Find all neighbors within specified distance using searcher object

searches for all neighbors (i.e., points, rows, or observations) in
`Idx`

= rangesearch(`Mdl`

,`Y`

,`r`

)`Mdl.X`

within radius `r`

of each point (i.e.,
row or observation) in the query data `Y`

using an exhaustive
search or a *K*d-tree. `rangesearch`

returns
`Idx`

, which is a column vector of the indices of
`Mdl.X`

within `r`

units.

returns the indices of the observation in `Idx`

= rangesearch(`Mdl`

,`Y`

,`r`

,`Name,Value`

)`Mdl.X`

within radius
`r`

of each observation in `Y`

with additional
options specified by one or more `Name,Value`

pair arguments. For
example, you can specify to use a different distance metric than is stored in
`Mdl.Distance`

or a different distance metric parameter than is
stored in `Mdl.DistParameter`

.

`[`

additionally returns the matrix `Idx`

,`D`

]
= rangesearch(___)`D`

using any of the input
arguments in the previous syntaxes. `D`

contains the distances
between the observations in `Mdl.X`

within radius
`r`

of each observation in `Y`

. By default,
the function arranges the columns of `D`

in ascending order by
closeness, with respect to the distance metric.

`knnsearch`

finds the *k*
(positive integer) points in `Mdl.X`

that are
*k*-nearest for each `Y`

point. In contrast,
`rangesearch`

finds all the points in `Mdl.X`

that are within distance `r`

(positive scalar) of each
`Y`

point.

`rangesearch`

is an object function that requires an `ExhaustiveSearcher`

or a `KDTreeSearcher`

model object, query data, and a distance. Under equivalent
conditions, `rangesearch`

returns the same results as `rangesearch`

when you specify the name-value pair argument
`'NSMethod','exhaustive'`

or
`'NSMethod','kdtree'`

, respectively.

`ExhaustiveSearcher`

| `KDTreeSearcher`

| `createns`

| `knnsearch`

| `rangesearch`