Traveler
Auto Ownership#
import logging
import traveler as tv
tv.log_to_stdout(logging.INFO)
[00:00.85] INFO: Logging to stdout set to level 20
import mtc
store = mtc.mini()
store
<Store with keys households, persons, skims, land_use, time_periods, tours>
from traveler.steps import SampleHouseholds
store = SampleHouseholds(n=100_000).run_on(store)
/Users/jpn/Git/traveler/src/traveler/steps/_sample_households.py:268: UserWarning: n (100000) is greater than the number of households in the store, returning all households.
return self.run_via_polars(store)
store.households.to_pandas()
| hhsize | num_workers | auto_ownership | TAZ | HHT | income | home_zone_id | |
|---|---|---|---|---|---|---|---|
| household_id | |||||||
| 2717868 | 2 | 1 | 1 | 25 | 1 | 361000 | 25 |
| 763899 | 1 | 0 | 1 | 6 | 4 | 59220 | 6 |
| 2222791 | 2 | 2 | 2 | 9 | 2 | 197000 | 9 |
| 112477 | 1 | 1 | 0 | 17 | 6 | 2200 | 17 |
| 370491 | 3 | 1 | 0 | 21 | 1 | 16500 | 21 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 109218 | 1 | 1 | 0 | 10 | 4 | 15000 | 10 |
| 570708 | 1 | 0 | 0 | 23 | 6 | 13100 | 23 |
| 2762199 | 1 | 0 | 0 | 21 | 0 | 0 | 21 |
| 2049372 | 1 | 1 | 1 | 18 | 4 | 103000 | 18 |
| 702559 | 2 | 0 | 0 | 13 | 1 | 14800 | 13 |
5000 rows × 7 columns
store.persons.to_pandas()
| household_id | age | PNUM | sex | pemploy | pstudent | ptype | _household_idx_ | |
|---|---|---|---|---|---|---|---|---|
| person_id | ||||||||
| 25671 | 25671 | 47 | 1 | 1 | 3 | 3 | 4 | 3817 |
| 25675 | 25675 | 27 | 1 | 2 | 3 | 2 | 3 | 4066 |
| 25678 | 25678 | 30 | 1 | 2 | 3 | 3 | 4 | 3498 |
| 25683 | 25683 | 23 | 1 | 1 | 3 | 3 | 4 | 4539 |
| 25684 | 25684 | 52 | 1 | 1 | 3 | 3 | 4 | 4710 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 7554848 | 2863513 | 68 | 1 | 1 | 3 | 3 | 5 | 1559 |
| 7554855 | 2863520 | 68 | 1 | 1 | 3 | 3 | 5 | 4711 |
| 7554859 | 2863524 | 93 | 1 | 2 | 3 | 3 | 5 | 1148 |
| 7554887 | 2863552 | 76 | 1 | 2 | 3 | 3 | 5 | 371 |
| 7554903 | 2863568 | 82 | 1 | 2 | 3 | 3 | 5 | 4690 |
8212 rows × 8 columns
store.info()
<mtc.tables.store.Store>
households = <JaxTable with shape (5000,)>
persons = <JaxTable with shape (8212,)>
skims = <Skims with 21 keys>
land_use = <JaxTable with shape (25,)>
time_periods = {'EA': 0, 'AM': 1, 'MD': 2, 'PM': 3, 'EV': 4}
tours = <JaxTable with shape (20000,)>
from mtc.models.annotate_households import (
annotate_households,
household_value_of_time,
)
from mtc.models.annotate_landuse import annotate_landuse
from mtc.models.annotate_persons import (
annotate_persons,
)
store.households.info()
<JaxTable>
- household_id (5000,) int32
- hhsize (5000,) int8
- num_workers (5000,) int8
- auto_ownership (5000,) int8
- TAZ (5000,) int32
- HHT (5000,) int8
- income (5000,) int32
- home_zone_id (5000,) categorical: (25 categories)
store.persons.info()
<JaxTable>
- person_id (8212,) int32
- household_id (8212,) int32
- age (8212,) int32
- PNUM (8212,) int32
- sex (8212,) int32
- pemploy (8212,) int32
- pstudent (8212,) int32
- ptype (8212,) int32
- _household_idx_ (8212,) int32
# household_value_of_time.__step__._compute_values.chunk_size = 10_000_000
# type: ignore
store = store.run_steps(
annotate_landuse, annotate_persons, annotate_households, household_value_of_time
)
[00:02.32] INFO: compute_values step annotate_landuse completed in 0:00:00.05
[00:02.69] INFO: compute_values step annotate_persons completed in 0:00:00.36
[00:02.86] INFO: compute_values step annotate_households completed in 0:00:00.16
[00:02.94] INFO: compute_values step household_value_of_time completed in 0:00:00.07
store.households.info()
<JaxTable>
- household_id (5000,) int32
- hhsize (5000,) int8
- num_workers (5000,) int8
- auto_ownership (5000,) int8
- TAZ (5000,) int32
- HHT (5000,) int8
- income (5000,) int32
- home_zone_id (5000,) categorical: (25 categories)
- income_in_thousands (5000,) float32
- income_segment (5000,) int8
- family (5000,) bool
- non_family (5000,) bool
- num_drivers (5000,) int8
- num_adults (5000,) int8
- num_children (5000,) int8
- num_young_children (5000,) int8
- num_children_5_to_15 (5000,) int8
- num_children_16_to_17 (5000,) int8
- num_college_age (5000,) int8
- num_young_adults (5000,) int8
- home_is_urban (5000,) bool
- home_is_rural (5000,) bool
- median_value_of_time (5000,) float32
- random_vot (5000,) float32
store.completed_steps
{'annotate_households',
'annotate_landuse',
'annotate_persons',
'household_value_of_time'}
store.persons.info()
<JaxTable>
- person_id (8212,) int32
- household_id (8212,) int32
- age (8212,) int32
- PNUM (8212,) int32
- sex (8212,) int32
- pemploy (8212,) int32
- pstudent (8212,) int32
- ptype (8212,) int32
- _household_idx_ (8212,) int32
- age_16_to_19 (8212,) bool
- age_16_p (8212,) bool
- adult (8212,) bool
- male (8212,) bool
- female (8212,) bool
- has_non_worker (8212,) bool
- has_retiree (8212,) bool
- has_preschool_kid (8212,) bool
- has_driving_kid (8212,) bool
- has_school_kid (8212,) bool
- has_full_time (8212,) bool
- has_part_time (8212,) bool
- has_university (8212,) bool
- student_is_employed (8212,) bool
- nonstudent_to_school (8212,) bool
- is_student (8212,) bool
- is_gradeschool (8212,) bool
- is_highschool (8212,) bool
- is_university (8212,) bool
- school_segment (8212,) int8
- is_worker (8212,) bool
- home_zone_id (8212,) categorical: (25 categories)
store.households.info()
<JaxTable>
- household_id (5000,) int32
- hhsize (5000,) int8
- num_workers (5000,) int8
- auto_ownership (5000,) int8
- TAZ (5000,) int32
- HHT (5000,) int8
- income (5000,) int32
- home_zone_id (5000,) categorical: (25 categories)
- income_in_thousands (5000,) float32
- income_segment (5000,) int8
- family (5000,) bool
- non_family (5000,) bool
- num_drivers (5000,) int8
- num_adults (5000,) int8
- num_children (5000,) int8
- num_young_children (5000,) int8
- num_children_5_to_15 (5000,) int8
- num_children_16_to_17 (5000,) int8
- num_college_age (5000,) int8
- num_young_adults (5000,) int8
- home_is_urban (5000,) bool
- home_is_rural (5000,) bool
- median_value_of_time (5000,) float32
- random_vot (5000,) float32
store.validate(verbose=1, coerce="safe", missing_fields="ignore")
Found 2 category groups for validation.
TIMEPERIOD: ['EA', 'AM', 'MD', 'PM', 'EV'] (ordered=False)
TAZ: [ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
25] (ordered=False)
table households is <mtc.tables.households.Households object at 0x12c5cce50>
Validating table 'households'...
Field 'household_id' validated as <class 'numpy.int32'>.
Field 'TAZ' validated as <class 'numpy.int32'>.
Field 'HHT' validated as <class 'numpy.int8'>.
Field 'hhsize' validated as <class 'numpy.int8'>.
Field 'income' validated as <class 'numpy.int32'>.
Field 'auto_ownership' validated as <class 'numpy.int8'>.
Field 'random_vot' validated as <class 'numpy.float32'>.
Field 'income_segment' validated as <class 'numpy.int8'>.
Field 'family' validated as <class 'bool'>.
Field 'non_family' validated as <class 'bool'>.
Field 'num_drivers' validated as <class 'numpy.int8'>.
Field 'num_adults' validated as <class 'numpy.int8'>.
Field 'num_children' validated as <class 'numpy.int8'>.
Field 'num_young_children' validated as <class 'numpy.int8'>.
Field 'num_children_5_to_15' validated as <class 'numpy.int8'>.
Field 'num_children_16_to_17' validated as <class 'numpy.int8'>.
Field 'num_college_age' validated as <class 'numpy.int8'>.
Field 'num_young_adults' validated as <class 'numpy.int8'>.
Field 'num_workers' validated as <class 'numpy.int8'>.
table tours is <mtc.tables.tours.Tours object at 0x12b2be650>
Validating table 'tours'...
Field 'tour_id' validated as <class 'numpy.int32'>.
Field 'person_id' validated as <class 'numpy.int32'>.
Field 'household_id' validated as <class 'numpy.int32'>.
Field '_household_idx_' validated as <class 'numpy.int32'>.
Field '_person_idx_' validated as <class 'numpy.int32'>.
table persons is <mtc.tables.persons.Persons object at 0x12c609a90>
Validating table 'persons'...
Field 'person_id' validated as <class 'numpy.int32'>.
Field 'household_id' validated as <class 'numpy.int32'>.
Field '_household_idx_' validated as <class 'numpy.int32'>.
Field 'age' coerced to <class 'jax.numpy.int8'> without loss of information.
Field 'sex' coerced to <class 'jax.numpy.int8'> without loss of information.
Field 'ptype' coerced to <class 'jax.numpy.int8'> without loss of information.
Field 'pemploy' coerced to <class 'jax.numpy.int8'> without loss of information.
Field 'pstudent' coerced to <class 'jax.numpy.int8'> without loss of information.
Field 'adult' validated as <class 'bool'>.
Field 'male' validated as <class 'bool'>.
Field 'female' validated as <class 'bool'>.
Field 'age_16_to_19' validated as <class 'numpy.bool'>.
Field 'age_16_p' validated as <class 'numpy.bool'>.
table land_use is <mtc.tables.landuse.LandUse object at 0x12c5e8f50>
Validating table 'land_use'...
Field 'TAZ' validated as <class 'numpy.int32'>.
Field 'TOTACRE' validated as <class 'numpy.float32'>.
Field 'county_id' validated as <class 'numpy.int8'>.
Field 'TOTHH' validated as <class 'numpy.int32'>.
Field 'TOTPOP' validated as <class 'numpy.int32'>.
Field 'TOTEMP' validated as <class 'numpy.int32'>.
Field 'RESACRE' validated as <class 'numpy.float32'>.
Field 'CIACRE' validated as <class 'numpy.float32'>.
Field 'DISTRICT' validated as <class 'numpy.int8'>.
Field 'SD' validated as <class 'numpy.int8'>.
Field 'AGE0519' validated as <class 'numpy.int32'>.
Field 'RETEMPN' validated as <class 'numpy.int32'>.
Field 'FPSEMPN' validated as <class 'numpy.int32'>.
Field 'HEREMPN' validated as <class 'numpy.int32'>.
Field 'density_index' validated as <class 'numpy.float32'>.
from mtc.models.auto_ownership import auto_ownership
store2 = auto_ownership.run_on(store)
store2.households.info()
<JaxTable>
- household_id (5000,) int32
- hhsize (5000,) int8
- num_workers (5000,) int8
- auto_ownership (5000,) int8
- TAZ (5000,) int32
- HHT (5000,) int8
- income (5000,) int32
- home_zone_id (5000,) categorical: (25 categories)
- income_in_thousands (5000,) float32
- income_segment (5000,) int8
- family (5000,) bool
- non_family (5000,) bool
- num_drivers (5000,) int8
- num_adults (5000,) int8
- num_children (5000,) int8
- num_young_children (5000,) int8
- num_children_5_to_15 (5000,) int8
- num_children_16_to_17 (5000,) int8
- num_college_age (5000,) int8
- num_young_adults (5000,) int8
- home_is_urban (5000,) bool
- home_is_rural (5000,) bool
- median_value_of_time (5000,) float32
- random_vot (5000,) float32
- auto_ownership_sim (5000,) int8
store2.households.to_polars()
shape: (5_000, 25)
| household_id | hhsize | num_workers | auto_ownership | TAZ | HHT | income | home_zone_id | income_in_thousands | income_segment | family | non_family | num_drivers | num_adults | num_children | num_young_children | num_children_5_to_15 | num_children_16_to_17 | num_college_age | num_young_adults | home_is_urban | home_is_rural | median_value_of_time | random_vot | auto_ownership_sim |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| i32 | i8 | i8 | i8 | i32 | i8 | i32 | enum | f32 | i8 | bool | bool | i8 | i8 | i8 | i8 | i8 | i8 | i8 | i8 | bool | bool | f32 | f32 | i8 |
| 2717868 | 2 | 1 | 1 | 25 | 1 | 361000 | "TAZ-25" | 361.0 | 4 | true | false | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | true | false | 60.0 | 0.351049 | 4 |
| 763899 | 1 | 0 | 1 | 6 | 4 | 59220 | "TAZ-6" | 59.220001 | 2 | false | false | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | false | false | 25.0 | 0.489543 | 1 |
| 2222791 | 2 | 2 | 2 | 9 | 2 | 197000 | "TAZ-9" | 197.0 | 4 | false | false | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | false | false | 60.0 | 1.229426 | 1 |
| 112477 | 1 | 1 | 0 | 17 | 6 | 2200 | "TAZ-17" | 2.2 | 1 | false | false | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | false | false | 15.0 | 1.188264 | 1 |
| 370491 | 3 | 1 | 0 | 21 | 1 | 16500 | "TAZ-21" | 16.5 | 1 | false | false | 2 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | false | false | 15.0 | 0.640115 | 1 |
| … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … | … |
| 109218 | 1 | 1 | 0 | 10 | 4 | 15000 | "TAZ-10" | 15.0 | 1 | false | false | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | false | false | 15.0 | 1.933671 | 1 |
| 570708 | 1 | 0 | 0 | 23 | 6 | 13100 | "TAZ-23" | 13.1 | 1 | false | false | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | false | false | 15.0 | 1.532178 | 1 |
| 2762199 | 1 | 0 | 0 | 21 | 0 | 0 | "TAZ-21" | 0.0 | 1 | false | false | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | false | false | 15.0 | 0.651419 | 0 |
| 2049372 | 1 | 1 | 1 | 18 | 4 | 103000 | "TAZ-18" | 103.0 | 4 | false | false | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | false | false | 60.0 | 2.995311 | 1 |
| 702559 | 2 | 0 | 0 | 13 | 1 | 14800 | "TAZ-13" | 14.8 | 1 | false | false | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | false | false | 15.0 | 0.999762 | 1 |
auto_ownership.simulate_choice(store)
Array([4, 1, 1, ..., 0, 1, 1], dtype=int8)
import pandas as pd
pd.DataFrame(auto_ownership.utility(store, trace=True).trace)
| income_in_thousands | three_drivers | two_drivers | |
|---|---|---|---|
| 0 | 361.000031 | False | True |
| 1 | 59.220001 | False | False |
| 2 | 197.000015 | False | True |
| 3 | 2.200000 | False | False |
| 4 | 16.500000 | False | True |
| ... | ... | ... | ... |
| 4995 | 15.000001 | False | False |
| 4996 | 13.100000 | False | False |
| 4997 | 0.000000 | False | False |
| 4998 | 103.000008 | False | False |
| 4999 | 14.800001 | False | True |
5000 rows × 3 columns
auto_ownership.simulate_choice(store)
Array([4, 1, 1, ..., 0, 1, 1], dtype=int8)
auto_ownership.analytic_share(store)
Array([0.26307496, 0.5855463 , 0.02963491, 0.00279747, 0.11894631], dtype=float32)
auto_ownership.analytic_share_loss(
store,
target_shares={
0: 0.1,
1: 0.3,
2: 0.4,
3: 0.1,
4: 0.1,
},
)
Array(0.25510776, dtype=float32)
g = auto_ownership.analytic_share_loss_grad(
store,
target_shares={
0: 0.1,
1: 0.3,
2: 0.4,
3: 0.1,
4: 0.1,
},
)
g
AutoOwnershipParams(
coef_cars1_asc=Array(0.07327148, dtype=float32, weak_type=True),
coef_cars1_asc_county=Array(0., dtype=float32, weak_type=True),
coef_cars1_asc_marin=Array(0., dtype=float32, weak_type=True),
coef_cars1_asc_san_francisco=Array(0., dtype=float32, weak_type=True),
coef_cars1_auto_time_saving_per_worker=Array(0., dtype=float32, weak_type=True),
coef_cars1_density_0_10_no_workers=Array(0.22252251, dtype=float32, weak_type=True),
coef_cars1_density_10_up_no_workers=Array(1.7848635, dtype=float32, weak_type=True),
coef_cars1_density_10_up_workers=Array(2.4006515, dtype=float32, weak_type=True),
coef_cars1_drivers_2=Array(0.03263084, dtype=float32, weak_type=True),
coef_cars1_drivers_3=Array(0.00229748, dtype=float32, weak_type=True),
coef_cars1_drivers_4_up=Array(0.0008286, dtype=float32, weak_type=True),
coef_cars1_hh_income_0_30k=Array(1.5066861, dtype=float32, weak_type=True),
coef_cars1_hh_income_30_up=Array(1.8867602, dtype=float32, weak_type=True),
coef_cars1_num_workers_clip_3=Array(0.07202546, dtype=float32, weak_type=True),
coef_cars1_persons_16_17=Array(0.00185995, dtype=float32, weak_type=True),
coef_cars1_persons_18_24=Array(0.01011639, dtype=float32, weak_type=True),
coef_cars1_persons_25_34=Array(0.02964097, dtype=float32, weak_type=True),
coef_cars1_presence_children_0_4=Array(0.00396558, dtype=float32, weak_type=True),
coef_cars1_presence_children_5_17=Array(0.00998919, dtype=float32, weak_type=True),
coef_cars234_asc_marin=Array(0., dtype=float32, weak_type=True),
coef_cars234_presence_children_0_4=Array(-0.00331472, dtype=float32, weak_type=True),
coef_cars2_asc=Array(-0.02221743, dtype=float32, weak_type=True),
coef_cars2_asc_county=Array(0., dtype=float32, weak_type=True),
coef_cars2_asc_san_francisco=Array(-0.02221743, dtype=float32, weak_type=True),
coef_cars2_auto_time_saving_per_worker=Array(0., dtype=float32, weak_type=True),
coef_cars2_density_0_10_no_workers=Array(-0.02364007, dtype=float32, weak_type=True),
coef_cars2_density_10_up_no_workers=Array(-0.21981671, dtype=float32, weak_type=True),
coef_cars2_density_10_up_workers=Array(-0.34944877, dtype=float32, weak_type=True),
coef_cars2_drivers_2=Array(-0.0156795, dtype=float32, weak_type=True),
coef_cars2_drivers_3=Array(-0.00141119, dtype=float32, weak_type=True),
coef_cars2_drivers_4_up=Array(-0.00030825, dtype=float32, weak_type=True),
coef_cars2_hh_income_0_30k=Array(-0.586473, dtype=float32, weak_type=True),
coef_cars2_hh_income_30_up=Array(-0.94900423, dtype=float32, weak_type=True),
coef_cars2_num_workers_clip_3=Array(-0.03074503, dtype=float32, weak_type=True),
coef_cars2_persons_16_17=Array(-0.0006489, dtype=float32, weak_type=True),
coef_cars2_persons_18_24=Array(-0.00259443, dtype=float32, weak_type=True),
coef_cars2_persons_25_34=Array(-0.01191693, dtype=float32, weak_type=True),
coef_cars2_presence_children_5_17=Array(-0.00367055, dtype=float32, weak_type=True),
coef_cars34_asc_county=Array(0., dtype=float32, weak_type=True),
coef_cars34_asc_san_francisco=Array(0., dtype=float32, weak_type=True),
coef_cars34_density_0_10_no_workers=Array(-0.00011447, dtype=float32, weak_type=True),
coef_cars34_density_10_up_no_workers=Array(-7.9052865e-05, dtype=float32, weak_type=True),
coef_cars34_persons_16_17=Array(-0.00098679, dtype=float32, weak_type=True),
coef_cars34_persons_18_24=Array(-0.00365545, dtype=float32, weak_type=True),
coef_cars34_persons_25_34=Array(-0.01036393, dtype=float32, weak_type=True),
coef_cars34_presence_children_5_17=Array(-0.00444413, dtype=float32, weak_type=True),
coef_cars3_asc=Array(0.00015584, dtype=float32, weak_type=True),
coef_cars3_auto_time_saving_per_worker=Array(0., dtype=float32, weak_type=True),
coef_cars3_drivers_2=Array(0.00024149, dtype=float32, weak_type=True),
coef_cars3_drivers_3=Array(5.5594464e-05, dtype=float32, weak_type=True),
coef_cars3_drivers_4_up=Array(-2.7979536e-07, dtype=float32, weak_type=True),
coef_cars3_hh_income_0_30k=Array(0.0056824, dtype=float32, weak_type=True),
coef_cars3_hh_income_30_up=Array(0.01130291, dtype=float32, weak_type=True),
coef_cars3_num_workers_clip_3=Array(0.00050934, dtype=float32, weak_type=True),
coef_cars4_asc=Array(-0.01873321, dtype=float32, weak_type=True),
coef_cars4_auto_time_saving_per_worker=Array(0., dtype=float32, weak_type=True),
coef_cars4_drivers_2=Array(-0.01417157, dtype=float32, weak_type=True),
coef_cars4_drivers_3=Array(-0.00117014, dtype=float32, weak_type=True),
coef_cars4_drivers_4_up=Array(-0.00054256, dtype=float32, weak_type=True),
coef_cars4_hh_income_0_30k=Array(-0.50299746, dtype=float32, weak_type=True),
coef_cars4_hh_income_30_up=Array(-0.76541436, dtype=float32, weak_type=True),
coef_cars4_num_workers_clip_3=Array(-0.0266102, dtype=float32, weak_type=True),
coef_retail_auto_no_workers=Array(0., dtype=float32, weak_type=True),
coef_retail_auto_workers=Array(0., dtype=float32, weak_type=True),
coef_retail_non_motor=Array(0., dtype=float32, weak_type=True),
coef_retail_transit_no_workers=Array(0., dtype=float32, weak_type=True),
coef_retail_transit_workers=Array(0., dtype=float32, weak_type=True),
)
auto_ownership.calibrate_analytic_share(
store,
target_shares={
0: 0.1,
1: 0.3,
2: 0.4,
3: 0.1,
4: 0.1,
},
calib_params=[
"coef_cars1_asc",
"coef_cars2_asc",
"coef_cars3_asc",
"coef_cars4_asc",
],
)
message: Optimization terminated successfully.
success: True
status: 0
fun: 0.012498477473855019
x: [ 1.679e+00 6.343e+00 -3.334e+00 -2.961e+00]
nit: 17
jac: [ 1.570e-06 -3.571e-06 -1.522e-06 5.138e-07]
hess_inv: [[ 4.255e+01 4.628e+01 -7.460e-01 5.284e+01]
[ 4.628e+01 9.476e+01 -1.335e+00 7.839e+01]
[-7.460e-01 -1.335e+00 1.021e+00 -1.567e+00]
[ 5.284e+01 7.839e+01 -1.567e+00 1.718e+02]]
nfev: 24
njev: 24
shares: [ 1.250e-01 3.250e-01 4.250e-01 6.097e-06 1.250e-01]
auto_ownership.analytic_share(
store,
)
Array([1.2501623e-01, 3.2500395e-01, 4.2497578e-01, 6.0965403e-06,
1.2499795e-01], dtype=float32)