def submit_observation(self, config, target):
    'Upload a pairing of a configuration alongside an observed target\n        variable.\n\n        Parameters:\n            config (dictionary): A dictionary mapping dimension names to values\n                indicating the configuration of parameters.\n            target (float): A number indicating the performance of this\n                configuration of model parameters.\n\n        Examples:\n            This utility is helpful in the event that a machine learning\n            practitioner already has a few existing evaluations of the system at\n            given inputs. For instance, the consumer may have already performed\n            a grid search to obtain parameter values.\n\n            Suppose that a particular experiment has two dimensions named "x"\n            and "y". Then to upload a configuration to the Thor server, we\n            proceed as follows:\n\n            >>> d = {"x": 1.5, "y": 3.1}\n            >>> v = f(d["x"], d["y"])\n            >>> exp.submit_observation(d, v)\n        '
    post_data = {'auth_token': self.auth_token, 'experiment_id': self.experiment_id, 'configuration': json.dumps(config), 'target': target}
    result = requests.post(url=self.base_url.format('submit_observation'), json=post_data)
    return json_parser(result, self.auth_token)
Upload a pairing of a configuration alongside an observed target
variable.

Parameters:
    config (dictionary): A dictionary mapping dimension names to values
        indicating the configuration of parameters.
    target (float): A number indicating the performance of this
        configuration of model parameters.

Examples:
    This utility is helpful in the event that a machine learning
    practitioner already has a few existing evaluations of the system at
    given inputs. For instance, the consumer may have already performed
    a grid search to obtain parameter values.

    Suppose that a particular experiment has two dimensions named "x"
    and "y". Then to upload a configuration to the Thor server, we
    proceed as follows:

    >>> d = {"x": 1.5, "y": 3.1}
    >>> v = f(d["x"], d["y"])
    >>> exp.submit_observation(d, v)
def submit_observation(self, config, target):
    'Upload a pairing of a configuration alongside an observed target\n        variable.\n\n        Parameters:\n            config (dictionary): A dictionary mapping dimension names to values\n                indicating the configuration of parameters.\n            target (float): A number indicating the performance of this\n                configuration of model parameters.\n\n        Examples:\n            This utility is helpful in the event that a machine learning\n            practitioner already has a few existing evaluations of the system at\n            given inputs. For instance, the consumer may have already performed\n            a grid search to obtain parameter values.\n\n            Suppose that a particular experiment has two dimensions named "x"\n            and "y". Then to upload a configuration to the Thor server, we\n            proceed as follows:\n\n            >>> d = {"x": 1.5, "y": 3.1}\n            >>> v = f(d["x"], d["y"])\n            >>> exp.submit_observation(d, v)\n        '
    post_data = {'auth_token': self.auth_token, 'experiment_id': self.experiment_id, 'configuration': json.dumps(config), 'target': target}
    result = requests.post(url=self.base_url.format('submit_observation'), json=post_data)
    return json_parser(result, self.auth_token)