def calc_error(truth_dict):
model = plda.Model(truth_dict['data'], truth_dict['labels'])
Phi_w = truth_dict['Phi_w']
likelihood_means = truth_dict['means']
dim = Phi_w.shape[0]
test_data = np.random.randint(-100, 100, (10, dim))
expected = []
predicted = []
for mean, label in zip(truth_dict['means'], truth_dict['labels']):
true_logps = gaussian(mean, Phi_w).logpdf(test_data)
true_logps -= logsumexp(true_logps)
test_U = model.transform(test_data, 'D', 'U_model')
predicted_logps = model.calc_logp_posterior_predictive(
test_U, label)
predicted_logps -= logsumexp(predicted_logps)
expected.append(true_logps)
predicted.append(predicted_logps)
expected = np.asarray(expected)
predicted = np.asarray(predicted)
error = calc_mean_squared_error(expected, predicted, as_log=True)
return error
def calc_error(truth_dict):
model = plda.Model(truth_dict['data'], truth_dict['labels'])
expected = truth_dict['Phi_w']
predicted = np.matmul(model.A, model.A.T)
error = calc_mean_squared_error(expected, predicted, as_log=True)
return error
def calc_error(truth_dict):
model = plda.Model(truth_dict['data'], truth_dict['labels'])
expected = truth_dict['prior_mean']
predicted = model.m
error = calc_mean_squared_error(expected, predicted, as_log=True)
return error
def calc_error(truth_dict):
X = truth_dict['data']
Y = truth_dict['labels']
expected = truth_dict['prior_mean']
predicted = optimize_maximum_likelihood(X, Y)[0]
error = calc_mean_squared_error(expected, predicted, as_log=True)
return error
def calc_error(truth_dict):
model = plda.Model(truth_dict['data'], truth_dict['labels'])
dim = truth_dict['data'].shape[-1]
Phi_b = truth_dict['Phi_b']
expected = Phi_b
predicted = np.matmul(model.A, model.Psi)
predicted = np.matmul(predicted, model.A.T)
error = calc_mean_squared_error(expected, predicted, as_log=True)
return error
def calc_error(truth_dict):
model = plda.Model(truth_dict['data'], truth_dict['labels'])
Phi_w = truth_dict['Phi_w']
Phi_b = truth_dict['Phi_b']
expected = Phi_b
predicted = np.matmul(Phi_w, model.Psi)
error = calc_mean_squared_error(expected, predicted, as_log=True)
print(error)
return error
def calc_error(truth_dict):
data = truth_dict['data']
labels = truth_dict['labels']
S_b, S_w = calc_scatter_matrices(data, labels)
n = truth_dict['n_k']
expected = truth_dict['Phi_b']
predicted = S_b - S_w / (n - 1)
error = calc_mean_squared_error(expected, predicted, as_log=True)
return error
def calc_off_diagonal_error(truth_dict):
model = plda.Model(truth_dict['data'], truth_dict['labels'])
dim = truth_dict['data'].shape[-1]
Phi_w = truth_dict['Phi_w']
expected = np.zeros((dim, dim))
predicted = np.matmul(model.inv_A, Phi_w)
predicted = np.matmul(predicted, model.inv_A.T)
predicted[range(dim), range(dim)] = 0
error = calc_mean_squared_error(expected, predicted, as_log=True)
return error
def calc_diagonal_error(truth_dict):
model = plda.Model(truth_dict['data'], truth_dict['labels'])
dim = truth_dict['data'].shape[-1]
Phi_w = truth_dict['Phi_w']
expected = np.ones(dim)
predicted = np.matmul(model.inv_A, Phi_w)
predicted = np.matmul(predicted, model.inv_A.T)
predicted = np.diag(predicted)
error = calc_mean_squared_error(expected, predicted, as_log=True)
return error
def calc_error(truth_dict):
model = plda.Model(truth_dict['data'], truth_dict['labels'])
expected = truth_dict['means']
predicted = []
for k, params in model.posterior_params.items():
predicted.append(params['mean'])
predicted = np.asarray(predicted)
predicted = model.transform(predicted, from_space='U_model',
to_space='D')
error = calc_mean_squared_error(expected, predicted, as_log=True)
return error
def calc_error(truth_dict):
model = plda.Model(truth_dict['data'], truth_dict['labels'])
Phi_b = truth_dict['Phi_b']
prior_mean = truth_dict['prior_mean']
dim = prior_mean.shape[0]
random_vectors = np.random.randint(-100, 100, (10, dim))
expected = gaussian(prior_mean, Phi_b).logpdf(random_vectors)
latent_vectors = model.transform(random_vectors, 'D', 'U_model')
predicted = model.calc_logp_prior(latent_vectors)
error = calc_mean_squared_error(expected, predicted, as_log=True)
print(error)
return error
