=======================
Apply to a New Dataset
=======================

In this notebook, we will guide you add your own dataset to use the ``LibMOON``.

Firstily we need to create a new dataset class, which should inherit from the ``BaseMOP`` class.

.. code-block:: python

    import numpy as np
    import torch
    from libmoon.problem.synthetic.mop import BaseMOP

    class F1(BaseMOP):
    def __init__(self,
                 n_var: int,
                 n_obj: int=None,
                 lbound: np.ndarray=None,
                 ubound: np.ndarray=None,
                 n_cons: int = 0,
                 ) -> None:

        self.n_dim = n_var
        self.n_obj = 2
        self.lbound = torch.zeros(n_var).float()
        self.ubound = torch.ones(n_var).float()


Then we can implement the ``_evaluate_torch`` method, which is used to evaluate the objective values of the dataset.
We can also implement the ``_evaluate_numpy`` method, which is implemented using numpy.

.. code-block:: python

    def _evaluate_torch(self, x):
        n = x.shape[1]

        sum1 = sum2 =  0.0
        count1 = count2 =  0.0

        for i in range(2,n+1):
            yi = x[:,i-1] - torch.pow(2 * x[:,0] - 1, 2)
            yi = yi * yi

            if i % 2 == 0:
                sum2 = sum2 + yi
                count2 = count2 + 1.0
            else:
                sum1 = sum1 + yi
                count1 = count1 + 1.0

        f1 = (1 + 1.0/count1  * sum1 ) * x[:,0]
        f2 = (1 + 1.0/count2 * sum2 ) * (1.0 - torch.sqrt(x[:,0] / (1 + 1.0/count2 * sum2 )))

        objs = torch.stack([f1,f2]).T

        return objs