def setParams(self, **kwargs):
        """ Set parameters for SVM training. Apart from the ones below, you can use all parameters 
        defined for the LIBSVM svm_model class, see their documentation.

        :key searchlog: Save a list of coordinates and the achieved CV accuracy to this file."""
        if kwargs in 'weight':
            self.params['nr_weight'] = len(kwargs['weight'])
        if 'log2C' in kwargs:
            self.params['C'] = 2 ** kwargs['log2C']
            kwargs.pop('log2C')
        if 'log2g' in kwargs:
            self.params['gamma'] = 2 ** kwargs['log2g']
            kwargs.pop('log2g')
        if 'searchlog' in kwargs:
            self.searchlog = kwargs['searchlog']
            kwargs.pop('searchlog')
        self.params.update(kwargs)
dataset/ETHPy150Open pybrain/pybrain/pybrain/supervised/trainers/svmtrainer.py/SVMTrainer.setParams/swapped operands of `in`