get param set param error

[gnodabb]

There is an issue where in the BaseNeuralNetwork class, the 'set_param' and 'get_param' function do not get and set all of the parameters. This causes an issue when using basic sklearn functions such as 'cross_validate'

Edit: Looks like this may be fixed in PR #55

to fix update the following functions like this:

    def get_params(self, deep=False):
        """Get parameters for this estimator.

        Returns
        -------
        params : dictionary
            Parameter names mapped to their values.
        """
        params = {'activation': self.activation,
                  'algorithm': self.algorithm,
                  'hidden_nodes': self.hidden_nodes,
                  'max_iters': self.max_iters,
                  'bias': self.bias,
                  'is_classifier': self.is_classifier,
                  'learning_rate': self.learning_rate,
                  'early_stopping': self.early_stopping,
                  'clip_max': self.clip_max,
                  'restarts': self.restarts,
                  'schedule': self.schedule,
                  'pop_size': self.pop_size,
                  'mutation_prob': self.mutation_prob,
                  'max_attempts': self.max_attempts,
                  'random_state': self.random_state,
                  'curve': self.curve}

        return params

    def set_params(self, **in_params):
        """Set the parameters of this estimator.

        Parameters
        -------
        in_params: dictionary
            Dictionary of parameters to be set and the value to be set to.
        """
        if 'hidden_nodes' in in_params.keys():
            self.hidden_nodes = in_params['hidden_nodes']
        if 'max_iters' in in_params.keys():
            self.max_iters = in_params['max_iters']
        if 'bias' in in_params.keys():
            self.bias = in_params['bias']
        if 'is_classifier' in in_params.keys():
            self.is_classifier = in_params['is_classifier']
        if 'learning_rate' in in_params.keys():
            self.learning_rate = in_params['learning_rate']
        if 'early_stopping' in in_params.keys():
            self.early_stopping = in_params['early_stopping']
        if 'clip_max' in in_params.keys():
            self.clip_max = in_params['clip_max']
        if 'restarts' in in_params.keys():
            self.restarts = in_params['restarts']
        if 'schedule' in in_params.keys():
            self.schedule = in_params['schedule']
        if 'pop_size' in in_params.keys():
            self.pop_size = in_params['pop_size']
        if 'mutation_prob' in in_params.keys():
            self.mutation_prob = in_params['mutation_prob']
        if 'activation' in in_params.keys():
            self.activation = in_params['activation']
        if 'algorithm' in in_params.keys():
            self.algorithm = in_params['algorithm']
        if 'max_attempts' in in_params.keys():
            self.max_attempts = in_params['max_attempts']
        if 'random_state' in in_params.keys():
            self.random_state = in_params['random_state']
        if 'curve' in in_params.keys():
            self.curve = in_params['curve']