svm_w_usage.py

来自「一种基于局部密度比权重设置模型的加权支持向量回归模型来单步求解多分类问题：该方法」· Python 代码 · 共 43 行

#!/usr/bin/env python

import string
from svm import *

f = open("../heart.10", "r")
labels = []
samples = []
weights = []
line = f.readline()
max_index = 0
while line:
	elems = string.split(line)
	sample = {}
	for e in elems[1:]:
		points = string.split(e, ":")
		sample[int(points[0])] = float(points[1])
                if max_index < int(points[0]):
                    max_index = int(points[0])
                    
	labels.append(float(elems[0]))
	samples.append(sample)
        weights.append(0.001)
	line = f.readline()
f.close()

print "%d samples loaded." % (len(samples))
param = svm_parameter(svm_type = C_SVC, kernel_type = RBF, gamma=1.0/max_index)

for i in range(10):
    print weights
    
    prob = svm_problem(labels, samples, weights)
    model=svm_model(prob, param)

    for i in range(len(samples)):
        if model.predict(samples[i]) != labels[i]:
	    print ("deemphasizing %d"%i)
            weights[i] = weights[i] / 2.0
        else:
            weights[i] = weights[i] * 2.0