code: def test_levenberg_marquardt(self):
      dataset = datasets.make_regression(n_samples=50, n_features=2)
      data, target = dataset
  
      data_scaler = preprocessing.MinMaxScaler()
      target_scaler = preprocessing.MinMaxScaler()
  
      x_train, x_test, y_train, y_test = train_test_split(
          data_scaler.fit_transform(data),
          target_scaler.fit_transform(target.reshape(-1, 1)),
          test_size=0.15,
      )
  
      lmnet = algorithms.LevenbergMarquardt(
          network=[
              layers.Input(2),
              layers.Sigmoid(6),
              layers.Sigmoid(1),
          ],
          mu_update_factor=2,
          mu=0.1,
          verbose=False,
          show_epoch=1,
      )
      lmnet.train(x_train, y_train, epochs=4)
      error = lmnet.score(x_test, y_test)
  
      self.assertGreater(0.01, error)
  
file_path: neupy/tests/algorithms/gd/test_levmarq.py
imports: from functools import partial
  import numpy
  import tensorflow
  from sklearn import datasets, preprocessing
  from sklearn.model_selection import train_test_split
  from neupy import algorithms, layers
  from neupy.utils import asfloat
  from neupy.algorithms.gd.lev_marq import compute_jacobian
  from base import BaseTestCase
label_desc: Use image node_id 3 for calling the LevenbergMarquardtTestCase obj's underlying member method code with example usage: obj.test_levenberg_marquardt() without return types