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Greedy furthest-neighbour path, compare methods
Parent 7c72f18
dkl9

dkl9 commited on 2025-185 23:46:05
Showing 1 changed files, with 45 additions and 3 deletions.

main.py
View file @ bbafe21
... ... 
@@ -1,4 +1,7 @@
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+import itertools
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 import math
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+import random
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+import turtle
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 import typing
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 import collections.abc
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... ... 
@@ -45,6 +48,16 @@ class BinaryTree:
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 def distance_table[T](points: list[T], metric: Func[[T, T], float]) -> DistTable:
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     return [[metric(x, y) for y in points] for x in points]
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+def show_mat(distances: DistTable) -> str:
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+    rows = []
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+    for i in range(len(distances)):
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+        first, last = i == 0, i == len(distances) - 1
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+        row = "/" if first else "\\" if last else "|"
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+        row += " ".join(f"{d:5.2f}" for d in distances[i])
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+        row += "\\" if first else "/" if last else "|"
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+        rows.append(row)
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+    return "\n".join(rows)
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+
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 def score_nearest(distances: DistTable, sample: IndSeq) -> float:
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     return sum(min(distances[i][j] for j in sample) for i in range(len(distances)))
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... ... 
@@ -62,6 +75,19 @@ def greedy_min_seq(distances: DistTable, score_func: Func[[DistTable, IndSeq], f
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         options.remove(best)
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     return seq
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+def furthest_nb(distances: DistTable) -> IndSeq:
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+    seq = []
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+    options = set(range(len(distances)))
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+    start = min(options, key=lambda o: score_nearest(distances, [o]))
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+    seq.append(start)
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+    options.remove(start)
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+    while options:
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+        dl = distances[seq[-1]]
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+        best = max(options, key=lambda o: dl[o])
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+        seq.append(best)
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+        options.remove(best)
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+    return seq
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+
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 def distance_hierarchy(distances: DistTable) -> BinaryTree:
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     n = len(distances)
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     forest = [BinaryTree(i) for i in range(n)]
... ... 
@@ -90,8 +116,24 @@ def scattered_hierarchy(hierarchy: BinaryTree) -> IndSeq:
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         seq.append(fb.a)
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     return seq
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-dt: DistTable = distance_table([(0, 2), (3, 2), (4, 2), (5, 0), (0, 0)], math.dist)
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-print(greedy_min_seq(dt, score_total))
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+def with_len(distances: DistTable, seq: IndSeq):
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+    return (sum(distances[i][j] for (i, j) in itertools.pairwise(seq)), seq)
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+
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+points: list[tuple[float, float]] = [(random.randint(0, 10), random.randint(0, 10)) for _ in range(10)]
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+print(points)
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+t: turtle.Turtle = turtle.Turtle()
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+t.hideturtle()
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+t.pen(speed=10)
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+for (i, (x, y)) in enumerate(points):
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+    t.teleport(50 * (x - 5), 50 * (y - 5))
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+    t.dot()
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+    t.write(i)
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+dt: DistTable = distance_table(points, math.dist)
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+print(show_mat(dt))
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 h: BinaryTree = distance_hierarchy(dt)
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 print(h)
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-print(scattered_hierarchy(h))
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+print("by graph", with_len(dt, scattered_hierarchy(h)))
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+print("by nearest-nb sum", with_len(dt, greedy_min_seq(dt, score_nearest)))
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+print("by total dist", with_len(dt, greedy_min_seq(dt, score_total)))
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+print("by furthest-nb", with_len(dt, furthest_nb(dt)))
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+input("done?")
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