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/dports/math/py-hdbscan/hdbscan-0.8.27/hdbscan/
H A Ddist_metrics.pxd24 DTYPE_t
35 cdef inline DTYPE_t euclidean_dist(DTYPE_t* x1, DTYPE_t* x2,
45 cdef inline DTYPE_t euclidean_rdist(DTYPE_t* x1, DTYPE_t* x2,
55 cdef inline DTYPE_t euclidean_dist_to_rdist(DTYPE_t dist) nogil except -1:
59 cdef inline DTYPE_t euclidean_rdist_to_dist(DTYPE_t dist) except -1:
70 cdef DTYPE_t p
81 cdef DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2,
84 cdef DTYPE_t rdist(self, DTYPE_t* x1, DTYPE_t* x2,
89 cdef int cdist(self, DTYPE_t[:, ::1] X, DTYPE_t[:, ::1] Y, argument
92 cdef DTYPE_t _rdist_to_dist(self, DTYPE_t rdist) except -1
[all …]
H A Ddist_metrics.pyx303 cdef DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2,
311 cdef DTYPE_t rdist(self, DTYPE_t* x1, DTYPE_t* x2,
424 cdef inline DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2,
428 cdef inline DTYPE_t rdist(self, DTYPE_t* x1, DTYPE_t* x2,
460 cdef inline DTYPE_t rdist(self, DTYPE_t* x1, DTYPE_t* x2,
472 cdef inline DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2,
501 cdef inline DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2,
522 cdef inline DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2,
561 cdef inline DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2,
620 cdef inline DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2,
[all …]
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/metrics/
H A D_dist_metrics.pxd11 cdef inline DTYPE_t euclidean_dist(const DTYPE_t* x1, const DTYPE_t* x2,
13 cdef DTYPE_t tmp, d=0
21 cdef inline DTYPE_t euclidean_rdist(const DTYPE_t* x1, const DTYPE_t* x2,
23 cdef DTYPE_t tmp, d=0
31 cdef inline DTYPE_t euclidean_dist_to_rdist(const DTYPE_t dist) nogil except -1:
46 cdef DTYPE_t p
53 cdef DTYPE_t dist(self, const DTYPE_t* x1, const DTYPE_t* x2,
56 cdef DTYPE_t rdist(self, const DTYPE_t* x1, const DTYPE_t* x2,
61 cdef int cdist(self, const DTYPE_t[:, ::1] X, const DTYPE_t[:, ::1] Y, argument
64 cdef DTYPE_t _rdist_to_dist(self, DTYPE_t rdist) nogil except -1
[all …]
H A D_dist_metrics.pyx294 cdef DTYPE_t dist(self, const DTYPE_t* x1, const DTYPE_t* x2,
302 cdef DTYPE_t rdist(self, const DTYPE_t* x1, const DTYPE_t* x2,
432 cdef inline DTYPE_t dist(self, const DTYPE_t* x1, const DTYPE_t* x2,
436 cdef inline DTYPE_t rdist(self, const DTYPE_t* x1, const DTYPE_t* x2,
471 cdef inline DTYPE_t rdist(self, const DTYPE_t* x1, const DTYPE_t* x2,
480 cdef inline DTYPE_t dist(self, const DTYPE_t* x1, const DTYPE_t* x2,
509 cdef inline DTYPE_t dist(self, const DTYPE_t* x1, const DTYPE_t* x2,
542 cdef inline DTYPE_t dist(self, const DTYPE_t* x1, const DTYPE_t* x2,
580 cdef inline DTYPE_t dist(self, const DTYPE_t* x1, const DTYPE_t* x2,
638 cdef inline DTYPE_t dist(self, const DTYPE_t* x1, const DTYPE_t* x2,
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neighbors/
H A D_binary_tree.pxi317 cdef DTYPE_t logaddexp(DTYPE_t x1, DTYPE_t x2):
325 cdef DTYPE_t logsubexp(DTYPE_t x1, DTYPE_t x2):
352 cdef inline DTYPE_t log_gaussian_kernel(DTYPE_t dist, DTYPE_t h):
357 cdef inline DTYPE_t log_tophat_kernel(DTYPE_t dist, DTYPE_t h):
365 cdef inline DTYPE_t log_epanechnikov_kernel(DTYPE_t dist, DTYPE_t h):
373 cdef inline DTYPE_t log_exponential_kernel(DTYPE_t dist, DTYPE_t h):
378 cdef inline DTYPE_t log_linear_kernel(DTYPE_t dist, DTYPE_t h):
386 cdef inline DTYPE_t log_cosine_kernel(DTYPE_t dist, DTYPE_t h):
394 cdef inline DTYPE_t compute_log_kernel(DTYPE_t dist, DTYPE_t h,
1121 cdef inline DTYPE_t dist(self, DTYPE_t* x1, DTYPE_t* x2,
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H A D_ball_tree.pyx53 cdef DTYPE_t radius
54 cdef DTYPE_t *this_pt
57 cdef DTYPE_t* data = &tree.data[0, 0]
58 cdef DTYPE_t* centroid = &tree.node_bounds[0, i_node, 0]
61 cdef DTYPE_t* sample_weight
62 cdef DTYPE_t sum_weight_node
104 DTYPE_t* pt) nogil except -1:
112 DTYPE_t* pt) except -1:
120 DTYPE_t* min_dist, DTYPE_t* max_dist) nogil except -1:
124 cdef DTYPE_t rad = tree.node_data[i_node].radius
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H A D_quad_tree.pxd9 ctypedef np.npy_float32 DTYPE_t # Type of X
37 DTYPE_t squared_max_width # Squared value of the maximum width w
43 DTYPE_t[3] center # Store the center for quick split of cells
44 DTYPE_t[3] barycenter # Keep track of the center of mass of the cell
47 DTYPE_t[3] min_bounds # Inferior boundaries of this cell (inclusive)
71 cdef int insert_point(self, DTYPE_t[3] point, SIZE_t point_index,
76 cdef SIZE_t _select_child(self, DTYPE_t[3] point, Cell* cell) nogil
77 cdef bint _is_duplicate(self, DTYPE_t[3] point1, DTYPE_t[3] point2) nogil
80 cdef long summarize(self, DTYPE_t[3] point, DTYPE_t* results,
86 cdef void _init_root(self, DTYPE_t[3] min_bounds, DTYPE_t[3] max_bounds
[all …]
H A D_kd_tree.pyx45 cdef DTYPE_t rad = 0
49 cdef DTYPE_t* data = &tree.data[0, 0]
52 cdef DTYPE_t* data_row
109 cdef DTYPE_t min_dist(BinaryTree tree, ITYPE_t i_node, DTYPE_t* pt) except -1:
138 cdef DTYPE_t max_dist(BinaryTree tree, ITYPE_t i_node, DTYPE_t* pt) except -1:
147 DTYPE_t* min_dist, DTYPE_t* max_dist) nogil except -1:
151 cdef DTYPE_t d, d_lo, d_hi
188 cdef DTYPE_t d, d1, d2, rdist=0.0
189 cdef DTYPE_t zero = 0.0
227 cdef DTYPE_t d, d1, d2, rdist=0.0
[all …]
H A D_quad_tree.pyx82 DTYPE_t[3] pt
119 cdef DTYPE_t n_frac
188 DTYPE_t[3] save_point
189 DTYPE_t width
251 cdef bint _is_duplicate(self, DTYPE_t[3] point1, DTYPE_t[3] point2) nogil:
285 cdef void _init_root(self, DTYPE_t[3] min_bounds, DTYPE_t[3] max_bounds
290 DTYPE_t width
371 cdef long summarize(self, DTYPE_t[3] point, DTYPE_t* results,
451 cdef DTYPE_t[3] query_pt
595 def _py_summarize(self, DTYPE_t[:] query_pt, DTYPE_t[:, :] X, float angle): argument
[all …]
/dports/graphics/py-scikit-image/scikit-image-0.19.0/skimage/measure/
H A D_ccomp.pxd4 ctypedef cnp.intp_t DTYPE_t
6 cdef DTYPE_t find_root(DTYPE_t *forest, DTYPE_t n) nogil
7 cdef void set_root(DTYPE_t *forest, DTYPE_t n, DTYPE_t root) nogil
8 cdef void join_trees(DTYPE_t *forest, DTYPE_t n, DTYPE_t m) nogil
H A D_ccomp.pyx71 DTYPE_t x
72 DTYPE_t y
73 DTYPE_t z
78 DTYPE_t ndim
220 cdef DTYPE_t find_root(DTYPE_t *forest, DTYPE_t n) nogil:
230 cdef inline void set_root(DTYPE_t *forest, DTYPE_t n, DTYPE_t root) nogil:
247 cdef inline void join_trees(DTYPE_t *forest, DTYPE_t n, DTYPE_t m) nogil:
364 cdef DTYPE_t *forest_p = <DTYPE_t*>forest.data
365 cdef DTYPE_t *data_p = <DTYPE_t*>cnp.PyArray_DATA(data)
406 cdef DTYPE_t resolve_labels(DTYPE_t *data_p, DTYPE_t *forest_p,
[all …]
/dports/textproc/py-gensim/gensim-4.0.1/gensim/
H A D_matutils.pyx9 ctypedef cython.floating DTYPE_t
44 cdef DTYPE_t _mean_absolute_difference(DTYPE_t[:] a, DTYPE_t[:] b) nogil:
61 cdef DTYPE_t result = 0.0
105 cdef DTYPE_t _logsumexp_2d(DTYPE_t[:, :] data) nogil: argument
225 cdef void _dirichlet_expectation_1d(DTYPE_t[:] alpha, DTYPE_t[:] out) nogil:
282 def digamma(DTYPE_t x):
300 cdef inline DTYPE_t _digamma(DTYPE_t x,) nogil:
329 cdef DTYPE_t c = 8.5;
331 cdef DTYPE_t r;
332 cdef DTYPE_t value;
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/dports/science/py-GPy/GPy-1.10.0/GPy/models/
H A Dstate_space_cython.pyx17 ctypedef np.float64_t DTYPE_t
22 cpdef f_a(self, int k, np.ndarray[DTYPE_t, ndim=2] m, np.ndarray[DTYPE_t, ndim=2] A): argument
45 cpdef f_h(self, int k, np.ndarray[DTYPE_t, ndim=2] m_pred, np.ndarray[DTYPE_t, ndim=2] Hk): argument
78 def __init__(self, np.ndarray[DTYPE_t, ndim=3] R, np.ndarray[DTYPE_t, ndim=2] index, argument
173 … np.ndarray[DTYPE_t, ndim=3] R, np.ndarray[DTYPE_t, ndim=2] index, int R_time_var_index,
183 cpdef f_h(self, int k, np.ndarray[DTYPE_t, ndim=2] m, np.ndarray[DTYPE_t, ndim=2] H): argument
222 def __init__(self, np.ndarray[DTYPE_t, ndim=3] Q, np.ndarray[DTYPE_t, ndim=2] index, argument
337 cpdef f_a(self, int k, np.ndarray[DTYPE_t, ndim=2] m, np.ndarray[DTYPE_t, ndim=2] A): argument
399 def __init__(self, np.ndarray[DTYPE_t, ndim=3] As, np.ndarray[DTYPE_t, ndim=3] Qs, argument
440 cpdef f_a(self, int k, np.ndarray[DTYPE_t, ndim=2] m, np.ndarray[DTYPE_t, ndim=2] A): argument
[all …]
/dports/science/py-scipy/scipy-1.7.1/scipy/signal/
H A D_upfirdn_apply.pyx47 ctypedef fused DTYPE_t:
109 cdef DTYPE_t _extend_left(DTYPE_t *x, np.intp_t idx, np.intp_t len_x,
111 cdef DTYPE_t le = 0.
175 cdef DTYPE_t _extend_right(DTYPE_t *x, np.intp_t idx, np.intp_t len_x,
178 cdef DTYPE_t re = 0.
245 cdef DTYPE_t xval
251 if DTYPE_t is float:
278 DTYPE_t cval):
340 temp_data = <DTYPE_t*>malloc(data_info.shape[axis] * sizeof(DTYPE_t))
420 cdef void _apply_impl(DTYPE_t *x, np.intp_t len_x, DTYPE_t *h_trans_flip,
[all …]
H A D_sosfilt.pyx12 ctypedef fused DTYPE_t:
19 # with nogil(DTYPE_t is not object):
79 def _sosfilt(DTYPE_t [:, ::1] sos, argument
80 DTYPE_t [:, ::1] x,
81 DTYPE_t [:, :, ::1] zi):
82 if DTYPE_t is object:
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/utils/
H A D_fast_dict.pyx26 #ctypedef np.float64_t DTYPE_t
42 np.ndarray[DTYPE_t, ndim=1] values): argument
75 cdef DTYPE_t [:] values = np.empty(size, dtype=np.float64)
79 cdef DTYPE_t value
99 cdef np.ndarray[DTYPE_t, ndim=1] values = np.empty(size,
104 cdef _to_arrays(self, ITYPE_t [:] keys, DTYPE_t [:] values):
128 def append(self, ITYPE_t key, DTYPE_t value):
133 cdef pair[ITYPE_t, DTYPE_t] args
143 cdef cpp_map[ITYPE_t, DTYPE_t].iterator it = d.my_map.begin()
144 cdef cpp_map[ITYPE_t, DTYPE_t].iterator end = d.my_map.end()
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H A D_logistic_sigmoid.pyx7 ctypedef np.float64_t DTYPE_t
10 cdef inline DTYPE_t _inner_log_logistic_sigmoid(const DTYPE_t x):
20 DTYPE_t[:, :] X, argument
21 DTYPE_t[:, :] out):
H A D_typedefs.pyx24 cdef DTYPE_t INF = np.inf
25 cdef DTYPE_t PI = np.pi
26 cdef DTYPE_t ROOT_2PI = sqrt(2 * PI)
H A D_fast_dict.pxd13 ctypedef np.float64_t DTYPE_t
21 cdef cpp_map[ITYPE_t, DTYPE_t] my_map
22 cdef _to_arrays(self, ITYPE_t [:] keys, DTYPE_t [:] values)
/dports/astro/py-astropy/astropy-5.0/astropy/timeseries/periodograms/bls/
H A D_impl.pyx13 ctypedef np.float64_t DTYPE_t
51 np.ndarray[DTYPE_t, mode='c'] t_array, argument
52 np.ndarray[DTYPE_t, mode='c'] y_array,
53 np.ndarray[DTYPE_t, mode='c'] ivar_array,
54 np.ndarray[DTYPE_t, mode='c'] period_array,
55 np.ndarray[DTYPE_t, mode='c'] duration_array,
60 cdef np.ndarray[DTYPE_t, mode='c'] out_objective = np.empty_like(period_array, dtype=DTYPE)
61 cdef np.ndarray[DTYPE_t, mode='c'] out_depth = np.empty_like(period_array, dtype=DTYPE)
62 cdef np.ndarray[DTYPE_t, mode='c'] out_depth_err = np.empty_like(period_array, dtype=DTYPE)
63 cdef np.ndarray[DTYPE_t, mode='c'] out_duration = np.empty_like(period_array, dtype=DTYPE)
[all …]
/dports/science/py-GPy/GPy-1.10.0/GPy/kern/src/
H A Dstationary_cython.pyx11 ctypedef np.float64_t DTYPE_t
20 np.ndarray[DTYPE_t, ndim=2] _X, argument
21 np.ndarray[DTYPE_t, ndim=2] _X2,
22 np.ndarray[DTYPE_t, ndim=2] _tmp,
23 np.ndarray[DTYPE_t, ndim=2] _grad):
42 np.ndarray[DTYPE_t, ndim=2] _tmp, argument
43 np.ndarray[DTYPE_t, ndim=2] _X,
44 np.ndarray[DTYPE_t, ndim=2] _X2,
45 np.ndarray[DTYPE_t, ndim=1] _grad):
/dports/astro/py-astropy/astropy-5.0/astropy/timeseries/periodograms/lombscargle/implementations/
H A Dcython_impl.pyx14 ctypedef np.float64_t DTYPE_t
103 cdef _standard_lomb_scargle(const DTYPE_t[::1] t, const DTYPE_t[::1] y, const DTYPE_t[::1] dy,
104 const DTYPE_t[::1] omega, DTYPE_t[::1] PLS):
108 cdef DTYPE_t w, omega_t, sin_omega_t, cos_omega_t
109 cdef DTYPE_t S2, C2, tau, Y, wsum, YY, YCtau, YStau, CCtau, SStau
166 cdef _generalized_lomb_scargle(const DTYPE_t[::1] t, const DTYPE_t[::1] y, const DTYPE_t[::1] dy,
167 const DTYPE_t[::1] omega, DTYPE_t[::1] PLS):
171 cdef DTYPE_t w, omega_t, sin_omega_t, cos_omega_t
172 cdef DTYPE_t S, C, S2, C2, tau, Y, wsum, YY
173 cdef DTYPE_t Stau, Ctau, YCtau, YStau, CCtau, SStau
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/cluster/
H A D_hierarchical_fast.pyx22 ctypedef np.float64_t DTYPE_t
195 cdef DTYPE_t value
249 cdef DTYPE_t value
250 cdef DTYPE_t n_out = <DTYPE_t> (n_a + n_b)
284 cdef public DTYPE_t weight
358 np.ndarray[DTYPE_t, ndim=2] L):
379 cdef DTYPE_t[:, ::1] result
382 cdef DTYPE_t delta
482 DTYPE_t right_value
483 DTYPE_t left_value
[all …]
/dports/math/py-yt/yt-4.0.1/yt/utilities/lib/
H A Dwrite_array.pyx15 ctypedef np.float64_t DTYPE_t
18 def write_3D_array(np.ndarray[DTYPE_t, ndim=3] data, fhandle):
32 def write_3D_vector_array(np.ndarray[DTYPE_t, ndim=3] data_x,
33 np.ndarray[DTYPE_t, ndim=3] data_y,
34 np.ndarray[DTYPE_t, ndim=3] data_z,
/dports/biology/py-scikit-bio/scikit-bio-0.5.6/skbio/diversity/
H A D_phylogenetic.pyx14 ctypedef np.int64_t DTYPE_t
20 np.ndarray[DTYPE_t, ndim=1] tip_indices): argument
68 cdef _traverse_reduce(np.ndarray[DTYPE_t, ndim=2] child_index, argument
69 np.ndarray[DTYPE_t, ndim=2] a):
127 DTYPE_t node, start, end
128 DTYPE_t n_envs = a.shape[1]
168 np.ndarray[DTYPE_t, ndim=2] count_array, counts_t
169 np.ndarray[DTYPE_t, ndim=1] observed_indices, otus_in_nodes
174 DTYPE_t n_count_vectors, n_count_otus

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