static public void TestMethod()
{
Console.WriteLine("The length of an adjacent matrix:");
int n = int.Parse(Console.ReadLine());
Console.WriteLine("The adjacent matrix:");
double[,] M = new double[n, n];
for (int i = 0; i < n;)
{
string X = Console.ReadLine();
string[] Y = X.Split(' ');
for (int j = 0; j < n;)
{
M[i, j] = double.Parse(Y[j]);
j++;
}
i++;
}
double[] rowsum = new double[n];
for (int i = 0; i < n; i++)
{
rowsum[i] = 0;
for (int j = 0; j < n; j++)
{
rowsum[i] = rowsum[i] + M[i, j];
}
if (rowsum[i] == 0)
{
M[i, i] = 1;
rowsum[i] = 1;
}
}
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
M[i, j] = 0.85 * M[i, j] / rowsum[i] + 0.15 / n;
}
}
Ket[] Base = new Ket[n];
Ket[,] Basetensor = new Ket[n, n];
Matrix[] Basematrix = new Matrix[n];
for (int j = 0; j < n; j++)
{
Matrix value = (Matrix)Matrix.Build.Dense(n, 1, Complex.Zero);
value[j, 0] = Complex.One;
Base[j] = new Ket(value);
Basematrix[j] = value;
}
Matrix[,] Basematrixtensor = new Matrix[n, n];
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
Matrix value = (Matrix)Basematrix[i].KroneckerProduct(Basematrix[j]);
Basetensor[i, j] = new Ket(value);
Basematrixtensor[i, j] = value;
}
}
Ket[] psi = new Ket[n];
for (int i = 0; i < n; i++)
{
Matrix value = (Matrix)Matrix.Build.Dense(n * n, 1, Complex.Zero);
Ket temvalue = new Ket(value);
for (int j = 0; j < n; j++)
{
temvalue.Value = (Matrix)(temvalue.Value + Math.Sqrt(M[i, j]) * Basetensor[i, j].Value);
}
psi[i] = new Ket(temvalue.Value);
}
Matrix value1 = (Matrix)Matrix.Build.Dense(n * n, 1, Complex.Zero);
Ket psi0 = new Ket(value1);
for (int i = 0; i < n; i++)
{
psi0.Value = (Matrix)(psi[i].Value + psi0.Value);
}
psi0.Value = (Matrix)(psi0.Value.Divide(Math.Sqrt(n)));
Matrix U = (Matrix)Matrix.Build.Dense(n * n, n * n, Complex.Zero);
for (int i = 0; i < n; i++)
{
var tempsi = (Matrix)psi[i].Value.ConjugateTranspose();
U = (Matrix)(psi[i].Value * tempsi + U);
}
Matrix S = (Matrix)Matrix.Build.Dense(n * n, n * n, Complex.Zero);
for (int i = 0; i < n; i++)
{
for (int j = 0; j < n; j++)
{
var tembasetensor = Basetensor[i, j].Value.ConjugateTranspose();
S = (Matrix)(Basetensor[j, i].Value * tembasetensor + S);
}
}
//Console.WriteLine(U);
Matrix I = (Matrix)Matrix.Build.DenseIdentity(n * n, n * n);
//Console.WriteLine(I);
//Console.WriteLine(S);
U = (Matrix)(S * (2 * U - I));
//Console.WriteLine(U);
U = (Matrix)U.Power(2);
Console.WriteLine("The step of quantum evolution");
int m = int.Parse(Console.ReadLine());
var Measurematrix = new Matrix[n];
Matrix sI = (Matrix)Matrix.Build.DenseIdentity(n, n);
for (int i = 0; i < n; i++)
{
Measurematrix[i] = (Matrix)(sI.KroneckerProduct(Basematrix[i]));
}
Matrix [] R = new Matrix[n];
double[] Rank = new double[n];
for (int i = 0; i < n; i++)
{
Matrix temR = (Matrix)Matrix.Build.Dense(1, n, Complex.Zero);
Matrix tem1 = (Matrix)Matrix.Build.Dense(1, n * n, Complex.Zero);
Matrix tem2 = (Matrix)Matrix.Build.Dense(n * n, n * n, Complex.Zero);
Matrix tem3 = (Matrix)Matrix.Build.Dense(n, 1, Complex.Zero);
tem1 = (Matrix)psi0.Value.ConjugateTranspose();
tem2 = (Matrix)U.ConjugateTranspose();
temR = (Matrix)(tem1 * tem2.Power(m) * Measurematrix[i]);
tem3 = (Matrix)temR.ConjugateTranspose();
R[i] = (Matrix)(temR * tem3);
Rank[i] = R[i].Real().At(0, 0);
}
Console.WriteLine("The PageRank (from high to low) is (the first column is pages and the second is the corresponding importantance)");
Rank2[] Rank1 = new Rank2[n];
for (int i = 0; i < n; i++)
{
Rank1[i] = new Rank2 {
Page = i, PageRank = Rank[i]
};
}
IEnumerable <Rank2> query = Rank1.OrderByDescending(Rank2 => Rank2.PageRank);
foreach (Rank2 rank in query)
{
Console.WriteLine("{0} - {1}", rank.Page, rank.PageRank);
}
Console.ReadKey();
}
static public void TestMethod()
{
var useDefault = false;
Console.WriteLine("Please enter the length of an adjacency matrix (Enter key to use Default config):\n");
if (!int.TryParse(Console.ReadLine(), out int n))
{
Console.WriteLine("Currently, we are using the default configuration.\n");
useDefault = true;
n = 7;
}
Console.WriteLine("The length of the adjacency matrix is {0}.\n", n);
var M = new double[n, n];
if (useDefault == false)
{
Console.WriteLine("Please enter the adjacency matrix:");
for (var i = 0; i < n;)
{
var X = Console.ReadLine();
var Y = X.Split(' ');
for (var j = 0; j < n;)
{
if (!double.TryParse(Y[j], out M[i, j]))
{
useDefault = true;
break;
}
j++;
}
if (useDefault)
{
break;
}
i++;
}
}
else
{
M = new double[7, 7]
{
{ 0, 0, 0, 0, 0, 0, 0 },
{ 1, 0, 0, 0, 0, 0, 0 },
{ 1, 0, 0, 0, 0, 0, 0 },
{ 0, 1, 0, 0, 0, 0, 0 },
{ 0, 1, 0, 0, 0, 0, 0 },
{ 0, 0, 1, 0, 0, 0, 0 },
{ 0, 0, 1, 0, 0, 0, 0 }
};
}
var outputMatrix = MathNet.Numerics.LinearAlgebra.Double.Matrix.Build.DenseOfArray(M);
Console.WriteLine("The adjacency matrix is:\n");
Console.WriteLine(outputMatrix);
var rowsum = new double[n];
for (var i = 0; i < n; i++)
{
rowsum[i] = 0;
for (var j = 0; j < n; j++)
{
rowsum[i] = rowsum[i] + M[i, j];
}
if (rowsum[i] == 0)
{
M[i, i] = 1;
rowsum[i] = 1;
}
}
for (var i = 0; i < n; i++)
{
for (var j = 0; j < n; j++)
{
M[i, j] = 0.85 * M[i, j] / rowsum[i] + 0.15 / n;
}
}
var Base = new Ket[n];
var Basetensor = new Ket[n, n];
var Basematrix = new Matrix[n];
for (var j = 0; j < n; j++)
{
var value = (Matrix)Matrix.Build.Dense(n, 1, Complex.Zero);
value[j, 0] = Complex.One;
Base[j] = new Ket(value);
Basematrix[j] = value;
}
var Basematrixtensor = new Matrix[n, n];
for (var i = 0; i < n; i++)
{
for (var j = 0; j < n; j++)
{
var value = (Matrix)Basematrix[i].KroneckerProduct(Basematrix[j]);
Basetensor[i, j] = new Ket(value);
Basematrixtensor[i, j] = value;
}
}
var psi = new Ket[n];
for (var i = 0; i < n; i++)
{
var value = (Matrix)Matrix.Build.Dense(n * n, 1, Complex.Zero);
var temvalue = new Ket(value);
for (var j = 0; j < n; j++)
{
temvalue.Value = (Matrix)(temvalue.Value + Math.Sqrt(M[i, j]) * Basetensor[i, j].Value);
}
psi[i] = new Ket(temvalue.Value);
}
var value1 = (Matrix)Matrix.Build.Dense(n * n, 1, Complex.Zero);
var psi0 = new Ket(value1);
for (var i = 0; i < n; i++)
{
psi0.Value = (Matrix)(psi[i].Value + psi0.Value);
}
psi0.Value = (Matrix)(psi0.Value.Divide(Math.Sqrt(n)));
var U = (Matrix)Matrix.Build.Dense(n * n, n * n, Complex.Zero);
for (var i = 0; i < n; i++)
{
var tempsi = (Matrix)psi[i].Value.ConjugateTranspose();
U = (Matrix)(psi[i].Value * tempsi + U);
}
var S = (Matrix)Matrix.Build.Dense(n * n, n * n, Complex.Zero);
for (var i = 0; i < n; i++)
{
for (var j = 0; j < n; j++)
{
var tembasetensor = Basetensor[i, j].Value.ConjugateTranspose();
S = (Matrix)(Basetensor[j, i].Value * tembasetensor + S);
}
}
//Console.WriteLine(U.ToComplexString());
var I = (Matrix)Matrix.Build.DenseIdentity(n * n, n * n);
//Console.WriteLine(I.ToComplexString());
//Console.WriteLine(S.ToComplexString());
U = (Matrix)(S * (2 * U - I));
//Console.WriteLine(U.ToComplexString());
U = (Matrix)U.Power(2);
int m = 0;
if (useDefault == false)
{
Console.WriteLine("Please enter the step of quantum evolution");
m = int.Parse(Console.ReadLine());
}
else
{
m = 5;
}
Console.WriteLine("The Step of quantum evolution is {0}.\n ", m);
var Measurematrix = new Matrix[n];
var sI = (Matrix)Matrix.Build.DenseIdentity(n, n);
for (var i = 0; i < n; i++)
{
Measurematrix[i] = (Matrix)(sI.KroneckerProduct(Basematrix[i]));
}
var R = new Matrix[n];
var Rank = new double[n];
for (var i = 0; i < n; i++)
{
var temR = (Matrix)Matrix.Build.Dense(1, n, Complex.Zero);
var tem1 = (Matrix)Matrix.Build.Dense(1, n * n, Complex.Zero);
var tem2 = (Matrix)Matrix.Build.Dense(n * n, n * n, Complex.Zero);
var tem3 = (Matrix)Matrix.Build.Dense(n, 1, Complex.Zero);
tem1 = (Matrix)psi0.Value.ConjugateTranspose();
tem2 = (Matrix)U.ConjugateTranspose();
temR = (Matrix)(tem1 * tem2.Power(m) * Measurematrix[i]);
tem3 = (Matrix)temR.ConjugateTranspose();
R[i] = (Matrix)(temR * tem3);
Rank[i] = R[i].Real().At(0, 0);
}
Console.WriteLine("The {0}-step quantum PageRank (from high to low) is ", m);
Console.WriteLine("(the first column is page and the second is the corresponding importantance)");
var Rank1 = new Rank2[n];
for (var i = 0; i < n; i++)
{
Rank1[i] = new Rank2 {
Page = i, PageRank = Rank[i]
};
}
var query = Rank1.OrderByDescending(Rank2 => Rank2.PageRank);
foreach (var rank in query)
{
Console.WriteLine("{0} - {1}", rank.Page + 1, rank.PageRank);
}
for (var i = 0; i < n; i++)
{
Rank[i] = 0;
for (var j = 0; j < 300; j++)
{
var temR = (Matrix)Matrix.Build.Dense(1, n, Complex.Zero);
var tem1 = (Matrix)Matrix.Build.Dense(1, n * n, Complex.Zero);
var tem2 = (Matrix)Matrix.Build.Dense(n * n, n * n, Complex.Zero);
var tem3 = (Matrix)Matrix.Build.Dense(n, 1, Complex.Zero);
tem1 = (Matrix)psi0.Value.ConjugateTranspose();
tem2 = (Matrix)U.ConjugateTranspose();
temR = (Matrix)(tem1 * tem2.Power(j) * Measurematrix[i]);
tem3 = (Matrix)temR.ConjugateTranspose();
R[i] = (Matrix)(temR * tem3);
Rank[i] = R[i].Real().At(0, 0) + Rank[i];
}
Rank[i] = Rank[i] / 300;
}
Console.WriteLine("\nThe averaged quantum PageRank (from high to low) is ");
var Rank3 = new Rank2[n];
for (var i = 0; i < n; i++)
{
Rank3[i] = new Rank2 {
Page = i, PageRank = Rank[i]
};
}
var query1 = Rank3.OrderByDescending(Rank2 => Rank2.PageRank);
foreach (var rank in query1)
{
Console.WriteLine("{0} - {1}", rank.Page + 1, rank.PageRank);
}
//Console.ReadKey();
}
