bdm.mtsne: Multi-core t-SNE (mtSNE)
In jgarriga65/bigMap: Big Data Mapping

bdm.mtsne

R Documentation

Multi-core t-SNE (mtSNE)

Description

Starts the multi-core t-SNE (mtSNE) algorithm.

Usage

bdm.mtsne(
  data,
  is.distance = F,
  is.sparse = F,
  ppx = 100,
  theta = 0.5,
  iters = 250,
  mpi.cl = NULL,
  threads = 4,
  infoRate = 25
)

Arguments

`data`	A `data.frame` or `matrix` with raw input-data. The dataset must not have duplicated rows.
`is.distance`	Default value is `is.distance = FALSE`. TRUE indicates that raw data is a distance matrix.
`is.sparse`	Default value is `is.sparse = FALSE`. TRUE indicates that the raw data is a sparse matrix.
`ppx`	The value of perplexity to compute similarities.
`theta`	Accuracy/speed trade-off factor, a value between 0.33 and 0.8. Default value is `theta = 0.5`. If `theta < 0.33` the algorithm uses the exact computation of the gradient. The closer it is this value to 1 the faster the computation and the coarser the approximation of the gradient.
`iters`	Number of iters/epoch. Default value is `iters = 250`.
`mpi.cl`	An MPI (inter-node parallelization) cluster as generated by `bdm.mpi.start()`. By default `mpi.cl = NULL`, i.e. a 'SOCK' (intra-node parallelization) cluster is generated.
`threads`	Number of parallel threads (according to data size and hardware resources, i.e. number of cores and available memory). Default value is `threads = 4`.
`infoRate`	Number of epochs to show output information. Default value is `infoRate = 25`.

Value

A bdm data mapping instance.

Examples


# --- load example dataset
bdm.example()
## Not run: 
# --- perform mtSNE
m <- bdm.mtsne(ex$data, ex$map, ppx = 250, iters = 250, threads = 4)
# --- plot the Cost function
bdm.cost(m)
# --- plot mtSNE output (use bdm.ptsne.plot() function)
bdm.ptsne.plot(m)

## End(Not run)

jgarriga65/bigMap documentation built on June 10, 2024, 7:05 a.m.