  We present a cosmography analysis of the Local Universe based on the recently
released Two-Micron All-Sky Redshift Survey (2MRS). Our method is based on a
Bayesian Networks Machine Learning algorithm (the Kigen-code) which
self-consistently samples the initial density fluctuations compatible with the
observed galaxy distribution and a structure formation model given by second
order Lagrangian perturbation theory (2LPT). From the initial conditions we
obtain an ensemble of reconstructed density and peculiar velocity fields which
characterize the local cosmic structure with high accuracy unveiling nonlinear
structures like filaments and voids in detail. Coherent redshift space
distortions are consistently corrected within 2LPT. From the ensemble of
cross-correlations between the reconstructions and the galaxy field and the
variance of the recovered density fields we find that our method is extremely
accurate up to k ~ 1 h Mpc^-1 and still yields reliable results down to scales
of about 3-4 h^-1 Mpc. The motion of the local group we obtain within ~ 80 h^-1
Mpc (v_LG=522+-86 km s^-1, l_LG=291^o +- 16^o, b_LG=34^o+-8^o) is in good
agreement with measurements derived from the CMB and from direct observations
of peculiar motions and is consistent with the predictions of LambdaCDM.
