VW(1) User Commands VW(1)
NAME
vw - Vowpal Wabbit -- fast online learning tool
DESCRIPTION
VW options:
-h [ --help ]
Look here: http://hunch.net/~vw/ and click on Tutorial.
--active_learning
active learning mode
--active_simulation
active learning simulation mode
--active_mellowness arg (=8)
active learning mellowness parameter c_0. Default 8
--adaptive
use adaptive, individual learning rates.
--exact_adaptive_norm
use a more expensive exact norm for adaptive learning rates.
-a [ --audit ]
print weights of features
-b [ --bit_precision ] arg
number of bits in the feature table
--bfgs use bfgs optimization
-c [ --cache ]
Use a cache. The default is <data>.cache
--cache_file arg
The location(s) of cache_file.
--compressed
use gzip format whenever possible. If a cache file is being created, this option creates a compressed cache file. A mixture of
raw-text & compressed inputs are supported with autodetection.
--conjugate_gradient
use conjugate gradient based optimization
--nonormalize
Do not normalize online updates
--l1 arg (=0)
l_1 lambda
--l2 arg (=0)
l_2 lambda
-d [ --data ] arg
Example Set
--daemon
persistent daemon mode on port 26542
--num_children arg (=10)
number of children for persistent daemon mode
--pid_file arg
Write pid file in persistent daemon mode
--decay_learning_rate arg (=1)
Set Decay factor for learning_rate between passes
--input_feature_regularizer arg
Per feature regularization input file
-f [ --final_regressor ] arg
Final regressor
--readable_model arg
Output human-readable final regressor
--hash arg
how to hash the features. Available options: strings, all
--hessian_on
use second derivative in line search
--version
Version information
--ignore arg
ignore namespaces beginning with character <arg>
--initial_weight arg (=0)
Set all weights to an initial value of 1.
-i [ --initial_regressor ] arg
Initial regressor(s)
--initial_pass_length arg (=18446744073709551615)
initial number of examples per pass
--initial_t arg (=1)
initial t value
--lda arg
Run lda with <int> topics
--lda_alpha arg (=0.100000001)
Prior on sparsity of per-document topic weights
--lda_rho arg (=0.100000001)
Prior on sparsity of topic distributions
--lda_D arg (=10000)
Number of documents
--minibatch arg (=1)
Minibatch size, for LDA
--span_server arg
Location of server for setting up spanning tree
--min_prediction arg
Smallest prediction to output
--max_prediction arg
Largest prediction to output
--mem arg (=15)
memory in bfgs
--noconstant
Don't add a constant feature
--noop do no learning
--output_feature_regularizer_binary arg
Per feature regularization output file
--output_feature_regularizer_text arg Per feature regularization output file,
in text
--port arg
port to listen on
--power_t arg (=0.5)
t power value
-l [ --learning_rate ] arg (=10)
Set Learning Rate
--passes arg (=1)
Number of Training Passes
--termination arg (=0.00100000005)
Termination threshold
-p [ --predictions ] arg
File to output predictions to
-q [ --quadratic ] arg
Create and use quadratic features
--quiet
Don't output diagnostics
--rank arg (=0)
rank for matrix factorization.
--random_weights arg
make initial weights random
-r [ --raw_predictions ] arg
File to output unnormalized predictions to
--save_per_pass
Save the model after every pass over data
--sendto arg
send examples to <host>
-t [ --testonly ]
Ignore label information and just test
--loss_function arg (=squared)
Specify the loss function to be used, uses squared by default. Currently available ones are squared, classic, hinge, logistic and
quantile.
--quantile_tau arg (=0.5)
Parameter au associated with Quantile loss. Defaults to 0.5
--unique_id arg (=0)
unique id used for cluster parallel jobs
--total arg (=1)
total number of nodes used in cluster parallel job
--node arg (=0)
node number in cluster parallel job
--sort_features
turn this on to disregard order in which features have been defined. This will lead to smaller cache sizes
--ngram arg
Generate N grams
--skips arg
Generate skips in N grams. This in conjunction with the ngram tag can be used to generate generalized n-skip-k-gram.
vw 6.1 June 2012 VW(1)