# encoding: utf-8 # module dipy.segment.metricspeed # from C:\Users\xukai\Downloads\发票2\venv\Lib\site-packages\dipy\segment\metricspeed.cp311-win_amd64.pyd # by generator 1.147 # no doc # imports import builtins as __builtins__ # import numpy as np # C:\Users\xukai\Downloads\发票2\venv\Lib\site-packages\numpy\__init__.py import math as math # # functions def dist(*args, **kwargs): # real signature unknown """ Computes a distance between `datum1` and `datum2`. A sequence of N-dimensional points is represented as a 2D array with shape (nb_points, nb_dimensions). Parameters ---------- metric : `Metric` object Tells how to compute the distance between `datum1` and `datum2`. datum1 : 2D array Sequence of N-dimensional points. datum2 : 2D array Sequence of N-dimensional points. Returns ------- double Distance between two data points. """ pass def distance_matrix(*args, **kwargs): # real signature unknown """ Computes the distance matrix between two lists of sequential data. The distance matrix is obtained by computing the pairwise distance of all tuples spawn by the Cartesian product of `data1` with `data2`. If `data2` is not provided, the Cartesian product of `data1` with itself is used instead. A sequence of N-dimensional points is represented as a 2D array with shape (nb_points, nb_dimensions). Parameters ---------- metric : `Metric` object Tells how to compute the distance between two sequential data. data1 : list of 2D arrays List of sequences of N-dimensional points. data2 : list of 2D arrays Llist of sequences of N-dimensional points. Returns ------- 2D array (double) Distance matrix. """ pass def __pyx_unpickle_AveragePointwiseEuclideanMetric(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_CosineMetric(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_CythonMetric(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_Enum(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_Metric(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_MinimumAverageDirectFlipMetric(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_SumPointwiseEuclideanMetric(*args, **kwargs): # real signature unknown pass # classes class Metric(object): """ Computes a distance between two sequential data. A sequence of N-dimensional points is represented as a 2D array with shape (nb_points, nb_dimensions). A `feature` object can be specified in order to calculate the distance between extracted features, rather than directly between the sequential data. Parameters ---------- feature : `Feature` object, optional It is used to extract features before computing the distance. Notes ----- When subclassing `Metric`, one only needs to override the `dist` and `are_compatible` methods. """ def are_compatible(self, *args, **kwargs): # real signature unknown """ Checks if features can be used by `metric.dist` based on their shape. Basically this method exists so we don't have to do this check inside the `metric.dist` function (speedup). Parameters ---------- shape1 : int, 1-tuple or 2-tuple shape of the first data point's features shape2 : int, 1-tuple or 2-tuple shape of the second data point's features Returns ------- are_compatible : bool whether or not shapes are compatible """ pass def dist(self, *args, **kwargs): # real signature unknown """ Computes a distance between two data points based on their features. Parameters ---------- features1 : 2D array Features of the first data point. features2 : 2D array Features of the second data point. Returns ------- double Distance between two data points. """ pass def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(*args, **kwargs): # real signature unknown """ Create and return a new object. See help(type) for accurate signature. """ pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass feature = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """ `Feature` object used to extract features from sequential data """ is_order_invariant = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """ Is this metric invariant to the sequence's ordering """ __pyx_vtable__ = None # (!) real value is '' class CythonMetric(Metric): """ Computes a distance between two sequential data. A sequence of N-dimensional points is represented as a 2D array with shape (nb_points, nb_dimensions). A `feature` object can be specified in order to calculate the distance between extracted features, rather than directly between the sequential data. Parameters ---------- feature : `Feature` object, optional It is used to extract features before computing the distance. Notes ----- When subclassing `CythonMetric`, one only needs to override the `c_dist` and `c_are_compatible` methods. """ def are_compatible(self, *args, **kwargs): # real signature unknown """ Checks if features can be used by `metric.dist` based on their shape. Basically this method exists so we don't have to do this check inside method `dist` (speedup). Parameters ---------- shape1 : int, 1-tuple or 2-tuple Shape of the first data point's features. shape2 : int, 1-tuple or 2-tuple Shape of the second data point's features. Returns ------- bool Whether or not shapes are compatible. Notes ----- This method calls its Cython version `self.c_are_compatible` accordingly. """ pass def dist(self, *args, **kwargs): # real signature unknown """ Computes a distance between two data points based on their features. Parameters ---------- features1 : 2D array Features of the first data point. features2 : 2D array Features of the second data point. Returns ------- double Distance between two data points. Notes ----- This method calls its Cython version `self.c_dist` accordingly. """ pass def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(*args, **kwargs): # real signature unknown """ Create and return a new object. See help(type) for accurate signature. """ pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass __pyx_vtable__ = None # (!) real value is '' class SumPointwiseEuclideanMetric(CythonMetric): """ Computes the sum of pointwise Euclidean distances between two sequential data. A sequence of N-dimensional points is represented as a 2D array with shape (nb_points, nb_dimensions). A `feature` object can be specified in order to calculate the distance between the features, rather than directly between the sequential data. Parameters ---------- feature : `Feature` object, optional It is used to extract features before computing the distance. Notes ----- The distance between two 2D sequential data:: s1 s2 0* a *0 \ | \ | 1* | | b *1 | \ 2* \ c *2 is equal to $a+b+c$ where $a$ is the Euclidean distance between s1[0] and s2[0], $b$ between s1[1] and s2[1] and $c$ between s1[2] and s2[2]. """ def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(*args, **kwargs): # real signature unknown """ Create and return a new object. See help(type) for accurate signature. """ pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass __pyx_vtable__ = None # (!) real value is '' class AveragePointwiseEuclideanMetric(SumPointwiseEuclideanMetric): """ Computes the average of pointwise Euclidean distances between two sequential data. A sequence of N-dimensional points is represented as a 2D array with shape (nb_points, nb_dimensions). A `feature` object can be specified in order to calculate the distance between the features, rather than directly between the sequential data. Parameters ---------- feature : `Feature` object, optional It is used to extract features before computing the distance. Notes ----- The distance between two 2D sequential data:: s1 s2 0* a *0 \ | \ | 1* | | b *1 | \ 2* \ c *2 is equal to $(a+b+c)/3$ where $a$ is the Euclidean distance between s1[0] and s2[0], $b$ between s1[1] and s2[1] and $c$ between s1[2] and s2[2]. """ def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(*args, **kwargs): # real signature unknown """ Create and return a new object. See help(type) for accurate signature. """ pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass __pyx_vtable__ = None # (!) real value is '' class CosineMetric(CythonMetric): """ Computes the cosine distance between two vectors. A vector (i.e. a N-dimensional point) is represented as a 2D array with shape (1, nb_dimensions). Notes ----- The distance between two vectors $v_1$ and $v_2$ is equal to $\frac{1}{\pi} \arccos\left(\frac{v_1 \cdot v_2}{\|v_1\| \|v_2\|}\right)$ and is bounded within $[0,1]$. """ def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(*args, **kwargs): # real signature unknown """ Create and return a new object. See help(type) for accurate signature. """ pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass __pyx_vtable__ = None # (!) real value is '' class MinimumAverageDirectFlipMetric(AveragePointwiseEuclideanMetric): """ Computes the MDF distance (minimum average direct-flip) between two sequential data. A sequence of N-dimensional points is represented as a 2D array with shape (nb_points, nb_dimensions). Notes ----- The distance between two 2D sequential data:: s1 s2 0* a *0 \ | \ | 1* | | b *1 | \ 2* \ c *2 is equal to $\min((a+b+c)/3, (a'+b'+c')/3)$ where $a$ is the Euclidean distance between s1[0] and s2[0], $b$ between s1[1] and s2[1], $c$ between s1[2] and s2[2], $a'$ between s1[0] and s2[2], $b'$ between s1[1] and s2[1] and $c'$ between s1[2] and s2[0]. """ def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(*args, **kwargs): # real signature unknown """ Create and return a new object. See help(type) for accurate signature. """ pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass is_order_invariant = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """ Is this metric invariant to the sequence's ordering """ __pyx_vtable__ = None # (!) real value is '' # variables with complex values __loader__ = None # (!) real value is '<_frozen_importlib_external.ExtensionFileLoader object at 0x000002DA7B1EF610>' __spec__ = None # (!) real value is "ModuleSpec(name='dipy.segment.metricspeed', loader=<_frozen_importlib_external.ExtensionFileLoader object at 0x000002DA7B1EF610>, origin='C:\\\\Users\\\\xukai\\\\Downloads\\\\??2\\\\venv\\\\Lib\\\\site-packages\\\\dipy\\\\segment\\\\metricspeed.cp311-win_amd64.pyd')" __test__ = {}