tensorflow_probability/python/sts/seasonal.py
def build_effects_to_residuals_matrix(num_seasons, dtype):
  """Build change-of-basis matrices for constrained seasonal effects.

  This method builds the matrix that transforms seasonal effects into
  effect residuals (differences from the mean effect), and additionally
  projects these residuals onto the subspace where the mean effect is zero.

  See `ConstrainedSeasonalStateSpaceModel` for mathematical details.

  Args:
    num_seasons: scalar `int` number of seasons.
    dtype: TensorFlow `dtype` for the returned values.
  Returns:
    effects_to_residuals: `Tensor` of shape
      `[num_seasons-1, num_seasons]`, such that `differences_from_mean_effect =
      matmul(effects_to_residuals, seasonal_effects)`.  In the
      notation of `ConstrainedSeasonalStateSpaceModel`, this is
      `effects_to_residuals = P * R`.
    residuals_to_effects: the (pseudo)-inverse of the above; a
      `Tensor` of shape `[num_seasons, num_seasons-1]`. In the
      notation of `ConstrainedSeasonalStateSpaceModel`, this is
      `residuals_to_effects = R^{-1} * P'`.
  """

  # Build the matrix that converts effects `e_i` into differences from the mean
  # effect `(e_i - sum(e_i)) / num_seasons`, with the mean effect in the last
  # row so that the transformation is invertible.
  effects_to_residuals_fullrank = np.eye(num_seasons) - 1./num_seasons
  effects_to_residuals_fullrank[-1, :] = 1./num_seasons  # compute mean effect
  residuals_to_effects_fullrank = np.linalg.inv(effects_to_residuals_fullrank)

  # Drop the final dimension, effectively setting the mean effect to zero.
  effects_to_residuals = effects_to_residuals_fullrank[:-1, :]
  residuals_to_effects = residuals_to_effects_fullrank[:, :-1]

  # Return Tensor values of the specified dtype.
  effects_to_residuals = tf.cast(
      effects_to_residuals, dtype=dtype, name='effects_to_residuals')
  residuals_to_effects = tf.cast(
      residuals_to_effects, dtype=dtype, name='residuals_to_effects')

  return effects_to_residuals, residuals_to_effects
def build_effects_to_residuals_matrix(num_seasons, dtype):
  # Build the matrix that converts effects `e_i` into differences from the mean
  # effect `(e_i - sum(e_i)) / num_seasons`, with the mean effect in the last
  # row so that the transformation is invertible.
  effects_to_residuals_fullrank = np.eye(num_seasons) - 1./num_seasons
  effects_to_residuals_fullrank[-1, :] = 1./num_seasons  # compute mean effect
  residuals_to_effects_fullrank = np.linalg.inv(effects_to_residuals_fullrank)

  # Drop the final dimension, effectively setting the mean effect to zero.
  effects_to_residuals = effects_to_residuals_fullrank[:-1, :]
  residuals_to_effects = residuals_to_effects_fullrank[:, :-1]

  # Return Tensor values of the specified dtype.
  effects_to_residuals = tf.cast(
      effects_to_residuals, dtype=dtype, name='effects_to_residuals')
  residuals_to_effects = tf.cast(
      residuals_to_effects, dtype=dtype, name='residuals_to_effects')

  return effects_to_residuals, residuals_to_effects
def build_effects_to_residuals_matrix ( num_seasons , dtype ) : # Build the matrix that converts effects `e_i` into differences from the mean # effect `(e_i - sum(e_i)) / num_seasons`, with the mean effect in the last # row so that the transformation is invertible. effects_to_residuals_fullrank = np . eye ( num_seasons ) - 1. / num_seasons effects_to_residuals_fullrank [ - 1 , : ] = 1. / num_seasons # compute mean effect residuals_to_effects_fullrank = np . linalg . inv ( effects_to_residuals_fullrank ) # Drop the final dimension, effectively setting the mean effect to zero. effects_to_residuals = effects_to_residuals_fullrank [ : - 1 , : ] residuals_to_effects = residuals_to_effects_fullrank [ : , : - 1 ] # Return Tensor values of the specified dtype. effects_to_residuals = tf . cast ( effects_to_residuals , dtype = dtype , name = 'effects_to_residuals' ) residuals_to_effects = tf . cast ( residuals_to_effects , dtype = dtype , name = 'residuals_to_effects' ) return effects_to_residuals , residuals_to_effects
Build change-of-basis matrices for constrained seasonal effects.

  This method builds the matrix that transforms seasonal effects into
  effect residuals (differences from the mean effect), and additionally
  projects these residuals onto the subspace where the mean effect is zero.

  See `ConstrainedSeasonalStateSpaceModel` for mathematical details.

  Args:
    num_seasons: scalar `int` number of seasons.
    dtype: TensorFlow `dtype` for the returned values.
  Returns:
    effects_to_residuals: `Tensor` of shape
      `[num_seasons-1, num_seasons]`, such that `differences_from_mean_effect =
      matmul(effects_to_residuals, seasonal_effects)`.  In the
      notation of `ConstrainedSeasonalStateSpaceModel`, this is
      `effects_to_residuals = P * R`.
    residuals_to_effects: the (pseudo)-inverse of the above; a
      `Tensor` of shape `[num_seasons, num_seasons-1]`. In the
      notation of `ConstrainedSeasonalStateSpaceModel`, this is
      `residuals_to_effects = R^{-1} * P'`.
Build change - of - basis matrices for constrained seasonal effects .