06-23-2011
Solaris Volume Manager (SVM)
RAID0 is striping, in which data is spread across multiple spindles (disks). Data written to the RAID0 is broken up into chunks of a specified size (interlace value) and spread across the disks.
For instance, if you put 8192 interlace, one 8K page can involve just one spindle. If you read 65536, and have 8 disks, you read 8 pages at once, subject to seek time, rotational latency and conflicts for bus or controller.
If your disk average spin covers always just less than 262144 bytes, a big interlace like that would mean that for one seek, you pick up most of the track, and a good design can start in the middle, killing rotational latency.
It is a tuning decision. Certainly, should be some multiple of the size of the disk record. Extra data is often fetched by buffering or caching to RAM somewhere along the read chain. UNIX data is not always laid out sequentially on a disk.
Good luck!
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..
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LEARN ABOUT DEBIAN
svm-train
svm-train(1) User Manuals svm-train(1)
NAME
svm-train - train one or more SVM instance(s) on a given data set to produce a model file
SYNOPSIS
svm-train [-s svm_type ] [ -t kernel_type ] [ -d degree ] [ -g gamma ] [ -r coef0 ] [ -c cost ] [ -n nu ] [ -p epsilon ] [ -m cachesize ] [
-e epsilon ] [ -h shrinking ] [ -b probability_estimates ] ] [ -wi weight ] [ -v n ] [ -q ]
training_set_file [ model_file ]
DESCRIPTION
svm-train trains a Support Vector Machine to learn the data indicated in the training_set_file
and produce a model_file
to save the results of the learning optimization. This model can be used later with svm_predict(1) or other LIBSVM enabled software.
OPTIONS
-s svm_type
svm_type defaults to 0 and can be any value between 0 and 4 as follows:
0 -- C-SVC
1 -- nu-SVC
2 -- one-class SVM
3 -- epsilon-SVR
4 -- nu-SVR
-t kernel_type
kernel_type defaults to 2 (Radial Basis Function (RBF) kernel) and can be any value between 0 and 4 as follows:
0 -- linear: u.v
1 -- polynomial: (gamma*u.v + coef0)^degree
2 -- radial basis function: exp(-gamma*|u-v|^2)
3 -- sigmoid: tanh(gamma*u.v + coef0)
4 -- precomputed kernel (kernel values in training_set_file) --
-d degree
Sets the degree of the kernel function, defaulting to 3
-g gamma
Adjusts the gamma in the kernel function (default 1/k)
-r coef0
Sets the coef0 (constant offset) in the kernel function (default 0)
-c cost
Sets the parameter C ( cost ) of C-SVC, epsilon-SVR, and nu-SVR (default 1)
-n nu Sets the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
-p epsilon
Set the epsilon in the loss function of epsilon-SVR (default 0.1)
-m cachesize
Set the cache memory size to cachesize in MB (default 100)
-e epsilon
Set the tolerance of termination criterion to epsilon (default 0.001)
-h shrinking
Whether to use the shrinking
heuristics, 0 or 1 (default 1)
-b probability-estimates
probability_estimates is a binary value indicating whether to calculate probability estimates when training the SVC or SVR model.
Values are 0 or 1 and defaults to 0 for speed.
-wi weight
Set the parameter C (cost) of class i to weight*C, for C-SVC (default 1)
-v n Set n for n -fold cross validation mode
-q quiet mode; suppress messages to stdout.
FILES
training_set_file must be prepared in the following simple sparse training vector format:
<label> <index1>:<value1> <index2>:<value2> . . .
.
.
.
There is one sample per line. Each sample consists of a target value (label or regression target) followed by a sparse representation of
the input vector. All unmentioned coordinates are assumed to be 0. For classification, <label> is an integer indicating the class label
(multi-class is supported). For regression, <label> is the target value which can be any real number. For one-class SVM, it's not used so
can be any number. Except using precomputed kernels (explained in another section), <index>:<value> gives a feature (attribute) value.
<index> is an integer starting from 1 and <value> is a real number. Indices must be in an ASCENDING order.
ENVIRONMENT
No environment variables.
DIAGNOSTICS
None documented; see Vapnik et al.
BUGS
Please report bugs to the Debian BTS.
AUTHOR
Chih-Chung Chang, Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, Chen-Tse Tsai <ctse.tsai@gmail.com> (packaging)
SEE ALSO
svm-predict(1), svm-scale(1)
Linux MAY 2006 svm-train(1)