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Hands-On Quantum Machine Learning with Python

Calculating the Parameters

Learn how we can calculate the parameters of norm and survival and run the QBN.

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Calculating the parameters of the norm

Javascript (babel-node)
def calculate_norm_params(passengers):
    # the different populations in our data
    pop_children = passengers[passengers.IsChild.eq(1)]
    pop_adults = passengers[passengers.IsChild.eq(0)]
    # combinations of being a child and gender
    pop_am = pop_adults[pop_adults.Sex.eq('male')]
    pop_af = pop_adults[pop_adults.Sex.eq('female')]
    pop_cm = pop_children[pop_children.Sex.eq('male')]
    pop_cf = pop_children[pop_children.Sex.eq('female')]
    norm_params = {
        'p_norm_am': pop_am.Norm.sum() /  len(pop_am),
        'p_norm_af': pop_af.Norm.sum() /  len(pop_af),
        'p_norm_cm': pop_cm.Norm.sum() /  len(pop_cm),
        'p_norm_cf': pop_cf.Norm.sum() /  len(pop_cf),
    }
    return norm_params

The function calculate_norm_params takes the Pandas dataframe of the passengers and returns the norm_params dictionary. From lines 2 to 10, we’ll specify different populations (groups) of passengers. Then, from lines 12 to 17, we’ll calculate the probabilities of a passenger being favored by a norm (Norm), given that the passenger belongs to a group.

In lines 3 and 4, we’ll separate the children from the adults in the data by evaluating whether the value of the column IsChild is 1 (children) or 0 (adults). In lines 7 to 10, we’ll further split these two groups into four based on whether the sex (Sex) is female or male.

Let’s pay some attention to how we calculate the probabilities of a passenger being favored by a norm in lines 13 to 16. We’ll sum the Norm of all passengers of a group and divide it by the number of passengers in the group. The Norm is the hidden variable. Similar to the example of a missing value, we’ll fill this column with a number between 0 and 1 that represents the probability of the respective passenger to be favored by a norm.

For example, if we have ten passengers and five have a value of 00, and five have a value of 11, we’ll get a resulting probability of P(Norm)=(5⋅1+5⋅0)/10=0.5P(Norm)=(5\cdot 1+5\cdot 0)/10=0.5 ...