old_contents: #!/usr/bin/env python
  
  from svm import *
  
  # a three-class problem
  labels = [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)
  
new_contents: #!/usr/bin/env python
  
  from svm import *
  
  # a three-class problem
  labels = [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])
  
subject: add a sample of prediction for precomputed kernel