deep_sort.nn_matching Namespace Reference

Classes
class	NearestNeighborDistanceMetric

Functions
def	_cosine_distance (a, b, data_is_normalized=False)
def	_nn_cosine_distance (x, y)
def	_nn_euclidean_distance (x, y)
def	_pdist (a, b)

Function Documentation

_cosine_distance()

def deep_sort.nn_matching._cosine_distance (   a,   b,   data_is_normalized = False  )

private

Compute pair-wise cosine distance between points in `a` and `b`.

Parameters
----------
a : array_like
    An NxM matrix of N samples of dimensionality M.
b : array_like
    An LxM matrix of L samples of dimensionality M.
data_is_normalized : Optional[bool]
    If True, assumes rows in a and b are unit length vectors.
    Otherwise, a and b are explicitly normalized to lenght 1.

Returns
-------
ndarray
    Returns a matrix of size len(a), len(b) such that eleement (i, j)
    contains the squared distance between `a[i]` and `b[j]`.

Definition at line 31 of file nn_matching.py.

_nn_cosine_distance()

def deep_sort.nn_matching._nn_cosine_distance (   x,   y  )

private

Helper function for nearest neighbor distance metric (cosine).

Parameters
----------
x : ndarray
    A matrix of N row-vectors (sample points).
y : ndarray
    A matrix of M row-vectors (query points).

Returns
-------
ndarray
    A vector of length M that contains for each entry in `y` the
    smallest cosine distance to a sample in `x`.

Definition at line 78 of file nn_matching.py.

_nn_euclidean_distance()

def deep_sort.nn_matching._nn_euclidean_distance (   x,   y  )

private

Helper function for nearest neighbor distance metric (Euclidean).

Parameters
----------
x : ndarray
    A matrix of N row-vectors (sample points).
y : ndarray
    A matrix of M row-vectors (query points).

Returns
-------
ndarray
    A vector of length M that contains for each entry in `y` the
    smallest Euclidean distance to a sample in `x`.

Definition at line 57 of file nn_matching.py.

_pdist()

def deep_sort.nn_matching._pdist (   a,   b  )

private

Compute pair-wise squared distance between points in `a` and `b`.

Parameters
----------
a : array_like
    An NxM matrix of N samples of dimensionality M.
b : array_like
    An LxM matrix of L samples of dimensionality M.

Returns
-------
ndarray
    Returns a matrix of size len(a), len(b) such that eleement (i, j)
    contains the squared distance between `a[i]` and `b[j]`.

Definition at line 5 of file nn_matching.py.