Init issue 11 implementation

Author: Mec-iS

Diff for: src/cluster/hierarchical.rs

@@ -0,0 +1,62 @@
+/// # Hierarchical clustering
+/// 
+/// Implement hierarchical clustering methods:
+/// * Agglomerative clustering (current)
+/// * Bisecting K-Means (future)
+/// * Fastcluster (future)
+/// 
+
+/* 
+class AgglomerativeClustering():
+    """
+    Parameters
+    ----------
+    n_clusters : int or None, default=2
+        The number of clusters to find. It must be ``None`` if
+        ``distance_threshold`` is not ``None``.
+    affinity : str or callable, default='euclidean'
+        If linkage is "ward", only "euclidean" is accepted.
+    linkage : {'ward',}, default='ward'
+        Which linkage criterion to use. The linkage criterion determines which
+        distance to use between sets of observation. The algorithm will merge
+        the pairs of cluster that minimize this criterion.
+        - 'ward' minimizes the variance of the clusters being merged.
+    compute_distances : bool, default=False
+        Computes distances between clusters even if `distance_threshold` is not
+        used. This can be used to make dendrogram visualization, but introduces
+        a computational and memory overhead.
+    """
+
+    def fit(X):
+        # compute tree 
+        parents, childern = ward_tree(X, ....)
+        # compute clusters
+        labels = _hierarchical.hc_get_heads(parents)
+        # assign cluster numbers
+        self.labels_ = np.searchsorted(np.unique(labels), labels)
+
+*/
+
+// implement ward tree
+
+
+// implement hierarchical cut (only needed if we want to allwo compute_full_tree) (future)
+
+
+// HOT: try to implement fastcluster <https://arxiv.org/pdf/1109.2378.pdf> (future)
+
+
+// additional: implement BisectingKMeans (future)
+
+
+mod tests {
+    // >>> from sklearn.cluster import AgglomerativeClustering
+    // >>> import numpy as np
+    // >>> X = np.array([[1, 2], [1, 4], [1, 0],
+    // ...               [4, 2], [4, 4], [4, 0]])
+    // >>> clustering = AgglomerativeClustering().fit(X)
+    // >>> clustering
+    // AgglomerativeClustering()
+    // >>> clustering.labels_
+    // array([1, 1, 1, 0, 0, 0])
+}