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Class svm_parameter

pattern/vector/svm/libsvm.py:117–234  ·  view source on GitHub ↗

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115 for i, xi in enumerate(self.x_space): self.x[i] = xi
116
117class svm_parameter(Structure):
118 _names = ["svm_type", "kernel_type", "degree", "gamma", "coef0",
119 "cache_size", "eps", "C", "nr_weight", "weight_label", "weight",
120 "nu", "p", "shrinking", "probability"]
121 _types = [c_int, c_int, c_int, c_double, c_double,
122 c_double, c_double, c_double, c_int, POINTER(c_int), POINTER(c_double),
123 c_double, c_double, c_int, c_int]
124 _fields_ = genFields(_names, _types)
125
126 def __init__(self, options = None):
127 if options == None:
128 options = ''
129 self.parse_options(options)
130
131 def __str__(self):
132 s = ''
133 attrs = svm_parameter._names + list(self.__dict__.keys())
134 values = map(lambda attr: getattr(self, attr), attrs)
135 for attr, val in zip(attrs, values):
136 s += (' %s: %s\n' % (attr, val))
137 s = s.strip()
138
139 return s
140
141 def set_to_default_values(self):
142 self.svm_type = C_SVC;
143 self.kernel_type = RBF
144 self.degree = 3
145 self.gamma = 0
146 self.coef0 = 0
147 self.nu = 0.5
148 self.cache_size = 100
149 self.C = 1
150 self.eps = 0.001
151 self.p = 0.1
152 self.shrinking = 1
153 self.probability = 0
154 self.nr_weight = 0
155 self.weight_label = (c_int*0)()
156 self.weight = (c_double*0)()
157 self.cross_validation = False
158 self.nr_fold = 0
159 self.print_func = None
160
161 def parse_options(self, options):
162 if isinstance(options, list):
163 argv = options
164 elif isinstance(options, str):
165 argv = options.split()
166 else:
167 raise TypeError("arg 1 should be a list or a str.")
168 self.set_to_default_values()
169 self.print_func = cast(None, PRINT_STRING_FUN)
170 weight_label = []
171 weight = []
172
173 i = 0
174 while i < len(argv):

Callers 1

svm_trainFunction · 0.85

Calls 1

genFieldsFunction · 0.70

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