import pandas as pd
import numpy as np

# Create an empty dataframe
data = pd.DataFrame()

# Create our target variable
data['Gender'] = ['male','male','male','male','female','female','female','female']

# Create our feature variables
data['Height'] = [6,5.92,5.58,5.92,5,5.5,5.42,5.75]
data['Weight'] = [180,190,170,165,100,150,130,150]
data['Foot_Size'] = [12,11,12,10,6,8,7,9]

# Create an empty dataframe
person = pd.DataFrame()

# Create some feature values for this single row
person['Height'] = [6]
person['Weight'] = [130]
person['Foot_Size'] = [8]

# Number of males
n_male = data['Gender'][data['Gender'] == 'male'].count()

# Number of males
n_female = data['Gender'][data['Gender'] == 'female'].count()

# Total rows
total_ppl = data['Gender'].count()

# Number of males divided by the total rows
P_male = n_male/total_ppl

# Number of females divided by the total rows
P_female = n_female/total_ppl

# Group the data by gender and calculate the means of each feature
data_means = data.groupby('Gender').mean()

# Group the data by gender and calculate the variance of each feature
data_variance = data.groupby('Gender').var()

# Means for male
male_height_mean = data_means['Height'][data_variance.index == 'male'].values[0]
male_weight_mean = data_means['Weight'][data_variance.index == 'male'].values[0]
male_footsize_mean = data_means['Foot_Size'][data_variance.index == 'male'].values[0]

# Variance for male
male_height_variance = data_variance['Height'][data_variance.index == 'male'].values[0]
male_weight_variance = data_variance['Weight'][data_variance.index == 'male'].values[0]
male_footsize_variance = data_variance['Foot_Size'][data_variance.index == 'male'].values[0]

# Means for female
female_height_mean = data_means['Height'][data_variance.index == 'female'].values[0]
female_weight_mean = data_means['Weight'][data_variance.index == 'female'].values[0]
female_footsize_mean = data_means['Foot_Size'][data_variance.index == 'female'].values[0]

# Variance for female
female_height_variance = data_variance['Height'][data_variance.index == 'female'].values[0]
female_weight_variance = data_variance['Weight'][data_variance.index == 'female'].values[0]
female_footsize_variance = data_variance['Foot_Size'][data_variance.index == 'female'].values[0]

# Create a function that calculates p(x | y):
def p_x_given_y(x, mean_y, variance_y):

    # Input the arguments into a probability density function
    p = 1/(np.sqrt(2*np.pi*variance_y)) * np.exp((-(x-mean_y)**2)/(2*variance_y))

    # return p
    return p
# Numerator of the posterior if the unclassified observation is a male
P_male * \
p_x_given_y(person['Height'][0], male_height_mean, male_height_variance) * \
p_x_given_y(person['Weight'][0], male_weight_mean, male_weight_variance) * \
p_x_given_y(person['Foot_Size'][0], male_footsize_mean, male_footsize_variance)

# Numerator of the posterior if the unclassified observation is a female
P_female * \
p_x_given_y(person['Height'][0], female_height_mean, female_height_variance) * \
p_x_given_y(person['Weight'][0], female_weight_mean, female_weight_variance) * \
p_x_given_y(person['Foot_Size'][0], female_footsize_mean, female_footsize_variance)
