MCMCLearnerTransitionDegenerateModelWithMNIWPrior):
pass
def rmse(state, estimate):
return la.norm(state-estimate)
figure_path = './notes/figures/'
# Load the test data
test_path = './results/toy-final/'
test_data_file = 'toy-test-data.p'
fh = open(test_path+test_data_file, 'rb')
model,state,observ = pickle.load(fh)
# Load the MCMC output
basic_learner = load_learner(test_path+'toy-mcmc-basic.p')
degenerate_learner = load_learner(test_path+'toy-mcmc-degenerate.p')
# Get state estimates from each algorithm
num_burn = 5000
basic_mn, basic_sd = basic_learner.estimate_state_trajectory(
numBurnIn=num_burn)
degenerate_mn, degenerate_sd = degenerate_learner.estimate_state_trajectory(
numBurnIn=num_burn)
# Assess RMSE
basic_rmse = rmse(state, basic_mn)
degenerate_rmse = rmse(state, degenerate_mn)
# Display results
print("Model | RMSE")
MCMCLearnerTransitionDegenerateModelWithMNIWPrior):
pass
def rmse(state, estimate):
return la.norm(state-estimate)
figure_path = './notes/figures/'
# Load the test data
test_path = './results/toy2-final/'
test_data_file = 'toy2-test-data.p'
fh = open(test_path+test_data_file, 'rb')
model,state,observ = pickle.load(fh)
# Load the MCMC output
degenerate_learner = load_learner(test_path+'toy2-mcmc-degenerate.p')
# Get state estimates from each algorithm
num_burn = 5000
degenerate_mn, degenerate_sd = degenerate_learner.estimate_state_trajectory(
numBurnIn=num_burn)
# Draw state
ds = degenerate_learner.model.ds
fig, axs = plt.subplots(nrows=ds,ncols=1,sharex=True)
for dd in range(ds):
axs[dd].plot(state[:,dd], 'k')
axs[dd].locator_params(axis='y',nbins=2)
fig.savefig(figure_path+'toy2-state.pdf', bbox_inches='tight')
# Rank plots
return la.norm(err[np.isnan(original)])
figure_path = './notes/figures/'
# Load the test data
data_path = './mocap-data/'
test_path = './results/mocap-final/'
markers_truth = np.genfromtxt(data_path + 'downsampled_head_markers_truth.csv',
delimiter=',')
test_data_file = 'mocap-test-data.p'
fh = open(test_path + test_data_file, 'rb')
markers = pickle.load(fh)
# Load the MCMC output
naive_learner = load_learner(test_path + 'mocap-mcmc-naive.p')
basic_learner = load_learner(test_path + 'mocap-mcmc-basic.p')
degenerate_learner = load_learner(test_path + 'mocap-mcmc-degenerate.p')
# Get state estimates from each algorithm
num_burn = 10000
basic_mn, basic_sd = basic_learner.estimate_state_trajectory(
numBurnIn=num_burn)
degenerate_mn, degenerate_sd = degenerate_learner.estimate_state_trajectory(
numBurnIn=num_burn)
naive_mn, naive_sd = naive_learner.estimate_state_trajectory(
numBurnIn=num_burn)
# Run MSVD as a comparison
msvd_markers = mocap_msvd(markers, naive_mn[:, :12])
def mocap_rmse(truth, original, estimate):
err = truth-estimate
return la.norm(err[np.isnan(original)])
figure_path = './notes/figures/'
# Load the test data
data_path = './mocap-data/'
test_path = './results/mocap-final/'
markers_truth = np.genfromtxt(data_path+'downsampled_head_markers_truth.csv', delimiter=',')
test_data_file = 'mocap-test-data.p'
fh = open(test_path+test_data_file, 'rb')
markers = pickle.load(fh)
# Load the MCMC output
naive_learner = load_learner(test_path+'mocap-mcmc-naive.p')
basic_learner = load_learner(test_path+'mocap-mcmc-basic.p')
degenerate_learner = load_learner(test_path+'mocap-mcmc-degenerate.p')
# Get state estimates from each algorithm
num_burn = 10000
basic_mn, basic_sd = basic_learner.estimate_state_trajectory(
numBurnIn=num_burn)
degenerate_mn, degenerate_sd = degenerate_learner.estimate_state_trajectory(
numBurnIn=num_burn)
naive_mn, naive_sd = naive_learner.estimate_state_trajectory(
numBurnIn=num_burn)
# Run MSVD as a comparison
msvd_markers = mocap_msvd(markers, naive_mn[:,:12])
def rmse(state, estimate):
return la.norm(state - estimate)
figure_path = './notes/figures/'
# Load the test data
test_path = './results/toy2-final/'
test_data_file = 'toy2-test-data.p'
fh = open(test_path + test_data_file, 'rb')
model, state, observ = pickle.load(fh)
# Load the MCMC output
degenerate_learner = load_learner(test_path + 'toy2-mcmc-degenerate.p')
# Get state estimates from each algorithm
num_burn = 5000
degenerate_mn, degenerate_sd = degenerate_learner.estimate_state_trajectory(
numBurnIn=num_burn)
# Draw state
ds = degenerate_learner.model.ds
fig, axs = plt.subplots(nrows=ds, ncols=1, sharex=True)
for dd in range(ds):
axs[dd].plot(state[:, dd], 'k')
axs[dd].locator_params(axis='y', nbins=2)
fig.savefig(figure_path + 'toy2-state.pdf', bbox_inches='tight')
# Rank plots
