Trees | Indices | Help |
|
---|
|
Informational Entropy functions The definitions used are the same as those in Tom Mitchell's book "Machine Learning"
|
|||
|
|||
|
|||
|
|||
|
|
|||
hascEntropy = 1
|
|||
_log2 = math.log(2)
|
Imports: numpy, math, xrange, cEntropy
|
Calculates the informational entropy of a set of results. **Arguments** results is a 1D Numeric array containing the number of times a given set hits each possible result. For example, if a function has 3 possible results, and the variable in question hits them 5, 6 and 1 times each, results would be [5,6,1] **Returns** the informational entropy |
calculates the information gain for a variable **Arguments** varMat is a Numeric array with the number of possible occurances of each result for reach possible value of the given variable. So, for a variable which adopts 4 possible values and a result which has 3 possible values, varMat would be 4x3 **Returns** The expected information gain |
Calculates the informational entropy of a set of results. **Arguments** results is a 1D Numeric array containing the number of times a given set hits each possible result. For example, if a function has 3 possible results, and the variable in question hits them 5, 6 and 1 times each, results would be [5,6,1] **Returns** the informational entropy |
calculates the information gain for a variable **Arguments** varMat is a Numeric array with the number of possible occurances of each result for reach possible value of the given variable. So, for a variable which adopts 4 possible values and a result which has 3 possible values, varMat would be 4x3 **Returns** The expected information gain |
Trees | Indices | Help |
|
---|
Generated by Epydoc 3.0.1 on Thu Feb 1 16:13:01 2018 | http://epydoc.sourceforge.net |