//
// C++ standalone version of fastcluster by Daniel Müllner
//
// Copyright: Christoph Dalitz, 2020
//            Daniel Müllner, 2011
// License:   BSD style license
//            (see the file LICENSE for details)
//


#include <assert.h>
#include <vector>
#include <algorithm>

#include "FastCluster/fastcluster.h"

// Code by Daniel Müllner
// workaround to make it usable as a standalone version (without R)
bool fc_isnan(double x)
{
    return false;
}
#include "fastcluster_dm.cpp.inc"
#include "fastcluster_R_dm.cpp.inc"

//
// Assigns cluster labels (0, ..., nclust-1) to the n points such
// that the cluster result is split into nclust clusters.
//
// Input arguments:
//   n      = number of observables
//   merge  = clustering result in R format
//   nclust = number of clusters
// Output arguments:
//   labels = allocated integer array of size n for result
//
void cutree_k(int n, const int* merge, int nclust, int* labels)
{

    int k,m1,m2,j,l;

    if (nclust > n || nclust < 2)
    {
        for (j=0; j<n; j++) labels[j] = 0;
        return;
    }

    // assign to each observable the number of its last merge step
    // beware: indices of observables in merge start at 1 (R convention)
    std::vector<int> last_merge(n, 0);
    for (k=1; k<=(n-nclust); k++)
    {
        // (m1,m2) = merge[k,]
        m1 = merge[k-1];
        m2 = merge[n-1+k-1];
        if (m1 < 0 && m2 < 0)   // both single observables
        {
            last_merge[-m1-1] = last_merge[-m2-1] = k;
        }
        else if (m1 < 0 || m2 < 0)   // one is a cluster
        {
            if(m1 < 0)
            {
                j = -m1;
                m1 = m2;
            }
            else j = -m2;
            // merging single observable and cluster
            for(l = 0; l < n; l++)
                if (last_merge[l] == m1)
                    last_merge[l] = k;
            last_merge[j-1] = k;
        }
        else   // both cluster
        {
            for(l=0; l < n; l++)
            {
                if( last_merge[l] == m1 || last_merge[l] == m2 )
                    last_merge[l] = k;
            }
        }
    }

    // assign cluster labels
    int label = 0;
    std::vector<int> z(n,-1);
    for (j=0; j<n; j++)
    {
        if (last_merge[j] == 0)   // still singleton
        {
            labels[j] = label++;
        }
        else
        {
            if (z[last_merge[j]] < 0)
            {
                z[last_merge[j]] = label++;
            }
            labels[j] = z[last_merge[j]];
        }
    }
}

//
// Assigns cluster labels (0, ..., nclust-1) to the n points such
// that the hierarchical clustering is stopped when cluster distance >= cdist
//
// Input arguments:
//   n      = number of observables
//   merge  = clustering result in R format
//   height = cluster distance at each merge step
//   cdist  = cutoff cluster distance
// Output arguments:
//   labels = allocated integer array of size n for result
//
void cutree_cdist(int n, const int* merge, double* height, double cdist, int* labels)
{

    int k;

    for (k=0; k<(n-1); k++)
    {
        if (height[k] >= cdist)
        {
            break;
        }
    }
    cutree_k(n, merge, n-k, labels);
}


//
// Hierarchical clustering with one of Daniel Muellner's fast algorithms
//
// Input arguments:
//   n       = number of observables
//   distmat = condensed distance matrix, i.e. an n*(n-1)/2 array representing
//             the upper triangle (without diagonal elements) of the distance
//             matrix, e.g. for n=4:
//               d00 d01 d02 d03
//               d10 d11 d12 d13   ->  d01 d02 d03 d12 d13 d23
//               d20 d21 d22 d23
//               d30 d31 d32 d33
//   method  = cluster metric (see enum hclust_fast_methods)
// Output arguments:
//   merge   = allocated (n-1)x2 matrix (2*(n-1) array) for storing result.
//             Result follows R hclust convention:
//              - observable indices start with one
//              - merge[i][] contains the merged nodes in step i
//              - merge[i][j] is negative when the node is an atom
//   height  = allocated (n-1) array with distances at each merge step
// Return code:
//   0 = ok
//   1 = invalid method
//
int hclust_fast(int n, double* distmat, int method, int* merge, double* height)
{

    // call appropriate clustering function
    cluster_result Z2(n-1);
    if (method == HCLUST_METHOD_SINGLE)
    {
        // single link
        MST_linkage_core(n, distmat, Z2);
    }
    else if (method == HCLUST_METHOD_COMPLETE)
    {
        // complete link
        NN_chain_core<METHOD_METR_COMPLETE, t_float>(n, distmat, NULL, Z2);
    }
    else if (method == HCLUST_METHOD_AVERAGE)
    {
        // best average distance
        double* members = new double[n];
        for (int i=0; i<n; i++) members[i] = 1;
        NN_chain_core<METHOD_METR_AVERAGE, t_float>(n, distmat, members, Z2);
        delete[] members;
    }
    else if (method == HCLUST_METHOD_MEDIAN)
    {
        // best median distance (beware: O(n^3))
        generic_linkage<METHOD_METR_MEDIAN, t_float>(n, distmat, NULL, Z2);
    }
    else
    {
        return 1;
    }

    int* order = new int[n];
    if (method == HCLUST_METHOD_MEDIAN)
    {
        generate_R_dendrogram<true>(merge, height, order, Z2, n);
    }
    else
    {
        generate_R_dendrogram<false>(merge, height, order, Z2, n);
    }

    delete[] order; // only needed for visualization

    return 0;
}
