Least Mean Square (LMS)

An example of least mean square algorithm to determine a linear model's parameter.

In this code, a linear equation is used to generate sample data using a slope and bias. Later a Gaussian noise is added to the desired output. The noisy output and original input is used to determine the slope and bias of the linear equation using LMS algorithm. This implementation of LMS is based on batch update rule of gradient decent algorithm in which we use the sum of error instead of sample error. You can modify this code to create sample based update rule easily.