R/Tests.R

In DawnTrisha/hclustHDCP: High Dimensional Change-point detection based on Hierarchical clustering

# test1 = function(a)
# {
#   if(a == "ori")
#   {
#     x = 1
#   }else{
#     x = 0
#   }
#
#   return(x)
# }
#
# test1("wgt")
#
# d = 5
# X1 = matrix(rnorm((10 * 5), mean = 0, sd = 1), nrow = 10, ncol = d)
# X2 = matrix(rnorm((10 * 5), mean = 4, sd = 1), nrow = 10, ncol = d)
# X3 = matrix(rnorm((10 * 5), mean = 8, sd = 1), nrow = 10, ncol = d)
# X = rbind(X1, X2, X3)
#
# detect_multiple_cp(X = X, numcp = 2, dist.method = "average")
# #
# # source("SingleChangePoint.R")
# #
# # source("MultipleChangePoint_NumKnown.R")
#
# # Multiple change-point
# d = 5
# X1 = matrix(rnorm((5 * 5), mean = 0, sd = 1), nrow = 5, ncol = d)
# X2 = matrix(rnorm((5 * 5), mean = 4, sd = 1), nrow = 5, ncol = d)
# X3 = matrix(rnorm((5 * 5), mean = 8, sd = 1), nrow = 5, ncol = d)
# X = rbind(X1, X2, X3)
#
# D = as.matrix(dist(X, method = "euclidean"))
#
# detect_estimated_cp(D = D, p = d)
#
# # Single change-point
# d = 5
# X1 = matrix(rnorm((5 * 5), mean = 0, sd = 1), nrow = 5, ncol = d)
# X2 = matrix(rnorm((5 * 5), mean = 4, sd = 1), nrow = 5, ncol = d)
# X = rbind(X1, X2)
#
# D = as.matrix(dist(X, method = "euclidean"))
#
#
# # Check if MADD or gen MADD works
# X1 = matrix(rnorm((10*15), mean = 0, sd = 2), nrow = 10, ncol = 15)
# X2 = matrix(stats::rt(10*15, df = 4, ncp = 0),nrow = 10, ncol = 15)
# X = rbind(X1, X2)
#
# D = distmat_HDLSS(X = X, option = "genMADD")
# detect_single_cp(D = D)
#
# detect_estimated_cp(D = D, p = 15)
#
# # Example in documentation for single chnage-point
# X1 = matrix(rnorm((15 * 50), mean = 0, sd = 1), nrow = 15, ncol = 50)
# X2 = matrix(rnorm((15 * 50), mean = 4, sd = 1), nrow = 15, ncol = 50)
# X = rbind(X1, X2)
#
# detect_single_cp(X = X)
# detect_single_cp(X = X, dist.method = "single")
#
# # Example in documentation for single chnage-point
# X1 = matrix(rnorm((15 * 50), mean = 0, sd = 1), nrow = 15, ncol = 50)
# X2 = matrix(rnorm((15 * 50), mean = 1, sd = 1), nrow = 15, ncol = 50)
# X3 = matrix(rnorm((15 * 50), mean = 2, sd = 1), nrow = 15, ncol = 50)
# X = rbind(X1, X2, X3)
#
# detect_multiple_cp(X = X, numcp = 2)
# detect_multiple_cp(X = X, numcp = 4, dist.method = "complete")
#
#
# X1 = matrix(rnorm((15 * 50), mean = 0, sd = 1), nrow = 15, ncol = 50)
# X2 = matrix(rnorm((15 * 50), mean = 1, sd = 1), nrow = 15, ncol = 50)
# X3 = matrix(rnorm((15 * 50), mean = 2, sd = 1), nrow = 15, ncol = 50)
# X = rbind(X1, X2, X3)
#
# detect_multiple_cp(X = X, numcp = 2)
# detect_multiple_cp(X = X, numcp = 4, dist.method = "complete")
#
# X1 = matrix(rnorm((15 * 200), mean = 0, sd = 1), nrow = 15, ncol = 200)
# X2 = matrix(rnorm((15 * 200), mean = 5, sd = 1), nrow = 15, ncol = 200)
# X3 = matrix(rnorm((15 * 200), mean = 10, sd = 1), nrow = 15, ncol = 200)
# X = rbind(X1, X2, X3)
#
# detect_estimated_cp(X = X)
# detect_estimated_cp(X = X, dist.method = "complete")
#
# ##########
# # Example for distance matrix
# X = matrix(stats::rt((10*5), ncp = 0, df = 4), nrow = 10, ncol = 5)
#
# X1 = matrix(rnorm((15 * 50), mean = 0, sd = 1), nrow = 15, ncol = 50)
# X2 = matrix(rnorm((15 * 50), mean = 1, sd = 1), nrow = 15, ncol = 50)
# X3 = matrix(rnorm((15 * 50), mean = 2, sd = 1), nrow = 15, ncol = 50)
# X = rbind(X1, X2, X3)
#
# detect_estimated_cp(X = X)
#
# X1 = matrix(rnorm((15 * 150), mean = 0, sd = 1), nrow = 15, ncol = 150)
# X2 = matrix(rnorm((15 * 150), mean = 5, sd = 1), nrow = 15, ncol = 150)
# X3 = matrix(rnorm((15 * 150), mean = 10, sd = 1), nrow = 15, ncol = 150)
# X = rbind(X1, X2, X3)
#
# detect_estimated_cp(X = X, dist.method = "single", lambda = 0.0005)
# detect_estimated_cp(X = X, dist.method = "average", lambda = 0.015)
# detect_estimated_cp(X = X, dist.method = "complete",  lambda = 0.015)
#
#
# dist_mat = as.matrix(stats::dist(X))
#
# detect_estimated_cp(D = dist_mat, p = 40)
#
# # Example 1
# set.seed(1)
# # Generate data matrix
# X1 = matrix(rnorm((15 * 200), mean = 0, sd = 1), nrow = 15, ncol = 200)
# X2 = matrix(rnorm((15 * 200), mean = 5, sd = 1), nrow = 15, ncol = 200)
# X3 = matrix(rnorm((15 * 200), mean = 10, sd = 1), nrow = 15, ncol = 200)
# X = rbind(X1, X2, X3)
#
# # Detect change-points with default average linkage
# detect_estimated_cp(X = X)