這可以從windows命令行執(zhí)行：c:\mallet\bin\mallet run

我試過了

^{pr2}$

然后得到一個錯誤WindowsError: [Error 2] The system cannot find the file specified

我試過了subprocess.call(['c:/mallet/bin/mallet', 'run'])

然后找出錯誤WindowsError: [Error 193] %1 is not a valid Win32 application

我要傳給什么subprocess.call()? 在

為了完整起見，我想傳遞的完整命令是：bin\mallet run cc.mallet.topics.tui.DMRLoader texts.txt features.txt instance.mallet

我模糊的想法是這是一個預(yù)編譯的java類，我正在以某種方式調(diào)用它，但我并不真正理解我在這里做什么。在

以下是文件夾bin中的兩個mallet文件：

mallet.bat@echo off

rem This batch file serves as a wrapper for several

rem MALLET command line tools.

if not "%MALLET_HOME%" == "" goto gotMalletHome

echo MALLET requires an environment variable MALLET_HOME.

goto :eof

:gotMalletHome

set MALLET_CLASSPATH=%MALLET_HOME%\class;%MALLET_HOME%\lib\mallet-deps.jar

set MALLET_MEMORY=1G

set MALLET_ENCODING=UTF-8

set CMD=%1

shift

set CLASS=

if "%CMD%"=="import-dir" set CLASS=cc.mallet.classify.tui.Text2Vectors

if "%CMD%"=="import-file" set CLASS=cc.mallet.classify.tui.Csv2Vectors

if "%CMD%"=="import-smvlight" set CLASS=cc.mallet.classify.tui.SvmLight2Vectors

if "%CMD%"=="train-classifier" set CLASS=cc.mallet.classify.tui.Vectors2Classify

if "%CMD%"=="train-topics" set CLASS=cc.mallet.topics.tui.Vectors2Topics

if "%CMD%"=="infer-topics" set CLASS=cc.mallet.topics.tui.InferTopics

if "%CMD%"=="estimate-topics" set CLASS=cc.mallet.topics.tui.EstimateTopics

if "%CMD%"=="hlda" set CLASS=cc.mallet.topics.tui.HierarchicalLDATUI

if "%CMD%"=="prune" set CLASS=cc.mallet.classify.tui.Vectors2Vectors

if "%CMD%"=="split" set CLASS=cc.mallet.classify.tui.Vectors2Vectors

if "%CMD%"=="bulk-load" set CLASS=cc.mallet.util.BulkLoader

if "%CMD%"=="run" set CLASS=%1 & shift

if not "%CLASS%" == "" goto gotClass

echo Mallet 2.0 commands:

echo import-dir load the contents of a directory into mallet instances (one per file)

echo import-file load a single file into mallet instances (one per line)

echo import-svmlight load a single SVMLight format data file into mallet instances (one per line)

echo train-classifier train a classifier from Mallet data files

echo train-topics train a topic model from Mallet data files

echo infer-topics use a trained topic model to infer topics for new documents

echo estimate-topics estimate the probability of new documents given a trained model

echo hlda train a topic model using Hierarchical LDA

echo prune remove features based on frequency or information gain

echo split divide data into testing, training, and validation portions

echo Include --help with any option for more information

goto :eof

:gotClass

set MALLET_ARGS=

:getArg

if "%1"=="" goto run

set MALLET_ARGS=%MALLET_ARGS% %1

shift

goto getArg

:run

java -Xmx%MALLET_MEMORY% -ea -Dfile.encoding=%MALLET_ENCODING% -classpath %MALLET_CLASSPATH% %CLASS% %MALLET_ARGS%

:eof

和mallet#!/bin/bash

malletdir=`dirname $0`

malletdir=`dirname $malletdir`

cp=$malletdir/class:$malletdir/lib/mallet-deps.jar:$CLASSPATH

#echo $cp

MEMORY=1g

JAVA_COMMAND="java -Xmx$MEMORY -ea -Djava.awt.headless=true -Dfile.encoding=UTF-8 -server -classpath $cp"

CMD=$1

shift

help()

{

cat <

Mallet 2.0 commands:

import-dir load the contents of a directory into mallet instances (one per file)

import-file load a single file into mallet instances (one per line)

import-svmlight load SVMLight format data files into Mallet instances

train-classifier train a classifier from Mallet data files

classify-dir classify data from a single file with a saved classifier

classify-file classify the contents of a directory with a saved classifier

classify-svmlight classify data from a single file in SVMLight format

train-topics train a topic model from Mallet data files

infer-topics use a trained topic model to infer topics for new documents

evaluate-topics estimate the probability of new documents under a trained model

hlda train a topic model using Hierarchical LDA

prune remove features based on frequency or information gain

split divide data into testing, training, and validation portions

Include --help with any option for more information

EOF

}

CLASS=

case $CMD in

import-dir) CLASS=cc.mallet.classify.tui.Text2Vectors;;

import-file) CLASS=cc.mallet.classify.tui.Csv2Vectors;;

import-svmlight) CLASS=cc.mallet.classify.tui.SvmLight2Vectors;;

train-classifier) CLASS=cc.mallet.classify.tui.Vectors2Classify;;

classify-dir) CLASS=cc.mallet.classify.tui.Text2Classify;;

classify-file) CLASS=cc.mallet.classify.tui.Csv2Classify;;

classify-svmlight) CLASS=cc.mallet.classify.tui.SvmLight2Classify;;

train-topics) CLASS=cc.mallet.topics.tui.Vectors2Topics;;

infer-topics) CLASS=cc.mallet.topics.tui.InferTopics;;

evaluate-topics) CLASS=cc.mallet.topics.tui.EvaluateTopics;;

hlda) CLASS=cc.mallet.topics.tui.HierarchicalLDATUI;;

prune) CLASS=cc.mallet.classify.tui.Vectors2Vectors;;

split) CLASS=cc.mallet.classify.tui.Vectors2Vectors;;

bulk-load) CLASS=cc.mallet.util.BulkLoader;;

run) CLASS=$1; shift;;

*) echo "Unrecognized command: $CMD"; help; exit 1;;

esac

$JAVA_COMMAND $CLASS $*

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