n = 300; noise = 0.05;
seed = 1782;
theta <- seq(0, pi, length.out = n / 2)
x1 <- cos(theta) + rnorm(n / 2, sd = noise)
y1 <- sin(theta) + rnorm(n / 2, sd = noise)
x2 <- cos(theta + pi) + rnorm(n / 2, sd = noise)
y2 <- sin(theta + pi) + rnorm(n / 2, sd = noise)
X <- rbind(cbind(x1, y1), cbind(x2, y2))
y <- c(rep(0, n / 2), rep(1, n / 2))
nfeatures <- ncol(X)
i <- 1
clusters <- unique(y)
objcl <- which(y == clusters[i])
nuobjcl <- length(objcl)
noiseLabel <- -1
distX <- as.matrix(dist(X))^2
distXy <- distX[y != noiseLabel, y != noiseLabel]
mr <- matrix_mutual_reachability_distance(nuobjcl, distXy[objcl, objcl], nfeatures)
d_ucore_cl <- rep(0, nrow(X))
d_ucore_cl[objcl] <- mr$d_ucore
G <- list(no_vertices = nuobjcl, MST_edges = matrix(0, nrow = nuobjcl - 1, ncol = 3),
MST_degrees = rep(0, nuobjcl), MST_parent = rep(0, nuobjcl))
g_start <- 1
mst_results <- MST_Edges(G, g_start, mr$G_edges_weights)
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