sDmat is supposed to calculate distance (measured in
high-dimensional input space) to neighbors (defined by based on 2D
output space) for each of hexagons/rectangles
sDmat(sMap, which_neigh = 1, distMeasure = c("median", "mean", "min", "max"))
dMat: a vector with the length of nHex. It stores the
distance a hexaon/rectangle is away from its output-space-defined
neighbors in high-dimensional input space
"which_neigh" is defined in output 2D space, but "distMeasure" is defined in high-dimensional input space
# 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 16:56:03 First, define topology of a map grid (2018-01-18 16:56:03)... Second, initialise the codebook matrix (61 X 10) using 'linear' initialisation, given a topology and input data (2018-01-18 16:56:03)... Third, get training at the rough stage (2018-01-18 16:56:03)... 1 out of 7 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 2 out of 7 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 3 out of 7 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 4 out of 7 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 5 out of 7 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 6 out of 7 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 7 out of 7 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) Fourth, get training at the finetune stage (2018-01-18 16:56:03)... 1 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 2 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 3 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 4 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 5 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 6 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 7 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 8 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 9 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 10 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 11 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 12 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 13 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 14 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 15 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 16 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 17 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 18 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 19 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 20 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 21 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 22 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 23 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 24 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) 25 out of 25 (2018-01-18 16:56:03) updated (2018-01-18 16:56:03) Next, identify the best-matching hexagon/rectangle for the input data (2018-01-18 16:56:03)... Finally, append the response data (hits and mqe) into the sMap object (2018-01-18 16:56:03)... 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: 4.87294660591056 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 16:56:03 Runtime in total is: 0 secs# 3) calculate "median" distances in INPUT space to different neighbors in 2D OUTPUT space # 3a) using direct neighbors in 2D OUTPUT space dMat <- sDmat(sMap=sMap, which_neigh=1, distMeasure="median") # 3b) using no more than 2-topological neighbors in 2D OUTPUT space # dMat <- sDmat(sMap=sMap, which_neigh=2, distMeasure="median")