Gaussian naive Bayes.txt

#Algorithm for using Gaussian naïve Bayes:
#Deciding if an item is in category A or B based on inference.
#Take two classicaly dimorphic traits as distributions, Height, & Waist to Hip ratio.
#Calculate p.d.f values for each input based on assumed Gaussian distribution.
#Evaluate the ratios of the two data point, giving a relative likelihood.
# Is an individual a Man or Female based on their height and Waist hip ratio using population norms.
import math
#Height M 
Sdev = 8
#Indivduals Height
x = 84.25
u = 80
#Height F
Sdev2 = 8
u2 = 63
#Waist to Hip Ratio F 
Sdev3 = 10
#Indivduals W 2 H ratio
q = 90
q = float(q)
u3 = 91
u3 = float(u3)
#Waist to Hip Ratio M 
Sdev4 = 12
u4 = 74
u4= float(u4)

def Gauss_pdf():
    G1 = (6.2831)**0.5
    G2 = G1*Sdev
    G3 = 1/G2
    G4 = -((x-u)**2)
    G5 = 2*((Sdev**2))
    G6 = G4/G5
    G7 = 2.718281**G6
    G8 = G3*G7 
    #G1 = int(G1)
    print('Male')
    print(G8)
    
    AG1 = (6.2831)**0.5
    AG2 = AG1*Sdev2
    AG3 = 1/AG2
    AG4 = -((x-u2)**2)
    AG5 = 2*((Sdev2**2))
    AG6 = AG4/AG5
    AG7 = 2.718281**AG6
    AG8 = AG3*AG7 
    #G1 = int(G1)
    print('Female')
    print(AG8)
    print('Based on their height this individual is',(G8)/(AG8),'times more likely to be a Man than a Woman')

    h1 = (6.2831)**0.5
    h2 = h1*Sdev3
    h3 = 1/h2
    h4 = -((q-u3)**2)
    h5 = 2*((Sdev3**2))
    h6 = h4/h5
    h7 = 2.718281**h6
    h8 = h3*h7 
    #h1 = int(h1)
    print('Male')
    print(h8)
    
    k1 = (6.2831)**0.5
    k2 = k1*Sdev4
    k3 = 1/k2
    k4 = -((q-u4)**2)
    k5 = 2*((Sdev4**2))
    k6 = k4/k5
    k7 = 2.718281**k6
    k8 = k3*k7 
    #k1 = int(k1)
    print('Female')
    print(k8)
    print('Based on their Waist to hip ratio this individual is',(h8)/(k8),'times more likely to be a Man than a Woman')
    print('One average this individual is',((G8/AG8)+(h8/k8))/2,'times more likely to be a Man than a Woman ' )
Gauss_pdf()