# R/dtm.R In TDA: Statistical Tools for Topological Data Analysis

#### Documented in dtm

```dtm <-
function(X, Grid, m0, r = 2, weight = 1) {

if (!is.numeric(X) && !is.data.frame(X)) {
stop("X should be a matrix of coordinates")
}
if (!is.numeric(Grid) && !is.data.frame(Grid)) {
stop("Grid should be a matrix of coordinates")
}
if (NCOL(X) != NCOL(Grid)) {
stop("dimensions of X and Grid do not match")
}
if (!is.numeric(m0) || length(m0) != 1 || m0 < 0 || m0 > 1) {
stop("m0 should be a number between 0 and 1")
}
if (!is.numeric(r) || length(r) != 1 || r < 1) {
stop("r should be a number greater than or equal to 1")
}
if (!is.numeric(weight) ||
(length(weight) != 1 && length(weight) != NROW(X))) {
stop("weight should be either a number or a vector of length equals the number of sample")
}

# without weight
if (length(weight) == 1) {
X <- as.matrix(X)
weightBound <- m0 * NROW(X)
knnDistance <- FNN::knnx.dist(
data = X, query = as.matrix(Grid), k = ceiling(weightBound),
algorithm = c("kd_tree"))
return (Dtm(knnDistance = knnDistance, weightBound = weightBound, r = r))

# with weight
} else {
X0 <- as.matrix(X[weight != 0, , drop = FALSE])
weight0 <- weight[weight != 0]
weight0sort <- sort(weight0)
weightBound <- m0 * sum(weight0)
weightSumTemp <- 0
for (k0 in seq(along = weight0)) {
weightSumTemp <- weightSumTemp + weight0sort[k0]
if (weightSumTemp >= weightBound) {
break
}
}
knnDistanceIndex <- FNN::get.knnx(
data = X0, query = as.matrix(Grid), k = k0, algorithm = c("kd_tree"))
return (DtmWeight(
knnDistance = knnDistanceIndex[["nn.dist"]], weightBound = weightBound,
r = r, knnIndex = knnDistanceIndex[["nn.index"]], weight = weight0))
}
}
```

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TDA documentation built on March 30, 2021, 5:10 p.m.