Question

我正在使用libsvm库来支持向量机，我试图在一个python代码中包含所有的包（svm-scale，svm-train和svm-predict），如下所示：

#!/usr/bin/env python

import sys
import os
from subprocess import *
from util import *
from svm import *
from svmutil import *

params={"gamma":0.1, "cost":100000, "kernel":2}
model = 0
kernels = ["Linear", "Polynomial", "RBF", "Sigmoid", "Precomputed"]

values={"lower":-1, "upper":1}

def scaleTrainData(trainData):
    global model    
    y, x = readDataSet(trainData)
    arg1 = "-l %s -u %s" % (str(values["lower"]), str(values["upper"]))
    model = svm_scale(y, x, arg)
    print arg

def trainModel( trainData ):
    global model
    y, x = readDataSet(trainData)
    arg = "-s 0 -t %s -g %s -c %s" % (str(params["kernel"]), str(params["gamma"]), str(params["cost"]))
    model = svm_train(y, x, arg)
    print arg

 def readDataSet( dataSet ):
    if type(dataSet) == type(""):
        y, x = svm_read_problem(dataSet)
    else:
        y, x = parseDataSet(dataSet)
    return y, x


def parseDataSet( dataSet ):
    y, x = [], []
    for line in dataSet:
        line = line.split(None, 1)
        if len(line) == 1:
            line += ['']
        label, features = line
        xi = {}
        for e in features.split():
            ind, val = e.split(":")
            xi[int(ind)] = float(val)
        y += [float(label)]
        x += [xi]
    return (y, x)


 def predictData( dataSet ):
    y, x = readDataSet(dataSet)
    label, acc, val = svm_predict(y, x, model)
    print label
    return label


 scaleTrainData("trainWeek")
 trainModel("train.scale")
 predictData("test.scale")

但我收到以下错误： model = svm_scale（y，x，arg） NameError：全局名称＆＃39; svm_scale＆＃39;未定义

此错误表示什么？我应该如何在我的代码中包含svm-scale包，以便使用上面提到的代码来扩展，训练和预测我的数据集？

gobal name＆＃s; svm_scale＆＃39;没有定义

0 个答案: