def entropies(pdf, variable, conditional_variable, logbase="log2"):
"""
Input:
pdf joint pdf class
variable the variable to calculate the entropy. Could fit multiple labels (e.g. "var" fits "var_1", "var_2", "var_3",...)
conditional_variable conditional variable
logbase Base for the logarithm ("log2", "log", "log10")
returns mutual information based on conditional entropy
"""
return shannon.calculate(pdf, variable, logbase) - shannon.conditional(pdf, variable, conditional_variable, logbase)
def entropies(pdf, cond_pdf, logbase="log2"):
'''
Mutual information metric based on entropies.
Input:
pdf_a probability A variable NxB
N = elements
B = bins
cond_pdf conditional probability NxBAxBB
N = elements
BA = bins A
BB = bins B
logbase Base for the logarithm ("log2", "log", "log10")
Returns:
mutual information N
N = elements
'''
return shannon.calculate(pdf, logbase) - shannon.conditional(pdf, cond_pdf, logbase)
