dNetReorder: Function to reorder the multiple graph colorings within a...

Description Usage Arguments Value Note See Also Examples

Description

dNetReorder is reorder the multiple graph colorings within a sheet-shape rectangle grid

Usage

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dNetReorder(
g,
data,
feature = c("node", "edge"),
node.normalise = c("none", "degree"),
xdim = NULL,
ydim = NULL,
amplifier = NULL,
metric = c("none", "pearson", "spearman", "kendall", "euclidean",
"manhattan", "cos",
"mi"),
init = c("linear", "uniform", "sample"),
algorithm = c("sequential", "batch"),
alphaType = c("invert", "linear", "power"),
neighKernel = c("gaussian", "bubble", "cutgaussian", "ep", "gamma")
)

Arguments

g

an object of class "igraph" or "graphNEL"

data

an input data matrix used to color-code vertices/nodes. One column corresponds to one graph node coloring. The input matrix must have row names, and these names should include all node names of input graph, i.e. V(g)$name, since there is a mapping operation. After mapping, the length of the patern vector should be the same as the number of nodes of input graph. The way of how to color-code is to map values in the pattern onto the whole colormap (see the next arguments: colormap, ncolors, zlim and colorbar)

feature

the type of the features used. It can be one of either 'edge' for the edge feature or 'node' for the node feature. See 'Note' for explanations.

node.normalise

the normalisation of the nodes. It can be one of either 'none' for no normalisation or 'degree' for a node being penalised by its degree.

xdim

an integer specifying x-dimension of the grid

ydim

an integer specifying y-dimension of the grid

amplifier

an integer specifying the amplifier (3 by default) of the number of component planes. The product of the component number and the amplifier constitutes the number of rectangles in the sheet grid

metric

distance metric used to define the similarity between component planes. It can be "none", which means directly using column-wise vectors of codebook/data matrix. Otherwise, first calculate the covariance matrix from the codebook/data matrix. The distance metric used for calculating the covariance matrix between component planes can be: "pearson" for pearson correlation, "spearman" for spearman rank correlation, "kendall" for kendall tau rank correlation, "euclidean" for euclidean distance, "manhattan" for cityblock distance, "cos" for cosine similarity, "mi" for mutual information.

init

an initialisation method. It can be one of "uniform", "sample" and "linear" initialisation methods

algorithm

the training algorithm. Currently, only "sequential" algorithm has been implemented

alphaType

the alpha type. It can be one of "invert", "linear" and "power" alpha types

neighKernel

the training neighbor kernel. It can be one of "gaussian", "bubble", "cutgaussian", "ep" and "gamma" kernels

Value

an object of class "sReorder", a list with following components:

Note

According to which features are used and whether nodes should be penalised by degrees, the feature data are constructed differently from the input data and input graph:

The size of "sheet"-shape rectangle grid depends on the input arguments:

See Also

visNetReorder

Examples

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# 1) generate a random graph according to the ER model
g <- erdos.renyi.game(100, 1/100)

# 2) produce the induced subgraph only based on the nodes in query
subg <- dNetInduce(g, V(g), knn=0)

# 3) reorder the module with vertices being color-coded by input data
nnodes <- vcount(subg)
nsamples <- 10
data <- matrix(runif(nnodes*nsamples), nrow=nnodes, ncol=nsamples)
rownames(data) <- V(subg)$name
sReorder <- dNetReorder(g=subg, data, feature="node",
node.normalise="none")

Example output

Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

Loading required package: supraHex
Loading required package: hexbin
Start at 2018-04-30 03:01:29

First, define topology of a map grid (2018-04-30 03:01:29)...
Second, initialise the codebook matrix (36 X 8) using 'linear' initialisation, given a topology and input data (2018-04-30 03:01:29)...
Third, get training at the rough stage (2018-04-30 03:01:29)...
	1 out of 360 (2018-04-30 03:01:29)
	36 out of 360 (2018-04-30 03:01:29)
	72 out of 360 (2018-04-30 03:01:29)
	108 out of 360 (2018-04-30 03:01:29)
	144 out of 360 (2018-04-30 03:01:29)
	180 out of 360 (2018-04-30 03:01:29)
	216 out of 360 (2018-04-30 03:01:29)
	252 out of 360 (2018-04-30 03:01:29)
	288 out of 360 (2018-04-30 03:01:29)
	324 out of 360 (2018-04-30 03:01:29)
	360 out of 360 (2018-04-30 03:01:29)
Fourth, get training at the finetune stage (2018-04-30 03:01:29)...
	1 out of 1440 (2018-04-30 03:01:29)
	144 out of 1440 (2018-04-30 03:01:29)
	288 out of 1440 (2018-04-30 03:01:29)
	432 out of 1440 (2018-04-30 03:01:29)
	576 out of 1440 (2018-04-30 03:01:29)
	720 out of 1440 (2018-04-30 03:01:29)
	864 out of 1440 (2018-04-30 03:01:29)
	1008 out of 1440 (2018-04-30 03:01:29)
	1152 out of 1440 (2018-04-30 03:01:29)
	1296 out of 1440 (2018-04-30 03:01:29)
	1440 out of 1440 (2018-04-30 03:01:29)
Next, identify the best-matching hexagon/rectangle for the input data (2018-04-30 03:01:29)...
Finally, append the response data (hits and mqe) into the sMap object (2018-04-30 03:01:29)...

Below are the summaries of the training results:
   dimension of input data: 10x8
   xy-dimension of map grid: xdim=6, ydim=6, r=3
   grid lattice: rect
   grid shape: sheet
   dimension of grid coord: 36x2
   initialisation method: linear
   dimension of codebook matrix: 36x8
   mean quantization error: 0.151466827895375

Below are the details of trainology:
   training algorithm: sequential
   alpha type: invert
   training neighborhood kernel: gaussian
   trainlength (x input data length): 36 at rough stage; 144 at finetune stage
   radius (at rough stage): from 1 to 1
   radius (at finetune stage): from 1 to 1

End at 2018-04-30 03:01:29
Runtime in total is: 0 secs

dnet documentation built on Feb. 20, 2020, 3:01 p.m.

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