def __init__(self,arg1,arg2=None):
		if arg2 == None:
			# create model from file
			filename = arg1
			self.model = svmc.svm_load_model(filename)
		else:
			# create model from problem and parameter
			prob,param = arg1,arg2
			self.prob = prob
			if param.gamma == 0:
				param.gamma = 1.0/prob.maxlen
			msg = svmc.svm_check_parameter(prob.prob,param.param)
			if msg: raise ValueError, msg
			self.model = svmc.svm_train(prob.prob,param.param)

		#setup some classwide variables
		self.nr_class = svmc.svm_get_nr_class(self.model)
		self.svm_type = svmc.svm_get_svm_type(self.model)
		#create labels(classes)
		intarr = svmc.new_int(self.nr_class)
		svmc.svm_get_labels(self.model,intarr)
		self.labels = _int_array_to_list(intarr, self.nr_class)
		svmc.delete_int(intarr)
		#check if valid probability model
		self.probability = svmc.svm_check_probability_model(self.model)
	def __init__(self, prob, param):
		if param is None:
			raise NotImplementedError, "svm_varma can't load a svm_model from a file (yet)"

		self.prob = prob
		if param.gamma == 0:
			param.gamma = 1.0/prob.maxlen
		msg = svmc.svm_check_parameter(prob.prob,param.param)
		if msg: raise ValueError, msg
		self.model = svmc.svm_train(prob.prob,param.param)

		#setup some classwide variables
		self.nr_class = nr_class = svmc.svm_get_nr_class(self.model)
		self.svm_type = svmc.svm_get_svm_type(self.model)
		#create labels(classes)
		intarr = svmc.new_int(self.nr_class)
		svmc.svm_get_labels(self.model,intarr)
		self.labels = labels = _int_array_to_list(intarr, self.nr_class)
		svmc.delete_int(intarr)
		if len(labels) != 2:
			raise NotImplementedError, "svm_varma doesn't handle problems with more than 2 labels (yet)"
		#check if valid probability model
		self.probability = svmc.svm_check_probability_model(self.model)

		model = self.model
		self.obj = svmc.svm_varma_get_obj(model, 0)
		self.rho = svmc.svm_varma_get_rho(model, 0)
		self.nSV = svmc.svm_varma_get_nSV(model, 0)
		self.total_sv = svmc.svm_varma_get_total_sv(model)

		# XXX: this only work for 2-class problems
		size = self.total_sv
		doublearr = svmc.new_double(size)
		svmc.svm_varma_get_sv_coef(model, 0, doublearr)
		self.sv_coef = _double_array_to_list(doublearr, size)
		svmc.delete_double(doublearr)
		
		# XXX: only works with PRECOMPUTED kernels
		size = self.total_sv
		doublearr = svmc.new_double(size)
		svmc.svm_varma_get_SV(model, doublearr)
		self.SV = _double_array_to_list(doublearr, size)
		svmc.delete_double(doublearr)
def get_model_params(model):
    """
    Extract the alpha and b parameters from the SVM model.
    returns (alpha, b)
    """
    rho = svmc.svm_get_model_rho(model.model)
    n = svmc.svm_get_model_num_coefs(model.model)
    coefs_dblarr = svmc.new_double(n)
    perm_intarr = svmc.new_int(n)
    try:
        svmc.svm_get_model_coefs(model.model,coefs_dblarr)
        svmc.svm_get_model_perm(model.model,perm_intarr)
        coefs = np.zeros(n,dtype=float)
        perm = np.zeros(n,dtype=int)
        for i in range(n):
            coefs[i] = svmc.double_getitem(coefs_dblarr,i)
            perm[i] = svmc.int_getitem(perm_intarr,i)
    finally:
        svmc.delete_double(coefs_dblarr)
        svmc.delete_int(perm_intarr)
    return (coefs, perm, rho)
def _free_int_array(x):
	if x != 'NULL' and x != None:
		svmc.delete_int(x)