def test_logistic_grouped_fpd_without_initial_approx_should_take_the_default_initial_approx(
self):
fit = ModelFitter().fit('logistic', 'mixed-waterfall-agile')
a, b, c = fit.get_lsq_parameters()
self.assertAlmostEqual(a, 835.410477, places=6)
self.assertAlmostEqual(b, 0.030741, places=6)
self.assertAlmostEqual(c, 75.801110, places=6)
def setUpClass(cls):
cls.fit_ntds_go = ModelFitter().fit('goel-okumoto',
'ntds',
initial_approx=(1, 0.5),
lsq_only=True,
mt_formula='regular')
cls.fit_mixed_ds = ModelFitter().fit('delayed-s-shaped',
'mixed-waterfall-agile',
initial_approx=(1, 0.5),
lsq_only=True,
mt_formula='regular')
cls.fit_agile1_log = ModelFitter().fit('logistic',
'agile-n1',
initial_approx=(100, 0.05, 20),
lsq_only=True,
mt_formula='regular')
cls.fit_ntds_ds = ModelFitter().fit('delayed-s-shaped',
'ntds',
initial_approx=(1, 0.5),
lsq_only=True,
mt_formula='regular')
cls.fit_ntds_log = ModelFitter().fit('logistic',
'ntds',
initial_approx=(100, 0.05, 20),
lsq_only=True,
mt_formula='regular')
def test_logistic_ttf_without_initial_approx_should_take_the_default_initial_approx(
self):
fit = ModelFitter().fit('logistic', 'ntds')
a, b, c = fit.get_lsq_parameters()
self.assertAlmostEqual(a, 24.640875, places=6)
self.assertAlmostEqual(b, 0.040930, places=6)
self.assertAlmostEqual(c, 76.510170, places=6)
def setUpClass(cls):
cls.go_ntds_fit = ModelFitter().fit('goel-okumoto',
'ntds',
lsq_only=True)
cls.ds_ntds_fit = ModelFitter().fit('delayed-s-shaped',
'ntds',
lsq_only=True)
cls.log_ntds_fit = ModelFitter().fit('logistic', 'ntds', lsq_only=True)
def setUpClass(cls):
cls.fit_ntds_go = ModelFitter().fit('goel-okumoto', 'ntds', initial_approx=(1, 0.5),
lsq_only=True, mt_formula='regular')
cls.fit_ntds_ds = ModelFitter().fit('delayed-s-shaped', 'ntds', initial_approx=(1, 0.5),
lsq_only=True, mt_formula='regular')
cls.fit_ntds_log = ModelFitter().fit('logistic', 'ntds', initial_approx=(10, 0.05, 20),
lsq_only=True, mt_formula='regular')
cls.fit_ntds_bc = ModelFitter().fit('barraza-contagion', 'ntds', initial_approx=(10, 0.01),
lsq_only=True, mt_formula='regular')
def setUpClass(cls):
cls.fit_ds_agile1_25samples = ModelFitter().fit('delayed-s-shaped',
'agile-n1',
end_sample=25,
mts=False)
cls.fit_log_agile1_25samples = ModelFitter().fit('logistic',
'agile-n1',
end_sample=25,
mts=False)
cls.fit_ds_mixed_35samples = ModelFitter().fit('delayed-s-shaped',
'mixed-waterfall-agile',
end_sample=35,
mts=False)
def test_wrong_initial_approx(self):
with self.assertRaises(InvalidArgumentException) as error:
ModelFitter().fit('goel-okumoto',
'ntds',
initial_approx=('Wine', 'Beer'))
self.assertEqual(
error.exception.strerror,
'Initial approximation must be a real tuple or array')
def setUpClass(cls):
cls.ntds_go_fit_from_sample_10 = ModelFitter().fit(
'goel-okumoto',
'ntds',
initial_sample=10,
mts=False,
initial_approx=(50, 0.005))
cls.ntds_go_fit_from_sample_0 = ModelFitter().fit('goel-okumoto',
'ntds',
initial_sample=0,
mts=False)
cls.mixed_ds_fit_from_sample_40 = ModelFitter().fit(
'delayed-s-shaped',
'mixed-waterfall-agile',
initial_sample=40,
mts=False)
cls.mixed_ds_fit_from_sample_0 = ModelFitter().fit(
'delayed-s-shaped',
'mixed-waterfall-agile',
initial_sample=0,
mts=False)
def setUpClass(cls):
cls.ttf_fit_ds = ModelFitter().fit('delayed-s-shaped',
'ntds',
t0=5,
mts=False)
cls.fpd_fit_ds = ModelFitter().fit('delayed-s-shaped',
'mixed-waterfall-agile',
t0=5,
mts=False)
cls.ttf_fit_go = ModelFitter().fit('goel-okumoto',
'ntds',
t0=5,
mts=False)
cls.fpd_fit_go = ModelFitter().fit('goel-okumoto',
'mixed-waterfall-agile',
t0=5,
mts=False)
cls.ttf_fit_log = ModelFitter().fit('logistic',
'ntds',
t0=5,
mts=False)
cls.fpd_fit_log = ModelFitter().fit('logistic',
'mixed-waterfall-agile',
t0=5,
mts=False,
initial_approx=(1000, 0.001, 100))
def setUpClass(cls):
cls.fit = ModelFitter().fit('delayed-s-shaped',
'ntds',
initial_approx=(1, 0.5),
lsq_only=True,
mts=False)
from src.domain.fitters.model_fitter import ModelFitter
ttf_fitter = ModelFitter()
fit = ttf_fitter.fit('logistic', 'agile-n3', initial_approx=(30, 0.03, 300))
fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.domain.fitters.model_fitter import ModelFitter
ttf_fitter = ModelFitter()
fit = ttf_fitter.fit('delayed-s-shaped', 'agile-n1')
fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.domain.fitters.model_fitter import ModelFitter
fitter = ModelFitter()
fit = fitter.fit('delayed-s-shaped', 'mixed-waterfall-agile')
fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.domain.fitters.model_fitter import ModelFitter
fitter = ModelFitter()
fit = fitter.fit('gompertz', 'mixed-waterfall-agile')
fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.domain.fitters.model_fitter import ModelFitter
fitter = ModelFitter()
fit = fitter.fit('logistic',
'mixed-waterfall-agile',
initial_approx=(1000, 0.001, 100))
fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.domain.fitters.model_fitter import ModelFitter
ttf_fitter = ModelFitter()
fit = ttf_fitter.fit('barraza-contagion', 'agile-n3')
fit.show_results(plot_mttf=True, plot_mtbf=True)
def setUpClass(cls):
cls.fit = ModelFitter().fit('barraza-contagion', 'mixed-waterfall-agile', initial_approx=(1, 0.5),
lsq_only=True, mts=False)
def setUpClass(cls):
cls.fit = ModelFitter().fit('delayed-s-shaped',
'mixed-waterfall-agile',
initial_approx=(1, 0.5),
mts=False)
def setUpClass(cls):
cls.fit = ModelFitter().fit('gompertz',
'ntds',
initial_approx=(25, 0.5, 0.9),
mts=False)
from src.domain.fitters.model_fitter import ModelFitter
ttf_fitter = ModelFitter()
fit = ttf_fitter.fit('musa-okumoto', 'ntds')
fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.domain.fitters.model_fitter import ModelFitter
ttf_fitter = ModelFitter()
fit = ttf_fitter.fit('delayed-s-shaped', 'ntds')
fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.data.data_repository import DataRepository
from src.domain.models.delayed_s_shaped.delayed_s_shaped_estimator import DelayedSShapedEstimator
from src.domain.models.logistic.logistic_estimator import LogisticEstimator
from src.domain.fitters.model_fitter import ModelFitter
fpd_fitter = ModelFitter()
data = DataRepository.provide_project_data('mixed-waterfall-agile')
x_axis_data = data.get_formated_times()
cumulative_failures = data.get_cumulative_failures()
ds = DelayedSShapedEstimator()
log = LogisticEstimator()
'''
ds_fit = fpd_fitter.fit('delayed-s-shaped', 'mixed-waterfall-agile')
log_fit = fpd_fitter.fit('logistic', 'mixed-waterfall-agile')
ds_lsq_a = ds_fit.get_lsq_parameters()[0]
ds_lsq_b = ds_fit.get_lsq_parameters()[1]
log_lsq_a = log_fit.get_lsq_parameters()[0]
log_lsq_b = log_fit.get_lsq_parameters()[1]
log_lsq_c = log_fit.get_lsq_parameters()[2]
fig, axes = plt.subplots()
axes.set_xlabel('Tiempo (días)')
axes.set_ylabel('Numero esperado de fallas')
axes.set_xlim(left=0, auto=True)
axes.set_ylim(auto=True)
axes.patch.set_facecolor("#ffffff")
from src.domain.fitters.model_fitter import ModelFitter
fpd_fitter = ModelFitter()
ds_fit_151 = fpd_fitter.fit('delayed-s-shaped',
'agile-n2',
end_sample=4,
initial_approx=(100, 0.001))
log_fit_151 = fpd_fitter.fit('logistic',
'agile-n2',
end_sample=4,
initial_approx=(8000, 0.003, 1))
bc_fit_151 = fpd_fitter.fit('barraza-contagion', 'agile-n2', end_sample=4)
ds_fit_248 = fpd_fitter.fit('delayed-s-shaped',
'agile-n2',
end_sample=5,
initial_approx=(500, 0.002))
#log_fit_248 = fpd_fitter.fit('logistic', 'agile-n2', end_sample=5, initial_approx=(8000, 0.003, 1))
bc_fit_248 = fpd_fitter.fit('barraza-contagion', 'agile-n2', end_sample=5)
ds_fit_480 = fpd_fitter.fit('delayed-s-shaped',
'agile-n2',
end_sample=6,
initial_approx=(100, 0.001))
log_fit_480 = fpd_fitter.fit('logistic',
'agile-n2',
end_sample=6,
initial_approx=(8000, 0.003, 1))
bc_fit_480 = fpd_fitter.fit('barraza-contagion', 'agile-n2', end_sample=6)
def setUpClass(cls):
cls.fit = ModelFitter().fit('poisson',
'ntds',
initial_approx=0.5,
lsq_only=True,
mts=False)
from src.domain.fitters.model_fitter import ModelFitter
fitter = ModelFitter()
fit = fitter.fit('goel-okumoto', 'mixed-waterfall-agile')
fit.show_results(plot_mttf=True, plot_mtbf=True)
def setUpClass(cls):
cls.fit_ntds_bc = ModelFitter().fit('barraza-contagion',
'ntds',
initial_approx=(10, 0.01),
lsq_only=True,
mt_formula='conditional')
from src.domain.fitters.model_fitter import ModelFitter
ttf_fitter = ModelFitter()
fit = ttf_fitter.fit('goel-okumoto', 'agile-n3', initial_approx=(20, 0.5))
fit.show_results(plot_mttf=True, plot_mtbf=True)
def setUpClass(cls):
cls.fit = ModelFitter().fit('goel-okumoto',
'mixed-waterfall-agile',
initial_approx=(1, 0.5),
lsq_only=True,
mts=False)
def setUpClass(cls):
cls.fit = ModelFitter().fit('logistic', 'mixed-waterfall-agile', initial_approx=(800, 0.03, 75), mts=False)
def setUpClass(cls):
cls.fit = ModelFitter().fit('goel-okumoto',
'ntds',
initial_approx=(1, 0.5),
lsq_only=True,
mts=False)
