Difference between revisions of "Monte Carlo Calculation of Pi"
From WLCS
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'''Directions:''' | '''Directions:''' | ||
− | # Prompt the user for a number N (this will be our total number of test points | + | # Prompt the user for a number N (this will be our total number of test points) |
− | # Create a variable for our '''numHits''' | + | # Create a variable for our '''numHits''' (this is *not* our loop counter) |
− | # Write a loop that runs N times | + | # Write a loop that runs N times (you should use a loop counter variable like '''count''' and avoid using '''x''' because we will use '''x''' for something else) |
## Generate random numbers for '''x''' and '''y''' between 0 and 1.0 by using '''random.random()''' | ## Generate random numbers for '''x''' and '''y''' between 0 and 1.0 by using '''random.random()''' | ||
## Use the distance formula to calculate the distance from (0, 0) to (x, y) | ## Use the distance formula to calculate the distance from (0, 0) to (x, y) |
Revision as of 18:16, 30 September 2014
Objective:
- To become well-learned in the way of the while loop
Resources:
Directions:
- Prompt the user for a number N (this will be our total number of test points)
- Create a variable for our numHits (this is *not* our loop counter)
- Write a loop that runs N times (you should use a loop counter variable like count and avoid using x because we will use x for something else)
- Generate random numbers for x and y between 0 and 1.0 by using random.random()
- Use the distance formula to calculate the distance from (0, 0) to (x, y)
- Increment (Increase by 1) numHits if the distance is less than 1
- Calculate an estimate of pi
- successProbability = numHits / N
- PI = successProbability * 4
- Print out your estimate of PI