def do_simulation(path):
"""
Uses a model identified by path to run a naive and a trained simulation
:param path: The model path
:return:
[0]: The facing angle bin centers
[1]: The occupancy of the naive model
[2]: The occupancy of the trained model
"""
global std_pt
bins = np.linspace(-np.pi, np.pi, 100)
# bin-centers in degress
bcenters = bins[:-1] + np.diff(bins) / 2
# naive simulation
mdata = c.ModelData(path)
model_naive = c.ZfGpNetworkModel()
model_naive.load(mdata.ModelDefinition, mdata.FirstCheckpoint)
model_trained = c.ZfGpNetworkModel()
model_trained.load(mdata.ModelDefinition, mdata.LastCheckpoint)
sim = MoTypes(False).pt_sim(model_naive, std_pt, 100)
pos_naive = sim.run_simulation(GlobalDefs.n_steps)
h_naive = a.bin_simulation_pt(pos_naive, bins)
sim = MoTypes(False).pt_sim(model_trained, std_pt, 100)
pos_trained = sim.run_simulation(GlobalDefs.n_steps)
h_trained = a.bin_simulation_pt(pos_trained, bins)
return bcenters, h_naive, h_trained
def run_flat_gradient(model_path, drop_list=None):
mdata = c.ModelData(model_path)
gpn = MoTypes(False).network_model()
gpn.load(mdata.ModelDefinition, mdata.LastCheckpoint)
flt_params = GlobalDefs.circle_sim_params.copy()
flt_params["t_max"] = flt_params["t_min"]
sim = MoTypes(False).rad_sim(gpn, std, **flt_params)
sim.t_max = sim.t_min # reset gradient to be flat
sim.remove = drop_list
evo_path = model_path + '/evolve/generation_weights.npy'
evo_weights = np.load(evo_path)
w = np.mean(evo_weights[-1, :, :], 0)
sim.bf_weights = w
return sim.run_simulation(GlobalDefs.n_steps, False)