Podcast
Questions and Answers
Pandas is a Python package that provides fast, flexible, and expressive ______ structures designed to make working with data both easy and intuitive.
Pandas is a Python package that provides fast, flexible, and expressive ______ structures designed to make working with data both easy and intuitive.
data
The general knapsack problem is an example of ______ programming.
The general knapsack problem is an example of ______ programming.
integer
To read an Excel file in Python, we can use the ______ function from the pandas library.
To read an Excel file in Python, we can use the ______ function from the pandas library.
read_excel
We can convert a DataFrame into a tuple list using the ______ function in pandas.
We can convert a DataFrame into a tuple list using the ______ function in pandas.
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The Greedy Algorithm is ______ to implement, but does not always yield the best solution.
The Greedy Algorithm is ______ to implement, but does not always yield the best solution.
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In the general knapsack problem, we are allowed to take multiple items of each type, and all items of a given type have the same ______.
In the general knapsack problem, we are allowed to take multiple items of each type, and all items of a given type have the same ______.
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The knapsack function takes two arguments: item_list and ______.
The knapsack function takes two arguments: item_list and ______.
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The item list is a list of tuples containing the item name, ______, and profit.
The item list is a list of tuples containing the item name, ______, and profit.
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The knapsack_by_profit function sorts the item list by descending order considering the ______.
The knapsack_by_profit function sorts the item list by descending order considering the ______.
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The knapsack_by_weight function sorts the item list by ascending order considering the ______.
The knapsack_by_weight function sorts the item list by ascending order considering the ______.
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The knapsack function is called with the sorted item list and ______ as arguments.
The knapsack function is called with the sorted item list and ______ as arguments.
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The greedy algorithm is used to solve the ______ problem.
The greedy algorithm is used to solve the ______ problem.
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The knapsack_by_weight function sorts the item list by ______ order considering the profit/weight.
The knapsack_by_weight function sorts the item list by ______ order considering the profit/weight.
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The greedy algorithm considers the ______ profit-weight ratio to find the optimal solution.
The greedy algorithm considers the ______ profit-weight ratio to find the optimal solution.
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The function knapsack_by_profit_per_weight is defined to take ______ and capacity as arguments.
The function knapsack_by_profit_per_weight is defined to take ______ and capacity as arguments.
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The sorted_items list is sorted by the ______ of the item's profit and weight.
The sorted_items list is sorted by the ______ of the item's profit and weight.
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The optimal solution is not always found by making ______ locally optimal choices.
The optimal solution is not always found by making ______ locally optimal choices.
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Increasing the capacity to ______ may change the optimal solution found by the greedy algorithm.
Increasing the capacity to ______ may change the optimal solution found by the greedy algorithm.
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Study Notes
Importing Data from Excel to Python
- Importing pandas library:
import pandas as pd - Reading Excel file:
df = pd.read_excel("kpData.xlsx") - Converting DataFrame to tuple list:
item_list = list(df.itertuples(index=False, name=None))
Pros and Cons of Greedy Algorithm
- Easy to implement
- Computationally efficient
- Does not always yield the best solution
- Unknown quality of the approximation
General Knapsack Problem
- Allowed to take multiple items of each type
- Not a 0/1 programming problem
- Integer programming problem
- Example: Cargo Loading Problem
Homework 1: Knapsack Problem
- Define a function
knapsackwith argumentsitem_listandcapacity - Check the weights of the items in the item list
- If the weight is less than capacity, add the item to the knapsack and decrease the capacity by the weight
- Increase the profit
- Loop ends when all items are checked
- Call the function
Greedy Algorithm Examples
- Example 1: C = 20, item list =
[("clock", 175, 10), ...] - Greedy algorithm 2: Sort item list by descending order of profit
- Greedy algorithm 3: Sort item list by ascending order of weight
- Greedy algorithm 4: Sort item list by descending order of profit/weight ratio
Comparing Greedy Algorithms
- Applied different rules: by given order, by highest profit, by highest profit-weight ratio, by lowest weight
- Results: profit = 275, 200, 255, 175 respectively
- None of them give the optimal answer
Limitations of Greedy Algorithm
- Sequence of locally "optimal" choices does not always yield a globally optimal solution
- Greedy algorithm by given order is not always the winner
- Profit-weight ratio is often the winner with larger data sets
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Description
Test your understanding of Greedy Algorithm, a fundamental concept in data structures and algorithms. This quiz covers the basics of greedy algorithm and its applications in solving complex problems. Assess your knowledge and learn more about this essential topic in computer science.
