
# The *NP takes as input a `NPRegressionDescription` namedtuple with fields:
#   `query`: a tuple containing ((context_x, context_y), target_x)
#   `target_y`: a tesor containing the ground truth for the targets to be
#     predicted
#   `num_total_points`: A vector containing a scalar that describes the total
#     number of datapoints used (context + target)
#   `num_context_points`: A vector containing a scalar that describes the number
#     of datapoints used as context
# The GPCurvesReader returns the newly sampled data in this format at each
# iteration

import collections

NPRegressionDescription = collections.namedtuple(
    "NPRegressionDescription",
    ("query", "target_y", "num_total_points", "num_context_points"))



# The *NP takes as input a `NPRegressionDescription` namedtuple with fields:
#   `query`: a tuple containing ((context_x, context_y), target_x)
#   `target_y`: a tesor containing the ground truth for the targets to be
#     predicted
#   `num_total_points`: A vector containing a scalar that describes the total
#     number of datapoints used (context + target)
#   `num_context_points`: A vector containing a scalar that describes the number
#     of datapoints used as context
# The GPCurvesReader returns the newly sampled data in this format at each
# iteration

import collections

NPRegressionDescription = collections.namedtuple(
    "NPRegressionDescription",
    ("query", "target_y", "num_total_points", "num_context_points"))


# The *NP takes as input a `NPRegressionDescription` namedtuple with fields: #   `query`: a tuple containing ((context_x, context_y), target_x) #   `target_y`: a tesor containing the ground truth for the targets to be #     predicted #   `num_total_points`: A vector containing a scalar that describes the total #     number of datapoints used (context + target) #   `num_context_points`: A vector containing a scalar that describes the number #     of datapoints used as context # The GPCurvesReader returns the newly sampled data in this format at each # iteration