如何在libsvm中使用svm_model函数'predict'？

Question

在最新版本的libsvm（v3.17 2013.04.01）中，类'svm_model'中的'predict'方法已被删除。

替代方法似乎是模块'svmutil'中的方法'svm_predict'。 但我无法理解这种方法的参数数据（y，x）。

def svm_predict(y, x, m, options=""):

 """ svm_predict(y, x, m [, options]) -> (p_labels, p_acc, p_vals) Predict data (y, x) with the SVM model m. options: -b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported. -q : quiet mode (no outputs). The return tuple contains p_labels: a list of predicted labels p_acc: a tuple including accuracy (for classification), mean-squared error, and squared correlation coefficient (for regression). p_vals: a list of decision values or probability estimates (if '-b 1' is specified). If k is the number of classes, for decision values, each element includes results of predicting k(k-1)/2 binary-class SVMs. For probabilities, each element contains k values indicating the probability that the testing instance is in each class. Note that the order of classes here is the same as 'model.label' field in the model structure. """ 

Answer 1

“y”是标签，与“x”中的数据点对应

这是我上个月做的一个例子：

#!/usr/bin/python
from svmutil import *

model = svm_load_model("train_yesterday.model")

values=[{1:1.37599, 2:1.37597, 3:1.37597, 4:1.37587, 5:1.37586}]
newcurve = []

for j in range(1,121):
    a,b,val = svm_predict([1],values,model)
    newval = val[0][0]

    for i in range(1,5):
        values[0][i] = values[0][i+1]
    values[0][5] = newval