Graphs : Greedy Best First (#2018)

* implement greedy best first

* implement Greedy Best First Search

* review changes

* add doctests

* >>> gbf.search()  # doctest: +NORMALIZE_WHITESPACE

Co-authored-by: Christian Clauss <cclauss@me.com>

graphs/greedy_best_first.py

@@ -0,0 +1,174 @@
+"""
+https://en.wikipedia.org/wiki/Best-first_search#Greedy_BFS
+"""
+
+from typing import List, Tuple
+
+grid = [
+    [0, 0, 0, 0, 0, 0, 0],
+    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
+    [0, 0, 0, 0, 0, 0, 0],
+    [0, 0, 1, 0, 0, 0, 0],
+    [1, 0, 1, 0, 0, 0, 0],
+    [0, 0, 0, 0, 0, 0, 0],
+    [0, 0, 0, 0, 1, 0, 0],
+]
+
+delta = ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right
+
+
+class Node:
+    """
+    >>> k = Node(0, 0, 4, 5, 0, None)
+    >>> k.calculate_heuristic()
+    9
+    >>> n = Node(1, 4, 3, 4, 2, None)
+    >>> n.calculate_heuristic()
+    2
+    >>> l = [k, n]
+    >>> n == l[0]
+    False
+    >>> l.sort()
+    >>> n == l[0]
+    True
+    """
+
+    def __init__(self, pos_x, pos_y, goal_x, goal_y, g_cost, parent):
+        self.pos_x = pos_x
+        self.pos_y = pos_y
+        self.pos = (pos_y, pos_x)
+        self.goal_x = goal_x
+        self.goal_y = goal_y
+        self.g_cost = g_cost
+        self.parent = parent
+        self.f_cost = self.calculate_heuristic()
+
+    def calculate_heuristic(self) -> float:
+        """
+        The heuristic here is the Manhattan Distance
+        Could elaborate to offer more than one choice
+        """
+        dy = abs(self.pos_x - self.goal_x)
+        dx = abs(self.pos_y - self.goal_y)
+        return dx + dy
+
+    def __lt__(self, other) -> bool:
+        return self.f_cost < other.f_cost
+
+
+class GreedyBestFirst:
+    """
+    >>> gbf = GreedyBestFirst((0, 0), (len(grid) - 1, len(grid[0]) - 1))
+    >>> [x.pos for x in gbf.get_successors(gbf.start)]
+    [(1, 0), (0, 1)]
+    >>> (gbf.start.pos_y + delta[3][0], gbf.start.pos_x + delta[3][1])
+    (0, 1)
+    >>> (gbf.start.pos_y + delta[2][0], gbf.start.pos_x + delta[2][1])
+    (1, 0)
+    >>> gbf.retrace_path(gbf.start)
+    [(0, 0)]
+    >>> gbf.search()  # doctest: +NORMALIZE_WHITESPACE
+    [(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 1), (5, 1), (6, 1),
+     (6, 2), (6, 3), (5, 3), (5, 4), (5, 5), (6, 5), (6, 6)]
+    """
+
+    def __init__(self, start, goal):
+        self.start = Node(start[1], start[0], goal[1], goal[0], 0, None)
+        self.target = Node(goal[1], goal[0], goal[1], goal[0], 99999, None)
+
+        self.open_nodes = [self.start]
+        self.closed_nodes = []
+
+        self.reached = False
+
+    def search(self) -> List[Tuple[int]]:
+        """
+        Search for the path,
+        if a path is not found, only the starting position is returned
+        """
+        while self.open_nodes:
+            # Open Nodes are sorted using __lt__
+            self.open_nodes.sort()
+            current_node = self.open_nodes.pop(0)
+
+            if current_node.pos == self.target.pos:
+                self.reached = True
+                return self.retrace_path(current_node)
+
+            self.closed_nodes.append(current_node)
+            successors = self.get_successors(current_node)
+
+            for child_node in successors:
+                if child_node in self.closed_nodes:
+                    continue
+
+                if child_node not in self.open_nodes:
+                    self.open_nodes.append(child_node)
+                else:
+                    # retrieve the best current path
+                    better_node = self.open_nodes.pop(self.open_nodes.index(child_node))
+
+                    if child_node.g_cost < better_node.g_cost:
+                        self.open_nodes.append(child_node)
+                    else:
+                        self.open_nodes.append(better_node)
+
+        if not (self.reached):
+            return [self.start.pos]
+
+    def get_successors(self, parent: Node) -> List[Node]:
+        """
+        Returns a list of successors (both in the grid and free spaces)
+        """
+        successors = []
+        for action in delta:
+            pos_x = parent.pos_x + action[1]
+            pos_y = parent.pos_y + action[0]
+
+            if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(grid) - 1):
+                continue
+
+            if grid[pos_y][pos_x] != 0:
+                continue
+
+            successors.append(
+                Node(
+                    pos_x,
+                    pos_y,
+                    self.target.pos_y,
+                    self.target.pos_x,
+                    parent.g_cost + 1,
+                    parent,
+                )
+            )
+        return successors
+
+    def retrace_path(self, node: Node) -> List[Tuple[int]]:
+        """
+        Retrace the path from parents to parents until start node
+        """
+        current_node = node
+        path = []
+        while current_node is not None:
+            path.append((current_node.pos_y, current_node.pos_x))
+            current_node = current_node.parent
+        path.reverse()
+        return path
+
+
+if __name__ == "__main__":
+    init = (0, 0)
+    goal = (len(grid) - 1, len(grid[0]) - 1)
+    for elem in grid:
+        print(elem)
+
+    print("------")
+
+    greedy_bf = GreedyBestFirst(init, goal)
+    path = greedy_bf.search()
+
+    for elem in path:
+        grid[elem[0]][elem[1]] = 2
+
+    for elem in grid:
+        print(elem)