Note
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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
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
See the data processing script: Data preparation for Swissmetro.
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')
Example b05normal_mixture_all_algos.py
Parameters to be estimated
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)
Define a random parameter, normally distributed, designed to be used for Monte-Carlo simulation.
B_TIME = Beta('B_TIME', 0, None, None, 0)
It is advised not to use 0 as starting value for the following parameter.
B_TIME_S = Beta('B_TIME_S', 1, None, None, 0)
B_TIME_RND = B_TIME + B_TIME_S * bioDraws('B_TIME_RND', 'NORMAL')
Definition of the utility functions.
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
Associate utility functions with the numbering of alternatives
V = {1: V1, 2: V2, 3: V3}
Associate the availability conditions with the alternatives
av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}
Conditional to B_TIME_RND, we have a logit model (called the kernel)
prob = models.logit(V, av, CHOICE)
We integrate over B_TIME_RND using Monte-Carlo
logprob = log(MonteCarlo(prob))
Options for the optimization algorithm
The conjugate gradient iteration can be constrained to stay feasible, or not.
infeasible_cg_values = [True, False]
The radius of the first trust region is tested with three different values.
initial_radius_values = [0.1, 1.0, 10.0]
The percentage of iterations such that the analytical second derivatives is evaluated.
second_derivatives_values = [0.0, 0.5, 1.0]
We run the optimization algorithm with all possible combinations of the parameters. The results are stored in a Pandas DataFrame called summary.
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)
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 ++
summary
SUMMARY_FILE = '05normalMixture_allAlgos.csv'
summary.to_csv(SUMMARY_FILE, index=False)
print(f'Summary reported in file {SUMMARY_FILE}')
Summary reported in file 05normalMixture_allAlgos.csv
Total running time of the script: (3 minutes 33.173 seconds)