@Test
public void testPDF() throws Exception {
System.out.println("Testing 10000 random pdf calls");
LogNormalDistributionModel logNormal = new LogNormalDistributionModel();
logNormal.init("1.0", "2.0");
for (int i = 0; i < 10000; i++) {
double M = Math.random() * 10.0 - 5.0;
double S = Math.random() * 10;
double x = -1;
while (x < 0) {
x = Math.log(Math.random() * 10);
}
logNormal.MParameterInput.setValue(M + "", logNormal);
logNormal.SParameterInput.setValue(S + "", logNormal);
logNormal.initAndValidate();
double pdf =
1.0
/ (x * S * Math.sqrt(2 * Math.PI))
* Math.exp(-Math.pow(Math.log(x) - M, 2) / (2 * S * S));
System.out.println("Testing logNormal[M=" + M + " S=" + S + "].pdf(" + x + ")");
double f = logNormal.density(x);
assertEquals(pdf, f, 1e-10);
}
}
@Test
public void testCalcLogP3() throws Exception {
// does the same as testCalcLogP(), but with by constructing object through init
LogNormalDistributionModel logNormal = new LogNormalDistributionModel();
logNormal.init("2000", "0.6", true, "1200");
RealParameter p = new RealParameter(new Double[] {2952.6747000000014});
double f0 = logNormal.calcLogP(p);
assertEquals(-7.880210654973873, f0, 1e-10);
}
@Test
public void testCalcLogP() throws Exception {
LogNormalDistributionModel logNormal = new LogNormalDistributionModel();
logNormal.hasMeanInRealSpaceInput.setValue("true", logNormal);
logNormal.offsetInput.setValue("1200", logNormal);
logNormal.MParameterInput.setValue("2000", logNormal);
logNormal.SParameterInput.setValue("0.6", logNormal);
logNormal.initAndValidate();
RealParameter p = new RealParameter(new Double[] {2952.6747000000014});
double f0 = logNormal.calcLogP(p);
assertEquals(-7.880210654973873, f0, 1e-10);
}
@Test
public void testCalcLogP2() throws Exception {
// does the same as testCalcLogP(), but with by constructing object through XML
String sXML =
"<input spec='beast.math.distributions.LogNormalDistributionModel' "
+ "offset='1200' "
+ "M='2000' "
+ "S='0.6' "
+ "meanInRealSpace='true'/>";
RealParameter p = new RealParameter(new Double[] {2952.6747000000014});
XMLParser parser = new XMLParser();
LogNormalDistributionModel logNormal =
(LogNormalDistributionModel) parser.parseBareFragment(sXML, true);
double f0 = logNormal.calcLogP(p);
assertEquals(-7.880210654973873, f0, 1e-10);
}