New translations svm.py (French)

lang/fr/gklearn/gedlib/lib/libsvm.3.22/python/svm.py

@@ -0,0 +1,330 @@
+#!/usr/bin/env python
+
+from ctypes import *
+from ctypes.util import find_library
+from os import path
+import sys
+
+if sys.version_info[0] >= 3:
+	xrange = range
+
+__all__ = ['libsvm', 'svm_problem', 'svm_parameter',
+           'toPyModel', 'gen_svm_nodearray', 'print_null', 'svm_node', 'C_SVC',
+           'EPSILON_SVR', 'LINEAR', 'NU_SVC', 'NU_SVR', 'ONE_CLASS',
+           'POLY', 'PRECOMPUTED', 'PRINT_STRING_FUN', 'RBF',
+           'SIGMOID', 'c_double', 'svm_model']
+
+try:
+	dirname = path.dirname(path.abspath(__file__))
+	if sys.platform == 'win32':
+		libsvm = CDLL(path.join(dirname, r'..\windows\libsvm.dll'))
+	else:
+		libsvm = CDLL(path.join(dirname, '../libsvm.so.2'))
+except:
+# 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:
+		raise Exception('LIBSVM library not found.')
+
+C_SVC = 0
+NU_SVC = 1
+ONE_CLASS = 2
+EPSILON_SVR = 3
+NU_SVR = 4
+
+LINEAR = 0
+POLY = 1
+RBF = 2
+SIGMOID = 3
+PRECOMPUTED = 4
+
+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 __str__(self):
+		return '%d:%g' % (self.index, self.value)
+
+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 __str__(self):
+		s = ''
+		attrs = svm_parameter._names + list(self.__dict__.keys())
+		values = map(lambda attr: getattr(self, attr), attrs)
+		for attr, val in zip(attrs, values):
+			s += (' %s: %s\n' % (attr, val))
+		s = s.strip()
+
+		return s
+
+	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 = None
+		self.weight = None
+		self.cross_validation = False
+		self.nr_fold = 0
+		self.print_func = cast(None, PRINT_STRING_FUN)
+
+	def parse_options(self, options):
+		if isinstance(options, list):
+			argv = options
+		elif isinstance(options, str):
+			argv = options.split()
+		else:
+			raise TypeError("arg 1 should be a list or a str.")
+		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
+				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', 'sv_indices', '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), 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 get_sv_indices(self):
+		total_sv = self.get_nr_sv()
+		sv_indices = (c_int * total_sv)()
+		libsvm.svm_get_sv_indices(self, sv_indices)
+		return sv_indices[:total_sv]
+
+	def get_nr_sv(self):
+		return libsvm.svm_get_nr_sv(self)
+
+	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_sv_indices, None, [POINTER(svm_model), POINTER(c_int)])
+fillprototype(libsvm.svm_get_nr_sv, c_int, [POINTER(svm_model)])
+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])