private OrgRank SingleRank(Matrix <double> matRanks, int rank_length, double[] awardNos, double[,] awards)
{
if (rank_length == 0)
{
//fill the top ranks of the length of awards as the last award
double next_award = awardNos[0];
for (int i = 0; i < awardNos.Length; i++)
{
if (i < matRanks.RowCount)
{
matRanks[i, 1] = next_award;
}
}
}
OrgRank orgRank = new OrgRank(matRanks.ToArray(), awards);
orgRank.AddSubRank(new Rank(matRanks.ToArray(), awards));
return(orgRank);
}
private OrgRank SplitRank(Matrix <double> matRanks, int rank_length, double[] awardNos, double[,] awards)
{
Vector <double> vecAwardNos = CreateVector.DenseOfArray(awardNos);
//fill the rest of ranks as the last award
double next_award = vecAwardNos[rank_length];
for (int i = rank_length; i < matRanks.RowCount; i++)
{
if (i < matRanks.RowCount)
{
matRanks[i, 1] = next_award;
}
}
OrgRank orgRank = new OrgRank(matRanks.ToArray(), awards);
// split ranks into two groups
Matrix <double> matRanks1 = matRanks.SubMatrix(0, rank_length, 0, matRanks.ColumnCount);
// split award_numbers into two sub_awards
double[,] awards1 = To2DAwards(vecAwardNos.SubVector(0, rank_length).ToArray());
Rank rank1 = new Rank(matRanks1.ToArray(), awards1);
orgRank.AddSubRank(rank1);
// split ranks into two groups
int length = matRanks.RowCount - rank_length;
Matrix <double> matRanks2 = matRanks.SubMatrix(rank_length, length, 0, matRanks.ColumnCount);
// split award_numbers into two sub_awards
length = awardNos.Length - rank_length;
double[,] awards2 = To2DAwards(vecAwardNos.SubVector(rank_length, length).ToArray());
Rank rank2 = new Rank(matRanks2.ToArray(), awards2);
orgRank.AddSubRank(rank2);
return(orgRank);
}
/// <summary>
/// Rank the votes
/// </summary>
/// <param name="votes">
/// each row presents a project grains the number of votes per award
/// int[P,A]
/// P: Projects, A: the awards
/// </param>
/// <param name="awards">
/// each column presents a award's weight and number
/// int[A,N]
/// A: awards, N: the number per award
/// </param>
public OrgRank InitRank(int[] projects, double[,] votes, double[,] awards, bool isRevoted = false)
{
// matV(P,A) = actual votes to matrix PxW
Matrix <double> matV = CreateMatrix.DenseOfArray(votes);
// matA(W,N) = the awards's name and number
Matrix <double> matA = CreateMatrix.DenseOfArray(awards);
double[] scores = null;
if (!isRevoted)
{
// vecA(W,1) = vector Ax1
// pick the award's weight into a single array
double[] weights = matA.Column(0).ToArray();
weights = weights.Select(a => (double)Math.Pow(10, weights.Length - a)).ToArray();
Vector <double> vecA = CreateVector.DenseOfArray(weights);
// S(P,1)=matV(P,A) X vecW(A,1)
Vector <double> vecS = matV.Multiply(vecA.Conjugate());
scores = vecS.ToArray();
}
else
{
scores = matV.Column(0).ToArray();
}
// Sort and Rank
// Sort by the scores
Array.Sort(scores, projects, Comparer <double> .Create((a, b) =>
{
if (a > b)
{
return(-1);
}
else if (a < b)
{
return(1);
}
return(0);
}));
// [1,2]
// [2,2] => [1,1,2,2,3,3]
// [3,2]
double[] awardNos = To1DAwards(awards);
// double[] awardItems = matA.Column(0).ToArray();
// column(0): projectId
// column(1): actual rank
// column(2): expected rank
// column(3): score
Matrix <double> matRanks = CreateMatrix.DenseOfColumnArrays(new double[][]
{
projects.Select(p => (double)p).ToArray(),
new double[projects.Length],
new double[projects.Length],
scores
});
//int rank_length = 0;
int current = 0;
while (true)
{
if (current >= projects.Length)
{
break;
}
if (current == 0)
{
matRanks[current, 1] = awardNos[current];
matRanks[current, 2] = awardNos[current];
}
else if ((int)scores[current] == (int)scores[current - 1])
{
matRanks[current, 1] = matRanks[current - 1, 1];
if (current < awardNos.Length)
{
matRanks[current, 2] = awardNos[current];
}
}
else if ((int)scores[current] < (int)scores[current - 1])
{
//if (current >= awardNos.Length)
// break;
if (current < awardNos.Length)
{
matRanks[current, 1] = awardNos[current];
}
if (current < awardNos.Length)
{
matRanks[current, 2] = awardNos[current];
}
}
else // (int) scores[current_score] > (int) scores[current_score - 1]
{
// won't be here
// do nothing
}
current++;
//rank_length++;
}
OrgRank orgRank = new OrgRank(matRanks.SubMatrix(0, matRanks.RowCount, 0, 2).ToArray(), awards);
Stack <Rank> stack_Ranks = new Stack <Rank>();
int rank_length = matRanks.RowCount;
for (int i = matRanks.RowCount - 1; i >= 0; i--)
{
if (i < matRanks.RowCount - 1)
{
// compare scores
int cur = (int)matRanks[i, 3];
int last = (int)matRanks[i + 1, 3];
if (matRanks[i + 1, 1] > 0 && cur != last)
// 0 rank means the project won't be involved into VoteVerify
{
int start_row = i + 1;
int length = rank_length - (i + 1);
// pick the [project,rank]
double[,] ranks = matRanks.SubMatrix(start_row, length, 0, 2).ToArray();
// pick the [award]
double[] sub_awardNos = matRanks.Column(2).SubVector(start_row, length).ToArray().Where(a => a > 0).ToArray();
double[,] sub_awards = this.To2DAwards(sub_awardNos);
stack_Ranks.Push(new Rank(ranks, sub_awards));
// drop the part being picked above
matRanks = matRanks.SubMatrix(0, start_row, 0, matRanks.ColumnCount);
rank_length = matRanks.RowCount;
}
}
if (i < matRanks.RowCount - 1 && (int)matRanks[i + 1, 1] <= 0)
{
// 0 rank means the project won't be involved into VoteVerify
rank_length--;
}
// pick the last part
if (i == 0)
{
int start_row = 0;
int length = rank_length;
// pick the [project,rank]
double[,] ranks = matRanks.SubMatrix(start_row, length, 0, 2).ToArray();
// pick the [award]
double[] sub_awardNos = matRanks.Column(2).SubVector(start_row, length).ToArray().Where(a => a > 0).ToArray();
double[,] sub_awards = this.To2DAwards(sub_awardNos);
stack_Ranks.Push(new Rank(ranks, sub_awards));
}
}
while (stack_Ranks.Count > 0)
{
orgRank.AddSubRank(stack_Ranks.Pop());
}
return(orgRank);
}
public OrgRank InitRankOld(int[] projects, double[,] votes, double[,] awards, bool isRevoted = false)
{
// matV(P,A) = actual votes to matrix PxW
Matrix <double> matV = CreateMatrix.DenseOfArray(votes);
// matA(W,N) = the awards's name and number
Matrix <double> matA = CreateMatrix.DenseOfArray(awards);
double[] scores = null;
if (!isRevoted)
{
// vecA(W,1) = vector Ax1
// pick the award's weight into a single array
double[] weights = matA.Column(0).ToArray();
weights = weights.Select(a => (double)Math.Pow(10, weights.Length - a)).ToArray();
Vector <double> vecA = CreateVector.DenseOfArray(weights);
// S(P,1)=matV(P,A) X vecW(A,1)
Vector <double> vecS = matV.Multiply(vecA.Conjugate());
scores = vecS.ToArray();
}
else
{
scores = matV.Column(0).ToArray();
}
// Sort and Rank
// Sort by the scores
Array.Sort(scores, projects, Comparer <double> .Create((a, b) =>
{
if (a > b)
{
return(-1);
}
else if (a < b)
{
return(1);
}
return(0);
}));
// [1,2]
// [2,2] => [1,1,2,2,3,3]
// [3,2]
double[] awardNos = To1DAwards(awards);
double[] awardItems = matA.Column(0).ToArray();
// Rank
// Grain the rank denpending on the scores one by one
// when the current score equals to the last, get the same level rank
// when the current score is less than the last, get the next level rank
int award_index = 0;
// int award_no_index = 0;
int rank_length = 0;
int count_number = 0;
int score_index = 0;
// double last_score = 0.0d;
Matrix <double> matRanks =
CreateMatrix.DenseOfColumnArrays(new double[][]
{
projects.Select(p => (double)p).ToArray(),
new double[projects.Length]
});
while (true)
{
if (score_index == scores.Length)
{
break;
}
var current_score = scores[score_index];
if (current_score == 0)
{
rank_length += count_number;
break;
}
if (score_index > 0 && current_score < scores[score_index - 1])
{
var current_award_no = matA[award_index, 1];
if (count_number >= current_award_no)
{
award_index++;
rank_length += count_number;
count_number = 0;
if (award_index == awardItems.Length)
{
break;
}
}
}
else if (score_index > 0 && current_score == scores[score_index - 1])
{
// do nothing
}
matRanks[score_index, 1] = awardItems[award_index];
score_index++;
count_number++;
}
OrgRank orgRank = new OrgRank();
if (rank_length == 0 || rank_length == awardNos.Length)
{
orgRank = SingleRank(matRanks, rank_length, awardNos, awards);
}
else if (rank_length > awardNos.Length)
{
int newRank_length = this.CalculateNearestLength(scores, rank_length, awardNos.Length);
if (newRank_length < awardNos.Length)
{
// split again
Vector <double> vecAwardNos = CreateVector.DenseOfArray(awardNos);
orgRank = new OrgRank(matRanks.ToArray(), awards);
// split ranks into two groups
Matrix <double> matRanks1 = matRanks.SubMatrix(0, newRank_length, 0, matRanks.ColumnCount);
// split award_numbers into two sub_awards
double[,] awards1 = To2DAwards(vecAwardNos.SubVector(0, newRank_length).ToArray());
Rank rank1 = new Rank(matRanks1.ToArray(), awards1);
orgRank.AddSubRank(rank1);
// here rank_length must great than newRank_length
// because it should always equals or less than rank_length
// it should go to the first SingleRank() if rank_length==newRank_length
if (rank_length > newRank_length)
{
// split ranks into two groups
int length = rank_length - newRank_length;
Matrix <double> matRanks2 = matRanks.SubMatrix(newRank_length, length, 0, matRanks.ColumnCount);
// split award_numbers into two sub_awards
length = awardNos.Length - newRank_length;
double[,] awards2 = To2DAwards(vecAwardNos.SubVector(newRank_length, length).ToArray());
Rank rank2 = new Rank(matRanks2.ToArray(), awards2);
orgRank.AddSubRank(rank2);
}
}
else // newRank_length >= awardNos.Length
{
// reset other's rank as 0
for (int i = newRank_length; i < matRanks.RowCount; i++)
{
matRanks[i, 1] = 0;
}
orgRank = SingleRank(matRanks, newRank_length, awardNos, awards);
}
}
else if (rank_length < awardNos.Length)
{
orgRank = SplitRank(matRanks, rank_length, awardNos, awards);
}
return(orgRank);
}
