#!/usr/bin/env python
from ctypes import *
from ctypes.util import find_library
import sys
import os
# For unix the prefix 'lib' is not considered.
if find_library('svm'):
libsvm = CDLL(find_library('svm'))
elif find_library('libsvm'):
libsvm = CDLL(find_library('libsvm'))
else:
if sys.platform == 'win32':
libsvm = CDLL(os.path.join(os.path.dirname(__file__),\
'../windows/libsvm.dll'))
else:
libsvm = CDLL(os.path.join(os.path.dirname(__file__),\
'../libsvm.so.2'))
# Construct constants
SVM_TYPE = ['C_SVC', 'NU_SVC', 'ONE_CLASS', 'EPSILON_SVR', 'NU_SVR' ]
KERNEL_TYPE = ['LINEAR', 'POLY', 'RBF', 'SIGMOID', 'PRECOMPUTED']
for i, s in enumerate(SVM_TYPE): exec("%s = %d" % (s , i))
for i, s in enumerate(KERNEL_TYPE): exec("%s = %d" % (s , i))
PRINT_STRING_FUN = CFUNCTYPE(None, c_char_p)
def print_null(s):
return
def genFields(names, types):
return list(zip(names, types))
def fillprototype(f, restype, argtypes):
f.restype = restype
f.argtypes = argtypes
class svm_node(Structure):
_names = ["index", "value"]
_types = [c_int, c_double]
_fields_ = genFields(_names, _types)
def gen_svm_nodearray(xi, feature_max=None, isKernel=None):
if isinstance(xi, dict):
index_range = xi.keys()
elif isinstance(xi, (list, tuple)):
if not isKernel:
xi = [0] + xi # idx should start from 1
index_range = range(len(xi))
else:
raise TypeError('xi should be a dictionary, list or tuple')
if feature_max:
assert(isinstance(feature_max, int))
index_range = filter(lambda j: j <= feature_max, index_range)
if not isKernel:
index_range = filter(lambda j:xi[j] != 0, index_range)
index_range = sorted(index_range)
ret = (svm_node * (len(index_range)+1))()
ret[-1].index = -1
for idx, j in enumerate(index_range):
ret[idx].index = j
ret[idx].value = xi[j]
max_idx = 0
if index_range:
max_idx = index_range[-1]
return ret, max_idx
class svm_problem(Structure):
_names = ["l", "y", "x"]
_types = [c_int, POINTER(c_double), POINTER(POINTER(svm_node))]
_fields_ = genFields(_names, _types)
def __init__(self, y, x, isKernel=None):
if len(y) != len(x):
raise ValueError("len(y) != len(x)")
self.l = l = len(y)
max_idx = 0
x_space = self.x_space = []
for i, xi in enumerate(x):
tmp_xi, tmp_idx = gen_svm_nodearray(xi,isKernel=isKernel)
x_space += [tmp_xi]
max_idx = max(max_idx, tmp_idx)
self.n = max_idx
self.y = (c_double * l)()
for i, yi in enumerate(y): self.y[i] = yi
self.x = (POINTER(svm_node) * l)()
for i, xi in enumerate(self.x_space): self.x[i] = xi
class svm_parameter(Structure):
_names = ["svm_type", "kernel_type", "degree", "gamma", "coef0",
"cache_size", "eps", "C", "nr_weight", "weight_label", "weight",
"nu", "p", "shrinking", "probability"]
_types = [c_int, c_int, c_int, c_double, c_double,
c_double, c_double, c_double, c_int, POINTER(c_int), POINTER(c_double),
c_double, c_double, c_int, c_int]
_fields_ = genFields(_names, _types)
def __init__(self, options = None):
if options == None:
options = ''
self.parse_options(options)
def show(self):
attrs = svm_parameter._names + self.__dict__.keys()
values = map(lambda attr: getattr(self, attr), attrs)
for attr, val in zip(attrs, values):
print(' %s: %s' % (attr, val))
def set_to_default_values(self):
self.svm_type = C_SVC;
self.kernel_type = RBF
self.degree = 3
self.gamma = 0
self.coef0 = 0
self.nu = 0.5
self.cache_size = 100
self.C = 1
self.eps = 0.001
self.p = 0.1
self.shrinking = 1
self.probability = 0
self.nr_weight = 0
self.weight_label = (c_int*0)()
self.weight = (c_double*0)()
self.cross_validation = False
self.nr_fold = 0
self.print_func = None
def parse_options(self, options):
argv = options.split()
self.set_to_default_values()
self.print_func = cast(None, PRINT_STRING_FUN)
weight_label = []
weight = []
i = 0
while i < len(argv):
if argv[i] == "-s":
i = i + 1
self.svm_type = int(argv[i])
elif argv[i] == "-t":
i = i + 1
self.kernel_type = int(argv[i])
elif argv[i] == "-d":
i = i + 1
self.degree = int(argv[i])
elif argv[i] == "-g":
i = i + 1
self.gamma = float(argv[i])
elif argv[i] == "-r":
i = i + 1
self.coef0 = float(argv[i])
elif argv[i] == "-n":
i = i + 1
self.nu = float(argv[i])
elif argv[i] == "-m":
i = i + 1
self.cache_size = float(argv[i])
elif argv[i] == "-c":
i = i + 1
self.C = float(argv[i])
elif argv[i] == "-e":
i = i + 1
self.eps = float(argv[i])
elif argv[i] == "-p":
i = i + 1
self.p = float(argv[i])
elif argv[i] == "-h":
i = i + 1
self.shrinking = int(argv[i])
elif argv[i] == "-b":
i = i + 1
self.probability = int(argv[i])
elif argv[i] == "-q":
self.print_func = PRINT_STRING_FUN(print_null)
elif argv[i] == "-v":
i = i + 1
self.cross_validation = 1
self.nr_fold = int(argv[i])
if self.nr_fold < 2:
raise ValueError("n-fold cross validation: n must >= 2")
elif argv[i].startswith("-w"):
i = i + 1
self.nr_weight += 1
nr_weight = self.nr_weight
weight_label += [int(argv[i-1][2:])]
weight += [float(argv[i])]
else:
raise ValueError("Wrong options")
i += 1
libsvm.svm_set_print_string_function(self.print_func)
self.weight_label = (c_int*self.nr_weight)()
self.weight = (c_double*self.nr_weight)()
for i in range(self.nr_weight):
self.weight[i] = weight[i]
self.weight_label[i] = weight_label[i]
class svm_model(Structure):
_names = ['param', 'nr_class', 'l', 'SV', 'sv_coef', 'rho',
'probA', 'probB', 'label', 'nSV', 'free_sv']
_types = [svm_parameter, c_int, c_int, POINTER(POINTER(svm_node)),
POINTER(POINTER(c_double)), POINTER(c_double),
POINTER(c_double), POINTER(c_double), POINTER(c_int),
POINTER(c_int), c_int]
_fields_ = genFields(_names, _types)
def __init__(self):
self.__createfrom__ = 'python'
def __del__(self):
# free memory created by C to avoid memory leak
if hasattr(self, '__createfrom__') and self.__createfrom__ == 'C':
libsvm.svm_free_and_destroy_model(pointer(self))
def get_svm_type(self):
return libsvm.svm_get_svm_type(self)
def get_nr_class(self):
return libsvm.svm_get_nr_class(self)
def get_svr_probability(self):
return libsvm.svm_get_svr_probability(self)
def get_labels(self):
nr_class = self.get_nr_class()
labels = (c_int * nr_class)()
libsvm.svm_get_labels(self, labels)
return labels[:nr_class]
def is_probability_model(self):
return (libsvm.svm_check_probability_model(self) == 1)
def get_sv_coef(self):
return [tuple(self.sv_coef[j][i] for j in xrange(self.nr_class - 1))
for i in xrange(self.l)]
def get_SV(self):
result = []
for sparse_sv in self.SV[:self.l]:
row = dict()
i = 0
while True:
row[sparse_sv[i].index] = sparse_sv[i].value
if sparse_sv[i].index == -1:
break
i += 1
result.append(row)
return result
def toPyModel(model_ptr):
"""
toPyModel(model_ptr) -> svm_model
Convert a ctypes POINTER(svm_model) to a Python svm_model
"""
if bool(model_ptr) == False:
raise ValueError("Null pointer")
m = model_ptr.contents
m.__createfrom__ = 'C'
return m
fillprototype(libsvm.svm_train, POINTER(svm_model), [POINTER(svm_problem), POINTER(svm_parameter)])
fillprototype(libsvm.svm_cross_validation, None, [POINTER(svm_problem), POINTER(svm_parameter), c_int, POINTER(c_double)])
fillprototype(libsvm.svm_save_model, c_int, [c_char_p, POINTER(svm_model)])
fillprototype(libsvm.svm_load_model, POINTER(svm_model), [c_char_p])
fillprototype(libsvm.svm_get_svm_type, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_get_nr_class, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_get_labels, None, [POINTER(svm_model), POINTER(c_int)])
fillprototype(libsvm.svm_get_svr_probability, c_double, [POINTER(svm_model)])
fillprototype(libsvm.svm_predict_values, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)])
fillprototype(libsvm.svm_predict, c_double, [POINTER(svm_model), POINTER(svm_node)])
fillprototype(libsvm.svm_predict_probability, c_double, [POINTER(svm_model), POINTER(svm_node), POINTER(c_double)])
fillprototype(libsvm.svm_free_model_content, None, [POINTER(svm_model)])
fillprototype(libsvm.svm_free_and_destroy_model, None, [POINTER(POINTER(svm_model))])
fillprototype(libsvm.svm_destroy_param, None, [POINTER(svm_parameter)])
fillprototype(libsvm.svm_check_parameter, c_char_p, [POINTER(svm_problem), POINTER(svm_parameter)])
fillprototype(libsvm.svm_check_probability_model, c_int, [POINTER(svm_model)])
fillprototype(libsvm.svm_set_print_string_function, None, [PRINT_STRING_FUN])