resolves TheAlgorithms#12128

graphs/travelling_salesman_problem.py

@@ -0,0 +1,354 @@
+import itertools
+from collections.abc import Generator, Hashable, Sequence
+from dataclasses import dataclass
+from typing import Generic, TypeVar
+
+T = TypeVar("T", bound=int | str | Hashable)
+
+
+@dataclass(frozen=True)
+class TSPEdge(Generic[T]):
+    """
+    Represents an edge in a graph for the Traveling Salesman Problem (TSP).
+
+    Attributes:
+        vertices (frozenset[T]): A pair of vertices representing the edge.
+        weight (float): The weight (or cost) of the edge.
+    """
+
+    vertices: frozenset[T]
+    weight: float
+
+    def __str__(self) -> str:
+        return f"({self.vertices}, {self.weight})"
+
+    def __post_init__(self):
+        # Ensures that there is no loop in a vertex
+        if len(self.vertices) != 2:
+            raise ValueError("frozenset must have exactly 2 elements")
+
+    @classmethod
+    def from_3_tuple(cls, x, y, w) -> "TSPEdge":
+        """
+        Construct TSPEdge from a 3-tuple (x, y, w).
+        x & y are vertices and w is the weight.
+        """
+        return cls(frozenset([x, y]), w)
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, TSPEdge):
+            return NotImplemented
+        return self.vertices == other.vertices
+
+    def __add__(self, other: "TSPEdge") -> float:
+        return self.weight + other.weight
+
+
+class TSPGraph(Generic[T]):
+    """
+    Represents a graph for the Traveling Salesman Problem (TSP).
+    The graph is:
+    - Simple (no loops or multiple edges between vertices).
+    - Undirected.
+    - Connected.
+    """
+
+    def __init__(self, edges: frozenset[TSPEdge] | None = None):
+        self._edges = edges or frozenset()
+
+    def __str__(self) -> str:
+        return f"{[str(edge) for edge in self._edges]}"
+
+    @classmethod
+    def from_3_tuples(cls, *edges) -> "TSPGraph":
+        return cls(frozenset(TSPEdge.from_3_tuple(x, y, w) for x, y, w in edges))
+
+    @classmethod
+    def from_weights(cls, weights: list) -> "TSPGraph":
+        """
+        Create TSPGraph from Weights (List of Lists) where the vertices
+        are labeled with integers.
+        """
+        triples = [
+            (x, y, weights[x][y])
+            for x, y in itertools.product(range(len(weights)), range(len(weights[0])))
+            if x != y  # Filter out self-loops
+        ]
+        # return cls.from_3_tuples(*cast(list[tuple[T, T, float]], triples))
+        return cls.from_3_tuples(*triples)
+
+    @property
+    def vertices(self) -> frozenset[T]:
+        return frozenset(vertex for edge in self._edges for vertex in edge.vertices)
+
+    @property
+    def edges(self) -> frozenset[TSPEdge]:
+        return self._edges
+
+    @property
+    def weight(self) -> float:
+        """Total Weight of TSPGraph."""
+        return sum(edge.weight for edge in self._edges)
+
+    def __contains__(self, obj: T | TSPEdge) -> bool:
+        if isinstance(obj, TSPEdge):
+            return any(obj == edge_ for edge_ in self._edges)
+        else:
+            return obj in self.vertices
+
+    def is_edge_in_graph(self, x: T, y: T) -> bool:
+        return frozenset([x, y]) in self.get_edges()
+
+    def add_edge(self, x: T, y: T, w: float) -> "TSPGraph":
+        # Validator to check if either x or y is in the vertex set to ensure
+        # that the graph would be connected
+        # Only use this validator if there exist at least 1 edge in the edge set.
+        if self._edges and x not in self and y not in self:
+            error_message = f"Adding the edge ({x}, {y}) may form a disconnected graph."
+            raise ValueError(error_message)
+
+        new_edge = TSPEdge.from_3_tuple(
+            x, y, w
+        )  # This would raise Vertex Loop error if x == y
+
+        # Raise error if Multi-Edges
+        if new_edge in self:
+            error_message = f"({x}, {y}, {w}) is invalid."
+            raise ValueError(error_message)
+
+        return TSPGraph(
+            edges=frozenset(self._edges | frozenset([TSPEdge.from_3_tuple(x, y, w)]))
+        )
+
+    def get_edges(self) -> list[frozenset[T]]:
+        return [edge.vertices for edge in self.edges]
+
+    def get_edge_weight(self, x: T, y: T) -> float:
+        if (x not in self) or (y not in self):
+            error_message = f"{x} or {y} does not belong to the graph vertices."
+            raise ValueError(error_message)
+
+        # Find the edge with vertices (x, y)
+        edge = next(
+            (edge for edge in self.edges if frozenset([x, y]) == edge.vertices), None
+        )
+
+        if edge is None:
+            error_message = f"No edge exists between {x} and {y}."
+            raise ValueError(error_message)
+
+        return edge.weight
+
+    def get_vertex_neighbors(self, x: T) -> frozenset[T]:
+        if x not in self.vertices:
+            error_message = f"{x} does not belong to the graph vertex set."
+            raise ValueError(error_message)
+        return frozenset(
+            next(iter(edge.vertices - frozenset([x])))
+            for edge in self.edges
+            if x in edge.vertices
+        )
+
+    def get_vertex_degree(self, x: T) -> int:
+        if x not in self.vertices:
+            error_message = f"{x} does not belong to the graph vertices."
+            raise ValueError(error_message)
+        return sum(1 for edge in self.edges if x in edge.vertices)
+
+    def get_vertex_argmin(self, x: T) -> T:
+        """Returns the Neighbor of a Vertex with the Minimum Weight."""
+        return min(
+            [(y, self.get_edge_weight(x, y)) for y in self.get_vertex_neighbors(x)],
+            key=lambda tup: tup[1],
+        )[0]
+
+    def get_vertex_argmax(self, x: T) -> T:
+        """Returns the Neighbor of a Vertex with the Maximum Weight."""
+        return max(
+            [(y, self.get_edge_weight(x, y)) for y in self.get_vertex_neighbors(x)],
+            key=lambda tup: tup[1],
+        )[0]
+
+    def get_vertex_neighbor_weights(self, x: T) -> Sequence[tuple[T, float]]:
+        # Sort by Smallest to Largest
+        return sorted(
+            [(y, self.get_edge_weight(x, y)) for y in self.get_vertex_neighbors(x)],
+            key=lambda tup: tup[1],  # pair[1] is the weight (float)
+        )
+
+
+def adjacent_tuples(path: list[T]) -> zip:
+    """
+    Generates adjacent pairs of elements from a path.
+
+    Args:
+        path (list[T]): A list of vertices representing a path.
+
+    Returns:
+        zip: A zip object containing tuples of adjacent vertices.
+
+    Examples
+        >>> list(adjacent_tuples([1, 2, 3, 4, 5]))
+        [(1, 2), (2, 3), (3, 4), (4, 5)]
+
+        >>> list(adjacent_tuples(["A", "B", "C", "D", "E"]))
+        [('A', 'B'), ('B', 'C'), ('C', 'D'), ('D', 'E')]
+    """
+    iter1, iter2 = itertools.tee(path)
+    next(iter2, None)
+    return zip(iter1, iter2)
+
+
+def path_weight(path: list[T], tsp_graph: TSPGraph) -> float:
+    """
+    Calculates the total weight of a given path in the graph.
+
+    Args:
+        path (list[T]): A list of vertices representing a path.
+        tsp_graph (TSPGraph): The graph containing the edges and weights.
+
+    Returns:
+        float: The total weight of the path.
+    """
+    return sum(tsp_graph.get_edge_weight(x, y) for x, y in adjacent_tuples(path))
+
+
+def generate_paths(start: T, end: T, tsp_graph: TSPGraph) -> Generator[list[T]]:
+    """
+    Generates all possible paths between two vertices in a
+    TSPGraph using Depth-First Search (DFS).
+
+    Args:
+        start (T): The starting vertex.
+        end (T): The target vertex.
+        tsp_graph (TSPGraph): The graph to traverse.
+
+    Yields:
+        Generator[list[T]]: A generator yielding paths as lists of vertices.
+
+    Raises:
+        AssertionError: If start or end is not in the graph, or if they are the same.
+    """
+
+    assert start in tsp_graph.vertices
+    assert end in tsp_graph.vertices
+    assert start != end
+
+    def dfs(
+        current: T, target: T, visited: set[T], path: list[T]
+    ) -> Generator[list[T]]:
+        visited.add(current)
+        path.append(current)
+
+        # If we reach the target, yield the current path
+        if current == target:
+            yield list(path)
+        else:
+            # Recur for all unvisited neighbors
+            for neighbor in tsp_graph.get_vertex_neighbors(current):
+                if neighbor not in visited:
+                    yield from dfs(neighbor, target, visited, path)
+
+        # Backtrack
+        path.pop()
+        visited.remove(current)
+
+    # Initialize DFS
+    yield from dfs(start, end, set(), [])
+
+
+def nearest_neighborhood(tsp_graph: TSPGraph, v, visited_=None) -> list[T] | None:
+    """
+    Approximates a solution to the Traveling Salesman Problem
+    using the Nearest Neighbor heuristic.
+
+    Args:
+        tsp_graph (TSPGraph): The graph to traverse.
+        v (T): The starting vertex.
+        visited_ (list[T] | None): A list of already visited vertices.
+
+    Returns:
+        list[T] | None: A complete Hamiltonian cycle if possible, otherwise None.
+    """
+    # Initialize visited list on first call
+    visited = visited_ or [v]
+
+    # Base case: if all vertices are visited
+    if len(visited) == len(tsp_graph.vertices):
+        # Check if there is an edge to return to the starting point
+        if tsp_graph.is_edge_in_graph(visited[-1], visited[0]):
+            return [*visited, visited[0]]
+        else:
+            return None
+
+    # Get unvisited neighbors
+    filtered_neighbors = [
+        tup for tup in tsp_graph.get_vertex_neighbor_weights(v) if tup[0] not in visited
+    ]
+
+    # If there are unvisited neighbors, continue to the nearest one
+    if filtered_neighbors:
+        next_v = min(filtered_neighbors, key=lambda tup: tup[1])[0]
+        return nearest_neighborhood(tsp_graph, v=next_v, visited_=[*visited, next_v])
+    else:
+        # No more neighbors, return None (cannot form a complete tour)
+        return None
+
+
+def sample_1():
+    # Reference: https://graphicmaths.com/computer-science/graph-theory/travelling-salesman-problem/
+
+    edges = [
+        ("A", "B", 7),
+        ("A", "D", 1),
+        ("A", "E", 1),
+        ("B", "C", 3),
+        ("B", "E", 8),
+        ("C", "E", 2),
+        ("C", "D", 6),
+        ("D", "E", 7),
+    ]
+
+    # Create the graph
+    graph = TSPGraph.from_3_tuples(*edges)
+
+    import random
+
+    init_v = random.choice(list(graph.vertices))
+    optim_path = nearest_neighborhood(graph, init_v)
+    # optim_path = nearest_neighborhood(graph, 'A')
+    print(f"Optimal Cycle: {optim_path}")
+    if optim_path:
+        print(f"Optimal Weight: {path_weight(optim_path, graph)}")
+
+
+def sample_2():
+    # Example 8x8 weight matrix (symmetric, no self-loops)
+    weights = [
+        [0, 1, 2, 3, 4, 5, 6, 7],
+        [1, 0, 8, 9, 10, 11, 12, 13],
+        [2, 8, 0, 14, 15, 16, 17, 18],
+        [3, 9, 14, 0, 19, 20, 21, 22],
+        [4, 10, 15, 19, 0, 23, 24, 25],
+        [5, 11, 16, 20, 23, 0, 26, 27],
+        [6, 12, 17, 21, 24, 26, 0, 28],
+        [7, 13, 18, 22, 25, 27, 28, 0],
+    ]
+
+    graph = TSPGraph.from_weights(weights)
+
+    import random
+
+    init_v = random.choice(list(graph.vertices))
+    optim_path = nearest_neighborhood(graph, init_v)
+    print(f"Optimal Cycle: {optim_path}")
+    if optim_path:
+        print(f"Optimal Weight: {path_weight(optim_path, graph)}")
+
+
+if __name__ == "__main__":
+    import doctest
+
+    doctest.testmod()
+    sample_1()
+    sample_2()