### Question:
GaussianProcessPrior extends WeightLearningApplication { protected ValueAndStd predictFnValAndStd(float[] x, List<WeightConfig> xKnown) { return predictFnValAndStd(x, xKnown, new float[blasYKnown.size()], new float[x.length], new float[x.length], new FloatMatrix(), new FloatMatrix(), new FloatMatrix(), FloatMatrix.zeroes(1, x.length)); } GaussianProcessPrior(List<Rule> rules, Database rvDB, Database observedDB);  GaussianProcessPrior(Model model, Database rvDB, Database observedDB);  static final String CONFIG_PREFIX; static final String KERNEL_KEY; static final String KERNEL_DEFAULT; static final String MAX_ITERATIONS_KEY; static final int MAX_ITERATIONS_DEFAULT; static final String MAX_CONFIGS_KEY; static final int MAX_CONFIGS_DEFAULT; static final String EXPLORATION_KEY; static final float EXPLORATION_DEFAULT; static final String RANDOM_CONFIGS_ONLY_KEY; static final boolean RANDOM_CONFIGS_ONLY_DEFAULT; static final String EARLY_STOPPING_KEY; static final boolean EARLY_STOPPING_DEFAULT; static final int MAX_RAND_INT_VAL; static final float SMALL_VALUE; }### Answer:
@Test public void testPredictFnValAndStd() { Config.setProperty(GaussianProcessKernel.REL_DEP_KEY, 100); Config.setProperty(GaussianProcessKernel.SCALE_KEY, 1.0); Config.setProperty(GaussianProcessKernel.SPACE_KEY, GaussianProcessKernel.Space.OS); GaussianProcessPrior wl = (GaussianProcessPrior) getWLA(); FloatMatrix inverseMat = FloatMatrix.zeroes(3, 3); inverseMat.set(0, 0, 1); inverseMat.set(0, 1, 1f); inverseMat.set(0, 2, 0.9999f); inverseMat.set(1, 0, 0.9999f); inverseMat.set(1, 1, 1); inverseMat.set(1, 2, 0.9999f); inverseMat.set(2, 0, 0.9999f); inverseMat.set(2, 1, 0.9999f); inverseMat.set(2, 2, 1); inverseMat = inverseMat.inverse(); wl.setKnownDataStdInvForTest(inverseMat); float[] x = new float[]{0.4f, 0.2f, 0.1f}; List<GaussianProcessPrior.WeightConfig> xKnown = new ArrayList<GaussianProcessPrior.WeightConfig>(); xKnown.add(new GaussianProcessPrior.WeightConfig(new float[]{0.1f, 0.2f, 0.3f})); xKnown.add(new GaussianProcessPrior.WeightConfig(new float[]{0.2f, 0.2f, 0.1f})); xKnown.add(new GaussianProcessPrior.WeightConfig(new float[]{0.4f, 0.3f, 0.2f})); float[] yKnown = new float[]{0.5f, 0.6f, 0.7f}; FloatMatrix blasYKnown = FloatMatrix.columnVector(yKnown); wl.setKernelForTest(GaussianProcessKernel.makeKernel(GaussianProcessKernel.KernelType.SQUARED_EXP, wl)); wl.setBlasYKnownForTest(blasYKnown); GaussianProcessPrior.ValueAndStd fnAndStd = wl.predictFnValAndStd(x, xKnown); Assert.assertEquals(0.84939, fnAndStd.value, 1e-4); Assert.assertEquals(0.99656, fnAndStd.std, 1e-4); }