520_Project2.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "import copy\n",
        "# import networkx as nx"
      ],
      "metadata": {
        "id": "DSQg8a0YkQ_u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s-S84YE2G83y"
      },
      "outputs": [],
      "source": [
        "\n",
        "class Graph:\n",
        "    def __init__ (self, node_num):\n",
        "        self.node_num = node_num\n",
        "        # self.node_count = range(node_num) ## not needed\n",
        "\n",
        "        ## need to change it to adjacency list for better read-time for edges ===> ALSO change 'add_edge' accordingly\n",
        "        # self.adj_matrix =  [[0 for column in range(node_num)] for row in range(node_num)] ## adjacency matrix initialised\n",
        "\n",
        "        self.adj_list = {node : set() for node in range(node_num)}\n",
        "        self.visual = [] ## VIS\n",
        "\n",
        "        i=0\n",
        "        while i<node_num-1:\n",
        "            self.add_edge(i, i+1)\n",
        "            i=i+1\n",
        "        self.add_edge(self.node_num-1,0)    \n",
        "        \n",
        "        self.add_rand_edges()\n",
        "\n",
        "        # self.prey_belief = [1/49]*50\n",
        "        # self.prey_belief[self.loc] = 0\n",
        "\n",
        "    def add_edge(self, node1, node2): ## --> O/P: updates adj_list\n",
        "        \n",
        "        ## since graph is undirected, we add bi-directional edges\n",
        "\n",
        "        # self.adj_matrix[node1][node2] = weight\n",
        "        # self.adj_matrix[node2][node1] = weight\n",
        "        \n",
        "        self.adj_list[node1].add(node2)\n",
        "        self.adj_list[node2].add(node1)\n",
        "\n",
        "        tmp = [node1, node2] ## VIS\n",
        "        self.visual.append(tmp) ## VIS\n",
        "        \n",
        "        ## if self.directed == False:\n",
        "        ##    self.adj_matrix[node2][node1] = weight\n",
        "\n",
        "    def get_deg(self, node): ## --> O/P: INT ## func to get degree of a node\n",
        "        return len(self.adj_list[node])\n",
        "\n",
        "    def get_next_moves(self, node):\n",
        "        return list(self.adj_list[node]) \n",
        "\n",
        "    def get_valid_neighbours(self, node): ## --> O/P: list of neighbors (e.g.: [1, 2, 3, ...] )\n",
        "\n",
        "        valid_neighbours = []\n",
        "        # make a list of valid neighbors (-5 <- node -> 5)\n",
        "        # make a new list from prev list with neighbors w/ degree = 2\n",
        "        # random.choice from the new list\n",
        "        ## I CAN INITIALISE i=-5 and then run till i<6 \n",
        "        ##\n",
        "        for i in range(-5, -1):\n",
        "            next = (node + i + self.node_num) % self.node_num ## the \"+ self.node_num\" handles negative cases\n",
        "            valid_neighbours.append(next)\n",
        "        for i in range(2, 6):\n",
        "            next = (node + i + self.node_num) % self.node_num ## the \"+ self.node_num\" handles negative cases\n",
        "            valid_neighbours.append(next)\n",
        "\n",
        "        return valid_neighbours\n",
        "    \n",
        "    def get_deg2_neighbours(self, node): ## --> O/P: list of neighbors with deg=2 [e.g.: [1, 2, 3, ...]]\n",
        "\n",
        "        # make a new list from prev list with neighbors w/ degree = 2\n",
        "        # random.choice from the new list\n",
        "        valid_neighbours = self.get_valid_neighbours(node)\n",
        "        deg2_neighbours = []\n",
        "        for i in valid_neighbours:\n",
        "            if(self.get_deg(i) == 2):\n",
        "                deg2_neighbours.append(i)\n",
        "\n",
        "        # print(valid_neighbours)\n",
        "        # print(\"\\n\")\n",
        "        # print(deg2_neighbours)\n",
        "        return deg2_neighbours\n",
        "\n",
        "    def get_rand_node(self):\n",
        "        return random.choice(list(self.adj_list.keys()))\n",
        "\n",
        "    def add_rand_edges(self): ## --> O/P: updates adj_list\n",
        "        \n",
        "        total_nodes = list(self.adj_list.keys()) ## --> total_nodes = [0,1,......,10]\n",
        "        while(total_nodes):\n",
        "            x = random.choice(total_nodes)\n",
        "            # print (x , \":\", total_nodes)\n",
        "            valid_nbrs = self.get_deg2_neighbours(x)\n",
        "            if valid_nbrs:\n",
        "                y = random.choice(valid_nbrs) ## failing where it doesn't get deg2_neighbours\n",
        "                self.add_edge(x, y)\n",
        "                total_nodes.remove(y) ### fails where 'y' is NULL\n",
        "                # print(y)\n",
        "            total_nodes.remove(x)\n",
        "\n",
        "    def getPath(self, goal, start, parent):\n",
        "\n",
        "        result = [goal]\n",
        "        while goal != start:\n",
        "            goal = parent[goal]    \n",
        "            result.append(goal)\n",
        "        # print (goal)\n",
        "\n",
        "        result.reverse()\n",
        "        return result\n",
        "\n",
        "    def BFS(self, start, goal):\n",
        "\n",
        "        fringe = [start]\n",
        "        visited = set()\n",
        "        parent = {}\n",
        "        visited.add(start)\n",
        "        \n",
        "        while(fringe):\n",
        "            current = fringe.pop(0) #Queue so pop(0) implementing FIFO\n",
        "            # visited.add(current) \n",
        "            if current == goal:\n",
        "            #print(len(visited))\n",
        "                return self.getPath(goal, start, parent)\n",
        "            #print (current,\"current\")\n",
        "            children = self.get_next_moves(current)\n",
        "            #print(children,\"test_BFS\")\n",
        "            for child in children:\n",
        "                if child not in visited:\n",
        "                    visited.add(child)\n",
        "                    fringe.append(child)\n",
        "                    parent[child] = current\n",
        "\n",
        "        return None\n",
        "\n",
        "    def print_adj_list(self):\n",
        "        for i in self.adj_list.keys():\n",
        "            print(i, self.adj_list[i])\n",
        "\n",
        "    ## VIS\n",
        "    def visualize(self):\n",
        "        G = nx.Graph()\n",
        "        G.add_edges_from(self.visual)\n",
        "        nx.draw_networkx(G)\n",
        "        plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "RzQnK9qi5xM-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Prey:\n",
        "    def __init__(self, graph):\n",
        "        self.loc = graph.get_rand_node()\n",
        "        self.graph = graph\n",
        "\n",
        "    # def get_neighbours(self):\n",
        "    #     return self.graph.adj_list[self.loc] ## --> neighbours is a set()\n",
        "        \n",
        "    def move(self):\n",
        "        # neighbours = list(self.get_neighbours())\n",
        "        neighbours = self.graph.get_next_moves(self.loc)\n",
        "        neighbours.append(self.loc)\n",
        "        next = random.choice(neighbours)\n",
        "        self.loc = next\n",
        "        return self.loc\n",
        "\n",
        "    def __str__(self):\n",
        "        return str(self.loc)\n",
        "\n",
        "    def __repr__(self):\n",
        "        return self.loc"
      ],
      "metadata": {
        "id": "-yxLDJFBOrI1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Predator:\n",
        "    def __init__(self, graph):\n",
        "        self.loc = graph.get_rand_node()\n",
        "        self.graph = graph\n",
        "\n",
        "    def move(self, agent_loc):\n",
        "        # assert self.loc != agent_loc ## but will this prevent agent from dying ??\n",
        "\n",
        "        neighbours = self.graph.get_next_moves(self.loc)\n",
        "\n",
        "        path = self.graph.BFS(self.loc, agent_loc)\n",
        "        self.loc = path[1]\n",
        "        return self.loc\n",
        "\n",
        "    def __str__(self):\n",
        "        return str(self.loc)\n",
        "\n",
        "    def __repr__(self):\n",
        "        return self.loc\n",
        "        "
      ],
      "metadata": {
        "id": "ov0kjS12TlE-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Agent1:\n",
        "\n",
        "    def __init__(self, graph, prey_loc, pred_loc, dbg=False):\n",
        "        \n",
        "        self.graph = graph\n",
        "        self.loc = graph.get_rand_node()\n",
        "        self.dbg = dbg\n",
        "        # self.loc = graph.get_rand_node()\n",
        "        # print(\"Agent1: \", prey_loc, pred_loc)\n",
        "        while prey_loc == self.loc or pred_loc == self.loc:\n",
        "            self.loc = graph.get_rand_node()\n",
        "\n",
        "    def debug(self, *args):\n",
        "        if self.dbg:\n",
        "            print(\"Agent1: \", args)\n",
        "\n",
        "    def isPrey(self, prey_loc):\n",
        "        if self.loc == prey_loc:\n",
        "            return True\n",
        "\n",
        "    def isPred(self, pred_loc):\n",
        "        if self.loc == pred_loc:\n",
        "            return True\n",
        "\n",
        "    def move(self, prey_loc, pred_loc):\n",
        "        \n",
        "        self.loc = self.move_helper(prey_loc, pred_loc)\n",
        "        return self.loc\n",
        "\n",
        "    def move_helper(self, prey_loc, pred_loc):\n",
        "        nbrs = self.graph.get_next_moves(self.loc)\n",
        "        prey_path_dict = {}\n",
        "        pred_path_dict = {}\n",
        "\n",
        "        curr_path_prey = len(self.graph.BFS(self.loc, prey_loc))\n",
        "        curr_path_pred = len(self.graph.BFS(self.loc, pred_loc))\n",
        "        self.debug(\"Curr Path:\", curr_path_prey, curr_path_pred)\n",
        "\n",
        "        for node in nbrs:\n",
        "            prey_path_dict[node] = len(self.graph.BFS(node, prey_loc)) ## {(1:3), (2:4)}\n",
        "            pred_path_dict[node] = len(self.graph.BFS(node, pred_loc))\n",
        "\n",
        "        self.debug(\"Prey Paths:\", prey_path_dict)\n",
        "        self.debug(\"Pred Paths:\", pred_path_dict)\n",
        "\n",
        "        nodes_lt_prey = []\n",
        "        nodes_eq_prey = []\n",
        "        for n, cost in prey_path_dict.items():\n",
        "            if cost < curr_path_prey:\n",
        "                nodes_lt_prey.append(n)\n",
        "            elif cost == curr_path_prey:\n",
        "                nodes_eq_prey.append(n)\n",
        "\n",
        "        nodes_gt_pred = []\n",
        "        nodes_eq_pred = []\n",
        "        for n, cost in pred_path_dict.items():\n",
        "            if cost > curr_path_pred:\n",
        "                nodes_gt_pred.append(n)\n",
        "            elif cost == curr_path_pred:\n",
        "                nodes_eq_pred.append(n)\n",
        "\n",
        "        valid_n = []\n",
        "\n",
        "        ## 1. closer to prey and farther from pred\n",
        "        if nodes_lt_prey:\n",
        "            for n in nodes_lt_prey:\n",
        "                if n in nodes_gt_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 1\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 2. closer to prey and not closer to pred\n",
        "        if nodes_lt_prey:\n",
        "            for n in nodes_lt_prey:\n",
        "                if n in nodes_eq_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 2\")\n",
        "                return random.choice(valid_n)\n",
        "\n",
        "        ## 3. not farther than prey and farther from pred\n",
        "        if nodes_eq_prey:\n",
        "            for n in nodes_eq_prey:\n",
        "                if n in nodes_gt_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 3\")\n",
        "                return random.choice(valid_n)\n",
        "\n",
        "        ## 4. not farther than prey and not closer to pred\n",
        "        if nodes_eq_prey:\n",
        "            for n in nodes_eq_prey:\n",
        "                if n in nodes_eq_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 4\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 5. farther from pred\n",
        "        if nodes_gt_pred:\n",
        "            self.debug(\"Rule 5\")\n",
        "            return random.choice(nodes_gt_pred)\n",
        "\n",
        "        ## 6. not closer to pred\n",
        "        if nodes_eq_pred:\n",
        "            self.debug(\"Rule 6\")\n",
        "            return random.choice(nodes_eq_pred)\n",
        "\n",
        "        ## 7. sit still and pray\n",
        "        else:\n",
        "            self.debug(\"Rule 7\")\n",
        "            return self.loc\n"
      ],
      "metadata": {
        "id": "bkOpmE0Jc951"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Agent2:\n",
        "\n",
        "    def __init__(self, graph, prey_loc, pred_loc, dbg=False):\n",
        "\n",
        "        self.graph = graph\n",
        "        self.loc = graph.get_rand_node()\n",
        "        self.dbg = dbg\n",
        "        # self.loc = graph.get_rand_node()\n",
        "        # print(\"Agent1: \", prey_loc, pred_loc)\n",
        "        while prey_loc == self.loc or pred_loc == self.loc:\n",
        "            self.loc = graph.get_rand_node()\n",
        "\n",
        "    def debug(self, *args):\n",
        "        if self.dbg:\n",
        "            print(\"Agent2: \", args)\n",
        "\n",
        "    def isPrey(self, prey_loc):\n",
        "        if self.loc == prey_loc:\n",
        "            return True\n",
        "\n",
        "    def isPred(self, pred_loc):\n",
        "        if self.loc == pred_loc:\n",
        "            return True\n",
        "    \n",
        "    def move(self, prey_loc, pred_loc):\n",
        "        self.loc = self.move_helper(prey_loc, pred_loc)\n",
        "        return self.loc\n",
        "\n",
        "    def move_helper(self,prey_loc, pred_loc):\n",
        "        \n",
        "        nbrs = self.graph.get_next_moves(self.loc)\n",
        "        prey_path_dict = {}\n",
        "        pred_path_dict = {}\n",
        "\n",
        "        curr_path_prey = len(self.graph.BFS(self.loc, prey_loc))\n",
        "        curr_path_pred = len(self.graph.BFS(self.loc, pred_loc))\n",
        "        self.debug(\"Curr Path:\", curr_path_prey, curr_path_pred)\n",
        "        for node in nbrs:\n",
        "            prey_path_dict[node] = len(self.graph.BFS(node, prey_loc)) ## {(1:3), (2:4)}\n",
        "            pred_path_dict[node] = len(self.graph.BFS(node, pred_loc))\n",
        "\n",
        "        self.debug(\"Prey Paths:\", prey_path_dict)\n",
        "        self.debug(\"Pred Paths:\", pred_path_dict)\n",
        "        nodes_lt_prey = []\n",
        "        nodes_eq_prey = []\n",
        "        for n, cost in prey_path_dict.items():\n",
        "            if cost < curr_path_prey:\n",
        "                nodes_lt_prey.append(n)\n",
        "            elif cost == curr_path_prey:\n",
        "                nodes_eq_prey.append(n)\n",
        "\n",
        "        nodes_gt_pred = []\n",
        "        nodes_eq_pred = []\n",
        "        for n, cost in pred_path_dict.items():\n",
        "            if cost > curr_path_pred:\n",
        "                nodes_gt_pred.append(n)\n",
        "            elif cost == curr_path_pred:\n",
        "                nodes_eq_pred.append(n)\n",
        "\n",
        "        valid_n = []\n",
        "\n",
        "        ## 3. not farther than prey and farther from pred\n",
        "        if nodes_eq_prey:\n",
        "            for n in nodes_eq_prey:\n",
        "                if n in nodes_gt_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 3\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 5. farther from pred\n",
        "        if nodes_gt_pred:\n",
        "            self.debug(\"Rule 5\")\n",
        "            return random.choice(nodes_gt_pred)\n",
        "\n",
        "        ## 1. closer to prey and farther from pred\n",
        "        if nodes_lt_prey:\n",
        "            for n in nodes_lt_prey:\n",
        "                if n in nodes_gt_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 1\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 2. closer to prey and not closer to pred\n",
        "        if nodes_lt_prey:\n",
        "            for n in nodes_lt_prey:\n",
        "                if n in nodes_eq_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 2\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 4. not farther than prey and not closer to pred\n",
        "        if nodes_eq_prey:\n",
        "            for n in nodes_eq_prey:\n",
        "                if n in nodes_eq_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 4\")\n",
        "                return random.choice(valid_n)\n",
        "\n",
        "        ## 6. not closer to pred\n",
        "        if nodes_eq_pred:\n",
        "            self.debug(\"Rule 6\")\n",
        "            return random.choice(nodes_eq_pred)\n",
        "\n",
        "        ## 7. sit still and pray\n",
        "        else:\n",
        "            self.debug(\"Rule 7\")\n",
        "            return self.loc\n"
      ],
      "metadata": {
        "id": "DWLBftwBWxbI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Agent3:\n",
        "\n",
        "    def __init__(self, graph, prey_loc, pred_loc, prey_belief, dbg=False):\n",
        "        \n",
        "        self.graph = graph\n",
        "        self.loc = graph.get_rand_node()\n",
        "        self.dbg = dbg\n",
        "        # self.loc = graph.get_rand_node()\n",
        "        # print(\"Agent1: \", prey_loc, pred_loc)\n",
        "        self.prey_loc = prey_loc\n",
        "        self.pred_loc = pred_loc\n",
        "\n",
        "        while prey_loc == self.loc or pred_loc == self.loc:\n",
        "            self.loc = graph.get_rand_node()\n",
        "\n",
        "        # self.prey_belief = [1/49]*50\n",
        "        self.prey_belief = prey_belief\n",
        "        self.prey_belief[self.loc] = 0\n",
        "\n",
        "        # self.new_prey_belief = []\n",
        "    \n",
        "    # def update_prey_belief(self, prey_belief):\n",
        "\n",
        "    def debug(self, *args):\n",
        "        if self.dbg:\n",
        "            print(\"Agent3: \", args)\n",
        "\n",
        "    def isPrey(self, loc, prey_loc):\n",
        "        if loc == prey_loc:\n",
        "            return True\n",
        "\n",
        "    def isPred(self, loc, pred_loc):\n",
        "        if loc == pred_loc:\n",
        "            return True\n",
        "\n",
        "    def survey(self, prey_belief):\n",
        "        ## look at current list of belief - select max prob nodes (if multiple - select one at random)\n",
        "        new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "        # new_prey_belief = prey_belief\n",
        "\n",
        "        val_max = max(prey_belief)\n",
        "        # idx_max = 0 ## not needed\n",
        "        idx_max_list = []\n",
        "\n",
        "        for i in range(len(prey_belief)):\n",
        "            if prey_belief[i] >= val_max:\n",
        "                # val_max = prey_belief[i]\n",
        "                idx_max_list.append(i)\n",
        "\n",
        "        rand_idx_max = random.choice(idx_max_list)\n",
        "        # print(prey_belief, val_max, idx_max_list)\n",
        "\n",
        "        # ### check if prey is there or not\n",
        "        # \n",
        "        if self.isPrey(rand_idx_max, self.prey_loc):\n",
        "            # print(\"IS PREY EXECUTED: \", rand_idx_max)\n",
        "            for i in range(self.graph.node_num):\n",
        "                prey_belief[i] = 0.0\n",
        "            prey_belief[rand_idx_max] = 1.0\n",
        "            # print (\"ISPREY: \", prey_belief)\n",
        "            return prey_belief\n",
        "        #     update_belief() ## according to prey found in survey logic\n",
        "                \n",
        "        else:\n",
        "        #     update_belief() ## according to prey not found in survey logic\n",
        "            for i in range(self.graph.node_num):\n",
        "                # denom = sum(prey_belief)-prey_belief[rand_idx_max]\n",
        "                denom = 1-prey_belief[rand_idx_max]\n",
        "                new_prey_belief[i] = prey_belief[i]/denom\n",
        "                new_prey_belief[rand_idx_max] = 0.0\n",
        "\n",
        "        prey_belief = new_prey_belief\n",
        "        return prey_belief\n",
        "\n",
        "    def agent_move_prey_belief(self, prey_loc, prey_belief):\n",
        "\n",
        "        new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "        # new_prey_belief = prey_belief\n",
        "\n",
        "        if self.isPrey(self.loc, prey_loc):\n",
        "            for i in range(self.graph.node_num): ###///this will not actually happen since the game would be over\n",
        "                prey_belief[i] = 0.0\n",
        "            prey_belief[self.loc] = 1.0\n",
        "            return prey_belief  \n",
        "        \n",
        "        else:\n",
        "        #   update_belief() ## according to prey not found in survey logic\n",
        "            for i in range(self.graph.node_num):\n",
        "                # denom = sum(prey_belief)-prey_belief[self.loc]\n",
        "                denom = 1.0-prey_belief[self.loc]\n",
        "                new_prey_belief[i] = prey_belief[i]/denom\n",
        "                new_prey_belief[self.loc] = 0.0\n",
        "                # new_prey_belief[curr_loc] = 0.0\n",
        "                # prey_belief[i] = prey_belief[i]/denom\n",
        "\n",
        "        prey_belief = new_prey_belief\n",
        "        return prey_belief\n",
        "\n",
        "    def prey_move_prey_belief(self, prey_loc, prey_belief):\n",
        "\n",
        "        new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "        # new_prey_belief = prey_belief\n",
        "\n",
        "        for i in range(self.graph.node_num):\n",
        "            nbrs_i = self.graph.get_next_moves(i)\n",
        "            nbrs_i.append(i)\n",
        "            for nbr in nbrs_i:\n",
        "                denom = self.graph.get_deg(nbr) + 1\n",
        "                new_prey_belief[i] += prey_belief[nbr] / denom\n",
        "\n",
        "        prey_belief = new_prey_belief\n",
        "\n",
        "        prey_belief = self.agent_move_prey_belief(prey_loc, prey_belief)\n",
        "\n",
        "        # prey_belief = new_prey_belief\n",
        "        return prey_belief\n",
        "\n",
        "    # ## #### #### ####     \n",
        "    # def prey_move_prey_belief(self, prey_loc, prey_belief):\n",
        "    #     new_prey_belief = []\n",
        "    #     for node in range(self.graph.node_num):\n",
        "    #         tmp = 0.0\n",
        "    #         for nbr in self.graph.get_next_moves(node):\n",
        "    #             tmp += prey_belief[nbr] / (self.graph.get_deg(nbr)+1)\n",
        "    #         tmp += prey_belief[node]/(self.graph.get_deg(node)+1)\n",
        "    #         new_prey_belief.append(tmp)\n",
        "    #     return new_prey_belief\n",
        "\n",
        "    def move(self, prey_loc, pred_loc, prey_belief):\n",
        "        self.debug('Prey Belief: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "        prey_belief = self.survey(prey_belief)\n",
        "        self.debug('Prey Belief after S: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "        curr_loc = self.loc ######\n",
        "        self.loc = self.move_helper(prey_loc, pred_loc, prey_belief)\n",
        "        prey_belief = self.agent_move_prey_belief(prey_loc, prey_belief)\n",
        "        # prey_belief[curr_loc] = 0.0\n",
        "        self.debug('Prey Belief after A: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "        # prey_belief = self.prey_move_prey_belief(prey3.loc, prey_belief)\n",
        "        # self.debug('Prey Belief after P: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "        return self.loc\n",
        "\n",
        "    # def move_helper(self, prey_loc, pred_loc, prey_belief):\n",
        "    def move_helper(self, prey_loc, pred_loc, prey_belief):\n",
        "\n",
        "        ## We select max belief node from list of max belief nodes and assume it to be where Prey is even if its not been found\n",
        "        val_max = max(prey_belief)\n",
        "        idx_max = 0\n",
        "        idx_max_list = []\n",
        "\n",
        "        for i in range(len(prey_belief)):\n",
        "            if prey_belief[i] >= val_max:\n",
        "                val_max = prey_belief[i]\n",
        "                idx_max_list.append(i)\n",
        "\n",
        "        prey_loc = random.choice(idx_max_list) ##need self.prey_loc or not? // why is prey_loc being or not being there as a parameter affecting code ??????\n",
        "        self.debug('Predicted prey location :', prey_loc)\n",
        "        \n",
        "        nbrs = self.graph.get_next_moves(self.loc)\n",
        "        prey_path_dict = {}\n",
        "        pred_path_dict = {}\n",
        "\n",
        "        curr_path_prey = len(self.graph.BFS(self.loc, prey_loc))\n",
        "        curr_path_pred = len(self.graph.BFS(self.loc, pred_loc))\n",
        "        self.debug(\"Curr Path:\", curr_path_prey, curr_path_pred)\n",
        "\n",
        "        for node in nbrs:\n",
        "            prey_path_dict[node] = len(self.graph.BFS(node, prey_loc)) ## {(1:3), (2:4)}\n",
        "            pred_path_dict[node] = len(self.graph.BFS(node, pred_loc))\n",
        "\n",
        "        self.debug(\"Prey Paths:\", prey_path_dict)\n",
        "        self.debug(\"Pred Paths:\", pred_path_dict)\n",
        "\n",
        "        nodes_lt_prey = []\n",
        "        nodes_eq_prey = []\n",
        "        for n, cost in prey_path_dict.items():\n",
        "            if cost < curr_path_prey:\n",
        "                nodes_lt_prey.append(n)\n",
        "            elif cost == curr_path_prey:\n",
        "                nodes_eq_prey.append(n)\n",
        "\n",
        "        nodes_gt_pred = []\n",
        "        nodes_eq_pred = []\n",
        "        for n, cost in pred_path_dict.items():\n",
        "            if cost > curr_path_pred:\n",
        "                nodes_gt_pred.append(n)\n",
        "            elif cost == curr_path_pred:\n",
        "                nodes_eq_pred.append(n)\n",
        "\n",
        "        valid_n = []\n",
        "\n",
        "        ## 1. closer to prey and farther from pred\n",
        "        if nodes_lt_prey:\n",
        "            for n in nodes_lt_prey:\n",
        "                if n in nodes_gt_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 1\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 2. closer to prey and not closer to pred\n",
        "        if nodes_lt_prey:\n",
        "            for n in nodes_lt_prey:\n",
        "                if n in nodes_eq_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 2\")\n",
        "                return random.choice(valid_n)\n",
        "\n",
        "        ## 3. not farther than prey and farther from pred\n",
        "        if nodes_eq_prey:\n",
        "            for n in nodes_eq_prey:\n",
        "                if n in nodes_gt_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 3\")\n",
        "                return random.choice(valid_n)\n",
        "\n",
        "        ## 4. not farther than prey and not closer to pred\n",
        "        if nodes_eq_prey:\n",
        "            for n in nodes_eq_prey:\n",
        "                if n in nodes_eq_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 4\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 5. farther from pred\n",
        "        if nodes_gt_pred:\n",
        "            self.debug(\"Rule 5\")\n",
        "            return random.choice(nodes_gt_pred)\n",
        "\n",
        "        ## 6. not closer to pred\n",
        "        if nodes_eq_pred:\n",
        "            self.debug(\"Rule 6\")\n",
        "            return random.choice(nodes_eq_pred)\n",
        "\n",
        "        ## 7. sit still and pray\n",
        "        else:\n",
        "            self.debug(\"Rule 7\")\n",
        "            return self.loc\n",
        "        "
      ],
      "metadata": {
        "id": "K4u8_bXv9yUs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Agent4:\n",
        "\n",
        "    def __init__(self, graph, prey_loc, pred_loc, prey_belief, dbg=False):\n",
        "        \n",
        "        self.graph = graph\n",
        "        self.loc = graph.get_rand_node()\n",
        "        self.dbg = dbg\n",
        "        self.prey_loc = prey_loc\n",
        "        self.pred_loc = pred_loc\n",
        "\n",
        "        while prey_loc == self.loc or pred_loc == self.loc:\n",
        "            self.loc = graph.get_rand_node()\n",
        "\n",
        "        self.prey_belief = prey_belief\n",
        "        self.prey_belief[self.loc] = 0\n",
        "\n",
        "    \n",
        "    def debug(self, *args):\n",
        "        if self.dbg:\n",
        "            print(\"Agent4: \", args)\n",
        "\n",
        "\n",
        "    def isPrey(self, loc, prey_loc):\n",
        "        if loc == prey_loc:\n",
        "            return True\n",
        "\n",
        "\n",
        "    def isPred(self, loc, pred_loc):\n",
        "        if loc == pred_loc:\n",
        "            return True\n",
        "\n",
        "\n",
        "    def survey(self, prey_belief):\n",
        "        ## look at current list of belief - select max prob nodes (if multiple - select one at random)\n",
        "        new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "\n",
        "        val_max = max(prey_belief)\n",
        "        idx_max_list = []\n",
        "\n",
        "        for i in range(len(prey_belief)):\n",
        "            if prey_belief[i] >= val_max:\n",
        "                idx_max_list.append(i)\n",
        "\n",
        "        rand_idx_max = random.choice(idx_max_list)\n",
        "\n",
        "        # ### check if prey is there or not\n",
        "        # \n",
        "        if self.isPrey(rand_idx_max, self.prey_loc):\n",
        "            for i in range(self.graph.node_num):\n",
        "                prey_belief[i] = 0.0\n",
        "            prey_belief[rand_idx_max] = 1.0\n",
        "            return prey_belief\n",
        "        #     update_belief() ## according to prey found in survey logic\n",
        "                \n",
        "        else:\n",
        "        #     update_belief() ## according to prey not found in survey logic\n",
        "            for i in range(self.graph.node_num):\n",
        "                denom = 1-prey_belief[rand_idx_max]\n",
        "                new_prey_belief[i] = prey_belief[i]/denom\n",
        "                new_prey_belief[rand_idx_max] = 0.0\n",
        "\n",
        "        prey_belief = new_prey_belief\n",
        "        return prey_belief\n",
        "\n",
        "\n",
        "    def agent_move_prey_belief(self, prey_loc, prey_belief):\n",
        "\n",
        "        new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "        # new_prey_belief = prey_belief\n",
        "\n",
        "        if self.isPrey(self.loc, prey_loc):\n",
        "            for i in range(self.graph.node_num): ###///this will not actually happen since the game would be over\n",
        "                prey_belief[i] = 0.0\n",
        "            prey_belief[self.loc] = 1.0\n",
        "            return prey_belief  \n",
        "        \n",
        "        else:\n",
        "        #   update_belief() ## according to prey not found in survey logic\n",
        "            for i in range(self.graph.node_num):\n",
        "                denom = 1.0-prey_belief[self.loc]\n",
        "                new_prey_belief[i] = prey_belief[i]/denom\n",
        "                new_prey_belief[self.loc] = 0.0\n",
        "\n",
        "        prey_belief = new_prey_belief\n",
        "        return prey_belief\n",
        "\n",
        "\n",
        "    def prey_move_prey_belief(self, prey_loc, prey_belief):\n",
        "\n",
        "        new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "\n",
        "        for i in range(self.graph.node_num):\n",
        "            nbrs_i = self.graph.get_next_moves(i)\n",
        "            nbrs_i.append(i)\n",
        "            for nbr in nbrs_i:\n",
        "                denom = self.graph.get_deg(nbr) + 1\n",
        "                new_prey_belief[i] += prey_belief[nbr] / denom\n",
        "\n",
        "        prey_belief = new_prey_belief\n",
        "\n",
        "        prey_belief = self.agent_move_prey_belief(prey_loc, prey_belief)\n",
        "\n",
        "        return prey_belief\n",
        "\n",
        "\n",
        "    def move(self, prey_loc, pred_loc, prey_belief, tmp_prey_belief):\n",
        "        self.debug('Prey Belief: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "        prey_belief = self.survey(prey_belief)\n",
        "        self.debug('Prey Belief after S: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "        \n",
        "        self.loc = self.move_helper(prey_loc, pred_loc, tmp_prey_belief)\n",
        "        \n",
        "        prey_belief = self.agent_move_prey_belief(prey_loc, prey_belief)\n",
        "        # prey_belief[curr_loc] = 0.0\n",
        "        self.debug('Prey Belief after A: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "        # prey_belief = self.prey_move_prey_belief(prey3.loc, prey_belief)\n",
        "        # self.debug('Prey Belief after P: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "        return self.loc\n",
        "\n",
        "\n",
        "    def move_helper(self, prey_loc, pred_loc, prey_belief):\n",
        "\n",
        "        ## We select max belief node from list of max belief nodes and assume it to be where Prey is even if its not been found\n",
        "        val_max = max(prey_belief)\n",
        "        idx_max = 0\n",
        "        idx_max_list = []\n",
        "\n",
        "        for i in range(len(prey_belief)):\n",
        "            if prey_belief[i] >= val_max:\n",
        "                val_max = prey_belief[i]\n",
        "                idx_max_list.append(i)\n",
        "\n",
        "        prey_loc = random.choice(idx_max_list) ##need self.prey_loc or not? // why is prey_loc being or not being there as a parameter affecting code ??????\n",
        "        self.debug('Predicted prey location :', prey_loc)\n",
        "        \n",
        "        nbrs = self.graph.get_next_moves(self.loc)\n",
        "        prey_path_dict = {}\n",
        "        pred_path_dict = {}\n",
        "\n",
        "        curr_path_prey = len(self.graph.BFS(self.loc, prey_loc))\n",
        "        curr_path_pred = len(self.graph.BFS(self.loc, pred_loc))\n",
        "        self.debug(\"Curr Path:\", curr_path_prey, curr_path_pred)\n",
        "\n",
        "        for node in nbrs:\n",
        "            prey_path_dict[node] = len(self.graph.BFS(node, prey_loc)) ## {(1:3), (2:4)}\n",
        "            pred_path_dict[node] = len(self.graph.BFS(node, pred_loc))\n",
        "\n",
        "        self.debug(\"Prey Paths:\", prey_path_dict)\n",
        "        self.debug(\"Pred Paths:\", pred_path_dict)\n",
        "\n",
        "        nodes_lt_prey = []\n",
        "        nodes_eq_prey = []\n",
        "        for n, cost in prey_path_dict.items():\n",
        "            if cost < curr_path_prey:\n",
        "                nodes_lt_prey.append(n)\n",
        "            elif cost == curr_path_prey:\n",
        "                nodes_eq_prey.append(n)\n",
        "\n",
        "        nodes_gt_pred = []\n",
        "        nodes_eq_pred = []\n",
        "        for n, cost in pred_path_dict.items():\n",
        "            if cost > curr_path_pred:\n",
        "                nodes_gt_pred.append(n)\n",
        "            elif cost == curr_path_pred:\n",
        "                nodes_eq_pred.append(n)\n",
        "\n",
        "        valid_n = []\n",
        "\n",
        "        ## 1. closer to prey and farther from pred\n",
        "        if nodes_lt_prey:\n",
        "            for n in nodes_lt_prey:\n",
        "                if n in nodes_gt_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 1\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 2. closer to prey and not closer to pred\n",
        "        if nodes_lt_prey:\n",
        "            for n in nodes_lt_prey:\n",
        "                if n in nodes_eq_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 2\")\n",
        "                return random.choice(valid_n)\n",
        "\n",
        "        ## 3. not farther than prey and farther from pred\n",
        "        if nodes_eq_prey:\n",
        "            for n in nodes_eq_prey:\n",
        "                if n in nodes_gt_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 3\")\n",
        "                return random.choice(valid_n)\n",
        "\n",
        "        ## 4. not farther than prey and not closer to pred\n",
        "        if nodes_eq_prey:\n",
        "            for n in nodes_eq_prey:\n",
        "                if n in nodes_eq_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 4\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 5. farther from pred\n",
        "        if nodes_gt_pred:\n",
        "            self.debug(\"Rule 5\")\n",
        "            return random.choice(nodes_gt_pred)\n",
        "\n",
        "        ## 6. not closer to pred\n",
        "        if nodes_eq_pred:\n",
        "            self.debug(\"Rule 6\")\n",
        "            return random.choice(nodes_eq_pred)\n",
        "\n",
        "        ## 7. sit still and pray\n",
        "        else:\n",
        "            self.debug(\"Rule 7\")\n",
        "            return self.loc\n",
        "        "
      ],
      "metadata": {
        "id": "obV2S85uq5H1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Agent5:\n",
        "\n",
        "    def __init__(self, graph, prey_loc, pred_loc, pred_belief, dbg=False):\n",
        "        \n",
        "        self.graph = graph\n",
        "        self.loc = graph.get_rand_node()\n",
        "        self.dbg = dbg\n",
        "        # self.loc = graph.get_rand_node()\n",
        "        # print(\"Agent1: \", prey_loc, pred_loc)\n",
        "        self.prey_loc = prey_loc\n",
        "        self.pred_loc = pred_loc\n",
        "\n",
        "        while prey_loc == self.loc or pred_loc == self.loc:\n",
        "            self.loc = graph.get_rand_node()\n",
        "\n",
        "        # self.pred_belief = [1/49]*50\n",
        "        self.pred_belief = pred_belief\n",
        "        self.pred_belief[self.loc] = 0\n",
        "\n",
        "    def debug(self, *args):\n",
        "        if self.dbg:\n",
        "            print(\"Agent5: \", args)\n",
        "\n",
        "    def isPrey(self, loc, prey_loc):\n",
        "        if loc == prey_loc:\n",
        "            return True\n",
        "\n",
        "    def isPred(self, loc, pred_loc):\n",
        "        if loc == pred_loc:\n",
        "            return True\n",
        "\n",
        "    def survey(self, pred_belief):\n",
        "        ## look at current list of belief - select max prob nodes (if multiple - select one at random)\n",
        "        new_pred_belief = [0.0]*(self.graph.node_num)\n",
        "        # new_prey_belief = prey_belief\n",
        "\n",
        "        val_max = max(pred_belief)\n",
        "        # idx_max = 0 ## not needed\n",
        "        idx_max_list = []\n",
        "\n",
        "        for i in range(len(pred_belief)):\n",
        "            if pred_belief[i] >= val_max:\n",
        "                # val_max = prey_belief[i]\n",
        "                idx_max_list.append(i)\n",
        "\n",
        "        # tmp_max_list = []\n",
        "        dist_list = []\n",
        "        dist_min_list = []\n",
        "\n",
        "        for node in idx_max_list:\n",
        "            dist_list.append(len(self.graph.BFS(node, self.pred_loc)))\n",
        "        \n",
        "        min_dist = min(dist_list) ## CALCULATING MINIMUM dist from agent to partcular pred_probable posi\n",
        "\n",
        "        for i in range(len(dist_list)):\n",
        "            if dist_list[i] <= min_dist:\n",
        "                dist_min_list.append(i)\n",
        "\n",
        "        rand_idx_max = random.choice(dist_min_list)\n",
        "        # print(prey_belief, val_max, idx_max_list)\n",
        "\n",
        "        # ### check if prey is there or not\n",
        "        # \n",
        "        if self.isPred(rand_idx_max, self.pred_loc):\n",
        "            # print(\"IS PREY EXECUTED: \", rand_idx_max)\n",
        "            for i in range(self.graph.node_num):\n",
        "                pred_belief[i] = 0.0\n",
        "            pred_belief[rand_idx_max] = 1.0\n",
        "            # print (\"ISPREY: \", prey_belief)\n",
        "            return pred_belief\n",
        "        #     update_belief() ## according to prey found in survey logic\n",
        "                \n",
        "        else:\n",
        "        #     update_belief() ## according to prey not found in survey logic\n",
        "            for i in range(self.graph.node_num):\n",
        "                # denom = sum(prey_belief)-prey_belief[rand_idx_max]\n",
        "                denom = 1-pred_belief[rand_idx_max]\n",
        "                new_pred_belief[i] = pred_belief[i]/denom\n",
        "                new_pred_belief[rand_idx_max] = 0.0\n",
        "\n",
        "        pred_belief = new_pred_belief\n",
        "        return pred_belief\n",
        "\n",
        "    def agent_move_pred_belief(self, pred_loc, pred_belief):\n",
        "\n",
        "        new_pred_belief = [0.0]*(self.graph.node_num)\n",
        "\n",
        "        if self.isPred(self.loc, pred_loc):\n",
        "            for i in range(self.graph.node_num): ###///this will not actually happen since the game would be over\n",
        "                pred_belief[i] = 0.0\n",
        "            pred_belief[self.loc] = 1.0\n",
        "            return pred_belief  \n",
        "        \n",
        "        else:\n",
        "        #   update_belief() ## according to prey not found in survey logic\n",
        "            for i in range(self.graph.node_num):\n",
        "                # denom = sum(prey_belief)-prey_belief[self.loc]\n",
        "                denom = 1.0-pred_belief[self.loc]\n",
        "                new_pred_belief[i] = pred_belief[i]/denom\n",
        "                new_pred_belief[self.loc] = 0.0\n",
        "                # new_prey_belief[curr_loc] = 0.0\n",
        "                # prey_belief[i] = prey_belief[i]/denom\n",
        "\n",
        "        pred_belief = new_pred_belief\n",
        "        return pred_belief\n",
        "\n",
        "    def pred_move_pred_belief(self, pred_loc, pred_belief):\n",
        "        pred_belief_tmp = [0.0]*self.graph.node_num\n",
        "        # print (\"before func PRED_MOVE_UPDATE : \", sum(pred_belief))\n",
        "        for i in range(self.graph.node_num):\n",
        "            tmp_belief = 0.0\n",
        "\n",
        "            nbrs_i = self.graph.get_next_moves(i)\n",
        "            \n",
        "            for nbr in nbrs_i:\n",
        "                nbr_bfs = {}\n",
        "                \n",
        "                nbr_nbrs_i = self.graph.get_next_moves(nbr)\n",
        "                for nbr_nbr in nbr_nbrs_i:\n",
        "                    bfs = self.graph.BFS(nbr_nbr, self.loc)\n",
        "                    nbr_bfs[nbr_nbr] = len(bfs)\n",
        "\n",
        "                # print (\"NBR_BFS: \", nbr_bfs)\n",
        "                min_bfs = min(nbr_bfs.values())\n",
        "\n",
        "                nbr_min_path_len = [key for key, value in nbr_bfs.items() if value == min_bfs]\n",
        "                # print(\"nbr_min_path_len : \", nbr_min_path_len)\n",
        "\n",
        "                x = len(nbr_min_path_len)\n",
        "                # print (\"DEBUG: \", i, \" \", nbr_min_path_len)\n",
        "                if i in nbr_min_path_len:\n",
        "                    tmp_belief += pred_belief[nbr]*((0.4/self.graph.get_deg(nbr))+(0.6/x))\n",
        "                    # print(\"tmp bel in 1 \", tmp_belief)\n",
        "                else:\n",
        "                    tmp_belief += pred_belief[nbr]*((0.4/self.graph.get_deg(nbr)))\n",
        "                    # print(\"tmp bel in 2 \", tmp_belief)\n",
        "\n",
        "                # print(\"i :\", i)\n",
        "                # print(\"nbrs_i\", nbrs_i)\n",
        "                # print(\"nbr_nbr\", nbr_nbrs_i)\n",
        "\n",
        "                # exit (0)\n",
        "            # print(\"TMP BELIEF: \", tmp_belief)\n",
        "            pred_belief_tmp[i] = tmp_belief\n",
        "\n",
        "        # print (\"after func PRED_MOVE_UPDATE OLD : \", pred_belief)\n",
        "\n",
        "        # print (\"after func PRED_MOVE_UPDATE : \", sum(pred_belief_tmp))\n",
        "\n",
        "        return pred_belief_tmp\n",
        "\n",
        "\n",
        "    def move(self, prey_loc, pred_loc, pred_belief):\n",
        "        self.debug('Pred Belief: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        pred_belief = self.survey(pred_belief)\n",
        "        self.debug('Pred Belief after S: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        curr_loc = self.loc ######\n",
        "        self.loc = self.move_helper(prey_loc, pred_loc, pred_belief)\n",
        "        pred_belief = self.agent_move_pred_belief(pred_loc, pred_belief)\n",
        "        # prey_belief[curr_loc] = 0.0\n",
        "        self.debug('Pred Belief after A: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "\n",
        "        return self.loc\n",
        "\n",
        "\n",
        "    def move_helper(self, prey_loc, pred_loc, pred_belief):\n",
        "\n",
        "        #####///// WE SELECT MAX BELIEF NODE for pred_loc\n",
        "        val_max = max(pred_belief)\n",
        "        idx_max = 0\n",
        "        idx_max_list = []\n",
        "\n",
        "        for i in range(len(pred_belief)):\n",
        "            if pred_belief[i] >= val_max:\n",
        "                # val_max = prey_belief[i]\n",
        "                idx_max_list.append(i)\n",
        "\n",
        "        pred_loc = random.choice(idx_max_list) ##need self.prey_loc or not? // why is prey_loc being or not being there as a parameter affecting code ??????\n",
        "        self.debug('Predicted pred location :', pred_loc)\n",
        "        \n",
        "        nbrs = self.graph.get_next_moves(self.loc)\n",
        "        prey_path_dict = {}\n",
        "        pred_path_dict = {}\n",
        "\n",
        "        curr_path_prey = len(self.graph.BFS(self.loc, prey_loc))\n",
        "        curr_path_pred = len(self.graph.BFS(self.loc, pred_loc))\n",
        "        self.debug(\"Curr Path:\", curr_path_prey, curr_path_pred)\n",
        "\n",
        "        for node in nbrs:\n",
        "            prey_path_dict[node] = len(self.graph.BFS(node, prey_loc)) ## {(1:3), (2:4)}\n",
        "            pred_path_dict[node] = len(self.graph.BFS(node, pred_loc))\n",
        "\n",
        "        self.debug(\"Prey Paths:\", prey_path_dict)\n",
        "        self.debug(\"Pred Paths:\", pred_path_dict)\n",
        "\n",
        "        nodes_lt_prey = []\n",
        "        nodes_eq_prey = []\n",
        "        for n, cost in prey_path_dict.items():\n",
        "            if cost < curr_path_prey:\n",
        "                nodes_lt_prey.append(n)\n",
        "            elif cost == curr_path_prey:\n",
        "                nodes_eq_prey.append(n)\n",
        "\n",
        "        nodes_gt_pred = []\n",
        "        nodes_eq_pred = []\n",
        "        for n, cost in pred_path_dict.items():\n",
        "            if cost > curr_path_pred:\n",
        "                nodes_gt_pred.append(n)\n",
        "            elif cost == curr_path_pred:\n",
        "                nodes_eq_pred.append(n)\n",
        "\n",
        "        valid_n = []\n",
        "\n",
        "        ## 1. closer to prey and farther from pred\n",
        "        if nodes_lt_prey:\n",
        "            for n in nodes_lt_prey:\n",
        "                if n in nodes_gt_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 1\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 2. closer to prey and not closer to pred\n",
        "        if nodes_lt_prey:\n",
        "            for n in nodes_lt_prey:\n",
        "                if n in nodes_eq_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 2\")\n",
        "                return random.choice(valid_n)\n",
        "\n",
        "        ## 3. not farther than prey and farther from pred\n",
        "        if nodes_eq_prey:\n",
        "            for n in nodes_eq_prey:\n",
        "                if n in nodes_gt_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 3\")\n",
        "                return random.choice(valid_n)\n",
        "\n",
        "        ## 4. not farther than prey and not closer to pred\n",
        "        if nodes_eq_prey:\n",
        "            for n in nodes_eq_prey:\n",
        "                if n in nodes_eq_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 4\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 5. farther from pred\n",
        "        if nodes_gt_pred:\n",
        "            self.debug(\"Rule 5\")\n",
        "            return random.choice(nodes_gt_pred)\n",
        "\n",
        "        ## 6. not closer to pred\n",
        "        if nodes_eq_pred:\n",
        "            self.debug(\"Rule 6\")\n",
        "            return random.choice(nodes_eq_pred)\n",
        "\n",
        "        ## 7. sit still and pray\n",
        "        else:\n",
        "            self.debug(\"Rule 7\")\n",
        "            return self.loc\n",
        "        "
      ],
      "metadata": {
        "id": "dVrCkstJY0IE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Agent6:\n",
        "\n",
        "    def __init__(self, graph, prey_loc, pred_loc, pred_belief, dbg=False):\n",
        "        \n",
        "        self.graph = graph\n",
        "        self.loc = graph.get_rand_node()\n",
        "        self.dbg = dbg\n",
        "        # self.loc = graph.get_rand_node()\n",
        "        # print(\"Agent1: \", prey_loc, pred_loc)\n",
        "        self.prey_loc = prey_loc\n",
        "        self.pred_loc = pred_loc\n",
        "\n",
        "        while prey_loc == self.loc or pred_loc == self.loc:\n",
        "            self.loc = graph.get_rand_node()\n",
        "\n",
        "        # self.pred_belief = [1/49]*50\n",
        "        self.pred_belief = pred_belief\n",
        "        self.pred_belief[self.loc] = 0\n",
        "\n",
        "    def debug(self, *args):\n",
        "        if self.dbg:\n",
        "            print(\"Agent6: \", args)\n",
        "\n",
        "    def isPrey(self, loc, prey_loc):\n",
        "        if loc == prey_loc:\n",
        "            return True\n",
        "\n",
        "    def isPred(self, loc, pred_loc):\n",
        "        if loc == pred_loc:\n",
        "            return True\n",
        "\n",
        "    def survey(self, pred_belief):\n",
        "        ## look at current list of belief - select max prob nodes (if multiple - select one at random)\n",
        "        new_pred_belief = [0.0]*(self.graph.node_num)\n",
        "        # new_prey_belief = prey_belief\n",
        "\n",
        "        val_max = max(pred_belief)\n",
        "        # idx_max = 0 ## not needed\n",
        "        idx_max_list = []\n",
        "\n",
        "        for i in range(len(pred_belief)):\n",
        "            if pred_belief[i] >= val_max:\n",
        "                # val_max = prey_belief[i]\n",
        "                idx_max_list.append(i)\n",
        "\n",
        "        # tmp_max_list = []\n",
        "        dist_list = []\n",
        "        dist_min_list = []\n",
        "\n",
        "        for node in idx_max_list:\n",
        "            dist_list.append(len(self.graph.BFS(node, self.pred_loc)))\n",
        "        \n",
        "        min_dist = min(dist_list) ## CALCULATING MINIMUM dist from agent to partcular pred_probable posi\n",
        "\n",
        "        for i in range(len(dist_list)):\n",
        "            if dist_list[i] <= min_dist:\n",
        "                dist_min_list.append(i)\n",
        "\n",
        "        rand_idx_max = random.choice(dist_min_list)\n",
        "        # print(prey_belief, val_max, idx_max_list)\n",
        "\n",
        "        # ### check if prey is there or not\n",
        "        # \n",
        "        if self.isPred(rand_idx_max, self.pred_loc):\n",
        "            # print(\"IS PREY EXECUTED: \", rand_idx_max)\n",
        "            for i in range(self.graph.node_num):\n",
        "                pred_belief[i] = 0.0\n",
        "            pred_belief[rand_idx_max] = 1.0\n",
        "            # print (\"ISPREY: \", prey_belief)\n",
        "            return pred_belief\n",
        "        #     update_belief() ## according to prey found in survey logic\n",
        "                \n",
        "        else:\n",
        "        #     update_belief() ## according to prey not found in survey logic\n",
        "            for i in range(self.graph.node_num):\n",
        "                # denom = sum(prey_belief)-prey_belief[rand_idx_max]\n",
        "                denom = 1-pred_belief[rand_idx_max]\n",
        "                new_pred_belief[i] = pred_belief[i]/denom\n",
        "                new_pred_belief[rand_idx_max] = 0.0\n",
        "\n",
        "        pred_belief = new_pred_belief\n",
        "        return pred_belief\n",
        "\n",
        "    def agent_move_pred_belief(self, pred_loc, pred_belief):\n",
        "\n",
        "        new_pred_belief = [0.0]*(self.graph.node_num)\n",
        "\n",
        "        if self.isPred(self.loc, pred_loc):\n",
        "            for i in range(self.graph.node_num): ###///this will not actually happen since the game would be over\n",
        "                pred_belief[i] = 0.0\n",
        "            pred_belief[self.loc] = 1.0\n",
        "            return pred_belief  \n",
        "        \n",
        "        else:\n",
        "        #   update_belief() ## according to prey not found in survey logic\n",
        "            for i in range(self.graph.node_num):\n",
        "                # denom = sum(prey_belief)-prey_belief[self.loc]\n",
        "                denom = 1.0-pred_belief[self.loc]\n",
        "                new_pred_belief[i] = pred_belief[i]/denom\n",
        "                new_pred_belief[self.loc] = 0.0\n",
        "                # new_prey_belief[curr_loc] = 0.0\n",
        "                # prey_belief[i] = prey_belief[i]/denom\n",
        "\n",
        "        pred_belief = new_pred_belief\n",
        "        return pred_belief\n",
        "\n",
        "    def pred_move_pred_belief(self, pred_loc, pred_belief):\n",
        "        pred_belief_tmp = [0.0]*self.graph.node_num\n",
        "        # print (\"before func PRED_MOVE_UPDATE : \", sum(pred_belief))\n",
        "        for i in range(self.graph.node_num):\n",
        "            tmp_belief = 0.0\n",
        "\n",
        "            nbrs_i = self.graph.get_next_moves(i)\n",
        "            \n",
        "            for nbr in nbrs_i:\n",
        "                nbr_bfs = {}\n",
        "                \n",
        "                nbr_nbrs_i = self.graph.get_next_moves(nbr)\n",
        "                for nbr_nbr in nbr_nbrs_i:\n",
        "                    bfs = self.graph.BFS(nbr_nbr, self.loc)\n",
        "                    nbr_bfs[nbr_nbr] = len(bfs)\n",
        "\n",
        "                # print (\"NBR_BFS: \", nbr_bfs)\n",
        "                min_bfs = min(nbr_bfs.values())\n",
        "\n",
        "                nbr_min_path_len = [key for key, value in nbr_bfs.items() if value == min_bfs]\n",
        "                # print(\"nbr_min_path_len : \", nbr_min_path_len)\n",
        "\n",
        "                x = len(nbr_min_path_len)\n",
        "                # print (\"DEBUG: \", i, \" \", nbr_min_path_len)\n",
        "                if i in nbr_min_path_len:\n",
        "                    tmp_belief += pred_belief[nbr]*((0.4/self.graph.get_deg(nbr))+(0.6/x))\n",
        "                    # print(\"tmp bel in 1 \", tmp_belief)\n",
        "                else:\n",
        "                    tmp_belief += pred_belief[nbr]*((0.4/self.graph.get_deg(nbr)))\n",
        "                    # print(\"tmp bel in 2 \", tmp_belief)\n",
        "\n",
        "                # print(\"i :\", i)\n",
        "                # print(\"nbrs_i\", nbrs_i)\n",
        "                # print(\"nbr_nbr\", nbr_nbrs_i)\n",
        "\n",
        "                # exit (0)\n",
        "            # print(\"TMP BELIEF: \", tmp_belief)\n",
        "            pred_belief_tmp[i] = tmp_belief\n",
        "\n",
        "        # print (\"after func PRED_MOVE_UPDATE OLD : \", pred_belief)\n",
        "\n",
        "        # print (\"after func PRED_MOVE_UPDATE : \", sum(pred_belief_tmp))\n",
        "\n",
        "        return pred_belief_tmp\n",
        "\n",
        "\n",
        "    def move(self, prey_loc, pred_loc, pred_belief):\n",
        "        self.debug('Pred Belief: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        pred_belief = self.survey(pred_belief)\n",
        "        self.debug('Pred Belief after S: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        curr_loc = self.loc ######\n",
        "        self.loc = self.move_helper(prey_loc, pred_loc, pred_belief)\n",
        "        pred_belief = self.agent_move_pred_belief(pred_loc, pred_belief)\n",
        "        # prey_belief[curr_loc] = 0.0\n",
        "        self.debug('Pred Belief after A: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "\n",
        "        return self.loc\n",
        "\n",
        "\n",
        "    def move_helper(self, prey_loc, pred_loc, pred_belief):\n",
        "\n",
        "        #####///// WE SELECT MAX BELIEF NODE for pred_loc\n",
        "        val_max = max(pred_belief)\n",
        "        idx_max = 0\n",
        "        idx_max_list = []\n",
        "\n",
        "        for i in range(len(pred_belief)):\n",
        "            if pred_belief[i] >= val_max:\n",
        "                # val_max = prey_belief[i]\n",
        "                idx_max_list.append(i)\n",
        "                \n",
        "        pred_loc = random.choice(idx_max_list) ##need self.prey_loc or not? // why is prey_loc being or not being there as a parameter affecting code ??????\n",
        "        self.debug('Predicted pred location :', pred_loc)\n",
        "        \n",
        "        nbrs = self.graph.get_next_moves(self.loc)\n",
        "        prey_path_dict = {}\n",
        "        pred_path_dict = {}\n",
        "\n",
        "        curr_path_prey = len(self.graph.BFS(self.loc, prey_loc))\n",
        "        curr_path_pred = len(self.graph.BFS(self.loc, pred_loc))\n",
        "        self.debug(\"Curr Path:\", curr_path_prey, curr_path_pred)\n",
        "\n",
        "        for node in nbrs:\n",
        "            prey_path_dict[node] = len(self.graph.BFS(node, prey_loc)) ## {(1:3), (2:4)}\n",
        "            pred_path_dict[node] = len(self.graph.BFS(node, pred_loc))\n",
        "\n",
        "        self.debug(\"Prey Paths:\", prey_path_dict)\n",
        "        self.debug(\"Pred Paths:\", pred_path_dict)\n",
        "\n",
        "        nodes_lt_prey = []\n",
        "        nodes_eq_prey = []\n",
        "        for n, cost in prey_path_dict.items():\n",
        "            if cost < curr_path_prey:\n",
        "                nodes_lt_prey.append(n)\n",
        "            elif cost == curr_path_prey:\n",
        "                nodes_eq_prey.append(n)\n",
        "\n",
        "        nodes_gt_pred = []\n",
        "        nodes_eq_pred = []\n",
        "        for n, cost in pred_path_dict.items():\n",
        "            if cost > curr_path_pred:\n",
        "                nodes_gt_pred.append(n)\n",
        "            elif cost == curr_path_pred:\n",
        "                nodes_eq_pred.append(n)\n",
        "\n",
        "        valid_n = []\n",
        "\n",
        "        # for i in range(len(idx_max_list)):\n",
        "            # dist = abs(abs(idx_max_list[i] - self.loc)-self.graph.node_num) % self.graph.node_num\n",
        "        dist = abs(abs(pred_loc - self.loc)-self.graph.node_num) % self.graph.node_num\n",
        "\n",
        "        if dist >= 5:\n",
        "\n",
        "            ## 1. closer to prey and farther from pred\n",
        "            if nodes_lt_prey:\n",
        "                for n in nodes_lt_prey:\n",
        "                    if n in nodes_gt_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 1\")\n",
        "                    return random.choice(valid_n)\n",
        "            \n",
        "            ## 2. closer to prey and not closer to pred\n",
        "            if nodes_lt_prey:\n",
        "                for n in nodes_lt_prey:\n",
        "                    if n in nodes_eq_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 2\")\n",
        "                    return random.choice(valid_n)\n",
        "\n",
        "            ## 3. not farther than prey and farther from pred\n",
        "            if nodes_eq_prey:\n",
        "                for n in nodes_eq_prey:\n",
        "                    if n in nodes_gt_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 3\")\n",
        "                    return random.choice(valid_n)\n",
        "\n",
        "            ## 4. not farther than prey and not closer to pred\n",
        "            if nodes_eq_prey:\n",
        "                for n in nodes_eq_prey:\n",
        "                    if n in nodes_eq_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 4\")\n",
        "                    return random.choice(valid_n)\n",
        "            \n",
        "            ## 5. farther from pred\n",
        "            if nodes_gt_pred:\n",
        "                self.debug(\"Rule 5\")\n",
        "                return random.choice(nodes_gt_pred)\n",
        "\n",
        "            ## 6. not closer to pred\n",
        "            if nodes_eq_pred:\n",
        "                self.debug(\"Rule 6\")\n",
        "                return random.choice(nodes_eq_pred)\n",
        "\n",
        "            ## 7. sit still and pray\n",
        "            else:\n",
        "                self.debug(\"Rule 7\")\n",
        "                return self.loc\n",
        "        \n",
        "        else:\n",
        "\n",
        "            ## 3. not farther than prey and farther from pred\n",
        "            if nodes_eq_prey:\n",
        "                for n in nodes_eq_prey:\n",
        "                    if n in nodes_gt_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 3\")\n",
        "                    return random.choice(valid_n)\n",
        "            \n",
        "            ## 5. farther from pred\n",
        "            if nodes_gt_pred:\n",
        "                self.debug(\"Rule 5\")\n",
        "                return random.choice(nodes_gt_pred)\n",
        "\n",
        "            ## 1. closer to prey and farther from pred\n",
        "            if nodes_lt_prey:\n",
        "                for n in nodes_lt_prey:\n",
        "                    if n in nodes_gt_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 1\")\n",
        "                    return random.choice(valid_n)\n",
        "            \n",
        "            ## 2. closer to prey and not closer to pred\n",
        "            if nodes_lt_prey:\n",
        "                for n in nodes_lt_prey:\n",
        "                    if n in nodes_eq_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 2\")\n",
        "                    return random.choice(valid_n)\n",
        "            \n",
        "            ## 4. not farther than prey and not closer to pred\n",
        "            if nodes_eq_prey:\n",
        "                for n in nodes_eq_prey:\n",
        "                    if n in nodes_eq_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 4\")\n",
        "                    return random.choice(valid_n)\n",
        "\n",
        "            ## 6. not closer to pred\n",
        "            if nodes_eq_pred:\n",
        "                self.debug(\"Rule 6\")\n",
        "                return random.choice(nodes_eq_pred)\n",
        "\n",
        "            ## 7. sit still and pray\n",
        "            self.debug(\"Rule 7\")\n",
        "            return self.loc\n",
        "\n",
        "    "
      ],
      "metadata": {
        "id": "hUaK6CLzs2xB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Agent7:\n",
        "\n",
        "    def __init__(self, graph, prey_loc, pred_loc, prey_belief, pred_belief, dbg=False):\n",
        "        \n",
        "        self.graph = graph\n",
        "        self.loc = graph.get_rand_node()\n",
        "        self.dbg = dbg\n",
        "        # self.loc = graph.get_rand_node()\n",
        "        # print(\"Agent1: \", prey_loc, pred_loc)\n",
        "        self.prey_loc = prey_loc\n",
        "        self.pred_loc = pred_loc\n",
        "\n",
        "        while prey_loc == self.loc or pred_loc == self.loc:\n",
        "            self.loc = graph.get_rand_node()\n",
        "\n",
        "        # self.pred_belief = [1/49]*50\n",
        "        self.pred_belief = pred_belief\n",
        "        self.pred_belief[self.loc] = 0\n",
        "\n",
        "        self.prey_belief = prey_belief\n",
        "        self.prey_belief[self.loc] = 0\n",
        "\n",
        "    def debug(self, *args):\n",
        "        if self.dbg:\n",
        "            print(\"Agent7: \", args)\n",
        "\n",
        "    def isPrey(self, loc, prey_loc):\n",
        "        if loc == prey_loc:\n",
        "            return True\n",
        "\n",
        "    def isPred(self, loc, pred_loc):\n",
        "        if loc == pred_loc:\n",
        "            return True\n",
        "\n",
        "    def survey(self, pred_belief, prey_belief):\n",
        "\n",
        "        # for i in range(len(self.graph.node_num)):\n",
        "        #     if pred_belief[i] == 1.0\n",
        "\n",
        "        if sum(pred_belief) in pred_belief: ##\n",
        "            #### PREY CODE\n",
        "            new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "            new_pred_belief = [0.0]*(self.graph.node_num)\n",
        "\n",
        "            val_max = max(prey_belief)\n",
        "            idx_max_list = []\n",
        "\n",
        "            for i in range(len(prey_belief)):\n",
        "                if prey_belief[i] >= val_max:\n",
        "                    # val_max = prey_belief[i]\n",
        "                    idx_max_list.append(i)\n",
        "\n",
        "            rand_idx_max = random.choice(idx_max_list)\n",
        "\n",
        "            # if self.isPred(rand_idx_max, self.pred.loc):\n",
        "            #     FLAG = 1\n",
        "\n",
        "            if self.isPrey(rand_idx_max, self.prey_loc):\n",
        "                # print(\"IS PREY EXECUTED: \", rand_idx_max)\n",
        "                for i in range(self.graph.node_num):\n",
        "                    # prey_belief[i] = 0.0\n",
        "                    new_prey_belief[i] = 0.0\n",
        "                # prey_belief[rand_idx_max] = 1.0\n",
        "                new_prey_belief[rand_idx_max] = 1.0\n",
        "                # print (\"ISPREY: \", prey_belief)\n",
        "                # return prey_belief, pred_belief\n",
        "                    \n",
        "            else:\n",
        "                for i in range(self.graph.node_num):\n",
        "                    denom = 1-prey_belief[rand_idx_max]\n",
        "                    new_prey_belief[i] = prey_belief[i]/denom\n",
        "                new_prey_belief[rand_idx_max] = 0.0 ###changed indent(earlier in for loop)\n",
        "\n",
        "            prey_belief = new_prey_belief\n",
        "\n",
        "            # #################################\n",
        "\n",
        "            if self.isPred(rand_idx_max, self.pred_loc):\n",
        "                for i in range(self.graph.node_num):\n",
        "                    # pred_belief[i] = 0.0\n",
        "                    new_pred_belief[i] = 0.0\n",
        "                # pred_belief[rand_idx_max] = 1.0\n",
        "                new_pred_belief[rand_idx_max] = 1.0\n",
        "                # return prey_belief, pred_belief\n",
        "\n",
        "            else:\n",
        "                for i in range(self.graph.node_num):\n",
        "                    denom = 1-pred_belief[rand_idx_max]\n",
        "                    new_pred_belief[i] = pred_belief[i]/denom\n",
        "                new_pred_belief[rand_idx_max] = 0.0 ###changed indent(earlier in for loop)\n",
        "\n",
        "            pred_belief = new_pred_belief\n",
        "\n",
        "            # #################################\n",
        "\n",
        "            return prey_belief, pred_belief     \n",
        "\n",
        "        else:\n",
        "            ### PRED CODE\n",
        "            new_pred_belief = [0.0]*(self.graph.node_num)\n",
        "            new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "\n",
        "            val_max = max(pred_belief)\n",
        "            idx_max_list = []\n",
        "\n",
        "            for i in range(len(pred_belief)):\n",
        "                if pred_belief[i] >= val_max:\n",
        "                    idx_max_list.append(i)\n",
        "\n",
        "            dist_list = []\n",
        "            dist_min_list = []\n",
        "\n",
        "            for node in idx_max_list:\n",
        "                dist_list.append(len(self.graph.BFS(node, self.pred_loc)))\n",
        "            \n",
        "            min_dist = min(dist_list) ## CALCULATING MINIMUM dist from agent to partcular pred_probable pos\n",
        "\n",
        "            for i in range(len(dist_list)):\n",
        "                if dist_list[i] <= min_dist:\n",
        "                    dist_min_list.append(i)\n",
        "\n",
        "            rand_idx_max = random.choice(dist_min_list)\n",
        "\n",
        "            if self.isPred(rand_idx_max, self.pred_loc):\n",
        "                for i in range(self.graph.node_num):\n",
        "                    # pred_belief[i] = 0.0\n",
        "                    new_pred_belief[i] = 0.0\n",
        "                # pred_belief[rand_idx_max] = 1.0\n",
        "                new_pred_belief[rand_idx_max] = 1.0\n",
        "                # return prey_belief, pred_belief\n",
        "\n",
        "            else:\n",
        "                for i in range(self.graph.node_num):\n",
        "                    denom = 1-pred_belief[rand_idx_max]\n",
        "                    new_pred_belief[i] = pred_belief[i]/denom\n",
        "                new_pred_belief[rand_idx_max] = 0.0 ###changed indent(earlier in for loop)\n",
        "\n",
        "            pred_belief = new_pred_belief\n",
        "            # return prey_belief, pred_belief\n",
        "\n",
        "                        \n",
        "            # ###########################\n",
        "\n",
        "            if self.isPrey(rand_idx_max, self.prey_loc):\n",
        "                # print(\"IS PREY EXECUTED: \", rand_idx_max)\n",
        "                for i in range(self.graph.node_num):\n",
        "                    # prey_belief[i] = 0.0\n",
        "                    new_prey_belief[i] = 0.0\n",
        "                # prey_belief[rand_idx_max] = 1.0\n",
        "                new_prey_belief[rand_idx_max] = 1.0\n",
        "                \n",
        "                # print (\"ISPREY: \", prey_belief)\n",
        "                # return prey_belief, pred_belief\n",
        "\n",
        "            else:\n",
        "                for i in range(self.graph.node_num):\n",
        "                    denom = 1-prey_belief[rand_idx_max]\n",
        "                    new_prey_belief[i] = prey_belief[i]/denom\n",
        "                new_prey_belief[rand_idx_max] = 0.0 ###changed indent(earlier in for loop)\n",
        "\n",
        "            prey_belief = new_prey_belief\n",
        "            # return prey_belief, pred_belief    \n",
        "\n",
        "            # ###########################\n",
        "\n",
        "            return prey_belief, pred_belief \n",
        "\n",
        "    \n",
        "\n",
        "    def agent_move_pred_belief(self, pred_loc, pred_belief):\n",
        "\n",
        "        new_pred_belief = [0.0]*(self.graph.node_num)\n",
        "\n",
        "        if self.isPred(self.loc, pred_loc):\n",
        "            for i in range(self.graph.node_num): ###///this will not actually happen since the game would be over\n",
        "                # pred_belief[i] = 0.0\n",
        "                new_pred_belief[i] = 0.0\n",
        "            # pred_belief[self.loc] = 1.0\n",
        "            new_pred_belief[self.loc] = 1.0\n",
        "            # return pred_belief\n",
        "        \n",
        "        else:\n",
        "            for i in range(self.graph.node_num):\n",
        "                denom = 1.0-pred_belief[self.loc]\n",
        "                new_pred_belief[i] = pred_belief[i]/denom\n",
        "            new_pred_belief[self.loc] = 0.0 ###changed indent(earlier in for loop)\n",
        "                # prey_belief[i] = prey_belief[i]/denom\n",
        "\n",
        "        pred_belief = new_pred_belief\n",
        "        return pred_belief\n",
        "\n",
        "    def pred_move_pred_belief(self, pred_loc, pred_belief):\n",
        "        pred_belief_tmp = [0.0]*self.graph.node_num\n",
        "        # print (\"before func PRED_MOVE_UPDATE : \", sum(pred_belief))\n",
        "        for i in range(self.graph.node_num):\n",
        "            tmp_belief = 0.0\n",
        "\n",
        "            nbrs_i = self.graph.get_next_moves(i)\n",
        "            \n",
        "            for nbr in nbrs_i:\n",
        "                nbr_bfs = {}\n",
        "                \n",
        "                nbr_nbrs_i = self.graph.get_next_moves(nbr)\n",
        "                for nbr_nbr in nbr_nbrs_i:\n",
        "                    bfs = self.graph.BFS(nbr_nbr, self.loc)\n",
        "                    nbr_bfs[nbr_nbr] = len(bfs)\n",
        "\n",
        "                # print (\"NBR_BFS: \", nbr_bfs)\n",
        "                min_bfs = min(nbr_bfs.values())\n",
        "\n",
        "                nbr_min_path_len = [key for key, value in nbr_bfs.items() if value == min_bfs]\n",
        "                # print(\"nbr_min_path_len : \", nbr_min_path_len)\n",
        "\n",
        "                x = len(nbr_min_path_len)\n",
        "                # print (\"DEBUG: \", i, \" \", nbr_min_path_len)\n",
        "                if i in nbr_min_path_len:\n",
        "                    tmp_belief += pred_belief[nbr]*((0.4/self.graph.get_deg(nbr))+(0.6/x))\n",
        "                    # print(\"tmp bel in 1 \", tmp_belief)\n",
        "                else:\n",
        "                    tmp_belief += pred_belief[nbr]*((0.4/self.graph.get_deg(nbr)))\n",
        "                    # print(\"tmp bel in 2 \", tmp_belief)\n",
        "\n",
        "                # print (\"i :\", i)\n",
        "                # print(\"nbrs_i\", nbrs_i)\n",
        "                # print(\"nbr_nbr\", nbr_nbrs_i)\n",
        "\n",
        "                # exit (0)\n",
        "            # print(\"TMP BELIEF: \", tmp_belief)\n",
        "            pred_belief_tmp[i] = tmp_belief\n",
        "\n",
        "        # print (\"after func PRED_MOVE_UPDATE OLD : \", pred_belief)\n",
        "\n",
        "        # print (\"after func PRED_MOVE_UPDATE : \", sum(pred_belief_tmp))\n",
        "\n",
        "        return pred_belief_tmp\n",
        "\n",
        "\n",
        "    def agent_move_prey_belief(self, prey_loc, prey_belief):\n",
        "\n",
        "        new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "        # new_prey_belief = prey_belief\n",
        "\n",
        "        if self.isPrey(self.loc, prey_loc):\n",
        "            for i in range(self.graph.node_num): ###///this will not actually happen since the game would be over\n",
        "                # prey_belief[i] = 0.0\n",
        "                new_prey_belief[i] = 0.0\n",
        "            # prey_belief[self.loc] = 1.0\n",
        "            new_prey_belief[self.loc] = 1.0\n",
        "            # return prey_belief  \n",
        "        \n",
        "        else:\n",
        "        #   update_belief() ## according to prey not found in survey logic\n",
        "            for i in range(self.graph.node_num):\n",
        "                # denom = sum(prey_belief)-prey_belief[self.loc]\n",
        "                denom = 1.0-prey_belief[self.loc]\n",
        "                new_prey_belief[i] = prey_belief[i]/denom\n",
        "            new_prey_belief[self.loc] = 0.0 ###changed indent(earlier in for loop)\n",
        "                # new_prey_belief[curr_loc] = 0.0\n",
        "                # prey_belief[i] = prey_belief[i]/denom\n",
        "\n",
        "        prey_belief = new_prey_belief\n",
        "        return prey_belief\n",
        "\n",
        "    def prey_move_prey_belief(self, prey_loc, prey_belief):\n",
        "\n",
        "        new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "        # new_prey_belief = prey_belief\n",
        "\n",
        "        for i in range(self.graph.node_num):\n",
        "            nbrs_i = self.graph.get_next_moves(i)\n",
        "            nbrs_i.append(i)\n",
        "            for nbr in nbrs_i:\n",
        "                denom = self.graph.get_deg(nbr) + 1\n",
        "                new_prey_belief[i] += prey_belief[nbr] / denom\n",
        "\n",
        "        prey_belief = new_prey_belief\n",
        "\n",
        "        prey_belief = self.agent_move_prey_belief(prey_loc, prey_belief)\n",
        "\n",
        "        # prey_belief = new_prey_belief\n",
        "        return prey_belief\n",
        "\n",
        "\n",
        "    def move(self, prey_loc, pred_loc, pred_belief, prey_belief):\n",
        "        self.debug('Pred Belief: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        self.debug('Prey Belief: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "\n",
        "        prey_belief, pred_belief = self.survey(pred_belief, prey_belief)\n",
        "        self.debug('Pred Belief after S: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        self.debug('Prey Belief after S: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "        curr_loc = self.loc ######\n",
        "        self.loc = self.move_helper(prey_loc, pred_loc, prey_belief, pred_belief)\n",
        "        prey_belief = self.agent_move_prey_belief(prey_loc, prey_belief)\n",
        "        pred_belief = self.agent_move_pred_belief(pred_loc, pred_belief)\n",
        "\n",
        "        # prey_belief[curr_loc] = 0.0\n",
        "        self.debug('Pred Belief after A: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        self.debug('Prey Belief after A: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "\n",
        "        return self.loc\n",
        "\n",
        "\n",
        "    def move_helper(self, prey_loc, pred_loc, prey_belief, pred_belief):\n",
        "\n",
        "        #####///// WE SELECT MAX BELIEF NODE for pred_loc\n",
        "        val_max1 = max(pred_belief)\n",
        "        idx_max1 = 0\n",
        "        idx_max_list1 = []\n",
        "\n",
        "        for i in range(len(pred_belief)):\n",
        "            if pred_belief[i] >= val_max1:\n",
        "                # val_max = prey_belief[i]\n",
        "                idx_max_list1.append(i)\n",
        "\n",
        "        pred_loc = random.choice(idx_max_list1) ##need self.prey_loc or not? // why is prey_loc being or not being there as a parameter affecting code ??????\n",
        "        self.debug('Predicted pred location :', pred_loc)\n",
        "\n",
        "        ## We select max belief node from list of max belief nodes and assume it to be where Prey is even if its not been found\n",
        "        val_max = max(prey_belief)\n",
        "        idx_max = 0\n",
        "        idx_max_list = []\n",
        "\n",
        "        for i in range(len(prey_belief)):\n",
        "            if prey_belief[i] >= val_max:\n",
        "                val_max = prey_belief[i]\n",
        "                idx_max_list.append(i)\n",
        "\n",
        "        prey_loc = random.choice(idx_max_list) ##need self.prey_loc or not? // why is prey_loc being or not being there as a parameter affecting code ??????\n",
        "        self.debug('Predicted prey location :', prey_loc)\n",
        "        \n",
        "        nbrs = self.graph.get_next_moves(self.loc)\n",
        "        prey_path_dict = {}\n",
        "        pred_path_dict = {}\n",
        "\n",
        "        curr_path_prey = len(self.graph.BFS(self.loc, prey_loc))\n",
        "        curr_path_pred = len(self.graph.BFS(self.loc, pred_loc))\n",
        "        self.debug(\"Curr Path:\", curr_path_prey, curr_path_pred)\n",
        "\n",
        "        for node in nbrs:\n",
        "            prey_path_dict[node] = len(self.graph.BFS(node, prey_loc)) ## {(1:3), (2:4)}\n",
        "            pred_path_dict[node] = len(self.graph.BFS(node, pred_loc))\n",
        "\n",
        "        self.debug(\"Prey Paths:\", prey_path_dict)\n",
        "        self.debug(\"Pred Paths:\", pred_path_dict)\n",
        "\n",
        "        nodes_lt_prey = []\n",
        "        nodes_eq_prey = []\n",
        "        for n, cost in prey_path_dict.items():\n",
        "            if cost < curr_path_prey:\n",
        "                nodes_lt_prey.append(n)\n",
        "            elif cost == curr_path_prey:\n",
        "                nodes_eq_prey.append(n)\n",
        "\n",
        "        nodes_gt_pred = []\n",
        "        nodes_eq_pred = []\n",
        "        for n, cost in pred_path_dict.items():\n",
        "            if cost > curr_path_pred:\n",
        "                nodes_gt_pred.append(n)\n",
        "            elif cost == curr_path_pred:\n",
        "                nodes_eq_pred.append(n)\n",
        "\n",
        "        valid_n = []\n",
        "\n",
        "        ## 1. closer to prey and farther from pred\n",
        "        if nodes_lt_prey:\n",
        "            for n in nodes_lt_prey:\n",
        "                if n in nodes_gt_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 1\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 2. closer to prey and not closer to pred\n",
        "        if nodes_lt_prey:\n",
        "            for n in nodes_lt_prey:\n",
        "                if n in nodes_eq_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 2\")\n",
        "                return random.choice(valid_n)\n",
        "\n",
        "        ## 3. not farther than prey and farther from pred\n",
        "        if nodes_eq_prey:\n",
        "            for n in nodes_eq_prey:\n",
        "                if n in nodes_gt_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 3\")\n",
        "                return random.choice(valid_n)\n",
        "\n",
        "        ## 4. not farther than prey and not closer to pred\n",
        "        if nodes_eq_prey:\n",
        "            for n in nodes_eq_prey:\n",
        "                if n in nodes_eq_pred:\n",
        "                    valid_n.append(n)\n",
        "            if valid_n:\n",
        "                self.debug(\"Rule 4\")\n",
        "                return random.choice(valid_n)\n",
        "        \n",
        "        ## 5. farther from pred\n",
        "        if nodes_gt_pred:\n",
        "            self.debug(\"Rule 5\")\n",
        "            return random.choice(nodes_gt_pred)\n",
        "\n",
        "        ## 6. not closer to pred\n",
        "        if nodes_eq_pred:\n",
        "            self.debug(\"Rule 6\")\n",
        "            return random.choice(nodes_eq_pred)\n",
        "\n",
        "        ## 7. sit still and pray\n",
        "        else:\n",
        "            self.debug(\"Rule 7\")\n",
        "            return self.loc\n",
        "        "
      ],
      "metadata": {
        "id": "4p_4eiEzw-lL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Agent8:\n",
        "\n",
        "    def __init__(self, graph, prey_loc, pred_loc, prey_belief, pred_belief, dbg=False):\n",
        "        \n",
        "        self.graph = graph\n",
        "        self.loc = graph.get_rand_node()\n",
        "        self.dbg = dbg\n",
        "        # self.loc = graph.get_rand_node()\n",
        "        # print(\"Agent1: \", prey_loc, pred_loc)\n",
        "        self.prey_loc = prey_loc\n",
        "        self.pred_loc = pred_loc\n",
        "\n",
        "        while prey_loc == self.loc or pred_loc == self.loc:\n",
        "            self.loc = graph.get_rand_node()\n",
        "\n",
        "        # self.pred_belief = [1/49]*50\n",
        "        self.pred_belief = pred_belief\n",
        "        self.pred_belief[self.loc] = 0\n",
        "\n",
        "        self.prey_belief = prey_belief\n",
        "        self.prey_belief[self.loc] = 0\n",
        "\n",
        "    def debug(self, *args):\n",
        "        if self.dbg:\n",
        "            print(\"Agent7: \", args)\n",
        "\n",
        "    def isPrey(self, loc, prey_loc):\n",
        "        if loc == prey_loc:\n",
        "            return True\n",
        "\n",
        "    def isPred(self, loc, pred_loc):\n",
        "        if loc == pred_loc:\n",
        "            return True\n",
        "\n",
        "    def survey(self, pred_belief, prey_belief):\n",
        "\n",
        "        # for i in range(len(self.graph.node_num)):\n",
        "        #     if pred_belief[i] == 1.0\n",
        "\n",
        "        if sum(pred_belief) in pred_belief: ##\n",
        "            #### PREY CODE\n",
        "            new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "            new_pred_belief = [0.0]*(self.graph.node_num)\n",
        "\n",
        "            val_max = max(prey_belief)\n",
        "            idx_max_list = []\n",
        "\n",
        "            for i in range(len(prey_belief)):\n",
        "                if prey_belief[i] >= val_max:\n",
        "                    # val_max = prey_belief[i]\n",
        "                    idx_max_list.append(i)\n",
        "\n",
        "            rand_idx_max = random.choice(idx_max_list)\n",
        "\n",
        "            # if self.isPred(rand_idx_max, self.pred.loc):\n",
        "            #     FLAG = 1\n",
        "\n",
        "            if self.isPrey(rand_idx_max, self.prey_loc):\n",
        "                # print(\"IS PREY EXECUTED: \", rand_idx_max)\n",
        "                for i in range(self.graph.node_num):\n",
        "                    # prey_belief[i] = 0.0\n",
        "                    new_prey_belief[i] = 0.0\n",
        "                # prey_belief[rand_idx_max] = 1.0\n",
        "                new_prey_belief[rand_idx_max] = 1.0\n",
        "                # print (\"ISPREY: \", prey_belief)\n",
        "                # return prey_belief, pred_belief\n",
        "                    \n",
        "            else:\n",
        "                for i in range(self.graph.node_num):\n",
        "                    denom = 1-prey_belief[rand_idx_max]\n",
        "                    new_prey_belief[i] = prey_belief[i]/denom\n",
        "                new_prey_belief[rand_idx_max] = 0.0 ###changed indent(earlier in for loop)\n",
        "\n",
        "            prey_belief = new_prey_belief\n",
        "\n",
        "            # #################################\n",
        "\n",
        "            if self.isPred(rand_idx_max, self.pred_loc):\n",
        "                for i in range(self.graph.node_num):\n",
        "                    # pred_belief[i] = 0.0\n",
        "                    new_pred_belief[i] = 0.0\n",
        "                # pred_belief[rand_idx_max] = 1.0\n",
        "                new_pred_belief[rand_idx_max] = 1.0\n",
        "                # return prey_belief, pred_belief\n",
        "\n",
        "            else:\n",
        "                for i in range(self.graph.node_num):\n",
        "                    denom = 1-pred_belief[rand_idx_max]\n",
        "                    new_pred_belief[i] = pred_belief[i]/denom\n",
        "                new_pred_belief[rand_idx_max] = 0.0 ###changed indent(earlier in for loop)\n",
        "\n",
        "            pred_belief = new_pred_belief\n",
        "\n",
        "            # #################################\n",
        "\n",
        "            return prey_belief, pred_belief     \n",
        "\n",
        "        else:\n",
        "            ### PRED CODE\n",
        "            new_pred_belief = [0.0]*(self.graph.node_num)\n",
        "            new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "\n",
        "            val_max = max(pred_belief)\n",
        "            idx_max_list = []\n",
        "\n",
        "            for i in range(len(pred_belief)):\n",
        "                if pred_belief[i] >= val_max:\n",
        "                    idx_max_list.append(i)\n",
        "\n",
        "            dist_list = []\n",
        "            dist_min_list = []\n",
        "\n",
        "            for node in idx_max_list:\n",
        "                dist_list.append(len(self.graph.BFS(node, self.pred_loc)))\n",
        "            \n",
        "            min_dist = min(dist_list) ## CALCULATING MINIMUM dist from agent to partcular pred_probable pos\n",
        "\n",
        "            for i in range(len(dist_list)):\n",
        "                if dist_list[i] <= min_dist:\n",
        "                    dist_min_list.append(i)\n",
        "\n",
        "            rand_idx_max = random.choice(dist_min_list)\n",
        "\n",
        "            if self.isPred(rand_idx_max, self.pred_loc):\n",
        "                for i in range(self.graph.node_num):\n",
        "                    # pred_belief[i] = 0.0\n",
        "                    new_pred_belief[i] = 0.0\n",
        "                # pred_belief[rand_idx_max] = 1.0\n",
        "                new_pred_belief[rand_idx_max] = 1.0\n",
        "                # return prey_belief, pred_belief\n",
        "\n",
        "            else:\n",
        "                for i in range(self.graph.node_num):\n",
        "                    denom = 1-pred_belief[rand_idx_max]\n",
        "                    new_pred_belief[i] = pred_belief[i]/denom\n",
        "                new_pred_belief[rand_idx_max] = 0.0 ###changed indent(earlier in for loop)\n",
        "\n",
        "            pred_belief = new_pred_belief\n",
        "            # return prey_belief, pred_belief\n",
        "\n",
        "                        \n",
        "            # ###########################\n",
        "\n",
        "            if self.isPrey(rand_idx_max, self.prey_loc):\n",
        "                # print(\"IS PREY EXECUTED: \", rand_idx_max)\n",
        "                for i in range(self.graph.node_num):\n",
        "                    # prey_belief[i] = 0.0\n",
        "                    new_prey_belief[i] = 0.0\n",
        "                # prey_belief[rand_idx_max] = 1.0\n",
        "                new_prey_belief[rand_idx_max] = 1.0\n",
        "                \n",
        "                # print (\"ISPREY: \", prey_belief)\n",
        "                # return prey_belief, pred_belief\n",
        "\n",
        "            else:\n",
        "                for i in range(self.graph.node_num):\n",
        "                    denom = 1-prey_belief[rand_idx_max]\n",
        "                    new_prey_belief[i] = prey_belief[i]/denom\n",
        "                new_prey_belief[rand_idx_max] = 0.0 ###changed indent(earlier in for loop)\n",
        "\n",
        "            prey_belief = new_prey_belief\n",
        "            # return prey_belief, pred_belief    \n",
        "\n",
        "            # ###########################\n",
        "\n",
        "            return prey_belief, pred_belief \n",
        "\n",
        "    \n",
        "\n",
        "    def agent_move_pred_belief(self, pred_loc, pred_belief):\n",
        "\n",
        "        new_pred_belief = [0.0]*(self.graph.node_num)\n",
        "\n",
        "        if self.isPred(self.loc, pred_loc):\n",
        "            for i in range(self.graph.node_num): ###///this will not actually happen since the game would be over\n",
        "                # pred_belief[i] = 0.0\n",
        "                new_pred_belief[i] = 0.0\n",
        "            # pred_belief[self.loc] = 1.0\n",
        "            new_pred_belief[self.loc] = 1.0\n",
        "            # return pred_belief\n",
        "        \n",
        "        else:\n",
        "            for i in range(self.graph.node_num):\n",
        "                denom = 1.0-pred_belief[self.loc]\n",
        "                new_pred_belief[i] = pred_belief[i]/denom\n",
        "            new_pred_belief[self.loc] = 0.0 ###changed indent(earlier in for loop)\n",
        "                # prey_belief[i] = prey_belief[i]/denom\n",
        "\n",
        "        pred_belief = new_pred_belief\n",
        "        return pred_belief\n",
        "\n",
        "    def pred_move_pred_belief(self, pred_loc, pred_belief):\n",
        "        pred_belief_tmp = [0.0]*self.graph.node_num\n",
        "        # print (\"before func PRED_MOVE_UPDATE : \", sum(pred_belief))\n",
        "        for i in range(self.graph.node_num):\n",
        "            tmp_belief = 0.0\n",
        "\n",
        "            nbrs_i = self.graph.get_next_moves(i)\n",
        "            \n",
        "            for nbr in nbrs_i:\n",
        "                nbr_bfs = {}\n",
        "                \n",
        "                nbr_nbrs_i = self.graph.get_next_moves(nbr)\n",
        "                for nbr_nbr in nbr_nbrs_i:\n",
        "                    bfs = self.graph.BFS(nbr_nbr, self.loc)\n",
        "                    nbr_bfs[nbr_nbr] = len(bfs)\n",
        "\n",
        "                # print (\"NBR_BFS: \", nbr_bfs)\n",
        "                min_bfs = min(nbr_bfs.values())\n",
        "\n",
        "                nbr_min_path_len = [key for key, value in nbr_bfs.items() if value == min_bfs]\n",
        "                # print(\"nbr_min_path_len : \", nbr_min_path_len)\n",
        "\n",
        "                x = len(nbr_min_path_len)\n",
        "                # print (\"DEBUG: \", i, \" \", nbr_min_path_len)\n",
        "                if i in nbr_min_path_len:\n",
        "                    tmp_belief += pred_belief[nbr]*((0.4/self.graph.get_deg(nbr))+(0.6/x))\n",
        "                    # print(\"tmp bel in 1 \", tmp_belief)\n",
        "                else:\n",
        "                    tmp_belief += pred_belief[nbr]*((0.4/self.graph.get_deg(nbr)))\n",
        "                    # print(\"tmp bel in 2 \", tmp_belief)\n",
        "\n",
        "                # print (\"i :\", i)\n",
        "                # print(\"nbrs_i\", nbrs_i)\n",
        "                # print(\"nbr_nbr\", nbr_nbrs_i)\n",
        "\n",
        "                # exit (0)\n",
        "            # print(\"TMP BELIEF: \", tmp_belief)\n",
        "            pred_belief_tmp[i] = tmp_belief\n",
        "\n",
        "        # print (\"after func PRED_MOVE_UPDATE OLD : \", pred_belief)\n",
        "\n",
        "        # print (\"after func PRED_MOVE_UPDATE : \", sum(pred_belief_tmp))\n",
        "\n",
        "        return pred_belief_tmp\n",
        "\n",
        "\n",
        "    def agent_move_prey_belief(self, prey_loc, prey_belief):\n",
        "\n",
        "        new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "        # new_prey_belief = prey_belief\n",
        "\n",
        "        if self.isPrey(self.loc, prey_loc):\n",
        "            for i in range(self.graph.node_num): ###///this will not actually happen since the game would be over\n",
        "                # prey_belief[i] = 0.0\n",
        "                new_prey_belief[i] = 0.0\n",
        "            # prey_belief[self.loc] = 1.0\n",
        "            new_prey_belief[self.loc] = 1.0\n",
        "            # return prey_belief  \n",
        "        \n",
        "        else:\n",
        "        #   update_belief() ## according to prey not found in survey logic\n",
        "            for i in range(self.graph.node_num):\n",
        "                # denom = sum(prey_belief)-prey_belief[self.loc]\n",
        "                denom = 1.0-prey_belief[self.loc]\n",
        "                new_prey_belief[i] = prey_belief[i]/denom\n",
        "            new_prey_belief[self.loc] = 0.0 ###changed indent(earlier in for loop)\n",
        "                # new_prey_belief[curr_loc] = 0.0\n",
        "                # prey_belief[i] = prey_belief[i]/denom\n",
        "\n",
        "        prey_belief = new_prey_belief\n",
        "        return prey_belief\n",
        "\n",
        "    def prey_move_prey_belief(self, prey_loc, prey_belief):\n",
        "\n",
        "        new_prey_belief = [0.0]*(self.graph.node_num)\n",
        "        # new_prey_belief = prey_belief\n",
        "\n",
        "        for i in range(self.graph.node_num):\n",
        "            nbrs_i = self.graph.get_next_moves(i)\n",
        "            nbrs_i.append(i)\n",
        "            for nbr in nbrs_i:\n",
        "                denom = self.graph.get_deg(nbr) + 1\n",
        "                new_prey_belief[i] += prey_belief[nbr] / denom\n",
        "\n",
        "        prey_belief = new_prey_belief\n",
        "\n",
        "        prey_belief = self.agent_move_prey_belief(prey_loc, prey_belief)\n",
        "\n",
        "        # prey_belief = new_prey_belief\n",
        "        return prey_belief\n",
        "\n",
        "\n",
        "    def move(self, prey_loc, pred_loc, pred_belief, prey_belief):\n",
        "        self.debug('Pred Belief: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        self.debug('Prey Belief: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "\n",
        "        prey_belief, pred_belief = self.survey(pred_belief, prey_belief)\n",
        "        self.debug('Pred Belief after S: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        self.debug('Prey Belief after S: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "        curr_loc = self.loc ######\n",
        "        self.loc = self.move_helper(prey_loc, pred_loc, prey_belief, pred_belief)\n",
        "        prey_belief = self.agent_move_prey_belief(prey_loc, prey_belief)\n",
        "        pred_belief = self.agent_move_pred_belief(pred_loc, pred_belief)\n",
        "\n",
        "        # prey_belief[curr_loc] = 0.0\n",
        "        self.debug('Pred Belief after A: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        self.debug('Prey Belief after A: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "\n",
        "        return self.loc\n",
        "\n",
        "\n",
        "    def move_helper(self, prey_loc, pred_loc, prey_belief, pred_belief):\n",
        "\n",
        "        #####///// WE SELECT MAX BELIEF NODE for pred_loc\n",
        "        val_max1 = max(pred_belief)\n",
        "        idx_max1 = 0\n",
        "        idx_max_list1 = []\n",
        "\n",
        "        for i in range(len(pred_belief)):\n",
        "            if pred_belief[i] >= val_max1:\n",
        "                # val_max = prey_belief[i]\n",
        "                idx_max_list1.append(i)\n",
        "\n",
        "        pred_loc = random.choice(idx_max_list1) ##need self.prey_loc or not? // why is prey_loc being or not being there as a parameter affecting code ??????\n",
        "        self.debug('Predicted pred location :', pred_loc)\n",
        "\n",
        "        ## We select max belief node from list of max belief nodes and assume it to be where Prey is even if its not been found\n",
        "        val_max = max(prey_belief)\n",
        "        idx_max = 0\n",
        "        idx_max_list = []\n",
        "\n",
        "        for i in range(len(prey_belief)):\n",
        "            if prey_belief[i] >= val_max:\n",
        "                val_max = prey_belief[i]\n",
        "                idx_max_list.append(i)\n",
        "\n",
        "        prey_loc = random.choice(idx_max_list) ##need self.prey_loc or not? // why is prey_loc being or not being there as a parameter affecting code ??????\n",
        "        self.debug('Predicted prey location :', prey_loc)\n",
        "        \n",
        "        nbrs = self.graph.get_next_moves(self.loc)\n",
        "        prey_path_dict = {}\n",
        "        pred_path_dict = {}\n",
        "\n",
        "        curr_path_prey = len(self.graph.BFS(self.loc, prey_loc))\n",
        "        curr_path_pred = len(self.graph.BFS(self.loc, pred_loc))\n",
        "        self.debug(\"Curr Path:\", curr_path_prey, curr_path_pred)\n",
        "\n",
        "        for node in nbrs:\n",
        "            prey_path_dict[node] = len(self.graph.BFS(node, prey_loc)) ## {(1:3), (2:4)}\n",
        "            pred_path_dict[node] = len(self.graph.BFS(node, pred_loc))\n",
        "\n",
        "        self.debug(\"Prey Paths:\", prey_path_dict)\n",
        "        self.debug(\"Pred Paths:\", pred_path_dict)\n",
        "\n",
        "        nodes_lt_prey = []\n",
        "        nodes_eq_prey = []\n",
        "        for n, cost in prey_path_dict.items():\n",
        "            if cost < curr_path_prey:\n",
        "                nodes_lt_prey.append(n)\n",
        "            elif cost == curr_path_prey:\n",
        "                nodes_eq_prey.append(n)\n",
        "\n",
        "        nodes_gt_pred = []\n",
        "        nodes_eq_pred = []\n",
        "        for n, cost in pred_path_dict.items():\n",
        "            if cost > curr_path_pred:\n",
        "                nodes_gt_pred.append(n)\n",
        "            elif cost == curr_path_pred:\n",
        "                nodes_eq_pred.append(n)\n",
        "\n",
        "        valid_n = []\n",
        "        \n",
        "        dist = abs(abs(pred_loc - self.loc)-self.graph.node_num) % self.graph.node_num\n",
        "\n",
        "        if dist >= 5:\n",
        "\n",
        "            ## 1. closer to prey and farther from pred\n",
        "            if nodes_lt_prey:\n",
        "                for n in nodes_lt_prey:\n",
        "                    if n in nodes_gt_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 1\")\n",
        "                    return random.choice(valid_n)\n",
        "            \n",
        "            ## 2. closer to prey and not closer to pred\n",
        "            if nodes_lt_prey:\n",
        "                for n in nodes_lt_prey:\n",
        "                    if n in nodes_eq_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 2\")\n",
        "                    return random.choice(valid_n)\n",
        "\n",
        "            ## 3. not farther than prey and farther from pred\n",
        "            if nodes_eq_prey:\n",
        "                for n in nodes_eq_prey:\n",
        "                    if n in nodes_gt_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 3\")\n",
        "                    return random.choice(valid_n)\n",
        "\n",
        "            ## 4. not farther than prey and not closer to pred\n",
        "            if nodes_eq_prey:\n",
        "                for n in nodes_eq_prey:\n",
        "                    if n in nodes_eq_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 4\")\n",
        "                    return random.choice(valid_n)\n",
        "            \n",
        "            ## 5. farther from pred\n",
        "            if nodes_gt_pred:\n",
        "                self.debug(\"Rule 5\")\n",
        "                return random.choice(nodes_gt_pred)\n",
        "\n",
        "            ## 6. not closer to pred\n",
        "            if nodes_eq_pred:\n",
        "                self.debug(\"Rule 6\")\n",
        "                return random.choice(nodes_eq_pred)\n",
        "\n",
        "            ## 7. sit still and pray\n",
        "            else:\n",
        "                self.debug(\"Rule 7\")\n",
        "                return self.loc\n",
        "        \n",
        "        else:\n",
        "\n",
        "            ## 3. not farther than prey and farther from pred\n",
        "            if nodes_eq_prey:\n",
        "                for n in nodes_eq_prey:\n",
        "                    if n in nodes_gt_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 3\")\n",
        "                    return random.choice(valid_n)\n",
        "            \n",
        "            ## 5. farther from pred\n",
        "            if nodes_gt_pred:\n",
        "                self.debug(\"Rule 5\")\n",
        "                return random.choice(nodes_gt_pred)\n",
        "\n",
        "            ## 1. closer to prey and farther from pred\n",
        "            if nodes_lt_prey:\n",
        "                for n in nodes_lt_prey:\n",
        "                    if n in nodes_gt_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 1\")\n",
        "                    return random.choice(valid_n)\n",
        "            \n",
        "            ## 2. closer to prey and not closer to pred\n",
        "            if nodes_lt_prey:\n",
        "                for n in nodes_lt_prey:\n",
        "                    if n in nodes_eq_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 2\")\n",
        "                    return random.choice(valid_n)\n",
        "            \n",
        "            ## 4. not farther than prey and not closer to pred\n",
        "            if nodes_eq_prey:\n",
        "                for n in nodes_eq_prey:\n",
        "                    if n in nodes_eq_pred:\n",
        "                        valid_n.append(n)\n",
        "                if valid_n:\n",
        "                    self.debug(\"Rule 4\")\n",
        "                    return random.choice(valid_n)\n",
        "\n",
        "            ## 6. not closer to pred\n",
        "            if nodes_eq_pred:\n",
        "                self.debug(\"Rule 6\")\n",
        "                return random.choice(nodes_eq_pred)\n",
        "\n",
        "            ## 7. sit still and pray\n",
        "            self.debug(\"Rule 7\")\n",
        "            return self.loc\n"
      ],
      "metadata": {
        "id": "LHcXckAs71mg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "node_num = 50\n",
        "g = Graph(node_num)\n",
        "# g.print_adj_list()\n",
        "\n",
        "# g.visualize()\n",
        "\n",
        "prey = Prey(g)\n",
        "# prey2 = prey1\n",
        "# prey2 = copy.deepcopy(prey1)\n",
        "prey1 = copy.copy(prey)\n",
        "prey2 = copy.copy(prey) ## ???? should I do deep or shallow copy of prey and pred if I want them to be separate for each agent but not have to write new code ?????\n",
        "prey3 = copy.copy(prey)\n",
        "prey4 = copy.copy(prey)\n",
        "prey5 = copy.copy(prey)\n",
        "prey6 = copy.copy(prey)\n",
        "prey7 = copy.copy(prey)\n",
        "prey8 = copy.copy(prey)\n",
        "prey9 = copy.copy(prey)\n",
        "\n",
        "pred = Predator(g)\n",
        "# pred2 = pred1\n",
        "# pred2 = copy.deepcopy(pred1)\n",
        "pred1 = copy.copy(pred)\n",
        "pred2 = copy.copy(pred)\n",
        "pred3 = copy.copy(pred)\n",
        "pred4 = copy.copy(pred)\n",
        "pred5 = copy.copy(pred)\n",
        "pred6 = copy.copy(pred)\n",
        "pred7 = copy.copy(pred)\n",
        "pred8 = copy.copy(pred)\n",
        "pred9 = copy.copy(pred)\n",
        "\n",
        "prey_belief = [1/(g.node_num-1)]*(g.node_num) ### [1/49]*50\n",
        "pred_belief = [1/(g.node_num-1)]*(g.node_num)\n",
        "\n",
        "threshold = 1000\n",
        "loops = 1000 ### CHANGE TO 3000"
      ],
      "metadata": {
        "id": "nDWchQhRJpG2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# threshold = 100\n",
        "node_num = 50\n",
        "\n",
        "cnt = 0\n",
        "hung = 0\n",
        "ans = 0\n",
        "\n",
        "survival = []\n",
        "\n",
        "for i in range(loops):\n",
        "    g = Graph(node_num)\n",
        "    prey = Prey(g)\n",
        "    pred = Predator(g)\n",
        "    prey8 = copy.copy(prey)\n",
        "    pred8 = copy.copy(pred)\n",
        "\n",
        "    \n",
        "    prey_belief = [1/(g.node_num-1)]*(g.node_num) ### [1/49]*50\n",
        "    pred_belief = [1/(g.node_num-1)]*(g.node_num)\n",
        "\n",
        "    ##------ AGENT-8 RUN MAIN ------##\n",
        "    agent8 = Agent8(g, prey8.loc, pred8.loc, prey_belief, pred_belief, False)\n",
        "\n",
        "    count = 0\n",
        "\n",
        "    while agent8.loc != pred8.loc and agent8.loc != prey8.loc:\n",
        "        if count > threshold:\n",
        "            print(\"HUNG SIMULATION\")\n",
        "            # break\n",
        "            hung += 1\n",
        "            continue\n",
        "\n",
        "        agent8.debug(\"BEFORE: \", agent8.loc, prey8.loc, pred8.loc)\n",
        "        agent8.loc = agent8.move(prey8.loc, pred8.loc, pred_belief, prey_belief)\n",
        "\n",
        "        if agent8.isPred(agent8.loc, pred8.loc) and agent8.isPrey(agent8.loc, prey8.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent8.isPred(agent8.loc, pred8.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent8.isPrey(agent8.loc, prey8.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "\n",
        "        prey8.loc = prey8.move()\n",
        "        prey_belief = agent8.prey_move_prey_belief(prey8.loc, prey_belief)\n",
        "        pred8.loc = pred8.move(agent8.loc)\n",
        "        pred_belief = agent8.pred_move_pred_belief(pred8.loc, pred_belief)\n",
        "        agent8.debug('Pred Belief after P: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        \n",
        "        agent8.debug(\"After: \", agent8.loc, prey8.loc, pred8.loc) ##\n",
        "        agent8.debug(\"prey8 found : \", agent8.isPrey(agent8.loc, prey8.loc),\" ; \", \"pred5 found : \", agent8.isPred(agent8.loc, pred8.loc)) ##\n",
        "\n",
        "        count = count + 1\n",
        "\n",
        "        if agent8.isPred(agent8.loc, pred8.loc) and agent8.isPrey(agent8.loc, prey8.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent8.isPred(agent8.loc, pred8.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent8.isPrey(agent8.loc, prey8.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "\n",
        "    cnt += 1\n",
        "    survival.append(count)\n",
        "    print(\"cnt: \", i, \" Steps : \", count)\n",
        "\n",
        "print(\"============================\")\n",
        "print(\"CNT: \", cnt)\n",
        "print(\"TRUE: \", ans)\n",
        "print(\"FALSE: \", cnt-(ans+hung))\n",
        "print(\"HUNG: \", hung)\n",
        "avg_steps = sum(survival)/len(survival)\n",
        "print(\"AVG. STEPS: \", avg_steps)\n",
        "print(\"============================\")\n"
      ],
      "metadata": {
        "id": "2nRqJryv-6nq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# threshold = 100\n",
        "node_num = 50\n",
        "\n",
        "cnt = 0\n",
        "hung = 0\n",
        "ans = 0\n",
        "\n",
        "survival = []\n",
        "\n",
        "for i in range(loops):\n",
        "    g = Graph(node_num)\n",
        "    prey = Prey(g)\n",
        "    pred = Predator(g)\n",
        "    prey7 = copy.copy(prey)\n",
        "    pred7 = copy.copy(pred)\n",
        "\n",
        "    \n",
        "    prey_belief = [1/(g.node_num-1)]*(g.node_num) ### [1/49]*50\n",
        "    pred_belief = [1/(g.node_num-1)]*(g.node_num)\n",
        "\n",
        "    ##------ AGENT-7 RUN MAIN ------##\n",
        "    agent7 = Agent7(g, prey7.loc, pred7.loc, prey_belief, pred_belief, False)\n",
        "\n",
        "    count = 0\n",
        "\n",
        "    while agent7.loc != pred7.loc and agent7.loc != prey7.loc:\n",
        "        if count > threshold:\n",
        "            print(\"HUNG SIMULATION\")\n",
        "            # break\n",
        "            hung += 1\n",
        "            continue\n",
        "\n",
        "        agent7.debug(\"BEFORE: \", agent7.loc, prey7.loc, pred7.loc)\n",
        "        agent7.loc = agent7.move(prey7.loc, pred7.loc, pred_belief, prey_belief)\n",
        "\n",
        "        if agent7.isPred(agent7.loc, pred7.loc) and agent7.isPrey(agent7.loc, prey7.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent7.isPred(agent7.loc, pred7.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent7.isPrey(agent7.loc, prey7.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "\n",
        "        prey7.loc = prey7.move()\n",
        "        prey_belief = agent7.prey_move_prey_belief(prey7.loc, prey_belief)\n",
        "        pred7.loc = pred7.move(agent7.loc)\n",
        "        pred_belief = agent7.pred_move_pred_belief(pred7.loc, pred_belief)\n",
        "        agent7.debug('Pred Belief after P: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        \n",
        "        agent7.debug(\"After: \", agent7.loc, prey7.loc, pred7.loc) ##\n",
        "        agent7.debug(\"prey7 found : \", agent7.isPrey(agent7.loc, prey7.loc),\" ; \", \"pred5 found : \", agent7.isPred(agent7.loc, pred7.loc)) ##\n",
        "\n",
        "        count = count + 1\n",
        "\n",
        "        if agent7.isPred(agent7.loc, pred7.loc) and agent7.isPrey(agent7.loc, prey7.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent7.isPred(agent7.loc, pred7.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent7.isPrey(agent7.loc, prey7.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "\n",
        "    cnt += 1\n",
        "    survival.append(count)\n",
        "    print(\"cnt: \", i, \" Steps : \", count)\n",
        "\n",
        "print(\"============================\")\n",
        "print(\"CNT: \", cnt)\n",
        "print(\"TRUE: \", ans)\n",
        "print(\"FALSE: \", cnt-(ans+hung))\n",
        "print(\"HUNG: \", hung)\n",
        "avg_steps = sum(survival)/len(survival)\n",
        "print(\"AVG. STEPS: \", avg_steps)\n",
        "print(\"============================\")\n"
      ],
      "metadata": {
        "id": "GDPe_wJ1sveV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# threshold = 100\n",
        "node_num = 50\n",
        "\n",
        "cnt = 0\n",
        "hung = 0\n",
        "ans = 0\n",
        "\n",
        "survival = []\n",
        "\n",
        "for i in range(loops):\n",
        "    g = Graph(node_num)\n",
        "    prey = Prey(g)\n",
        "    pred = Predator(g)\n",
        "    prey6 = copy.copy(prey)\n",
        "    pred6 = copy.copy(pred)\n",
        "\n",
        "    \n",
        "    # prey_belief = [1/(g.node_num-1)]*(g.node_num) ### [1/49]*50\n",
        "    pred_belief = [1/(g.node_num-1)]*(g.node_num)    \n",
        "    \n",
        "    ##------ AGENT-6 RUN MAIN ------##\n",
        "    agent6 = Agent6(g, prey6.loc, pred6.loc, pred_belief, False)\n",
        "\n",
        "    count = 0\n",
        "\n",
        "    while agent6.loc != pred6.loc and agent6.loc != prey6.loc:\n",
        "        if count > threshold:\n",
        "            print(\"HUNG SIMULATION\")\n",
        "            hung += 1\n",
        "            continue\n",
        "\n",
        "        agent6.debug(\"BEFORE: \", agent6.loc, prey6.loc, pred6.loc)\n",
        "        \n",
        "        agent6.loc = agent6.move(prey6.loc, pred6.loc, pred_belief)\n",
        "\n",
        "        if agent6.isPred(agent6.loc, pred6.loc) and agent6.isPrey(agent6.loc, prey6.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent6.isPred(agent6.loc, pred6.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent6.isPrey(agent6.loc, prey6.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue    \n",
        "        \n",
        "        prey6.loc = prey6.move()\n",
        "        pred6.loc = pred6.move(agent6.loc)\n",
        "        pred_belief = agent6.pred_move_pred_belief(pred6.loc, pred_belief)\n",
        "        agent6.debug('Pred Belief after P: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        \n",
        "        agent6.debug(\"After: \", agent6.loc, prey6.loc, pred6.loc) ##\n",
        "        agent6.debug(\"prey6 found : \", agent6.isPrey(agent6.loc, prey6.loc),\" ; \", \"pred6 found : \", agent6.isPred(agent6.loc, pred6.loc)) ##\n",
        "\n",
        "        count = count + 1\n",
        "\n",
        "        if agent6.isPred(agent6.loc, pred6.loc) and agent6.isPrey(agent6.loc, prey6.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent6.isPred(agent6.loc, pred6.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent6.isPrey(agent6.loc, prey6.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        \n",
        "    cnt += 1\n",
        "    survival.append(count)\n",
        "\n",
        "    print(\"cnt: \", cnt, \"Steps : \", count)\n",
        "\n",
        "\n",
        "print(\"============================\")\n",
        "print(\"CNT: \", cnt)\n",
        "print(\"TRUE: \", ans)\n",
        "print(\"FALSE: \", cnt-(ans+hung))\n",
        "print(\"HUNG: \", hung)\n",
        "avg_steps = sum(survival)/len(survival)\n",
        "print(\"AVG. STEPS: \", avg_steps)\n",
        "print(\"============================\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2XFgTHoufrf",
        "outputId": "c71e57fd-beb0-46c0-c94e-7e21e20d444c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "cnt:  504 Steps :  12\n",
            "False\n",
            "cnt:  505 Steps :  21\n",
            "False\n",
            "cnt:  506 Steps :  11\n",
            "True\n",
            "cnt:  507 Steps :  7\n",
            "True\n",
            "cnt:  508 Steps :  4\n",
            "False\n",
            "cnt:  509 Steps :  7\n",
            "False\n",
            "cnt:  510 Steps :  4\n",
            "False\n",
            "cnt:  511 Steps :  13\n",
            "False\n",
            "cnt:  512 Steps :  3\n",
            "False\n",
            "cnt:  513 Steps :  11\n",
            "False\n",
            "cnt:  514 Steps :  0\n",
            "False\n",
            "cnt:  515 Steps :  11\n",
            "True\n",
            "cnt:  516 Steps :  7\n",
            "True\n",
            "cnt:  517 Steps :  0\n",
            "False\n",
            "cnt:  518 Steps :  14\n",
            "True\n",
            "cnt:  519 Steps :  1\n",
            "True\n",
            "cnt:  520 Steps :  9\n",
            "True\n",
            "cnt:  521 Steps :  10\n",
            "False\n",
            "cnt:  522 Steps :  4\n",
            "True\n",
            "cnt:  523 Steps :  2\n",
            "True\n",
            "cnt:  524 Steps :  13\n",
            "False\n",
            "cnt:  525 Steps :  12\n",
            "False\n",
            "cnt:  526 Steps :  16\n",
            "False\n",
            "cnt:  527 Steps :  9\n",
            "False\n",
            "cnt:  528 Steps :  5\n",
            "False\n",
            "cnt:  529 Steps :  9\n",
            "True\n",
            "cnt:  530 Steps :  9\n",
            "False\n",
            "cnt:  531 Steps :  8\n",
            "False\n",
            "cnt:  532 Steps :  5\n",
            "True\n",
            "cnt:  533 Steps :  0\n",
            "False\n",
            "cnt:  534 Steps :  2\n",
            "False\n",
            "cnt:  535 Steps :  8\n",
            "True\n",
            "cnt:  536 Steps :  2\n",
            "True\n",
            "cnt:  537 Steps :  15\n",
            "False\n",
            "cnt:  538 Steps :  15\n",
            "True\n",
            "cnt:  539 Steps :  2\n",
            "False\n",
            "cnt:  540 Steps :  11\n",
            "False\n",
            "cnt:  541 Steps :  7\n",
            "False\n",
            "cnt:  542 Steps :  5\n",
            "True\n",
            "cnt:  543 Steps :  11\n",
            "False\n",
            "cnt:  544 Steps :  16\n",
            "False\n",
            "cnt:  545 Steps :  13\n",
            "False\n",
            "cnt:  546 Steps :  5\n",
            "False\n",
            "cnt:  547 Steps :  3\n",
            "False\n",
            "cnt:  548 Steps :  8\n",
            "True\n",
            "cnt:  549 Steps :  5\n",
            "False\n",
            "cnt:  550 Steps :  1\n",
            "False\n",
            "cnt:  551 Steps :  11\n",
            "False\n",
            "cnt:  552 Steps :  7\n",
            "True\n",
            "cnt:  553 Steps :  11\n",
            "False\n",
            "cnt:  554 Steps :  9\n",
            "True\n",
            "cnt:  555 Steps :  2\n",
            "False\n",
            "cnt:  556 Steps :  13\n",
            "False\n",
            "cnt:  557 Steps :  1\n",
            "False\n",
            "cnt:  558 Steps :  10\n",
            "False\n",
            "cnt:  559 Steps :  7\n",
            "False\n",
            "cnt:  560 Steps :  3\n",
            "True\n",
            "cnt:  561 Steps :  10\n",
            "False\n",
            "cnt:  562 Steps :  81\n",
            "False\n",
            "cnt:  563 Steps :  13\n",
            "False\n",
            "cnt:  564 Steps :  10\n",
            "True\n",
            "cnt:  565 Steps :  4\n",
            "False\n",
            "cnt:  566 Steps :  11\n",
            "True\n",
            "cnt:  567 Steps :  7\n",
            "False\n",
            "cnt:  568 Steps :  8\n",
            "False\n",
            "cnt:  569 Steps :  5\n",
            "False\n",
            "cnt:  570 Steps :  8\n",
            "True\n",
            "cnt:  571 Steps :  10\n",
            "False\n",
            "cnt:  572 Steps :  12\n",
            "True\n",
            "cnt:  573 Steps :  6\n",
            "True\n",
            "cnt:  574 Steps :  3\n",
            "True\n",
            "cnt:  575 Steps :  1\n",
            "False\n",
            "cnt:  576 Steps :  3\n",
            "False\n",
            "cnt:  577 Steps :  3\n",
            "True\n",
            "cnt:  578 Steps :  10\n",
            "False\n",
            "cnt:  579 Steps :  8\n",
            "True\n",
            "cnt:  580 Steps :  9\n",
            "False\n",
            "cnt:  581 Steps :  8\n",
            "False\n",
            "cnt:  582 Steps :  6\n",
            "False\n",
            "cnt:  583 Steps :  6\n",
            "False\n",
            "cnt:  584 Steps :  10\n",
            "False\n",
            "cnt:  585 Steps :  16\n",
            "False\n",
            "cnt:  586 Steps :  12\n",
            "True\n",
            "cnt:  587 Steps :  12\n",
            "False\n",
            "cnt:  588 Steps :  21\n",
            "True\n",
            "cnt:  589 Steps :  3\n",
            "False\n",
            "cnt:  590 Steps :  4\n",
            "False\n",
            "cnt:  591 Steps :  8\n",
            "True\n",
            "cnt:  592 Steps :  0\n",
            "False\n",
            "cnt:  593 Steps :  4\n",
            "False\n",
            "cnt:  594 Steps :  12\n",
            "False\n",
            "cnt:  595 Steps :  3\n",
            "True\n",
            "cnt:  596 Steps :  7\n",
            "False\n",
            "cnt:  597 Steps :  8\n",
            "False\n",
            "cnt:  598 Steps :  11\n",
            "True\n",
            "cnt:  599 Steps :  3\n",
            "False\n",
            "cnt:  600 Steps :  9\n",
            "False\n",
            "cnt:  601 Steps :  7\n",
            "False\n",
            "cnt:  602 Steps :  13\n",
            "False\n",
            "cnt:  603 Steps :  11\n",
            "False\n",
            "cnt:  604 Steps :  12\n",
            "True\n",
            "cnt:  605 Steps :  13\n",
            "False\n",
            "cnt:  606 Steps :  1\n",
            "True\n",
            "cnt:  607 Steps :  1\n",
            "True\n",
            "cnt:  608 Steps :  0\n",
            "True\n",
            "cnt:  609 Steps :  18\n",
            "True\n",
            "cnt:  610 Steps :  22\n",
            "False\n",
            "cnt:  611 Steps :  21\n",
            "False\n",
            "cnt:  612 Steps :  8\n",
            "False\n",
            "cnt:  613 Steps :  11\n",
            "True\n",
            "cnt:  614 Steps :  4\n",
            "False\n",
            "cnt:  615 Steps :  11\n",
            "False\n",
            "cnt:  616 Steps :  14\n",
            "False\n",
            "cnt:  617 Steps :  2\n",
            "False\n",
            "cnt:  618 Steps :  5\n",
            "False\n",
            "cnt:  619 Steps :  71\n",
            "False\n",
            "cnt:  620 Steps :  8\n",
            "False\n",
            "cnt:  621 Steps :  7\n",
            "False\n",
            "cnt:  622 Steps :  8\n",
            "False\n",
            "cnt:  623 Steps :  11\n",
            "True\n",
            "cnt:  624 Steps :  5\n",
            "True\n",
            "cnt:  625 Steps :  5\n",
            "False\n",
            "cnt:  626 Steps :  6\n",
            "False\n",
            "cnt:  627 Steps :  14\n",
            "False\n",
            "cnt:  628 Steps :  7\n",
            "True\n",
            "cnt:  629 Steps :  10\n",
            "True\n",
            "cnt:  630 Steps :  1\n",
            "True\n",
            "cnt:  631 Steps :  3\n",
            "False\n",
            "cnt:  632 Steps :  7\n",
            "False\n",
            "cnt:  633 Steps :  12\n",
            "True\n",
            "cnt:  634 Steps :  4\n",
            "True\n",
            "cnt:  635 Steps :  2\n",
            "False\n",
            "cnt:  636 Steps :  7\n",
            "True\n",
            "cnt:  637 Steps :  8\n",
            "False\n",
            "cnt:  638 Steps :  11\n",
            "False\n",
            "cnt:  639 Steps :  10\n",
            "False\n",
            "cnt:  640 Steps :  3\n",
            "False\n",
            "cnt:  641 Steps :  12\n",
            "False\n",
            "cnt:  642 Steps :  2\n",
            "False\n",
            "cnt:  643 Steps :  12\n",
            "True\n",
            "cnt:  644 Steps :  13\n",
            "True\n",
            "cnt:  645 Steps :  1\n",
            "False\n",
            "cnt:  646 Steps :  4\n",
            "False\n",
            "cnt:  647 Steps :  5\n",
            "False\n",
            "cnt:  648 Steps :  18\n",
            "False\n",
            "cnt:  649 Steps :  10\n",
            "False\n",
            "cnt:  650 Steps :  2\n",
            "False\n",
            "cnt:  651 Steps :  14\n",
            "False\n",
            "cnt:  652 Steps :  7\n",
            "True\n",
            "cnt:  653 Steps :  3\n",
            "False\n",
            "cnt:  654 Steps :  12\n",
            "False\n",
            "cnt:  655 Steps :  4\n",
            "True\n",
            "cnt:  656 Steps :  8\n",
            "True\n",
            "cnt:  657 Steps :  8\n",
            "True\n",
            "cnt:  658 Steps :  5\n",
            "False\n",
            "cnt:  659 Steps :  11\n",
            "True\n",
            "cnt:  660 Steps :  9\n",
            "False\n",
            "cnt:  661 Steps :  12\n",
            "False\n",
            "cnt:  662 Steps :  11\n",
            "False\n",
            "cnt:  663 Steps :  6\n",
            "False\n",
            "cnt:  664 Steps :  9\n",
            "True\n",
            "cnt:  665 Steps :  12\n",
            "True\n",
            "cnt:  666 Steps :  0\n",
            "False\n",
            "cnt:  667 Steps :  4\n",
            "False\n",
            "cnt:  668 Steps :  4\n",
            "True\n",
            "cnt:  669 Steps :  0\n",
            "True\n",
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            "True\n",
            "cnt:  2984 Steps :  9\n",
            "True\n",
            "cnt:  2985 Steps :  0\n",
            "True\n",
            "cnt:  2986 Steps :  15\n",
            "False\n",
            "cnt:  2987 Steps :  1\n",
            "True\n",
            "cnt:  2988 Steps :  14\n",
            "True\n",
            "cnt:  2989 Steps :  13\n",
            "True\n",
            "cnt:  2990 Steps :  8\n",
            "False\n",
            "cnt:  2991 Steps :  10\n",
            "False\n",
            "cnt:  2992 Steps :  8\n",
            "False\n",
            "cnt:  2993 Steps :  5\n",
            "True\n",
            "cnt:  2994 Steps :  9\n",
            "False\n",
            "cnt:  2995 Steps :  7\n",
            "True\n",
            "cnt:  2996 Steps :  5\n",
            "True\n",
            "cnt:  2997 Steps :  6\n",
            "True\n",
            "cnt:  2998 Steps :  10\n",
            "False\n",
            "cnt:  2999 Steps :  0\n",
            "False\n",
            "cnt:  3000 Steps :  12\n",
            "============================\n",
            "CNT:  3000\n",
            "TRUE:  1148\n",
            "FALSE:  1852\n",
            "HUNG:  0\n",
            "AVG. STEPS:  8.443333333333333\n",
            "============================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# threshold = 100\n",
        "node_num = 50\n",
        "\n",
        "cnt = 0\n",
        "hung = 0\n",
        "ans = 0\n",
        "\n",
        "survival = []\n",
        "\n",
        "for i in range(loops):\n",
        "    g = Graph(node_num)\n",
        "    prey = Prey(g)\n",
        "    pred = Predator(g)\n",
        "    prey5 = copy.copy(prey)\n",
        "    pred5 = copy.copy(pred)\n",
        "\n",
        "    \n",
        "    # prey_belief = [1/(g.node_num-1)]*(g.node_num) ### [1/49]*50\n",
        "    pred_belief = [1/(g.node_num-1)]*(g.node_num)\n",
        "\n",
        "    ##------ AGENT-5 RUN MAIN ------##\n",
        "    agent5 = Agent5(g, prey5.loc, pred5.loc, pred_belief, False)\n",
        "\n",
        "    count = 0\n",
        "\n",
        "    while agent5.loc != pred5.loc and agent5.loc != prey5.loc:\n",
        "        if count > threshold:\n",
        "            print(\"HUNG SIMULATION\")\n",
        "            hung += 1\n",
        "            continue\n",
        "\n",
        "        agent5.debug(\"BEFORE: \", agent5.loc, prey5.loc, pred5.loc)\n",
        "        \n",
        "        agent5.loc = agent5.move(prey5.loc, pred5.loc, pred_belief)\n",
        "\n",
        "        if agent5.isPred(agent5.loc, pred5.loc) and agent5.isPrey(agent5.loc, prey5.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent5.isPred(agent5.loc, pred5.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent5.isPrey(agent5.loc, prey5.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "\n",
        "        prey5.loc = prey5.move()\n",
        "        pred5.loc = pred5.move(agent5.loc)\n",
        "        pred_belief = agent5.pred_move_pred_belief(pred5.loc, pred_belief)\n",
        "        agent5.debug('Pred Belief after P: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "        \n",
        "        agent5.debug(\"After: \", agent5.loc, prey5.loc, pred5.loc) ##\n",
        "        agent5.debug(\"prey5 found : \", agent5.isPrey(agent5.loc, prey5.loc),\" ; \", \"pred5 found : \", agent5.isPred(agent5.loc, pred5.loc)) ##\n",
        "\n",
        "        count = count + 1\n",
        "\n",
        "        if agent5.isPred(agent5.loc, pred5.loc) and agent5.isPrey(agent5.loc, prey5.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent5.isPred(agent5.loc, pred5.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent5.isPrey(agent5.loc, prey5.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "\n",
        "    cnt += 1\n",
        "    survival.append(count)\n",
        "    print(\"cnt: \", cnt, \"Steps : \", count)\n",
        "\n",
        "\n",
        "print(\"============================\")\n",
        "print(\"CNT: \", cnt)\n",
        "print(\"TRUE: \", ans)\n",
        "print(\"FALSE: \", cnt-(ans+hung))\n",
        "print(\"HUNG: \", hung)\n",
        "avg_steps = sum(survival)/len(survival)\n",
        "print(\"AVG. STEPS: \", avg_steps)\n",
        "print(\"============================\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RN53C0DrzOhM",
        "outputId": "bc57f7bc-791d-4abd-92ea-115f425eed04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "True\n",
            "cnt:  2903 Steps :  6\n",
            "True\n",
            "cnt:  2904 Steps :  9\n",
            "False\n",
            "cnt:  2905 Steps :  1\n",
            "False\n",
            "cnt:  2906 Steps :  12\n",
            "True\n",
            "cnt:  2907 Steps :  0\n",
            "False\n",
            "cnt:  2908 Steps :  8\n",
            "True\n",
            "cnt:  2909 Steps :  3\n",
            "True\n",
            "cnt:  2910 Steps :  13\n",
            "True\n",
            "cnt:  2911 Steps :  7\n",
            "True\n",
            "cnt:  2912 Steps :  15\n",
            "True\n",
            "cnt:  2913 Steps :  5\n",
            "True\n",
            "cnt:  2914 Steps :  7\n",
            "True\n",
            "cnt:  2915 Steps :  2\n",
            "False\n",
            "cnt:  2916 Steps :  4\n",
            "True\n",
            "cnt:  2917 Steps :  8\n",
            "False\n",
            "cnt:  2918 Steps :  7\n",
            "True\n",
            "cnt:  2919 Steps :  3\n",
            "True\n",
            "cnt:  2920 Steps :  6\n",
            "True\n",
            "cnt:  2921 Steps :  11\n",
            "False\n",
            "cnt:  2922 Steps :  2\n",
            "False\n",
            "cnt:  2923 Steps :  1\n",
            "True\n",
            "cnt:  2924 Steps :  11\n",
            "True\n",
            "cnt:  2925 Steps :  12\n",
            "False\n",
            "cnt:  2926 Steps :  7\n",
            "True\n",
            "cnt:  2927 Steps :  7\n",
            "False\n",
            "cnt:  2928 Steps :  13\n",
            "True\n",
            "cnt:  2929 Steps :  3\n",
            "False\n",
            "cnt:  2930 Steps :  5\n",
            "False\n",
            "cnt:  2931 Steps :  1\n",
            "True\n",
            "cnt:  2932 Steps :  9\n",
            "False\n",
            "cnt:  2933 Steps :  8\n",
            "True\n",
            "cnt:  2934 Steps :  5\n",
            "False\n",
            "cnt:  2935 Steps :  4\n",
            "True\n",
            "cnt:  2936 Steps :  7\n",
            "False\n",
            "cnt:  2937 Steps :  3\n",
            "True\n",
            "cnt:  2938 Steps :  13\n",
            "True\n",
            "cnt:  2939 Steps :  21\n",
            "True\n",
            "cnt:  2940 Steps :  11\n",
            "False\n",
            "cnt:  2941 Steps :  8\n",
            "False\n",
            "cnt:  2942 Steps :  15\n",
            "False\n",
            "cnt:  2943 Steps :  23\n",
            "False\n",
            "cnt:  2944 Steps :  4\n",
            "True\n",
            "cnt:  2945 Steps :  4\n",
            "False\n",
            "cnt:  2946 Steps :  0\n",
            "False\n",
            "cnt:  2947 Steps :  12\n",
            "True\n",
            "cnt:  2948 Steps :  4\n",
            "True\n",
            "cnt:  2949 Steps :  15\n",
            "True\n",
            "cnt:  2950 Steps :  14\n",
            "False\n",
            "cnt:  2951 Steps :  4\n",
            "True\n",
            "cnt:  2952 Steps :  4\n",
            "True\n",
            "cnt:  2953 Steps :  8\n",
            "True\n",
            "cnt:  2954 Steps :  9\n",
            "False\n",
            "cnt:  2955 Steps :  15\n",
            "True\n",
            "cnt:  2956 Steps :  15\n",
            "False\n",
            "cnt:  2957 Steps :  11\n",
            "True\n",
            "cnt:  2958 Steps :  2\n",
            "True\n",
            "cnt:  2959 Steps :  5\n",
            "True\n",
            "cnt:  2960 Steps :  8\n",
            "True\n",
            "cnt:  2961 Steps :  8\n",
            "True\n",
            "cnt:  2962 Steps :  8\n",
            "False\n",
            "cnt:  2963 Steps :  6\n",
            "False\n",
            "cnt:  2964 Steps :  5\n",
            "False\n",
            "cnt:  2965 Steps :  10\n",
            "False\n",
            "cnt:  2966 Steps :  3\n",
            "True\n",
            "cnt:  2967 Steps :  5\n",
            "False\n",
            "cnt:  2968 Steps :  6\n",
            "True\n",
            "cnt:  2969 Steps :  8\n",
            "True\n",
            "cnt:  2970 Steps :  4\n",
            "False\n",
            "cnt:  2971 Steps :  15\n",
            "True\n",
            "cnt:  2972 Steps :  3\n",
            "False\n",
            "cnt:  2973 Steps :  18\n",
            "False\n",
            "cnt:  2974 Steps :  4\n",
            "False\n",
            "cnt:  2975 Steps :  3\n",
            "True\n",
            "cnt:  2976 Steps :  5\n",
            "True\n",
            "cnt:  2977 Steps :  5\n",
            "True\n",
            "cnt:  2978 Steps :  12\n",
            "False\n",
            "cnt:  2979 Steps :  13\n",
            "False\n",
            "cnt:  2980 Steps :  2\n",
            "True\n",
            "cnt:  2981 Steps :  16\n",
            "True\n",
            "cnt:  2982 Steps :  8\n",
            "True\n",
            "cnt:  2983 Steps :  10\n",
            "True\n",
            "cnt:  2984 Steps :  17\n",
            "True\n",
            "cnt:  2985 Steps :  3\n",
            "True\n",
            "cnt:  2986 Steps :  1\n",
            "False\n",
            "cnt:  2987 Steps :  5\n",
            "True\n",
            "cnt:  2988 Steps :  3\n",
            "True\n",
            "cnt:  2989 Steps :  3\n",
            "True\n",
            "cnt:  2990 Steps :  10\n",
            "False\n",
            "cnt:  2991 Steps :  2\n",
            "False\n",
            "cnt:  2992 Steps :  3\n",
            "True\n",
            "cnt:  2993 Steps :  9\n",
            "True\n",
            "cnt:  2994 Steps :  5\n",
            "False\n",
            "cnt:  2995 Steps :  3\n",
            "False\n",
            "cnt:  2996 Steps :  5\n",
            "True\n",
            "cnt:  2997 Steps :  3\n",
            "True\n",
            "cnt:  2998 Steps :  21\n",
            "False\n",
            "cnt:  2999 Steps :  6\n",
            "True\n",
            "cnt:  3000 Steps :  2\n",
            "============================\n",
            "CNT:  3000\n",
            "TRUE:  1746\n",
            "FALSE:  1254\n",
            "HUNG:  0\n",
            "AVG. STEPS:  7.3196666666666665\n",
            "============================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# threshold = 100\n",
        "node_num = 50\n",
        "\n",
        "cnt = 0\n",
        "hung = 0\n",
        "ans = 0\n",
        "\n",
        "survival = []\n",
        "\n",
        "for i in range(loops):\n",
        "    g = Graph(node_num)\n",
        "    prey = Prey(g)\n",
        "    pred = Predator(g)\n",
        "    prey4 = copy.copy(prey)\n",
        "    pred4 = copy.copy(pred)\n",
        "\n",
        "    \n",
        "    prey_belief = [1/(g.node_num-1)]*(g.node_num) ### [1/49]*50\n",
        "    # pred_belief = [1/(g.node_num-1)]*(g.node_num)    \n",
        "    \n",
        "    ##------ AGENT-4 RUN MAIN ------##\n",
        "    agent4 = Agent4(g, prey4.loc, pred4.loc, prey_belief, False)\n",
        "\n",
        "    tmp_prey_belief = prey_belief\n",
        "\n",
        "    count = 0\n",
        "\n",
        "    while agent4.loc != pred4.loc and agent4.loc != prey4.loc:\n",
        "        if count > threshold:\n",
        "            print(\"HUNG SIMULATION\")\n",
        "            hung += 1\n",
        "            continue\n",
        "\n",
        "        agent4.debug(\"Before: \", agent4.loc, prey4.loc, pred4.loc) ##\n",
        "        \n",
        "        agent4.loc = agent4.move(prey4.loc, pred4.loc, prey_belief, tmp_prey_belief)\n",
        "        \n",
        "        if agent4.isPred(agent4.loc, pred4.loc) and agent4.isPrey(agent4.loc, prey4.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent4.isPred(agent4.loc, pred4.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent4.isPrey(agent4.loc, prey4.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        \n",
        "        prey4.loc = prey4.move()\n",
        "        prey_belief = agent4.prey_move_prey_belief(prey4.loc, prey_belief)\n",
        "        agent4.debug('Prey Belief after P: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "        pred4.loc = pred4.move(agent4.loc)\n",
        "        \n",
        "        agent4.debug(\"After: \", agent4.loc, prey4.loc, pred4.loc) ##\n",
        "        agent4.debug(\"prey4 found : \", agent4.isPrey(agent4.loc, prey4.loc),\" ; \", \"pred4 found : \", agent4.isPred(agent4.loc, pred4.loc)) ##\n",
        "        \n",
        "        tmp_prey_belief = agent4.prey_move_prey_belief(prey4.loc, prey_belief)\n",
        "        agent4.debug('TMP_BELIEF: ', tmp_prey_belief, 'SUM: ', sum(tmp_prey_belief))\n",
        "        \n",
        "        count = count + 1\n",
        "\n",
        "        if agent4.isPred(agent4.loc, pred4.loc) and agent4.isPrey(agent4.loc, prey4.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent4.isPred(agent4.loc, pred4.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent4.isPrey(agent4.loc, prey4.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "\n",
        "    cnt += 1\n",
        "    survival.append(count)\n",
        "    print(\"cnt: \", cnt, \"Steps : \", count)\n",
        "\n",
        "\n",
        "print(\"============================\")\n",
        "print(\"CNT: \", cnt)\n",
        "print(\"TRUE: \", ans)\n",
        "print(\"FALSE: \", cnt-(ans+hung))\n",
        "print(\"HUNG: \", hung)\n",
        "avg_steps = sum(survival)/len(survival)\n",
        "print(\"AVG. STEPS: \", avg_steps)\n",
        "print(\"============================\")\n"
      ],
      "metadata": {
        "id": "862kufYM2Qmk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "threshold = 5000\n",
        "loops = 3000\n",
        "node_num = 50\n",
        "\n",
        "cnt = 0\n",
        "hung = 0\n",
        "ans = 0\n",
        "\n",
        "survival = []\n",
        "\n",
        "for i in range(loops):\n",
        "    g = Graph(node_num)\n",
        "    prey = Prey(g)\n",
        "    pred = Predator(g)\n",
        "    prey3 = copy.copy(prey)\n",
        "    pred3 = copy.copy(pred)\n",
        "\n",
        "    \n",
        "    prey_belief = [1/(g.node_num-1)]*(g.node_num) ### [1/49]*50\n",
        "    # pred_belief = [1/(g.node_num-1)]*(g.node_num)    \n",
        "\n",
        "    ##------ AGENT-3 RUN MAIN ------##\n",
        "    agent3 = Agent3(g, prey3.loc, pred3.loc, prey_belief, False)\n",
        "\n",
        "    count = 0\n",
        "    while agent3.loc != pred3.loc and agent3.loc != prey3.loc:\n",
        "        if count > threshold:\n",
        "            print(\"HUNG SIMULATION\")\n",
        "            hung += 1\n",
        "            continue\n",
        "\n",
        "        agent3.debug(\"Before: \", agent3.loc, prey3.loc, pred3.loc) ##\n",
        "        \n",
        "        agent3.loc = agent3.move(prey3.loc, pred3.loc, prey_belief)\n",
        "        \n",
        "        if agent3.isPred(agent3.loc, pred3.loc) and agent3.isPrey(agent3.loc, prey3.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent3.isPred(agent3.loc, pred3.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent3.isPrey(agent3.loc, prey3.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "\n",
        "        prey3.loc = prey3.move()\n",
        "        prey_belief = agent3.prey_move_prey_belief(prey3.loc, prey_belief)\n",
        "        agent3.debug('Prey Belief after P: ', prey_belief, \" ; SUM :\", sum(prey_belief))\n",
        "        pred3.loc = pred3.move(agent3.loc)\n",
        "        \n",
        "        agent3.debug(\"After: \", agent3.loc, prey3.loc, pred3.loc) ##\n",
        "        agent3.debug(\"prey3 found : \", agent3.isPrey(agent3.loc, prey3.loc),\" ; \", \"pred3 found : \", agent3.isPred(agent3.loc, pred3.loc)) ##\n",
        "        \n",
        "        count = count + 1\n",
        "\n",
        "        if agent3.isPred(agent3.loc, pred3.loc) and agent3.isPrey(agent3.loc, prey3.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent3.isPred(agent3.loc, pred3.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent3.isPrey(agent3.loc, prey3.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "\n",
        "\n",
        "    cnt += 1\n",
        "    survival.append(count)\n",
        "    print(\"cnt: \", cnt, \"Steps : \", count)\n",
        "\n",
        "\n",
        "print(\"============================\")\n",
        "print(\"CNT: \", cnt)\n",
        "print(\"TRUE: \", ans)\n",
        "print(\"FALSE: \", cnt-(ans+hung))\n",
        "print(\"HUNG: \", hung)\n",
        "avg_steps = sum(survival)/len(survival)\n",
        "print(\"AVG. STEPS: \", avg_steps)\n",
        "print(\"============================\")"
      ],
      "metadata": {
        "id": "jEiIl48Z1J8E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "threshold = 5000\n",
        "loops = 3000\n",
        "node_num = 50\n",
        "\n",
        "cnt = 0\n",
        "hung = 0\n",
        "ans = 0\n",
        "\n",
        "survival = []\n",
        "\n",
        "for i in range(loops):\n",
        "    g = Graph(node_num)\n",
        "    prey = Prey(g)\n",
        "    pred = Predator(g)\n",
        "    prey2 = copy.copy(prey)\n",
        "    pred2 = copy.copy(pred)\n",
        "\n",
        "    ##------ AGENT-2 RUN ------##\n",
        "\n",
        "    agent2 = Agent2(g, prey2.loc, pred2.loc, False)\n",
        "\n",
        "    count = 0\n",
        "\n",
        "    while agent2.loc != pred2.loc and agent2.loc != prey2.loc:\n",
        "        if count > threshold:\n",
        "            print(\"HUNG SIMULATION\")\n",
        "            hung += 1\n",
        "            continue\n",
        "\n",
        "        agent2.debug(\"Before: \", agent2.loc, prey2.loc, pred2.loc)\n",
        "        agent2.loc = agent2.move(prey2.loc, pred2.loc)\n",
        "\n",
        "        if agent2.isPred(pred2.loc) and agent2.isPrey(prey2.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent2.isPred(pred2.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent2.isPrey(prey2.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue        \n",
        "\n",
        "        prey2.loc = prey2.move()\n",
        "        pred2.loc = pred2.move(agent2.loc)\n",
        "        agent2.debug(\"After: \", agent2.loc, prey2.loc, pred2.loc)\n",
        "        # print(\"After: \", agent2.move(prey2.loc, pred2.loc), prey2.move(), pred2.move(agent2.loc))\n",
        "        agent2.debug(\"prey2 found : \", agent2.isPrey(prey2.loc),\" ; \", \"pred2 found : \", agent2.isPred(pred2.loc))\n",
        "        \n",
        "        count = count + 1\n",
        "        \n",
        "        if agent2.isPred(pred2.loc) and agent2.isPrey(prey2.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent2.isPred(pred2.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent2.isPrey(prey2.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue        \n",
        "\n",
        "    cnt += 1\n",
        "    survival.append(count)\n",
        "\n",
        "    print(\"cnt: \", cnt, \"Steps : \", count)\n",
        "\n",
        "\n",
        "print(\"============================\")\n",
        "print(\"CNT: \", cnt)\n",
        "print(\"TRUE: \", ans)\n",
        "print(\"FALSE: \", cnt-(ans+hung))\n",
        "print(\"HUNG: \", hung)\n",
        "avg_steps = sum(survival)/len(survival)\n",
        "print(\"AVG. STEPS: \", avg_steps)\n",
        "print(\"============================\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vPHRFhB-xOWA",
        "outputId": "f1eb4ba5-9e35-482c-906e-7dd2fde71c7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "cnt:  504 Steps :  8\n",
            "True\n",
            "cnt:  505 Steps :  60\n",
            "True\n",
            "cnt:  506 Steps :  37\n",
            "True\n",
            "cnt:  507 Steps :  308\n",
            "True\n",
            "cnt:  508 Steps :  6\n",
            "True\n",
            "cnt:  509 Steps :  28\n",
            "True\n",
            "cnt:  510 Steps :  5\n",
            "True\n",
            "cnt:  511 Steps :  58\n",
            "True\n",
            "cnt:  512 Steps :  24\n",
            "True\n",
            "cnt:  513 Steps :  715\n",
            "True\n",
            "cnt:  514 Steps :  12\n",
            "True\n",
            "cnt:  515 Steps :  42\n",
            "True\n",
            "cnt:  516 Steps :  14\n",
            "True\n",
            "cnt:  517 Steps :  33\n",
            "False\n",
            "cnt:  518 Steps :  70\n",
            "True\n",
            "cnt:  519 Steps :  24\n",
            "True\n",
            "cnt:  520 Steps :  166\n",
            "True\n",
            "cnt:  521 Steps :  20\n",
            "True\n",
            "cnt:  522 Steps :  134\n",
            "True\n",
            "cnt:  523 Steps :  91\n",
            "True\n",
            "cnt:  524 Steps :  266\n",
            "True\n",
            "cnt:  525 Steps :  6\n",
            "True\n",
            "cnt:  526 Steps :  243\n",
            "True\n",
            "cnt:  527 Steps :  111\n",
            "True\n",
            "cnt:  528 Steps :  8\n",
            "True\n",
            "cnt:  529 Steps :  17\n",
            "True\n",
            "cnt:  530 Steps :  239\n",
            "True\n",
            "cnt:  531 Steps :  9\n",
            "True\n",
            "cnt:  532 Steps :  27\n",
            "True\n",
            "cnt:  533 Steps :  20\n",
            "True\n",
            "cnt:  534 Steps :  26\n",
            "True\n",
            "cnt:  535 Steps :  2\n",
            "True\n",
            "cnt:  536 Steps :  251\n",
            "True\n",
            "cnt:  537 Steps :  1\n",
            "True\n",
            "cnt:  538 Steps :  32\n",
            "True\n",
            "cnt:  539 Steps :  1\n",
            "True\n",
            "cnt:  540 Steps :  189\n",
            "False\n",
            "cnt:  541 Steps :  157\n",
            "True\n",
            "cnt:  542 Steps :  22\n",
            "False\n",
            "cnt:  543 Steps :  149\n",
            "True\n",
            "cnt:  544 Steps :  29\n",
            "False\n",
            "cnt:  545 Steps :  115\n",
            "True\n",
            "cnt:  546 Steps :  10\n",
            "True\n",
            "cnt:  547 Steps :  19\n",
            "True\n",
            "cnt:  548 Steps :  6\n",
            "True\n",
            "cnt:  549 Steps :  17\n",
            "True\n",
            "cnt:  550 Steps :  36\n",
            "True\n",
            "cnt:  551 Steps :  21\n",
            "True\n",
            "cnt:  552 Steps :  125\n",
            "True\n",
            "cnt:  553 Steps :  434\n",
            "True\n",
            "cnt:  554 Steps :  18\n",
            "True\n",
            "cnt:  555 Steps :  641\n",
            "True\n",
            "cnt:  556 Steps :  11\n",
            "True\n",
            "cnt:  557 Steps :  64\n",
            "True\n",
            "cnt:  558 Steps :  7\n",
            "True\n",
            "cnt:  559 Steps :  7\n",
            "True\n",
            "cnt:  560 Steps :  360\n",
            "True\n",
            "cnt:  561 Steps :  0\n",
            "True\n",
            "cnt:  562 Steps :  58\n",
            "True\n",
            "cnt:  563 Steps :  36\n",
            "True\n",
            "cnt:  564 Steps :  2\n",
            "True\n",
            "cnt:  565 Steps :  64\n",
            "False\n",
            "cnt:  566 Steps :  74\n",
            "True\n",
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            "cnt:  2963 Steps :  15\n",
            "True\n",
            "cnt:  2964 Steps :  4\n",
            "True\n",
            "cnt:  2965 Steps :  220\n",
            "True\n",
            "cnt:  2966 Steps :  4\n",
            "True\n",
            "cnt:  2967 Steps :  225\n",
            "True\n",
            "cnt:  2968 Steps :  133\n",
            "True\n",
            "cnt:  2969 Steps :  106\n",
            "True\n",
            "cnt:  2970 Steps :  961\n",
            "True\n",
            "cnt:  2971 Steps :  10\n",
            "True\n",
            "cnt:  2972 Steps :  14\n",
            "True\n",
            "cnt:  2973 Steps :  41\n",
            "True\n",
            "cnt:  2974 Steps :  61\n",
            "True\n",
            "cnt:  2975 Steps :  22\n",
            "True\n",
            "cnt:  2976 Steps :  2\n",
            "True\n",
            "cnt:  2977 Steps :  2\n",
            "True\n",
            "cnt:  2978 Steps :  364\n",
            "True\n",
            "cnt:  2979 Steps :  43\n",
            "True\n",
            "cnt:  2980 Steps :  0\n",
            "True\n",
            "cnt:  2981 Steps :  81\n",
            "True\n",
            "cnt:  2982 Steps :  20\n",
            "True\n",
            "cnt:  2983 Steps :  52\n",
            "True\n",
            "cnt:  2984 Steps :  22\n",
            "True\n",
            "cnt:  2985 Steps :  127\n",
            "True\n",
            "cnt:  2986 Steps :  1073\n",
            "True\n",
            "cnt:  2987 Steps :  73\n",
            "True\n",
            "cnt:  2988 Steps :  4\n",
            "True\n",
            "cnt:  2989 Steps :  12\n",
            "True\n",
            "cnt:  2990 Steps :  31\n",
            "True\n",
            "cnt:  2991 Steps :  28\n",
            "True\n",
            "cnt:  2992 Steps :  324\n",
            "True\n",
            "cnt:  2993 Steps :  3\n",
            "True\n",
            "cnt:  2994 Steps :  567\n",
            "True\n",
            "cnt:  2995 Steps :  23\n",
            "True\n",
            "cnt:  2996 Steps :  17\n",
            "True\n",
            "cnt:  2997 Steps :  117\n",
            "True\n",
            "cnt:  2998 Steps :  28\n",
            "True\n",
            "cnt:  2999 Steps :  600\n",
            "True\n",
            "cnt:  3000 Steps :  334\n",
            "============================\n",
            "CNT:  3000\n",
            "TRUE:  2826\n",
            "FALSE:  174\n",
            "HUNG:  0\n",
            "AVG. STEPS:  114.78\n",
            "============================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "threshold = 5000\n",
        "loops = 3000\n",
        "node_num = 50\n",
        "\n",
        "cnt = 0\n",
        "hung = 0\n",
        "ans = 0\n",
        "\n",
        "survival = []\n",
        "\n",
        "for i in range(loops):\n",
        "    g = Graph(node_num)\n",
        "    prey = Prey(g)\n",
        "    pred = Predator(g)\n",
        "    prey1 = copy.copy(prey)\n",
        "    pred1 = copy.copy(pred)\n",
        "\n",
        "    ##------ AGENT-1 RUN ------##\n",
        "\n",
        "    agent1 = Agent1(g, prey1.loc, pred1.loc, False)\n",
        "\n",
        "    count = 0\n",
        "\n",
        "    while agent1.loc != pred1.loc and agent1.loc != prey1.loc:\n",
        "        if count > threshold:\n",
        "            print(\"HUNG SIMULATION\")\n",
        "            hung += 1\n",
        "            continue\n",
        "\n",
        "        agent1.debug(\"Before: \", agent1.loc, prey1.loc, pred1.loc)\n",
        "        agent1.loc = agent1.move(prey1.loc, pred1.loc)\n",
        "\n",
        "        if agent1.isPred(pred1.loc) and agent1.isPrey(prey1.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent1.isPred(pred1.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent1.isPrey(prey1.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue        \n",
        "\n",
        "        prey1.loc = prey1.move()\n",
        "        pred1.loc = pred1.move(agent1.loc)\n",
        "        agent1.debug(\"After: \", agent1.loc, prey1.loc, pred1.loc)\n",
        "        # print(\"After: \", agent1.move(prey1.loc, pred1.loc), prey1.move(), pred1.move(agent1.loc))\n",
        "        agent1.debug(\"prey1 found : \", agent1.isPrey(prey1.loc),\" ; \", \"pred1 found : \", agent1.isPred(pred1.loc))\n",
        "        \n",
        "        count = count + 1\n",
        "        \n",
        "        if agent1.isPred(pred1.loc) and agent1.isPrey(prey1.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue\n",
        "        elif agent1.isPred(pred1.loc):\n",
        "            print(False)\n",
        "            continue\n",
        "        elif agent1.isPrey(prey1.loc):\n",
        "            print(True)\n",
        "            ans += 1\n",
        "            continue        \n",
        "\n",
        "    cnt += 1\n",
        "    survival.append(count)\n",
        "    print(\"cnt: \", cnt, \"Steps : \", count)\n",
        "\n",
        "\n",
        "print(\"============================\")\n",
        "print(\"CNT: \", cnt)\n",
        "print(\"TRUE: \", ans)\n",
        "print(\"FALSE: \", cnt-(ans+hung))\n",
        "print(\"HUNG: \", hung)\n",
        "avg_steps = sum(survival)/len(survival)\n",
        "print(\"AVG. STEPS: \", avg_steps)\n",
        "print(\"============================\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2B6WKaozs239",
        "outputId": "6657a998-f5c8-4c5c-92cb-601877e2f5c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "cnt:  504 Steps :  12\n",
            "True\n",
            "cnt:  505 Steps :  11\n",
            "True\n",
            "cnt:  506 Steps :  5\n",
            "True\n",
            "cnt:  507 Steps :  4\n",
            "True\n",
            "cnt:  508 Steps :  11\n",
            "True\n",
            "cnt:  509 Steps :  9\n",
            "True\n",
            "cnt:  510 Steps :  12\n",
            "True\n",
            "cnt:  511 Steps :  5\n",
            "False\n",
            "cnt:  512 Steps :  4\n",
            "True\n",
            "cnt:  513 Steps :  11\n",
            "True\n",
            "cnt:  514 Steps :  6\n",
            "True\n",
            "cnt:  515 Steps :  1\n",
            "True\n",
            "cnt:  516 Steps :  19\n",
            "True\n",
            "cnt:  517 Steps :  9\n",
            "True\n",
            "cnt:  518 Steps :  2\n",
            "True\n",
            "cnt:  519 Steps :  21\n",
            "True\n",
            "cnt:  520 Steps :  11\n",
            "True\n",
            "cnt:  521 Steps :  17\n",
            "True\n",
            "cnt:  522 Steps :  9\n",
            "True\n",
            "cnt:  523 Steps :  8\n",
            "True\n",
            "cnt:  524 Steps :  8\n",
            "True\n",
            "cnt:  525 Steps :  23\n",
            "True\n",
            "cnt:  526 Steps :  9\n",
            "True\n",
            "cnt:  527 Steps :  7\n",
            "False\n",
            "cnt:  528 Steps :  14\n",
            "True\n",
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            "cnt:  530 Steps :  18\n",
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            "True\n",
            "cnt:  533 Steps :  1\n",
            "False\n",
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            "True\n",
            "cnt:  539 Steps :  1\n",
            "False\n",
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            "cnt:  548 Steps :  7\n",
            "True\n",
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            "True\n",
            "cnt:  552 Steps :  18\n",
            "True\n",
            "cnt:  553 Steps :  2\n",
            "True\n",
            "cnt:  554 Steps :  10\n",
            "False\n",
            "cnt:  555 Steps :  7\n",
            "True\n",
            "cnt:  556 Steps :  16\n",
            "True\n",
            "cnt:  557 Steps :  5\n",
            "True\n",
            "cnt:  558 Steps :  5\n",
            "True\n",
            "cnt:  559 Steps :  6\n",
            "True\n",
            "cnt:  560 Steps :  8\n",
            "False\n",
            "cnt:  561 Steps :  6\n",
            "True\n",
            "cnt:  562 Steps :  14\n",
            "True\n",
            "cnt:  563 Steps :  4\n",
            "True\n",
            "cnt:  564 Steps :  14\n",
            "False\n",
            "cnt:  565 Steps :  1\n",
            "True\n",
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            "True\n",
            "cnt:  567 Steps :  2\n",
            "True\n",
            "cnt:  568 Steps :  6\n",
            "True\n",
            "cnt:  569 Steps :  12\n",
            "True\n",
            "cnt:  570 Steps :  1\n",
            "True\n",
            "cnt:  571 Steps :  12\n",
            "True\n",
            "cnt:  572 Steps :  101\n",
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            "cnt:  573 Steps :  8\n",
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            "True\n",
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            "True\n",
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            "True\n",
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            "True\n",
            "cnt:  580 Steps :  1\n",
            "True\n",
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            "True\n",
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            "True\n",
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            "True\n",
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            "True\n",
            "cnt:  585 Steps :  22\n",
            "True\n",
            "cnt:  586 Steps :  5\n",
            "True\n",
            "cnt:  587 Steps :  8\n",
            "False\n",
            "cnt:  588 Steps :  5\n",
            "True\n",
            "cnt:  589 Steps :  5\n",
            "True\n",
            "cnt:  590 Steps :  22\n",
            "True\n",
            "cnt:  591 Steps :  3\n",
            "True\n",
            "cnt:  592 Steps :  0\n",
            "True\n",
            "cnt:  593 Steps :  12\n",
            "True\n",
            "cnt:  594 Steps :  11\n",
            "True\n",
            "cnt:  595 Steps :  12\n",
            "True\n",
            "cnt:  596 Steps :  28\n",
            "True\n",
            "cnt:  597 Steps :  1\n",
            "True\n",
            "cnt:  598 Steps :  11\n",
            "True\n",
            "cnt:  599 Steps :  13\n",
            "False\n",
            "cnt:  600 Steps :  10\n",
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            "cnt:  601 Steps :  7\n",
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            "cnt:  602 Steps :  0\n",
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            "True\n",
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            "cnt:  613 Steps :  62\n",
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            "cnt:  614 Steps :  36\n",
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            "cnt:  620 Steps :  10\n",
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            "True\n",
            "cnt:  622 Steps :  21\n",
            "True\n",
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            "True\n",
            "cnt:  624 Steps :  0\n",
            "False\n",
            "cnt:  625 Steps :  24\n",
            "True\n",
            "cnt:  626 Steps :  15\n",
            "True\n",
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            "True\n",
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            "cnt:  634 Steps :  7\n",
            "True\n",
            "cnt:  635 Steps :  412\n",
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            "cnt:  636 Steps :  6\n",
            "True\n",
            "cnt:  637 Steps :  42\n",
            "True\n",
            "cnt:  638 Steps :  4\n",
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            "True\n",
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            "False\n",
            "cnt:  671 Steps :  11\n",
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            "True\n",
            "cnt:  673 Steps :  10\n",
            "True\n",
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            "True\n",
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            "cnt:  683 Steps :  12\n",
            "True\n",
            "cnt:  684 Steps :  8\n",
            "True\n",
            "cnt:  685 Steps :  0\n",
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            "True\n",
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            "cnt:  691 Steps :  13\n",
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            "cnt:  693 Steps :  3\n",
            "True\n",
            "cnt:  694 Steps :  15\n",
            "True\n",
            "cnt:  695 Steps :  16\n",
            "False\n",
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            "True\n",
            "cnt:  706 Steps :  8\n",
            "True\n",
            "cnt:  707 Steps :  0\n",
            "True\n",
            "cnt:  708 Steps :  14\n",
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            "cnt:  709 Steps :  9\n",
            "False\n",
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            "cnt:  753 Steps :  17\n",
            "False\n",
            "cnt:  754 Steps :  1\n",
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            "False\n",
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            "False\n",
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            "False\n",
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            "True\n",
            "cnt:  2987 Steps :  3\n",
            "False\n",
            "cnt:  2988 Steps :  9\n",
            "True\n",
            "cnt:  2989 Steps :  23\n",
            "True\n",
            "cnt:  2990 Steps :  9\n",
            "True\n",
            "cnt:  2991 Steps :  10\n",
            "True\n",
            "cnt:  2992 Steps :  12\n",
            "False\n",
            "cnt:  2993 Steps :  10\n",
            "True\n",
            "cnt:  2994 Steps :  11\n",
            "True\n",
            "cnt:  2995 Steps :  11\n",
            "True\n",
            "cnt:  2996 Steps :  5\n",
            "True\n",
            "cnt:  2997 Steps :  23\n",
            "True\n",
            "cnt:  2998 Steps :  49\n",
            "True\n",
            "cnt:  2999 Steps :  14\n",
            "True\n",
            "cnt:  3000 Steps :  17\n",
            "============================\n",
            "CNT:  3000\n",
            "TRUE:  2679\n",
            "FALSE:  321\n",
            "HUNG:  0\n",
            "AVG. STEPS:  15.881\n",
            "============================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "##------ AGENT-1 RUN ------##\n",
        "\n",
        "agent1 = Agent1(g, prey1.loc, pred1.loc, True)\n",
        "\n",
        "count = 0\n",
        "while agent1.loc != pred1.loc and agent1.loc != prey1.loc:\n",
        "    if count > threshold:\n",
        "        print(\"HUNG SIMULATION\")\n",
        "        break\n",
        "        \n",
        "    print(\"Before: \", agent1.loc, prey1.loc, pred1.loc)\n",
        "    agent1.loc = agent1.move(prey1.loc, pred1.loc)\n",
        "    prey1.loc = prey1.move()\n",
        "    pred1.loc = pred1.move(agent1.loc)\n",
        "    print(\"After: \", agent1.loc, prey1.loc, pred1.loc)\n",
        "    # print(\"After: \", agent1.move(prey1.loc, pred1.loc), prey1.move(), pred1.move(agent1.loc))\n",
        "    print(\"prey1 found : \", agent1.isPrey(prey1.loc),\" ; \", \"pred1 found : \", agent1.isPred(pred1.loc))\n",
        "    \n",
        "    count = count + 1\n",
        "    \n",
        "    if agent1.isPred(pred1.loc) or agent1.isPrey(prey1.loc):\n",
        "        break\n",
        "\n",
        "print(\"Steps : \", count)"
      ],
      "metadata": {
        "id": "k0ixUx9I6Gnb",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**REFERENCES:**\n",
        "\n",
        "[Graph Implementation](https://stackabuse.com/courses/graphs-in-python-theory-and-implementation/lessons/representing-graphs-in-code/)"
      ],
      "metadata": {
        "id": "u7HMhRzQZfUk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n"
      ],
      "metadata": {
        "id": "v6vR--c_ZaXe",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n"
      ],
      "metadata": {
        "cellView": "form",
        "id": "YqFDqpXzniFl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "# ##------ AGENT-7 RUN MAIN ------##\n",
        "# agent7 = Agent7(g, prey7.loc, pred7.loc, prey_belief, pred_belief, True)\n",
        "\n",
        "# count = 0\n",
        "\n",
        "# while agent7.loc != pred7.loc and agent7.loc != prey7.loc:\n",
        "#     if count > threshold:\n",
        "#         print(\"HUNG SIMULATION\")\n",
        "#         break\n",
        "\n",
        "#     agent7.debug(\"BEFORE: \", agent7.loc, prey7.loc, pred7.loc)\n",
        "#     agent7.loc = agent7.move(prey7.loc, pred7.loc, pred_belief, prey_belief)\n",
        "    \n",
        "#     if agent7.isPred(agent7.loc, pred7.loc) or agent7.isPrey(agent7.loc, prey7.loc):\n",
        "#         break\n",
        "\n",
        "#     prey7.loc = prey7.move()\n",
        "#     prey_belief = agent7.prey_move_prey_belief(prey7.loc, prey_belief)\n",
        "#     pred7.loc = pred7.move(agent7.loc)\n",
        "#     pred_belief = agent7.pred_move_pred_belief(pred7.loc, pred_belief)\n",
        "#     agent7.debug('Pred Belief after P: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "    \n",
        "#     agent7.debug(\"After: \", agent7.loc, prey7.loc, pred7.loc) ##\n",
        "#     agent7.debug(\"prey7 found : \", agent7.isPrey(agent7.loc, prey7.loc),\" ; \", \"pred5 found : \", agent7.isPred(agent7.loc, pred7.loc)) ##\n",
        "\n",
        "#     count = count + 1\n",
        "\n",
        "#     if agent7.isPred(agent7.loc, pred7.loc) or agent7.isPrey(agent7.loc, prey7.loc):\n",
        "#         break\n",
        "\n",
        "# print(\"Steps : \", count)\n"
      ],
      "metadata": {
        "id": "L2eSEmdcrJUq",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "# ##------ AGENT-7 RUN MAIN ------##\n",
        "# agent7 = Agent7(g, prey7.loc, pred7.loc, prey_belief, pred_belief, True)\n",
        "\n",
        "# count = 0\n",
        "\n",
        "# while agent7.loc != pred7.loc and agent7.loc != prey7.loc:\n",
        "#     if count > threshold:\n",
        "#         print(\"HUNG SIMULATION\")\n",
        "#         break\n",
        "\n",
        "#     agent7.debug(\"BEFORE: \", agent7.loc, prey7.loc, pred7.loc)\n",
        "#     agent7.loc = agent7.move(prey7.loc, pred7.loc, pred_belief, prey_belief)\n",
        "    \n",
        "#     if agent7.isPred(agent7.loc, pred7.loc) or agent7.isPrey(agent7.loc, prey7.loc):\n",
        "#         break\n",
        "\n",
        "#     prey7.loc = prey7.move()\n",
        "#     prey_belief = agent7.prey_move_prey_belief(prey7.loc, prey_belief)\n",
        "#     pred7.loc = pred7.move(agent7.loc)\n",
        "#     pred_belief = agent7.pred_move_pred_belief(pred7.loc, pred_belief)\n",
        "#     agent7.debug('Pred Belief after P: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "    \n",
        "#     agent7.debug(\"After: \", agent7.loc, prey7.loc, pred7.loc) ##\n",
        "#     agent7.debug(\"prey7 found : \", agent7.isPrey(agent7.loc, prey7.loc),\" ; \", \"pred5 found : \", agent7.isPred(agent7.loc, pred7.loc)) ##\n",
        "\n",
        "#     count = count + 1\n",
        "\n",
        "#     if agent7.isPred(agent7.loc, pred7.loc) or agent7.isPrey(agent7.loc, prey7.loc):\n",
        "#         break\n",
        "\n",
        "# print(\"Steps : \", count)\n"
      ],
      "metadata": {
        "id": "Y7T-d2ft_uy9",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "# ##------ AGENT-7 RUN MAIN ------##\n",
        "# agent7 = Agent7(g, prey7.loc, pred7.loc, prey_belief, pred_belief, True)\n",
        "\n",
        "# count = 0\n",
        "\n",
        "# while agent7.loc != pred7.loc and agent7.loc != prey7.loc:\n",
        "#     if count > threshold:\n",
        "#         print(\"HUNG SIMULATION\")\n",
        "#         break\n",
        "\n",
        "#     agent7.debug(\"BEFORE: \", agent7.loc, prey7.loc, pred7.loc)\n",
        "#     agent7.loc = agent7.move(prey7.loc, pred7.loc, pred_belief, prey_belief)\n",
        "    \n",
        "#     if agent7.isPred(agent7.loc, pred7.loc) or agent7.isPrey(agent7.loc, prey7.loc):\n",
        "#         break\n",
        "\n",
        "#     prey7.loc = prey7.move()\n",
        "#     prey_belief = agent7.prey_move_prey_belief(prey7.loc, prey_belief)\n",
        "#     pred7.loc = pred7.move(agent7.loc)\n",
        "#     pred_belief = agent7.pred_move_pred_belief(pred7.loc, pred_belief)\n",
        "#     agent7.debug('Pred Belief after P: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "    \n",
        "#     agent7.debug(\"After: \", agent7.loc, prey7.loc, pred7.loc) ##\n",
        "#     agent7.debug(\"prey7 found : \", agent7.isPrey(agent7.loc, prey7.loc),\" ; \", \"pred5 found : \", agent7.isPred(agent7.loc, pred7.loc)) ##\n",
        "\n",
        "#     count = count + 1\n",
        "\n",
        "#     if agent7.isPred(agent7.loc, pred7.loc) or agent7.isPrey(agent7.loc, prey7.loc):\n",
        "#         break\n",
        "\n",
        "# print(\"Steps : \", count)\n"
      ],
      "metadata": {
        "id": "g5KeB9Nh97z3",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "# ##------ AGENT-7 RUN MAIN ------##\n",
        "# agent7 = Agent7(g, prey7.loc, pred7.loc, prey_belief, pred_belief, True)\n",
        "\n",
        "# count = 0\n",
        "\n",
        "# while agent7.loc != pred7.loc and agent7.loc != prey7.loc:\n",
        "#     if count > threshold:\n",
        "#         print(\"HUNG SIMULATION\")\n",
        "#         break\n",
        "\n",
        "#     agent7.debug(\"BEFORE: \", agent7.loc, prey7.loc, pred7.loc)\n",
        "#     agent7.loc = agent7.move(prey7.loc, pred7.loc, pred_belief, prey_belief)\n",
        "    \n",
        "#     if agent7.isPred(agent7.loc, pred7.loc) or agent7.isPrey(agent7.loc, prey7.loc):\n",
        "#         break\n",
        "\n",
        "#     prey7.loc = prey7.move()\n",
        "#     prey_belief = agent7.prey_move_prey_belief(prey7.loc, prey_belief)\n",
        "#     pred7.loc = pred7.move(agent7.loc)\n",
        "#     pred_belief = agent7.pred_move_pred_belief(pred7.loc, pred_belief)\n",
        "#     agent7.debug('Pred Belief after P: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "    \n",
        "#     agent7.debug(\"After: \", agent7.loc, prey7.loc, pred7.loc) ##\n",
        "#     agent7.debug(\"prey7 found : \", agent7.isPrey(agent7.loc, prey7.loc),\" ; \", \"pred5 found : \", agent7.isPred(agent7.loc, pred7.loc)) ##\n",
        "\n",
        "#     count = count + 1\n",
        "\n",
        "#     if agent7.isPred(agent7.loc, pred7.loc) or agent7.isPrey(agent7.loc, prey7.loc):\n",
        "#         break\n",
        "\n",
        "# print(\"Steps : \", count)\n"
      ],
      "metadata": {
        "id": "Yjpt8tjRwcFT",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "# ##------ AGENT-7 RUN MAIN ------##\n",
        "# agent7 = Agent7(g, prey7.loc, pred7.loc, prey_belief, pred_belief, True)\n",
        "\n",
        "# count = 0\n",
        "\n",
        "# while agent7.loc != pred7.loc and agent7.loc != prey7.loc:\n",
        "#     if count > threshold:\n",
        "#         print(\"HUNG SIMULATION\")\n",
        "#         break\n",
        "\n",
        "#     agent7.debug(\"BEFORE: \", agent7.loc, prey7.loc, pred7.loc)\n",
        "#     agent7.loc = agent7.move(prey7.loc, pred7.loc, pred_belief, prey_belief)\n",
        "#     prey7.loc = prey7.move()\n",
        "\n",
        "#     if agent7.isPred(agent7.loc, pred7.loc) or agent7.isPrey(agent7.loc, prey7.loc):\n",
        "#         break\n",
        "    \n",
        "#     prey_belief = agent7.prey_move_prey_belief(prey7.loc, prey_belief)\n",
        "#     pred7.loc = pred7.move(agent7.loc)\n",
        "#     pred_belief = agent7.pred_move_pred_belief(pred7.loc, pred_belief)\n",
        "#     agent7.debug('Pred Belief after P: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "    \n",
        "#     agent7.debug(\"After: \", agent7.loc, prey7.loc, pred7.loc) ##\n",
        "#     agent7.debug(\"prey7 found : \", agent7.isPrey(agent7.loc, prey7.loc),\" ; \", \"pred5 found : \", agent7.isPred(agent7.loc, pred7.loc)) ##\n",
        "\n",
        "#     count = count + 1\n",
        "\n",
        "#     if agent7.isPred(agent7.loc, pred7.loc) or agent7.isPrey(agent7.loc, prey7.loc):\n",
        "#         break\n",
        "\n",
        "# print(\"Steps : \", count)\n"
      ],
      "metadata": {
        "id": "i0LV8S-5s7xP",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "# ##------ AGENT-7 RUN MAIN ------##\n",
        "# agent7 = Agent7(g, prey7.loc, pred7.loc, prey_belief, pred_belief, True)\n",
        "\n",
        "# count = 0\n",
        "\n",
        "# while agent7.loc != pred7.loc and agent7.loc != prey7.loc:\n",
        "#     if count > threshold:\n",
        "#         print(\"HUNG SIMULATION\")\n",
        "#         break\n",
        "\n",
        "#     agent7.debug(\"BEFORE: \", agent7.loc, prey7.loc, pred7.loc)\n",
        "#     agent7.loc = agent7.move(prey7.loc, pred7.loc, pred_belief, prey_belief)\n",
        "#     prey7.loc = prey7.move()\n",
        "#     prey_belief = agent7.prey_move_prey_belief(prey7.loc, prey_belief)\n",
        "#     pred7.loc = pred7.move(agent7.loc)\n",
        "#     pred_belief = agent7.pred_move_pred_belief(pred7.loc, pred_belief)\n",
        "#     agent7.debug('Pred Belief after P: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "    \n",
        "#     agent7.debug(\"After: \", agent7.loc, prey7.loc, pred7.loc) ##\n",
        "#     agent7.debug(\"prey7 found : \", agent7.isPrey(agent7.loc, prey7.loc),\" ; \", \"pred5 found : \", agent7.isPred(agent7.loc, pred7.loc)) ##\n",
        "\n",
        "#     count = count + 1\n",
        "\n",
        "#     if agent7.isPred(agent7.loc, pred7.loc) or agent7.isPrey(agent7.loc, prey7.loc):\n",
        "#         break\n",
        "        \n",
        "# print(\"Steps : \", count)\n"
      ],
      "metadata": {
        "id": "kA7BOkP96zJ6",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title\n",
        "# ##------ AGENT-7 RUN MAIN ------##\n",
        "# agent7 = Agent7(g, prey7.loc, pred7.loc, prey_belief, pred_belief, True)\n",
        "\n",
        "# count = 0\n",
        "\n",
        "# while agent7.loc != pred7.loc and agent7.loc != prey7.loc:\n",
        "#     if count > threshold:\n",
        "#         print(\"HUNG SIMULATION\")\n",
        "#         break\n",
        "\n",
        "#     agent7.debug(\"BEFORE: \", agent7.loc, prey7.loc, pred7.loc)\n",
        "#     agent7.loc = agent7.move(prey7.loc, pred7.loc, pred_belief, prey_belief)\n",
        "#     prey7.loc = prey7.move()\n",
        "#     prey_belief = agent7.prey_move_prey_belief(prey7.loc, prey_belief)\n",
        "#     pred7.loc = pred7.move(agent7.loc)\n",
        "#     pred_belief = agent7.pred_move_pred_belief(pred7.loc, pred_belief)\n",
        "#     agent7.debug('Pred Belief after P: ', pred_belief, \" ; SUM :\", sum(pred_belief))\n",
        "    \n",
        "#     agent7.debug(\"After: \", agent7.loc, prey7.loc, pred7.loc) ##\n",
        "#     agent7.debug(\"prey7 found : \", agent7.isPrey(agent7.loc, prey7.loc),\" ; \", \"pred5 found : \", agent7.isPred(agent7.loc, pred7.loc)) ##\n",
        "\n",
        "#     count = count + 1\n",
        "\n",
        "#     if agent7.isPred(agent7.loc, pred7.loc) or agent7.isPrey(agent7.loc, prey7.loc):\n",
        "#         break\n",
        "\n",
        "# print(\"Steps : \", count)\n"
      ],
      "metadata": {
        "id": "WabbVfl6RfBg",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}