This design is not standardized and currently unstable.
We implement the least-squares linear regression algorithm. Linear regression is a popular machine learning algorithm that models the relationship between two variables as linear. In a simple linear regression, the linear regression line has an equation of the form Y = a + b ∗ X where X is an explanatory variable and Y is a dependent variable.
While more efficient implementations of least-squares linear regression can easily be achieved, we show a simple implementation for ease of readability. For convenience, we define a Point circuit to abstract the notion of a two-dimensional point, as shown in Figure 15. Figure 16 shows the code used to instantiate the least-squares linear regression circuit.