def test_patchmcgsm_stationary(self):
xmask = ones([2, 2], dtype='bool')
ymask = zeros([2, 2], dtype='bool')
xmask[-1, -1] = False
ymask[-1, -1] = True
model = PatchMCGSM(3,
3,
xmask,
ymask,
model=MCGSM(sum(xmask), 1, 2, 2))
data = randn(4, 10000)
model.initialize(data)
model.train(data,
parameters={
'verbosity': 0,
'max_iter': 10,
'stationary': True,
'treshold': 1e-4
})
self.assertTrue(all(model[0, 2].predictors == model[0, 1].predictors))
self.assertFalse(all(model[1, 0].predictors == model[0, 1].predictors))
self.assertTrue(all(model[1, 2].weights == model[1, 1].weights))
self.assertTrue(all(model[1, 2].features == model[1, 1].features))
self.assertTrue(all(model[1, 2].scales == model[1, 1].scales))
self.assertTrue(all(model[1, 2].priors == model[1, 1].priors))
xmask, ymask = generate_masks(3)
model = PatchMCGSM(3,
3,
xmask,
ymask,
model=MCGSM(sum(xmask), 1, 2, 2))
data = randn(4, 10000)
model.initialize(data)
model.train(data,
parameters={
'verbosity': 0,
'max_iter': 10,
'stationary': True,
'treshold': 1e-4
})
self.assertTrue(all(model[0, 2].weights == model[0, 1].weights))
self.assertTrue(all(model[2, 0].features == model[1, 0].features))
self.assertTrue(all(model[2, 2].scales == model[1, 2].scales))
def test_patchmcgsm_train(self):
xmask = ones([2, 2], dtype="bool")
ymask = zeros([2, 2], dtype="bool")
xmask[-1, -1] = False
ymask[-1, -1] = True
model = PatchMCGSM(2, 2, xmask, ymask, model=MCGSM(sum(xmask), 1, 1, 1))
data = randn(4, 10000)
model.initialize(data)
converged = model.train(data, parameters={"verbosity": 0, "max_iter": 200, "treshold": 1e-4})
self.assertTrue(converged)
def test_patchmcgsm_stationary(self):
xmask = ones([2, 2], dtype='bool')
ymask = zeros([2, 2], dtype='bool')
xmask[-1, -1] = False
ymask[-1, -1] = True
model = PatchMCGSM(3, 3, xmask, ymask, model=MCGSM(sum(xmask), 1, 2, 2))
data = randn(4, 10000)
model.initialize(data)
model.train(data, parameters={
'verbosity': 0,
'max_iter': 10,
'stationary': True,
'treshold': 1e-4})
self.assertTrue(all(model[0, 2].predictors == model[0, 1].predictors))
self.assertFalse(all(model[1, 0].predictors == model[0, 1].predictors))
self.assertTrue(all(model[1, 2].weights == model[1, 1].weights))
self.assertTrue(all(model[1, 2].features == model[1, 1].features))
self.assertTrue(all(model[1, 2].scales == model[1, 1].scales))
self.assertTrue(all(model[1, 2].priors == model[1, 1].priors))
xmask, ymask = generate_masks(3)
model = PatchMCGSM(3, 3, xmask, ymask, model=MCGSM(sum(xmask), 1, 2, 2))
data = randn(4, 10000)
model.initialize(data)
model.train(data, parameters={
'verbosity': 0,
'max_iter': 10,
'stationary': True,
'treshold': 1e-4})
self.assertTrue(all(model[0, 2].weights == model[0, 1].weights))
self.assertTrue(all(model[2, 0].features == model[1, 0].features))
self.assertTrue(all(model[2, 2].scales == model[1, 2].scales))
def test_patchmcgsm_train(self):
xmask = ones([2, 2], dtype='bool')
ymask = zeros([2, 2], dtype='bool')
xmask[-1, -1] = False
ymask[-1, -1] = True
model = PatchMCGSM(2,
2,
xmask,
ymask,
model=MCGSM(sum(xmask), 1, 1, 1))
data = randn(4, 10000)
model.initialize(data)
converged = model.train(data,
parameters={
'verbosity': 0,
'max_iter': 200,
'treshold': 1e-4
})
self.assertTrue(converged)