#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on April 2022
@author: Simon Delarue <sdelarue@enst.fr>
"""
import numpy as np
[docs]class BaseActivation:
"""Base class for activation functions.
Parameters
----------
name : str
Name of the activation function.
"""
def __init__(self, name: str = 'custom'):
self.name = name
[docs] @staticmethod
def output(signal: np.ndarray) -> np.ndarray:
"""Output of the activation function.
Parameters
----------
signal : np.ndarray, shape (n_samples, n_channels)
Input signal.
Returns
-------
output : np.ndarray, shape (n_samples, n_channels)
Output signal.
"""
output = signal
return output
[docs] @staticmethod
def gradient(signal: np.ndarray, direction: np.ndarray) -> np.ndarray:
"""Gradient of the activation function.
Parameters
----------
signal : np.ndarray, shape (n_samples, n_channels)
Input signal.
direction : np.ndarray, shape (n_samples, n_channels)
Direction where the gradient is taken.
Returns
-------
gradient : np.ndarray, shape (n_samples, n_channels)
Gradient.
"""
gradient = direction
return gradient
[docs]class BaseLoss(BaseActivation):
"""Base class for loss functions."""
[docs] @staticmethod
def loss(signal: np.ndarray, labels: np.ndarray) -> float:
"""Get the loss value.
Parameters
----------
signal : np.ndarray, shape (n_samples, n_channels)
Input signal (before activation).
labels : np.ndarray, shape (n_samples)
True labels.
"""
return 0
[docs] @staticmethod
def loss_gradient(signal: np.ndarray, labels: np.ndarray) -> np.ndarray:
"""Gradient of the loss function.
Parameters
----------
signal : np.ndarray, shape (n_samples, n_channels)
Input signal.
labels : np.ndarray, shape (n_samples,)
True labels.
Returns
-------
gradient : np.ndarray, shape (n_samples, n_channels)
Gradient.
"""
gradient = np.ones_like(signal)
return gradient