def predict_new_data(argv):
'''
Implements output prediction for new data
Arguments:
argv -- system inputs
Returns:
Y -- predicted Y value
'''
# file1 = saved model, file2 = excel file with new data
print(argv)
_, file1, file2 = argv
print(file1)
print(file2)
# Process the excel data
X = process_data(file2)
# Load model
model = ELM(20, 1, tprint=5)
model.load('{}'.format(file1))
# Predict Y
Y_predicted = model.predict(X)
return Y_predicted
def test_ELM_SaveLoad(self):
X = np.array([1, 2, 3, 1, 2, 3])
T = np.array([[1, 0], [1, 0], [1, 0], [0, 1], [0, 1], [0, 1]])
elm = ELM(1, 2, precision='32', norm=0.02)
elm.add_neurons(1, "lin")
elm.add_neurons(2, "tanh")
elm.train(X, T, "wc", w=(0.7, 0.3))
B1 = elm.nnet.get_B()
try:
f, fname = tempfile.mkstemp()
elm.save(fname)
elm2 = ELM(3, 3)
elm2.load(fname)
finally:
os.close(f)
self.assertEqual(elm2.nnet.inputs, 1)
self.assertEqual(elm2.nnet.outputs, 2)
self.assertEqual(elm2.classification, "wc")
self.assertIs(elm.precision, np.float32)
self.assertIs(elm2.precision, np.float64) # precision has changed
np.testing.assert_allclose(np.array([0.7, 0.3]), elm2.wc)
np.testing.assert_allclose(0.02, elm2.nnet.norm)
np.testing.assert_allclose(B1, elm2.nnet.get_B())
self.assertEqual(elm2.nnet.get_neurons()[0][1], "lin")
self.assertEqual(elm2.nnet.get_neurons()[1][1], "tanh")
def test_ELM_SaveLoad(self):
X = np.array([1, 2, 3, 1, 2, 3])
T = np.array([[1, 0], [1, 0], [1, 0], [0, 1], [0, 1], [0, 1]])
elm = ELM(1, 2, precision='32', norm=0.02)
elm.add_neurons(1, "lin")
elm.add_neurons(2, "tanh")
elm.train(X, T, "wc", w=(0.7, 0.3))
B1 = elm.nnet.get_B()
try:
f, fname = tempfile.mkstemp()
elm.save(fname)
elm2 = ELM(3, 3)
elm2.load(fname)
finally:
os.close(f)
self.assertEqual(elm2.nnet.inputs, 1)
self.assertEqual(elm2.nnet.outputs, 2)
self.assertEqual(elm2.classification, "wc")
self.assertIs(elm.precision, np.float32)
self.assertIs(elm2.precision, np.float64) # precision has changed
np.testing.assert_allclose(np.array([0.7, 0.3]), elm2.wc)
np.testing.assert_allclose(0.02, elm2.nnet.norm)
np.testing.assert_allclose(B1, elm2.nnet.get_B())
self.assertEqual(elm2.nnet.get_neurons()[0][1], "lin")
self.assertEqual(elm2.nnet.get_neurons()[1][1], "tanh")
def t10k_test(model_path='../models/elm.model'):
images, labels = read_mnist.load_mnist('../data/', kind='t10k')
images = map(read_mnist.up_to_2D, images)
images = map(get_hog, images)
images = np.mat(np.array(images))
labels = np.mat(map(read_mnist.handle_label, labels))
elm = ELM(images.shape[1], labels.shape[1])
# print images.shape[1], images.shape[1]
elm.load(model_path)
results = elm.predict(images)
labels = map(get_labels, np.array(labels))
results = map(get_labels, np.array(results))
yes, tot = 0, len(labels)
for i in range(0, len(labels)):
if labels[i] == results[i]:
yes += 1
print 'YES :', yes
print 'TOT :', tot
print 'ACC : ', str(float(yes) / tot * 100.0) + '%'
return float(yes) / tot * 100.0
def predictor_init(model_path):
elm = ELM(324, 324)
elm.load(model_path)
if elm == None:
print 'Error: elm is None.'
exit()
print elm
return elm
