page updates, added spaces around table syntax

adimail · May 22, 2024 · cc22449 · cc22449
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diff --git a/docs/AI/8-puzzle.md b/docs/AI/8-puzzle.md
@@ -6,7 +6,7 @@ parent: AI
 
 # 8-Puzzle Problem
 
-The 8-puzzle is a sliding puzzle consisting of eight tiles, numbered 1 through 8, placed in a 3x3 grid with one empty space. The goal is to rearrange the tiles from a given initial configuration to a goal configuration by sliding tiles into the empty space.
+> The 8-puzzle is a sliding puzzle consisting of eight tiles, numbered 1 through 8, placed in a 3x3 grid with one empty space. The goal is to rearrange the tiles from a given initial configuration to a goal configuration by sliding tiles into the empty space.
 
 ![puzzle](https://www.aiai.ed.ac.uk/~gwickler/images/8-puzzle-states.png)
 
@@ -159,7 +159,7 @@ for soln in solution:
 
 
 ---
-## Here is the entire code for you to copy paste
+## Here is the entire code for you to try
 
 ```python
 class PuzzleState:

diff --git a/docs/AI/flight-schedulling-greedy-search.md b/docs/AI/flight-schedulling-greedy-search.md
@@ -1,9 +1,11 @@
 ---
 layout: default
-title: Flight Scheduling with Greedy Algorithms
+title: Flight Scheduling
 parent: AI
 ---
 
+#  Flight Scheduling with Greedy Algorithms
+
 This code performs the following steps:
 
 - Define a Flight class to hold flight details.
@@ -124,7 +126,7 @@ print(overlapedflights_df)
 
 
 ---
-## Here is the entire code for you to copy paste
+## Here is the entire code for you to try
 
 ```python
 import pandas as pd
@@ -185,13 +187,15 @@ print(overlapedflights_df)
 Output:
 
 Scheduled flights:
+
 | Flight Number | Departure Time | Arrival Time | Maintenance Time |
 |---------------|----------------|--------------|------------------|
 | flight1       | 08:00          | 10:30        | 30               |
 | flight3       | 14:00          | 16:30        | 20               |
 | flight5       | 20:00          | 22:30        | 25               |
 
 Delayed Flights due to overlap
+
 | Flight Number | Departure Time | Arrival Time | Maintenance Time |
 |---------------|----------------|--------------|------------------|
 | flight2       | 11:00          | 13:30        | 45               |

diff --git a/docs/AI/n-queen.md b/docs/AI/n-queen.md
@@ -6,7 +6,7 @@ parent: AI
 
 # N-Queens Problem
 
-Our goal is to develop a solution to place N chess queens on an N×N chessboard so that no two queens threaten each other.
+> Our goal is to develop a solution to place N chess queens on an N×N chessboard so that no two queens threaten each other.
 
 ![problem](https://miro.medium.com/v2/resize:fit:457/0*ScgscJU4q5zWf6lk.png)
 
@@ -33,7 +33,7 @@ graph TD;
 ```
 
 ---
-## Here is the entire code for you to copy paste
+## Here is the entire code for you to try
 
 ```python
 def IsSafe(board, row, col):

diff --git a/docs/AI/tsp.md b/docs/AI/tsp.md
@@ -6,7 +6,7 @@ parent: AI
 
 # Travelling Salesman Problem in python
 
-Given a list of cities and the distances between them, the objective is to find the optimal tour that visits each city exactly once and returns to the starting city, minimizing the total distance traveled.
+> Given a list of cities and the distances between them, the objective is to find the optimal tour that visits each city exactly once and returns to the starting city, minimizing the total distance traveled.
 
 ## Algorithm
 
@@ -25,22 +25,26 @@ graph TD;
 ```
 
 ---
-## Here is the entire code for you to copy paste
+## Here is the entire code for you to try
 
 ```python
 import math
+import pandas as pd
 
 def TSP(cities):
     currentCity = cities[0]
     tour = [currentCity]
     SetVisited(currentCity)
+    totalDistance = 0
     while Check(cities):
-        nearest_city = FindNearestCity(currentCity, cities)
+        nearest_city, distance = FindNearestCity(currentCity, cities)
         tour.append(nearest_city)
         SetVisited(nearest_city)
         currentCity = nearest_city
+        totalDistance = totalDistance + distance
+
     tour.append(tour[0]) 
-    return tour
+    return tour, totalDistance
 
 def Check(cities):
     return any(city['visited'] == False for city in cities)
@@ -54,7 +58,7 @@ def FindNearestCity(currentCity, cities):
             if distance < nearest_distance:
                 nearest_distance = distance
                 nearest_city = city
-    return nearest_city
+    return nearest_city, distance
 
 def SetVisited(city):
     city['visited'] = True
@@ -70,13 +74,37 @@ cities = [
     {'name': 'PCMC', 'x': 0, 'y': 2, 'visited': False}
 ]
 
-print("Cities")
-for city in cities:
-    print(f"{city['name']} - ({city['x']}, {city['y']})")
+tour, totalDistance = TSP(cities)
 
-tour = TSP(cities)
+df = pd.DataFrame(cities)
+
+print(df)
 
 print("\nTour:")
 for i, city in enumerate(tour):
         print(f"Step {i+1}: Visit {city['name']}")
+
+print(f'\nTotal trip cost: {totalDistance}')
+
+```
+
+output:
+
+```
+name      x  y  visited
+Pune      0  0     True
+Wakad     1  1     True
+Moshi     2  2     True
+Hadapsar  1  3     True
+PCMC      0  2     True
+
+Tour:
+Step 1: Visit Pune
+Step 2: Visit Wakad
+Step 3: Visit Moshi
+Step 4: Visit Hadapsar
+Step 5: Visit PCMC
+Step 6: Visit Pune
+
+Total trip cost: 6.82842712474619
 ```