"""
Copyright 2018 Johns Hopkins University (Author: Jesus Villalba)
Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
import numpy as np
import h5py
import scipy.linalg as la
from ..hyp_model import HypModel
from ..pdfs import Normal
[docs]class CentWhiten(HypModel):
"""Class to do centering and whitening of i-vectors."""
[docs] def __init__(self, mu=None, T=None, update_mu=True, update_T=True, **kwargs):
super().__init__(**kwargs)
self.mu = mu
self.T = T
self.update_mu = update_mu
self.update_T = update_T
[docs] def predict(self, x):
if self.mu is not None:
x = x - self.mu
if self.T is not None:
if self.T.ndim == 1:
x = x * T
else:
x = np.dot(x, self.T)
return x
[docs] def fit(self, x=None, sample_weight=None, mu=None, S=None):
if x is not None:
if x.shape[0] > x.shape[1]:
gauss = Normal(x_dim=x.shape[1])
gauss.fit(x=x, sample_weight=sample_weight)
mu = gauss.mu
S = gauss.Sigma
else:
mu = np.mean(x, axis=0)
S = np.eye(x.shape[1])
if self.update_mu:
self.mu = mu
if self.update_T:
d, V = la.eigh(S)
V *= np.sqrt(1 / d)
V = np.fliplr(V)
p = V[0, :] < 0
V[:, p] *= -1
nonzero = d > 0
if not np.all(nonzero):
V = V[:, nonzero[::-1]]
self.T = V
[docs] def get_config(self):
config = {"update_mu": self.update_mu, "update_t": self.update_T}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
[docs] def save_params(self, f):
params = {"mu": self.mu, "T": self.T}
self._save_params_from_dict(f, params)
[docs] @classmethod
def load_params(cls, f, config):
param_list = ["mu", "T"]
params = cls._load_params_to_dict(f, config["name"], param_list)
return cls(mu=params["mu"], T=params["T"], name=config["name"])
[docs] @classmethod
def load_mat(cls, file_path):
with h5py.File(file_path, "r") as f:
mu = np.asarray(f["mu"], dtype="float32")
T = np.asarray(f["T"], dtype="float32")
return cls(mu, T)
[docs] def save_mat(self, file_path):
with h5py.File(file_path, "w") as f:
f.create_dataset("mu", data=self.mu)
f.create_dataset("T", data=self.T)
[docs] @staticmethod
def filter_args(**kwargs):
valid_args = ("update_mu", "update_T", "name")
return dict((k, kwargs[k]) for k in valid_args if k in kwargs)
[docs] @staticmethod
def add_class_args(parser, prefix=None):
if prefix is None:
p1 = "--"
else:
p1 = "--" + prefix + "."
parser.add_argument(
p1 + "update-mu",
default=True,
type=bool,
help=("updates centering parameter"),
)
parser.add_argument(
p1 + "update-T",
default=True,
type=bool,
help=("updates whitening parameter"),
)
parser.add_argument(p1 + "name", default="lnorm")
add_argparse_args = add_class_args