我知道有一些库允许从 Python 代码中使用支持向量机，但我特别寻找允许在线教学的库（也就是说，不必一次性给出所有数据）。

有这样的库吗？

回答：

LibSVM 包含一个通过 SWIG 工作的 Python 包装器。

来自其发行版的示例 svm-test.py：

#!/usr/bin/env pythonfrom svm import *# a three-class problemlabels = [0, 1, 1, 2]samples = [[0, 0], [0, 1], [1, 0], [1, 1]]problem = svm_problem(labels, samples);size = len(samples)kernels = [LINEAR, POLY, RBF]kname = ['linear','polynomial','rbf']param = svm_parameter(C = 10,nr_weight = 2,weight_label = [1,0],weight = [10,1])for k in kernels:    param.kernel_type = k;    model = svm_model(problem,param)    errors = 0    for i in range(size):        prediction = model.predict(samples[i])        probability = model.predict_probability        if (labels[i] != prediction):            errors = errors + 1    print "##########################################"    print " kernel %s: error rate = %d / %d" % (kname[param.kernel_type], errors, size)    print "##########################################"param = svm_parameter(kernel_type = RBF, C=10)model = svm_model(problem, param)print "##########################################"print " Decision values of predicting %s" % (samples[0])print "##########################################"print "Numer of Classes:", model.get_nr_class()d = model.predict_values(samples[0])for i in model.get_labels():    for j in model.get_labels():        if j>i:            print "{%d, %d} = %9.5f" % (i, j, d[i,j])param = svm_parameter(kernel_type = RBF, C=10, probability = 1)model = svm_model(problem, param)pred_label, pred_probability = model.predict_probability(samples[1])print "##########################################"print " Probability estimate of predicting %s" % (samples[1])print "##########################################"print "predicted class: %d" % (pred_label)for i in model.get_labels():    print "prob(label=%d) = %f" % (i, pred_probability[i])print "##########################################"print " Precomputed kernels"print "##########################################"samples = [[1, 0, 0, 0, 0], [2, 0, 1, 0, 1], [3, 0, 0, 1, 1], [4, 0, 1, 1, 2]]problem = svm_problem(labels, samples);param = svm_parameter(kernel_type=PRECOMPUTED,C = 10,nr_weight = 2,weight_label = [1,0],weight = [10,1])model = svm_model(problem, param)pred_label = model.predict(samples[0])