Data¶

In [1]:

import matplotlib.pyplot as plt
import arviz as az

from lqg.io import load_tracking_data

data, blob_widths = load_tracking_data(data_path="../../data/")
data.shape

import matplotlib.pyplot as plt import arviz as az from lqg.io import load_tracking_data data, blob_widths = load_tracking_data(data_path="../../data/") data.shape

Out[1]:

(6, 20, 1068, 2)

In [2]:

plt.plot(data[0, 0, :, 0])
plt.plot(data[0, 0, :, 1])

plt.show()

plt.plot(data[0, 0, :, 0]) plt.plot(data[0, 0, :, 1]) plt.show()

In [3]:

seed = 1
models = ["BoundedActor", "SubjectiveActor"]

results = {model_name: az.from_netcdf(f"../../data/processed/{model_name}-{seed}.nc") for model_name in models}

seed = 1 models = ["BoundedActor", "SubjectiveActor"] results = {model_name: az.from_netcdf(f"../../data/processed/{model_name}-{seed}.nc") for model_name in models}

In [4]:

for model_name, inference_data in sorted(results.items()):
    summary = az.summary(inference_data.posterior, var_names=[f"sigma_{i}" for i in range(6)])
    display(summary)

    plt.plot(blob_widths, summary["mean"], label=model_name)
    plt.fill_between(blob_widths, summary["hdi_3%"], summary["hdi_97%"], alpha=0.5)
    plt.ylim(0, )

plt.legend()

plt.show()

for model_name, inference_data in sorted(results.items()): summary = az.summary(inference_data.posterior, var_names=[f"sigma_{i}" for i in range(6)]) display(summary) plt.plot(blob_widths, summary["mean"], label=model_name) plt.fill_between(blob_widths, summary["hdi_3%"], summary["hdi_97%"], alpha=0.5) plt.ylim(0, ) plt.legend() plt.show()

	mean	sd	hdi_3%	hdi_97%	mcse_mean	mcse_sd	ess_bulk	ess_tail	r_hat
sigma_0	8.544	0.273	8.032	9.056	0.002	0.001	22415.0	14534.0	1.0
sigma_1	9.726	0.337	9.104	10.373	0.002	0.002	21472.0	15742.0	1.0
sigma_2	11.812	0.360	11.147	12.502	0.002	0.002	20882.0	14644.0	1.0
sigma_3	19.854	0.918	18.193	21.622	0.006	0.004	23440.0	15436.0	1.0
sigma_4	28.512	1.516	25.677	31.347	0.011	0.008	19540.0	14415.0	1.0
sigma_5	51.588	3.234	45.550	57.624	0.024	0.017	18498.0	16077.0	1.0

	mean	sd	hdi_3%	hdi_97%	mcse_mean	mcse_sd	ess_bulk	ess_tail	r_hat
sigma_0	11.844	0.390	11.126	12.590	0.003	0.002	17120.0	14474.0	1.0
sigma_1	12.188	0.419	11.396	12.956	0.003	0.002	19297.0	14892.0	1.0
sigma_2	14.951	0.548	13.965	16.028	0.004	0.003	18766.0	15385.0	1.0
sigma_3	19.438	0.899	17.733	21.098	0.007	0.005	19041.0	14257.0	1.0
sigma_4	27.277	1.390	24.766	30.001	0.010	0.007	18600.0	14759.0	1.0
sigma_5	49.542	3.561	43.016	56.399	0.026	0.019	18428.0	15201.0	1.0

In [ ]: