# Preface:

Using Bayesian formula, guess the gender of the other party according to the Chinese name. Without further ado, let’s begin happily

# The development tools

Python version: 3.6.4

Related modules:

Pyqt5 module;

And some of the modules that come with Python.

# Environment set up

Install Python and add it to the environment variable, and the PIP will install the appropriate module.

# Introduction of the principle

Let’s start with a brief introduction to the Bayesian formula, and then move on to the code implementation. As we all know, the probability of event A occurring under the condition that event B has already occurred is:

If A and B are two independent events, then:

Obviously, we can use the above formula to determine whether two events are independent. Let’s introduce the total probability formula (superscript C stands for complement) :

The above formula is easy to understand if you draw a Venn diagram (source network) :

Based on the above conclusions, we can easily derive the Bayesian formula:

The practical application of our name to guess gender is to ask:

Obviously, we have:

Here we know how often each character appears in male and female names:

We can assume that they are independent, for example:

``````def genderprob(name, probs, type_='male'):
assert type_ in ['male', 'female']
if type_ == 'male':
p = self.male_total / self.total
for c in name:
p *= probs.get(c, (0, 0))[0]
else:
p = self.female_total / self.total
for c in name:
p *= probs.get(c, (0, 0))[1]
return p``````

Take Liu Yifei as an example:

``P (female) = the number of occurrences of female name/total occurrences P = liu (liu | female) in the women's name the number of occurrences of total number/female name``

The denominator cancels out when we do division, so we don’t have to calculate it, which is:

``````male_prob = genderprob(name, self.name_probs, 'male')
female_prob = genderprob(name, self.name_probs, 'female')
result = {'male': male_prob / (male_prob + female_prob), 'female': female_prob / (male_prob + female_prob)}``````

Then we use PyQT5 to create a simple visual interface for this small model of name predictive gender:

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