# Similarity Clustering as in paper. 

# Database must be open.
# Distributed array T with attribute Pos of type point must be present. 
# Workers relation must be present.

# restore Workers from WorkersNewton
# let S = 'S' ffeed5 consume
# let T = S feed ddistribute3["T", 160, TRUE, Workers];

# Variable myPort must be set below to an exclusively used port.



# Step 1

let Card = T dmap["", . count] getValue tie[. + ..];
query share("Card", TRUE, Workers)

let SS = T dmap["", . feedNth[Card div 10000, FALSE]] dsummarize consume


# Step 2

@&Scripts/SimilarityPartitioning.sec;
let n = PC count;
let MinPts = 10;
let Eps = 100.0;
let wgs84 = create_geoid("WGS1984");
let myPort = 1238;


# Step 3

query share("PC", TRUE, Workers);
query share("MinPts", TRUE, Workers);
query share("Eps", TRUE, Workers);
query share("wgs84", TRUE, Workers);
query share("n", TRUE, Workers);


# Step 4

query memclear();
query T dcommand['query memclear()'] consume;
query T dcommand['query meminit(3600)'] consume;

query T dlet["PCm", 'PC feed mconsume'] consume;
query T dlet["PCm_Pos_mtree", 'PCm mcreatemtree[Pos, wgs84]'] consume

let V = T 
  dmap["",  . feed 
    loopjoin[fun(t: TUPLE) PCm_Pos_mtree PCm mdistScan[attr(t, Pos)] head[1] 
      projectextend[N; Dist: distance(attr(t, Pos), .Pos, wgs84)]]
    loopjoin[fun(u: TUPLE) PCm_Pos_mtree PCm mdistRange[attr(u, Pos), 
      attr(u, Dist) + (2 * Eps)] projectextend[; N2: .N]]
  ]
  partition["", .N2, n] 
  collect2["V", myPort]




# Step 5

let ControlN = createintdarray("ControlN", Workers, n);

let X = V ControlN 
  dmap2["X", $1 feed extend[Pos2: gk(.Pos)] dbscanM[Pos2, CID0, Eps, MinPts]
    extend[CID: (.CID0 * n) + $2] consume, myPort
  ]


# Step 6

query T dcommand['query memclear()'] filter[.Ok] count;

let Wm = X dmap["Wm", . feed filter[.N = .N2] mconsume];
let Wm_Pos_mtree = Wm dmap["Wm_Pos_mtree", . mcreatemtree[Pos, wgs84]];

let Neighbors = X Wm_Pos_mtree Wm 
  dmap3["Neighbors", $1 feed filter[.N # .N2] 
    loopsel[fun(t: TUPLE) $2 $3 mdistRange[attr(t, Pos), Eps] 
      projectextend[; P: .Osm_id, PosP: .Pos,  CID0: .CID0, CIDp: .CID, 
        IsCoreP: .IsCore, Np: .N, Q: attr(t, Osm_id), QPos: attr(t, Pos)]] 
    consume, myPort
  ]

let NeighborsByP = Neighbors partition["", hashvalue(.P, 999997), 0]
  collect2["NeighborsByP", myPort];
let NeighborsByQ = Neighbors partition["", hashvalue(.Q, 999997), 0]
  collect2["NeighborsByQ", myPort];


# Step 7

query T dcommand['query memclear()'] filter[.Ok] count;
query T dcommand['query meminit(3600)'] consume;




let Pairs = NeighborsByQ NeighborsByP 
  dmap2["Pairs", . feed {n1} .. feed {n2} itHashJoin[Q_n1, P_n2] mconsume, myPort]

let Merge = Pairs 
  dmap["Merge", . mfeed filter[.IsCoreP_n1 and .IsCoreP_n2] 
    project[CIDp_n1, CIDp_n2] sort rdup consume
  ]

let Assignments = Pairs 
  dmap["", 
    . mfeed filter[.IsCoreP_n1 and not(.IsCoreP_n2)] 
      projectextend[; P: .P_n2, N: .Np_n2,  CID: .CIDp_n1]
    . mfeed filter[.IsCoreP_n2 and not(.IsCoreP_n1)] 
      projectextend[; P: .P_n1, N: .Np_n1, CID: .CIDp_n2]
    concat sort krdup[P] consume
  ]
  partition["", .N, 0]
  collect2["Assignments", myPort]


# Step 8

let MergeM = Merge dsummarize sort rdup createmgraph2[CIDp_n1, CIDp_n2, 1.0];
let MaxCN = X dmap["", . feed max[CID] feed transformstream] dsummarize max[Elem];


# Step 9

let R = MergeM mg2connectedcomponents projectextend[; CID: .CIDp_n1, 
  CIDnew: .CompNo + MaxCN] sort rdup consume


# Step 10

query share("R", TRUE, Workers);


# Step 11

query X Assignments 
  dmap2["",  $1 feed addid filter[.N = .N2] $2 feed sort krdup[P] {a} 
    itHashJoin[Osm_id, P_a] $1 updatedirect2[TID; CID: .CID_a] count, myPort
  ] 
  getValue tie[. + ..]

query X 
  dmap["", $1 feed addid filter[.N = .N2] R feed sort krdup[CID] {a} 
    itHashJoin[CID, CID_a] $1 updatedirect2[TID; CID: .CIDnew_a] count
  ] 
  getValue tie[. + ..]