = ff.literal(2);
PropertyName exp = ff.property("foo");
Function func = ff.function("Quantile", exp, classes);
Object value = func.evaluate(featureCollection);
assertTrue(value instanceof RangedClassifier);
RangedClassifier ranged = (RangedClassifier) value;
// the values being quantiled are
// {4,90,20,43,29,61,8,12};
// so there should be two groups:
// {4, 8, 12, 20} 4 <= x < 29
// {29, 43, 61, 90} 29 <= x <= 90
assertEquals(2, ranged.getSize());
assertEquals("4..29", ranged.getTitle(0));
assertEquals("29..90", ranged.getTitle(1));
}
/**
* Test a feature collection where each feature will be in it's own bin.
*
* <p>Creates a feature collection with five features 1-5. Then uses the quantile function to put
* these features in 5 bins. Each bin should have a single feature.
*
* @throws Exception
*/
public void testSingleBin() throws Exception {
// create a feature collection with five features values 1-5
SimpleFeatureType dataType = DataUtilities.createType("classification.test1", "id:0,value:int");
int iVal[] = new int[] {1, 2, 3, 4, 5};
SimpleFeature[] myfeatures = new SimpleFeature[iVal.length];
for (int i = 0; i < iVal.length; i++) {
myfeatures[i] =
SimpleFeatureBuilder.build(
dataType,
new Object[] {new Integer(i + 1), new Integer(iVal[i])},
"classification.test1" + (i + 1));
}
MemoryDataStore store = new MemoryDataStore();
store.createSchema(dataType);
store.addFeatures(myfeatures);
FeatureCollection<SimpleFeatureType, SimpleFeature> myFeatureCollection =
store.getFeatureSource("test1").getFeatures();
// run the quantile function
org.opengis.filter.expression.Expression function =
ff.function("Quantile", ff.property("value"), ff.literal(5));
Classifier classifier = (Classifier) function.evaluate(myFeatureCollection);
// verify the results
assertNotNull(classifier);
assertEquals(classifier.getClass(), RangedClassifier.class);
RangedClassifier range = (RangedClassifier) classifier;
assertEquals(5, range.getSize());
for (int i = 0; i < 5; i++) {
assertTrue(i + 1 == ((Number) range.getMin(i)).doubleValue());
if (i != 4) {
assertTrue(i + 2 == ((Number) range.getMax(i)).doubleValue());
assertEquals((i + 1) + ".." + (i + 2), range.getTitle(i));
} else {
assertTrue(i + 1 == ((Number) range.getMax(i)).doubleValue());
assertEquals((i + 1) + ".." + (i + 1), range.getTitle(i));
}
}
}
