# Path: tests/context.py

# 
# Path: tests/context.py

# 
# Path: tests/context.py



import numpy as np
import copy
import pdb
import os
import time
import matplotlib
import matplotlib.pylab as plt
from scipy.optimize import check_grad
from .context import aep
from .context import flatten_dict, unflatten_dict
from .context import PROP_MC, PROP_MM, PROP_LIN
from test_utils import kink, check_grad
matplotlib.use('Agg')


np.random.seed(42)
        
def test_gplvm_aep_gaussian(nat_param=True, stoc=False, prop_mode=PROP_MM):
    N_train = 10
    alpha = 0.5
    M = 5
    D = 2
    Q = 3
    y_train = np.random.randn(N_train, Q)

# Path: tests/context.py

# 
# Path: tests/context.py

# 
# Path: tests/context.py



# Path: tests/test_grads_aep.py
import numpy as np
import copy
import pdb
import os
import time
import matplotlib
import matplotlib.pylab as plt
from scipy.optimize import check_grad
from .context import aep
from .context import flatten_dict, unflatten_dict
from .context import PROP_MC, PROP_MM, PROP_LIN
from test_utils import kink, check_grad

matplotlib.use('Agg')


np.random.seed(42)
        
def test_gplvm_aep_gaussian(nat_param=True, stoc=False, prop_mode=PROP_MM):
    N_train = 10
    alpha = 0.5
    M = 5
    D = 2
    Q = 3
    y_train = np.random.randn(N_train, Q)

    lvm = aep.SGPLVM(y_train, D, M, lik='Gaussian', nat_param=nat_param)