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  1. Split Lab 1.0 Free
  2. Split Lab 1.0 Test
  3. Split Lab 1.0 Vs
  4. Split Lab 1.0 Full
Split Data into Test and Train Set

Split data from vector Y into two sets in predefined ratio while preserving relative ratios of different labels in Y. Used to split the data used during classification into train and test subsets.

  • SplitLab: A shear-wave splitting environment in Matlab $ Andreas Wu. Uses simultaneously three different techniques. 93.286 SKS 50 1.35 g 49 51 51 1.40 1.40 1.40 1 0.05.
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Keywords
classif
Usage
Arguments
Y
Vector of data labels. If there are only a few labels (as is expected) than relative ratio of data in both subsets will be the same.
SplitRatio

Split Lab 1.0 Free

Splitting ratio:
  • if (0<=splitratio<1)< code='> then SplitRatio fraction of points from Y will be set toTRUE
  • if (SplitRatio1) then one random point from Y will be set to TRUE
  • if (SplitRatio>1) then SplitRatio number of points from Y will be set to TRUE
group
Optional vector/list used when multiple copies of each sample are present. In such a case group contains unique sample labels, marking all copies of the same sample with the same label, and the function tries to place all copies in either train or test subset. If provided than has to have the same length as Y.
Details

Function msc.sample.split is the old name of the sample.split function. To be retired soon.

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Value
SplitRatio*length(Y) elements set to TRUE.
See Also
  • Similar to sample function.
  • Variable group is used in the same way as f argument in split and INDEX argument in tapply
Aliases
  • sample.split
Examples
library(caTools) library(MASS) data(cats) # load cats data Y = cats[,1] # extract labels from the data msk = sample.split(Y, SplitRatio=3/4) table(Y,msk) t=sum( msk) # number of elements in one class f=sum(!msk) # number of elements in the other class stopifnot( round((t+f)*3/4) t ) # test ratios # example of using group variable g = rep(seq(length(Y)/4), each=4); g[48]=12; msk = sample.split(Y, SplitRatio=1/2, group=g) table(Y,msk) # try to get correct split ratios .. split(msk,g) # .. while keeping samples with the same group label together # test results print(paste( 'All Labels numbers: total=',t+f,', train=',t,', test=',f, ', ratio=', t/(t+f) ) ) U = unique(Y) # extract all unique labels for( i in 1:length(U)) { # check for all labels lab = (YU[i]) # mask elements that have label U[i] t=sum( msk[lab]) # number of elements with label U[i] in one class f=sum(!msk[lab]) # number of elements with label U[i] in the other class print(paste( 'Label',U[i],'numbers: total=',t+f,', train=',t,', test=',f, ', ratio=', t/(t+f) ) ) } # use results train = cats[ msk,2:3] # use output of sample.split to .. test = cats[!msk,2:3] # create train and test subsets z = lda(train, Y[msk]) # perform classification table(predict(z, test)$class, Y[!msk]) # predicted & true labels # see also LogitBoost example
Documentation reproduced from package caTools, version 1.17.1, License: GPL-3

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Split Lab 1.0 Test

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Split Lab 1.0 Vs

functionSL_RFviewer(idx)
global config rf
data_info = regexp(rf(idx).seisfile,'_', 'split');
titlestr = {sprintf(['Event: %s; Station: %s; (' num2str(idx) '/' num2str(length(rf)) ')'],rf(idx).seisfile, config.stnname);
sprintf(['M_w = %3.1f Backazimuth: %6.2f' char(186) ' Distance: %6.2f' char(186) ' Depth: %3.0fkm'],rf(idx).Mw, rf(idx).bazi, rf(idx).dis, rf(idx).depth)};
time = - config.extime_before + rf(idx).dt*(0:1:rf(idx).RFlength-1);
plot(time,rf(idx).RadialRF,'k','LineWidth',2.0);hold on;
plot(time,rf(idx).TransverseRF);hold on
plot(xlim,[00],'g--');
set(gca,'xlim',[-330],'xtick',(0:2:30),'Xgrid','on')
title(gca,titlestr,'FontSize',16)
hold off;
return

Split Lab 1.0 Full

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