def create_covariance(mu, sigma):
mu = z.convert_to_tensor([z.convert_to_tensor(m) for m in mu])
sigma = z.convert_to_tensor(sigma) # TODO (Mayou36): fix as above?
params_tensor = z.convert_to_tensor(params)
if sigma.shape.ndims > 1:
covariance = sigma
elif sigma.shape.ndims == 1:
covariance = tf.linalg.tensor_diag(z.pow(sigma, 2.))
else:
sigma = tf.reshape(sigma, [1])
covariance = tf.linalg.tensor_diag(z.pow(sigma, 2.))
if not params_tensor.shape[0] == mu.shape[0] == covariance.shape[
0] == covariance.shape[1]:
raise ShapeIncompatibleError(
f"params_tensor, mu and sigma have to have the same length. Currently"
f"param: {params_tensor.shape[0]}, mu: {mu.shape[0]}, "
f"covariance (from sigma): {covariance.shape[0:2]}")
return covariance
def create_covariance(mu, sigma):
mu = z.convert_to_tensor(mu)
sigma = z.convert_to_tensor(sigma) # TODO (Mayou36): fix as above?
params_tensor = z.convert_to_tensor(params)
if sigma.shape.ndims > 1:
covariance = sigma # TODO: square as well?
elif sigma.shape.ndims == 1:
covariance = tf.linalg.tensor_diag(z.pow(sigma, 2.0))
else:
sigma = znp.reshape(sigma, [1])
covariance = tf.linalg.tensor_diag(z.pow(sigma, 2.0))
if (not params_tensor.shape[0] == mu.shape[0] ==
covariance.shape[0] == covariance.shape[1]):
raise ShapeIncompatibleError(
f"params_tensor, observation and uncertainty have to have the"
" same length. Currently"
f"param: {params_tensor.shape[0]}, mu: {mu.shape[0]}, "
f"covariance (from uncertainty): {covariance.shape[0:2]}")
return covariance