Source code for moe_examples.hyper_opt_of_gp_from_historical_data

# -*- coding: utf-8 -*-
"""An example for accessing the gp_mean_var simple endpoint.

:func:`moe.easy_interface.simple_endpoint.gp_mean_var`

The function requires some historical information to inform the Gaussian Process

The optimal hyperparameters are returned.
"""
import numpy

from moe.easy_interface.simple_endpoint import gp_hyper_opt
from moe.optimal_learning.python.data_containers import SamplePoint

# Randomly generate some historical data
# points_sampled is an iterable of iterables of the form [point_as_a_list, objective_function_value, value_variance]
points_sampled = [
        SamplePoint(numpy.array([x]), numpy.random.uniform(-1, 1), 0.01) for x in numpy.arange(0, 1, 0.1)
        ]


[docs]def run_example(verbose=True, **kwargs):
    """Run the example, aksing MOE for optimal hyperparameters given historical data."""
    covariance_info = gp_hyper_opt(
            points_sampled,
            **kwargs
            )

    if verbose:
        print covariance_info


if __name__ == '__main__':
    run_example()