Function svm_predict

pattern/vector/svm/libsvmutil.py:167–254

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). T

(y, x, m, options="")

		return m

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.
	"""

	def info(s):
		print(s)

	predict_probability = 0
	argv = options.split()
	i = 0
	while i < len(argv):
		if argv[i] == '-b':
			i += 1
			predict_probability = int(argv[i])
		elif argv[i] == '-q':
			info = print_null
		else:
			raise ValueError("Wrong options")
		i+=1

	svm_type = m.get_svm_type()
	is_prob_model = m.is_probability_model()
	nr_class = m.get_nr_class()
	pred_labels = []
	pred_values = []

	if predict_probability:
		if not is_prob_model:
			raise ValueError("Model does not support probabiliy estimates")

		if svm_type in [NU_SVR, EPSILON_SVR]:
			info("Prob. model for test data: target value = predicted value + z,\n"
			"z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g" % m.get_svr_probability());
			nr_class = 0

		prob_estimates = (c_double * nr_class)()
		for xi in x:
			xi, idx = gen_svm_nodearray(xi, isKernel=(m.param.kernel_type == PRECOMPUTED))
			label = libsvm.svm_predict_probability(m, xi, prob_estimates)