extreme_learning_machine.py

# Written by Yuta Koreeda
# CC-BY
import numpy as np


class ExtremeLearningMachine(object):
    def __init__(self, n_unit, activation=None):
        self._activation = self._sig if activation is None else activation
        self._n_unit = n_unit

    @staticmethod
    def _sig(x):
        return 1. / (1 + np.exp(-x))

    @staticmethod
    def _add_bias(x):
        return np.hstack((x, np.ones((x.shape[0], 1))))

    def fit(self, X, y):
        self.W0 = np.random.random((X.shape[1], self._n_unit))
        z = self._add_bias(self._activation(X.dot(self.W0)))
        self.W1 = np.linalg.lstsq(z, y)[0]

    def transform(self, X):
        if not hasattr(self, 'W0'):
            raise UnboundLocalError('must fit before transform')
        z = self._add_bias(self._activation(X.dot(self.W0)))
        return z.dot(self.W1)

    def fit_transform(self, X, y):
        self.W0 = np.random.random((X.shape[1], self._n_unit))
        z = self._add_bias(self._activation(X.dot(self.W0)))
        self.W1 = np.linalg.lstsq(z, y)[0]
        return z.dot(self.W1)