12-14-2011
Why dont you using SVM? You can expand the FS with growfs. VXvm is the powerfull VM, but in case of local rootdisk, easy to use SVM. The best solution for Solaris root disk is SVM, except ZFS. IMHO
Last edited by rua; 12-14-2011 at 06:02 AM..
Reason: add
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I don't understood why on SPARC-Platforms have not present RAID-Controller ? Sorry for my bad english, but it's crazy always setup software RAID !!! I whanna Hardware RAID and when i can find solution ? (7 Replies)
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Hi,
I have t5120 sparc and I have 2 146 G drives in the system. I will be installing solaris 10 and also want the system mirrored using Hardware RAID "1"
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AVAILABLE DISK SELECTIONS:
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Can someone tell me what are the differences between software and hardware raid ?
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Hi,
I have a question. Do LiveUpgrade supports hardware raid?
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Dear All ,
Pl find the below command ,
# raidctl -l
Controller: 1
Volume:c1t0d0
Disk: 0.0.0
Disk: 0.1.0
Disk: 0.3.0
#
raidctl -l c1t0d0
Volume Size Stripe Status Cache RAID
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LEARN ABOUT DEBIAN
liblinear-train
LIBLINEAR-TRAIN(1) General Commands Manual LIBLINEAR-TRAIN(1)
NAME
liblinear-train - train a linear classifier and produce a model
SYNOPSIS
liblinear-train [options] training_set_file [model_file]
DESCRIPTION
liblinear-train trains a linear classifier using liblinear and produces a model suitable for use with liblinear-predict(1).
training_set_file is the file containing the data used for training. model_file is the file to which the model will be saved. If
model_file is not provided, it defaults to training_set_file.model.
To obtain good performances, sometimes one needs to scale the data. This can be done with svm-scale(1).
OPTIONS
A summary of options is included below.
-s type
Set the type of the solver:
0 ... L2-regularized logistic regression
1 ... L2-regularized L2-loss support vector classification (dual) (default)
2 ... L2-regularized L2-loss support vector classification (primal)
3 ... L2-regularized L1-loss support vector classification (dual)
4 ... multi-class support vector classification
5 ... L1-regularized L2-loss support vector classification
6 ... L1-regularized logistic regression
7 ... L2-regularized logistic regression (dual)
-c cost
Set the parameter C (default: 1)
-e epsilon
Set the tolerance of the termination criterion
For -s 0 and 2:
|f'(w)|_2 <= epsilon*min(pos,neg)/l*|f'(w0)_2, where f is
the primal function and pos/neg are the number of positive/negative data
(default: 0.01)
For -s 1, 3, 4 and 7:
Dual maximal violation <= epsilon; similar to libsvm (default: 0.1)
For -s 5 and 6:
|f'(w)|_inf <= epsilon*min(pos,neg)/l*|f'(w0)|_inf, where f is the primal
function (default: 0.01)
-B bias
If bias >= 0, then instance x becomes [x; bias]; if bias < 0, then
no bias term is added (default: -1)
-wi weight
Weight-adjusts the parameter C of class i by the value weight
-v n n-fold cross validation mode
-q Quiet mode (no outputs).
EXAMPLES
Train a linear SVM using L2-loss function:
liblinear-train data_file
Train a logistic regression model:
liblinear-train -s 0 data_file
Do five-fold cross-validation using L2-loss SVM, using a smaller stopping tolerance 0.001 instead of the default 0.1 for more accurate
solutions:
liblinear-train -v 5 -e 0.001 data_file
Train four classifiers:
positive negative Cp Cn
class 1 class 2,3,4 20 10
class 2 class 1,3,4 50 10
class 3 class 1,2,4 20 10
class 4 class 1,2,3 10 10
liblinear-train -c 10 -w1 2 -w2 5 -w3 2 four_class_data_file
If there are only two classes, we train ONE model. The C values for the two classes are 10 and 50:
liblinear-train -c 10 -w3 1 -w2 5 two_class_data_file
Output probability estimates (for logistic regression only) using liblinear-predict(1):
liblinear-predict -b 1 test_file data_file.model output_file
SEE ALSO
liblinear-predict(1), svm-predict(1), svm-train(1)
AUTHORS
liblinear-train was written by the LIBLINEAR authors at National Taiwan university for the LIBLINEAR Project.
This manual page was written by Christian Kastner <debian@kvr.at>, for the Debian project (and may be used by others).
March 08, 2011 LIBLINEAR-TRAIN(1)