Matlab & linear regression with non linear method
Budget: $30 – $250 USD
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(2(pi)(x)) + e(varepsilon) , where e (varepsilon) contains samples from a Gaussian distribution
N(0, sigma =0.3).
2. Select a set of permissible values for the regularization parameter ??.
3. For each value of lambda , use the method of “linear regression with non-linear models”
to fit Gaussian basis functions to each of the datasets. Use lambda = 0.1.
4. Produce the plot as shown below, where (refer to attached image)
5. The test error curve is the average error for a test data set of 1000 points.
a. L=100
b. N=25
c. X contains samples from a uniform distribution U(0,1).
d. t = sin(2(pi)(x)) + e(varepsilon) , where e (varepsilon) contains samples from a Gaussian distribution
N(0, sigma =0.3).
2. Select a set of permissible values for the regularization parameter ??.
3. For each value of lambda , use the method of “linear regression with non-linear models”
to fit Gaussian basis functions to each of the datasets. Use lambda = 0.1.
4. Produce the plot as shown below, where (refer to attached image)
5. The test error curve is the average error for a test data set of 1000 points.