GNU/Linux man pages
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Syntaxe et mise en oeuvre :
GNU/Linux |
Debian 7.5.0(Wheezy) |
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svm-predict(1) |
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svm-predict − make predictions based on a trained SVM model file and test data
svm-predict [ -b probability_estimates ] [ -q ] test_data model_file [ output_file ]
svm-predict
uses a Support Vector Machine specified by a given input
model_file to make predictions for each of the
samples in test_data
The format of this file is identical to the training_data
file used in svm_train(1) and is just a sparse vector
as follows:
<label> <index1>:<value1>
<index2>:<value2> . . .
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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. If you have label data
available for
testing then you can enter these values in the test_data
file. If they
are not available you can just enter 0 and will not know
real accuracy
for the SVM directly, however you can still get the results
of its
prediction for the data point.
If output_file is given, it will be used to specify the filename to store the predicted results, one per line, in the same order as the test_data file.
-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.
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-q |
quiet mode; suppress messages to stdout. |
training_set_file
must be prepared in the following simple sparse training
vector format:
<label> <index1>:<value1>
<index2>:<value2> . . .
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There is one sample per line.
Each sample consist 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.
No environment variables.
None documented; see Vapnik et al.
Please report bugs to the Debian BTS.
Chih-Chung Chang, Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, Chen-Tse Tsai <ctse.tsai@gmail.com> (packaging)
svm-train(1), svm-scale(1)
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svm-predict(1) | |