Prompt:
Implement the Python class `Pipeline` described below.

Class description:
Implement the Pipeline class.

Method signatures and docstrings:
- def __init__(self, data_path, metadata, support, target_image: list, validation_eqs: set): Args: param1: param2: support: target_image: target_image_l: validation_eqs: A set containing all the validation equations in a str format and without constant placeholders. This argument is used for the symbol checking
- def is_valid_and_not_in_validation_set(self, idx: int) -> bool: Assert both symbolically and numerically that the equation is not in the validation set Args: idx: index to the Eq in the dataset

Implement the Python class `Pipeline` described below.

Class description:
Implement the Pipeline class.

Method signatures and docstrings:
- def __init__(self, data_path, metadata, support, target_image: list, validation_eqs: set): Args: param1: param2: support: target_image: target_image_l: validation_eqs: A set containing all the validation equations in a str format and without constant placeholders. This argument is used for the symbol checking
- def is_valid_and_not_in_validation_set(self, idx: int) -> bool: Assert both symbolically and numerically that the equation is not in the validation set Args: idx: index to the Eq in the dataset

<|skeleton|>
class Pipeline:

    def __init__(self, data_path, metadata, support, target_image: list, validation_eqs: set):
        """Args: param1: param2: support: target_image: target_image_l: validation_eqs: A set containing all the validation equations in a str format and without constant placeholders. This argument is used for the symbol checking"""
        <|body_0|>

    def is_valid_and_not_in_validation_set(self, idx: int) -> bool:
        """Assert both symbolically and numerically that the equation is not in the validation set Args: idx: index to the Eq in the dataset"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        self.data_path = data_path
        self.metadata = metadata
        self.support = support
        self.target_image_l = target_image
        self.target_image = set(target_image)
        self.validation_eqs_l = validation_eqs
        self.validation_eqs = set(validation_eqs)
        self.res = Manager().dict()
<|end_body_0|>

<|body_start_1|>
        eq = load_eq(self.data_path, idx, self.metadata.eqs_per_hdf)
        dict_costs = return_dict_metadata_dummy_constant(self.metadata)
        consts = torch.stack([torch.ones([int(self.support.shape[1])]) * dict_costs[key] for key in dict_costs.keys()])
        input_lambdi = torch.cat([self.support, consts], axis=0)
        assert input_lambdi.shape[0] == len(self.metadata.total_coefficients) + len(self.metadata.total_variables)
        const, dummy_const = sample_symbolic_constants(eq)
        eq_str = sympify(eq.expr.format(**dummy_const))
        if str(eq_str) in self.validation_eqs:
            print('EQUATION IN VAL')
        args = [eq.code, input_lambdi]
        y = evaluate_fun(args)
        curr = [x if not np.isnan(x) else 'nan' for x in y]
        val = tuple(curr)
        if val in self.target_image:
            index = self.target_image_l.index(val)
            print('EQUATION IN VAL')
            if not index in self.res:
                self.res[index] = self.validation_eqs_l[index]
                print(len(self.res))
<|end_body_1|>

Solution:
class Pipeline:
    def __init__(self, data_path, metadata, support, target_image: list, validation_eqs: set):
        """Args: param1: param2: support: target_image: target_image_l: validation_eqs: A set containing all the validation equations in a str format and without constant placeholders. This argument is used for the symbol checking"""
        self.data_path = data_path
        self.metadata = metadata
        self.support = support
        self.target_image_l = target_image
        self.target_image = set(target_image)
        self.validation_eqs_l = validation_eqs
        self.validation_eqs = set(validation_eqs)
        self.res = Manager().dict()

    def is_valid_and_not_in_validation_set(self, idx: int) -> bool:
        """Assert both symbolically and numerically that the equation is not in the validation set Args: idx: index to the Eq in the dataset"""
        eq = load_eq(self.data_path, idx, self.metadata.eqs_per_hdf)
        dict_costs = return_dict_metadata_dummy_constant(self.metadata)
        consts = torch.stack([torch.ones([int(self.support.shape[1])]) * dict_costs[key] for key in dict_costs.keys()])
        input_lambdi = torch.cat([self.support, consts], axis=0)
        assert input_lambdi.shape[0] == len(self.metadata.total_coefficients) + len(self.metadata.total_variables)
        const, dummy_const = sample_symbolic_constants(eq)
        eq_str = sympify(eq.expr.format(**dummy_const))
        if str(eq_str) in self.validation_eqs:
            print('EQUATION IN VAL')
        args = [eq.code, input_lambdi]
        y = evaluate_fun(args)
        curr = [x if not np.isnan(x) else 'nan' for x in y]
        val = tuple(curr)
        if val in self.target_image:
            index = self.target_image_l.index(val)
            print('EQUATION IN VAL')
            if not index in self.res:
                self.res[index] = self.validation_eqs_l[index]
                print(len(self.res))


provenance

Prompt:
Implement the Python class `getCrimeZip` described below.

Class description:
Implement the getCrimeZip class.

Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None): Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event.

Implement the Python class `getCrimeZip` described below.

Class description:
Implement the getCrimeZip class.

Method signatures and docstrings:
- def execute(trial=False): Retrieve some data sets (not using the API here for the sake of simplicity).
- def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None): Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event.

<|skeleton|>
class getCrimeZip:

    def execute(trial=False):
        """Retrieve some data sets (not using the API here for the sake of simplicity)."""
        <|body_0|>

    def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
        """Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        startTime = datetime.datetime.now()
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('yuxiao_yzhang11', 'yuxiao_yzhang11')
        crime = repo['yuxiao_yzhang11.crime']
        group = {'_id': '$Location', 'count': {'$sum': 1}}
        crimeCor = crime.aggregate([{'$group': group}])
        repo.dropCollection('crimeCor')
        repo.createCollection('crimeCor')
        repo['yuxiao_yzhang11.crimeCor'].insert_many(crimeCor)
        repo.dropCollection('crimeZip')
        repo.createCollection('crimeZip')
        crime_zips = []
        for i in crimeCor:
            cor = i['_id'].replace('(', '')
            cor = cor.replace(')', '')
            if cor != '0.0, 0.0':
                geolocator = Nominatim()
                location = geolocator.reverse(cor, timeout=10)
                try:
                    zip = location.raw['address']['postcode']
                    i['zip'] = zip
                    print(i)
                    crime_zips.append(i)
                    repo['yuxiao_yzhang11.crimeZip'].insert(i)
                except KeyError as e:
                    print('Error: ', e, ', at cord: ', cor, ', and zip: ', zip, ', and location: ', location)
        endTime = datetime.datetime.now()
        return {'start': startTime, 'end': endTime}
<|end_body_0|>

<|body_start_1|>
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('yuxiao_yzhang11', 'yuxiao_yzhang11')
        doc.add_namespace('alg', 'http://datamechanics.io/algorithm/')
        doc.add_namespace('dat', 'http://datamechanics.io/data/')
        doc.add_namespace('ont', 'http://datamechanics.io/ontology#')
        doc.add_namespace('log', 'http://datamechanics.io/log/')
        this_script = doc.agent('alg:yuxiao_yzhang11#getCrimeZip', {prov.model.PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'})
        resource = doc.entity('dat:20127to20158crimeincident1edit', {'prov:label': '311, Service Requests', prov.model.PROV_TYPE: 'ont:DataResource', 'ont:Extension': 'json'})
        this_run = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
        doc.usage(this_run, resource, startTime, None, {prov.model.PROV_TYPE: 'ont:Retrieval'})
        output = doc.entity('dat:yuxiao_yzhang11.crimeZip', {prov.model.PROV_LABEL: 'CrimeZip', prov.model.PROV_TYPE: 'ont:DataSet'})
        doc.wasAssociatedWith(this_run, this_script)
        doc.used(this_run, resource, startTime)
        doc.wasAttributedTo(output, this_script)
        doc.wasGeneratedBy(output, this_run, endTime)
        doc.wasDerivedFrom(output, resource, this_run, this_run, this_run)
        repo.logout()
        return doc
<|end_body_1|>

Solution:
class getCrimeZip:
    def execute(trial=False):
        """Retrieve some data sets (not using the API here for the sake of simplicity)."""
        startTime = datetime.datetime.now()
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('yuxiao_yzhang11', 'yuxiao_yzhang11')
        crime = repo['yuxiao_yzhang11.crime']
        group = {'_id': '$Location', 'count': {'$sum': 1}}
        crimeCor = crime.aggregate([{'$group': group}])
        repo.dropCollection('crimeCor')
        repo.createCollection('crimeCor')
        repo['yuxiao_yzhang11.crimeCor'].insert_many(crimeCor)
        repo.dropCollection('crimeZip')
        repo.createCollection('crimeZip')
        crime_zips = []
        for i in crimeCor:
            cor = i['_id'].replace('(', '')
            cor = cor.replace(')', '')
            if cor != '0.0, 0.0':
                geolocator = Nominatim()
                location = geolocator.reverse(cor, timeout=10)
                try:
                    zip = location.raw['address']['postcode']
                    i['zip'] = zip
                    print(i)
                    crime_zips.append(i)
                    repo['yuxiao_yzhang11.crimeZip'].insert(i)
                except KeyError as e:
                    print('Error: ', e, ', at cord: ', cor, ', and zip: ', zip, ', and location: ', location)
        endTime = datetime.datetime.now()
        return {'start': startTime, 'end': endTime}

    def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
        """Create the provenance document describing everything happening in this script. Each run of the script will generate a new document describing that invocation event."""
        client = dml.pymongo.MongoClient()
        repo = client.repo
        repo.authenticate('yuxiao_yzhang11', 'yuxiao_yzhang11')
        doc.add_namespace('alg', 'http://datamechanics.io/algorithm/')
        doc.add_namespace('dat', 'http://datamechanics.io/data/')
        doc.add_namespace('ont', 'http://datamechanics.io/ontology#')
        doc.add_namespace('log', 'http://datamechanics.io/log/')
        this_script = doc.agent('alg:yuxiao_yzhang11#getCrimeZip', {prov.model.PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'})
        resource = doc.entity('dat:20127to20158crimeincident1edit', {'prov:label': '311, Service Requests', prov.model.PROV_TYPE: 'ont:DataResource', 'ont:Extension': 'json'})
        this_run = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
        doc.usage(this_run, resource, startTime, None, {prov.model.PROV_TYPE: 'ont:Retrieval'})
        output = doc.entity('dat:yuxiao_yzhang11.crimeZip', {prov.model.PROV_LABEL: 'CrimeZip', prov.model.PROV_TYPE: 'ont:DataSet'})
        doc.wasAssociatedWith(this_run, this_script)
        doc.used(this_run, resource, startTime)
        doc.wasAttributedTo(output, this_script)
        doc.wasGeneratedBy(output, this_run, endTime)
        doc.wasDerivedFrom(output, resource, this_run, this_run, this_run)
        repo.logout()
        return doc


Prompt:
Implement the Python class `Geometry` described below.

Class description:
Implement the Geometry class.

Method signatures and docstrings:
- def __init__(self, geo_cfg, alpha, s): Set up the room geometry from the .dae file Geometry consists of: Volume, Total ara and an array of plane objects. Each plane object will be processed in a c++ class and have the following att: - name (string) - bounding box (bool) - list of vertices (Eigen<double> - Nvert x 3) - normal (Eigen<double> - 1 x 3) - vert_x - 2D polygon x coord (Eigen<double> - 1 x Nvert) - vert_y - 2D polygon y coord (Eigen<double> - 1 x Nvert) - nig - 2D normal components index (Eigen<int> - 1 x 2) - area (double) - centroid (Eigen<double> - 1 x 3) - alpha - absorption coefficient (Eigen<double> - 1 x Nfreq) - s - scattering coefficient (double)
- def plot_dae_room(self, normals='off'): a simple plot of the room - not redered

Implement the Python class `Geometry` described below.

Class description:
Implement the Geometry class.

Method signatures and docstrings:
- def __init__(self, geo_cfg, alpha, s): Set up the room geometry from the .dae file Geometry consists of: Volume, Total ara and an array of plane objects. Each plane object will be processed in a c++ class and have the following att: - name (string) - bounding box (bool) - list of vertices (Eigen<double> - Nvert x 3) - normal (Eigen<double> - 1 x 3) - vert_x - 2D polygon x coord (Eigen<double> - 1 x Nvert) - vert_y - 2D polygon y coord (Eigen<double> - 1 x Nvert) - nig - 2D normal components index (Eigen<int> - 1 x 2) - area (double) - centroid (Eigen<double> - 1 x 3) - alpha - absorption coefficient (Eigen<double> - 1 x Nfreq) - s - scattering coefficient (double)
- def plot_dae_room(self, normals='off'): a simple plot of the room - not redered

<|skeleton|>
class Geometry:

    def __init__(self, geo_cfg, alpha, s):
        """Set up the room geometry from the .dae file Geometry consists of: Volume, Total ara and an array of plane objects. Each plane object will be processed in a c++ class and have the following att: - name (string) - bounding box (bool) - list of vertices (Eigen<double> - Nvert x 3) - normal (Eigen<double> - 1 x 3) - vert_x - 2D polygon x coord (Eigen<double> - 1 x Nvert) - vert_y - 2D polygon y coord (Eigen<double> - 1 x Nvert) - nig - 2D normal components index (Eigen<int> - 1 x 2) - area (double) - centroid (Eigen<double> - 1 x 3) - alpha - absorption coefficient (Eigen<double> - 1 x Nfreq) - s - scattering coefficient (double)"""
        <|body_0|>

    def plot_dae_room(self, normals='off'):
        """a simple plot of the room - not redered"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        daepath = geo_cfg['room']
        self.planes = []
        mesh = co.Collada(daepath)
        for obj in mesh.scene.objects('geometry'):
            for triset in obj.primitives():
                if type(triset) != co.triangleset.BoundTriangleSet:
                    log.info('Warning: non-supported primitive ignored!')
                    continue
                for jp, tri in enumerate(triset):
                    name = '{}-{}'.format(obj.original.name, jp)
                    vertices = np.array(tri.vertices)
                    normal = np.float32(tri.normals[0] / np.linalg.norm(tri.normals[0]))
                    vert_x, vert_y, normal_nig = vert_2d(normal, vertices)
                    area = np.float64(triangle_area(vertices))
                    centroid = np.float32(triangle_centroid(vertices))
                    alpha_v = np.float32(alpha[jp])
                    plane = ra_cpp.Planecpp(name, False, vertices, normal, vert_x, vert_y, normal_nig, area, centroid, alpha_v, s[jp])
                    self.planes.append(plane)
        self.total_area = total_area(self.planes)
        self.volume = volume(self.planes)
<|end_body_0|>

<|body_start_1|>
        fig = plt.figure()
        ax = fig.gca(projection='3d')
        for plane in self.planes:
            ax.scatter(plane.vertices[:, 0], plane.vertices[:, 1], plane.vertices[:, 2], color='blue')
            verts = [list(zip(plane.vertices[:, 0], plane.vertices[:, 1], plane.vertices[:, 2]))]
            collection = Poly3DCollection(verts, linewidths=1, alpha=0.5, edgecolor='gray')
            face_color = 'silver'
            collection.set_facecolor(face_color)
            ax.add_collection3d(collection)
            if normals == 'on':
                ax.quiver(plane.centroid[0], plane.centroid[1], plane.centroid[2], plane.normal[0], plane.normal[1], plane.normal[2], length=1, color='red', normalize=True)
        ax.set_xlabel('X axis')
        ax.set_ylabel('Y axis')
        ax.set_zlabel('Z axis')
        plt.show()
<|end_body_1|>

Solution:
class Geometry:
    def __init__(self, geo_cfg, alpha, s):
        """Set up the room geometry from the .dae file Geometry consists of: Volume, Total ara and an array of plane objects. Each plane object will be processed in a c++ class and have the following att: - name (string) - bounding box (bool) - list of vertices (Eigen<double> - Nvert x 3) - normal (Eigen<double> - 1 x 3) - vert_x - 2D polygon x coord (Eigen<double> - 1 x Nvert) - vert_y - 2D polygon y coord (Eigen<double> - 1 x Nvert) - nig - 2D normal components index (Eigen<int> - 1 x 2) - area (double) - centroid (Eigen<double> - 1 x 3) - alpha - absorption coefficient (Eigen<double> - 1 x Nfreq) - s - scattering coefficient (double)"""
        daepath = geo_cfg['room']
        self.planes = []
        mesh = co.Collada(daepath)
        for obj in mesh.scene.objects('geometry'):
            for triset in obj.primitives():
                if type(triset) != co.triangleset.BoundTriangleSet:
                    log.info('Warning: non-supported primitive ignored!')
                    continue
                for jp, tri in enumerate(triset):
                    name = '{}-{}'.format(obj.original.name, jp)
                    vertices = np.array(tri.vertices)
                    normal = np.float32(tri.normals[0] / np.linalg.norm(tri.normals[0]))
                    vert_x, vert_y, normal_nig = vert_2d(normal, vertices)
                    area = np.float64(triangle_area(vertices))
                    centroid = np.float32(triangle_centroid(vertices))
                    alpha_v = np.float32(alpha[jp])
                    plane = ra_cpp.Planecpp(name, False, vertices, normal, vert_x, vert_y, normal_nig, area, centroid, alpha_v, s[jp])
                    self.planes.append(plane)
        self.total_area = total_area(self.planes)
        self.volume = volume(self.planes)

    def plot_dae_room(self, normals='off'):
        """a simple plot of the room - not redered"""
        fig = plt.figure()
        ax = fig.gca(projection='3d')
        for plane in self.planes:
            ax.scatter(plane.vertices[:, 0], plane.vertices[:, 1], plane.vertices[:, 2], color='blue')
            verts = [list(zip(plane.vertices[:, 0], plane.vertices[:, 1], plane.vertices[:, 2]))]
            collection = Poly3DCollection(verts, linewidths=1, alpha=0.5, edgecolor='gray')
            face_color = 'silver'
            collection.set_facecolor(face_color)
            ax.add_collection3d(collection)
            if normals == 'on':
                ax.quiver(plane.centroid[0], plane.centroid[1], plane.centroid[2], plane.normal[0], plane.normal[1], plane.normal[2], length=1, color='red', normalize=True)
        ax.set_xlabel('X axis')
        ax.set_ylabel('Y axis')
        ax.set_zlabel('Z axis')
        plt.show()


forward

Prompt:
Implement the Python class `CNN` described below.

Class description:
CNN Layer, i.e. a layer of cnn network that takes the output of convolutional network as input

Method signatures and docstrings:
- def __init__(self, input_channel_count, output_channel_count, kernel_size=5): Init HighWay Instance. @param input_channel_count: int @param output_channel_count: int @param kernel_size: int
- def forward(self, x_reshaped): Run a forward step that map a batch of x_reshaped to x_conv_out @param x_reshaped: tensor of (max_sentence_length * batch_size, e_char, max_word_length) @return x_conv_out: tensor of (max_sentence_length * batch_size, e_word)

Implement the Python class `CNN` described below.

Class description:
CNN Layer, i.e. a layer of cnn network that takes the output of convolutional network as input

Method signatures and docstrings:
- def __init__(self, input_channel_count, output_channel_count, kernel_size=5): Init HighWay Instance. @param input_channel_count: int @param output_channel_count: int @param kernel_size: int
- def forward(self, x_reshaped): Run a forward step that map a batch of x_reshaped to x_conv_out @param x_reshaped: tensor of (max_sentence_length * batch_size, e_char, max_word_length) @return x_conv_out: tensor of (max_sentence_length * batch_size, e_word)

<|skeleton|>
class CNN:
    """CNN Layer, i.e. a layer of cnn network that takes the output of convolutional network as input"""

    def __init__(self, input_channel_count, output_channel_count, kernel_size=5):
        """Init HighWay Instance. @param input_channel_count: int @param output_channel_count: int @param kernel_size: int"""
        <|body_0|>

    def forward(self, x_reshaped):
        """Run a forward step that map a batch of x_reshaped to x_conv_out @param x_reshaped: tensor of (max_sentence_length * batch_size, e_char, max_word_length) @return x_conv_out: tensor of (max_sentence_length * batch_size, e_word)"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(CNN, self).__init__()
        self.input_channel_count = input_channel_count
        self.output_channel_count = output_channel_count
        self.conv = nn.Conv1d(in_channels=input_channel_count, out_channels=output_channel_count, kernel_size=kernel_size)
        self.max_pool = nn.AdaptiveMaxPool1d(output_size=1)
        self.relu = nn.ReLU()
<|end_body_0|>

<|body_start_1|>
        assert x_reshaped.size()[1] == self.input_channel_count, print(f'{x_reshaped.size()} x_reshaped size')
        x_conv = self.conv(x_reshaped)
        assert x_conv.size()[1] == self.output_channel_count, print(f'{x_conv.size()} x_conv size')
        x_conv_out = self.max_pool(x_conv).squeeze(dim=2)
        assert x_conv_out.size()[1] == self.output_channel_count, print(f'{x_conv_out.size()} x_conv_out size')
        return x_conv_out
<|end_body_1|>

Solution:
class CNN:
    """CNN Layer, i.e. a layer of cnn network that takes the output of convolutional network as input"""

    def __init__(self, input_channel_count, output_channel_count, kernel_size=5):
        """Init HighWay Instance. @param input_channel_count: int @param output_channel_count: int @param kernel_size: int"""
        super(CNN, self).__init__()
        self.input_channel_count = input_channel_count
        self.output_channel_count = output_channel_count
        self.conv = nn.Conv1d(in_channels=input_channel_count, out_channels=output_channel_count, kernel_size=kernel_size)
        self.max_pool = nn.AdaptiveMaxPool1d(output_size=1)
        self.relu = nn.ReLU()

    def forward(self, x_reshaped):
        """Run a forward step that map a batch of x_reshaped to x_conv_out @param x_reshaped: tensor of (max_sentence_length * batch_size, e_char, max_word_length) @return x_conv_out: tensor of (max_sentence_length * batch_size, e_word)"""
        assert x_reshaped.size()[1] == self.input_channel_count, print(f'{x_reshaped.size()} x_reshaped size')
        x_conv = self.conv(x_reshaped)
        assert x_conv.size()[1] == self.output_channel_count, print(f'{x_conv.size()} x_conv size')
        x_conv_out = self.max_pool(x_conv).squeeze(dim=2)
        assert x_conv_out.size()[1] == self.output_channel_count, print(f'{x_conv_out.size()} x_conv_out size')
        return x_conv_out


Prompt:
Implement the Python class `_BatchNormImpl` described below.

Class description:
The implementor class of the Batch Normalization layer for double numbers in regular representation. Implements the functionality specific to double numbers and needed by the 'BatchNorm' class. This includes: getting the norm of double number, applying scaling and shift to a double-valued tensor, and updating the running mean and variance, which are used during inference. Args: affine (bool) - A bool value. When set to True, gamma and beta can be learned. use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false, use the mean value and variance value of specified value. If None, the training process will use the mean and variance of current b

Method signatures and docstrings:
- def scale_and_shift(self, u_x: Tensor, u_y: Tensor, scale_x: Tensor, scale_y: Tensor, shift_x: Tensor, shift_y: Tensor) -> Tuple[Tensor, Tensor]: Applies double scaling and shift to an input tensor in regular representation. This function implements the operation as: .. math:: \\begin{align} \\text{Re(out)} = \\text{Re(inp)} * \\text{Re(scale)} + \\text{Db(inp)} * \\text{Db(scale)} + \\text{Re(shift)}\\\\ \\text{Db(out)} = \\text{Re(inp)} * \\text{Db(scale)} + \\text{Db(inp)} * \\text{Re(scale)} + \\text{Db(shift)}, \\end{align} where :math:`inp` is the double input tensors, :math:`scale` and :math:`shift` are double parameters representing the scaling and shift coefficients respectively. :math:`\\text{Re(...)}` and :math:`\\text{Db(...)}` are respectively real and double parts of the double-valued expression inside the parentheses. A
- def get_norm(self, u: Tensor) -> Tensor: Calculates norm of double elements of an input tensor in regular representation. Norm is a non-negative real number that is a characteristic of 'magnitude' of that number, i.e. how far away it is from zero. The function implements the operation as: .. math:: \\text{out} = |Re(inp)| + |Db(inp)|, where :math:`inp` is the double input tensors, :math:`\\text{Re(...)}` and :math:`\\text{Db(...)}` are respectively real and double parts of the double-valued expression inside the parentheses. Args: u (Tensor): Tensor of shape (2, *, ..., *). '2' denotes that the input tensor belongs to the double domain and has two components. Returns: Tensor of shape (*, ..., *). The count and size of dimensions of
- def get_square_norm(self, u: Tensor) -> Tensor: Calculates element-wise squared norm of double elements of an input tensor in regular representation. Norm is a non-negative real number that is a characteristic of 'magnitude' of that number, i.e. how far away it is from zero. The function implements the operation as: .. math:: \\text{out} = \\left(|Re(inp)| + |Db(inp)|\\right)^2, where :math:`inp` is the double input tensors, :math:`\\text{Re(...)}` and :math:`\\text{Du(...)}` are respectively real and double parts of the double-valued expression inside the parentheses. Args: u (Tensor): Tensor of shape (2, *, ..., *). '2' denotes that the input tensor belongs to the double domain and has a real and a double parts. Returns: Tensor of shape

Implement the Python class `_BatchNormImpl` described below.

Class description:
The implementor class of the Batch Normalization layer for double numbers in regular representation. Implements the functionality specific to double numbers and needed by the 'BatchNorm' class. This includes: getting the norm of double number, applying scaling and shift to a double-valued tensor, and updating the running mean and variance, which are used during inference. Args: affine (bool) - A bool value. When set to True, gamma and beta can be learned. use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false, use the mean value and variance value of specified value. If None, the training process will use the mean and variance of current b

Method signatures and docstrings:
- def scale_and_shift(self, u_x: Tensor, u_y: Tensor, scale_x: Tensor, scale_y: Tensor, shift_x: Tensor, shift_y: Tensor) -> Tuple[Tensor, Tensor]: Applies double scaling and shift to an input tensor in regular representation. This function implements the operation as: .. math:: \\begin{align} \\text{Re(out)} = \\text{Re(inp)} * \\text{Re(scale)} + \\text{Db(inp)} * \\text{Db(scale)} + \\text{Re(shift)}\\\\ \\text{Db(out)} = \\text{Re(inp)} * \\text{Db(scale)} + \\text{Db(inp)} * \\text{Re(scale)} + \\text{Db(shift)}, \\end{align} where :math:`inp` is the double input tensors, :math:`scale` and :math:`shift` are double parameters representing the scaling and shift coefficients respectively. :math:`\\text{Re(...)}` and :math:`\\text{Db(...)}` are respectively real and double parts of the double-valued expression inside the parentheses. A
- def get_norm(self, u: Tensor) -> Tensor: Calculates norm of double elements of an input tensor in regular representation. Norm is a non-negative real number that is a characteristic of 'magnitude' of that number, i.e. how far away it is from zero. The function implements the operation as: .. math:: \\text{out} = |Re(inp)| + |Db(inp)|, where :math:`inp` is the double input tensors, :math:`\\text{Re(...)}` and :math:`\\text{Db(...)}` are respectively real and double parts of the double-valued expression inside the parentheses. Args: u (Tensor): Tensor of shape (2, *, ..., *). '2' denotes that the input tensor belongs to the double domain and has two components. Returns: Tensor of shape (*, ..., *). The count and size of dimensions of
- def get_square_norm(self, u: Tensor) -> Tensor: Calculates element-wise squared norm of double elements of an input tensor in regular representation. Norm is a non-negative real number that is a characteristic of 'magnitude' of that number, i.e. how far away it is from zero. The function implements the operation as: .. math:: \\text{out} = \\left(|Re(inp)| + |Db(inp)|\\right)^2, where :math:`inp` is the double input tensors, :math:`\\text{Re(...)}` and :math:`\\text{Du(...)}` are respectively real and double parts of the double-valued expression inside the parentheses. Args: u (Tensor): Tensor of shape (2, *, ..., *). '2' denotes that the input tensor belongs to the double domain and has a real and a double parts. Returns: Tensor of shape

<|skeleton|>
class _BatchNormImpl:
    """The implementor class of the Batch Normalization layer for double numbers in regular representation. Implements the functionality specific to double numbers and needed by the 'BatchNorm' class. This includes: getting the norm of double number, applying scaling and shift to a double-valued tensor, and updating the running mean and variance, which are used during inference. Args: affine (bool) - A bool value. When set to True, gamma and beta can be learned. use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false, use the mean value and variance value of specified value. If None, the training process will use the mean and variance of current b"""

    def scale_and_shift(self, u_x: Tensor, u_y: Tensor, scale_x: Tensor, scale_y: Tensor, shift_x: Tensor, shift_y: Tensor) -> Tuple[Tensor, Tensor]:
        """Applies double scaling and shift to an input tensor in regular representation. This function implements the operation as: .. math:: \\begin{align} \\text{Re(out)} = \\text{Re(inp)} * \\text{Re(scale)} + \\text{Db(inp)} * \\text{Db(scale)} + \\text{Re(shift)}\\\\ \\text{Db(out)} = \\text{Re(inp)} * \\text{Db(scale)} + \\text{Db(inp)} * \\text{Re(scale)} + \\text{Db(shift)}, \\end{align} where :math:`inp` is the double input tensors, :math:`scale` and :math:`shift` are double parameters representing the scaling and shift coefficients respectively. :math:`\\text{Re(...)}` and :math:`\\text{Db(...)}` are respectively real and double parts of the double-valued expression inside the parentheses. A"""
        <|body_0|>

    def get_norm(self, u: Tensor) -> Tensor:
        """Calculates norm of double elements of an input tensor in regular representation. Norm is a non-negative real number that is a characteristic of 'magnitude' of that number, i.e. how far away it is from zero. The function implements the operation as: .. math:: \\text{out} = |Re(inp)| + |Db(inp)|, where :math:`inp` is the double input tensors, :math:`\\text{Re(...)}` and :math:`\\text{Db(...)}` are respectively real and double parts of the double-valued expression inside the parentheses. Args: u (Tensor): Tensor of shape (2, *, ..., *). '2' denotes that the input tensor belongs to the double domain and has two components. Returns: Tensor of shape (*, ..., *). The count and size of dimensions of"""
        <|body_1|>

    def get_square_norm(self, u: Tensor) -> Tensor:
        """Calculates element-wise squared norm of double elements of an input tensor in regular representation. Norm is a non-negative real number that is a characteristic of 'magnitude' of that number, i.e. how far away it is from zero. The function implements the operation as: .. math:: \\text{out} = \\left(|Re(inp)| + |Db(inp)|\\right)^2, where :math:`inp` is the double input tensors, :math:`\\text{Re(...)}` and :math:`\\text{Du(...)}` are respectively real and double parts of the double-valued expression inside the parentheses. Args: u (Tensor): Tensor of shape (2, *, ..., *). '2' denotes that the input tensor belongs to the double domain and has a real and a double parts. Returns: Tensor of shape"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        out_x = u_x * scale_x + u_y * scale_y + shift_x
        out_y = u_x * scale_y + u_y * scale_x + shift_y
        return (out_x, out_y)
<|end_body_0|>

<|body_start_1|>
        u_r, u_d = get_real_and_double(u)
        out = ops.abs(u_r) + ops.abs(u_d)
        return out
<|end_body_1|>

<|body_start_2|>
        norm = self.get_norm(u)
        out = norm ** 2
        return out
<|end_body_2|>

Solution:
class _BatchNormImpl:
    """The implementor class of the Batch Normalization layer for double numbers in regular representation. Implements the functionality specific to double numbers and needed by the 'BatchNorm' class. This includes: getting the norm of double number, applying scaling and shift to a double-valued tensor, and updating the running mean and variance, which are used during inference. Args: affine (bool) - A bool value. When set to True, gamma and beta can be learned. use_batch_statistics (bool): If true, use the mean value and variance value of current batch data. If false, use the mean value and variance value of specified value. If None, the training process will use the mean and variance of current b"""

    def scale_and_shift(self, u_x: Tensor, u_y: Tensor, scale_x: Tensor, scale_y: Tensor, shift_x: Tensor, shift_y: Tensor) -> Tuple[Tensor, Tensor]:
        """Applies double scaling and shift to an input tensor in regular representation. This function implements the operation as: .. math:: \\begin{align} \\text{Re(out)} = \\text{Re(inp)} * \\text{Re(scale)} + \\text{Db(inp)} * \\text{Db(scale)} + \\text{Re(shift)}\\\\ \\text{Db(out)} = \\text{Re(inp)} * \\text{Db(scale)} + \\text{Db(inp)} * \\text{Re(scale)} + \\text{Db(shift)}, \\end{align} where :math:`inp` is the double input tensors, :math:`scale` and :math:`shift` are double parameters representing the scaling and shift coefficients respectively. :math:`\\text{Re(...)}` and :math:`\\text{Db(...)}` are respectively real and double parts of the double-valued expression inside the parentheses. A"""
        out_x = u_x * scale_x + u_y * scale_y + shift_x
        out_y = u_x * scale_y + u_y * scale_x + shift_y
        return (out_x, out_y)

    def get_norm(self, u: Tensor) -> Tensor:
        """Calculates norm of double elements of an input tensor in regular representation. Norm is a non-negative real number that is a characteristic of 'magnitude' of that number, i.e. how far away it is from zero. The function implements the operation as: .. math:: \\text{out} = |Re(inp)| + |Db(inp)|, where :math:`inp` is the double input tensors, :math:`\\text{Re(...)}` and :math:`\\text{Db(...)}` are respectively real and double parts of the double-valued expression inside the parentheses. Args: u (Tensor): Tensor of shape (2, *, ..., *). '2' denotes that the input tensor belongs to the double domain and has two components. Returns: Tensor of shape (*, ..., *). The count and size of dimensions of"""
        u_r, u_d = get_real_and_double(u)
        out = ops.abs(u_r) + ops.abs(u_d)
        return out

    def get_square_norm(self, u: Tensor) -> Tensor:
        """Calculates element-wise squared norm of double elements of an input tensor in regular representation. Norm is a non-negative real number that is a characteristic of 'magnitude' of that number, i.e. how far away it is from zero. The function implements the operation as: .. math:: \\text{out} = \\left(|Re(inp)| + |Db(inp)|\\right)^2, where :math:`inp` is the double input tensors, :math:`\\text{Re(...)}` and :math:`\\text{Du(...)}` are respectively real and double parts of the double-valued expression inside the parentheses. Args: u (Tensor): Tensor of shape (2, *, ..., *). '2' denotes that the input tensor belongs to the double domain and has a real and a double parts. Returns: Tensor of shape"""
        norm = self.get_norm(u)
        out = norm ** 2
        return out


Prompt:
Implement the Python class `CartesianConversion` described below.

Class description:
A helper class for defining new domain classes. Say you are implementing a new class, perhaps the :code:`Rotation` domain transform where the interesting part is the adjustments to the :code:`r` and :code:`t` coordinates. The implementation of :code:`x` and :code:`y` is then done with respect to the transformed :code:`r` and :code:`t` variables. This class provides implementations of the :code:`_get_xs` and :code:`_get_ys` methods to handle this conversion for you automatically, leaving you to focus on implementing the transform that interest you. To use this class, simply include it in your :code:`class` definition as follows .. code-block:: python class MyDomain(CartesianConversion, RealDo

Method signatures and docstrings:
- def _get_x(self): The conversion to the :math:`x` component in terms of :math:`r` and :math:`\\theta` .. math:: x = r\\cos{(\\theta)}
- def _get_y(self): The conversion to the :math:`y` component in terms of :math:`r` and :math:`\\theta` .. math:: y = r\\sin{(\\theta)}

Implement the Python class `CartesianConversion` described below.

Class description:
A helper class for defining new domain classes. Say you are implementing a new class, perhaps the :code:`Rotation` domain transform where the interesting part is the adjustments to the :code:`r` and :code:`t` coordinates. The implementation of :code:`x` and :code:`y` is then done with respect to the transformed :code:`r` and :code:`t` variables. This class provides implementations of the :code:`_get_xs` and :code:`_get_ys` methods to handle this conversion for you automatically, leaving you to focus on implementing the transform that interest you. To use this class, simply include it in your :code:`class` definition as follows .. code-block:: python class MyDomain(CartesianConversion, RealDo

Method signatures and docstrings:
- def _get_x(self): The conversion to the :math:`x` component in terms of :math:`r` and :math:`\\theta` .. math:: x = r\\cos{(\\theta)}
- def _get_y(self): The conversion to the :math:`y` component in terms of :math:`r` and :math:`\\theta` .. math:: y = r\\sin{(\\theta)}

<|skeleton|>
class CartesianConversion:
    """A helper class for defining new domain classes. Say you are implementing a new class, perhaps the :code:`Rotation` domain transform where the interesting part is the adjustments to the :code:`r` and :code:`t` coordinates. The implementation of :code:`x` and :code:`y` is then done with respect to the transformed :code:`r` and :code:`t` variables. This class provides implementations of the :code:`_get_xs` and :code:`_get_ys` methods to handle this conversion for you automatically, leaving you to focus on implementing the transform that interest you. To use this class, simply include it in your :code:`class` definition as follows .. code-block:: python class MyDomain(CartesianConversion, RealDo"""

    def _get_x(self):
        """The conversion to the :math:`x` component in terms of :math:`r` and :math:`\\theta` .. math:: x = r\\cos{(\\theta)}"""
        <|body_0|>

    def _get_y(self):
        """The conversion to the :math:`y` component in terms of :math:`r` and :math:`\\theta` .. math:: y = r\\sin{(\\theta)}"""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        rs = self._get_r()
        ts = self._get_t()

        def mk_xs(width, height):
            r = rs(width, height)
            t = ts(width, height)
            return r * np.cos(t)
        return mk_xs
<|end_body_0|>

<|body_start_1|>
        rs = self._get_r()
        ts = self._get_t()

        def mk_ys(width, height):
            r = rs(width, height)
            t = ts(width, height)
            return r * np.sin(t)
        return mk_ys
<|end_body_1|>

Solution:
class CartesianConversion:
    """A helper class for defining new domain classes. Say you are implementing a new class, perhaps the :code:`Rotation` domain transform where the interesting part is the adjustments to the :code:`r` and :code:`t` coordinates. The implementation of :code:`x` and :code:`y` is then done with respect to the transformed :code:`r` and :code:`t` variables. This class provides implementations of the :code:`_get_xs` and :code:`_get_ys` methods to handle this conversion for you automatically, leaving you to focus on implementing the transform that interest you. To use this class, simply include it in your :code:`class` definition as follows .. code-block:: python class MyDomain(CartesianConversion, RealDo"""

    def _get_x(self):
        """The conversion to the :math:`x` component in terms of :math:`r` and :math:`\\theta` .. math:: x = r\\cos{(\\theta)}"""
        rs = self._get_r()
        ts = self._get_t()

        def mk_xs(width, height):
            r = rs(width, height)
            t = ts(width, height)
            return r * np.cos(t)
        return mk_xs

    def _get_y(self):
        """The conversion to the :math:`y` component in terms of :math:`r` and :math:`\\theta` .. math:: y = r\\sin{(\\theta)}"""
        rs = self._get_r()
        ts = self._get_t()

        def mk_ys(width, height):
            r = rs(width, height)
            t = ts(width, height)
            return r * np.sin(t)
        return mk_ys


Prompt:
Implement the Python class `SimpleCnn` described below.

Class description:
Simple CNN network.

Method signatures and docstrings:
- def __init__(self, desc): Initialize.
- def forward(self, x): Forward.

Implement the Python class `SimpleCnn` described below.

Class description:
Simple CNN network.

Method signatures and docstrings:
- def __init__(self, desc): Initialize.
- def forward(self, x): Forward.

<|skeleton|>
class SimpleCnn:
    """Simple CNN network."""

    def __init__(self, desc):
        """Initialize."""
        <|body_0|>

    def forward(self, x):
        """Forward."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        super(SimpleCnn, self).__init__()
        self.conv_num = 3
        self.conv_layers = nn.ModuleList([None] * self.conv_num)
        self.bn_layers = nn.ModuleList([None] * self.conv_num)
        self.relu_layers = nn.ModuleList([None] * self.conv_num)
        self.pool_layers = nn.ModuleList([None] * self.conv_num)
        conv_layer_names = ['conv_layer_{}'.format(i) for i in range(self.conv_num)]
        inp_filters = 3
        out_size = 32
        for i, key in enumerate(conv_layer_names):
            out_filters = desc[key]['filters']
            kernel_size = desc[key]['kernel_size']
            padding = (kernel_size - 1) // 2
            self.conv_layers[i] = nn.Conv2d(inp_filters, out_filters, padding=padding, kernel_size=kernel_size)
            if 'bn' in desc[key].keys():
                if desc[key]['bn']:
                    self.bn_layers[i] = nn.BatchNorm2d(out_filters)
            if 'relu' in desc[key].keys():
                if desc[key]['relu']:
                    self.relu_layers[i] = nn.ReLU(inplace=False)
            self.pool_layers[i] = nn.MaxPool2d(2, stride=2)
            inp_filters = out_filters
            out_size = out_size // 2
        fc_inp_size = inp_filters * out_size * out_size
        fc_out_size = desc['fully_connect']['output_unit']
        self.fc0 = nn.Linear(fc_inp_size, fc_out_size)
        self.fc0_relu = nn.ReLU(inplace=True)
        fc_inp_size = fc_out_size
        fc_out_size = 10
        self.fc1 = nn.Linear(fc_inp_size, fc_out_size)
<|end_body_0|>

<|body_start_1|>
        for i in range(self.conv_num):
            x = self.conv_layers[i](x)
            if self.bn_layers[i] is not None:
                x = self.bn_layers[i](x)
            if self.relu_layers[i] is not None:
                x = self.relu_layers[i](x)
            x = self.pool_layers[i](x)
        x = self.fc0(x.view(x.size(0), -1))
        x = self.fc0_relu(x)
        x = self.fc1(x)
        return x
<|end_body_1|>

Solution:
class SimpleCnn:
    """Simple CNN network."""

    def __init__(self, desc):
        """Initialize."""
        super(SimpleCnn, self).__init__()
        self.conv_num = 3
        self.conv_layers = nn.ModuleList([None] * self.conv_num)
        self.bn_layers = nn.ModuleList([None] * self.conv_num)
        self.relu_layers = nn.ModuleList([None] * self.conv_num)
        self.pool_layers = nn.ModuleList([None] * self.conv_num)
        conv_layer_names = ['conv_layer_{}'.format(i) for i in range(self.conv_num)]
        inp_filters = 3
        out_size = 32
        for i, key in enumerate(conv_layer_names):
            out_filters = desc[key]['filters']
            kernel_size = desc[key]['kernel_size']
            padding = (kernel_size - 1) // 2
            self.conv_layers[i] = nn.Conv2d(inp_filters, out_filters, padding=padding, kernel_size=kernel_size)
            if 'bn' in desc[key].keys():
                if desc[key]['bn']:
                    self.bn_layers[i] = nn.BatchNorm2d(out_filters)
            if 'relu' in desc[key].keys():
                if desc[key]['relu']:
                    self.relu_layers[i] = nn.ReLU(inplace=False)
            self.pool_layers[i] = nn.MaxPool2d(2, stride=2)
            inp_filters = out_filters
            out_size = out_size // 2
        fc_inp_size = inp_filters * out_size * out_size
        fc_out_size = desc['fully_connect']['output_unit']
        self.fc0 = nn.Linear(fc_inp_size, fc_out_size)
        self.fc0_relu = nn.ReLU(inplace=True)
        fc_inp_size = fc_out_size
        fc_out_size = 10
        self.fc1 = nn.Linear(fc_inp_size, fc_out_size)

    def forward(self, x):
        """Forward."""
        for i in range(self.conv_num):
            x = self.conv_layers[i](x)
            if self.bn_layers[i] is not None:
                x = self.bn_layers[i](x)
            if self.relu_layers[i] is not None:
                x = self.relu_layers[i](x)
            x = self.pool_layers[i](x)
        x = self.fc0(x.view(x.size(0), -1))
        x = self.fc0_relu(x)
        x = self.fc1(x)
        return x


execute

Prompt:
Implement the Python class `FISTA` described below.

Class description:
Fast Iterative Shrinkage-Thresholding Algorithm implementation. Attributes ---------- f: function :math:`C^{1,1}` convex function. df: function derivative function of f. L: float Lipshitz contant of f. g: function Non-smooth function. pg: function g poximal operator. shape: tuple The data shape. Nit: None, int Number of iteration. If None, the iterations will stop as soon as the functional no longer evolve. Default is None. init: numpy array Init point which shape is the same as the data. If None, a random initailization is drawn. Default is None. verbose: bool If True, process informations are sent to the output. Default is True. Nit_max: int Maximum number of iterations. tau: float Descent

Method signatures and docstrings:
- def __init__(self, f, df, L, g, pg, shape, Nit=None, init=None, verbose=True): Initialization function for FISTA. Arguments --------- f: function :math:`C^{1,1}` convex function. df: function derivative function of f. L: float Lipshitz contant of f. g: function Non-smooth function. pg: function g poximal operator. shape: tuple The data shape. Nit: None, int Number of iteration. If None, the iterations will stop as soon as the functional no longer evolve. Default is None. init: numpy array Init point which shape is the same as the data. If None, a random initailization is drawn. Default is None. verbose: bool If True, process informations are sent to the output. Default is True.
- def StopCritera(self, n): This function computes a critera that informs about the algorithm convergence at step n. Arguments --------- n: int Current step Returns ------- float Value of the critera.
- def StopTest(self, n): This function choose if iterations should be stopped at step n. If Nit is not None, it returns True as long as n is smaller than Nit. If Nit is None, it returns True as long as the functional is evolving fast. Arguments --------- n: int Current step. Returns ------- bool Should the iterations go on ?
- def execute(self): Method that executes the FISTA algorithm. Returns ------- numpy array The optimum of the optimization problem. dict Extra informations about convergence. Note ---- Infos in output dictionary: * :code:`E`: Evolution of the functional along the iterations. * :code:`time`: Execution time.

Implement the Python class `FISTA` described below.

Class description:
Fast Iterative Shrinkage-Thresholding Algorithm implementation. Attributes ---------- f: function :math:`C^{1,1}` convex function. df: function derivative function of f. L: float Lipshitz contant of f. g: function Non-smooth function. pg: function g poximal operator. shape: tuple The data shape. Nit: None, int Number of iteration. If None, the iterations will stop as soon as the functional no longer evolve. Default is None. init: numpy array Init point which shape is the same as the data. If None, a random initailization is drawn. Default is None. verbose: bool If True, process informations are sent to the output. Default is True. Nit_max: int Maximum number of iterations. tau: float Descent

Method signatures and docstrings:
- def __init__(self, f, df, L, g, pg, shape, Nit=None, init=None, verbose=True): Initialization function for FISTA. Arguments --------- f: function :math:`C^{1,1}` convex function. df: function derivative function of f. L: float Lipshitz contant of f. g: function Non-smooth function. pg: function g poximal operator. shape: tuple The data shape. Nit: None, int Number of iteration. If None, the iterations will stop as soon as the functional no longer evolve. Default is None. init: numpy array Init point which shape is the same as the data. If None, a random initailization is drawn. Default is None. verbose: bool If True, process informations are sent to the output. Default is True.
- def StopCritera(self, n): This function computes a critera that informs about the algorithm convergence at step n. Arguments --------- n: int Current step Returns ------- float Value of the critera.
- def StopTest(self, n): This function choose if iterations should be stopped at step n. If Nit is not None, it returns True as long as n is smaller than Nit. If Nit is None, it returns True as long as the functional is evolving fast. Arguments --------- n: int Current step. Returns ------- bool Should the iterations go on ?
- def execute(self): Method that executes the FISTA algorithm. Returns ------- numpy array The optimum of the optimization problem. dict Extra informations about convergence. Note ---- Infos in output dictionary: * :code:`E`: Evolution of the functional along the iterations. * :code:`time`: Execution time.

<|skeleton|>
class FISTA:
    """Fast Iterative Shrinkage-Thresholding Algorithm implementation. Attributes ---------- f: function :math:`C^{1,1}` convex function. df: function derivative function of f. L: float Lipshitz contant of f. g: function Non-smooth function. pg: function g poximal operator. shape: tuple The data shape. Nit: None, int Number of iteration. If None, the iterations will stop as soon as the functional no longer evolve. Default is None. init: numpy array Init point which shape is the same as the data. If None, a random initailization is drawn. Default is None. verbose: bool If True, process informations are sent to the output. Default is True. Nit_max: int Maximum number of iterations. tau: float Descent"""

    def __init__(self, f, df, L, g, pg, shape, Nit=None, init=None, verbose=True):
        """Initialization function for FISTA. Arguments --------- f: function :math:`C^{1,1}` convex function. df: function derivative function of f. L: float Lipshitz contant of f. g: function Non-smooth function. pg: function g poximal operator. shape: tuple The data shape. Nit: None, int Number of iteration. If None, the iterations will stop as soon as the functional no longer evolve. Default is None. init: numpy array Init point which shape is the same as the data. If None, a random initailization is drawn. Default is None. verbose: bool If True, process informations are sent to the output. Default is True."""
        <|body_0|>

    def StopCritera(self, n):
        """This function computes a critera that informs about the algorithm convergence at step n. Arguments --------- n: int Current step Returns ------- float Value of the critera."""
        <|body_1|>

    def StopTest(self, n):
        """This function choose if iterations should be stopped at step n. If Nit is not None, it returns True as long as n is smaller than Nit. If Nit is None, it returns True as long as the functional is evolving fast. Arguments --------- n: int Current step. Returns ------- bool Should the iterations go on ?"""
        <|body_2|>

    def execute(self):
        """Method that executes the FISTA algorithm. Returns ------- numpy array The optimum of the optimization problem. dict Extra informations about convergence. Note ---- Infos in output dictionary: * :code:`E`: Evolution of the functional along the iterations. * :code:`time`: Execution time."""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        if L <= 0:
            raise ValueError('Input L parameter should be strct. positive.')
        if Nit is not None:
            if Nit <= 0:
                raise ValueError('Input number of iteration is non-positive.')
            if Nit > 1000000.0:
                raise ValueError('Input number of iterations is really high.')
        if init is not None:
            if init.shape != shape:
                raise ValueError('Input init shape and shape parameter do not match.')
        else:
            np.random.seed(1)
            init = np.random.randn(*shape)
        if not isinstance(verbose, bool):
            raise ValueError('Input verbose parameter is not boolean.')
        _logger.info('Setting up new FISTA optimizer.')
        self.f = f
        self.df = df
        self.g = g
        self.pg = pg
        self.L = L
        self.shape = shape
        self.Nit = Nit
        self.init = init
        self.verbose = verbose
        self.Nit_max = 1000 if self.Nit is None else self.Nit
        self.tau = 0.99 / self.L
        self.E = np.zeros(self.Nit_max)
        self.lim = 0.0001
<|end_body_0|>

<|body_start_1|>
        if np.allclose(self.E[n - 2], 0):
            return None
        else:
            return np.abs(self.E[n - 1] - self.E[n - 2]) / (self.E[n - 2] * self.tau)
<|end_body_1|>

<|body_start_2|>
        if self.Nit is not None:
            return n < self.Nit
        else:
            if n < 2:
                return True
            if n >= self.Nit_max:
                return False
            else:
                critera = self.StopCritera(n)
                if critera is None:
                    if self.verbose:
                        print('Iterations stopped as the functional is allclose to 0.')
                    return False
                else:
                    return critera > self.lim
<|end_body_2|>

<|body_start_3|>
        _logger.info('Starting FISTA optimization.')
        start = time.time()
        X0 = self.init
        n = 0
        theta = 1
        Xm1 = X0
        Xy = X0
        while self.StopTest(n):
            if self.verbose:
                if n >= 2:
                    critera = self.StopCritera(n)
                    print('n: {}, f + g: {:.3e}, critera: {:.5f} (goal: {:.1e})'.format(n, self.E[n - 1], 0 if critera is None else critera, self.lim))
                else:
                    print('n: {}'.format(n))
            X = Xy - self.tau * self.df(Xy)
            X = self.pg(X)
            thetap1 = 0.5 * (1 + math.sqrt(1 + 4 * theta ** 2))
            Xy = X + (theta - 1) / thetap1 * (X - Xm1)
            theta = thetap1
            Xm1 = X
            self.E[n] = self.f(X) + self.g(X)
            n = n + 1
        self.E = self.E[:n]
        InfoOut = {'E': self.E, 'time': time.time() - start}
        _logger.info('FISTA optimization finished.')
        return (X, InfoOut)
<|end_body_3|>

Solution:
class FISTA:
    """Fast Iterative Shrinkage-Thresholding Algorithm implementation. Attributes ---------- f: function :math:`C^{1,1}` convex function. df: function derivative function of f. L: float Lipshitz contant of f. g: function Non-smooth function. pg: function g poximal operator. shape: tuple The data shape. Nit: None, int Number of iteration. If None, the iterations will stop as soon as the functional no longer evolve. Default is None. init: numpy array Init point which shape is the same as the data. If None, a random initailization is drawn. Default is None. verbose: bool If True, process informations are sent to the output. Default is True. Nit_max: int Maximum number of iterations. tau: float Descent"""

    def __init__(self, f, df, L, g, pg, shape, Nit=None, init=None, verbose=True):
        """Initialization function for FISTA. Arguments --------- f: function :math:`C^{1,1}` convex function. df: function derivative function of f. L: float Lipshitz contant of f. g: function Non-smooth function. pg: function g poximal operator. shape: tuple The data shape. Nit: None, int Number of iteration. If None, the iterations will stop as soon as the functional no longer evolve. Default is None. init: numpy array Init point which shape is the same as the data. If None, a random initailization is drawn. Default is None. verbose: bool If True, process informations are sent to the output. Default is True."""
        if L <= 0:
            raise ValueError('Input L parameter should be strct. positive.')
        if Nit is not None:
            if Nit <= 0:
                raise ValueError('Input number of iteration is non-positive.')
            if Nit > 1000000.0:
                raise ValueError('Input number of iterations is really high.')
        if init is not None:
            if init.shape != shape:
                raise ValueError('Input init shape and shape parameter do not match.')
        else:
            np.random.seed(1)
            init = np.random.randn(*shape)
        if not isinstance(verbose, bool):
            raise ValueError('Input verbose parameter is not boolean.')
        _logger.info('Setting up new FISTA optimizer.')
        self.f = f
        self.df = df
        self.g = g
        self.pg = pg
        self.L = L
        self.shape = shape
        self.Nit = Nit
        self.init = init
        self.verbose = verbose
        self.Nit_max = 1000 if self.Nit is None else self.Nit
        self.tau = 0.99 / self.L
        self.E = np.zeros(self.Nit_max)
        self.lim = 0.0001

    def StopCritera(self, n):
        """This function computes a critera that informs about the algorithm convergence at step n. Arguments --------- n: int Current step Returns ------- float Value of the critera."""
        if np.allclose(self.E[n - 2], 0):
            return None
        else:
            return np.abs(self.E[n - 1] - self.E[n - 2]) / (self.E[n - 2] * self.tau)

    def StopTest(self, n):
        """This function choose if iterations should be stopped at step n. If Nit is not None, it returns True as long as n is smaller than Nit. If Nit is None, it returns True as long as the functional is evolving fast. Arguments --------- n: int Current step. Returns ------- bool Should the iterations go on ?"""
        if self.Nit is not None:
            return n < self.Nit
        else:
            if n < 2:
                return True
            if n >= self.Nit_max:
                return False
            else:
                critera = self.StopCritera(n)
                if critera is None:
                    if self.verbose:
                        print('Iterations stopped as the functional is allclose to 0.')
                    return False
                else:
                    return critera > self.lim

    def execute(self):
        """Method that executes the FISTA algorithm. Returns ------- numpy array The optimum of the optimization problem. dict Extra informations about convergence. Note ---- Infos in output dictionary: * :code:`E`: Evolution of the functional along the iterations. * :code:`time`: Execution time."""
        _logger.info('Starting FISTA optimization.')
        start = time.time()
        X0 = self.init
        n = 0
        theta = 1
        Xm1 = X0
        Xy = X0
        while self.StopTest(n):
            if self.verbose:
                if n >= 2:
                    critera = self.StopCritera(n)
                    print('n: {}, f + g: {:.3e}, critera: {:.5f} (goal: {:.1e})'.format(n, self.E[n - 1], 0 if critera is None else critera, self.lim))
                else:
                    print('n: {}'.format(n))
            X = Xy - self.tau * self.df(Xy)
            X = self.pg(X)
            thetap1 = 0.5 * (1 + math.sqrt(1 + 4 * theta ** 2))
            Xy = X + (theta - 1) / thetap1 * (X - Xm1)
            theta = thetap1
            Xm1 = X
            self.E[n] = self.f(X) + self.g(X)
            n = n + 1
        self.E = self.E[:n]
        InfoOut = {'E': self.E, 'time': time.time() - start}
        _logger.info('FISTA optimization finished.')
        return (X, InfoOut)


predict

Prompt:
Implement the Python class `BaseTreeQuantileRegressor` described below.

Class description:
Implement the BaseTreeQuantileRegressor class.

Method signatures and docstrings:
- def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None): Child of BaseDecisionTree (sklearn), which use a single DecisionTree to do the same kind of Quantile things. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like, shape = [n_samples] or [n_samples, n_outputs] The target values (class labels) as integers or strings. sample_weight : array-like, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each nod
- def predict(self, X, u, check_input=False): Predict regression value for X. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. quantile : int, optional Value ranging from 0 to 100. By default, the mean is returned. check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- y : array of shape = [n_samples] If quantile is set to None, then return E(Y | X). Else return y such that F(Y=y | x) = quantile.

Implement the Python class `BaseTreeQuantileRegressor` described below.

Class description:
Implement the BaseTreeQuantileRegressor class.

Method signatures and docstrings:
- def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None): Child of BaseDecisionTree (sklearn), which use a single DecisionTree to do the same kind of Quantile things. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like, shape = [n_samples] or [n_samples, n_outputs] The target values (class labels) as integers or strings. sample_weight : array-like, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each nod
- def predict(self, X, u, check_input=False): Predict regression value for X. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. quantile : int, optional Value ranging from 0 to 100. By default, the mean is returned. check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- y : array of shape = [n_samples] If quantile is set to None, then return E(Y | X). Else return y such that F(Y=y | x) = quantile.

<|skeleton|>
class BaseTreeQuantileRegressor:

    def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None):
        """Child of BaseDecisionTree (sklearn), which use a single DecisionTree to do the same kind of Quantile things. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like, shape = [n_samples] or [n_samples, n_outputs] The target values (class labels) as integers or strings. sample_weight : array-like, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each nod"""
        <|body_0|>

    def predict(self, X, u, check_input=False):
        """Predict regression value for X. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. quantile : int, optional Value ranging from 0 to 100. By default, the mean is returned. check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- y : array of shape = [n_samples] If quantile is set to None, then return E(Y | X). Else return y such that F(Y=y | x) = quantile."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        y = np.asarray(y)
        if np.ndim(y) == 2 and y.shape[1] == 1:
            y = np.ravel(y)
        X, y = check_X_y(X, y, accept_sparse='csc', dtype=np.float32, multi_output=1)
        super(BaseTreeQuantileRegressor, self).fit(X, y, sample_weight=sample_weight, check_input=check_input, X_idx_sorted=X_idx_sorted)
        self.y_train_ = y
        self.y_train_leaves_ = self.tree_.apply(X)
        return self
<|end_body_0|>

<|body_start_1|>
        X = check_array(X, dtype=np.float32, accept_sparse='csc')
        if quantile is None:
            return super(BaseTreeQuantileRegressor, self).predict(X, check_input=check_input)
        B = u.shape[0]
        r_cos, r_sin = (np.zeros((X.shape[0], B)), np.zeros((X.shape[0], B)))
        X_leaves = self.apply(X)
        unique_leaves = np.unique(X_leaves)
        for leaf in unique_leaves:
            y = self.y_train_[self.y_train_leaves_ == leaf]
            r_cos[X_leaves == leaf, :] = np.mean(np.cos(y.dot(uv.T)), axis=0)
            r_sin[X_leaves == leaf, :] = np.mean(np.sin(y.dot(uv.T)), axis=0)
        return (r_cos, r_sin)
<|end_body_1|>

Solution:
class BaseTreeQuantileRegressor:
    def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None):
        """Child of BaseDecisionTree (sklearn), which use a single DecisionTree to do the same kind of Quantile things. Parameters ---------- X : array-like or sparse matrix, shape = [n_samples, n_features] The training input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csc_matrix``. y : array-like, shape = [n_samples] or [n_samples, n_outputs] The target values (class labels) as integers or strings. sample_weight : array-like, shape = [n_samples] or None Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each nod"""
        y = np.asarray(y)
        if np.ndim(y) == 2 and y.shape[1] == 1:
            y = np.ravel(y)
        X, y = check_X_y(X, y, accept_sparse='csc', dtype=np.float32, multi_output=1)
        super(BaseTreeQuantileRegressor, self).fit(X, y, sample_weight=sample_weight, check_input=check_input, X_idx_sorted=X_idx_sorted)
        self.y_train_ = y
        self.y_train_leaves_ = self.tree_.apply(X)
        return self

    def predict(self, X, u, check_input=False):
        """Predict regression value for X. Parameters ---------- X : array-like or sparse matrix of shape = [n_samples, n_features] The input samples. Internally, it will be converted to ``dtype=np.float32`` and if a sparse matrix is provided to a sparse ``csr_matrix``. quantile : int, optional Value ranging from 0 to 100. By default, the mean is returned. check_input : boolean, (default=True) Allow to bypass several input checking. Don't use this parameter unless you know what you do. Returns ------- y : array of shape = [n_samples] If quantile is set to None, then return E(Y | X). Else return y such that F(Y=y | x) = quantile."""
        X = check_array(X, dtype=np.float32, accept_sparse='csc')
        if quantile is None:
            return super(BaseTreeQuantileRegressor, self).predict(X, check_input=check_input)
        B = u.shape[0]
        r_cos, r_sin = (np.zeros((X.shape[0], B)), np.zeros((X.shape[0], B)))
        X_leaves = self.apply(X)
        unique_leaves = np.unique(X_leaves)
        for leaf in unique_leaves:
            y = self.y_train_[self.y_train_leaves_ == leaf]
            r_cos[X_leaves == leaf, :] = np.mean(np.cos(y.dot(uv.T)), axis=0)
            r_sin[X_leaves == leaf, :] = np.mean(np.sin(y.dot(uv.T)), axis=0)
        return (r_cos, r_sin)


Decode

Prompt:
Implement the Python class `AlgorandMnemonicDecoder` described below.

Class description:
Algorand mnemonic decoder class. It decodes a mnemonic phrase to bytes.

Method signatures and docstrings:
- def __init__(self, lang: Optional[AlgorandLanguages]=AlgorandLanguages.ENGLISH) -> None: Construct class. Language is set to English by default because Algorand mnemonic only support one language, so it's useless (and slower) to automatically detect the language. Args: lang (AlgorandLanguages, optional): Language, None for automatic detection Raises: TypeError: If the language is not a AlgorandLanguages enum ValueError: If loaded words list is not valid
- def Decode(self, mnemonic: Union[str, Mnemonic]) -> bytes: Decode a mnemonic phrase to bytes (no checksum). Args: mnemonic (str or Mnemonic object): Mnemonic Returns: bytes: Decoded bytes Raises: MnemonicChecksumError: If checksum is not valid ValueError: If mnemonic is not valid
- def __ValidateChecksum(entropy_bytes: bytes, chksum_word_idx_exp: int, words_list: MnemonicWordsList) -> None: Validate a mnemonic checksum. Args: entropy_bytes (list) : Entropy bytes chksum_word_idx_exp (int) : Expected checksum word index words_list (MnemonicWordsList): Words list Raises: MnemonicChecksumError: If checksum is not valid

Implement the Python class `AlgorandMnemonicDecoder` described below.

Class description:
Algorand mnemonic decoder class. It decodes a mnemonic phrase to bytes.

Method signatures and docstrings:
- def __init__(self, lang: Optional[AlgorandLanguages]=AlgorandLanguages.ENGLISH) -> None: Construct class. Language is set to English by default because Algorand mnemonic only support one language, so it's useless (and slower) to automatically detect the language. Args: lang (AlgorandLanguages, optional): Language, None for automatic detection Raises: TypeError: If the language is not a AlgorandLanguages enum ValueError: If loaded words list is not valid
- def Decode(self, mnemonic: Union[str, Mnemonic]) -> bytes: Decode a mnemonic phrase to bytes (no checksum). Args: mnemonic (str or Mnemonic object): Mnemonic Returns: bytes: Decoded bytes Raises: MnemonicChecksumError: If checksum is not valid ValueError: If mnemonic is not valid
- def __ValidateChecksum(entropy_bytes: bytes, chksum_word_idx_exp: int, words_list: MnemonicWordsList) -> None: Validate a mnemonic checksum. Args: entropy_bytes (list) : Entropy bytes chksum_word_idx_exp (int) : Expected checksum word index words_list (MnemonicWordsList): Words list Raises: MnemonicChecksumError: If checksum is not valid

<|skeleton|>
class AlgorandMnemonicDecoder:
    """Algorand mnemonic decoder class. It decodes a mnemonic phrase to bytes."""

    def __init__(self, lang: Optional[AlgorandLanguages]=AlgorandLanguages.ENGLISH) -> None:
        """Construct class. Language is set to English by default because Algorand mnemonic only support one language, so it's useless (and slower) to automatically detect the language. Args: lang (AlgorandLanguages, optional): Language, None for automatic detection Raises: TypeError: If the language is not a AlgorandLanguages enum ValueError: If loaded words list is not valid"""
        <|body_0|>

    def Decode(self, mnemonic: Union[str, Mnemonic]) -> bytes:
        """Decode a mnemonic phrase to bytes (no checksum). Args: mnemonic (str or Mnemonic object): Mnemonic Returns: bytes: Decoded bytes Raises: MnemonicChecksumError: If checksum is not valid ValueError: If mnemonic is not valid"""
        <|body_1|>

    def __ValidateChecksum(entropy_bytes: bytes, chksum_word_idx_exp: int, words_list: MnemonicWordsList) -> None:
        """Validate a mnemonic checksum. Args: entropy_bytes (list) : Entropy bytes chksum_word_idx_exp (int) : Expected checksum word index words_list (MnemonicWordsList): Words list Raises: MnemonicChecksumError: If checksum is not valid"""
        <|body_2|>

<|end_skeleton|>

<|body_start_0|>
        if lang is not None and (not isinstance(lang, AlgorandLanguages)):
            raise TypeError('Language is not an enumerative of AlgorandLanguages')
        super().__init__(lang.value if lang is not None else lang, Bip39WordsListFinder, Bip39WordsListGetter)
<|end_body_0|>

<|body_start_1|>
        mnemonic_obj = AlgorandMnemonic.FromString(mnemonic) if isinstance(mnemonic, str) else mnemonic
        if mnemonic_obj.WordsCount() not in AlgorandMnemonicConst.MNEMONIC_WORD_NUM:
            raise ValueError(f'Mnemonic words count is not valid ({mnemonic_obj.WordsCount()})')
        words = mnemonic_obj.ToList()
        words_list, _ = self._FindLanguage(mnemonic_obj)
        word_indexes = [words_list.GetWordIdx(w) for w in words]
        entropy_list = AlgorandMnemonicUtils.ConvertBits(word_indexes[:-1], 11, 8)
        assert entropy_list is not None
        entropy_bytes = BytesUtils.FromList(entropy_list)[:-1]
        self.__ValidateChecksum(entropy_bytes, word_indexes[-1], words_list)
        return entropy_bytes
<|end_body_1|>

<|body_start_2|>
        chksum_word_idx = AlgorandMnemonicUtils.ComputeChecksumWordIndex(entropy_bytes)
        if chksum_word_idx != chksum_word_idx_exp:
            raise MnemonicChecksumError(f'Invalid checksum (expected {words_list.GetWordAtIdx(chksum_word_idx)}, got {words_list.GetWordAtIdx(chksum_word_idx_exp)})')
<|end_body_2|>

Solution:
class AlgorandMnemonicDecoder:
    """Algorand mnemonic decoder class. It decodes a mnemonic phrase to bytes."""

    def __init__(self, lang: Optional[AlgorandLanguages]=AlgorandLanguages.ENGLISH) -> None:
        """Construct class. Language is set to English by default because Algorand mnemonic only support one language, so it's useless (and slower) to automatically detect the language. Args: lang (AlgorandLanguages, optional): Language, None for automatic detection Raises: TypeError: If the language is not a AlgorandLanguages enum ValueError: If loaded words list is not valid"""
        if lang is not None and (not isinstance(lang, AlgorandLanguages)):
            raise TypeError('Language is not an enumerative of AlgorandLanguages')
        super().__init__(lang.value if lang is not None else lang, Bip39WordsListFinder, Bip39WordsListGetter)

    def Decode(self, mnemonic: Union[str, Mnemonic]) -> bytes:
        """Decode a mnemonic phrase to bytes (no checksum). Args: mnemonic (str or Mnemonic object): Mnemonic Returns: bytes: Decoded bytes Raises: MnemonicChecksumError: If checksum is not valid ValueError: If mnemonic is not valid"""
        mnemonic_obj = AlgorandMnemonic.FromString(mnemonic) if isinstance(mnemonic, str) else mnemonic
        if mnemonic_obj.WordsCount() not in AlgorandMnemonicConst.MNEMONIC_WORD_NUM:
            raise ValueError(f'Mnemonic words count is not valid ({mnemonic_obj.WordsCount()})')
        words = mnemonic_obj.ToList()
        words_list, _ = self._FindLanguage(mnemonic_obj)
        word_indexes = [words_list.GetWordIdx(w) for w in words]
        entropy_list = AlgorandMnemonicUtils.ConvertBits(word_indexes[:-1], 11, 8)
        assert entropy_list is not None
        entropy_bytes = BytesUtils.FromList(entropy_list)[:-1]
        self.__ValidateChecksum(entropy_bytes, word_indexes[-1], words_list)
        return entropy_bytes

    def __ValidateChecksum(entropy_bytes: bytes, chksum_word_idx_exp: int, words_list: MnemonicWordsList) -> None:
        """Validate a mnemonic checksum. Args: entropy_bytes (list) : Entropy bytes chksum_word_idx_exp (int) : Expected checksum word index words_list (MnemonicWordsList): Words list Raises: MnemonicChecksumError: If checksum is not valid"""
        chksum_word_idx = AlgorandMnemonicUtils.ComputeChecksumWordIndex(entropy_bytes)
        if chksum_word_idx != chksum_word_idx_exp:
            raise MnemonicChecksumError(f'Invalid checksum (expected {words_list.GetWordAtIdx(chksum_word_idx)}, got {words_list.GetWordAtIdx(chksum_word_idx_exp)})')


scaled

Prompt:
Implement the Python class `SubselectionAlgorithm` described below.

Class description:
A class to collect all subselection algorithms

Method signatures and docstrings:
- def _numpify(data): Convert a Python array to a Numpy array
- def cutoff(data, cutoff=0.287): A cutoff-defined subselection algorithm Description ----------- This algorithm removes a decoy, if its score is l ess than the cutoff. Parameters ---------- data : list, tuple A 1D array of scores cutoff : float, optional The cutoff of keeping decoys Returns ------- list The decoy indices to keep list The decoy indices to throw
- def linear(data, cutoff=0.5): A linearly-defined subselection algorithm Description ----------- This algorithm removes the worst 500 decoys. Parameters ---------- data : list, tuple A 1D array of scores cutoff : float, optional The porportion of the total number of decoys to keep Returns ------- list The decoy indices to keep list The decoy indices to throw
- def scaled(data, cutoff=0.5): A scaling-defined subselection algorithm Description ----------- This algorithm removes a decoy, if its scaled score is less than 0.5. The scaled score is calculated by dividing the satisfaction score by the average of the set. Parameters ---------- data : list, tuple A 1D array of scores cutoff : float, optional The cutoff of keeping decoys Returns ------- list The decoy indices to keep list The decoy indices to throw

Implement the Python class `SubselectionAlgorithm` described below.

Class description:
A class to collect all subselection algorithms

Method signatures and docstrings:
- def _numpify(data): Convert a Python array to a Numpy array
- def cutoff(data, cutoff=0.287): A cutoff-defined subselection algorithm Description ----------- This algorithm removes a decoy, if its score is l ess than the cutoff. Parameters ---------- data : list, tuple A 1D array of scores cutoff : float, optional The cutoff of keeping decoys Returns ------- list The decoy indices to keep list The decoy indices to throw
- def linear(data, cutoff=0.5): A linearly-defined subselection algorithm Description ----------- This algorithm removes the worst 500 decoys. Parameters ---------- data : list, tuple A 1D array of scores cutoff : float, optional The porportion of the total number of decoys to keep Returns ------- list The decoy indices to keep list The decoy indices to throw
- def scaled(data, cutoff=0.5): A scaling-defined subselection algorithm Description ----------- This algorithm removes a decoy, if its scaled score is less than 0.5. The scaled score is calculated by dividing the satisfaction score by the average of the set. Parameters ---------- data : list, tuple A 1D array of scores cutoff : float, optional The cutoff of keeping decoys Returns ------- list The decoy indices to keep list The decoy indices to throw

<|skeleton|>
class SubselectionAlgorithm:
    """A class to collect all subselection algorithms"""

    def _numpify(data):
        """Convert a Python array to a Numpy array"""
        <|body_0|>

    def cutoff(data, cutoff=0.287):
        """A cutoff-defined subselection algorithm Description ----------- This algorithm removes a decoy, if its score is l ess than the cutoff. Parameters ---------- data : list, tuple A 1D array of scores cutoff : float, optional The cutoff of keeping decoys Returns ------- list The decoy indices to keep list The decoy indices to throw"""
        <|body_1|>

    def linear(data, cutoff=0.5):
        """A linearly-defined subselection algorithm Description ----------- This algorithm removes the worst 500 decoys. Parameters ---------- data : list, tuple A 1D array of scores cutoff : float, optional The porportion of the total number of decoys to keep Returns ------- list The decoy indices to keep list The decoy indices to throw"""
        <|body_2|>

    def scaled(data, cutoff=0.5):
        """A scaling-defined subselection algorithm Description ----------- This algorithm removes a decoy, if its scaled score is less than 0.5. The scaled score is calculated by dividing the satisfaction score by the average of the set. Parameters ---------- data : list, tuple A 1D array of scores cutoff : float, optional The cutoff of keeping decoys Returns ------- list The decoy indices to keep list The decoy indices to throw"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        if type(data).__module__ == numpy.__name__:
            return data
        else:
            return numpy.asarray(data)
<|end_body_0|>

<|body_start_1|>
        data = SubselectionAlgorithm._numpify(data)
        keep = numpy.where(data >= cutoff)[0]
        throw = numpy.where(data < cutoff)[0]
        return (keep.tolist(), throw.tolist())
<|end_body_1|>

<|body_start_2|>
        sorted_indices = SubselectionAlgorithm._numpify(data).argsort()[::-1]
        point = numpy.ceil(sorted_indices.shape[0] * cutoff)
        keep = sorted_indices[:point]
        throw = sorted_indices[point:]
        return (keep.tolist(), throw.tolist())
<|end_body_2|>

<|body_start_3|>
        data = SubselectionAlgorithm._numpify(data)
        data_scaled = data / numpy.mean(data)
        keep = numpy.where(data_scaled >= cutoff)[0]
        throw = numpy.where(data_scaled < cutoff)[0]
        return (keep.tolist(), throw.tolist())
<|end_body_3|>

Solution:
class SubselectionAlgorithm:
    """A class to collect all subselection algorithms"""

    def _numpify(data):
        """Convert a Python array to a Numpy array"""
        if type(data).__module__ == numpy.__name__:
            return data
        else:
            return numpy.asarray(data)

    def cutoff(data, cutoff=0.287):
        """A cutoff-defined subselection algorithm Description ----------- This algorithm removes a decoy, if its score is l ess than the cutoff. Parameters ---------- data : list, tuple A 1D array of scores cutoff : float, optional The cutoff of keeping decoys Returns ------- list The decoy indices to keep list The decoy indices to throw"""
        data = SubselectionAlgorithm._numpify(data)
        keep = numpy.where(data >= cutoff)[0]
        throw = numpy.where(data < cutoff)[0]
        return (keep.tolist(), throw.tolist())

    def linear(data, cutoff=0.5):
        """A linearly-defined subselection algorithm Description ----------- This algorithm removes the worst 500 decoys. Parameters ---------- data : list, tuple A 1D array of scores cutoff : float, optional The porportion of the total number of decoys to keep Returns ------- list The decoy indices to keep list The decoy indices to throw"""
        sorted_indices = SubselectionAlgorithm._numpify(data).argsort()[::-1]
        point = numpy.ceil(sorted_indices.shape[0] * cutoff)
        keep = sorted_indices[:point]
        throw = sorted_indices[point:]
        return (keep.tolist(), throw.tolist())

    def scaled(data, cutoff=0.5):
        """A scaling-defined subselection algorithm Description ----------- This algorithm removes a decoy, if its scaled score is less than 0.5. The scaled score is calculated by dividing the satisfaction score by the average of the set. Parameters ---------- data : list, tuple A 1D array of scores cutoff : float, optional The cutoff of keeping decoys Returns ------- list The decoy indices to keep list The decoy indices to throw"""
        data = SubselectionAlgorithm._numpify(data)
        data_scaled = data / numpy.mean(data)
        keep = numpy.where(data_scaled >= cutoff)[0]
        throw = numpy.where(data_scaled < cutoff)[0]
        return (keep.tolist(), throw.tolist())


Prompt:
Implement the Python class `CommentFilterSet` described below.

Class description:
FilterSet for CommentViewSet.

Method signatures and docstrings:
- def get_content_type(): Return list for `content_type` choices.
- def get_is_parent(self, queryset, name, value): Filters queryset based on `is_parent` field.

Implement the Python class `CommentFilterSet` described below.

Class description:
FilterSet for CommentViewSet.

Method signatures and docstrings:
- def get_content_type(): Return list for `content_type` choices.
- def get_is_parent(self, queryset, name, value): Filters queryset based on `is_parent` field.

<|skeleton|>
class CommentFilterSet:
    """FilterSet for CommentViewSet."""

    def get_content_type():
        """Return list for `content_type` choices."""
        <|body_0|>

    def get_is_parent(self, queryset, name, value):
        """Filters queryset based on `is_parent` field."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        item_list = []
        content_list = ContentType.objects.all()
        for item in content_list:
            item_list.append((item.name, item.name))
        return item_list
<|end_body_0|>

<|body_start_1|>
        if value == True:
            queryset = queryset.filter(parent__isnull=True)
        else:
            queryset = queryset.filter(parent__isnull=False)
        return queryset
<|end_body_1|>

Solution:
class CommentFilterSet:
    """FilterSet for CommentViewSet."""

    def get_content_type():
        """Return list for `content_type` choices."""
        item_list = []
        content_list = ContentType.objects.all()
        for item in content_list:
            item_list.append((item.name, item.name))
        return item_list

    def get_is_parent(self, queryset, name, value):
        """Filters queryset based on `is_parent` field."""
        if value == True:
            queryset = queryset.filter(parent__isnull=True)
        else:
            queryset = queryset.filter(parent__isnull=False)
        return queryset


Prompt:
Implement the Python class `HCN2` described below.

Class description:
HCN2 model from 21 day old dorsal root ganglion of mouse. HCN channels are voltage-gated ionic channels, regulated by cyclic nucleotides, such as cyclic adenosine-mono-phosphate (cAMP) (not modelled here). In contrast to most Na+ and K+ ionic channels, which open when membrane potential is depolarized, they are opened when the membrane potential hyperpolarizes below -50 mV. This channel is expressed in brain and heart tissue, though the specific function is unknown. Reference: Moosmang S. et al. Cellular expression and functional characterization of four hyperpolarization-activated pacemaker channels in cardiac and neuronal tissues. Eur. J. Biochem., 2001 Mar , 268 (1646-52).

Method signatures and docstrings:
- def _init_state(self, V): Run initialization calculation for m and h gates of the channel at starting Vmem value.
- def _calculate_state(self, V): Update the state of m and h gates of the channel given their present value and present simulation Vmem.

Implement the Python class `HCN2` described below.

Class description:
HCN2 model from 21 day old dorsal root ganglion of mouse. HCN channels are voltage-gated ionic channels, regulated by cyclic nucleotides, such as cyclic adenosine-mono-phosphate (cAMP) (not modelled here). In contrast to most Na+ and K+ ionic channels, which open when membrane potential is depolarized, they are opened when the membrane potential hyperpolarizes below -50 mV. This channel is expressed in brain and heart tissue, though the specific function is unknown. Reference: Moosmang S. et al. Cellular expression and functional characterization of four hyperpolarization-activated pacemaker channels in cardiac and neuronal tissues. Eur. J. Biochem., 2001 Mar , 268 (1646-52).

Method signatures and docstrings:
- def _init_state(self, V): Run initialization calculation for m and h gates of the channel at starting Vmem value.
- def _calculate_state(self, V): Update the state of m and h gates of the channel given their present value and present simulation Vmem.

<|skeleton|>
class HCN2:
    """HCN2 model from 21 day old dorsal root ganglion of mouse. HCN channels are voltage-gated ionic channels, regulated by cyclic nucleotides, such as cyclic adenosine-mono-phosphate (cAMP) (not modelled here). In contrast to most Na+ and K+ ionic channels, which open when membrane potential is depolarized, they are opened when the membrane potential hyperpolarizes below -50 mV. This channel is expressed in brain and heart tissue, though the specific function is unknown. Reference: Moosmang S. et al. Cellular expression and functional characterization of four hyperpolarization-activated pacemaker channels in cardiac and neuronal tissues. Eur. J. Biochem., 2001 Mar , 268 (1646-52)."""

    def _init_state(self, V):
        """Run initialization calculation for m and h gates of the channel at starting Vmem value."""
        <|body_0|>

    def _calculate_state(self, V):
        """Update the state of m and h gates of the channel given their present value and present simulation Vmem."""
        <|body_1|>

<|end_skeleton|>

<|body_start_0|>
        logs.log_info('You are using the funny current channel: HCN2')
        self.time_unit = 1000.0
        self.v_corr = 0
        V = V - 10
        self.m = 1.0 / (1 + np.exp((V - -99) / 6.2))
        self.h = 1
        self._mpower = 1
        self._hpower = 0
        self._PmCa = 0.05
        self._PmNa = 0.2
<|end_body_0|>

<|body_start_1|>
        self.vrev = -45
        V = V - 10
        self._mInf = 1.0 / (1 + np.exp((V - -99) / 6.2))
        self._mTau = 184.0
        self._hInf = 1
        self._hTau = 1
<|end_body_1|>

Solution:
class HCN2:
    """HCN2 model from 21 day old dorsal root ganglion of mouse. HCN channels are voltage-gated ionic channels, regulated by cyclic nucleotides, such as cyclic adenosine-mono-phosphate (cAMP) (not modelled here). In contrast to most Na+ and K+ ionic channels, which open when membrane potential is depolarized, they are opened when the membrane potential hyperpolarizes below -50 mV. This channel is expressed in brain and heart tissue, though the specific function is unknown. Reference: Moosmang S. et al. Cellular expression and functional characterization of four hyperpolarization-activated pacemaker channels in cardiac and neuronal tissues. Eur. J. Biochem., 2001 Mar , 268 (1646-52)."""

    def _init_state(self, V):
        """Run initialization calculation for m and h gates of the channel at starting Vmem value."""
        logs.log_info('You are using the funny current channel: HCN2')
        self.time_unit = 1000.0
        self.v_corr = 0
        V = V - 10
        self.m = 1.0 / (1 + np.exp((V - -99) / 6.2))
        self.h = 1
        self._mpower = 1
        self._hpower = 0
        self._PmCa = 0.05
        self._PmNa = 0.2

    def _calculate_state(self, V):
        """Update the state of m and h gates of the channel given their present value and present simulation Vmem."""
        self.vrev = -45
        V = V - 10
        self._mInf = 1.0 / (1 + np.exp((V - -99) / 6.2))
        self._mTau = 184.0
        self._hInf = 1
        self._hTau = 1


VGGs神经网络结构搭建, 前几层fc换为全局池化, 尝试深度可分离卷积

Prompt:
Implement the Python class `VGGs` described below.

Class description:
VGGs神经网络结构搭建, 前几层fc换为全局池化, 尝试深度可分离卷积

Method signatures and docstrings:
- def __init__(self, vgg_name, class_num, attention): 网络初始化 param: vgg_name 选择的VGG网络名 param: class_num 目标分类数 param: attention 注意力
- def _make_layers(self): 建立特征提取部分网络 return vgg_feature_net VGG特征提取部分
- def _classifer_layers(self): 建立分类部分网络 return vgg_classifer_net VGG分类部分
- def forward(self, x): 前向传播 :param: x 图片变量 return out: 前向传播输出

Implement the Python class `VGGs` described below.

Class description:
VGGs神经网络结构搭建, 前几层fc换为全局池化, 尝试深度可分离卷积

Method signatures and docstrings:
- def __init__(self, vgg_name, class_num, attention): 网络初始化 param: vgg_name 选择的VGG网络名 param: class_num 目标分类数 param: attention 注意力
- def _make_layers(self): 建立特征提取部分网络 return vgg_feature_net VGG特征提取部分
- def _classifer_layers(self): 建立分类部分网络 return vgg_classifer_net VGG分类部分
- def forward(self, x): 前向传播 :param: x 图片变量 return out: 前向传播输出

<|skeleton|>
class VGGs:
    """VGGs神经网络结构搭建, 前几层fc换为全局池化, 尝试深度可分离卷积"""

    def __init__(self, vgg_name, class_num, attention):
        """网络初始化 param: vgg_name 选择的VGG网络名 param: class_num 目标分类数 param: attention 注意力"""
        <|body_0|>

    def _make_layers(self):
        """建立特征提取部分网络 return vgg_feature_net VGG特征提取部分"""
        <|body_1|>

    def _classifer_layers(self):
        """建立分类部分网络 return vgg_classifer_net VGG分类部分"""
        <|body_2|>

    def forward(self, x):
        """前向传播 :param: x 图片变量 return out: 前向传播输出"""
        <|body_3|>

<|end_skeleton|>

<|body_start_0|>
        super(VGGs, self).__init__()
        self.class_num = class_num
        self.vgg_name = vgg_name
        self.attention = attention
        self.in_channels = 1
        self.features = self._make_layers()
        self.classifier = self._classifer_layers()
<|end_body_0|>

<|body_start_1|>
        layers = []
        for layer_type in self.cfg[self.vgg_name]:
            if layer_type != 'M':
                layer = [nn.Conv2d(self.in_channels, out_channels=self.in_channels, kernel_size=3, stride=1, padding=1), nn.Conv2d(self.in_channels, out_channels=layer_type, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(num_features=layer_type), nn.ReLU(inplace=True)]
                self.in_channels = layer_type
            else:
                layer = [nn.MaxPool2d(kernel_size=self.pool_kernel_size, stride=self.pool_stride)]
            layers += layer
        if self.attention:
            layers.append(Attention(self.in_channels, self.in_channels))
        layers.append(nn.AdaptiveAvgPool2d(output_size=(1, 1)))
        vgg_feature_net = nn.Sequential(*layers)
        return vgg_feature_net
<|end_body_1|>

<|body_start_2|>
        layers = []
        layers.append(nn.Dropout(p=0.5))
        layers.append(nn.Linear(in_features=self.in_channels, out_features=self.class_num, bias=True))
        vgg_classifer_net = nn.Sequential(*layers)
        return vgg_classifer_net
<|end_body_2|>

<|body_start_3|>
        feature = self.features(x)
        flat_feature = feature.view(feature.size(0), -1)
        out = self.classifier(flat_feature)
        return out
<|end_body_3|>

Solution:
class VGGs:
    """VGGs神经网络结构搭建, 前几层fc换为全局池化, 尝试深度可分离卷积"""

    def __init__(self, vgg_name, class_num, attention):
        """网络初始化 param: vgg_name 选择的VGG网络名 param: class_num 目标分类数 param: attention 注意力"""
        super(VGGs, self).__init__()
        self.class_num = class_num
        self.vgg_name = vgg_name
        self.attention = attention
        self.in_channels = 1
        self.features = self._make_layers()
        self.classifier = self._classifer_layers()

    def _make_layers(self):
        """建立特征提取部分网络 return vgg_feature_net VGG特征提取部分"""
        layers = []
        for layer_type in self.cfg[self.vgg_name]:
            if layer_type != 'M':
                layer = [nn.Conv2d(self.in_channels, out_channels=self.in_channels, kernel_size=3, stride=1, padding=1), nn.Conv2d(self.in_channels, out_channels=layer_type, kernel_size=1, stride=1, padding=0), nn.BatchNorm2d(num_features=layer_type), nn.ReLU(inplace=True)]
                self.in_channels = layer_type
            else:
                layer = [nn.MaxPool2d(kernel_size=self.pool_kernel_size, stride=self.pool_stride)]
            layers += layer
        if self.attention:
            layers.append(Attention(self.in_channels, self.in_channels))
        layers.append(nn.AdaptiveAvgPool2d(output_size=(1, 1)))
        vgg_feature_net = nn.Sequential(*layers)
        return vgg_feature_net

    def _classifer_layers(self):
        """建立分类部分网络 return vgg_classifer_net VGG分类部分"""
        layers = []
        layers.append(nn.Dropout(p=0.5))
        layers.append(nn.Linear(in_features=self.in_channels, out_features=self.class_num, bias=True))
        vgg_classifer_net = nn.Sequential(*layers)
        return vgg_classifer_net

    def forward(self, x):
        """前向传播 :param: x 图片变量 return out: 前向传播输出"""
        feature = self.features(x)
        flat_feature = feature.view(feature.size(0), -1)
        out = self.classifier(flat_feature)
        return out