def test_AddDataToFile_SingleAddition(self):
X = self.makeh5(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]))
T = self.makeh5(np.array([[1], [2], [3], [4]]))
hpelm = HPELM(2, 1)
hpelm.add_neurons(3, "lin")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
def test_AddDataToFile_SingleAddition(self):
X = self.makeh5(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]))
T = self.makeh5(np.array([[1], [2], [3], [4]]))
hpelm = HPELM(2, 1)
hpelm.add_neurons(3, "lin")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
def test_AddDataToFile_MixedSequentialAsync(self):
X = self.makeh5(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]))
T = self.makeh5(np.array([[1], [2], [3], [4]]))
hpelm = HPELM(2, 1)
hpelm.add_neurons(3, "lin")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
hpelm.add_data_async(X, T, fHH=fHH, fHT=fHT)
def test_AddDataToFile_MixedSequentialAsync(self):
X = self.makeh5(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]))
T = self.makeh5(np.array([[1], [2], [3], [4]]))
hpelm = HPELM(2, 1)
hpelm.add_neurons(3, "lin")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
hpelm.add_data_async(X, T, fHH=fHH, fHT=fHT)
def test_ValidationCorr_ReturnsConfusion(self):
X = self.makeh5(np.random.rand(10, 3))
T = self.makeh5(np.random.rand(10, 2))
hpelm = HPELM(3, 2, classification="c")
hpelm.add_neurons(6, "tanh")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
_, _, confs = hpelm.validation_corr(fHH, fHT, X, T, steps=3)
self.assertGreater(np.sum(confs[0]), 1)
def test_ValidationCorr_Works(self):
X = self.makeh5(np.random.rand(30, 3))
T = self.makeh5(np.random.rand(30, 2))
hpelm = HPELM(3, 2, norm=1e-6)
hpelm.add_neurons(6, "tanh")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
nns, err, confs = hpelm.validation_corr(fHH, fHT, X, T, steps=3)
self.assertGreater(err[0], err[-1])
def test_SolveCorr_Works(self):
X = self.makeh5(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]))
T = self.makeh5(np.array([[1], [2], [3], [4]]))
hpelm = HPELM(2, 1)
hpelm.add_neurons(3, "lin")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
hpelm.solve_corr(fHH, fHT)
self.assertIsNot(hpelm.nnet.get_B(), None)
def test_ValidationCorr_ReturnsConfusion(self):
X = self.makeh5(np.random.rand(10, 3))
T = self.makeh5(np.random.rand(10, 2))
hpelm = HPELM(3, 2, classification="c")
hpelm.add_neurons(6, "tanh")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
_, _, confs = hpelm.validation_corr(fHH, fHT, X, T, steps=3)
self.assertGreater(np.sum(confs[0]), 1)
def test_ValidationCorr_Works(self):
X = self.makeh5(np.random.rand(30, 3))
T = self.makeh5(np.random.rand(30, 2))
hpelm = HPELM(3, 2, norm=1e-6)
hpelm.add_neurons(6, "tanh")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
nns, err, confs = hpelm.validation_corr(fHH, fHT, X, T, steps=3)
self.assertGreater(err[0], err[-1])
def test_SolveCorr_Works(self):
X = self.makeh5(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]))
T = self.makeh5(np.array([[1], [2], [3], [4]]))
hpelm = HPELM(2, 1)
hpelm.add_neurons(3, "lin")
fHH = self.makefile()
fHT = self.makefile()
hpelm.add_data(X, T, fHH=fHH, fHT=fHT)
hpelm.solve_corr(fHH, fHT)
self.assertIsNot(hpelm.nnet.get_B(), None)
def test_ParallelBasicPython_Works(self):
X = np.random.rand(1000, 10)
T = np.random.rand(1000, 3)
hX = modules.make_hdf5(X, self.fnameX)
hT = modules.make_hdf5(T, self.fnameT)
model0 = HPELM(10, 3)
model0.add_neurons(10, 'lin')
model0.add_neurons(5, 'tanh')
model0.add_neurons(15, 'sigm')
model0.save(self.fmodel)
model1 = HPELM(10, 3)
model1.load(self.fmodel)
os.remove(self.fnameHT)
os.remove(self.fnameHH)
model1.add_data(self.fnameX,
self.fnameT,
istart=0,
icount=100,
fHH=self.fnameHH,
fHT=self.fnameHT)
model2 = HPELM(10, 3)
model2.load(self.fmodel)
model2.add_data(self.fnameX,
self.fnameT,
istart=100,
icount=900,
fHH=self.fnameHH,
fHT=self.fnameHT)
model3 = HPELM(10, 3)
model3.load(self.fmodel)
model3.solve_corr(self.fnameHH, self.fnameHT)
model3.save(self.fmodel)
model4 = HPELM(10, 3)
model4.load(self.fmodel)
model4.predict(self.fnameX, self.fnameY)
err = model4.error(self.fnameT, self.fnameY, istart=0, icount=198)
self.assertLess(err, 1)
err = model4.error(self.fnameT, self.fnameY, istart=379, icount=872)
self.assertLess(err, 1)
def main():
for neurons in range(100, 2100, 100):
elm = HPELM(X_train.shape[1], 1, 'c', None, 100, None, 'single')
elm.add_neurons(neurons, 'sigm')
print("Starting incremental HH and HT files")
for x in range(0, X_train.shape[0], 100):
elm.add_data(X_train,
y_train,
istart=x,
icount=100,
fHH="HH_" + str(neurons) + ".hdf5",
fHT="HT_" + str(neurons) + ".hdf5")
print("Finished creating HH and HT files with " + str(neurons) +
" neurons.")
print("Starting to solve ELM with " + str(neurons) + " neurons.")
elm.solve_corr("HH_" + str(neurons) + ".hdf5",
"HT_" + str(neurons) + ".hdf5")
elm.predict(X_train, "trainpreds_101_" + str(neurons) + "neurons.hdf5")
elm.predict(X_test, "testpreds_101_" + str(neurons) + "neurons.hdf5")
def test_ParallelBasicPython_Works(self):
X = np.random.rand(1000, 10)
T = np.random.rand(1000, 3)
hX = modules.make_hdf5(X, self.fnameX)
hT = modules.make_hdf5(T, self.fnameT)
model0 = HPELM(10, 3)
model0.add_neurons(10, 'lin')
model0.add_neurons(5, 'tanh')
model0.add_neurons(15, 'sigm')
model0.save(self.fmodel)
model1 = HPELM(10, 3)
model1.load(self.fmodel)
os.remove(self.fnameHT)
os.remove(self.fnameHH)
model1.add_data(self.fnameX, self.fnameT, istart=0, icount=100, fHH=self.fnameHH, fHT=self.fnameHT)
model2 = HPELM(10, 3)
model2.load(self.fmodel)
model2.add_data(self.fnameX, self.fnameT, istart=100, icount=900, fHH=self.fnameHH, fHT=self.fnameHT)
model3 = HPELM(10, 3)
model3.load(self.fmodel)
model3.solve_corr(self.fnameHH, self.fnameHT)
model3.save(self.fmodel)
model4 = HPELM(10, 3)
model4.load(self.fmodel)
model4.predict(self.fnameX, self.fnameY)
err = model4.error(self.fnameT, self.fnameY, istart=0, icount=198)
self.assertLess(err, 1)
err = model4.error(self.fnameT, self.fnameY, istart=379, icount=872)
self.assertLess(err, 1)
