如何在 libsvm 中使用 svm_model 的函数 'predict'？

Question

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

替代方法似乎是模块“svmutil”中的方法“svm_predict”。但是看不懂这个方法的参数data(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，对应“x”中的数据点

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