JSX_TTS / sklearn /metrics /_dist_metrics.pxd
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cimport numpy as np
from libc.math cimport sqrt, exp
from ..utils._typedefs cimport DTYPE_t, ITYPE_t
######################################################################
# Inline distance functions
#
# We use these for the default (euclidean) case so that they can be
# inlined. This leads to faster computation for the most common case
cdef inline DTYPE_t euclidean_dist(const DTYPE_t* x1, const DTYPE_t* x2,
ITYPE_t size) nogil except -1:
cdef DTYPE_t tmp, d=0
cdef np.intp_t j
for j in range(size):
tmp = x1[j] - x2[j]
d += tmp * tmp
return sqrt(d)
cdef inline DTYPE_t euclidean_rdist(const DTYPE_t* x1, const DTYPE_t* x2,
ITYPE_t size) nogil except -1:
cdef DTYPE_t tmp, d=0
cdef np.intp_t j
for j in range(size):
tmp = x1[j] - x2[j]
d += tmp * tmp
return d
cdef inline DTYPE_t euclidean_dist_to_rdist(const DTYPE_t dist) nogil except -1:
return dist * dist
cdef inline DTYPE_t euclidean_rdist_to_dist(const DTYPE_t dist) nogil except -1:
return sqrt(dist)
######################################################################
# DistanceMetric base class
cdef class DistanceMetric:
# The following attributes are required for a few of the subclasses.
# we must define them here so that cython's limited polymorphism will work.
# Because we don't expect to instantiate a lot of these objects, the
# extra memory overhead of this setup should not be an issue.
cdef DTYPE_t p
cdef DTYPE_t[::1] vec
cdef DTYPE_t[:, ::1] mat
cdef ITYPE_t size
cdef object func
cdef object kwargs
cdef DTYPE_t dist(self, const DTYPE_t* x1, const DTYPE_t* x2,
ITYPE_t size) nogil except -1
cdef DTYPE_t rdist(self, const DTYPE_t* x1, const DTYPE_t* x2,
ITYPE_t size) nogil except -1
cdef int pdist(self, const DTYPE_t[:, ::1] X, DTYPE_t[:, ::1] D) except -1
cdef int cdist(self, const DTYPE_t[:, ::1] X, const DTYPE_t[:, ::1] Y,
DTYPE_t[:, ::1] D) except -1
cdef DTYPE_t _rdist_to_dist(self, DTYPE_t rdist) nogil except -1
cdef DTYPE_t _dist_to_rdist(self, DTYPE_t dist) nogil except -1