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,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 _int_array(seq):
size = len(seq)
array = svmc.new_int(size)
i = 0
for item in seq:
svmc.int_setitem(array, i, item)
i = i + 1
return array
def _int_array(seq): size = len(seq) array = svmc.new_int(size) i = 0 for item in seq: svmc.int_setitem(array,i,item) i = i + 1 return array
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 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)