def test_ProjectELM_WorksCorrectly(self):
X = np.array([[1], [2], [3]])
elm = ELM(1, 1)
elm.add_neurons(1, "tanh", np.array([[1]]), np.array([0]))
H = elm.project(X)
np.testing.assert_allclose(H, np.tanh(X))
npos = sum((p == 1))
nneg = sum((p == 0))
print "\n Testing results"
print "Tpos:", ntpos, " / ", npos, "TD:", ntpos / float(npos)
print "Tneg:", ntneg, " / ", nneg, "TN:", ntneg / float(nneg)
print "Acc: ", nhit / (float)(len(p)), "total", len(p)
mn_error[i] = nhit / (float)(len(p))
print mn_error
print "mean error", np.mean(mn_error)
else:
elmInput = ELM(input_shape, input_shape)
elmInput.add_neurons(ninputsig, "tanh")
#elmInput.add_neurons(500, "lin")
elmInput.train(XXtrainIn, XXtrainOut, "r")
elmInputProjection = elmInput.project(XXtrainIn)
print "\n Trained input elm ", elmInput
print "Projection Max :", np.max(elmInputProjection), "Min :", np.min(
elmInputProjection)
# do norm before continuing.
elmInputProjectionNormed = mapMinMax(elmInputProjection)
print "Normed Projection Max :", np.max(
elmInputProjectionNormed), "Min :", np.min(elmInputProjectionNormed)
## HIDDEN LAYER
elmHidden1 = ELM(elmInputProjectionNormed.shape[1],
elmInputProjectionNormed.shape[1])
elmHidden1.add_neurons(ninputsig, "tanh")
elmHidden1.add_neurons(ninputlin, "lin")
elmHidden1.train(elmInputProjectionNormed, elmInputProjectionNormed, "r")
def test_ProjectELM_WorksCorrectly(self):
X = np.array([[1], [2], [3]])
elm = ELM(1, 1)
elm.add_neurons(1, "tanh", np.array([[1]]), np.array([0]))
H = elm.project(X)
np.testing.assert_allclose(H, np.tanh(X))