From 10aa214fcb2a05fd479e23a0d9c1d1ea617a7769 Mon Sep 17 00:00:00 2001 From: Hasenn Date: Mon, 14 Sep 2020 14:40:27 +0200 Subject: [PATCH] Docstrings and formatting improvements (#2418) * Fix spelling in docstrings * Improve comments and formatting * Update print statement to reflect doctest change * improve phrasing and apply black * Update rat_in_maze.py This method is recursive starting from (i, j) and going in one of four directions: up, down, left, right. If a path is found to destination it returns True otherwise it returns False. Co-authored-by: Christian Clauss --- backtracking/hamiltonian_cycle.py | 6 +++--- backtracking/rat_in_maze.py | 23 +++++++++++------------ boolean_algebra/quine_mc_cluskey.py | 2 +- cellular_automata/one_dimensional.py | 5 +++-- 4 files changed, 18 insertions(+), 18 deletions(-) diff --git a/backtracking/hamiltonian_cycle.py b/backtracking/hamiltonian_cycle.py index 3bd61fc66..7be1ea350 100644 --- a/backtracking/hamiltonian_cycle.py +++ b/backtracking/hamiltonian_cycle.py @@ -51,7 +51,7 @@ def util_hamilton_cycle(graph: List[List[int]], path: List[int], curr_ind: int) """ Pseudo-Code Base Case: - 1. Chceck if we visited all of vertices + 1. Check if we visited all of vertices 1.1 If last visited vertex has path to starting vertex return True either return False Recursive Step: @@ -59,8 +59,8 @@ def util_hamilton_cycle(graph: List[List[int]], path: List[int], curr_ind: int) Check if next vertex is valid for transiting from current vertex 2.1 Remember next vertex as next transition 2.2 Do recursive call and check if going to this vertex solves problem - 2.3 if next vertex leads to solution return True - 2.4 else backtrack, delete remembered vertex + 2.3 If next vertex leads to solution return True + 2.4 Else backtrack, delete remembered vertex Case 1: Use exact graph as in main function, with initialized values >>> graph = [[0, 1, 0, 1, 0], diff --git a/backtracking/rat_in_maze.py b/backtracking/rat_in_maze.py index ba96d6a52..788aeac13 100644 --- a/backtracking/rat_in_maze.py +++ b/backtracking/rat_in_maze.py @@ -1,13 +1,13 @@ def solve_maze(maze: list) -> bool: """ - This method solves rat in maze algorithm. - In this problem we have n by n matrix and we have start point and end point - we want to go from source to distination. In this matrix 0 are block paths - 1 are open paths we can use. + This method solves the "rat in maze" problem. + In this problem we have some n by n matrix, a start point and an end point. + We want to go from the start to the end. In this matrix zeroes represent walls + and ones paths we can use. Parameters : maze(2D matrix) : maze Returns: - Return: True is maze has a solution or False if it does not. + Return: True if the maze has a solution or False if it does not. >>> maze = [[0, 1, 0, 1, 1], ... [0, 0, 0, 0, 0], ... [1, 0, 1, 0, 1], @@ -47,13 +47,13 @@ def solve_maze(maze: list) -> bool: ... [0, 1, 0], ... [1, 0, 0]] >>> solve_maze(maze) - Solution does not exists! + No solution exists! False >>> maze = [[0, 1], ... [1, 0]] >>> solve_maze(maze) - Solution does not exists! + No solution exists! False """ size = len(maze) @@ -63,16 +63,15 @@ def solve_maze(maze: list) -> bool: if solved: print("\n".join(str(row) for row in solutions)) else: - print("Solution does not exists!") + print("No solution exists!") return solved def run_maze(maze, i, j, solutions): """ - This method is recursive method which starts from i and j - and goes with 4 direction option up, down, left, right - if path found to destination it breaks and return True - otherwise False + This method is recursive starting from (i, j) and going in one of four directions: + up, down, left, right. + If a path is found to destination it returns True otherwise it returns False. Parameters: maze(2D matrix) : maze i, j : coordinates of matrix diff --git a/boolean_algebra/quine_mc_cluskey.py b/boolean_algebra/quine_mc_cluskey.py index 036cfbe63..a55b62448 100644 --- a/boolean_algebra/quine_mc_cluskey.py +++ b/boolean_algebra/quine_mc_cluskey.py @@ -146,7 +146,7 @@ def main(): minterms = [ int(x) for x in input( - "Enter the decimal representation of Minterms 'Spaces Seprated'\n" + "Enter the decimal representation of Minterms 'Spaces Separated'\n" ).split() ] binary = decimal_to_binary(no_of_variable, minterms) diff --git a/cellular_automata/one_dimensional.py b/cellular_automata/one_dimensional.py index 7819088c8..a6229dd90 100644 --- a/cellular_automata/one_dimensional.py +++ b/cellular_automata/one_dimensional.py @@ -32,8 +32,9 @@ def new_generation(cells: List[List[int]], rule: List[int], time: int) -> List[i next_generation = [] for i in range(population): # Get the neighbors of each cell - left_neighbor = 0 if i == 0 else cells[time][i - 1] # special: leftmost cell - right_neighbor = 0 if i == population - 1 else cells[time][i + 1] # rightmost + # Handle neighbours outside bounds by using 0 as their value + left_neighbor = 0 if i == 0 else cells[time][i - 1] + right_neighbor = 0 if i == population - 1 else cells[time][i + 1] # Define a new cell and add it to the new generation situation = 7 - int(f"{left_neighbor}{cells[time][i]}{right_neighbor}", 2) next_generation.append(rule[situation])