Method returns a generator type object whose each iteration yields a sample
        using Hamiltonian Monte Carlo

        Parameters
        ----------
        initial_pos: A 1d array like object
            Vector representing values of parameter position, the starting
            state in markov chain.

        num_adapt: int
            The number of interations to run the adaptation of stepsize

        num_samples: int
            Number of samples to be generated

        trajectory_length: int or float
            Target trajectory length, stepsize * number of steps(L),
            where L is the number of steps taken to propose new values of position and momentum
            per HMC iteration and stepsize is step size.

        stepsize: float , defaults to None
            The stepsize for proposing new values of position and momentum in simulate_dynamics
            If None, then will be choosen suitably

        Returns
        -------
        genrator: yielding a numpy.array type object for a sample

        Examples
        --------
        >>> from pgmpy.sampling import HamiltonianMCDA as HMCda, GradLogPDFGaussian as GLPG, LeapFrog
        >>> from pgmpy.factors.continuous import GaussianDistribution as JGD
        >>> import numpy as np
        >>> mean = np.array([1, 1])
        >>> covariance = np.array([[1, 0.7], [0.7, 3]])
        >>> model = JGD(['x', 'y'], mean, covariance)
        >>> sampler = HMCda(model=model, grad_log_pdf=GLPG, simulate_dynamics=LeapFrog)
        >>> gen_samples = sampler.generate_sample(np.array([1, 1]), num_adapt=10000,
        ...                                       num_samples = 10000, trajectory_length=2, stepsize=None)
        >>> samples_array = np.array([sample for sample in gen_samples])
        >>> np.cov(samples_array.T)
        array([[ 0.98432155,  0.69517394],
               [ 0.69517394,  2.95449533]])

def generate_sample(self, initial_pos, num_adapt, num_samples, trajectory_length, stepsize=None):
        """
        Method returns a generator type object whose each iteration yields a sample
        using Hamiltonian Monte Carlo

        Parameters
        ----------
        initial_pos: A 1d array like object
            Vector representing values of parameter position, the starting
            state in markov chain.

        num_adapt: int
            The number of interations to run the adaptation of stepsize

        num_samples: int
            Number of samples to be generated

        trajectory_length: int or float
            Target trajectory length, stepsize * number of steps(L),
            where L is the number of steps taken to propose new values of position and momentum
            per HMC iteration and stepsize is step size.

        stepsize: float , defaults to None
            The stepsize for proposing new values of position and momentum in simulate_dynamics
            If None, then will be choosen suitably

        Returns
        -------
        genrator: yielding a numpy.array type object for a sample

        Examples
        --------
        >>> from pgmpy.sampling import HamiltonianMCDA as HMCda, GradLogPDFGaussian as GLPG, LeapFrog
        >>> from pgmpy.factors.continuous import GaussianDistribution as JGD
        >>> import numpy as np
        >>> mean = np.array([1, 1])
        >>> covariance = np.array([[1, 0.7], [0.7, 3]])
        >>> model = JGD(['x', 'y'], mean, covariance)
        >>> sampler = HMCda(model=model, grad_log_pdf=GLPG, simulate_dynamics=LeapFrog)
        >>> gen_samples = sampler.generate_sample(np.array([1, 1]), num_adapt=10000,
        ...                                       num_samples = 10000, trajectory_length=2, stepsize=None)
        >>> samples_array = np.array([sample for sample in gen_samples])
        >>> np.cov(samples_array.T)
        array([[ 0.98432155,  0.69517394],
               [ 0.69517394,  2.95449533]])
        """
        self.accepted_proposals = 0
        initial_pos = _check_1d_array_object(initial_pos, 'initial_pos')
        _check_length_equal(initial_pos, self.model.variables, 'initial_pos', 'model.variables')

        if stepsize is None:
            stepsize = self._find_reasonable_stepsize(initial_pos)

        if num_adapt <= 1:  # return sample generated using Simple HMC algorithm
            for sample in HamiltonianMC.generate_sample(self, initial_pos, num_samples, trajectory_length, stepsize):
                yield sample
            return
        mu = np.log(10.0 * stepsize)

        stepsize_bar = 1.0
        h_bar = 0.0

        position_m = initial_pos.copy()
        num_adapt += 1

        for i in range(1, num_samples + 1):

            position_m, alpha = self._sample(position_m, trajectory_length, stepsize)

            if i <= num_adapt:
                stepsize, stepsize_bar, h_bar = self._adapt_params(stepsize, stepsize_bar, h_bar, mu, i, alpha)
            else:
                stepsize = stepsize_bar

            yield position_m

        self.acceptance_rate = self.accepted_proposals / num_samples