Write a computer program to perform a local linear discriminant analysis. at each query point x0, the training data receive weights kλ(x0, xy) from a weighting kernel, and the ingredients for the linear discriminant function are computed by weighted averages. try out your program on the zip code data, and show the training and test errors for a series of five pre-chosen values of λ.

In a heat transfer course, we can derive the equation for the temperature distribution in a flat rectangular plate. in this example, we will look at a plate at steadystate with three sides being held at t1, and one side held at t2. the temperature for any location on the plate, t(x,y), can be calculated by where create a function (prob3_5) that will take inputs of vectors x and y in feet, scalar n, scalars l and w in feet and scalars t1 and t2 in degrees fahrenheit. it will output a matrix t which is the temperature of each x and y locations. t will have the number of columns equal to the number of elements in x and rows equal to the number of elements in y. though this can be done without loops (perhaps more efficiently), your program must use a nested loop.