"""
 Copyright 2018 Johns Hopkins University  (Author: Jesus Villalba)
 Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
"""
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
from six.moves import xrange

import numpy as np
import h5py

import scipy.linalg as la

from ..hyp_model import HypModel

class MVN(HypModel):
    """Class to do global mean and variance normalization.
    """
    def __init__(self, mu=None, s=None, **kwargs):
        super(MVN, self).__init__(**kwargs)
        self.mu = mu
        self.s = s

        
    def predict(self, x):
        if self.mu is not None:
            x = x - self.mu
        if self.s is not None:
            x = x/self.s
        return x
    
    def fit(self, x):
        self.mu = np.mean(x, axis=0)
        self.s = np.std(x, axis=0)

        
    def save_params(self, f):
        params = {'mu': self.mu,
                  's': self.s}
        self._save_params_from_dict(f, params)


    @classmethod
    def load_params(cls, f, config):
        param_list = ['mu', 's']
        params = cls._load_params_to_dict(f, config['name'], param_list)
        return cls(mu=params['mu'], s=params['s'], name=config['name'])
