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)
def test_patchmcgsm(self):
xmask = ones([8, 8], dtype='bool')
ymask = zeros([8, 8], dtype='bool')
xmask[-1, -1] = False
ymask[-1, -1] = True
model = PatchMCGSM(8, 8, xmask, ymask, model=MCGSM(sum(xmask), 1))
self.assertLess(max(abs(model.input_mask() - xmask)), 1e-8)
self.assertLess(max(abs(model.output_mask() - ymask)), 1e-8)
for i in range(8):
for j in range(8):
self.assertEqual(model[i, j].dim_in, (i + 1) * (j + 1) - 1)
self.assertTrue(isinstance(model[i, j], MCGSM))
# random pixel ordering
rows, cols = 7, 5
order = [(i // cols, i % cols) for i in permutation(rows * cols)]
model = PatchMCGSM(rows, cols, xmask, ymask, order,
MCGSM(sum(xmask), 1))
self.assertLess(max(abs(model.input_mask() - xmask)), 1e-8)
self.assertLess(max(abs(model.output_mask() - ymask)), 1e-8)
for i in range(rows):
for j in range(cols):
self.assertEqual(
model.input_mask(i, j).sum(), model[i, j].dim_in)
# test constructors
model0 = PatchMCGSM(rows, cols, max_pcs=3)
model1 = PatchMCGSM(rows, cols, model0.input_mask(),
model0.output_mask(), model0.order)
self.assertLess(max(abs(model0.input_mask() - model1.input_mask())),
1e-8)
self.assertLess(max(abs(model0.output_mask() - model1.output_mask())),
1e-8)
self.assertLess(
max(abs(asarray(model0.order) - asarray(model1.order))), 1e-8)
# test computation of input masks
model = PatchMCGSM(rows, cols, order, max_pcs=3)
i, j = model0.order[0]
input_mask = model.input_mask(i, j)
for i, j in model.order[1:]:
self.assertEqual(sum(model.input_mask(i, j) - input_mask), 1)
input_mask = model.input_mask(i, j)
def test_patchmcgsm(self):
xmask = ones([8, 8], dtype="bool")
ymask = zeros([8, 8], dtype="bool")
xmask[-1, -1] = False
ymask[-1, -1] = True
model = PatchMCGSM(8, 8, xmask, ymask, model=MCGSM(sum(xmask), 1))
self.assertLess(max(abs(model.input_mask() - xmask)), 1e-8)
self.assertLess(max(abs(model.output_mask() - ymask)), 1e-8)
for i in range(8):
for j in range(8):
self.assertEqual(model[i, j].dim_in, (i + 1) * (j + 1) - 1)
self.assertTrue(isinstance(model[i, j], MCGSM))
# random pixel ordering
rows, cols = 7, 5
order = [(i // cols, i % cols) for i in permutation(rows * cols)]
model = PatchMCGSM(rows, cols, xmask, ymask, order, MCGSM(sum(xmask), 1))
self.assertLess(max(abs(model.input_mask() - xmask)), 1e-8)
self.assertLess(max(abs(model.output_mask() - ymask)), 1e-8)
for i in range(rows):
for j in range(cols):
self.assertEqual(model.input_mask(i, j).sum(), model[i, j].dim_in)
# test constructors
model0 = PatchMCGSM(rows, cols, max_pcs=3)
model1 = PatchMCGSM(rows, cols, model0.input_mask(), model0.output_mask(), model0.order)
self.assertLess(max(abs(model0.input_mask() - model1.input_mask())), 1e-8)
self.assertLess(max(abs(model0.output_mask() - model1.output_mask())), 1e-8)
self.assertLess(max(abs(asarray(model0.order) - asarray(model1.order))), 1e-8)
# test computation of input masks
model = PatchMCGSM(rows, cols, order, max_pcs=3)
i, j = model0.order[0]
input_mask = model.input_mask(i, j)
for i, j in model.order[1:]:
self.assertEqual(sum(model.input_mask(i, j) - input_mask), 1)
input_mask = model.input_mask(i, j)
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))
