def _train(self):
        # Note: LIBLINEAR feature indices start from 1 (not 0).
        M  = [v for type, v in self._vectors]                        # List of vectors.
        H1 = dict((w, i+1) for i, w in enumerate(self.features))     # Feature => integer hash.
        H2 = dict((w, i+1) for i, w in enumerate(self.classes))      # Class => integer hash.
        H3 = dict((i+1, w) for i, w in enumerate(self.classes))      # Class reversed hash.
        x  = map(lambda v: dict(map(lambda k: (H1[k], v[k]), v)), M) # Hashed vectors.
        y  = map(lambda v: H2[v[0]], self._vectors)                  # Hashed classes.
        # For linear SVC, use LIBLINEAR which is faster.
        # For kernel SVC, use LIBSVM.
        if self.extension == LIBLINEAR:
            f = self._svm.liblinearutil.train
            o = "-s 1 -c %s -p %s -q" % (
                self._cost,       # -c
                self._epsilon     # -p
            )
        else:
            f = self._svm.libsvmutil.svm_train
            o = "-s %s -t %s -d %s -g %s -r %s -c %s -p %s -n %s -m %s -h %s -b %s -q" % (
                self._type,       # -s
                self._kernel,     # -t
                self._degree,     # -d
                self._gamma,      # -g
                self._coeff0,     # -r
                self._cost,       # -c
                self._epsilon,    # -p
                self._nu,         # -n
                self._cache,      # -m
            int(self._shrinking), # -h
            int(self._type != DETECTION), # -b
            )
        # Cache the model and the feature hash.
        # SVM.train() will remove the cached model (since it needs to be retrained).
        self._model = (f(y, x, o), H1, H2, H3)
Load the llvm value of the variable named *name*.
dataset/ETHPy150Open clips/pattern/pattern/vector/__init__.py/SVM._train--crossover with--dataset/ETHPy150Open numba/numba/numba/objmode.py/PyLower.loadvar