from src.domain.fitters.model_fitter import ModelFitter fpd_fitter = ModelFitter() fit = fpd_fitter.fit('barraza-contagion', 'agile-n2') fit.show_results()
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)
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)
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)
label='Datos reales (Proyecto Mixto Cascada/Ágil)') axes.plot(x_axis_data, ds.calculate_mean_failure_numbers(x_axis_data, ds_ml_a, ds_ml_b), linewidth=1, color='#ca3e47', linestyle='-', label='Delayed S-Shaped') axes.plot(x_axis_data, log.calculate_mean_failure_numbers(x_axis_data, log_ml_a, log_ml_b, log_ml_c), linewidth=1, color='#58b368', linestyle='-', label='Logístico') axes.legend() plt.show() ''' #ds_fit_50 = fpd_fitter.fit('delayed-s-shaped', 'mixed-waterfall-agile', end_sample=50, initial_approx=(20, 0.002)) #log_fit_50 = fpd_fitter.fit('logistic', 'mixed-waterfall-agile', end_sample=50, initial_approx=(1000, 0.00001, 10)) #ds_fit_60 = fpd_fitter.fit('delayed-s-shaped', 'mixed-waterfall-agile', end_sample=60, initial_approx=(20, 0.002)) #log_fit_60 = fpd_fitter.fit('logistic', 'mixed-waterfall-agile', end_sample=60, initial_approx=(1000, 0.00001, 100)) #ds_fit_70 = fpd_fitter.fit('delayed-s-shaped', 'mixed-waterfall-agile', end_sample=70, initial_approx=(10, 0.01)) #log_fit_70 = fpd_fitter.fit('logistic', 'mixed-waterfall-agile', end_sample=70, initial_approx=(1000, 0.00001, 100)) ds_fit_90 = fpd_fitter.fit('delayed-s-shaped', 'mixed-waterfall-agile', end_sample=90, initial_approx=(10, 0.01)) log_fit_70 = fpd_fitter.fit('logistic', 'mixed-waterfall-agile', end_sample=90, initial_approx=(1000, 0.00001, 100)) a = 2
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 ttf_fitter = ModelFitter() fit = ttf_fitter.fit('musa-okumoto', 'ntds') fit.show_results(plot_mttf=True, plot_mtbf=True)
axes.set_facecolor("#ffffff") axes.grid(color='black', linestyle='--', linewidth=0.5) axes.plot(x_axis_data, cumulative_failures, linewidth=1, color='#263859', linestyle='--', label='Datos reales (Proyecto Ágil #2)') axes.plot(x_axis_data, ds.calculate_mean_failure_numbers(x_axis_data, ds_ml_a, ds_ml_b), linewidth=1, color='#ca3e47', linestyle='-', label='Delayed S-Shaped') axes.plot(x_axis_data, log.calculate_mean_failure_numbers(x_axis_data, log_ml_a, log_ml_b, log_ml_c), linewidth=1, color='#58b368', linestyle='-', label='Logístico') axes.legend() plt.show() ''' #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)) 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)) #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)) #ds_fit_690 = fpd_fitter.fit('delayed-s-shaped', 'agile-n2', end_sample=10, initial_approx=(500, 0.002)) #log_fit_690 = fpd_fitter.fit('logistic', 'agile-n2', end_sample=10, initial_approx=(8000, 0.003, 0.001)) a = 2
from src.domain.fitters.model_fitter import ModelFitter fitter = ModelFitter() fit = fitter.fit('poisson', 'mixed-waterfall-agile') fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.domain.fitters.model_fitter import ModelFitter ttf_fitter = ModelFitter() fit = ttf_fitter.fit('poisson', 'agile-n1') fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.domain.fitters.model_fitter import ModelFitter ttf_fitter = ModelFitter() fit = ttf_fitter.fit('gompertz', 'agile-n1') fit.show_results()
from src.domain.fitters.model_fitter import ModelFitter ttf_fitter = ModelFitter() fit = ttf_fitter.fit('barraza-contagion', 'agile-n1', mt_formula='conditional') fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.domain.fitters.model_fitter import ModelFitter fitter = ModelFitter() fit = fitter.fit('musa-okumoto', 'mixed-waterfall-agile') fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.domain.fitters.model_fitter import ModelFitter ttf_fitter = ModelFitter() fit = ttf_fitter.fit('logistic', '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', 'agile-n4', initial_approx=(100, 0.001)) fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.domain.models.barraza_contagion.barraza_contagion_estimator import BarrazaContagionEstimator import numpy as np import matplotlib from matplotlib import pyplot as plt matplotlib.rcParams['pdf.fonttype'] = 42 matplotlib.rcParams['ps.fonttype'] = 42 bc = BarrazaContagionEstimator() ntds = DataRepository.provide_project_data('ntds') ntds_times = ntds.get_times() ntds_cf = ntds.get_cumulative_failures() ttf_fitter = ModelFitter() ntds_fit = ttf_fitter.fit('barraza-contagion', 'ntds') a_bc, b_bc = ntds_fit.get_lsq_parameters() mean_values_bc = bc.calculate_mean_failure_numbers(ntds_times, a_bc, b_bc) coef_r2 = np.var(mean_values_bc) / np.var(ntds_cf) print("rho: ", a_bc) print("gamma: ", b_bc * a_bc) print("gamma/rho: ", b_bc) print("r2: ", coef_r2) plt.style.use('bmh') fig, axes = plt.subplots(figsize=(8, 5)) axes.set_xlabel('Time (days)') axes.set_ylabel('Number of failures')
import numpy as np from src.data.data_repository import DataRepository from src.domain.fitters.model_fitter import ModelFitter from matplotlib import pyplot as plt from matplotlib import rc import matplotlib.font_manager rc('font', **{'family': 'serif', 'serif': ['CMU Sans Serif']}) plt.rcParams['font.family'] = 'Calibri' ttf_fitter = ModelFitter() ds_fit = ttf_fitter.fit('delayed-s-shaped', 'agile-n4', initial_approx=(100, 0.001), lsq_only=True, mt_formula='regular') log_fit = ttf_fitter.fit('logistic', 'agile-n4', initial_approx=(30, 0.03, 300), lsq_only=True, mt_formula='regular') bc_fit = ttf_fitter.fit('barraza-contagion', 'agile-n4', lsq_only=True, mt_formula='conditional') data = DataRepository.provide_project_data('agile-n4') real_tbf = data.get_time_between_failures() x_axis_data = np.linspace(1, len(real_tbf), len(real_tbf))
from src.domain.fitters.model_fitter import ModelFitter ttf_fitter = ModelFitter() fit = ttf_fitter.fit('delayed-s-shaped', 'agile-n1', end_sample=20) fit.show_results()
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.domain.fitters.model_fitter import ModelFitter fpd_fitter = ModelFitter() fit = fpd_fitter.fit('delayed-s-shaped', 'agile-n2', initial_approx=(10, 0.01)) 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 ttf_fitter = ModelFitter() #fit_go_10 = ttf_fitter.fit('goel-okumoto', 'ntds', end_sample=9, lsq_only=True) #fit_ds_10 = ttf_fitter.fit('delayed-s-shaped', 'ntds', end_sample=9, lsq_only=True) #fit_log_10 = ttf_fitter.fit('logistic', 'ntds', end_sample=9, lsq_only=True) #fit_bc_10 = ttf_fitter.fit('barraza-contagion', 'ntds', end_sample=9, lsq_only=True) #fit_go_20 = ttf_fitter.fit('goel-okumoto', 'ntds', end_sample=14, lsq_only=True) #fit_ds_20 = ttf_fitter.fit('delayed-s-shaped', 'ntds', end_sample=14, lsq_only=True) #fit_log_20 = ttf_fitter.fit('logistic', 'ntds', end_sample=14, lsq_only=True) fit_bc_20 = ttf_fitter.fit('barraza-contagion', 'ntds', end_sample=14, lsq_only=True) a=2
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)
import numpy as np from src.data.data_repository import DataRepository from src.domain.fitters.model_fitter import ModelFitter from matplotlib import pyplot as plt from matplotlib import rc import matplotlib.font_manager rc('font', **{'family': 'serif', 'serif': ['CMU Sans Serif']}) plt.rcParams['pdf.fonttype'] = 42 ttf_fitter = ModelFitter() ds_fit = ttf_fitter.fit('delayed-s-shaped', 'agile-n4', initial_approx=(100, 0.001)) log_fit = ttf_fitter.fit('logistic', 'agile-n4', initial_approx=(30, 0.03, 300)) bc_fit = ttf_fitter.fit('barraza-contagion', 'agile-n4') data = DataRepository.provide_project_data('agile-n4') real_tbf = data.get_time_between_failures() x_axis_data = np.linspace(1, len(real_tbf), len(real_tbf)) ds_mtbf = ds_fit.get_all_mtbf() log_mtbf = log_fit.get_all_mtbf() bc_mtbf = bc_fit.get_all_mtbf() fig, axes = plt.subplots() axes.set_xlabel('Número de falla') axes.set_ylabel('Tiempo medio entre fallas')
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 fpd_fitter = ModelFitter() #fit_go_50 = fpd_fitter.fit('goel-okumoto', 'mixed-waterfall-agile', end_sample=49, lsq_only=True) #fit_bc_50 = fpd_fitter.fit('barraza-contagion', 'mixed-waterfall-agile', end_sample=49, lsq_only=True) #fit_go_60 = fpd_fitter.fit('goel-okumoto', 'mixed-waterfall-agile', end_sample=59, lsq_only=True) #fit_bc_60 = fpd_fitter.fit('barraza-contagion', 'mixed-waterfall-agile', end_sample=59, lsq_only=True) #fit_go_70 = fpd_fitter.fit('goel-okumoto', 'mixed-waterfall-agile', end_sample=69, lsq_only=True) #fit_bc_70 = fpd_fitter.fit('barraza-contagion', 'mixed-waterfall-agile', end_sample=69, lsq_only=True) #fit_go_90 = fpd_fitter.fit('goel-okumoto', 'mixed-waterfall-agile', end_sample=89, lsq_only=True) fit_bc_90 = fpd_fitter.fit('barraza-contagion', 'mixed-waterfall-agile', end_sample=89, lsq_only=True) fit_log_90 = fpd_fitter.fit('logistic', 'mixed-waterfall-agile', end_sample=89, lsq_only=True) a=2
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)
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 from matplotlib import pyplot as plt ttf_fitter = ModelFitter() data = DataRepository.provide_project_data('agile-n1') x_axis_data = data.get_formated_times() cumulative_failures = data.get_cumulative_failures() ds = DelayedSShapedEstimator() log = LogisticEstimator() ds_fit = ttf_fitter.fit('delayed-s-shaped', 'agile-n1') log_fit = ttf_fitter.fit('logistic', 'agile-n1') 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 ttf_fitter = ModelFitter() fit = ttf_fitter.fit('barraza-contagion', 'agile-n3') fit.show_results(plot_mttf=True, plot_mtbf=True)
from src.domain.fitters.model_fitter import ModelFitter fpd_fitter = ModelFitter() fit = fpd_fitter.fit('barraza-contagion', 'mixed-waterfall-agile') fit.show_results()