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mod in-script user guide

pull/4/head
Kai Staats 2016-07-07 23:32:51 -06:00
parent 87c525e843
commit c94ab21006
2 changed files with 2 additions and 2 deletions
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2
tools/karoo_data_sort.py
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@ -13,7 +13,7 @@ the subset, if we grab a series of datapoints (rows in a .csv) from the larger d
the top, middle, or bottom, we will likely bias the new dataset and incorrectly train the machine learning algorithm. the top, middle, or bottom, we will likely bias the new dataset and incorrectly train the machine learning algorithm.
Therefore, it is imperative that we engage a random function, guided only by the number of data points for each class. Therefore, it is imperative that we engage a random function, guided only by the number of data points for each class.
This script can be used before or after karoo_normalise.py but assumes no header has yet been applied to the .csv. This script can be used *before* karoo_normalise.py, and assumes no header has yet been applied to the .csv.
''' '''
### USER INTERACTION ### ### USER INTERACTION ###

2
tools/karoo_normalise.py
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@ -12,7 +12,7 @@ This script works with a raw dataset to prepare a new, normalised dataset. It do
given column, finding the maximum and minimum values, and then modifying each value to fall between a high of 1 and given column, finding the maximum and minimum values, and then modifying each value to fall between a high of 1 and
low of 0. The modified values are written to a new file, the original remaining untouched. low of 0. The modified values are written to a new file, the original remaining untouched.
This script can be used before or after karoo_features_sort.py but assumes no header has yet been applied to the .csv. This script can be used *after* karoo_features_sort.py, and assumes no header has yet been applied to the .csv.
''' '''
def normalise(array): def normalise(array):