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'''
This project is for automatically tuning parameters for a toy function.
y = np.exp(-(x - 2)**2) + np.exp(-(x - 6)**2/10) + 1/ (x**2 + 1)
'''
import logging
import numpy as np
import nni

LOG = logging.getLogger('auto-toy')


def target(x):
    res = np.exp(-(x - 2)**2) + np.exp(-(x - 6)**2/10) + 1 / (x**2 + 1)
    return -res


def get_default_parameters():
    '''
    specify configurations as a dict
    '''
    params = {
        'x': 0
    }
    return params


def run(params):
    # predict
    y = target(params['x'])
    nni.report_final_result(y)


if __name__ == '__main__':
    try:
        # get parameters from tuner
        RECEIVED_PARAMS = nni.get_next_parameter()
        LOG.debug(RECEIVED_PARAMS)
        PARAMS = get_default_parameters()
        PARAMS.update(RECEIVED_PARAMS)
        LOG.debug(PARAMS)

        # train
        run(PARAMS)
    except Exception as exception:
        LOG.exception(exception)
        raise