affinityMatrix: Calculate the similarity matrix

In OSNMTF: Orthogonal Sparse Non-Negative Matrix Tri-Factorization

Description

To calculate the similarity matrix with the same method in package M2SMF, for asymmetric case

Usage

affinityMatrix(Diff, K = 20, sigma = 0.5)

Arguments

Diff	The distance matrix to culculate the similarity
K	The number of neighbours to culculate the similarity
sigma	A hyper-parameter to culculate the similarity

Value

The similarity matrix

Author(s)

Xiaoyao Yin

Examples

data1 <- matrix(0,100,100)
data2 <- matrix(0,80,100)
for (i in 1:20)
{
  data1[i,] <- rnorm(100,10,1)
}
for (i in 21:40)
{
  data1[i,] <- rnorm(100,20,1)
}
for (i in 41:60)
{
  data1[i,] <- rnorm(100,30,1)
}
for (i in 61:80)
{
  data1[i,] <- rnorm(100,40,1)
}
for (i in 81:100)
{
  data1[i,] <- rnorm(100,50,1)
}
for (i in 1:20)
{
  data2[i,] <- rnorm(100,5,1)
}
for (i in 21:40)
{
  data2[i,] <- rnorm(100,10,1)
}
for (i in 41:60)
{
  data2[i,] <- rnorm(100,15,1)
}
for (i in 61:80)
{
  data2[i,] <- rnorm(100,20,1)
}
new_data1 <- Standard_Normalization(data1)
new_data2 <- Standard_Normalization(data2)
Diff <- dist2eu(new_data1,new_data2)
simi_matr1 <- affinityMatrix(Diff, K = 20, sigma = 0.5)

OSNMTF documentation built on Dec. 1, 2019, 1:22 a.m.