#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from elm import ELM
class MLELM(ELM):
"""
Multi Layer Extreme Learning Machine
"""
def __init__(self, hidden_units, a=1):
self.hidden_units = hidden_units
self.betas = []
self.a = a
def __calc_hidden_layer(self, X):
"""
Args:
X np.array input feature vector
"""
for beta in self.betas:
X = np.dot(beta, X.T).T
return X
def fit(self, X, y):
self.out_num = max(y)
X = self._add_bias(X)
for hid_num in self.hidden_units[:-1]:
_X = self.__calc_hidden_layer(X)
np.random.seed()
W = np.random.uniform(-1., 1.,
(hid_num, _X.shape[1]))
_H = np.linalg.pinv(self._sigmoid(np.dot(W, _X.T)))
beta = np.dot(_H.T, _X)
self.betas.append(beta)
_X = self.__calc_hidden_layer(X)
self.elm = ELM(hid_num=self.hidden_units[-1])
self.elm.fit(_X, y)
return self
def predict(self, X):
X = self.__calc_hidden_layer(self._add_bias(X))
return self.elm.predict(X)
def main():
from sklearn import preprocessing
from sklearn.datasets import fetch_openml as fetch_mldata
from sklearn.model_selection import train_test_split
db_name = 'diabetes'
data_set = fetch_mldata(db_name)
data_set.data = preprocessing.normalize(data_set.data)
tmp = data_set.target
tmpL = [ 1 if i == "tested_positive" else -1 for i in tmp]
data_set.target = tmpL
X_train, X_test, y_train, y_test = train_test_split(
data_set.data, data_set.target, test_size=0.4)
mlelm = MLELM(hidden_units=(10, 30, 200)).fit(X_train, y_train)
elm = ELM(200).fit(X_train, y_train)
print("MLELM Accuracy %0.3f " % mlelm.score(X_test, y_test))
print("ELM Accuracy %0.3f " % elm.score(X_test, y_test))
if __name__ == "__main__":
main()
