parameters: (query_points, train_points, w, v, order)
return_statement: return rbf_term + linear_term
docstring: Apply polyharmonic interpolation model to data.
  
    Given coefficients w and v for the interpolation model, we evaluate
    interpolated function values at query_points.
  
    Args:
      query_points: `[b, m, d]` x values to evaluate the interpolation at
      train_points: `[b, n, d]` x values that act as the interpolation centers
                      ( the c variables in the wikipedia article)
      w: `[b, n, k]` weights on each interpolation center
      v: `[b, d, k]` weights on each input dimension
      order: order of the interpolation
  
    Returns:
      Polyharmonic interpolation evaluated at points defined in query_points.
docstring_summary: Apply polyharmonic interpolation model to data.
docstring_tokens: Apply polyharmonic interpolation model to data .
function: def _apply_interpolation(query_points, train_points, w, v, order):
    """Apply polyharmonic interpolation model to data.
  
    Given coefficients w and v for the interpolation model, we evaluate
    interpolated function values at query_points.
  
    Args:
      query_points: `[b, m, d]` x values to evaluate the interpolation at
      train_points: `[b, n, d]` x values that act as the interpolation centers
                      ( the c variables in the wikipedia article)
      w: `[b, n, k]` weights on each interpolation center
      v: `[b, d, k]` weights on each input dimension
      order: order of the interpolation
  
    Returns:
      Polyharmonic interpolation evaluated at points defined in query_points.
    """
  
    # First, compute the contribution from the rbf term.
    pairwise_dists = _cross_squared_distance_matrix(query_points, train_points)
    phi_pairwise_dists = _phi(pairwise_dists, order)
  
    rbf_term = math_ops.matmul(phi_pairwise_dists, w)
  
    # Then, compute the contribution from the linear term.
    # Pad query_points with ones, for the bias term in the linear model.
    query_points_pad = array_ops.concat([
        query_points,
        array_ops.ones_like(query_points[..., :1], train_points.dtype)
    ], 2)
    linear_term = math_ops.matmul(query_points_pad, v)
  
    return rbf_term + linear_term
function_tokens: def _apply_interpolation ( query_points , train_points , w , v , order ) : # First, compute the contribution from the rbf term. pairwise_dists = _cross_squared_distance_matrix ( query_points , train_points ) phi_pairwise_dists = _phi ( pairwise_dists , order ) rbf_term = math_ops . matmul ( phi_pairwise_dists , w ) # Then, compute the contribution from the linear term. # Pad query_points with ones, for the bias term in the linear model. query_points_pad = array_ops . concat ( [ query_points , array_ops . ones_like ( query_points [ ... , : 1 ] , train_points . dtype ) ] , 2 ) linear_term = math_ops . matmul ( query_points_pad , v ) return rbf_term + linear_term