====== 01 Intro and Solvability ====== We log on, discuss the rules, answer first questions, create first simple Python program. Read about [[https://cyber.felk.cvut.cz/study/computer-labs/|computer labs]] how to set-up initial password, access home directory, links to other university services, ... Reading about Python and programming essentials from the Programming Essentials course. [[https://cw.fel.cvut.cz/old/courses/be5b33prg/tutorials/start|Tutorials]] help with setting Python, discuss how to upload homework correctly, and add some class examples and some most frequent Python modules. After solving the logging issues we will go into Objective Python. We will program the NPuzzle class that would represent the classical n-1 puzzle. Necessary basis will be explained at the first lecture. Python is well on-line documented/discussed, in case of problems, google is your friend. ===== NPuzzle class ===== Besides the constructor ''%%__init__%%'' we will need a few basic method, namely ''reset, visualise, read_tile''. You can create some auxiliary methods/function as it suits to you class NPuzzle: ''' sliding puzzle class of a general size https://en.wikipedia.org/wiki/15_puzzle ''' def __init__(self,size): ''' create the list of symbols, typically from 1 to 8 or 15. Empty tile is represented by None :param size: the board will be size x size, size=3 - 8-puzzle; 4 - 15 puzzle ''' def reset(self): ''' initialize the board by a random shuffle of symbols :return: None ''' def visualise(self): ''' just print itself to the standard output :return: None ''' def read_tile(self, row, col): ''' returns a symbol on row, col position :param row: index of the row :param column: index of the column :return: value of the tile - int 1 to size^2-1, None for empty tile The function raises IndexError exception if outside the board ''' ===== Programming task - solvability n-1 puzzle ===== Is the given configuration/state solvable at all? You can use also the code from {{ :courses:b3b33kui:cviceni:program_po_tydnech:npuzzle.py |}} and compare it to your solution of ''npuzzle.py''. The task is to implement a method ''is_solvable(env)'', which decides whether a given ''env'' (NPuzzle instance) is solvable or not. Implement the function in a module named ''solvability_check.py'' and upload it to [[https://cw.felk.cvut.cz/brute/|BRUTE]]. A correct solution is worth 2 points. Example structure of (''solvability_check.py''): import npuzzle def is_solvable(env): ''' True or False? ''' # your code comes here if __name__=="__main__": # testing suite env = npuzzle.NPuzzle(3) # instance of NPuzzle class env.reset() # random shuffle env.visualise() # just to show the board # just check print(is_solvable(env)) # should output True or False Basic communication with the NPuzzle object is through the function ''env.read_tile(row, col)'', which returns the value of the tile; ''None'' for empty or an exception is generated (''IndexError'') in case the index is out of range. An example is provided in ''npuzzle.py'' When done, upload to the [[https://cw.felk.cvut.cz/brute|BRUTE]] ==== Quiz ==== > {{page>courses:be5b33kui:internal:quizzes#4-1-puzzle}} ===== Completeness and optimality ===== We will discuss how to best search when we don't know how far is the goal. What does it mean that a search algorithm is //complete, optimal//. ==== Quiz ==== > {{page>courses:be5b33kui:internal:quizzes#A forgetful traveler }} ===== Assignment: searching in a maze ===== The full description of the assignment is here [[https://cw.fel.cvut.cz/b182/courses/be5b33kui/labs/search/start| Search (1st assignment)]]. In a maze environment, you will program the algorithm to find the shortest path. Do not forget that the cost of transition between different positions does not have to be the same.