<filename>celloutline/utils/greedy.py
from __future__ import print_function, division
from itertools import islice
from array import array as pyarray

""" greedy solver of unknown provenance, would be nice to replace - CDW """
################################################################################
# A simple algorithm for solving the Travelling Salesman Problem
# Finds a suboptimal solution
################################################################################
if "xrange" not in globals():
    #py3
    xrange = range
else:
    #py2
    pass
    

def optimize_solution( distances, connections, endpoints ):
    """Tries to optimize solution, found by the greedy algorithm"""
    N = len(connections)
    path = restore_path( connections, endpoints )
    def ds(i,j): #distance between ith and jth points of path
        pi = path[i]
        pj = path[j]
        if pi < pj:
            return distances[pj][pi]
        else:
            return distances[pi][pj]
            
    d_total = 0.0
    optimizations = 0
    for a in xrange(N-1):
        b = a+1
        for c in xrange( b+2, N-1):
            d = c+1
            delta_d = ds(a,b)+ds(c,d) -( ds(a,c)+ds(b,d))
            if delta_d > 0:
                d_total += delta_d
                optimizations += 1
                connections[path[a]].remove(path[b])
                connections[path[a]].append(path[c])
                connections[path[b]].remove(path[a])
                connections[path[b]].append(path[d])

                connections[path[c]].remove(path[d])
                connections[path[c]].append(path[a])
                connections[path[d]].remove(path[c])
                connections[path[d]].append(path[b])
                path[:] = restore_path( connections, endpoints )
    
    return optimizations, d_total
        
def restore_path( connections, endpoints ):
    """Takes array of connections and returns a path.
    Connections is array of lists with 1 or 2 elements.
    These elements are indices of teh vertices, connected to this vertex
    Guarantees that first index < last index
    """
    if endpoints is None:
        #there are 2 nodes with valency 1 - start and end. Get them.
        start, end = [idx 
                      for idx, conn in enumerate(connections)
                      if len(conn)==1 ]
    else:
        start, end = endpoints

    path = [start]
    prev_point = None
    cur_point = start
    while True:
        next_points = [pnt for pnt in connections[cur_point] 
                       if pnt != prev_point ]
        if not next_points: break
        next_point = next_points[0]
        path.append(next_point)
        prev_point, cur_point = cur_point, next_point
    return path

def _assert_triangular(distances):
    """Ensure that matrix is left-triangular at least.
    """
    for i, row in enumerate(distances):
        if len(row) < i: raise ValueError( "Distance matrix must be left-triangular at least. Row {row} must have at least {i} items".format(**locals()))
    

def pairs_by_dist(N, distances):
    """returns list of coordinate pairs (i,j), sorted by distances; such that i < j"""
    #Sort coordinate pairs by distance
    indices = []
    for i in xrange(N):
        for j in xrange(i):
            indices.append((i,j))

    indices.sort(key = lambda ij: distances[ij[0]][ij[1]])
    return indices

def solve_tsp( distances, optim_steps=3, pairs_by_dist=pairs_by_dist, endpoints=None ):
    """Given a distance matrix, finds a solution for the TSP problem.
    Returns list of vertex indices. 
    Guarantees that the first index is lower than the last

    :arg: distances : left-triangular matrix of distances. array of arrays
    :arg: optim_steps (int) number of additional optimization steps, allows to improve solution but costly.
    :arg: pairs_by_dist (function) an implementtion of the pairs_by_dist function. for optimization purposes.
    :arg: endpoinds : None or pair (int,int)
    """
    N = len(distances)
    if N == 0: return []
    if N == 1: return [0]

    _assert_triangular(distances)

    #State of the TSP solver algorithm.
    node_valency = pyarray('i', [2])*N #Initially, each node has 2 sticky ends
    if endpoints is not None:
        start, end = endpoints
        if start == end: raise ValueError("start=end is not supported")
        node_valency[start]=1
        node_valency[end]=1
        
        
    #for each node, stores 1 or 2 connected nodes
    connections = [[] for i in xrange(N)] 

    def join_segments(sorted_pairs):
        #segments of nodes. Initially, each segment contains only 1 node
        segments = [ [i] for i in xrange(N) ]
  
        def possible_edges():
            #Generate sequence of graph edges, that are possible and connect different segments.
            #print("#### sorted pairs:", sorted_pairs)
            for ij in sorted_pairs:
                i,j = ij
                #if both start and end could have connections,
                #  and both nodes connect to a different segments:
                if node_valency[i] and node_valency[j] and\
                   (segments[i] is not segments[j]): 
                    yield ij
                    
        def connect_vertices(i,j):
            node_valency[i] -= 1
            node_valency[j] -= 1
            connections[i].append(j)
            connections[j].append(i)
            #Merge segment J into segment I.
            seg_i = segments[i]
            seg_j = segments[j]
            if len(seg_j) > len(seg_i):
                seg_i, seg_j = seg_j, seg_i
                i, j = j, i
            for node_idx in seg_j:
                segments[node_idx] = seg_i
            seg_i.extend(seg_j)
            
        def edge_connects_endpoint_segments(i,j):
            #return True, if given ede merges 2 segments that have endpoints in them
            si,sj = segments[i],segments[j]
            ss,se = segments[start], segments[end]
            return (si is ss) and (sj is se) or (sj is ss) and (si is se)
                
            
        #Take first N-1 possible edge. they are already sorted by distance
        edges_left = N-1
        for i,j in possible_edges():
            if endpoints and edges_left!=1 and edge_connects_endpoint_segments(i,j):
                #print(f"#### disallow {i}, {j} because premature termination")
                continue #don't allow premature path termination
            
            
            #print(f"####add edge {i}, {j} of len {distances[i][j]}")
            
            connect_vertices(i,j)
            edges_left -= 1
            if edges_left == 0:
                break

    #invoke main greedy algorithm
    join_segments(pairs_by_dist(N, distances))

    #now call additional optiomization procedure.
    for passn in range(optim_steps):
        nopt, dtotal = optimize_solution( distances, connections, endpoints )
        if nopt == 0:
            break
    #restore path from the connections map (graph) and return it
    return restore_path( connections, endpoints=endpoints )

<filename>celloutline/utils/greedy.py
from __future__ import print_function, division
from itertools import islice
from array import array as pyarray

""" greedy solver of unknown provenance, would be nice to replace - CDW """
################################################################################
# A simple algorithm for solving the Travelling Salesman Problem
# Finds a suboptimal solution
################################################################################
if "xrange" not in globals():
    #py3
    xrange = range
else:
    #py2
    pass
    

def optimize_solution( distances, connections, endpoints ):
    """Tries to optimize solution, found by the greedy algorithm"""
    N = len(connections)
    path = restore_path( connections, endpoints )
    def ds(i,j): #distance between ith and jth points of path
        pi = path[i]
        pj = path[j]
        if pi < pj:
            return distances[pj][pi]
        else:
            return distances[pi][pj]
            
    d_total = 0.0
    optimizations = 0
    for a in xrange(N-1):
        b = a+1
        for c in xrange( b+2, N-1):
            d = c+1
            delta_d = ds(a,b)+ds(c,d) -( ds(a,c)+ds(b,d))
            if delta_d > 0:
                d_total += delta_d
                optimizations += 1
                connections[path[a]].remove(path[b])
                connections[path[a]].append(path[c])
                connections[path[b]].remove(path[a])
                connections[path[b]].append(path[d])

                connections[path[c]].remove(path[d])
                connections[path[c]].append(path[a])
                connections[path[d]].remove(path[c])
                connections[path[d]].append(path[b])
                path[:] = restore_path( connections, endpoints )
    
    return optimizations, d_total
        
def restore_path( connections, endpoints ):
    """Takes array of connections and returns a path.
    Connections is array of lists with 1 or 2 elements.
    These elements are indices of teh vertices, connected to this vertex
    Guarantees that first index < last index
    """
    if endpoints is None:
        #there are 2 nodes with valency 1 - start and end. Get them.
        start, end = [idx 
                      for idx, conn in enumerate(connections)
                      if len(conn)==1 ]
    else:
        start, end = endpoints

    path = [start]
    prev_point = None
    cur_point = start
    while True:
        next_points = [pnt for pnt in connections[cur_point] 
                       if pnt != prev_point ]
        if not next_points: break
        next_point = next_points[0]
        path.append(next_point)
        prev_point, cur_point = cur_point, next_point
    return path

def _assert_triangular(distances):
    """Ensure that matrix is left-triangular at least.
    """
    for i, row in enumerate(distances):
        if len(row) < i: raise ValueError( "Distance matrix must be left-triangular at least. Row {row} must have at least {i} items".format(**locals()))
    

def pairs_by_dist(N, distances):
    """returns list of coordinate pairs (i,j), sorted by distances; such that i < j"""
    #Sort coordinate pairs by distance
    indices = []
    for i in xrange(N):
        for j in xrange(i):
            indices.append((i,j))

    indices.sort(key = lambda ij: distances[ij[0]][ij[1]])
    return indices

def solve_tsp( distances, optim_steps=3, pairs_by_dist=pairs_by_dist, endpoints=None ):
    """Given a distance matrix, finds a solution for the TSP problem.
    Returns list of vertex indices. 
    Guarantees that the first index is lower than the last

    :arg: distances : left-triangular matrix of distances. array of arrays
    :arg: optim_steps (int) number of additional optimization steps, allows to improve solution but costly.
    :arg: pairs_by_dist (function) an implementtion of the pairs_by_dist function. for optimization purposes.
    :arg: endpoinds : None or pair (int,int)
    """
    N = len(distances)
    if N == 0: return []
    if N == 1: return [0]

    _assert_triangular(distances)

    #State of the TSP solver algorithm.
    node_valency = pyarray('i', [2])*N #Initially, each node has 2 sticky ends
    if endpoints is not None:
        start, end = endpoints
        if start == end: raise ValueError("start=end is not supported")
        node_valency[start]=1
        node_valency[end]=1
        
        
    #for each node, stores 1 or 2 connected nodes
    connections = [[] for i in xrange(N)] 

    def join_segments(sorted_pairs):
        #segments of nodes. Initially, each segment contains only 1 node
        segments = [ [i] for i in xrange(N) ]
  
        def possible_edges():
            #Generate sequence of graph edges, that are possible and connect different segments.
            #print("#### sorted pairs:", sorted_pairs)
            for ij in sorted_pairs:
                i,j = ij
                #if both start and end could have connections,
                #  and both nodes connect to a different segments:
                if node_valency[i] and node_valency[j] and\
                   (segments[i] is not segments[j]): 
                    yield ij
                    
        def connect_vertices(i,j):
            node_valency[i] -= 1
            node_valency[j] -= 1
            connections[i].append(j)
            connections[j].append(i)
            #Merge segment J into segment I.
            seg_i = segments[i]
            seg_j = segments[j]
            if len(seg_j) > len(seg_i):
                seg_i, seg_j = seg_j, seg_i
                i, j = j, i
            for node_idx in seg_j:
                segments[node_idx] = seg_i
            seg_i.extend(seg_j)
            
        def edge_connects_endpoint_segments(i,j):
            #return True, if given ede merges 2 segments that have endpoints in them
            si,sj = segments[i],segments[j]
            ss,se = segments[start], segments[end]
            return (si is ss) and (sj is se) or (sj is ss) and (si is se)
                
            
        #Take first N-1 possible edge. they are already sorted by distance
        edges_left = N-1
        for i,j in possible_edges():
            if endpoints and edges_left!=1 and edge_connects_endpoint_segments(i,j):
                #print(f"#### disallow {i}, {j} because premature termination")
                continue #don't allow premature path termination
            
            
            #print(f"####add edge {i}, {j} of len {distances[i][j]}")
            
            connect_vertices(i,j)
            edges_left -= 1
            if edges_left == 0:
                break

    #invoke main greedy algorithm
    join_segments(pairs_by_dist(N, distances))

    #now call additional optiomization procedure.
    for passn in range(optim_steps):
        nopt, dtotal = optimize_solution( distances, connections, endpoints )
        if nopt == 0:
            break
    #restore path from the connections map (graph) and return it
    return restore_path( connections, endpoints=endpoints )

greedy solver of unknown provenance, would be nice to replace - CDW ################################################################################ # A simple algorithm for solving the Travelling Salesman Problem # Finds a suboptimal solution ################################################################################ #py3 #py2 Tries to optimize solution, found by the greedy algorithm #distance between ith and jth points of path Takes array of connections and returns a path.     Connections is array of lists with 1 or 2 elements.     These elements are indices of teh vertices, connected to this vertex     Guarantees that first index < last index #there are 2 nodes with valency 1 - start and end. Get them. Ensure that matrix is left-triangular at least. returns list of coordinate pairs (i,j), sorted by distances; such that i < j #Sort coordinate pairs by distance Given a distance matrix, finds a solution for the TSP problem.     Returns list of vertex indices.      Guarantees that the first index is lower than the last      :arg: distances : left-triangular matrix of distances. array of arrays     :arg: optim_steps (int) number of additional optimization steps, allows to improve solution but costly.     :arg: pairs_by_dist (function) an implementtion of the pairs_by_dist function. for optimization purposes.     :arg: endpoinds : None or pair (int,int) #State of the TSP solver algorithm. #Initially, each node has 2 sticky ends #for each node, stores 1 or 2 connected nodes #segments of nodes. Initially, each segment contains only 1 node #Generate sequence of graph edges, that are possible and connect different segments. #print("#### sorted pairs:", sorted_pairs) #if both start and end could have connections, #  and both nodes connect to a different segments: #Merge segment J into segment I. #return True, if given ede merges 2 segments that have endpoints in them #Take first N-1 possible edge. they are already sorted by distance #print(f"#### disallow {i}, {j} because premature termination") #don't allow premature path termination #print(f"####add edge {i}, {j} of len {distances[i][j]}") #invoke main greedy algorithm #now call additional optiomization procedure. #restore path from the connections map (graph) and return it