Description

sWriteData is supposed to write out the best-matching hexagons and/or cluster bases in terms of data.

Usage

sWriteData(sMap, data, sBase = NULL, filename = NULL, keep.data = FALSE)

Arguments

sMap

an object of class "sMap" or a codebook matrix

data

a data frame or matrix of input data

sBase

an object of class "sBase"

filename

a character string naming a filename

keep.data

logical to indicate whether or not to also write out the input data. By default, it sets to false for not keeping it. It is highly expensive to keep the large data sets

Value

a data frame with following components:

• ID: ID for data. It inherits the rownames of data (if exists). Otherwise, it is sequential integer values starting with 1 and ending with dlen, the total number of rows of the input data
• Hexagon_index: the index for best-matching hexagons
• Cluster_base: optional, it is only appended when sBase is given. It stores the cluster memberships/bases
• data: optional, it is only appended when keep.data is true

Note

If "filename" is not NULL, a tab-delimited text file will be also written out. If "sBase" is not NULL and comes from the "sMap" partition, then cluster bases are also appended. if "keep.data" is true, the data will be part of output.

Examples

# 1) generate an iid normal random matrix of 100x10 
data <- matrix( rnorm(100*10,mean=0,sd=1), nrow=100, ncol=10)

# 2) get trained using by default setup 
sMap <- sPipeline(data=data)

Start at 2018-01-18 16:56:13

First, define topology of a map grid (2018-01-18 16:56:13)...
Second, initialise the codebook matrix (61 X 10) using 'linear' initialisation, given a topology and input data (2018-01-18 16:56:13)...
Third, get training at the rough stage (2018-01-18 16:56:13)...
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Next, identify the best-matching hexagon/rectangle for the input data (2018-01-18 16:56:13)...
Finally, append the response data (hits and mqe) into the sMap object (2018-01-18 16:56:13)...

Below are the summaries of the training results:
   dimension of input data: 100x10
   xy-dimension of map grid: xdim=9, ydim=9, r=5
   grid lattice: hexa
   grid shape: suprahex
   dimension of grid coord: 61x2
   initialisation method: linear
   dimension of codebook matrix: 61x10
   mean quantization error: 5.04879912872829

Below are the details of trainology:
   training algorithm: batch
   alpha type: invert
   training neighborhood kernel: gaussian
   trainlength (x input data length): 7 at rough stage; 25 at finetune stage
   radius (at rough stage): from 3 to 1
   radius (at finetune stage): from 1 to 1

End at 2018-01-18 16:56:13
Runtime in total is: 0 secs


# 3) write data's BMH hitting the trained map
output <- sWriteData(sMap=sMap, data=data, filename="sData_output.txt")

# 4) partition the grid map into cluster bases
sBase <- sDmatCluster(sMap=sMap, which_neigh=1,
distMeasure="median", clusterLinkage="average")

# 5) write data's BMH and cluster bases
output <- sWriteData(sMap=sMap, data=data, sBase=sBase,
filename="sData_base_output.txt")

Source code

Source man

See also