bdm.ptsne: Parallelized t-SNE (ptSNE)

In jgarriga65/bigMap: Big Data Mapping

Parallelized t-SNE (ptSNE)

Description

Starts the parallelized t-SNE algorithm (pt-SNE). This is the first step of the mapping protocol.

Usage

bdm.ptsne(
  data,
  bdm,
  theta = 0.5,
  Y.init = NULL,
  mpi.cl = NULL,
  threads = 4,
  layers = 2,
  info = 0
)

Arguments

data	Input data (a matrix, a big.matrix or a .csv file name).
bdm	A bdm data mapping instance.
theta	Accuracy/speed trade-off factor, a value between 0.33 and 0.8. (Default value is theta = 0.0). If theta < 0.33 the algorithm uses the exact computation of the gradient. The closer is this value to 1 the faster is the computation but the coarser is the approximation of the gradient.
Y.init	A n *2 *layers matrix with initial mapping positions. (By default Y.init=NULL will use random initial positions).
mpi.cl	MPI (inter-node parallelization) cluster as generated by bdm.mpi.start(). (By default mpi.cl = NULL 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).
layers	Number of layers (minimum 2, maximum the number of threads). Default value is layers = 2.
info	Output information: 1 yields inter-round results, 0 disables intermediate results. Default value is info = 0.

Value

A bdm data mapping instance.

Examples

# --- load example dataset
bdm.example()
# --- perform ptSNE
## Not run: 
# --- run ptSNE
m <- bdm.ptsne(ex$data, ex$map, threads = 10, layers = 2)
# --- plot the Cost function
bdm.cost(m)
# --- plot ptSNE output
bdm.ptsne.plot(m, class.lbls = ex$labels)

## End(Not run)

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