public async Task <IActionResult> OnGetAsync(int?id)
{
if (id == null)
{
return(NotFound());
}
DCN = await _context.DCNs.FirstOrDefaultAsync(m => m.Id == id);
if (DCN == null)
{
return(NotFound());
}
return(Page());
}
public async Task <IActionResult> OnPostAsync(int?id)
{
if (id == null)
{
return(NotFound());
}
DCN = await _context.DCNs.FindAsync(id);
if (DCN != null)
{
_context.DCNs.Remove(DCN);
await _context.SaveChangesAsync();
}
return(RedirectToPage("./Index"));
}
static void Main(string[] args)
{
var inputFile = "../data/insample_music.rpt";
//读入数据，形成sequence类型的seq
var seq = File.ReadAllLines(inputFile)
.Select(dp =>
{
var sp = dp.Split('\t');
return new SequenceEle()
{
uid = sp[0],
time = DateTime.Parse(sp[2]),
tid = sp[1],
};
}).Where(dp => dp != null)
.OrderBy(dp => dp.time)
.ToList();
var inputFile1 = "../data/insample_movie.rpt";
var seq1 = File.ReadAllLines(inputFile1)
.Select(dp =>
{
var sp = dp.Split('\t');
return new SequenceEle()
{
uid = sp[0],
time = DateTime.Parse(sp[2]),
tid = sp[1],
};
}
catch (Exception e)
{
return null;
}).Where(dp => dp != null)
.OrderBy(dp => dp.uid).ThenBy(dp => dp.time)
.ToList();
int index = 0;
var tidIndexDic = seq.Select(dp => dp.tid).Distinct().ToDictionary(dp => dp, dp => index++);
for (int i = 0; i < seq.Count; i++)
{
seq[i].timeIndex = i;
seq[i].tIndex = tidIndexDic[seq[i].tid];
}
var selfMatrix = DCN.ComputeSelf(seq);
int n = seq.Count();
int m = tidIndexDic.Count();
int K = 9;
int index1 = 0;
var tidIndexDic1 = seq1.Select(dp => dp.tid).Distinct().ToDictionary(dp => dp, dp => index1++);
for (int i = 0; i < seq1.Count; i++)
{
seq1[i].timeIndex = i;
seq1[i].tIndex = tidIndexDic1[seq1[i].tid];
}
var selfMatrix1 = DCN.ComputeSelf(seq1);
int n1 = seq1.Count();
int m1 = tidIndexDic1.Count();
int k1 = 9;
//此时是对此进行训练
for (int kkk = 1; kkk <= 6; kkk++)
{
Console.WriteLine(kkk + "\t" + "kkkkk");
Inference.InitialParameter(m1, n1 - 1, k1);
Inference.GibbsSampling_0(seq1.Take(n1 - 1).ToList(), selfMatrix1, 10);
Inference.InitialParameter1(m, n, K);
Inference.GibbsSampling_0(seq.Take(n - 1).ToList(), selfMatrix, 10);
var predicts = new List<Tuple<int, double>>();
for (int x = 0; x < m; x++)
{
double p = 0.0;
for (int k = 0; k < K; k++)
{
try
{
p += Math.Pow(1000, 10) * Inference.theta.P[k] * Inference.phi[seq[n - 2].tIndex].P[x] *
Math.Exp(
(-1) * Math.Pow((k + 1 - selfMatrix[n - 2][x]
// / ((double)matrix[i].Max()) * k
), 2)
);
}
catch (Exception e)
{
}
}
predicts.Add(Tuple.Create(x, p));
}
predicts = predicts.OrderByDescending(dp => dp.Item2).ToList();
double mean = 0.0;
for (int k = 0; k < 9; k++)
{
mean += (k + 1) * Inference.theta.P[k];
}
int ns = (int)Math.Round(mean);
res.Add(seq[0].uid.ToString());
res.Add(ns.ToString());
var MAP = 0.0;
for (int i = 0; i < predicts.Count(); i++)
{
if (tidIndexDic.Where(dp => dp.Value == predicts.ElementAt(i).Item1).First().Key == seq[n - 1].tid && n > 3)
{
MAP = i + 1;
}
}
res.Add(MAP.ToString());
}
}
