added statistic and Q computation methods

src/shogun/statistical_testing/MMD.cpp

@@ -68,6 +68,7 @@ struct CMMD::Self
 	void compute_jobs(ComputationManager&) const;
 
 	std::pair<float64_t, float64_t> compute_statistic_variance();
+	std::pair<SGVector<float64_t>, SGMatrix<float64_t>> compute_statistic_and_Q();
 	SGVector<float64_t> sample_null();
 
 	CMMD& owner;
@@ -252,6 +253,73 @@ std::pair<float64_t, float64_t> CMMD::Self::compute_statistic_variance()
 	return std::make_pair(statistic, variance);
 }
 
+std::pair<SGVector<float64_t>, SGMatrix<float64_t>> CMMD::Self::compute_statistic_and_Q()
+{
+	REQUIRE(kernel_selection_mgr.num_kernels()>0, "No kernels specified for kernel learning! "
+		"Please add kernels using add_kernel() method!\n");
+
+	const size_t num_kernels=kernel_selection_mgr.num_kernels();
+	SGVector<float64_t> statistic(num_kernels);
+	SGMatrix<float64_t> Q(num_kernels, num_kernels);
+
+	std::fill(statistic.data(), statistic.data()+statistic.size(), 0);
+	std::fill(Q.data(), Q.data()+Q.size(), 0);
+
+	std::vector<index_t> term_counters_statistic(num_kernels, 1);
+	SGMatrix<index_t> term_counters_Q(num_kernels, num_kernels);
+	std::fill(term_counters_Q.data(), term_counters_Q.data()+term_counters_Q.size(), 1);
+
+	DataManager& dm=owner.get_data_manager();
+	ComputationManager cm;
+	create_computation_jobs();
+	cm.enqueue_job(statistic_job);
+
+	dm.start();
+	auto next_burst=dm.next();
+	std::vector<CFeatures*> blocks;
+	std::vector<std::vector<float32_t>> mmds(num_kernels);
+	while (!next_burst.empty())
+	{
+		merge_samples(next_burst, blocks);
+		REQUIRE(blocks.size()%2==0, "The number of blocks per burst (%d this burst) has to be even!\n", blocks.size());
+		for (size_t k=0; k<num_kernels; ++k)
+		{
+			CKernel* kernel=kernel_selection_mgr.kernel_at(k);
+			compute_kernel(cm, blocks, kernel);
+			compute_jobs(cm);
+			mmds[k]=cm.result(0);
+			for (size_t i=0; i<mmds[k].size(); ++i)
+			{
+				auto delta=mmds[k][i]-statistic[k];
+				statistic[k]+=delta/term_counters_statistic[k]++;
+			}
+		}
+		for (size_t i=0; i<num_kernels; ++i)
+		{
+			for (size_t j=0; j<=i; ++j)
+			{
+				for (size_t k=0; k<blocks.size()-1; k+=2)
+				{
+					auto term=(mmds[i][k]-mmds[i][k+1])*(mmds[i][k]-mmds[i][k+1]);
+					Q(i, j)+=(term-Q(i, j))/term_counters_Q(i, j)++;
+				}
+				Q(j, i)=Q(i, j);
+			}
+		}
+		next_burst=dm.next();
+	}
+	mmds.clear();
+
+	dm.end();
+	cm.done();
+
+	std::for_each(statistic.data(), statistic.data()+statistic.size(), [this](float64_t val)
+	{
+		val=owner.normalize_statistic(val);
+	});
+	return std::make_pair(statistic, Q);
+}
+
 SGVector<float64_t> CMMD::Self::sample_null()
 {
 	const KernelManager& km=owner.get_kernel_manager();
@@ -341,7 +409,8 @@ void CMMD::select_kernel(EKernelSelectionMethod kmethod)
 			break;
 		}
 		default:
-			SG_ERROR("Unsupported kernel selection method specified!\n");
+			SG_ERROR("Unsupported kernel selection method specified! "
+					"Presently only accepted values are MAXIMIZE_MMD, MAXIMIZE_POWER!\n");
 			break;
 	}
 	SG_DEBUG("Leaving!\n");