logic behind weighted KNN

Question

I am reading about KNN

So I made another example to make things clearer

In this example (Image attached)

You can see there are in total 5 Greed Circles and 20 Blue Squares

by standard KNN (k=3) , X should be a Blue Square

This is obvious 2 Blue Squares vs 1 Green Circle.

But in weighted KNN things are difference

In this case we have to calculate the weight (Likelihood) for each instance

Each Green Circle likelihood is $\frac{1}{5} $ , we have 5 Green Circles

While for Blue Squares it is $\frac{1}{20} $ , we have 20 Blue Squares

Therefore the weights around X will be $\frac{1}{5} $ Green Circle, and $\frac{2}{20} $ Blue Squares.

which means $\frac{1}{5} > \frac{2}{20} $

Then X is Green Circle

But if try to think of it logically then there are more Blue Squares than Green Circles which means X more likely to be Blue Square than Green Circle.

My question is :

Am I doing anything wrong here? Can someone explain why the equation is showing Green Circle while logic says Blue Square?