PLDA.py 文件源码

def _train_pca(self, training_set):
    """Trains and returns a LinearMachine that is trained using PCA"""
    data = numpy.vstack(feature for feature in training_set)

    logger.info("  -> Training LinearMachine using PCA ")
    trainer = bob.learn.linear.PCATrainer()
    machine, eigen_values = trainer.train(data)

    if isinstance(self.subspace_dimension_pca, float):
      cummulated = numpy.cumsum(eigen_values) / numpy.sum(eigen_values)
      for index in range(len(cummulated)):
        if cummulated[index] > self.subspace_dimension_pca:
          self.subspace_dimension_pca = index
          break
      self.subspace_dimension_pca = index

    # limit number of pcs
    logger.info("  -> limiting PCA subspace to %d dimensions", self.subspace_dimension_pca)
    machine.resize(machine.shape[0], self.subspace_dimension_pca)
    return machine