"""
import numpy as np
import pytest
+import sys
+import platform
+import re
+i386_looser_tolerances=bool(re.match('i.?86|x86',platform.uname()[4])) and np.log2(sys.maxsize)<33
from statsmodels.tsa.statespace import (
mlemodel, sarimax, structural, varmax, dynamic_factor)
# Test the output when the multivariate filter switches to the univariate
# filter
mod = get_model(univariate, missing, init)
- check_filter_output(mod, periods)
+ check_filter_output(mod, periods, atol=1e-10 if i386_looser_tolerances else 0)
@pytest.mark.parametrize('univariate', [True, False])
if init == 'diffuse':
return
mod.ssm.timing_init_filtered = True
- atol = 1e-12
+ atol = 1e-8 if i386_looser_tolerances else 1e-12
# Tolerance is lower for approximate diffuse for one attribute in this case
if missing == 'init' and init == 'approximate_diffuse':
atol = 1e-6
SMOOTH_UNIVARIATE)
current_path = os.path.dirname(os.path.abspath(__file__))
-
-
+import sys
+import platform
+import re
+i386_looser_tolerances=bool(re.match('i.?86|x86',platform.uname()[4])) and np.log2(sys.maxsize)<33
class TestStatesAR3(object):
@classmethod
def setup_class(cls, alternate_timing=False, *args, **kwargs):
def test_forecasts_error_cov(self):
assert_allclose(
self.results.forecasts_error_cov.diagonal(),
- self.desired[['F1', 'F2', 'F3']]
+ self.desired[['F1', 'F2', 'F3']],rtol=2e-7 if i386_looser_tolerances else 1e-7
)
def test_predicted_states(self):
def test_smoothed_measurement_disturbance_cov(self):
assert_allclose(
self.results.smoothed_measurement_disturbance_cov.diagonal(),
- self.desired[['Veps1', 'Veps2', 'Veps3']]
+ self.desired[['Veps1', 'Veps2', 'Veps3']],rtol=2e-7 if i386_looser_tolerances else 1e-7
)