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dist_metrics.pxd
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dist_metrics.pxd
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#!python
#cython: boundscheck=False
#cython: wraparound=False
#cython: cdivision=True
cimport cython
cimport numpy as np
from libc.math cimport fmax, fmin, fabs, sqrt, exp, cos, pow
from typedefs cimport DTYPE_t, ITYPE_t, DITYPE_t
from typedefs import DTYPE, ITYPE
######################################################################
# 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(DTYPE_t* x1, DTYPE_t* x2,
ITYPE_t size):
cdef DTYPE_t tmp, d=0
for j in range(size):
tmp = x1[j] - x2[j]
d += tmp * tmp
return sqrt(d)
cdef inline DTYPE_t euclidean_rdist(DTYPE_t* x1, DTYPE_t* x2,
ITYPE_t size):
cdef DTYPE_t tmp, d=0
for j in range(size):
tmp = x1[j] - x2[j]
d += tmp * tmp
return d
cdef inline DTYPE_t euclidean_dist_to_rdist(DTYPE_t dist):
return dist * dist
cdef inline DTYPE_t euclidean_rdist_to_dist(DTYPE_t dist):
return sqrt(dist)
cdef DTYPE_t[:, ::1] euclidean_cdist(DTYPE_t[:, ::1] X, DTYPE_t[:, ::1] Y)
######################################################################
# 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 DTYPE_t* vec_ptr
cdef DTYPE_t* mat_ptr
cdef ITYPE_t size
cdef DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2, ITYPE_t size)
cdef DTYPE_t rdist(self, DTYPE_t* x1, DTYPE_t* x2, ITYPE_t size)
cdef DTYPE_t[:, ::1] pdist(self, DTYPE_t[:, ::1] X)
cdef DTYPE_t[:, ::1] cdist(self, DTYPE_t[:, ::1] X, DTYPE_t[:, ::1] Y)
cdef DTYPE_t rdist_to_dist(self, DTYPE_t rdist)
cdef DTYPE_t dist_to_rdist(self, DTYPE_t dist)