# Path: megaman/relaxation/tests/utils.py
# def gen_data(name, opts):
#     test_data = copy.deepcopy(data[name])
#     test_dict = test_data[opts]
#     test_dict['laplacian'] = test_data['L']
#     test_dict['d'] = test_data['d']
#     return test_dict
# 
# class Bunch(object):
#     def __init__(self, adict):
#         self.__dict__.update(adict)


from megaman.relaxation import *
from functools import wraps
from .utils import gen_data, Bunch
import numpy as np
import numpy.testing
import shutil
                calculated_Y_list.append(rr.Y)

            np.testing.assert_allclose(
                calculated_loss_list, var.loss_list,
                err_msg='Loss calculated from matlab should be similar to that calculated from python, in {}'.format(__name__)
            )
            np.testing.assert_allclose(
                calculated_DL_list[:-1], var.DL_list,
                err_msg='gradient difference calculated from matlab should be similar to that calculated from python, in {}'.format(__name__)
            )
            np.testing.assert_allclose(
                calculated_Y_list, var.Y_list[1:],
                err_msg='Y calculated from linesearch should be similar, in {}'.format(__name__)
            )

        return wrapper
    return _test_deco

@_regression_test(True)
def test_whole_eps():
    return gen_data('eps_halfdome','whole_eps')

@_regression_test(False)
def test_whole_rloss():
    return gen_data('rloss_halfdome','whole_eps')

@_regression_test(True)
def test_half_eps():
    return gen_data('eps_halfdome','half_eps')


# Path: megaman/relaxation/tests/utils.py
# def gen_data(name, opts):
#     test_data = copy.deepcopy(data[name])
#     test_dict = test_data[opts]
#     test_dict['laplacian'] = test_data['L']
#     test_dict['d'] = test_data['d']
#     return test_dict
# 
# class Bunch(object):
#     def __init__(self, adict):
#         self.__dict__.update(adict)


# Path: megaman/relaxation/tests/test_regression_test.py
from megaman.relaxation import *
from functools import wraps
from .utils import gen_data, Bunch
import numpy as np
import numpy.testing
import shutil

                calculated_Y_list.append(rr.Y)

            np.testing.assert_allclose(
                calculated_loss_list, var.loss_list,
                err_msg='Loss calculated from matlab should be similar to that calculated from python, in {}'.format(__name__)
            )
            np.testing.assert_allclose(
                calculated_DL_list[:-1], var.DL_list,
                err_msg='gradient difference calculated from matlab should be similar to that calculated from python, in {}'.format(__name__)
            )
            np.testing.assert_allclose(
                calculated_Y_list, var.Y_list[1:],
                err_msg='Y calculated from linesearch should be similar, in {}'.format(__name__)
            )

        return wrapper
    return _test_deco

@_regression_test(True)
def test_whole_eps():
    return gen_data('eps_halfdome','whole_eps')

@_regression_test(False)
def test_whole_rloss():
    return gen_data('rloss_halfdome','whole_eps')

@_regression_test(True)
def test_half_eps():
    return gen_data('eps_halfdome','half_eps')


@_regression_test(False)