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)