In matlab please

1. Generate the data set D as follows: a. L=100 b. N=25 c. X contains samples from a uniform distribution U(0,1). d. t=sin(2X)+, where contains samples from a Gaussian distribution N(0,=0.3) 2. Select a set of permissible values for the regularization parameter . 3. For each value of , use the method of "linear regression with non-linear models" to fit Gaussian basis functions to each of the datasets. Use s=0.1. 4. Produce the plot as shown below, where f(x)=L1l=1Lf(l)(x)(bias)2=N1n=1N[f(x(n))h(x(n))]2variance=N1n=1NL1l=1L[f(l)(x(n))f(x(n))]2 5. The test error curve is the average error for a test data set of 1000 points