import numpy as np

class Gaussian:
    def __init__(self,data,variance=1.,normalize=False):
        self.data = data
        self.N,D = data.shape
        self.Z = 0. # a correction factor which accounts for the approximation made

        #normalisation
        if normalize:
            self._mean = data.mean(0)[None,:]
            self._std = data.std(0)[None,:]
            self.Y = (self.data - self._mean)/self._std
        else:
            self._mean = np.zeros((1,D))
            self._std = np.ones((1,D))
            self.Y = self.data

        self.YYT = np.dot(self.Y,self.Y.T)
        self._set_params(np.asarray(variance))

    def _get_params(self):
        return np.asarray(self._variance)

    def _get_param_names(self):
        return ["noise variance"]

    def _set_params(self,x):
        self._variance = x
        self.variance = np.eye(self.N)*self._variance

    def predictive_values(self,mu,var):
        """
        Un-normalise the prediction and add the likelihood variance, then return the 5%, 95% interval
        """
        mean = mu*self._std + self._mean
        true_var = (var + self._variance)*self._std**2
        _5pc = mean + mean - 2.*np.sqrt(var)
        _95pc = mean + 2.*np.sqrt(var)
        return mean, _5pc, _95pc

    def fit_full(self):
        """
        No approximations needed
        """
        pass

    def _gradients(self,partial):
        return np.sum(np.diag(partial))