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{{py:
implementation_specific_values = [
# Values are the following ones:
#
# name_suffix, INPUT_DTYPE_t, INPUT_DTYPE
('64', 'DistanceMetric64', 'float64_t'),
('32', 'DistanceMetric32', 'float32_t')
]
}}
from ...utils._typedefs cimport float64_t, float32_t, int32_t, intp_t
from ...metrics._dist_metrics cimport DistanceMetric64, DistanceMetric32, DistanceMetric
{{for name_suffix, DistanceMetric, INPUT_DTYPE_t in implementation_specific_values}}
cdef class DatasetsPair{{name_suffix}}:
cdef:
{{DistanceMetric}} distance_metric
intp_t n_features
cdef intp_t n_samples_X(self) noexcept nogil
cdef intp_t n_samples_Y(self) noexcept nogil
cdef float64_t dist(self, intp_t i, intp_t j) noexcept nogil
cdef float64_t surrogate_dist(self, intp_t i, intp_t j) noexcept nogil
cdef class DenseDenseDatasetsPair{{name_suffix}}(DatasetsPair{{name_suffix}}):
cdef:
const {{INPUT_DTYPE_t}}[:, ::1] X
const {{INPUT_DTYPE_t}}[:, ::1] Y
cdef class SparseSparseDatasetsPair{{name_suffix}}(DatasetsPair{{name_suffix}}):
cdef:
const {{INPUT_DTYPE_t}}[:] X_data
const int32_t[::1] X_indices
const int32_t[::1] X_indptr
const {{INPUT_DTYPE_t}}[:] Y_data
const int32_t[::1] Y_indices
const int32_t[::1] Y_indptr
cdef class SparseDenseDatasetsPair{{name_suffix}}(DatasetsPair{{name_suffix}}):
cdef:
const {{INPUT_DTYPE_t}}[:] X_data
const int32_t[::1] X_indices
const int32_t[::1] X_indptr
const {{INPUT_DTYPE_t}}[:] Y_data
const int32_t[::1] Y_indices
intp_t n_Y
cdef class DenseSparseDatasetsPair{{name_suffix}}(DatasetsPair{{name_suffix}}):
cdef:
# As distance metrics are commutative, we can simply rely
# on the implementation of SparseDenseDatasetsPair and
# swap arguments.
DatasetsPair{{name_suffix}} datasets_pair
{{endfor}}
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