VSZM

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Nov 26th, 2019
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  1. #!/bin/python3
  2. import math
  3. import os
  4. import random
  5. import re
  6. import sys
  7.  
  8.  
  9. #
  10. # Complete the 'predictMissingPrices' function below.
  11. #
  12. # The function is expected to return an INTEGER_ARRAY.
  13. # The function accepts following parameters:
  14. #  1. STRING startDate
  15. #  2. STRING endDate
  16. #  3. STRING_ARRAY knownTimestamps
  17. #  4. INTEGER_ARRAY pricesAtKnownTimeStamps
  18. #  5. STRING_ARRAY unknownTimestamps
  19. #
  20.  
  21. # y = mx + b
  22. def makeLinearPrediction(value_map, i, X):
  23.     # need two points
  24.     X1 = None
  25.     X2 = None
  26.     if i == 0:
  27.         j = i + 1
  28.         while X1 == None or X2 == None:
  29.             if X[j] in value_map:
  30.                 if X1 == None:
  31.                     X1 = (j, value_map[X[j]])
  32.                 else:
  33.                     X2 = (j, value_map[X[j]])
  34.             j += 1
  35.     elif i == len(X) - 1:
  36.         j = i - 1
  37.         while X1 == None or X2 == None:
  38.             if X[j] in value_map:
  39.                 if X1 == None:
  40.                     X1 = (j, value_map[X[j]])
  41.                 else:
  42.                     X2 = (j, value_map[X[j]])
  43.             j -= 1
  44.     else:
  45.         distance = 1
  46.         while X1 == None or X2 == None:
  47.             j = i - distance
  48.             if 0 < j and X[j] in value_map:
  49.                 if X1 == None:
  50.                     X1 = (j, value_map[X[j]])
  51.                 else:
  52.                     X2 = (j, value_map[X[j]])
  53.             j = i + distance
  54.             if j < len(X) and X[j] in value_map:
  55.                 if X1 == None:
  56.                     X1 = (j, value_map[X[j]])
  57.                 else:
  58.                     X2 = (j, value_map[X[j]])
  59.            
  60.             distance += 1
  61.  
  62.     m = (X2[1]-X1[1]) / float((X2[0]-X1[0]))
  63.     b = X1[1] - (m * X1[0])
  64.  
  65.     x = i
  66.     return round(m * x + b)
  67.  
  68. def timestampToFeatures(ts):
  69.     ts_split = ts.split(' ')
  70.     date_split = ts_split[0].split('-')
  71.     year = int(date_split[0])
  72.     month = int(date_split[1])
  73.     day = int(date_split[2])
  74.     hour = int(ts_split[1].split(':')[0])
  75.  
  76.     return (year, month, day, hour)
  77.  
  78.  
  79. def predictMissingPrices(startDate, endDate, knownTimestamps, pricesAtKnownTimeStamps, unknownTimestamps):
  80.     value_map = {}
  81.     X_train = [timestampToFeatures(ts) for ts in knownTimestamps]
  82.     for i in range(len(pricesAtKnownTimeStamps)):
  83.         value_map[X_train[i]] = pricesAtKnownTimeStamps[i]
  84.  
  85.     X_test = [timestampToFeatures(ts) for ts in unknownTimestamps]
  86.     X = sorted(X_train + X_test)
  87.  
  88.     predictions = []
  89.     for i in range(len(X)):
  90.         Xi = X[i]
  91.         if Xi not in value_map:
  92.             predictions.append(makeLinearPrediction(value_map, i, X))
  93.  
  94.     return predictions
  95.  
  96. """
  97. from sklearn.svm import SVR
  98.  
  99. def predictMissingPrices(startDate, endDate, knownTimestamps, pricesAtKnownTimeStamps, unknownTimestamps):
  100.    X_train = [timestampToFeatures(ts) for ts in knownTimestamps]
  101.    y_train = pricesAtKnownTimeStamps
  102.    X_test = [timestampToFeatures(ts) for ts in unknownTimestamps]
  103.  
  104.    model = SVR(kernel='rbf', C=10, gamma=0.1, epsilon=.1)
  105.  
  106.    model.fit(X_train, y_train)
  107.    
  108.    y_test = model.predict(X_test)
  109.    return y_test
  110. """
  111.  
  112.  
  113. if __name__ == '__main__':
  114.     # IO handling and parsing
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