"""
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 ..hyp_defs import float_cpu
[docs]class NDA(HypModel):
"""Class to do nearest-neighbors discriminant analysis"""
[docs] def __init__(self, mu=None, T=None, **kwargs):
super().__init__(**kwargs)
self.mu = mu
self.T = T
[docs] def predict(self, x):
if self.mu is not None:
x = x - self.mu
return np.dot(x, self.T)
[docs] def fit(self, mu, Sb, Sw, nda_dim=None):
self.mu = mu
assert Sb.shape == Sw.shape
d, V = la.eigh(Sb, Sw)
V = np.fliplr(V)
p = V[0, :] < 0
V[:, p] *= -1
if nda_dim is not None:
assert nda_dim <= V.shape[1]
V = V[:, :nda_dim]
self.T = V
[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)