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How to use _get_matrix method in molecule

Best Python code snippet using molecule_python

test_sparse_gpr.py

Source:test_sparse_gpr.py

...30        else:31            outputs = outputs[:, np.newaxis] if outputs.ndim == 1 else outputs32        return outputs33    return wrapped34def _get_matrix(name):35    return np.loadtxt(os.path.join(_data_dir, name + ".dat"))36class _InducingData(object):37    """38    A few pieces in common with these models39    """40    @staticmethod41    @atleast_col42    def _xy():43        return _get_matrix("x"), _get_matrix("y")44    @staticmethod45    @atleast_col46    def _x_test():47        return _get_matrix("x_test")48    @staticmethod49    @atleast_col50    def _z():51        return _get_matrix("z")52class TestVFE(_InducingData):53    def test_init(self):54        x, y = _InducingData._xy()55        kernel = Matern32(x.shape[1], ARD=True)56        VFE(x, y, kernel)57        VFE(x, y, kernel, inducing_points=_InducingData._z())58        # TODO mean59    def test_compute_loss(self):60        x, y = _InducingData._xy()61        z = _InducingData._z()62        kernel = Matern32(1)63        kernel.length_scales.data = torch.zeros(1, dtype=torch_dtype)64        kernel.variance.data = torch.zeros(1, dtype=torch_dtype)65        likelihood = likelihoods.Gaussian(variance=1.0)66        model = VFE(67            x,68            y,69            kernel,70            inducing_points=z,71            likelihood=likelihood,72            mean_function=mean_functions.Zero(1),73        )74        loss = model.loss()75        assert isinstance(loss, torch.Tensor)76        assert loss.ndimension() == 077        # Computed while I trust the result.78        assert loss.item() == pytest.approx(8.842242323920674)79        # Test ability to specify x and y80        loss_xy = model.loss(x=TensorType(x), y=TensorType(y))81        assert isinstance(loss_xy, torch.Tensor)82        assert loss_xy.item() == loss.item()83        with pytest.raises(ValueError):84            # Size mismatch85            model.loss(x=TensorType(x[: x.shape[0] // 2]))86    @needs_cuda87    def test_compute_loss_cuda(self):88        model = self._get_model()89        model.cuda()90        loss = model.loss()91        assert loss.is_cuda92    def test_predict(self):93        """94        Just the ._predict() method95        """96        x, y = _InducingData._xy()97        z = _InducingData._z()98        kernel = Matern32(1)99        kernel.length_scales.data = torch.zeros(1, dtype=torch_dtype)100        kernel.variance.data = torch.zeros(1, dtype=torch_dtype)101        likelihood = likelihoods.Gaussian(variance=1.0)102        model = VFE(103            x,104            y,105            kernel,106            inducing_points=z,107            likelihood=likelihood,108            mean_function=mean_functions.Zero(1),109        )110        x_test = torch.Tensor(_InducingData._x_test())111        mu, s = TestVFE._y_pred()112        gaussian_predictions(model, x_test, mu, s)113    @needs_cuda114    def test_predict_cuda(self):115        model = self._get_model()116        model.cuda()117        x_test = torch.randn(4, model.input_dimension, dtype=torch_dtype).cuda()118        for t in model._predict(x_test):119            assert t.is_cuda120    @staticmethod121    @atleast_col122    def _y_pred():123        return _get_matrix("vfe_y_mean"), _get_matrix("vfe_y_cov")124    @staticmethod125    def _get_model():126        x, y = _InducingData._xy()127        z = _InducingData._z()128        kernel = Matern32(1)129        kernel.length_scales.data = torch.zeros(1, dtype=torch_dtype)130        kernel.variance.data = torch.zeros(1, dtype=torch_dtype)131        likelihood = likelihoods.Gaussian(variance=1.0)132        model = VFE(133            x,134            y,135            kernel,136            inducing_points=z,137            likelihood=likelihood,138            mean_function=mean_functions.Zero(1),139        )140        return model141class TestSVGP(_InducingData):142    def test_init(self):143        x, y = _InducingData._xy()144        kernel = Matern32(x.shape[1], ARD=True)145        SVGP(x, y, kernel)146        SVGP(x, y, kernel, inducing_points=_InducingData._z())147        SVGP(x, y, kernel, mean_function=mean_functions.Constant(y.shape[1]))148        SVGP(149            x,150            y,151            kernel,152            mean_function=torch.nn.Linear(x.shape[1], y.shape[1], dtype=torch_dtype),153        )154    def test_compute_loss(self):155        x, y = _InducingData._xy()156        z = _InducingData._z()157        u_mu, u_l_s = TestSVGP._induced_outputs()158        kernel = Matern32(1)159        kernel.length_scales.data = torch.zeros(1, dtype=torch_dtype)160        kernel.variance.data = torch.zeros(1, dtype=torch_dtype)161        likelihood = likelihoods.Gaussian(variance=1.0)162        model = SVGP(163            x,164            y,165            kernel,166            inducing_points=z,167            likelihood=likelihood,168            mean_function=mean_functions.Zero(1),169        )170        model.induced_output_mean.data = TensorType(u_mu)171        model.induced_output_chol_cov.data = model.induced_output_chol_cov._transform.inv(172            TensorType(u_l_s)173        )174        loss = model.loss()175        assert isinstance(loss, torch.Tensor)176        assert loss.ndimension() == 0177        # Computed while I trust the result.178        assert loss.item() == pytest.approx(9.534628739243518)179        # Test ability to specify x and y180        loss_xy = model.loss(x=TensorType(x), y=TensorType(y))181        assert isinstance(loss_xy, torch.Tensor)182        assert loss_xy.item() == loss.item()183        with pytest.raises(ValueError):184            # Size mismatch185            model.loss(x=TensorType(x[: x.shape[0] // 2]), y=TensorType(y))186        model_minibatch = SVGP(x, y, kernel, batch_size=1)187        loss_mb = model_minibatch.loss()188        assert isinstance(loss_mb, torch.Tensor)189        assert loss_mb.ndimension() == 0190        model_full_mb = SVGP(191            x,192            y,193            kernel,194            inducing_points=z,195            likelihood=likelihood,196            mean_function=mean_functions.Zero(1),197            batch_size=x.shape[0],198        )199        model_full_mb.induced_output_mean.data = TensorType(u_mu)200        model_full_mb.induced_output_chol_cov.data = model_full_mb.induced_output_chol_cov._transform.inv(201            TensorType(u_l_s)202        )203        loss_full_mb = model_full_mb.loss()204        assert isinstance(loss_full_mb, torch.Tensor)205        assert loss_full_mb.ndimension() == 0206        assert loss_full_mb.item() == pytest.approx(loss.item())207        model.loss(model.X, model.Y)  # Just make sure it works!208    @needs_cuda209    def test_compute_loss_cuda(self):210        model = self._get_model()211        model.cuda()212        loss = model.loss()213        assert loss.is_cuda214    def test_predict(self):215        """216        Just the ._predict() method217        """218        x, y = _InducingData._xy()219        z = _InducingData._z()220        u_mu, u_l_s = TestSVGP._induced_outputs()221        kernel = Matern32(1)222        kernel.length_scales.data = torch.zeros(1, dtype=torch_dtype)223        kernel.variance.data = torch.zeros(1, dtype=torch_dtype)224        likelihood = likelihoods.Gaussian(variance=1.0)225        model = SVGP(226            x,227            y,228            kernel,229            inducing_points=z,230            likelihood=likelihood,231            mean_function=mean_functions.Zero(1),232        )233        model.induced_output_mean.data = TensorType(u_mu)234        model.induced_output_chol_cov.data = model.induced_output_chol_cov._transform.inv(235            TensorType(u_l_s)236        )237        x_test = TensorType(_InducingData._x_test())238        mu, s = TestSVGP._y_pred()239        gaussian_predictions(model, x_test, mu, s)240    @needs_cuda241    def test_predict_cuda(self):242        model = self._get_model()243        model.cuda()244        x_test = torch.randn(4, model.input_dimension, dtype=torch_dtype).cuda()245        for t in model._predict(x_test):246            assert t.is_cuda247    @staticmethod248    @atleast_col249    def _induced_outputs():250        return _get_matrix("q_mu"), _get_matrix("l_s")251    @staticmethod252    @atleast_col253    def _y_pred():254        return _get_matrix("svgp_y_mean"), _get_matrix("svgp_y_cov")255    @staticmethod256    def _get_model():257        x, y = _InducingData._xy()258        z = _InducingData._z()259        u_mu, u_l_s = TestSVGP._induced_outputs()260        kernel = Matern32(1)261        kernel.length_scales.data = torch.zeros(1, dtype=torch_dtype)262        kernel.variance.data = torch.zeros(1, dtype=torch_dtype)263        likelihood = likelihoods.Gaussian(variance=1.0)264        model = SVGP(265            x,266            y,267            kernel,268            inducing_points=z,...

transforms.py

Source:transforms.py

...24            return morphology.dilation(img, disk)25        else:26            return morphology.erosion(img, disk)27class LinearDeformation(Deformation):28    def _get_matrix(self, moments: ImageMoments, morph: ImageMorphology) -> np.ndarray:29        raise NotImplementedError30    def warp(self, xy: np.ndarray, morph: ImageMorphology) -> np.ndarray:31        moments = ImageMoments(morph.binary_image)32        centroid = np.array(moments.centroid)33        matrix = self._get_matrix(moments, morph)34        xy_ = (xy - centroid) @ matrix.T + centroid35        return xy_36class SetSlant(LinearDeformation):37    def __init__(self, target_slant_rad: float):38        self.target_shear = -np.tan(target_slant_rad)39    def _get_matrix(self, moments: ImageMoments, morph: ImageMorphology) -> np.ndarray:40        source_shear = moments.horizontal_shear41        delta = self.target_shear - source_shear42        return np.array([[1., -delta], [0., 1.]])43def _measure_width(morph: ImageMorphology, frac=.02, moments: ImageMoments = None):44    top_left, top_right = bounding_parallelogram(morph.hires_image,45                                                 frac=frac, moments=moments)[:2]46    return (top_right[0] - top_left[0]) / morph.scale47class SetWidth(LinearDeformation):48    _tolerance = 1.49    def __init__(self, target_width: float, validate=False):50        self.target_width = target_width51        self._validate = validate52    def _get_matrix(self, moments: ImageMoments, morph: ImageMorphology) -> np.ndarray:53        source_width = _measure_width(morph, moments=moments)54        factor = source_width / self.target_width55        shear = moments.horizontal_shear56        return np.array([[factor, shear * (1. - factor)], [0., 1.]])57    def __call__(self, morph: ImageMorphology) -> np.ndarray:58        pert_hires_image = super().__call__(morph)59        pert_image = morph.downscale(pert_hires_image)60        if self._validate:61            pert_morph = ImageMorphology(pert_image, threshold=morph.threshold, scale=morph.scale)62            width = _measure_width(pert_morph)63            if abs(width - self.target_width) > self._tolerance:64                print(f"!!! Incorrect width after transformation: {width:.1f}, "65                      f"expected {self.target_width:.1f}.")66                pert_hires_image = self(pert_morph)...

__init__.py

Source:__init__.py

1from qulacs_core import *2import qulacs.observable._get_matrix3Observable.get_matrix = \4    lambda obs: qulacs.observable._get_matrix._get_matrix(obs)5GeneralQuantumOperator.get_matrix = \...

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