this_residual = np.empty((5, BATCH_SIZE))
this_coefficient = np.empty((5, 24, BATCH_SIZE))
this_mu_test_posterior = np.empty((5, 24))
this_cov_test_posterior = np.empty((5, 24, 24))
for iteration in range(MAX_ITERATION):
this_train_index = np.arange(TOTAL_SIZE - (iteration + 1) * BATCH_SIZE,
TOTAL_SIZE - iteration * BATCH_SIZE)
this_time_train = time_train[this_train_index]
this_data_train = data_train[:, this_train_index]
# Marginal distribution of the k-th segment of data
for i in range(5):
this_mu_train[i, :] = functions.meanvec(mean_busday[i, :],
mean_holiday[i, :],
this_time_train)
# Conditional distribution of the k-th segment of data
for i in range(5):
this_cov_train[i, :, :] = functions.covmat(acf[i, :],
this_time_train)
this_cov_train_test[i, :, :] = functions.xcovmat(
acf[i, :], this_time_train, time_test)
this_cov_test_train[i, :, :] = np.transpose(
this_cov_train_test[i, :, :])
this_H[i, :, :] = np.dot(this_cov_train_test[i, :, :],
np.linalg.inv(cov_test_prior[i, :, :]))
this_mu_train_given_test[i, :] = this_mu_train[i, :] + np.dot(
this_cov_train_given_test = np.empty((5, BATCH_SIZE, BATCH_SIZE))
this_H = np.empty((5, BATCH_SIZE, 24))
this_G = np.empty((5, BATCH_SIZE, BATCH_SIZE))
this_residual = np.empty((5, BATCH_SIZE))
this_mu_test_posterior = np.empty((5, 24))
this_cov_test_posterior = np.empty((5, 24, 24))
for iteration in range(MAX_ITERATION):
this_train_index = np.arange(TOTAL_SIZE - (iteration + 1) * BATCH_SIZE,
TOTAL_SIZE - iteration * BATCH_SIZE)
this_time_train = time_train[this_train_index]
this_data_train = data_train[:, this_train_index]
this_mu_train = functions.meanvec(mean_busday, mean_holiday,
this_time_train)
for i in range(5):
this_cov_train[i, :, :] = functions.covmat(acf[i, :], this_time_train)
this_cov_train_test[i, :, :] = functions.xcovmat(
acf[i, :], this_time_train, time_test)
this_cov_test_train[i, :, :] = np.transpose(
this_cov_train_test[i, :, :])
# Conditional distribution for the k-th segment of data
this_mu_train_given_test[i, :] = this_mu_train[i, :] + np.dot(
np.dot(this_cov_train_test[i, :, :],
np.linalg.inv(cov_test_prior[i, :, :])),
(this_mu_test_prior[i, :] - mu_test_prior[i, :]))
this_cov_train_given_test[i, :, :] = this_cov_train[i, :, :] - np.dot(
np.dot(this_cov_train_test[i, :, :],
