functions.py 文件源码

def maximize(samples):
    '''
    Use Kernel Density Estimation to find a continuous PDF
    from the discrete sampling. Maximize that distribution.

    Parameters
    ----------
    samples : list (shape = (nsamples, ndim))
        Observations drawn from the distribution which is
        going to be fit.

    Returns
    -------
    maximum : list(ndim)
        Maxima of the probability distributions along each axis.
    '''
    from scipy.optimize import minimize
    from scipy.stats import gaussian_kde as kde

    # Give the samples array the proper shape.
    samples = np.transpose(samples)

    # Estimate the continous PDF.
    estimate = kde(samples)

    # Take the minimum of the estimate.
    def PDF(x):
        return -estimate(x)

    # Initial guess on maximum is made from 50th percentile of the sample.
    p0 = [np.percentile(samples[i], 50) for i in range(samples.shape[0])]

    # Calculate the maximum of the distribution.
    maximum = minimize(PDF, p0).x

    return maximum