VW

NAME

DESCRIPTION

VW options:

--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.

-a [ --audit ]

print weights of features

-b [ --bit_precision ] arg

number of bits in the feature table

--backprop

turn on delayed backprop

-c [ --cache ]

Use a cache. The default is <data>.cache

--cache_file arg

The location(s) of cache_file.

--compressed

use gzip format whenever appropriate. If a cache file is being created, this option creates a compressed cache file. A mixture of raw-text & compressed inputs are supported if this option is on

--conjugate_gradient

use conjugate gradient based optimization

--regularization arg (=0)

minimize weight magnitude

--corrective

turn on corrective updates

-d [ --data ] arg

Example Set

--daemon

read data from port 39523

--decay_learning_rate arg (=1)

Set Decay factor for learning_rate between passes

-f [ --final_regressor ] arg

Final regressor

--global_multiplier arg (=1)

Global update multiplier

--delayed_global

Do delayed global updates

--hash arg

how to hash the features. Available options: strings, all

-h [ --help ]

Output Arguments

--version

Version information

--initial_weight arg (=0)

Set all weights to an initial value of 1.

-i [ --initial_regressor ] arg

Initial regressor(s)

--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

--min_prediction arg

Smallest prediction to output

--max_prediction arg

Largest prediction to output

--multisource arg

multiple sources for daemon input

--noop

do no learning

--port arg

port to listen on

--power_t arg (=0.5)

t power value

--predictto arg

host to send predictions to

-l [ --learning_rate ] arg (=10) Set Learning Rate

--passes arg (=1)

Number of Training Passes

-p [ --predictions ] arg

File to output predictions to

-q [ --quadratic ] arg

Create and use quadratic features

--quiet

Don't output diagnostics

--random_weights arg

make initial weights random

-r [ --raw_predictions ] arg

File to output unnormalized predictions to

--sendto arg

send example to <hosts>

-t [ --testonly ]

Ignore label information and just test

--thread_bits arg (=0)

log_2 threads

--loss_function arg (=squared)

Specify the loss function to be used, uses squared by default. Currently available ones are squared, hinge, logistic and quantile.

--quantile_tau arg (=0.5)

Parameter \tau associated with Quantile loss. Defaults to 0.5

--unique_id arg (=0)

unique id used for cluster parallel

--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.

Index

NAME

DESCRIPTION

VW options: