<gh_stars>1-10
import collections
from collections import UserList
from copy import deepcopy
from dataclasses import dataclass
from typing import List

import numpy as np


@dataclass
class ObjectMetadata:
    object_class: str
    confidence: float
    size: np.ndarray


class EstimationObjectMetadata(ObjectMetadata):
    track_id: int


@dataclass
class Observation:
    measurement: np.ndarray
    metadata: ObjectMetadata


@dataclass
class Estimation:
    state: np.ndarray
    covariance: np.ndarray
    metadata: EstimationObjectMetadata


class ObservationList(UserList):
    def __init__(self, initial_data):
        self.data = initial_data

    @property
    def states(self):
        measurements = [observation.measurement for observation in self.data]
        return np.array(measurements)


@dataclass
class Gaussian:
    """Describes state of object

    Parameters
    ----------
    x : np.ndarray (N), N - state dimension
        describes state vector of object
    P : np.ndarray (N x N), N - state dimension
        describes covariance of state of object
    """

    x: np.ndarray
    P: np.ndarray

    def __post_init__(self):
        assert isinstance(self.x, np.ndarray), "Argument of wrong type!"
        assert isinstance(self.P, np.ndarray), "Argument of wrong type!"
        assert self.x.ndim == 1, "x must be N - vector"
        assert self.P.ndim == 2, "P must be N x N matrix"
        assert self.P.shape[0] == self.P.shape[1], "Covariance matrix should be square!"
        assert self.P.shape[0] == self.x.shape[0], "size of vector should be equal P column size!"

    def __repr__(self) -> str:
        np.set_printoptions(linewidth=np.inf)
        return f"{self.__class__.__name__} " f"x = {np.array2string(self.x, max_line_width=np.inf, precision =1)}"

    def __eq__(self, other) -> bool:
        assert isinstance(other, Gaussian)
        if self.x.__eq__(other.x).all() and self.P.__eq__(other.P).all():
            return True
        else:
            return False

    @property
    def states_np(self):
        return self.x

    @property
    def covariances_np(self):
        return self.P


@dataclass
class WeightedGaussian:
    log_weight: float
    gaussian: Gaussian

    def __post_init__(self):
        assert not np.isnan(self.log_weight), "nan weight"

    def __repr__(self) -> str:
        return (
            f"\n"
            f"weighted Gaussian with: "
            f"w = {self.log_weight:.2f}   "
            f"x = {np.array2string(self.gaussian.x, max_line_width=np.inf, precision =2)}  "
        )


class GaussianMixture(UserList):
    def __init__(self, initial_components: List[WeightedGaussian] = None):
        self.data = initial_components

    @property
    def log_weights(self):
        if self.data:
            weights = [x.log_weight for x in self.data]
            return weights
        else:
            return None

    @log_weights.setter
    def log_weights(self, log_weights):
        assert len(log_weights) == len(self.data)
        for idx in range(len(self.data)):
            self.weighted_components[idx].log_weights = log_weights[idx]

    @property
    def size(self):
        return len(self.data)

    @property
    def states(self):
        return [x.gaussian for x in self.data]

    @property
    def states_np(self):
        return np.array([state.gaussian.x for state in self.data])

    @property
    def covariances_np(self):
        return np.array([state.gaussian.P for state in self.data])


class _GaussianMixture(collections.abc.MutableSequence):
    def __init__(self, weighted_components: List[WeightedGaussian] = (None)):
        self.weighted_components = deepcopy(weighted_components)

    @property
    def log_weights(self):
        if self.weighted_components:
            weights = [x.log_weight for x in self.weighted_components]
            return weights
        else:
            return None

    @log_weights.setter
    def log_weights(self, log_weights):
        assert len(log_weights) == len(self.weighted_components)
        for idx in range(len(self.weighted_components)):
            self.weighted_components[idx].log_weights = log_weights[idx]

    @property
    def size(self):
        return len(self.weighted_components)

    @property
    def states(self):
        return [x.gaussian for x in self.weighted_components]

    @property
    def states_np(self):
        return np.array([state.gaussian.x for state in self.weighted_components])

    @property
    def covariances_np(self):
        return np.array([state.gaussian.P for state in self.weighted_components])

    def __copy__(self):
        return GaussianMixture(weighted_components=self.weighted_components)

    def check(self, item):
        if not isinstance(item, (WeightedGaussian)):
            raise TypeError

    def __len__(self):
        if self.weighted_components:
            return len(self.weighted_components)
        else:
            return 0

    def __getitem__(self, idx):
        return self.weighted_components[idx]

    def __delitem__(self, idx):
        del self.weighted_components[idx]

    def __setitem__(self, idx, item):
        self.check(item)
        self.weighted_components[idx] = item

    def insert(self, idx, item):
        self.check(item)
        self.weighted_components.insert(idx, item)

    def append(self, other):
        assert isinstance(other, WeightedGaussian)
        if not self.weighted_components:
            self.weighted_components = []
        self.weighted_components.append(other)

    def extend(self, other):
        assert isinstance(other, GaussianMixture)
        for idx in range(len(other)):
            self.append(other[idx])

    def __repr__(self) -> str:
        return f"Gaussian Mixture " f"components={self.weighted_components}"


class NumpyGaussian(np.recarray):
    def __new__(cls, mean, covariance, ndim):
        # Input array is an already formed ndarray instance
        # We first cast to be our class type
        dtype = np.dtype([("means", np.float32, (ndim,)), ("covariances", np.float32, (ndim, ndim))])
        obj = np.asarray((mean, covariance), dtype=dtype).view(cls)
        # Finally, we must return the newly created object:
        return obj

    def __array_finalize__(self, obj):
        # see InfoArray.__array_finalize__ for comments
        if obj is None:
            return
        self.info = getattr(obj, "info", None)


class NumpyWeightedGaussian(np.recarray):
    def __new__(cls, log_weight, gaussian):
        # Input array is an already formed ndarray instance
        # We first cast to be our class type
        dtype = np.dtype([("log_weight", log_weight.dtype), ("gaussian", gaussian.dtype)])
        obj = np.asarray((log_weight, gaussian), dtype=dtype).view(cls)
        # Finally, we must return the newly created object:
        return obj


if __name__ == "__main__":
    a = Gaussian(mean=[2.0, 2.0, 3.0], covariance=np.eye(3), ndim=3)
    b = WeightedGaussian(log_weight=0.03)
    import pdb

    pdb.set_trace()

import collections
from collections import UserList
from copy import deepcopy
from dataclasses import dataclass
from typing import List

import numpy as np


@dataclass
class ObjectMetadata:
    object_class: str
    confidence: float
    size: np.ndarray


class EstimationObjectMetadata(ObjectMetadata):
    track_id: int


@dataclass
class Observation:
    measurement: np.ndarray
    metadata: ObjectMetadata


@dataclass
class Estimation:
    state: np.ndarray
    covariance: np.ndarray
    metadata: EstimationObjectMetadata


class ObservationList(UserList):
    def __init__(self, initial_data):
        self.data = initial_data

    @property
    def states(self):
        measurements = [observation.measurement for observation in self.data]
        return np.array(measurements)


@dataclass
class Gaussian:
    """Describes state of object

    Parameters
    ----------
    x : np.ndarray (N), N - state dimension
        describes state vector of object
    P : np.ndarray (N x N), N - state dimension
        describes covariance of state of object
    """

    x: np.ndarray
    P: np.ndarray

    def __post_init__(self):
        assert isinstance(self.x, np.ndarray), "Argument of wrong type!"
        assert isinstance(self.P, np.ndarray), "Argument of wrong type!"
        assert self.x.ndim == 1, "x must be N - vector"
        assert self.P.ndim == 2, "P must be N x N matrix"
        assert self.P.shape[0] == self.P.shape[1], "Covariance matrix should be square!"
        assert self.P.shape[0] == self.x.shape[0], "size of vector should be equal P column size!"

    def __repr__(self) -> str:
        np.set_printoptions(linewidth=np.inf)
        return f"{self.__class__.__name__} " f"x = {np.array2string(self.x, max_line_width=np.inf, precision =1)}"

    def __eq__(self, other) -> bool:
        assert isinstance(other, Gaussian)
        if self.x.__eq__(other.x).all() and self.P.__eq__(other.P).all():
            return True
        else:
            return False

    @property
    def states_np(self):
        return self.x

    @property
    def covariances_np(self):
        return self.P


@dataclass
class WeightedGaussian:
    log_weight: float
    gaussian: Gaussian

    def __post_init__(self):
        assert not np.isnan(self.log_weight), "nan weight"

    def __repr__(self) -> str:
        return (
            f"\n"
            f"weighted Gaussian with: "
            f"w = {self.log_weight:.2f}   "
            f"x = {np.array2string(self.gaussian.x, max_line_width=np.inf, precision =2)}  "
        )


class GaussianMixture(UserList):
    def __init__(self, initial_components: List[WeightedGaussian] = None):
        self.data = initial_components

    @property
    def log_weights(self):
        if self.data:
            weights = [x.log_weight for x in self.data]
            return weights
        else:
            return None

    @log_weights.setter
    def log_weights(self, log_weights):
        assert len(log_weights) == len(self.data)
        for idx in range(len(self.data)):
            self.weighted_components[idx].log_weights = log_weights[idx]

    @property
    def size(self):
        return len(self.data)

    @property
    def states(self):
        return [x.gaussian for x in self.data]

    @property
    def states_np(self):
        return np.array([state.gaussian.x for state in self.data])

    @property
    def covariances_np(self):
        return np.array([state.gaussian.P for state in self.data])


class _GaussianMixture(collections.abc.MutableSequence):
    def __init__(self, weighted_components: List[WeightedGaussian] = (None)):
        self.weighted_components = deepcopy(weighted_components)

    @property
    def log_weights(self):
        if self.weighted_components:
            weights = [x.log_weight for x in self.weighted_components]
            return weights
        else:
            return None

    @log_weights.setter
    def log_weights(self, log_weights):
        assert len(log_weights) == len(self.weighted_components)
        for idx in range(len(self.weighted_components)):
            self.weighted_components[idx].log_weights = log_weights[idx]

    @property
    def size(self):
        return len(self.weighted_components)

    @property
    def states(self):
        return [x.gaussian for x in self.weighted_components]

    @property
    def states_np(self):
        return np.array([state.gaussian.x for state in self.weighted_components])

    @property
    def covariances_np(self):
        return np.array([state.gaussian.P for state in self.weighted_components])

    def __copy__(self):
        return GaussianMixture(weighted_components=self.weighted_components)

    def check(self, item):
        if not isinstance(item, (WeightedGaussian)):
            raise TypeError

    def __len__(self):
        if self.weighted_components:
            return len(self.weighted_components)
        else:
            return 0

    def __getitem__(self, idx):
        return self.weighted_components[idx]

    def __delitem__(self, idx):
        del self.weighted_components[idx]

    def __setitem__(self, idx, item):
        self.check(item)
        self.weighted_components[idx] = item

    def insert(self, idx, item):
        self.check(item)
        self.weighted_components.insert(idx, item)

    def append(self, other):
        assert isinstance(other, WeightedGaussian)
        if not self.weighted_components:
            self.weighted_components = []
        self.weighted_components.append(other)

    def extend(self, other):
        assert isinstance(other, GaussianMixture)
        for idx in range(len(other)):
            self.append(other[idx])

    def __repr__(self) -> str:
        return f"Gaussian Mixture " f"components={self.weighted_components}"


class NumpyGaussian(np.recarray):
    def __new__(cls, mean, covariance, ndim):
        # Input array is an already formed ndarray instance
        # We first cast to be our class type
        dtype = np.dtype([("means", np.float32, (ndim,)), ("covariances", np.float32, (ndim, ndim))])
        obj = np.asarray((mean, covariance), dtype=dtype).view(cls)
        # Finally, we must return the newly created object:
        return obj

    def __array_finalize__(self, obj):
        # see InfoArray.__array_finalize__ for comments
        if obj is None:
            return
        self.info = getattr(obj, "info", None)


class NumpyWeightedGaussian(np.recarray):
    def __new__(cls, log_weight, gaussian):
        # Input array is an already formed ndarray instance
        # We first cast to be our class type
        dtype = np.dtype([("log_weight", log_weight.dtype), ("gaussian", gaussian.dtype)])
        obj = np.asarray((log_weight, gaussian), dtype=dtype).view(cls)
        # Finally, we must return the newly created object:
        return obj


if __name__ == "__main__":
    a = Gaussian(mean=[2.0, 2.0, 3.0], covariance=np.eye(3), ndim=3)
    b = WeightedGaussian(log_weight=0.03)
    import pdb

    pdb.set_trace()
Describes state of object      Parameters     ----------     x : np.ndarray (N), N - state dimension         describes state vector of object     P : np.ndarray (N x N), N - state dimension         describes covariance of state of object # Input array is an already formed ndarray instance # We first cast to be our class type # Finally, we must return the newly created object: # see InfoArray.__array_finalize__ for comments # Input array is an already formed ndarray instance # We first cast to be our class type # Finally, we must return the newly created object: