# 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
        def wrapper():
            test_dict = func()
            var = Bunch(test_dict)

            rr = run_riemannian_relaxation(var.laplacian, var.Y_list[0], var.d, var.relaxation_kwds)

            calculated_loss_list = []
            calculated_DL_list = []
            calculated_Y_list = []

            for idx,Y in enumerate(var.Y_list):
                rr.Y = Y
                rr.H = np.copy(var.H_list[idx])
                if if_epsilon and idx >= 1:
                    rr.UU, rr.IUUEPS = compute_principal_plane(var.H_list[idx-1],rr.epsI,var.d)
                calculated_loss_list.append(rr.rieman_loss())

            for idx,H in enumerate(var.H_list):
                rr.H = H
                rr.Y = np.copy(var.Y_list[idx])
                calculated_DL_list.append(rr.compute_gradient())

            for idx,grad in enumerate(var.grad_list):
                rr.grad = grad
                rr.Y = np.copy(var.Y_list[idx])
                rr.loss = var.loss_list[idx]
                if if_epsilon:
                    rr.H = rr.compute_dual_rmetric()
                    rr.UU, rr.IUUEPS = compute_principal_plane(rr.H,rr.epsI,var.d)
                rr.make_optimization_step(first_iter=(idx == 0))

# 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

        def wrapper():
            test_dict = func()
            var = Bunch(test_dict)

            rr = run_riemannian_relaxation(var.laplacian, var.Y_list[0], var.d, var.relaxation_kwds)

            calculated_loss_list = []
            calculated_DL_list = []
            calculated_Y_list = []

            for idx,Y in enumerate(var.Y_list):
                rr.Y = Y
                rr.H = np.copy(var.H_list[idx])
                if if_epsilon and idx >= 1:
                    rr.UU, rr.IUUEPS = compute_principal_plane(var.H_list[idx-1],rr.epsI,var.d)
                calculated_loss_list.append(rr.rieman_loss())

            for idx,H in enumerate(var.H_list):
                rr.H = H
                rr.Y = np.copy(var.Y_list[idx])
                calculated_DL_list.append(rr.compute_gradient())

            for idx,grad in enumerate(var.grad_list):
                rr.grad = grad
                rr.Y = np.copy(var.Y_list[idx])
                rr.loss = var.loss_list[idx]
                if if_epsilon:
                    rr.H = rr.compute_dual_rmetric()
                    rr.UU, rr.IUUEPS = compute_principal_plane(rr.H,rr.epsI,var.d)
                rr.make_optimization_step(first_iter=(idx == 0))

                calculated_Y_list.append(rr.Y)