public void TestConvergence()
{
double[,] features = { { 1d, 0d }, { 1d, 1d }, { 1d, 2d }, { 1d, 3d }, { 1d, 4d } };
double[] initialTheta = { 0d, 0d };
double[] expectedTheta = { 1d, -1d };
double[] labels = { 1d, 0d, -1d, -2d, -3d };
double learningRate = .1d;
int numberOfIterations = 200;
double[] learnedTheta = LinearRegression.Learn(features, labels, initialTheta, learningRate, numberOfIterations);
for (int index = 0; index < learnedTheta.Length; index++)
{
Assert.AreEqual(expectedTheta[index], learnedTheta[index], 0.01d);
}
}
private void TrainPostFeatures(string postId, double lambda)
{
var postRepository = _unitOfWork.GetRepository <Post>();
var post = postRepository.Get(p => p.Id == postId)
.SingleOrDefault();
var rateRepository = _unitOfWork.GetRepository <Rate>();
var postRates = rateRepository.Get()
.Where(r => r.PostId == postId)
.Select(r => new { r.Value, r.User.Weights });
var values = postRates.Select(r => (double)r.Value)
.ToArray();
var usersWeights = postRates.Select(r => r.Weights)
.ToArray();
post.Features = LinearRegression.Learn(usersWeights, values, post.Features, lambda);
_unitOfWork.SaveChanges();
}
private void TrainUserWeights(string userId, double lambda)
{
var userRepostiory = _unitOfWork.GetRepository <User>();
var user = userRepostiory.Get(u => u.Id == userId)
.SingleOrDefault();
var rateRepository = _unitOfWork.GetRepository <Rate>();
var userRates = rateRepository.Get()
.Where(r => r.UserId == userId)
.Select(r => new { r.Value, r.Post.Features })
.ToList();
var values = userRates.Select(r => (double)r.Value)
.ToArray();
var postsFeatures = userRates.Select(r => r.Features)
.ToArray();
user.Weights = LinearRegression.Learn(postsFeatures, values, user.Weights, lambda);
_unitOfWork.SaveChanges();
}
