import numpy as np
from .abstract_learner import Learner
class ExtremeLearningMachine(Learner):
def __init__(self, layers, cost="mse", name="", solve_mode="pseudoinverse", **kw):
super().__init__(layers, cost, name, **kw)
self.solve = {
"pseudoinverse": self.solve_with_pseudo_inverse,
"covariance": self.solve_with_covariance_matrices,
"correlation": self.solve_with_covariance_matrices
}[solve_mode]
for layer in layers[:-1]:
layer.trainable = False
def solve_with_pseudo_inverse(self, Z, Y):
A = np.linalg.pinv(Z)
Wo = A @ Y
self.layers[-1].set_weights([Wo, np.array([0] * self.layers[-1].neurons)], fold=False)
def solve_with_covariance_matrices(self, Z, Y):
A = np.cov(Z.T)
B = np.cov(Z.T, Y.T)
W = np.invert(A) @ B
self.layers[-1].set_weights([W, np.array([0] * self.layers[-1].neurons)], fold=False)
def learn_batch(self, X, Y, **kwarg):
for layer in self.layers[:-1]:
X = layer.feedforward(X)
self.solve(X, Y)
