.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/swissmetro/plot_b05normal_mixture_all_algos.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_swissmetro_plot_b05normal_mixture_all_algos.py: Mixture of logit ================ Example of the use of different algorithms to estimate the model. :author: Michel Bierlaire, EPFL :date: Sun Apr 9 17:38:34 2023 .. GENERATED FROM PYTHON SOURCE LINES 12-22 .. code-block:: default import itertools import pandas as pd from biogeme.tools import format_timedelta import biogeme.biogeme_logging as blog import biogeme.biogeme as bio from biogeme import models import biogeme.exceptions as excep from biogeme.expressions import Beta, bioDraws, log, MonteCarlo .. GENERATED FROM PYTHON SOURCE LINES 23-24 See the data processing script: :ref:`swissmetro_data`. .. GENERATED FROM PYTHON SOURCE LINES 24-41 .. code-block:: default from swissmetro_data import ( database, CHOICE, SM_AV, CAR_AV_SP, TRAIN_AV_SP, TRAIN_TT_SCALED, TRAIN_COST_SCALED, SM_TT_SCALED, SM_COST_SCALED, CAR_TT_SCALED, CAR_CO_SCALED, ) logger = blog.get_screen_logger(level=blog.INFO) logger.info('Example b05normal_mixture_all_algos.py') .. rst-class:: sphx-glr-script-out .. code-block:: none Example b05normal_mixture_all_algos.py .. GENERATED FROM PYTHON SOURCE LINES 42-43 Parameters to be estimated .. GENERATED FROM PYTHON SOURCE LINES 43-48 .. code-block:: default ASC_CAR = Beta('ASC_CAR', 0, None, None, 0) ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0) ASC_SM = Beta('ASC_SM', 0, None, None, 1) B_COST = Beta('B_COST', 0, None, None, 0) .. GENERATED FROM PYTHON SOURCE LINES 49-51 Define a random parameter, normally distributed, designed to be used for Monte-Carlo simulation. .. GENERATED FROM PYTHON SOURCE LINES 51-53 .. code-block:: default B_TIME = Beta('B_TIME', 0, None, None, 0) .. GENERATED FROM PYTHON SOURCE LINES 54-55 It is advised not to use 0 as starting value for the following parameter. .. GENERATED FROM PYTHON SOURCE LINES 55-58 .. code-block:: default B_TIME_S = Beta('B_TIME_S', 1, None, None, 0) B_TIME_RND = B_TIME + B_TIME_S * bioDraws('B_TIME_RND', 'NORMAL') .. GENERATED FROM PYTHON SOURCE LINES 59-60 Definition of the utility functions. .. GENERATED FROM PYTHON SOURCE LINES 60-64 .. code-block:: default V1 = ASC_TRAIN + B_TIME_RND * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED V2 = ASC_SM + B_TIME_RND * SM_TT_SCALED + B_COST * SM_COST_SCALED V3 = ASC_CAR + B_TIME_RND * CAR_TT_SCALED + B_COST * CAR_CO_SCALED .. GENERATED FROM PYTHON SOURCE LINES 65-66 Associate utility functions with the numbering of alternatives .. GENERATED FROM PYTHON SOURCE LINES 66-68 .. code-block:: default V = {1: V1, 2: V2, 3: V3} .. GENERATED FROM PYTHON SOURCE LINES 69-70 Associate the availability conditions with the alternatives .. GENERATED FROM PYTHON SOURCE LINES 70-72 .. code-block:: default av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP} .. GENERATED FROM PYTHON SOURCE LINES 73-74 Conditional to B_TIME_RND, we have a logit model (called the kernel) .. GENERATED FROM PYTHON SOURCE LINES 74-76 .. code-block:: default prob = models.logit(V, av, CHOICE) .. GENERATED FROM PYTHON SOURCE LINES 77-78 We integrate over B_TIME_RND using Monte-Carlo .. GENERATED FROM PYTHON SOURCE LINES 78-80 .. code-block:: default logprob = log(MonteCarlo(prob)) .. GENERATED FROM PYTHON SOURCE LINES 81-83 Options for the optimization algorithm -------------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 85-86 The conjugate gradient iteration can be constrained to stay feasible, or not. .. GENERATED FROM PYTHON SOURCE LINES 86-88 .. code-block:: default infeasible_cg_values = [True, False] .. GENERATED FROM PYTHON SOURCE LINES 89-90 The radius of the first trust region is tested with three different values. .. GENERATED FROM PYTHON SOURCE LINES 90-92 .. code-block:: default initial_radius_values = [0.1, 1.0, 10.0] .. GENERATED FROM PYTHON SOURCE LINES 93-94 The percentage of iterations such that the analytical second derivatives is evaluated. .. GENERATED FROM PYTHON SOURCE LINES 94-96 .. code-block:: default second_derivatives_values = [0.0, 0.5, 1.0] .. GENERATED FROM PYTHON SOURCE LINES 97-98 We run the optimization algorithm with all possible combinations of the parameters. The results are stored in a Pandas DataFrame called ``summary``. .. GENERATED FROM PYTHON SOURCE LINES 98-165 .. code-block:: default results = {} summary = pd.DataFrame( columns=[ 'LogLikelihood', 'GradientNorm', 'Optimization time', 'TerminationCause', 'Status', ] ) for infeasible_cg, initial_radius, second_derivatives in itertools.product( infeasible_cg_values, initial_radius_values, second_derivatives_values ): # Create the Biogeme object the_biogeme = bio.BIOGEME(database, logprob, parameter_file='few_draws.toml') # We set the parameters of the optimization algorithm the_biogeme.infeasible_cg = infeasible_cg the_biogeme.initial_radius = initial_radius the_biogeme.second_derivatives = second_derivatives # We cancel the generation of the outputfiles the_biogeme.generate_html = False the_biogeme.generate_pickle = False name = ( f'cg_{infeasible_cg}_radius_{initial_radius}_second_deriv_{second_derivatives}' ) the_biogeme.modelName = f'b05normal_mixture_algo_{name}'.strip() result_data = { 'InfeasibleCG': infeasible_cg, 'InitialRadius': initial_radius, 'SecondDerivatives': second_derivatives, 'Status': 'Success', # Assume success unless an exception is caught } try: results[name] = the_biogeme.estimate() opt_time = format_timedelta( results[name].data.optimizationMessages["Optimization time"] ) result_data.update( { 'LogLikelihood': results[name].data.logLike, 'GradientNorm': results[name].data.gradientNorm, 'Optimization time': opt_time, 'TerminationCause': results[name].data.optimizationMessages[ "Cause of termination" ], } ) except excep.BiogemeError as e: print(e) result_data.update( { 'Status': 'Failed', 'LogLikelihood': None, 'GradientNorm': None, 'Optimization time': None, 'TerminationCause': str(e), } ) results[name] = None summary = pd.concat([summary, pd.DataFrame([result_data])], ignore_index=True) .. rst-class:: sphx-glr-script-out .. code-block:: none File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_True_radius_0.1_second_deriv_0.0.iter Parameter values restored from __b05normal_mixture_algo_cg_True_radius_0.1_second_deriv_0.0.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: BFGS with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 -0.055 -0.8 -1.2 -1.4 0.9 5.3e+03 0.029 0.1 0.35 + 1 -0.15 -0.7 -1.1 -1.5 0.92 5.2e+03 0.014 0.1 0.57 + 2 -0.055 -0.71 -1.2 -1.6 0.97 5.2e+03 0.012 0.1 0.69 + 3 -0.13 -0.61 -1.2 -1.7 1 5.2e+03 0.015 0.1 0.56 + 4 -0.028 -0.67 -1.3 -1.7 1.1 5.2e+03 0.01 0.1 0.72 + 5 -0.061 -0.57 -1.2 -1.8 1.2 5.2e+03 0.01 0.1 0.78 + 6 0.039 -0.57 -1.2 -1.9 1.2 5.2e+03 0.0077 0.1 0.61 + 7 0.032 -0.47 -1.2 -1.9 1.3 5.2e+03 0.0096 0.1 0.66 + 8 0.059 -0.49 -1.2 -2 1.3 5.2e+03 0.0052 0.1 0.66 + 9 0.059 -0.49 -1.2 -2 1.3 5.2e+03 0.0052 0.05 -0.2 - 10 0.082 -0.46 -1.3 -2 1.4 5.2e+03 0.0097 0.05 0.43 + 11 0.06 -0.46 -1.2 -2.1 1.4 5.2e+03 0.0036 0.05 0.66 + 12 0.06 -0.46 -1.2 -2.1 1.4 5.2e+03 0.0036 0.025 0.012 - 13 0.085 -0.48 -1.3 -2.1 1.4 5.2e+03 0.0042 0.025 0.17 + 14 0.071 -0.46 -1.3 -2.1 1.5 5.2e+03 0.004 0.025 0.68 + 15 0.093 -0.46 -1.3 -2.1 1.5 5.2e+03 0.0015 0.025 0.86 + 16 0.087 -0.43 -1.3 -2.1 1.5 5.2e+03 0.0022 0.025 0.59 + 17 0.11 -0.43 -1.3 -2.1 1.5 5.2e+03 0.0012 0.025 0.8 + 18 0.1 -0.43 -1.3 -2.2 1.5 5.2e+03 0.0013 0.025 0.73 + 19 0.12 -0.42 -1.3 -2.2 1.6 5.2e+03 0.0026 0.025 0.6 + 20 0.12 -0.42 -1.3 -2.2 1.6 5.2e+03 0.0026 0.012 -0.84 - 21 0.12 -0.42 -1.3 -2.2 1.6 5.2e+03 0.00081 0.012 0.52 + 22 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00086 0.012 0.74 + 23 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0015 0.012 0.38 + 24 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00043 0.012 0.8 + 25 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00043 0.0062 -1.2 - 26 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00025 0.0062 0.35 + 27 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00025 0.0031 -0.92 - 28 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00025 0.0016 0.096 - 29 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00019 0.0016 0.57 + 30 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00021 0.0016 0.49 + 31 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6e-05 0.0016 0.17 + 32 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.2e-05 0.0016 0.59 + 33 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.2e-05 0.00078 -4.2 - 34 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.2e-05 0.00039 -0.63 - 35 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.6e-06 0.0039 0.99 ++ 36 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.6e-06 0.0014 -10 - 37 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.6e-06 0.00069 -7.9 - 38 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.6e-06 0.00034 -3.8 - 39 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.6e-06 0.00017 -1.8 - 40 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.6e-06 8.6e-05 -0.49 - 41 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.6e-06 4.3e-05 0.05 - 42 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6.3e-06 4.3e-05 0.61 + 43 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6.9e-06 0.00043 0.92 ++ 44 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6.9e-06 0.00021 -0.67 - 45 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6e-06 0.00021 0.28 - /Users/bierlair/OnlineFiles/FilesOnGoogleDrive/github/biogeme/docs/examples/swissmetro/plot_b05normal_mixture_all_algos.py:163: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation. summary = pd.concat([summary, pd.DataFrame([result_data])], ignore_index=True) File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_True_radius_0.1_second_deriv_0.5.iter Parameter values restored from __b05normal_mixture_algo_cg_True_radius_0.1_second_deriv_0.5.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Hybrid Newton 50.0%/BFGS with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 -0.15 -0.8 -1.1 -1.4 0.95 5.3e+03 0.021 1 1 ++ 1 0.049 -0.5 -1.2 -1.9 1.3 5.2e+03 0.013 10 1.2 ++ 2 0.12 -0.42 -1.3 -2.2 1.5 5.2e+03 0.0027 1e+02 1.1 ++ 3 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0005 1e+03 1.2 ++ 4 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 3.5e-06 1e+03 1 ++ File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_True_radius_0.1_second_deriv_1.0.iter Parameter values restored from __b05normal_mixture_algo_cg_True_radius_0.1_second_deriv_1.0.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 -0.15 -0.8 -1.1 -1.4 0.95 5.3e+03 0.021 1 1 ++ 1 0.072 -0.47 -1.2 -2 1.4 5.2e+03 0.012 10 1.1 ++ 2 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0016 1e+02 1.1 ++ 3 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.2e-05 1e+03 1 ++ 4 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.5e-08 1e+03 1 ++ File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_True_radius_1.0_second_deriv_0.0.iter Parameter values restored from __b05normal_mixture_algo_cg_True_radius_1.0_second_deriv_0.0.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: BFGS with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.5 -3.9 - 1 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.25 -2 - 2 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.12 -0.58 - 3 -0.03 -0.83 -1.2 -1.4 0.88 5.3e+03 0.026 0.12 0.19 + 4 -0.15 -0.7 -1.1 -1.5 1 5.2e+03 0.013 0.12 0.57 + 5 -0.03 -0.69 -1.2 -1.6 1 5.2e+03 0.015 0.12 0.83 + 6 -0.1 -0.59 -1.2 -1.7 1.1 5.2e+03 0.014 0.12 0.6 + 7 0.023 -0.56 -1.2 -1.8 1.1 5.2e+03 0.013 0.12 0.72 + 8 -0.0025 -0.54 -1.2 -1.9 1.2 5.2e+03 0.0063 0.12 0.64 + 9 0.045 -0.45 -1.2 -1.9 1.3 5.2e+03 0.017 0.12 0.38 + 10 0.045 -0.45 -1.2 -1.9 1.3 5.2e+03 0.017 0.062 -0.57 - 11 0.059 -0.51 -1.2 -2 1.3 5.2e+03 0.0044 0.062 0.71 + 12 0.051 -0.46 -1.2 -2 1.4 5.2e+03 0.004 0.062 0.75 + 13 0.11 -0.47 -1.3 -2.1 1.4 5.2e+03 0.0067 0.062 0.14 + 14 0.078 -0.47 -1.2 -2.1 1.5 5.2e+03 0.0061 0.062 0.43 + 15 0.078 -0.47 -1.2 -2.1 1.5 5.2e+03 0.0061 0.031 -0.43 - 16 0.097 -0.43 -1.3 -2.1 1.5 5.2e+03 0.007 0.031 0.43 + 17 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0014 0.31 0.95 ++ 18 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0014 0.16 -7.1 - 19 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0014 0.078 -2.2 - 20 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0014 0.039 -0.6 - 21 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0014 0.02 -0.00066 - 22 0.094 -0.42 -1.3 -2.1 1.5 5.2e+03 0.0022 0.02 0.37 + 23 0.11 -0.43 -1.3 -2.2 1.5 5.2e+03 0.0012 0.02 0.72 + 24 0.1 -0.43 -1.3 -2.2 1.5 5.2e+03 0.0021 0.02 0.12 + 25 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0045 0.02 0.19 + 26 0.12 -0.43 -1.3 -2.2 1.6 5.2e+03 0.0014 0.02 0.47 + 27 0.11 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0014 0.02 0.46 + 28 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.001 0.02 0.54 + 29 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00068 0.02 0.12 + 30 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00068 0.0098 -2.8 - 31 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00082 0.0098 0.49 + 32 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00082 0.0049 -3.6 - 33 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00082 0.0024 -0.35 - 34 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00018 0.0024 0.89 + 35 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00019 0.0024 0.14 + 36 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00018 0.0024 0.5 + 37 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00018 0.0012 -1.2 - 38 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00018 0.00061 -0.38 - 39 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00011 0.00061 0.42 + 40 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.8e-05 0.00061 0.8 + 41 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 7.3e-05 0.00061 0.34 + 42 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.6e-05 0.00061 0.31 + 43 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.6e-05 0.00031 -0.004 - 44 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.6e-05 0.00031 0.8 + 45 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.4e-05 0.00031 0.51 + 46 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.4e-05 0.00015 -0.69 - 47 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.5e-05 0.00015 0.27 + 48 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6.6e-06 0.0015 0.9 ++ 49 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6.6e-06 0.0004 -1.3 - 50 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 7.5e-06 0.0004 0.5 + 51 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.4e-06 0.0004 0.4 + File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_True_radius_1.0_second_deriv_0.5.iter Parameter values restored from __b05normal_mixture_algo_cg_True_radius_1.0_second_deriv_0.5.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Hybrid Newton 50.0%/BFGS with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 0.042 -0.49 -1.2 -1.9 1.2 5.2e+03 0.012 10 1.1 ++ 1 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0081 1e+02 1.3 ++ 2 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00068 1e+03 1.1 ++ 3 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6.2e-05 1e+04 1.1 ++ 4 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6e-08 1e+04 1 ++ File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_True_radius_1.0_second_deriv_1.0.iter Parameter values restored from __b05normal_mixture_algo_cg_True_radius_1.0_second_deriv_1.0.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 0.042 -0.49 -1.2 -1.9 1.2 5.2e+03 0.012 10 1.1 ++ 1 0.11 -0.42 -1.3 -2.2 1.5 5.2e+03 0.0035 1e+02 1.1 ++ 2 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00013 1e+03 1 ++ 3 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.3e-07 1e+03 1 ++ File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_True_radius_10.0_second_deriv_0.0.iter Parameter values restored from __b05normal_mixture_algo_cg_True_radius_10.0_second_deriv_0.0.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: BFGS with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 5 -3.3 - 1 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 2.5 -5.4 - 2 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 1.2 -5.4 - 3 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.62 -4.4 - 4 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.31 -2.6 - 5 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.16 -0.96 - 6 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.078 -0.0066 - 7 -0.076 -0.78 -1.2 -1.4 0.92 5.3e+03 0.033 0.078 0.49 + 8 -0.15 -0.7 -1.1 -1.4 0.85 5.2e+03 0.015 0.078 0.47 + 9 -0.076 -0.78 -1.2 -1.5 0.93 5.2e+03 0.015 0.078 0.4 + 10 -0.15 -0.7 -1.1 -1.6 1 5.2e+03 0.015 0.078 0.57 + 11 -0.076 -0.62 -1.2 -1.6 1.1 5.2e+03 0.025 0.078 0.44 + 12 -0.055 -0.7 -1.2 -1.7 1.1 5.2e+03 0.012 0.078 0.32 + 13 -0.13 -0.62 -1.2 -1.7 1.1 5.2e+03 0.016 0.078 0.32 + 14 -0.055 -0.6 -1.3 -1.8 1.2 5.2e+03 0.013 0.078 0.6 + 15 0.023 -0.53 -1.2 -1.9 1.3 5.2e+03 0.016 0.078 0.68 + 16 0.023 -0.53 -1.2 -1.9 1.3 5.2e+03 0.016 0.039 0.029 - 17 0.023 -0.56 -1.2 -1.9 1.3 5.2e+03 0.0049 0.039 0.57 + 18 0.023 -0.53 -1.2 -1.9 1.3 5.2e+03 0.0059 0.039 0.86 + 19 0.062 -0.52 -1.2 -2 1.4 5.2e+03 0.0052 0.039 0.81 + 20 0.04 -0.48 -1.2 -2 1.4 5.2e+03 0.0043 0.039 0.74 + 21 0.079 -0.48 -1.3 -2 1.4 5.2e+03 0.0033 0.039 0.81 + 22 0.072 -0.46 -1.3 -2.1 1.4 5.2e+03 0.002 0.39 0.95 ++ 23 0.072 -0.46 -1.3 -2.1 1.4 5.2e+03 0.002 0.2 -0.99 - 24 0.14 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00094 0.2 0.69 + 25 0.14 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00094 0.098 -14 - 26 0.14 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00094 0.049 -6.5 - 27 0.14 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00094 0.024 -0.6 - 28 0.13 -0.4 -1.3 -2.2 1.6 5.2e+03 0.0019 0.024 0.2 + 29 0.13 -0.4 -1.3 -2.2 1.6 5.2e+03 0.0019 0.012 -1 - 30 0.13 -0.4 -1.3 -2.2 1.6 5.2e+03 0.0019 0.0061 0.03 - 31 0.13 -0.4 -1.3 -2.2 1.6 5.2e+03 0.00077 0.0061 0.69 + 32 0.13 -0.4 -1.3 -2.2 1.6 5.2e+03 0.00032 0.0061 0.48 + 33 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00046 0.0061 0.4 + 34 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0002 0.0061 0.45 + 35 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0002 0.0031 -0.051 - 36 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.1e-05 0.0031 0.86 + 37 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.9e-05 0.0031 0.4 + 38 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.9e-05 0.0015 -2.7 - 39 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.9e-05 0.00076 -0.85 - 40 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.7e-05 0.00076 0.38 + 41 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.9e-05 0.00076 0.22 + 42 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.4e-05 0.00076 0.24 + 43 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.7e-05 0.00076 0.61 + 44 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.7e-05 0.00038 -0.59 - 45 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.9e-05 0.00038 0.11 + 46 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.2e-06 0.00038 0.97 + File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_True_radius_10.0_second_deriv_0.5.iter Parameter values restored from __b05normal_mixture_algo_cg_True_radius_10.0_second_deriv_0.5.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Hybrid Newton 50.0%/BFGS with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 0.042 -0.49 -1.2 -1.9 1.2 5.2e+03 0.012 1e+02 1.1 ++ 1 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0081 1e+03 1.3 ++ 2 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00068 1e+04 1.1 ++ 3 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6.2e-05 1e+05 1.1 ++ 4 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6e-08 1e+05 1 ++ File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_True_radius_10.0_second_deriv_1.0.iter Parameter values restored from __b05normal_mixture_algo_cg_True_radius_10.0_second_deriv_1.0.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 0.042 -0.49 -1.2 -1.9 1.2 5.2e+03 0.012 1e+02 1.1 ++ 1 0.11 -0.42 -1.3 -2.2 1.5 5.2e+03 0.0035 1e+03 1.1 ++ 2 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00013 1e+04 1 ++ 3 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.3e-07 1e+04 1 ++ File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_False_radius_0.1_second_deriv_0.0.iter Parameter values restored from __b05normal_mixture_algo_cg_False_radius_0.1_second_deriv_0.0.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: BFGS with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 -0.055 -0.8 -1.2 -1.4 0.9 5.3e+03 0.029 0.1 0.35 + 1 -0.15 -0.7 -1.1 -1.5 0.92 5.2e+03 0.014 0.1 0.57 + 2 -0.055 -0.71 -1.2 -1.6 0.97 5.2e+03 0.012 0.1 0.69 + 3 -0.13 -0.61 -1.2 -1.7 1 5.2e+03 0.015 0.1 0.56 + 4 -0.028 -0.67 -1.3 -1.7 1.1 5.2e+03 0.01 0.1 0.72 + 5 -0.061 -0.57 -1.2 -1.8 1.2 5.2e+03 0.01 0.1 0.78 + 6 0.039 -0.57 -1.2 -1.9 1.2 5.2e+03 0.0077 0.1 0.61 + 7 0.032 -0.47 -1.2 -1.9 1.3 5.2e+03 0.0096 0.1 0.66 + 8 0.059 -0.49 -1.2 -2 1.3 5.2e+03 0.0051 0.1 0.67 + 9 0.059 -0.49 -1.2 -2 1.3 5.2e+03 0.0051 0.05 -0.19 - 10 0.082 -0.46 -1.3 -2 1.4 5.2e+03 0.0097 0.05 0.43 + 11 0.061 -0.46 -1.2 -2.1 1.4 5.2e+03 0.0035 0.05 0.67 + 12 0.061 -0.46 -1.2 -2.1 1.4 5.2e+03 0.0035 0.025 0.011 - 13 0.086 -0.48 -1.3 -2.1 1.4 5.2e+03 0.0043 0.025 0.15 + 14 0.07 -0.46 -1.3 -2.1 1.5 5.2e+03 0.0039 0.025 0.69 + 15 0.094 -0.46 -1.3 -2.1 1.5 5.2e+03 0.0016 0.025 0.86 + 16 0.087 -0.43 -1.3 -2.1 1.5 5.2e+03 0.0022 0.025 0.59 + 17 0.11 -0.43 -1.3 -2.1 1.5 5.2e+03 0.0012 0.025 0.8 + 18 0.1 -0.43 -1.3 -2.2 1.5 5.2e+03 0.0013 0.025 0.71 + 19 0.12 -0.42 -1.3 -2.2 1.6 5.2e+03 0.0027 0.025 0.58 + 20 0.12 -0.42 -1.3 -2.2 1.6 5.2e+03 0.0027 0.012 -0.38 - 21 0.12 -0.42 -1.3 -2.2 1.6 5.2e+03 0.00081 0.012 0.54 + 22 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00084 0.012 0.76 + 23 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0013 0.012 0.53 + 24 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00029 0.012 0.74 + 25 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00029 0.0062 -1.7 - 26 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00029 0.0031 -0.12 - 27 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00023 0.0031 0.9 + 28 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00023 0.0016 -0.24 - 29 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0001 0.0016 0.43 + 30 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0001 0.0016 0.4 + 31 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.3e-05 0.0016 0.72 + 32 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.3e-05 0.00078 -2.3 - 33 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 4.9e-05 0.00078 0.26 + 34 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 4.9e-05 0.00039 0.098 - 35 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 4.9e-05 0.0002 -0.15 - 36 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 4.4e-05 0.0002 0.51 + 37 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.9e-05 0.0002 0.63 + 38 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.4e-05 0.002 0.96 ++ 39 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.4e-05 0.00098 -2.2 - 40 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.4e-05 0.00049 -0.17 - 41 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.3e-05 0.00049 0.57 + 42 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.3e-05 0.00024 -0.41 - 43 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.9e-06 0.00024 0.66 + 44 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 5e-06 0.00024 0.79 + File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_False_radius_0.1_second_deriv_0.5.iter Parameter values restored from __b05normal_mixture_algo_cg_False_radius_0.1_second_deriv_0.5.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Hybrid Newton 50.0%/BFGS with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 -0.15 -0.8 -1.1 -1.4 0.95 5.3e+03 0.021 1 1 ++ 1 0.049 -0.5 -1.2 -1.9 1.3 5.2e+03 0.013 10 1.2 ++ 2 0.12 -0.42 -1.3 -2.2 1.5 5.2e+03 0.0027 1e+02 1.1 ++ 3 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0005 1e+03 1.2 ++ 4 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 3.5e-06 1e+03 1 ++ File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_False_radius_0.1_second_deriv_1.0.iter Parameter values restored from __b05normal_mixture_algo_cg_False_radius_0.1_second_deriv_1.0.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 -0.15 -0.8 -1.1 -1.4 0.95 5.3e+03 0.021 1 1 ++ 1 0.072 -0.47 -1.2 -2 1.4 5.2e+03 0.012 10 1.1 ++ 2 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0016 1e+02 1.1 ++ 3 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.2e-05 1e+03 1 ++ 4 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.5e-08 1e+03 1 ++ File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_False_radius_1.0_second_deriv_0.0.iter Parameter values restored from __b05normal_mixture_algo_cg_False_radius_1.0_second_deriv_0.0.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: BFGS with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.5 -3.9 - 1 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.25 -2 - 2 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.12 -0.58 - 3 -0.03 -0.83 -1.2 -1.4 0.88 5.3e+03 0.026 0.12 0.19 + 4 -0.15 -0.7 -1.1 -1.5 1 5.2e+03 0.013 0.12 0.57 + 5 -0.03 -0.69 -1.2 -1.6 1 5.2e+03 0.015 0.12 0.83 + 6 -0.1 -0.59 -1.2 -1.7 1.1 5.2e+03 0.014 0.12 0.6 + 7 0.023 -0.56 -1.2 -1.8 1.1 5.2e+03 0.013 0.12 0.72 + 8 -0.0025 -0.54 -1.2 -1.9 1.2 5.2e+03 0.0063 0.12 0.64 + 9 0.045 -0.45 -1.2 -1.9 1.3 5.2e+03 0.017 0.12 0.38 + 10 0.045 -0.45 -1.2 -1.9 1.3 5.2e+03 0.017 0.062 -0.57 - 11 0.059 -0.51 -1.2 -2 1.3 5.2e+03 0.0044 0.062 0.71 + 12 0.051 -0.46 -1.2 -2 1.4 5.2e+03 0.004 0.062 0.75 + 13 0.11 -0.47 -1.3 -2.1 1.4 5.2e+03 0.0067 0.062 0.14 + 14 0.078 -0.47 -1.2 -2.1 1.5 5.2e+03 0.0061 0.062 0.43 + 15 0.078 -0.47 -1.2 -2.1 1.5 5.2e+03 0.0061 0.031 -0.43 - 16 0.097 -0.43 -1.3 -2.1 1.5 5.2e+03 0.007 0.031 0.43 + 17 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0014 0.31 0.95 ++ 18 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0014 0.16 -7.1 - 19 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0014 0.078 -2.2 - 20 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0014 0.039 -0.6 - 21 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0014 0.02 -0.00066 - 22 0.094 -0.42 -1.3 -2.1 1.5 5.2e+03 0.0022 0.02 0.37 + 23 0.11 -0.43 -1.3 -2.2 1.5 5.2e+03 0.0012 0.02 0.72 + 24 0.1 -0.43 -1.3 -2.2 1.5 5.2e+03 0.0021 0.02 0.12 + 25 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0045 0.02 0.19 + 26 0.12 -0.43 -1.3 -2.2 1.6 5.2e+03 0.0014 0.02 0.47 + 27 0.11 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0014 0.02 0.46 + 28 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.001 0.02 0.54 + 29 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00068 0.02 0.12 + 30 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00068 0.0098 -2.8 - 31 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00082 0.0098 0.49 + 32 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00082 0.0049 -3.6 - 33 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00082 0.0024 -0.35 - 34 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00018 0.0024 0.89 + 35 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00019 0.0024 0.14 + 36 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00018 0.0024 0.5 + 37 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00018 0.0012 -1.2 - 38 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00018 0.00061 -0.38 - 39 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00011 0.00061 0.42 + 40 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.8e-05 0.00061 0.8 + 41 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 7.3e-05 0.00061 0.34 + 42 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.6e-05 0.00061 0.31 + 43 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.6e-05 0.00031 -0.004 - 44 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.6e-05 0.00031 0.8 + 45 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.4e-05 0.00031 0.51 + 46 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.4e-05 0.00015 -0.69 - 47 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.5e-05 0.00015 0.27 + 48 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6.6e-06 0.0015 0.9 ++ 49 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6.6e-06 0.0004 -1.3 - 50 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 7.5e-06 0.0004 0.5 + 51 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.4e-06 0.0004 0.4 + File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_False_radius_1.0_second_deriv_0.5.iter Parameter values restored from __b05normal_mixture_algo_cg_False_radius_1.0_second_deriv_0.5.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Hybrid Newton 50.0%/BFGS with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 0.042 -0.49 -1.2 -1.9 1.2 5.2e+03 0.012 10 1.1 ++ 1 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0081 1e+02 1.3 ++ 2 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00068 1e+03 1.1 ++ 3 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6.2e-05 1e+04 1.1 ++ 4 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6e-08 1e+04 1 ++ File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_False_radius_1.0_second_deriv_1.0.iter Parameter values restored from __b05normal_mixture_algo_cg_False_radius_1.0_second_deriv_1.0.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 0.042 -0.49 -1.2 -1.9 1.2 5.2e+03 0.012 10 1.1 ++ 1 0.11 -0.42 -1.3 -2.2 1.5 5.2e+03 0.0035 1e+02 1.1 ++ 2 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00013 1e+03 1 ++ 3 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.3e-07 1e+03 1 ++ File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_False_radius_10.0_second_deriv_0.0.iter Parameter values restored from __b05normal_mixture_algo_cg_False_radius_10.0_second_deriv_0.0.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: BFGS with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 5 -3.3 - 1 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 2.5 -5.4 - 2 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 1.2 -5.4 - 3 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.62 -4.4 - 4 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.31 -2.6 - 5 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.16 -0.96 - 6 -0.15 -0.7 -1.1 -1.3 1 5.3e+03 0.047 0.078 -0.0066 - 7 -0.076 -0.78 -1.2 -1.4 0.92 5.3e+03 0.033 0.078 0.49 + 8 -0.15 -0.7 -1.1 -1.4 0.85 5.2e+03 0.015 0.078 0.47 + 9 -0.076 -0.78 -1.2 -1.5 0.93 5.2e+03 0.015 0.078 0.4 + 10 -0.15 -0.7 -1.1 -1.6 1 5.2e+03 0.015 0.078 0.57 + 11 -0.076 -0.62 -1.2 -1.6 1.1 5.2e+03 0.025 0.078 0.44 + 12 -0.055 -0.7 -1.2 -1.7 1.1 5.2e+03 0.012 0.078 0.32 + 13 -0.13 -0.62 -1.2 -1.7 1.1 5.2e+03 0.016 0.078 0.32 + 14 -0.055 -0.6 -1.3 -1.8 1.2 5.2e+03 0.013 0.078 0.6 + 15 0.023 -0.53 -1.2 -1.9 1.3 5.2e+03 0.016 0.078 0.68 + 16 0.023 -0.53 -1.2 -1.9 1.3 5.2e+03 0.016 0.039 0.029 - 17 0.023 -0.56 -1.2 -1.9 1.3 5.2e+03 0.0049 0.039 0.57 + 18 0.023 -0.53 -1.2 -1.9 1.3 5.2e+03 0.0059 0.039 0.86 + 19 0.062 -0.52 -1.2 -2 1.4 5.2e+03 0.0052 0.039 0.81 + 20 0.04 -0.48 -1.2 -2 1.4 5.2e+03 0.0043 0.039 0.74 + 21 0.079 -0.48 -1.3 -2 1.4 5.2e+03 0.0033 0.039 0.81 + 22 0.072 -0.46 -1.3 -2.1 1.4 5.2e+03 0.002 0.39 0.95 ++ 23 0.072 -0.46 -1.3 -2.1 1.4 5.2e+03 0.002 0.2 -0.99 - 24 0.14 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00094 0.2 0.69 + 25 0.14 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00094 0.098 -14 - 26 0.14 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00094 0.049 -6.5 - 27 0.14 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00094 0.024 -0.6 - 28 0.13 -0.4 -1.3 -2.2 1.6 5.2e+03 0.0019 0.024 0.2 + 29 0.13 -0.4 -1.3 -2.2 1.6 5.2e+03 0.0019 0.012 -1 - 30 0.13 -0.4 -1.3 -2.2 1.6 5.2e+03 0.0019 0.0061 0.03 - 31 0.13 -0.4 -1.3 -2.2 1.6 5.2e+03 0.00077 0.0061 0.69 + 32 0.13 -0.4 -1.3 -2.2 1.6 5.2e+03 0.00032 0.0061 0.48 + 33 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00046 0.0061 0.4 + 34 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0002 0.0061 0.45 + 35 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.0002 0.0031 -0.051 - 36 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.1e-05 0.0031 0.86 + 37 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.9e-05 0.0031 0.4 + 38 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.9e-05 0.0015 -2.7 - 39 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.9e-05 0.00076 -0.85 - 40 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 9.7e-05 0.00076 0.38 + 41 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.9e-05 0.00076 0.22 + 42 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 3.4e-05 0.00076 0.24 + 43 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.7e-05 0.00076 0.61 + 44 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.7e-05 0.00038 -0.59 - 45 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 1.9e-05 0.00038 0.11 + 46 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.2e-06 0.00038 0.97 + File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_False_radius_10.0_second_deriv_0.5.iter Parameter values restored from __b05normal_mixture_algo_cg_False_radius_10.0_second_deriv_0.5.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Hybrid Newton 50.0%/BFGS with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 0.042 -0.49 -1.2 -1.9 1.2 5.2e+03 0.012 1e+02 1.1 ++ 1 0.098 -0.44 -1.3 -2.1 1.5 5.2e+03 0.0081 1e+03 1.3 ++ 2 0.12 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00068 1e+04 1.1 ++ 3 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6.2e-05 1e+05 1.1 ++ 4 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 6e-08 1e+05 1 ++ File few_draws.toml has been parsed. *** Initial values of the parameters are obtained from the file __b05normal_mixture_algo_cg_False_radius_10.0_second_deriv_1.0.iter Parameter values restored from __b05normal_mixture_algo_cg_False_radius_10.0_second_deriv_1.0.iter Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR ASC_TRAIN B_COST B_TIME B_TIME_S Function Relgrad Radius Rho 0 0.042 -0.49 -1.2 -1.9 1.2 5.2e+03 0.012 1e+02 1.1 ++ 1 0.11 -0.42 -1.3 -2.2 1.5 5.2e+03 0.0035 1e+03 1.1 ++ 2 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 0.00013 1e+04 1 ++ 3 0.13 -0.41 -1.3 -2.2 1.6 5.2e+03 2.3e-07 1e+04 1 ++ .. GENERATED FROM PYTHON SOURCE LINES 166-168 .. code-block:: default summary .. raw:: html
LogLikelihood GradientNorm Optimization time TerminationCause Status InfeasibleCG InitialRadius SecondDerivatives
0 -5216.339884 0.030958 19.3s Relative gradient = 6e-06 <= 6.1e-06 Success True 0.1 0.0
1 -5216.339884 0.012097 4.7s Relative gradient = 3.5e-06 <= 6.1e-06 Success True 0.1 0.5
2 -5216.339883 0.000054 5.8s Relative gradient = 1.5e-08 <= 6.1e-06 Success True 0.1 1.0
3 -5216.339883 0.013562 23.9s Relative gradient = 2.4e-06 <= 6.1e-06 Success True 1.0 0.0
4 -5216.339883 0.000230 5.1s Relative gradient = 6e-08 <= 6.1e-06 Success True 1.0 0.5
5 -5216.339883 0.000796 4.6s Relative gradient = 2.3e-07 <= 6.1e-06 Success True 1.0 1.0
6 -5216.339883 0.009656 18.0s Relative gradient = 2.2e-06 <= 6.1e-06 Success True 10.0 0.0
7 -5216.339883 0.000230 4.7s Relative gradient = 6e-08 <= 6.1e-06 Success True 10.0 0.5
8 -5216.339883 0.000796 4.5s Relative gradient = 2.3e-07 <= 6.1e-06 Success True 10.0 1.0
9 -5216.339884 0.022387 18.9s Relative gradient = 5e-06 <= 6.1e-06 Success False 0.1 0.0
10 -5216.339884 0.012091 4.8s Relative gradient = 3.5e-06 <= 6.1e-06 Success False 0.1 0.5
11 -5216.339883 0.000054 5.8s Relative gradient = 1.5e-08 <= 6.1e-06 Success False 0.1 1.0
12 -5216.339883 0.013562 21.8s Relative gradient = 2.4e-06 <= 6.1e-06 Success False 1.0 0.0
13 -5216.339883 0.000230 4.6s Relative gradient = 6e-08 <= 6.1e-06 Success False 1.0 0.5
14 -5216.339883 0.000796 4.7s Relative gradient = 2.3e-07 <= 6.1e-06 Success False 1.0 1.0
15 -5216.339883 0.009656 18.9s Relative gradient = 2.2e-06 <= 6.1e-06 Success False 10.0 0.0
16 -5216.339883 0.000230 4.6s Relative gradient = 6e-08 <= 6.1e-06 Success False 10.0 0.5
17 -5216.339883 0.000796 4.6s Relative gradient = 2.3e-07 <= 6.1e-06 Success False 10.0 1.0


.. GENERATED FROM PYTHON SOURCE LINES 169-172 .. code-block:: default SUMMARY_FILE = '05normalMixture_allAlgos.csv' summary.to_csv(SUMMARY_FILE, index=False) print(f'Summary reported in file {SUMMARY_FILE}') .. rst-class:: sphx-glr-script-out .. code-block:: none Summary reported in file 05normalMixture_allAlgos.csv .. rst-class:: sphx-glr-timing **Total running time of the script:** (3 minutes 33.173 seconds) .. _sphx_glr_download_auto_examples_swissmetro_plot_b05normal_mixture_all_algos.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_b05normal_mixture_all_algos.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_b05normal_mixture_all_algos.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_