examples_presvm = numpy.transpose(examples_presvm) feat_presvm = RealFeatures(examples_presvm) lab_presvm = Labels(numpy.array(labels_presvm)) wdk_presvm = LinearKernel(feat_presvm, feat_presvm) presvm_liblinear = LibLinear(1, feat_presvm, lab_presvm) presvm_liblinear.set_max_iterations(10000) presvm_liblinear.set_bias_enabled(False) presvm_liblinear.train() presvm_libsvm = LibSVM(1, wdk_presvm, lab_presvm) #presvm_libsvm = SVMLight(1, wdk_presvm, lab_presvm) #presvm_libsvm.io.set_loglevel(MSG_DEBUG) presvm_libsvm.set_bias_enabled(False) presvm_libsvm.train() my_w = presvm_liblinear.get_w() presvm_liblinear = LibLinear(1, feat_presvm, lab_presvm) presvm_liblinear.set_w(my_w) ############################################# # compute linear term manually ############################################# examples = numpy.array(examples, dtype=numpy.float64) examples = numpy.transpose(examples) feat = RealFeatures(examples) lab = Labels(numpy.array(labels))
lab_presvm = Labels(numpy.array(labels_presvm)) wdk_presvm = LinearKernel(feat_presvm, feat_presvm) presvm_liblinear = LibLinear(1, feat_presvm, lab_presvm) presvm_liblinear.set_max_iterations(10000) presvm_liblinear.set_bias_enabled(False) presvm_liblinear.train() presvm_libsvm = LibSVM(1, wdk_presvm, lab_presvm) #presvm_libsvm = SVMLight(1, wdk_presvm, lab_presvm) #presvm_libsvm.io.set_loglevel(MSG_DEBUG) presvm_libsvm.set_bias_enabled(False) presvm_libsvm.train() my_w = presvm_liblinear.get_w() presvm_liblinear = LibLinear(1, feat_presvm, lab_presvm) presvm_liblinear.set_w(my_w) ############################################# # compute linear term manually ############################################# examples = numpy.array(examples, dtype=numpy.float64) examples = numpy.transpose(examples) feat = RealFeatures(examples)
