如何使用libsvm中svm_model的'predict'函数？

Question

在最近版本的libsvm(v3.17 2013.04.01)中，类'svm_model'中的'method 'predict''已经被移除。替代方法看起来是模块'svmutil'中的'method 'svm_predict''。但是，我不太理解该方法中参数'data(y,x)'的含义。

以下是该方法的示例代码：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.
"""

- LeafiWan

Answer 1

"y"是标签，对应于"x"中的数据点。

这是我上个月做的一个示例：

#!/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

- Pedrom

---

如果我们想要预测“X”中点的分类，为什么要输入相应的“y”呢？这不是我们正在尝试找到的吗？ - mxdg

@mxdg 如果你想获得模型的精度，这是可选的，否则你可以忽略它们。 - Pedrom