/usr/share/pyshared/svm.py is in python-libsvm 3.12-1.1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 | #!/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])
|