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- #!/usr/bin/python
- import math
- def outlierCleaner(predictions, ages, net_worths):
- """
- Clean away the 10% of points that have the largest
- residual errors (difference between the prediction
- and the actual net worth).
- Return a list of tuples named cleaned_data where
- each tuple is of the form (age, net_worth, error).
- """
-
- #cleaned_data = []
- temp = []
- #print len(predictions)
- ### your code goes here
- for num in range(0,len(predictions)):
- error = abs(predictions[num] - net_worths[num])
- temp.append([ages[num],net_worths[num],error])
- temp = sorted(temp, key=lambda x: (x[2]))
- #print temp,len(temp)
- cuted_num = 0 - int(math.ceil(len(temp)/10))
- #print cuted_num
- cleaned_data = temp[:cuted_num]
- #print cleaned_data,len(cleaned_data)
- #print len(cleaned_data)
- return cleaned_data
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