josiahcoad

192.168.0.13

josiahcoad

# Install TensorFlow
import tensorflow as tf

# Load and prepare the MNIST dataset.
# Convert the samples from integers to floating-point numbers:
mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

josiahcoad

from typing import Dict, List, Tuple
from copy import deepcopy

Matrix = List[List[int]]

def move_empty_tile(matrix: Matrix, empty_tile: Tuple[int, int], new_empty_tile: Tuple[int, int]) -> Matrix:
    row, col = empty_tile
    new_row, new_col = new_empty_tile
    new_matrix = deepcopy(matrix)

josiahcoad

def get_adjacent_matrices(matrix: Matrix, empty_tile: Tuple[int, int] = None) -> List[Matrix]:
    def find_empty_tile(matrix):
        for i in range(len(matrix)):
            for j in range(len(matrix[i])):
                if matrix[i][j] == 0:
                    return (i, j)

    if empty_tile is None:
        empty_tile = find_empty_tile(matrix)

josiahcoad

from typing import Callable, Tuple
from copy import deepcopy
from dataclasses import dataclass, field
from typing import Dict, List, Tuple
from copy import deepcopy
import heapq

@dataclass
class Node:
    matrix: Matrix

josiahcoad

def a_star(initial_state: Matrix, end_state: Matrix, heuristic: Callable[[Matrix, Matrix], int] = uniform_cost) -> Node:
    def convert_to_nodes(matrices: List[Matrix]):
        neighbors = []
        for m in matrices:
            key = str(m)
            if key not in graph:
                graph[key] = Node(m, cost=heuristic(m, end_state))
            neighbors.append(graph[key])
        return neighbors