Support Agglomerative clustering. (#1384)

We use the open-source implementation from
https://github.com/cdalitz/hclust-cpp

sherpa-onnx/csrc/fast-clustering-test.cc

@@ -0,0 +1,69 @@
+// sherpa-onnx/csrc/fast-clustering-test.cc
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/fast-clustering.h"
+
+#include <vector>
+
+#include "gtest/gtest.h"
+
+namespace sherpa_onnx {
+
+TEST(FastClustering, TestTwoClusters) {
+  std::vector<float> features = {
+      // point 0
+      0.1,
+      0.1,
+      // point 2
+      0.4,
+      -0.5,
+      // point 3
+      0.6,
+      -0.7,
+      // point 1
+      0.2,
+      0.3,
+  };
+
+  FastClusteringConfig config;
+  config.num_clusters = 2;
+
+  FastClustering clustering(config);
+  auto labels = clustering.Cluster(features.data(), 4, 2);
+  int32_t k = 0;
+  for (auto i : labels) {
+    std::cout << "point " << k << ": label " << i << "\n";
+    ++k;
+  }
+}
+
+TEST(FastClustering, TestClusteringWithThreshold) {
+  std::vector<float> features = {
+      // point 0
+      0.1,
+      0.1,
+      // point 2
+      0.4,
+      -0.5,
+      // point 3
+      0.6,
+      -0.7,
+      // point 1
+      0.2,
+      0.3,
+  };
+
+  FastClusteringConfig config;
+  config.threshold = 0.5;
+
+  FastClustering clustering(config);
+  auto labels = clustering.Cluster(features.data(), 4, 2);
+  int32_t k = 0;
+  for (auto i : labels) {
+    std::cout << "point " << k << ": label " << i << "\n";
+    ++k;
+  }
+}
+
+}  // namespace sherpa_onnx