tensorflow_probability/python/sts/internal/util.py
def empirical_statistics(observed_time_series):
  """Compute statistics of a provided time series, as heuristic initialization.

  Args:
    observed_time_series: `Tensor` representing a time series, or batch of time
       series, of shape either `batch_shape + [num_timesteps, 1]` or
       `batch_shape + [num_timesteps]` (allowed if `num_timesteps > 1`).

  Returns:
    observed_mean: `Tensor` of shape `batch_shape`, giving the empirical
      mean of each time series in the batch.
    observed_stddev: `Tensor` of shape `batch_shape`, giving the empirical
      standard deviation of each time series in the batch.
    observed_initial_centered: `Tensor of shape `batch_shape`, giving the
      initial value of each time series in the batch after centering
      (subtracting the mean).
  """

  with tf.compat.v1.name_scope(
      'empirical_statistics', values=[observed_time_series]):

    [
        observed_time_series,
        mask
    ] = canonicalize_observed_time_series_with_mask(observed_time_series)

    squeezed_series = observed_time_series[..., 0]
    if mask is None:
      observed_mean, observed_variance = tf.nn.moments(
          x=squeezed_series, axes=-1)
      observed_initial = squeezed_series[..., 0]
    else:
      broadcast_mask = tf.broadcast_to(tf.cast(mask, tf.bool),
                                       tf.shape(input=squeezed_series))
      observed_mean, observed_variance = (
          missing_values_util.moments_of_masked_time_series(
              squeezed_series, broadcast_mask=broadcast_mask))
      try:
        observed_initial = (
            missing_values_util.initial_value_of_masked_time_series(
                squeezed_series, broadcast_mask=broadcast_mask))
      except NotImplementedError:
        tf.compat.v1.logging.warn(
            'Cannot compute initial values for a masked time series'
            'with dynamic shape; using the mean instead. This will'
            'affect heuristic priors and may change the results of'
            'inference.')
        observed_initial = observed_mean

    observed_stddev = tf.sqrt(observed_variance)
    observed_initial_centered = observed_initial - observed_mean
    return observed_mean, observed_stddev, observed_initial_centered
def empirical_statistics(observed_time_series):
  with tf.compat.v1.name_scope(
      'empirical_statistics', values=[observed_time_series]):

    [
        observed_time_series,
        mask
    ] = canonicalize_observed_time_series_with_mask(observed_time_series)

    squeezed_series = observed_time_series[..., 0]
    if mask is None:
      observed_mean, observed_variance = tf.nn.moments(
          x=squeezed_series, axes=-1)
      observed_initial = squeezed_series[..., 0]
    else:
      broadcast_mask = tf.broadcast_to(tf.cast(mask, tf.bool),
                                       tf.shape(input=squeezed_series))
      observed_mean, observed_variance = (
          missing_values_util.moments_of_masked_time_series(
              squeezed_series, broadcast_mask=broadcast_mask))
      try:
        observed_initial = (
            missing_values_util.initial_value_of_masked_time_series(
                squeezed_series, broadcast_mask=broadcast_mask))
      except NotImplementedError:
        tf.compat.v1.logging.warn(
            'Cannot compute initial values for a masked time series'
            'with dynamic shape; using the mean instead. This will'
            'affect heuristic priors and may change the results of'
            'inference.')
        observed_initial = observed_mean

    observed_stddev = tf.sqrt(observed_variance)
    observed_initial_centered = observed_initial - observed_mean
    return observed_mean, observed_stddev, observed_initial_centered
def empirical_statistics ( observed_time_series ) : with tf . compat . v1 . name_scope ( 'empirical_statistics' , values = [ observed_time_series ] ) : [ observed_time_series , mask ] = canonicalize_observed_time_series_with_mask ( observed_time_series ) squeezed_series = observed_time_series [ ... , 0 ] if mask is None : observed_mean , observed_variance = tf . nn . moments ( x = squeezed_series , axes = - 1 ) observed_initial = squeezed_series [ ... , 0 ] else : broadcast_mask = tf . broadcast_to ( tf . cast ( mask , tf . bool ) , tf . shape ( input = squeezed_series ) ) observed_mean , observed_variance = ( missing_values_util . moments_of_masked_time_series ( squeezed_series , broadcast_mask = broadcast_mask ) ) try : observed_initial = ( missing_values_util . initial_value_of_masked_time_series ( squeezed_series , broadcast_mask = broadcast_mask ) ) except NotImplementedError : tf . compat . v1 . logging . warn ( 'Cannot compute initial values for a masked time series' 'with dynamic shape; using the mean instead. This will' 'affect heuristic priors and may change the results of' 'inference.' ) observed_initial = observed_mean observed_stddev = tf . sqrt ( observed_variance ) observed_initial_centered = observed_initial - observed_mean return observed_mean , observed_stddev , observed_initial_centered
Compute statistics of a provided time series, as heuristic initialization.

  Args:
    observed_time_series: `Tensor` representing a time series, or batch of time
       series, of shape either `batch_shape + [num_timesteps, 1]` or
       `batch_shape + [num_timesteps]` (allowed if `num_timesteps > 1`).

  Returns:
    observed_mean: `Tensor` of shape `batch_shape`, giving the empirical
      mean of each time series in the batch.
    observed_stddev: `Tensor` of shape `batch_shape`, giving the empirical
      standard deviation of each time series in the batch.
    observed_initial_centered: `Tensor of shape `batch_shape`, giving the
      initial value of each time series in the batch after centering
      (subtracting the mean).
Compute statistics of a provided time series as heuristic initialization .