/dports/science/py-GPy/GPy-1.10.0/GPy/util/ |
H A D | cluster_with_offset.py | 27 X = np.zeros([0,1]) 28 Y = np.zeros([0,S]) 32 for p in clust: 68 Y = np.zeros([0,S]) 74 for i,p in enumerate(clust): 111 for p in range(0,N): 112 active.append([p]) 114 loglikes = np.zeros(len(active)) 164 pairloglikes = np.delete(pairloglikes,top[1],0) 165 pairloglikes = np.delete(pairloglikes,top[1],1) [all …]
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/dports/science/py-obspy/obspy-1.2.2/obspy/signal/ |
H A D | _sosfilt.py | 316 p = np.concatenate((p, np.zeros(max(len(z) - len(p), 0)))) 317 z = np.concatenate((z, np.zeros(max(len(p) - len(z), 0)))) 319 sos = np.zeros((n_sections, 6)) 329 p = np.concatenate(_cplxreal(p)) 331 p_sos = np.zeros((n_sections, 2), np.complex128) 337 p = np.delete(p, p1_idx) 345 z = np.delete(z, z1_idx) 357 z = np.delete(z, z1_idx) 369 z = np.delete(z, z2_idx) 387 z = np.delete(z, z2_idx) [all …]
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/dports/cad/sumo/sumo-1.2.0/tools/contributed/sumopy/coremodules/network/ |
H A D | publictransportnet_wxgui.py | 39 self.delete('offsets') 40 self.delete('widths') 41 self.delete('lengths') 42 self.delete('rotangles_xy') 93 offsets = np.zeros((n, 3), np.float32) 94 coords_to = np.zeros((n, 3), np.float32) 126 zeros = np.zeros(n, np.float32) 127 vertices = np.zeros((n, 4, 3), np.float32) 131 vertices_rot = rotate_vertices(widths, zeros, 341 p = pt.StopAccessProvider(self._net, logger=self._mainframe.get_logger()) [all …]
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/dports/science/lamprop/lamprop-2020.12.28/lp/ |
H A D | matrix.py | 24 def zeros(num): function 35 c = zeros(s) 59 for p in range(k - 1, -1, -1): 64 copy[p][j] = 0.0 79 res = zeros(s) 91 res = zeros(s) 102 res = zeros(s) 112 r = zeros(s) 119 def delete(m, r, k): function 150 for p in range(k + 1, size): [all …]
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/dports/science/py-GPyOpt/GPyOpt-1.2.6/GPyOpt/util/ |
H A D | epmgp.py | 75 logP = np.zeros(mu.shape) 125 logS = np.zeros((D - 1,)) 127 MP = np.zeros((D - 1,)) 130 P = np.zeros((D - 1,)) 170 C = np.delete(C, k, 1) 211 def lt_factor(s, l, M, V, mp, p, gamma): argument 216 cVnic = np.max([cVc / (1 - p * cVc), 0]) 217 cmni = cM + cVnic * (p * cM - mp) 233 dp = np.max([-p + eps, gamma * (pnew - p)]) # at worst, remove message 237 pnew = p + dp [all …]
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/dports/cad/py-pyfda/pyfda-0.1rc6/pyfda/ |
H A D | pyfda_lib.py | 413 p = np.asarray(p) 542 p,indx = cmplx_sort(p) 650 impulse = np.zeros(N) 1053 p = np.zeros(len(z)) 1078 a = np.zeros(len(b)) 1101 a = np.append(a, np.zeros(D)) 1104 b = np.append(b, np.zeros(-D)) # make filter causal, fill up b with zeros 1153 fil_dict['zpk'][0] = np.delete(fil_dict['zpk'][0],z_0) 1154 fil_dict['zpk'][1] = np.delete(fil_dict['zpk'][1],p_0) 1164 fil_dict['ba'][0] = np.delete(fil_dict['ba'][0],-1) [all …]
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/dports/science/py-scikit-fuzzy/scikit-fuzzy-0.4.2/skfuzzy/fuzzymath/ |
H A D | fuzzy_ops.py | 213 z, mfz = np.zeros(0), np.zeros(0) 317 np.delete(y, index) 318 np.delete(b, index) 489 p = r.shape[1] 490 t = np.zeros((m, p)) 492 for pp in range(p): 521 p = r.shape[1] 522 t = np.zeros((m, p)) 525 for pp in range(p):
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/dports/finance/myphpmoney/myphpmoney-1.3RC3/public_html/inc/ |
H A D | function.inc | 230 # delete leading zeros 245 # delete trailing zeros 258 # delete trailing zeros 339 # delete trailing zeros 345 # delete leading zeros 382 # delete leading zeros 392 # delete trailing zeros 405 # delete trailing zeros 479 # delete trailing zeros 485 # delete leading zeros [all …]
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/dports/science/py-gpaw/gpaw-21.6.0/gpaw/response/ |
H A D | bse.py | 534 chi0_wGG = np.zeros((1, nG, nG), complex) 537 chi0_wvv = np.zeros((1, 3, 3), complex) 564 einv_GG = np.zeros((nG, nG), complex) 657 self.H_SS = np.delete(self.H_SS, self.excludef_S, axis=0) 658 self.H_SS = np.delete(self.H_SS, self.excludef_S, axis=1) 661 self.df_S = np.delete(self.df_S, self.excludef_S) 662 self.rhoG0_S = np.delete(self.rhoG0_S, self.excludef_S) 679 H_tmp = desc2.zeros(dtype=complex) 688 self.v_St = desc.zeros(dtype=complex) 739 vchi_w = np.zeros(len(w_w), dtype=complex) [all …]
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/dports/science/py-scipy/scipy-1.7.1/scipy/optimize/ |
H A D | _remove_redundancy.py | 97 LU, p = plu 105 return LU, p 164 A = np.zeros((m, m + n), order='F') 167 e = np.zeros(m) 197 LU, p = lu 199 for i1, i2 in enumerate(p): 293 e = np.zeros(m) 445 A = np.delete(A, i_remove, axis=0) 446 b = np.delete(b, i_remove)
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/dports/comms/gnuradio/gnuradio-3.8.4.0/gr-fec/python/fec/polar/ |
H A D | testbed.py | 49 frozenbits = np.zeros(n - k) 90 res = np.zeros(llrs.size) 104 frozenbits = np.zeros(n - k) 109 channel_counter = np.zeros(k) 128 good_indices = np.zeros(channel_counter.size) 146 frozen_bit_positions = np.delete(np.arange(channel_counter.size), info_bit_positions) 241 lock_mask = np.zeros(block_size, dtype=int) 256 l0 = lock[p] 257 l1 = lock[p + 1] 258 first = mask[p] [all …]
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/dports/science/dynare/dynare-4.6.4/matlab/particles/src/ |
H A D | SMC_samplers_initialization.m | 10 % o xparam1 [double] (p*1) vector of parameters to be estimated (initial values). 11 % o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters. 74 % delete([BaseName '_dsmh*_blck*.mat']); 80 % delete([ MetropolisFolder '/dsmh.log']); 95 ix2 = zeros(npar,NumberOfParticles); 96 temperedlogpost = zeros(NumberOfParticles,1); 97 loglik = zeros(NumberOfParticles,1);
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H A D | DSMH_initialization.m | 10 % o xparam1 [double] (p*1) vector of parameters to be estimated (initial values). 11 % o mh_bounds [double] (p*2) matrix defining lower and upper bounds for the parameters. 75 % delete([BaseName '_dsmh*_blck*.mat']); 81 % delete([ MetropolisFolder '/dsmh.log']); 96 ix2 = zeros(npar,NumberOfParticles); 97 temperedlogpost = zeros(NumberOfParticles,1); 98 loglik = zeros(NumberOfParticles,1);
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/dports/archivers/gtar/tar-1.34/tests/ |
H A D | delete02.at | 24 AT_KEYWORDS([delete delete02]) 27 genfile -l 3073 -p zeros --file 1 32 cat archive | tar f - --delete 2 > archive2
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/dports/graphics/py-scikit-image/scikit-image-0.19.0/skimage/_shared/ |
H A D | coord.py | 33 indices = tree.query_ball_point(coord, r=spacing, p=p_norm) 43 p=p_norm).reshape(-1) 54 output = np.delete(coord, tuple(rejected_peaks_indices), axis=0) 114 output = np.zeros((0, coords.shape[1]), dtype=coords.dtype)
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/dports/cad/py-pyfda/pyfda-0.1rc6/pyfda/input_widgets/ |
H A D | filter_pz.py | 499 self.zpk[0] = np.delete(self.zpk[0], Z) 500 self.zpk[1] = np.delete(self.zpk[1], P) 505 self.zpk[1] = np.append(self.zpk[1], np.zeros(D)) 507 self.zpk[0] = np.append(self.zpk[0], np.zeros(-D)) 526 self.zpk[0] = np.insert(self.zpk[0], row, np.zeros(sel)) 527 self.zpk[1] = np.insert(self.zpk[1], row, np.zeros(sel)) 558 for p in range(len(self.zpk[1])-1, -1, -1): 559 if np.isclose(self.zpk[0][z], self.zpk[1][p], rtol = eps, atol = 1e-08): 560 self.zpk[0] = np.delete(self.zpk[0], z) 561 self.zpk[1] = np.delete(self.zpk[1], p)
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/dports/math/reduce/Reduce-svn5758-src/packages/algint/ |
H A D | coates.red | 250 % on the remaining zeros/poles. 288 fz:=delete(zfound,fz); 328 scalar m,p; 332 p:=(car pzero).p; 346 scalar m,p; 348 p:=ppole; 350 p:=cdr p; 387 % else interr "Removezero") delete(place,l) 465 delete(assoc(uu,car sqrtsavelist), 467 delete(u,cdr sqrtsavelist)>>; [all …]
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/dports/math/py-numpy/numpy-1.20.3/numpy/lib/tests/ |
H A D | test_function_base.py | 813 a_del = delete(self.a, indices) 833 delete(self.a, [100]) 835 delete(self.a, [-100]) 843 delete(self.a, True) 845 delete(self.a, False) 849 delete(self.a, [False]*4) 858 delete(a, [], axis=0) 1207 arr = np.zeros(0) 3035 o = np.zeros(()) 3166 p = p.tolist() [all …]
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/dports/science/py-scipy/scipy-1.7.1/scipy/signal/ |
H A D | filter_design.py | 1243 p = np.zeros(n_sections*2, np.complex128) 1439 p = np.concatenate((p, np.zeros(max(len(z) - len(p), 0)))) 1440 z = np.concatenate((z, np.zeros(max(len(p) - len(z), 0)))) 1460 p = np.delete(p, p1_idx) 1468 z = np.delete(z, z1_idx) 1480 z = np.delete(z, z1_idx) 1492 z = np.delete(z, z2_idx) 1510 z = np.delete(z, z2_idx) 1511 p = np.delete(p, p2_idx) 5188 b = np.zeros(N + 1) [all …]
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H A D | ltisys.py | 1000 def zeros(self, zeros): member in ZerosPolesGain 1039 self.zeros = system.zeros 2696 for p in poles: 2697 if sum(p == poles) > r: 2731 if np.conj(p) in poles: 2732 im_poles.extend((p, np.conj(p))) 2751 transfer_matrix_not_j = np.delete(transfer_matrix, j, axis=1) 2996 transfer_matrix_not_i_j = np.delete(transfer_matrix, (i, j), 3273 p = poles[idx] 3274 diag_poles[idx, idx] = np.real(p) [all …]
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/dports/misc/mxnet/incubator-mxnet-1.9.0/example/ssd/evaluate/ |
H A D | eval_metric.py | 138 pred = np.delete(pred, indices, axis=0) 141 pred = np.delete(pred, indices, axis=0) 144 records = np.hstack((dets[:, 1][:, np.newaxis], np.zeros((dets.shape[0], 1)))) 148 label = np.delete(label, label_indices, axis=0) 191 label = np.delete(label, label_indices, axis=0) 216 record = np.delete(record, np.where(record[:, 1].astype(int) == 0)[0], axis=0) 291 p = 0 293 p = np.max(prec[rec >= t]) 294 ap += p / 11.
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/ssd/evaluate/ |
H A D | eval_metric.py | 138 pred = np.delete(pred, indices, axis=0) 141 pred = np.delete(pred, indices, axis=0) 144 records = np.hstack((dets[:, 1][:, np.newaxis], np.zeros((dets.shape[0], 1)))) 148 label = np.delete(label, label_indices, axis=0) 191 label = np.delete(label, label_indices, axis=0) 216 record = np.delete(record, np.where(record[:, 1].astype(int) == 0)[0], axis=0) 291 p = 0 293 p = np.max(prec[rec >= t]) 294 ap += p / 11.
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/dports/science/qmcpack/qmcpack-3.11.0/nexus/lib/ |
H A D | gaussian_process.py | 266 Dm = np.zeros([n,n]) 424 K = np.zeros([n,m]) 638 best_E = np.delete(best_E,us_ind[1:us_ind.shape[0],0],0) 639 best_pos = np.delete(best_pos,us_ind[1:us_ind.shape[0],0],0) 640 ind_T = np.delete(ind_T,us_ind[1:us_ind.shape[0],0],0) 653 best_E = np.delete(best_E,us_ind[1:us_ind.shape[0],0],0) 654 best_pos = np.delete(best_pos,us_ind[1:us_ind.shape[0],0],0) 688 best_E = np.delete(best_E,us_ind[1:us_ind.shape[0],0],0) 818 E_parm = np.zeros(3) 1807 for p in P: [all …]
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/dports/math/octave-forge-statistics/statistics-1.4.3/inst/ |
H A D | runstest.m | 17 ## @deftypefn {Function File} {@var{h}, @var{p}, @var{stats} =} runstest (@var{x}, @var{v}) 35 ## @var{p} is the probablity of obtaining a test statistic of the magnitude found under the null hy… 41 ## Note: the large-sample normal approximation is used to find @var{h} and @var{p}. This is accurat… 52 function [h, p, stats] = runstest (x, x2) 65 x = x(~isnan(x)); #delete missing values 67 x = x(x ~= 0); #delete any zeros 84 p = 2 * normcdf(-abs(Z)); variable 86 h = p < alpha; 101 %! [h, p, stats] = runstest (data); 106 %! assert (p, expected_p, 1E-6);
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/dports/science/dynare/dynare-4.6.4/matlab/ |
H A D | hessian.m | 19 % and 25.3.27 p. 884 59 f1 = zeros(size(f0, 1), n); 76 hessian_mat = zeros(size(f0,1), n*n); 110 %$ fprintf(fid,' g = zeros(2,1);\\n'); 115 %$ fprintf(fid,' H = zeros(2,2);\\n'); 126 %$ t = zeros(5,1); 140 %$ delete('exfun.m');
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