861 lines
34 KiB
Plaintext
861 lines
34 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3cf53c1e",
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"metadata": {},
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"source": [
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"# 8 Queen Problem\n",
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"## Install Requirements\n",
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"create a virtual environment in current directory by\n",
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"\n",
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"```\n",
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"python3 -m venv .env # macos\n",
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"python -m venv .env # linux\n",
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"```\n",
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"## Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "39ee826a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import random"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f6decafe",
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"metadata": {},
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"source": [
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"## Field"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "33790f0d",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Field:\n",
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" \"\"\"\n",
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" Represents the 8 Queens problem state and provides methods for state manipulation. Encodes initial state, actions, transition model, and heuristic functions.\n",
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" \"\"\"\n",
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"\n",
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" def __init__(self, init_state=None, model=None):\n",
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" \"\"\"\n",
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" Initialize a new Field instance for the 8 Queens problem.\n",
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" \n",
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" Args:\n",
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" init_state (list, optional): Initial queen positions [1-8] for a number of columns.\n",
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" If None, generates random positions.\n",
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" model (str, optional): Solution method to use (\"genetic\" or \"backtrack\").\n",
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" \"\"\"\n",
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" self.state = []\n",
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" self.domain_values = [1, 2, 3, 4, 5, 6, 7, 8]\n",
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"\n",
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" if init_state is None:\n",
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" self.move_all_queens()\n",
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" else:\n",
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" self.move_all_queens(init_state)\n",
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"\n",
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" self.threats = self.collisions(self.state)\n",
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" self.fitness = 28 - self.threats\n",
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"\n",
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" self.model = model\n",
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"\n",
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" def get_fitness(self):\n",
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" \"\"\"\n",
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" Returns the fitness value of the current state.\n",
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" \n",
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" Returns:\n",
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" int: Fitness\n",
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" \"\"\"\n",
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" return self.fitness\n",
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"\n",
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" def get_state(self):\n",
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" \"\"\"\n",
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" Returns the current state representation.\n",
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" \n",
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" Returns:\n",
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" list: List of queen row positions for each column [1-8].\n",
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" \"\"\"\n",
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" return self.state\n",
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"\n",
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" def get_domain_values(self):\n",
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" \"\"\"\n",
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" Returns the current domain values for constraint satisfaction.\n",
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" \n",
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" Returns:\n",
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" list: Available row positions for queen placement in next column.\n",
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" \"\"\"\n",
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" return self.domain_values\n",
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"\n",
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" # Actions\n",
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" def set_state(self, column, row=None):\n",
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" \"\"\"\n",
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" Sets the queen position for a specific column and updates fitness and threats.\n",
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" \n",
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" Args:\n",
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" column (int): Column index [0-7] to place queen.\n",
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" row (int, optional): Row position [1-8]. If None, places randomly.\n",
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" \"\"\"\n",
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" if row is None:\n",
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" if column == len(self.state):\n",
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" self.state.append(random.randint(1, 8))\n",
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" elif 0 < row < 9:\n",
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" if column < len(self.state):\n",
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" self.state[column] = row\n",
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" elif column == len(self.state):\n",
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" self.state.append(row)\n",
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"\n",
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" self.threats = self.collisions()\n",
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" self.fitness = 28 - self.threats\n",
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"\n",
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" def set_domain_values(self, new_domain):\n",
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" \"\"\"\n",
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" Updates the domain values for constraint satisfaction.\n",
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" \n",
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" Args:\n",
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" new_domain (list): New set of valid row positions.\n",
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" \"\"\"\n",
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" self.domain_values = new_domain\n",
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"\n",
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" def add_queen(self, row):\n",
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" \"\"\"\n",
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" Adds a new queen to the next available column.\n",
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" \n",
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" Args:\n",
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" row (int): Row position [1-8] for the new queen.\n",
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" \"\"\"\n",
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" if len(self.get_state()) < 8:\n",
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" self.set_state(len(self.get_state()), row)\n",
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"\n",
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" def move_queen(self, column, new_row=None):\n",
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" \"\"\"\n",
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" Moves a queen to a new row position.\n",
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" \n",
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" Args:\n",
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" column (int): Column index [0-7] of queen to move.\n",
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" new_row (int, optional): New row position [1-8]. If None, moves randomly.\n",
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" \"\"\"\n",
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" if column <= len(self.get_state()):\n",
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" self.set_state(column, new_row)\n",
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"\n",
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" def move_all_queens(self, new_state=None):\n",
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" \"\"\"\n",
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" Moves all queens to new positions.\n",
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" \n",
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" Args:\n",
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" new_state (list, optional): Complete new state configuration.\n",
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" If None, moves all queens randomly.\n",
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" \"\"\"\n",
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" if new_state is None:\n",
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" for i in range(8):\n",
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" self.move_queen(i)\n",
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" else:\n",
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" for i, new_row in enumerate(new_state):\n",
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" self.move_queen(i, new_row)\n",
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"\n",
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" # heuristics functions\n",
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" def collisions(self, current_state=None):\n",
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" \"\"\"\n",
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" Calculates the number of queen threats (heuristic function).\n",
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" Counts horizontal and diagonal attacks between queens.\n",
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" \n",
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" Args:\n",
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" current_state (list, optional): State to evaluate. Uses self.state if None.\n",
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" \n",
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" Returns:\n",
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" float: Number of conflicting queen pairs.\n",
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" \"\"\"\n",
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" # wagerechte haben die gleiche row zahl stehe\n",
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" # diagonale haben einen wert der um den spalten-abstand gemindert ist => gleichseitiges rechtwinkliges Dreieck\n",
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" # Beachte die Spalten/ Linien Nr ist um eins verringert [0, 1, ...,7]\n",
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" if current_state is None:\n",
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" current_state = self.get_state()\n",
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"\n",
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" threats = 0\n",
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" for i, row_i in enumerate(current_state):\n",
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" for j, row_j in enumerate(current_state):\n",
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" if j != i:\n",
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" # horizontal diagonal in both sides up and down and counting \"twice\"\n",
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" if row_i == row_j or row_j == (row_i + abs(j - i)) or row_j == (row_i - abs(j - i)):\n",
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" threats += 1\n",
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" # print(f\"{i+1}-{row_i} <=> {j+1}-{row_j}\") # Debugging\n",
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" return threats / 2\n",
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"\n",
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" def print_field(self):\n",
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" \"\"\"\n",
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" Displays an ASCII representation of the chess board with queens.\n",
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" \"\"\"\n",
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" print(\"\\n ┌───┬───┬───┬───┬───┬───┬───┬───┐\")\n",
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" for row in range(8, 0, -1): # (0:8]\n",
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" row_string = \"\"\n",
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" for line in range(8):\n",
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" if line < len(self.state) and row == self.state[line]: # is there a Queen in this line (spalte) in this row\n",
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" if (row + line) % 2 == 0:\n",
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" row_string += \"▌Q▐│\"\n",
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" else:\n",
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" row_string += \" Q │\"\n",
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"\n",
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" elif (row + line) % 2 == 0:\n",
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" row_string += \"███│\"\n",
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" else:\n",
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" row_string += \" │\"\n",
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"\n",
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" print(f\"{row} │{row_string}\")\n",
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" if row > 1: print(\" ├───┼───┼───┼───┼───┼───┼───┼───┤\")\n",
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"\n",
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" print(\" └───┴───┴───┴───┴───┴───┴───┴───┘\")\n",
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" print(\" A B C D E F G H \\n\")\n",
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" print(f\"Threats: {self.threats}\")\n",
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" print(f\"Fitness: {self.fitness}\")\n",
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"\n",
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" def calc(self):\n",
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" \"\"\"\n",
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" Executes the specified solution algorithm based on the model type.\n",
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" Updates based on the selected model the current state with the solution and displays the result.\n",
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" \"\"\"\n",
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" if self.model == \"genetic\":\n",
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" best_field = Genetic().calc()\n",
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" self.move_all_queens(best_field.get_state())\n",
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" self.print_field()\n",
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"\n",
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" elif self.model == \"backtrack\":\n",
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" Backtrack().calc()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "10c258e5",
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"metadata": {},
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"source": [
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"## Genetic Algorithm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "2ca3efc5",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Genetic:\n",
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" \"\"\"\n",
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" Implements genetic algorithm for solving the 8 Queens problem.\n",
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" Uses fitness-based selection, crossover, and mutation operations.\n",
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" \"\"\"\n",
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" def __init__(self, size=1000):\n",
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" \"\"\"\n",
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" Initialize genetic algorithm with random population.\n",
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" \n",
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" Args:\n",
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" size (int): Population size for the genetic algorithm.\n",
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" \"\"\"\n",
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" self.initial_population = []\n",
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" self.p_mutation = 0.1\n",
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"\n",
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" for i in range(size):\n",
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" self.initial_population.append(Field())\n",
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"\n",
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" def random_selection(self, population):\n",
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" \"\"\"\n",
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" Performs fitness-proportionate selection from population.\n",
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" Higher fitness individuals have higher probability of selection.\n",
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" Basierend auf der Verteilung der heuristischen Werte (Fitness) soll zufällig ein Eintrag (Field) gewählt werden, d.h. je höher der heuritische Wert (Fitness) ist, umso höher soll die Wahrscheinlichkeit sein, dass ein Field ausgewählt wird\n",
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" \n",
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" Args:\n",
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" population (list): List of Field instances to select from.\n",
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" \n",
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" Returns:\n",
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" Field: Selected individual based on fitness distribution.\n",
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" \"\"\"\n",
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" fitness = []\n",
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" for field in population:\n",
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" fitness.append(field.get_fitness())\n",
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"\n",
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" # Weighted random selection based on fitness values\n",
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" chosen = random.choices(population, weights=fitness, k=1)[0]\n",
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"\n",
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" return chosen\n",
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"\n",
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" def mutation(self, field):\n",
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" \"\"\"\n",
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" Performs single random mutation on an individual by moving one queen.\n",
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" \n",
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" Args:\n",
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" field (Field): an Individual to mutate.\n",
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" \"\"\"\n",
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" field.move_queen(random.randint(0, 7), random.randint(1, 8))\n",
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"\n",
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" def reproduce(self, x, y):\n",
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" \"\"\"\n",
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" Creates child individual through crossover of two parent individuals.\n",
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" Uses single-point crossover at random position c.\n",
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" \n",
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" Args:\n",
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" x (Field): First parent individual.\n",
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" y (Field): Second parent individual.\n",
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" \n",
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" Returns:\n",
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" Field: Child individual\n",
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" \"\"\"\n",
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" child = []\n",
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" n = len(x.get_state())\n",
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" c = random.randint(1, n)\n",
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"\n",
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" # Slice operator Syntax [a:b)\n",
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" child.extend(x.get_state()[:c]) # [0:c)\n",
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" child.extend(y.get_state()[c:]) # [c:end)\n",
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"\n",
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" return Field(child)\n",
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" \n",
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" def remove_unfit(self, population, limit):\n",
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" new_population = []\n",
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" for i, field in enumerate(population):\n",
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" if field.get_fitness() >= limit:\n",
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" new_population.append(field) \n",
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" \n",
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" # filling up the removed fields\n",
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" if len(new_population) < len(population):\n",
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" for i in range(len(population) - len(new_population)):\n",
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" random_field = new_population[random.randint(0,len(new_population))]\n",
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" new_population.append(random_field)\n",
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" return new_population\n",
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"\n",
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"\n",
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" def genetic_algorithm(self, n):\n",
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" \"\"\"\n",
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" Main genetic algorithm loop for evolving population.\n",
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" \n",
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" Args:\n",
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" n (int): Maximum number of generations to run.\n",
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" \n",
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" Returns:\n",
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" Field: Best individual found after n generations.\n",
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" \"\"\"\n",
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" current_population = self.initial_population\n",
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" new_population = []\n",
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" best_field = self.initial_population[0]\n",
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"\n",
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" for i in range(n):\n",
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" current_population = self.remove_unfit(current_population, 14)\n",
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" for j in range(len(current_population)):\n",
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" x = self.random_selection(current_population)\n",
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" y = self.random_selection(current_population)\n",
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"\n",
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" child = self.reproduce(x, y)\n",
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"\n",
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" if random.random() < self.p_mutation:\n",
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" self.mutation(child)\n",
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"\n",
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" new_population.append(child)\n",
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"\n",
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" if child.get_fitness() > best_field.get_fitness():\n",
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" best_field = child\n",
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" if best_field.get_fitness() == 28:\n",
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" break\n",
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"\n",
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" print(f\"{i} {best_field.get_state()} {best_field.get_fitness()}\")\n",
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" if best_field.get_fitness() == 28:\n",
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" break\n",
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"\n",
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" current_population = new_population\n",
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" new_population = []\n",
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"\n",
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" return best_field\n",
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"\n",
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" def calc(self, n=100):\n",
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" \"\"\"\n",
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" Executes genetic algorithm for specified number of generations.\n",
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" \n",
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" Args:\n",
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" n (int): Number of generations to evolve (default: 100).\n",
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" \n",
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" Returns:\n",
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" Field: Best solution found by genetic algorithm.\n",
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" \"\"\"\n",
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" best_genetic_field = self.genetic_algorithm(n)\n",
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" return best_genetic_field"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bc171a9e",
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"metadata": {},
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"source": [
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"## Backtracking"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "14a6ab56",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Backtrack:\n",
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" \"\"\"\n",
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" Implements backtracking search for finding all solutions to 8 Queens problem.\n",
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" Uses constraint satisfaction with consistency checking and inference.\n",
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" \"\"\"\n",
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"\n",
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" def __init__(self):\n",
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" \"\"\"\n",
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" Initialize backtracking solver with empty results list.\n",
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" \"\"\"\n",
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" self.results = []\n",
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"\n",
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" def consistency(self, field, new_row):\n",
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" \"\"\"\n",
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" Checks if adding a queen at new_row maintains consistency.\n",
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" Tests if the new configuration violates any constraints.\n",
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" \n",
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" Args:\n",
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" field (Field): Current partial solution.\n",
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" new_row (int): Row position for new queen.\n",
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" \n",
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" Returns:\n",
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" bool: True if placement is consistent, False otherwise.\n",
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" \"\"\"\n",
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" current_state = field.get_state().copy()\n",
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" current_state.append(new_row)\n",
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" new_field = Field(current_state)\n",
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"\n",
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" if new_field.threats > 0:\n",
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" return False\n",
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" else:\n",
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" return True\n",
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"\n",
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" def inference(self, field):\n",
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" \"\"\"\n",
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" Performs constraint propagation to reduce domain values.\n",
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" Eliminates impossible row positions for the next column based on current state.\n",
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" \n",
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" Args:\n",
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" field (Field): Current partial solution.\n",
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" \n",
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" Returns:\n",
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" bool: True if domain is not empty, False if no valid moves remain.\n",
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" \"\"\"\n",
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" if len(field.get_state()) >= 8:\n",
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" return True\n",
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" \n",
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" # Reset domain for current column\n",
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" field.set_domain_values([1, 2, 3, 4, 5, 6, 7, 8]) \n",
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" inferences = []\n",
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"\n",
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" for new_row in range(1, 9):\n",
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" if not self.consistency(field, new_row):\n",
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" inferences.append(new_row)\n",
|
|
"\n",
|
|
" for row in inferences:\n",
|
|
" if row in field.get_domain_values():\n",
|
|
" field.get_domain_values().remove(row)\n",
|
|
"\n",
|
|
" if len(field.get_domain_values()) == 0:\n",
|
|
" return False\n",
|
|
"\n",
|
|
" return True\n",
|
|
"\n",
|
|
" def backtracing(self, field):\n",
|
|
" \"\"\"\n",
|
|
" Recursive backtracking search to find all valid solutions.\n",
|
|
" \n",
|
|
" Args:\n",
|
|
" field (Field): Current partial solution.\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" list: List of complete solutions found from this state.\n",
|
|
" \"\"\"\n",
|
|
" if len(field.get_state()) == 8:\n",
|
|
" return [Field(field.get_state().copy())]\n",
|
|
"\n",
|
|
" solutions = []\n",
|
|
"\n",
|
|
" for row in field.get_domain_values():\n",
|
|
" # old_domain_values = field.get_domain_values().copy()\n",
|
|
" if self.consistency(field, row):\n",
|
|
" field.add_queen(row)\n",
|
|
" if self.inference(field): # nur für die nächste Spalte\n",
|
|
" result = self.backtracing(field)\n",
|
|
" if len(result) != 0:\n",
|
|
" solutions.extend(result)\n",
|
|
"\n",
|
|
" field.get_state().pop()\n",
|
|
" # field.domain_values = old_domain_values\n",
|
|
"\n",
|
|
" return solutions\n",
|
|
"\n",
|
|
" def calc(self):\n",
|
|
" for i in range(1, 9):\n",
|
|
" result = self.backtracing(Field([i]))\n",
|
|
" self.results.extend(result)\n",
|
|
" for i, result in enumerate(self.results):\n",
|
|
" print(f\"{i + 1} {result.get_state()}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"id": "0ba915ff",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"------- Genetic -------\n",
|
|
"0 [8, 4, 7, 1, 2, 6, 1, 3] 26.0\n",
|
|
"1 [8, 4, 7, 1, 2, 6, 1, 3] 26.0\n",
|
|
"2 [8, 4, 7, 1, 2, 6, 1, 3] 26.0\n",
|
|
"3 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"4 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"5 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"6 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"7 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"8 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"9 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"10 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"11 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"12 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"13 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"14 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"15 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"16 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"17 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"18 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"19 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"20 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"21 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"22 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"23 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"24 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"25 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"26 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"27 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"28 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"29 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"30 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"31 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"32 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"33 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"34 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"35 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"36 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"37 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"38 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"39 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"40 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"41 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"42 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"43 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"44 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"45 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"46 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"47 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"48 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"49 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"50 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"51 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"52 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"53 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"54 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"55 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"56 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"57 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"58 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"59 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"60 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"61 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"62 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"63 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"64 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"65 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"66 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"67 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"68 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"69 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"70 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"71 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"72 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"73 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"74 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"75 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"76 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"77 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"78 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"79 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"80 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"81 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"82 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"83 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"84 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"85 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"86 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"87 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"88 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"89 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"90 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"91 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"92 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"93 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"94 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"95 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"96 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"97 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"98 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"99 [3, 6, 8, 1, 5, 7, 2, 7] 27.0\n",
|
|
"\n",
|
|
" ┌───┬───┬───┬───┬───┬───┬───┬───┐\n",
|
|
"8 │███│ │▌Q▐│ │███│ │███│ │\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"7 │ │███│ │███│ │▌Q▐│ │▌Q▐│\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"6 │███│ Q │███│ │███│ │███│ │\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"5 │ │███│ │███│ Q │███│ │███│\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"4 │███│ │███│ │███│ │███│ │\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"3 │ Q │███│ │███│ │███│ │███│\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"2 │███│ │███│ │███│ │▌Q▐│ │\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"1 │ │███│ │▌Q▐│ │███│ │███│\n",
|
|
" └───┴───┴───┴───┴───┴───┴───┴───┘\n",
|
|
" A B C D E F G H \n",
|
|
"\n",
|
|
"Threats: 1.0\n",
|
|
"Fitness: 27.0\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"def genetic():\n",
|
|
" print(\"------- Genetic -------\")\n",
|
|
" gen_field = Field(model=\"genetic\")\n",
|
|
" gen_field.calc()\n",
|
|
"\n",
|
|
"genetic()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "8be85fd2",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"------- Backtrack -------\n",
|
|
"1 [1, 5, 8, 6, 3, 7, 2, 4]\n",
|
|
"2 [1, 6, 8, 3, 7, 4, 2, 5]\n",
|
|
"3 [1, 7, 4, 6, 8, 2, 5, 3]\n",
|
|
"4 [1, 7, 5, 8, 2, 4, 6, 3]\n",
|
|
"5 [2, 4, 6, 8, 3, 1, 7, 5]\n",
|
|
"6 [2, 5, 7, 1, 3, 8, 6, 4]\n",
|
|
"7 [2, 5, 7, 4, 1, 8, 6, 3]\n",
|
|
"8 [2, 6, 1, 7, 4, 8, 3, 5]\n",
|
|
"9 [2, 6, 8, 3, 1, 4, 7, 5]\n",
|
|
"10 [2, 7, 3, 6, 8, 5, 1, 4]\n",
|
|
"11 [2, 7, 5, 8, 1, 4, 6, 3]\n",
|
|
"12 [2, 8, 6, 1, 3, 5, 7, 4]\n",
|
|
"13 [3, 1, 7, 5, 8, 2, 4, 6]\n",
|
|
"14 [3, 5, 2, 8, 1, 7, 4, 6]\n",
|
|
"15 [3, 5, 2, 8, 6, 4, 7, 1]\n",
|
|
"16 [3, 5, 7, 1, 4, 2, 8, 6]\n",
|
|
"17 [3, 5, 8, 4, 1, 7, 2, 6]\n",
|
|
"18 [3, 6, 2, 5, 8, 1, 7, 4]\n",
|
|
"19 [3, 6, 2, 7, 1, 4, 8, 5]\n",
|
|
"20 [3, 6, 2, 7, 5, 1, 8, 4]\n",
|
|
"21 [3, 6, 4, 1, 8, 5, 7, 2]\n",
|
|
"22 [3, 6, 4, 2, 8, 5, 7, 1]\n",
|
|
"23 [3, 6, 8, 1, 4, 7, 5, 2]\n",
|
|
"24 [3, 6, 8, 1, 5, 7, 2, 4]\n",
|
|
"25 [3, 6, 8, 2, 4, 1, 7, 5]\n",
|
|
"26 [3, 7, 2, 8, 5, 1, 4, 6]\n",
|
|
"27 [3, 7, 2, 8, 6, 4, 1, 5]\n",
|
|
"28 [3, 8, 4, 7, 1, 6, 2, 5]\n",
|
|
"29 [4, 1, 5, 8, 2, 7, 3, 6]\n",
|
|
"30 [4, 1, 5, 8, 6, 3, 7, 2]\n",
|
|
"31 [4, 2, 5, 8, 6, 1, 3, 7]\n",
|
|
"32 [4, 2, 7, 3, 6, 8, 1, 5]\n",
|
|
"33 [4, 2, 7, 3, 6, 8, 5, 1]\n",
|
|
"34 [4, 2, 7, 5, 1, 8, 6, 3]\n",
|
|
"35 [4, 2, 8, 5, 7, 1, 3, 6]\n",
|
|
"36 [4, 2, 8, 6, 1, 3, 5, 7]\n",
|
|
"37 [4, 6, 1, 5, 2, 8, 3, 7]\n",
|
|
"38 [4, 6, 8, 2, 7, 1, 3, 5]\n",
|
|
"39 [4, 6, 8, 3, 1, 7, 5, 2]\n",
|
|
"40 [4, 7, 1, 8, 5, 2, 6, 3]\n",
|
|
"41 [4, 7, 3, 8, 2, 5, 1, 6]\n",
|
|
"42 [4, 7, 5, 2, 6, 1, 3, 8]\n",
|
|
"43 [4, 7, 5, 3, 1, 6, 8, 2]\n",
|
|
"44 [4, 8, 1, 3, 6, 2, 7, 5]\n",
|
|
"45 [4, 8, 1, 5, 7, 2, 6, 3]\n",
|
|
"46 [4, 8, 5, 3, 1, 7, 2, 6]\n",
|
|
"47 [5, 1, 4, 6, 8, 2, 7, 3]\n",
|
|
"48 [5, 1, 8, 4, 2, 7, 3, 6]\n",
|
|
"49 [5, 1, 8, 6, 3, 7, 2, 4]\n",
|
|
"50 [5, 2, 4, 6, 8, 3, 1, 7]\n",
|
|
"51 [5, 2, 4, 7, 3, 8, 6, 1]\n",
|
|
"52 [5, 2, 6, 1, 7, 4, 8, 3]\n",
|
|
"53 [5, 2, 8, 1, 4, 7, 3, 6]\n",
|
|
"54 [5, 3, 1, 6, 8, 2, 4, 7]\n",
|
|
"55 [5, 3, 1, 7, 2, 8, 6, 4]\n",
|
|
"56 [5, 3, 8, 4, 7, 1, 6, 2]\n",
|
|
"57 [5, 7, 1, 3, 8, 6, 4, 2]\n",
|
|
"58 [5, 7, 1, 4, 2, 8, 6, 3]\n",
|
|
"59 [5, 7, 2, 4, 8, 1, 3, 6]\n",
|
|
"60 [5, 7, 2, 6, 3, 1, 4, 8]\n",
|
|
"61 [5, 7, 2, 6, 3, 1, 8, 4]\n",
|
|
"62 [5, 7, 4, 1, 3, 8, 6, 2]\n",
|
|
"63 [5, 8, 4, 1, 3, 6, 2, 7]\n",
|
|
"64 [5, 8, 4, 1, 7, 2, 6, 3]\n",
|
|
"65 [6, 1, 5, 2, 8, 3, 7, 4]\n",
|
|
"66 [6, 2, 7, 1, 3, 5, 8, 4]\n",
|
|
"67 [6, 2, 7, 1, 4, 8, 5, 3]\n",
|
|
"68 [6, 3, 1, 7, 5, 8, 2, 4]\n",
|
|
"69 [6, 3, 1, 8, 4, 2, 7, 5]\n",
|
|
"70 [6, 3, 1, 8, 5, 2, 4, 7]\n",
|
|
"71 [6, 3, 5, 7, 1, 4, 2, 8]\n",
|
|
"72 [6, 3, 5, 8, 1, 4, 2, 7]\n",
|
|
"73 [6, 3, 7, 2, 4, 8, 1, 5]\n",
|
|
"74 [6, 3, 7, 2, 8, 5, 1, 4]\n",
|
|
"75 [6, 3, 7, 4, 1, 8, 2, 5]\n",
|
|
"76 [6, 4, 1, 5, 8, 2, 7, 3]\n",
|
|
"77 [6, 4, 2, 8, 5, 7, 1, 3]\n",
|
|
"78 [6, 4, 7, 1, 3, 5, 2, 8]\n",
|
|
"79 [6, 4, 7, 1, 8, 2, 5, 3]\n",
|
|
"80 [6, 8, 2, 4, 1, 7, 5, 3]\n",
|
|
"81 [7, 1, 3, 8, 6, 4, 2, 5]\n",
|
|
"82 [7, 2, 4, 1, 8, 5, 3, 6]\n",
|
|
"83 [7, 2, 6, 3, 1, 4, 8, 5]\n",
|
|
"84 [7, 3, 1, 6, 8, 5, 2, 4]\n",
|
|
"85 [7, 3, 8, 2, 5, 1, 6, 4]\n",
|
|
"86 [7, 4, 2, 5, 8, 1, 3, 6]\n",
|
|
"87 [7, 4, 2, 8, 6, 1, 3, 5]\n",
|
|
"88 [7, 5, 3, 1, 6, 8, 2, 4]\n",
|
|
"89 [8, 2, 4, 1, 7, 5, 3, 6]\n",
|
|
"90 [8, 2, 5, 3, 1, 7, 4, 6]\n",
|
|
"91 [8, 3, 1, 6, 2, 5, 7, 4]\n",
|
|
"92 [8, 4, 1, 3, 6, 2, 7, 5]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"def backtrack():\n",
|
|
" print(\"------- Backtrack -------\")\n",
|
|
" back_field = Field(model=\"backtrack\")\n",
|
|
" back_field.calc()\n",
|
|
"\n",
|
|
"backtrack()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "b5371c6e",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"------- My Field -------\n",
|
|
"\n",
|
|
" ┌───┬───┬───┬───┬───┬───┬───┬───┐\n",
|
|
"8 │▌Q▐│ │███│ │███│ │███│ │\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"7 │ │███│ │███│ │▌Q▐│ │███│\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"6 │███│ │███│ │███│ │███│ │\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"5 │ │███│ Q │▌Q▐│ Q │███│ │███│\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"4 │███│ │███│ │███│ │▌Q▐│ Q │\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"3 │ │███│ │███│ │███│ │███│\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"2 │███│ │███│ │███│ │███│ │\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"1 │ │▌Q▐│ │███│ │███│ │███│\n",
|
|
" └───┴───┴───┴───┴───┴───┴───┴───┘\n",
|
|
" A B C D E F G H \n",
|
|
"\n",
|
|
"Threats: 6.0\n",
|
|
"Fitness: 22.0\n",
|
|
"[8, 1, 5, 5, 5, 7, 4, 4]\n",
|
|
"\n",
|
|
" ┌───┬───┬───┬───┬───┬───┬───┬───┐\n",
|
|
"8 │▌Q▐│ │███│ │███│ │███│ │\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"7 │ │███│ │███│ │▌Q▐│ │███│\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"6 │███│ │███│ │███│ │███│ │\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"5 │ │███│ Q │▌Q▐│ Q │███│ │███│\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"4 │███│ Q │███│ │███│ │▌Q▐│ Q │\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"3 │ │███│ │███│ │███│ │███│\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"2 │███│ │███│ │███│ │███│ │\n",
|
|
" ├───┼───┼───┼───┼───┼───┼───┼───┤\n",
|
|
"1 │ │███│ │███│ │███│ │███│\n",
|
|
" └───┴───┴───┴───┴───┴───┴───┴───┘\n",
|
|
" A B C D E F G H \n",
|
|
"\n",
|
|
"Threats: 9.0\n",
|
|
"Fitness: 19.0\n",
|
|
"[8, 4, 5, 5, 5, 7, 4, 4]\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"\n",
|
|
"def main():\n",
|
|
" print(\"------- My Field -------\")\n",
|
|
" myField = Field()\n",
|
|
" myField.print_field()\n",
|
|
" print(myField.get_state())\n",
|
|
" myField.move_queen(1,4)\n",
|
|
" myField.print_field()\n",
|
|
" print(myField.get_state())\n",
|
|
"\n",
|
|
"main()"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".env",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.18"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|