Complete the Todos
Complete the TODOs.
class NeuralNetworkRegressor(NeuralNetwork): def __init__(self, **kwargs): super().__init__(**kwargs)
for key, value in kwargs.items(): setattr(self, key, value) self.param_names = list(kwargs.keys(
def get_params(self, deep=True): """ Gets all class variables
This is a method for compatibility with Sklearn's GridSearchCv
return {param: getattr(self, param)
for param in self.param_names}
def set_params(self, **parameters): """ Sets all class variables
This is a method for compatibility with Sklearn's GridSearchCv
for parameter, value in parameters.items():
setattr(self, parameter, value)
return self
def predict(self, X: np.ndarray) -> np.ndarray: """ Make predictions using parameters learned during training.
Args: X: Features/data to make predictions with
TODO: Finish this method by adding code to make a prediction
Store the predicted labels into y_hat'
# TODO (REQUIRED) Add code below
# TODO (REQUIRED) Store predictions below by replacing np.ones() y_hat = np.ones([len(x), 1]) # Makes sure predictions are given as a 2D array return y_hat.reshape(-1, 1)
