Exemplo n.º 1
0
    def __init__(self, model, dataset, kernel):
        self.kernel = kernel
        modelc = model.contents
        self.svm_type = modelc.param.svm_type
        if is_classification_problem(self.svm_type) \
                and modelc.nSV[0] == 0:
            raise ValueError, 'model contains no support vectors'
        if is_classification_problem(self.svm_type):
            self.nr_class = modelc.nr_class
            self.labels = N.array(modelc.labels[:self.nr_class])
            nrho = self.nr_class * (self.nr_class - 1) / 2
            self.rho = modelc.rho[:nrho]
            self.sv_coef = [modelc.sv_coef[i][:modelc.l]
                            for i in range(self.nr_class - 1)]
            self.nSV = [modelc.nSV[i] for i in range(self.nr_class)]
            start = N.zeros((self.nr_class,), N.intc)
            for i in range(1, self.nr_class):
                start[i] = start[i - 1] + modelc.nSV[i - 1]
            self.start = start
        else:
            self.rho = modelc.rho[0]
            self.sv_coef = modelc.sv_coef[0][:modelc.l]

        if modelc.param.kernel_type != libsvm.PRECOMPUTED:
            svptrs = [modelc.SV[i] for i in range(modelc.l)]
            support_vectors = [dataset.iddatamap[addressof(svptr[0])]
                               for svptr in svptrs]
        else:
            ids = [int(modelc.SV[i][0].value) for i in range(modelc.l)]
            support_vectors = [dataset[id] for id in ids]
        self.support_vectors = support_vectors
        self.is_compact = False
        libsvm.svm_destroy_model(model)
Exemplo n.º 2
0
    def __init__(self, model, dataset, kernel):
        self.kernel = kernel
        modelc = model.contents
        self.svm_type = modelc.param.svm_type
        if is_classification_problem(self.svm_type) \
                and modelc.nSV[0] == 0:
            raise ValueError, 'model contains no support vectors'
        if is_classification_problem(self.svm_type):
            self.nr_class = modelc.nr_class
            self.labels = N.array(modelc.labels[:self.nr_class])
            nrho = self.nr_class * (self.nr_class - 1) / 2
            self.rho = modelc.rho[:nrho]
            self.sv_coef = [modelc.sv_coef[i][:modelc.l]
                            for i in range(self.nr_class - 1)]
            self.nSV = [modelc.nSV[i] for i in range(self.nr_class)]
            start = N.zeros((self.nr_class,), N.intc)
            for i in range(1, self.nr_class):
                start[i] = start[i - 1] + modelc.nSV[i - 1]
            self.start = start
        else:
            self.rho = modelc.rho[0]
            self.sv_coef = modelc.sv_coef[0][:modelc.l]

        if modelc.param.kernel_type != libsvm.PRECOMPUTED:
            svptrs = [modelc.SV[i] for i in range(modelc.l)]
            support_vectors = [dataset.iddatamap[addressof(svptr[0])]
                               for svptr in svptrs]
        else:
            ids = [int(modelc.SV[i][0].value) for i in range(modelc.l)]
            support_vectors = [dataset[id] for id in ids]
        self.support_vectors = support_vectors
        self.is_compact = False
        libsvm.svm_destroy_model(model)
Exemplo n.º 3
0
 def __del__(self):
     libsvm.svm_destroy_model(self.model)
Exemplo n.º 4
0
 def __del__(self):
     libsvm.svm_destroy_model(self.model)