Support Agglomerative clustering. (#1384)

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

sherpa-onnx/csrc/fast-clustering.h

@@ -0,0 +1,43 @@
+// sherpa-onnx/csrc/fast-clustering.h
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_CSRC_FAST_CLUSTERING_H_
+#define SHERPA_ONNX_CSRC_FAST_CLUSTERING_H_
+
+#include <memory>
+#include <vector>
+
+#include "sherpa-onnx/csrc/fast-clustering-config.h"
+
+namespace sherpa_onnx {
+
+class FastClustering {
+ public:
+  explicit FastClustering(const FastClusteringConfig &config);
+  ~FastClustering();
+
+  /**
+   * @param features Pointer to a 2-D feature matrix in row major. Each row
+   *                 is a feature frame. It is changed in-place. We will
+   *                 convert each feature frame to a normalized vector.
+   *                 That is, the L2-norm of each vector will be equal to 1.
+   *                 It uses cosine dissimilarity,
+   *                 which is 1 - (cosine similarity)
+   * @param num_rows Number of feature frames
+   * @param num-cols The feature dimension.
+   *
+   * @return Return a vector of size num_rows. ans[i] contains the label
+   *         for the i-th feature frame, i.e., the i-th row of the feature
+   *         matrix.
+   */
+  std::vector<int32_t> Cluster(float *features, int32_t num_rows,
+                               int32_t num_cols);
+
+ private:
+  class Impl;
+  std::unique_ptr<Impl> impl_;
+};
+
+}  // namespace sherpa_onnx
+#endif  // SHERPA_ONNX_CSRC_FAST_CLUSTERING_H_