/dports/math/py-hdbscan/hdbscan-0.8.27/hdbscan/ |
H A D | dist_metrics.pxd | 24 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 …]
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H A D | dist_metrics.pyx | 303 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 …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/metrics/ |
H A D | _dist_metrics.pxd | 11 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 …]
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H A D | _dist_metrics.pyx | 294 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, [all …]
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neighbors/ |
H A D | _binary_tree.pxi | 317 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, [all …]
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H A D | _ball_tree.pyx | 53 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 [all …]
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H A D | _quad_tree.pxd | 9 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 …]
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H A D | _kd_tree.pyx | 45 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 …]
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H A D | _quad_tree.pyx | 82 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 …]
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/dports/graphics/py-scikit-image/scikit-image-0.19.0/skimage/measure/ |
H A D | _ccomp.pxd | 4 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
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H A D | _ccomp.pyx | 71 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 …]
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/dports/textproc/py-gensim/gensim-4.0.1/gensim/ |
H A D | _matutils.pyx | 9 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; [all …]
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/dports/science/py-GPy/GPy-1.10.0/GPy/models/ |
H A D | state_space_cython.pyx | 17 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 …]
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/dports/science/py-scipy/scipy-1.7.1/scipy/signal/ |
H A D | _upfirdn_apply.pyx | 47 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 …]
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H A D | _sosfilt.pyx | 12 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:
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/utils/ |
H A D | _fast_dict.pyx | 26 #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() [all …]
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H A D | _logistic_sigmoid.pyx | 7 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):
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H A D | _typedefs.pyx | 24 cdef DTYPE_t INF = np.inf 25 cdef DTYPE_t PI = np.pi 26 cdef DTYPE_t ROOT_2PI = sqrt(2 * PI)
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H A D | _fast_dict.pxd | 13 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)
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/dports/astro/py-astropy/astropy-5.0/astropy/timeseries/periodograms/bls/ |
H A D | _impl.pyx | 13 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 …]
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/dports/science/py-GPy/GPy-1.10.0/GPy/kern/src/ |
H A D | stationary_cython.pyx | 11 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):
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/dports/astro/py-astropy/astropy-5.0/astropy/timeseries/periodograms/lombscargle/implementations/ |
H A D | cython_impl.pyx | 14 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
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/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/cluster/ |
H A D | _hierarchical_fast.pyx | 22 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 …]
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/dports/math/py-yt/yt-4.0.1/yt/utilities/lib/ |
H A D | write_array.pyx | 15 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,
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/dports/biology/py-scikit-bio/scikit-bio-0.5.6/skbio/diversity/ |
H A D | _phylogenetic.pyx | 14 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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