In [ ]: maxProfit([13,7,-30,15,10, -5,12,7,-11,6]) Empirical Analysis Now, compare between the growth rate of the two algorithms In [ ]: # feel free to import what you want %matplotlib inline import matplotlib import matplotlib.pyplot as plt import time In [ ]: # recording the time for various calls In [ 1: # the code to plot the curve Theoretical Analysis Brute Force [Write your analysis for the brute force case] Divide and Conquer [Write your analysis for the divide-and-conquer case]
In [ ]: maxProfit([13,7,-30,15,10, -5,12,7,-11,6]) Empirical Analysis Now, compare between the growth rate of the two algorithms In [ ]: # feel free to import what you want %matplotlib inline import matplotlib import matplotlib.pyplot as plt import time In [ ]: # recording the time for various calls In [ 1: # the code to plot the curve Theoretical Analysis Brute Force [Write your analysis for the brute force case] Divide and Conquer [Write your analysis for the divide-and-conquer case]
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
Related questions
Question
![During tough times like these, investment becomes more uncertain with more dangers. To solve it, we might try to train a model to decide when to buy or sell.
Therefore, to provide it with correct data, we plan to design an algorithm that answers:
what is the perfect moment to buy and when to sell to maximize your profit?
Assume you must buy
Input:
changes : array listing changes in the prices, where indices represent days; it has at least two values
Output:
i : index of the change before which we buy
j: index of the change before which we sell
maxProfit : the profit of this interval
Example:
Assume the below table contains the prices of a particular stock over days
prices changes
Day Value
50
1
63
13
70
7
3
40
-30
55
15
65
10
6
60
-5
7
72
12
8
79
7
9
68
-11
10
74
6
Therefore, the output of maxProfit([13,7,-30,15,10, -5,12,7, -11,6]) should be (3, 7, 39) . This is because our maximum profit would be 39
when we buy the stock at day 4, index of 3, and sell after day 8, index of 7. Then, the total profit is 15+ 10 – 5+ 12 +7 = 39
We will try to solve the problem using various techniques:
Brute-Force
Divide-and-Conquer](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3dfe85b1-e5f0-4a73-ab4f-8ab481027dba%2F2921a474-e702-47c5-b3ae-2bfb82515c17%2F1mj2cxn_processed.png&w=3840&q=75)
Transcribed Image Text:During tough times like these, investment becomes more uncertain with more dangers. To solve it, we might try to train a model to decide when to buy or sell.
Therefore, to provide it with correct data, we plan to design an algorithm that answers:
what is the perfect moment to buy and when to sell to maximize your profit?
Assume you must buy
Input:
changes : array listing changes in the prices, where indices represent days; it has at least two values
Output:
i : index of the change before which we buy
j: index of the change before which we sell
maxProfit : the profit of this interval
Example:
Assume the below table contains the prices of a particular stock over days
prices changes
Day Value
50
1
63
13
70
7
3
40
-30
55
15
65
10
6
60
-5
7
72
12
8
79
7
9
68
-11
10
74
6
Therefore, the output of maxProfit([13,7,-30,15,10, -5,12,7, -11,6]) should be (3, 7, 39) . This is because our maximum profit would be 39
when we buy the stock at day 4, index of 3, and sell after day 8, index of 7. Then, the total profit is 15+ 10 – 5+ 12 +7 = 39
We will try to solve the problem using various techniques:
Brute-Force
Divide-and-Conquer
![- changes: the list holding the changes in prices; the value whose index is k represents
the change between day <k> and day <k+1>
<changes> has at least a single change [two days]
Output:
- i: the index of the change before which we buy
- j: the index of the change after which we sell
- maxProfit: the value of the maximum profit
return (0,0,0)
In [ ]: maxProfit([13,7,-30,15,10, -5,12,7,-11,6])
Empirical Analysis
Now, compare between the growth rate of the two algorithms
In [ ]: # feel free to import what you want
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import time
In [ ]: # recording the time for various calls
In [ ]: # the code to plot the curve
Theoretical Analysis
Brute Force
[Write your analysis for the brute force case]
Divide and Conquer
[Write your analysis for the divide-and-conquer case]](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3dfe85b1-e5f0-4a73-ab4f-8ab481027dba%2F2921a474-e702-47c5-b3ae-2bfb82515c17%2Fuj0em8g_processed.png&w=3840&q=75)
Transcribed Image Text:- changes: the list holding the changes in prices; the value whose index is k represents
the change between day <k> and day <k+1>
<changes> has at least a single change [two days]
Output:
- i: the index of the change before which we buy
- j: the index of the change after which we sell
- maxProfit: the value of the maximum profit
return (0,0,0)
In [ ]: maxProfit([13,7,-30,15,10, -5,12,7,-11,6])
Empirical Analysis
Now, compare between the growth rate of the two algorithms
In [ ]: # feel free to import what you want
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import time
In [ ]: # recording the time for various calls
In [ ]: # the code to plot the curve
Theoretical Analysis
Brute Force
[Write your analysis for the brute force case]
Divide and Conquer
[Write your analysis for the divide-and-conquer case]
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