Support Agglomerative clustering. (#1384)

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

sherpa-onnx/csrc/fast-clustering.cc

@@ -0,0 +1,83 @@
+// sherpa-onnx/csrc/fast-clustering.cc
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#include "sherpa-onnx/csrc/fast-clustering.h"
+
+#include <vector>
+
+#include "Eigen/Dense"
+#include "fastcluster-all-in-one.h"  // NOLINT
+
+namespace sherpa_onnx {
+
+class FastClustering::Impl {
+ public:
+  explicit Impl(const FastClusteringConfig &config) : config_(config) {}
+
+  std::vector<int32_t> Cluster(float *features, int32_t num_rows,
+                               int32_t num_cols) {
+    if (num_rows <= 0) {
+      return {};
+    }
+
+    if (num_rows == 1) {
+      return {0};
+    }
+
+    Eigen::Map<
+        Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
+        m(features, num_rows, num_cols);
+    m.rowwise().normalize();
+
+    std::vector<double> distance((num_rows * (num_rows - 1)) / 2);
+
+    int32_t k = 0;
+    for (int32_t i = 0; i != num_rows; ++i) {
+      auto v = m.row(i);
+      for (int32_t j = i + 1; j != num_rows; ++j) {
+        double cosine_similarity = v.dot(m.row(j));
+        double consine_dissimilarity = 1 - cosine_similarity;
+
+        if (consine_dissimilarity < 0) {
+          consine_dissimilarity = 0;
+        }
+
+        distance[k] = consine_dissimilarity;
+        ++k;
+      }
+    }
+
+    std::vector<int32_t> merge(2 * (num_rows - 1));
+    std::vector<double> height(num_rows - 1);
+
+    fastclustercpp::hclust_fast(num_rows, distance.data(),
+                                fastclustercpp::HCLUST_METHOD_SINGLE,
+                                merge.data(), height.data());
+
+    std::vector<int32_t> labels(num_rows);
+    if (config_.num_clusters > 0) {
+      fastclustercpp::cutree_k(num_rows, merge.data(), config_.num_clusters,
+                               labels.data());
+    } else {
+      fastclustercpp::cutree_cdist(num_rows, merge.data(), height.data(),
+                                   config_.threshold, labels.data());
+    }
+
+    return labels;
+  }
+
+ private:
+  FastClusteringConfig config_;
+};
+
+FastClustering::FastClustering(const FastClusteringConfig &config)
+    : impl_(std::make_unique<Impl>(config)) {}
+
+FastClustering::~FastClustering() = default;
+
+std::vector<int32_t> FastClustering::Cluster(float *features, int32_t num_rows,
+                                             int32_t num_cols) {
+  return impl_->Cluster(features, num_rows, num_cols);
+}
+}  // namespace sherpa_onnx