def test_Classifier_PartialFit(self):
X, T = self.data_class
elm0 = ELMClassifier(n_neurons=4, alpha=1, random_state=0)
elm1 = ELMClassifier(n_neurons=4, alpha=1, random_state=0)
elm0.fit(X, T)
elm1.partial_fit(X[::2], T[::2])
elm1.partial_fit(X[1::2], T[1::2])
assert_allclose(elm0.solver_.coef_, elm1.solver_.coef_)
def test_Classifier_PartialFit(data_class):
X, T = data_class
elm0 = ELMClassifier(n_neurons=4, alpha=1, random_state=0)
elm1 = ELMClassifier(n_neurons=4, alpha=1, random_state=0)
elm0.fit(X, T)
elm1.partial_fit(X[::2], T[::2])
elm1.partial_fit(X[1::2], T[1::2])
assert elm0.solver_.coef_ == approx(elm1.solver_.coef_)
def test_IterativeSolver_SkipIntermediateSolution(self):
X, T = self.data_class
elm = ELMClassifier(classes=[0, 1, 2], n_neurons=10, alpha=1)
X0 = X[T == 0]
Y0 = T[T == 0]
elm.partial_fit(X0, Y0, compute_output_weights=False)
X1 = X[T == 1]
Y1 = T[T == 1]
elm.partial_fit(X1, Y1, compute_output_weights=False)
X2 = X[T == 2]
Y2 = T[T == 2]
elm.partial_fit(X2, Y2)
Yh = elm.predict(X)
self.assertEqual(set(Yh), {0, 1, 2})
def test_IterativeClassification_FeedClassesOneByOne(self):
X, T = self.data_class
elm = ELMClassifier(classes=[0, -1, -2], n_neurons=10, alpha=1)
X0 = X[T == 0]
Y0 = T[T == 0]
elm.partial_fit(X0, Y0)
X1 = X[T == 1]
Y1 = T[T == 1]
elm.partial_fit(X1, Y1, update_classes=True)
X2 = X[T == 2]
Y2 = T[T == 2]
elm.partial_fit(X2, Y2, update_classes=True)
Yh = elm.predict(X)
self.assertEqual(set(Yh), {0, 1, 2})
