/dports/math/R-cran-mvtnorm/mvtnorm/tests/ |
H A D | test-getInt.R | 5 sigmas <- rbind( globalVar 11 qmvnorm(p = p, tail = "lower.tail", mean = mean, sigma = sigmas, 13 qmvnorm(p = p, tail = "upper.tail", mean = mean, sigma = sigmas, 15 qmvnorm(p = p, tail = "both.tails", mean = mean, sigma = sigmas, 22 qmvt(p = p, tail = "lower.tail", delta = mean, sigma = sigmas, 24 qmvt(p = p, tail = "upper.tail", delta = mean, sigma = sigmas, 26 qmvt(p = p, tail = "both.tails", delta = mean, sigma = sigmas, 28 mvtnorm:::getInt(p,delta=mean, sigma=sigmas,tail="lower.tail", 30 mvtnorm:::getInt(p,delta=mean, sigma=sigmas,tail="upper.tail", 32 mvtnorm:::getInt(p,delta=mean, sigma=sigmas,tail="both.tails", [all …]
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/dports/finance/quantlib/QuantLib-1.20/test-suite/ |
H A D | riskstats.cpp | 43 Real sigmas[] = { 0.1, 1.0, 100.0 }; in testResults() local 50 for (j=0; j<LENGTH(sigmas); j++) { in testResults() 154 << sigmas[j] << ")\n" in testResults() 163 << sigmas[j] << ")\n" in testResults() 171 expected = sigmas[j]*sigmas[j]; in testResults() 178 << sigmas[j] << ")\n" in testResults() 187 << sigmas[j] << ")\n" in testResults() 195 expected = sigmas[j]; in testResults() 396 - sigmas[j]*sigmas[j] in testResults() 499 expected = sigmas[j]*sigmas[j]; in testResults() [all …]
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/dports/graphics/py-scikit-image/scikit-image-0.19.0/skimage/filters/ |
H A D | ridges.py | 84 def _check_sigmas(sigmas): argument 102 sigmas = np.asarray(sigmas).ravel() 103 if np.any(sigmas < 0.0): 106 return sigmas 222 sigmas = _check_sigmas(sigmas) 243 for i, sigma in enumerate(sigmas): 327 sigmas = _check_sigmas(sigmas) 340 for i, sigma in enumerate(sigmas): 438 sigmas = _check_sigmas(sigmas) 460 for i, sigma in enumerate(sigmas): [all …]
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/dports/science/InsightToolkit/ITK-5.0.1/Modules/Filtering/ImageGradient/test/ |
H A D | itkGradientRecursiveGaussianFilterTest4.cxx | 74 itk::FixedArray< DoubleType, 2 > sigmas; in itkGradientRecursiveGaussianFilterTest4() local 84 sigmas = filter->GetSigmaArray(); in itkGradientRecursiveGaussianFilterTest4() 85 if (sigmas[0] != 2.5 || sigmas[1] != 2.5) in itkGradientRecursiveGaussianFilterTest4() 88 std::cerr << "Sigma Array: " << sigmas[0] << ", " << sigmas[1] << std::endl; in itkGradientRecursiveGaussianFilterTest4() 94 sigmas[0] = 1.8; in itkGradientRecursiveGaussianFilterTest4() 95 sigmas[1] = 1.8; in itkGradientRecursiveGaussianFilterTest4() 96 filter->SetSigmaArray(sigmas); in itkGradientRecursiveGaussianFilterTest4() 98 sigmas = filter->GetSigmaArray(); in itkGradientRecursiveGaussianFilterTest4() 99 …if (itk::Math::NotExactlyEquals(sigmas[0], 1.8) || itk::Math::NotExactlyEquals(sigmas[1], 1.8) || … in itkGradientRecursiveGaussianFilterTest4() 102 std::cerr << "Sigma Array: " << sigmas[0] << ", " << sigmas[1] << std::endl; in itkGradientRecursiveGaussianFilterTest4()
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/dports/math/stanmath/math-4.2.0/test/unit/math/prim/prob/ |
H A D | skew_double_exponential_ccdf_log_test.cpp | 35 for (double sigmas : {0.1, 0.5, 1.1, 10.1}) { in TEST() 39 stan::math::var sigma = sigmas; in TEST() 53 stan::math::var sigma_true = sigmas; in TEST() 82 for (double sigmas : {0.1, 1.1, 3.2}) { in TEST() 100 for (double sigmas : {0.1, 1.1, 3.2}) { in TEST() 104 x += skew_de_ccdf_test(y, mus, sigmas, taus); in TEST() 118 for (double sigmas : {0.1, 1.1, 3.2}) { in TEST() 134 std::vector<double> sigmas{0.1, 1.1, 3.2}; in TEST() local 155 for (double sigmas : {0.1, 1.1, 3.2}) { in TEST() 171 std::vector<double> sigmas{0.1, 1.1, 3.2}; in TEST() local [all …]
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H A D | skew_double_exponential_cdf_log_test.cpp | 34 for (double sigmas : {0.1, 1.1, 3.2}) { in TEST() 38 stan::math::var sigma = sigmas; in TEST() 52 stan::math::var sigma_true = sigmas; in TEST() 80 for (double sigmas : {0.1, 1.1, 3.2}) { in TEST() 97 for (double sigmas : {0.1, 1.1, 3.2}) { in TEST() 101 x += skew_de_cdf_test(y, mus, sigmas, taus); in TEST() 116 for (double sigmas : {0.1, 1.1, 3.2}) { in TEST() 132 std::vector<double> sigmas{0.1, 1.1, 3.2}; in TEST() local 153 for (double sigmas : {0.1, 1.1, 3.2}) { in TEST() 169 std::vector<double> sigmas{0.1, 1.1, 3.2}; in TEST() local [all …]
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/dports/biology/gmap/gmap-2020-09-12/src/ |
H A D | iit-write-univ.c | 119 iota = sigmas[lambda]; in is_valid_input() 209 omegas[q] = sigmas[lambda]; in Node_make() 210 sigmas[lambda] = 0; in Node_make() 218 if (sigmas[lambda] != 0) { in Node_make() 219 sigmas[iota] = sigmas[lambda]; in Node_make() 266 int *sigmas; in IIT_count_nnodes() local 281 sigmas[i] = i; in IIT_count_nnodes() 294 FREE(sigmas); in IIT_count_nnodes() 323 (*sigmas)[i] = i; in IIT_build_univ() 634 int *sigmas, *omegas; in IIT_write_univ() local [all …]
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H A D | iit-write.c | 129 iota = sigmas[lambda]; in is_valid_input() 220 omegas[q] = sigmas[lambda]; in Node_make() 221 sigmas[lambda] = 0; in Node_make() 230 sigmas[iota] = sigmas[lambda]; in Node_make() 279 int *sigmas; in IIT_count_nnodes() local 294 sigmas[i] = i; in IIT_count_nnodes() 307 FREE(sigmas); in IIT_count_nnodes() 345 (*sigmas)[i] = i; in IIT_build_one_div() 860 new->sigmas[divno][i] = sigmas[i]; in IIT_create_one_div() 1537 FREE(sigmas); in IIT_write() [all …]
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/dports/math/R-cran-nloptr/nloptr/src/nlopt_src/isres/ |
H A D | isres.c | 109 xs = sigmas + population*n; in isres_minimize() 236 sigmas[rk*n+j] = sigmas[ri*n+j] in isres_minimize() 238 if (sigmas[rk*n+j] > sigmamax) in isres_minimize() 239 sigmas[rk*n+j] = sigmamax; in isres_minimize() 244 sigmas[rk*n+j] = sigmas[ri*n+j] + ALPHA*(sigmas[rk*n+j] in isres_minimize() 245 - sigmas[ri*n+j]); in isres_minimize() 262 double sigi = sigmas[rk*n+j]; in isres_minimize() 265 if (sigmas[rk*n+j] > sigmamax) in isres_minimize() 266 sigmas[rk*n+j] = sigmamax; in isres_minimize() 271 sigmas[rk*n+j] = sigi in isres_minimize() [all …]
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/dports/math/nlopt/nlopt-2.7.1/src/algs/isres/ |
H A D | isres.c | 114 xs = sigmas + population*n; in isres_minimize() 241 sigmas[rk*n+j] = sigmas[ri*n+j] in isres_minimize() 243 if (sigmas[rk*n+j] > sigmamax) in isres_minimize() 244 sigmas[rk*n+j] = sigmamax; in isres_minimize() 249 sigmas[rk*n+j] = sigmas[ri*n+j] + ALPHA*(sigmas[rk*n+j] in isres_minimize() 250 - sigmas[ri*n+j]); in isres_minimize() 267 double sigi = sigmas[rk*n+j]; in isres_minimize() 270 if (sigmas[rk*n+j] > sigmamax) in isres_minimize() 271 sigmas[rk*n+j] = sigmamax; in isres_minimize() 276 sigmas[rk*n+j] = sigi in isres_minimize() [all …]
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/dports/cad/repsnapper/repsnapper-2.5a4/libraries/vmmlib/include/vmmlib/ |
H A D | matrix_pseudoinverse.hpp | 47 vec_n_type sigmas; member 58 vec_m_type sigmas; member 88 vec_m_type& sigmas = _work_inv->sigmas; in compute_inv() local 93 … bool svd_ok = svd.compute(in_data, U, sigmas, Vt); // FIXME it always gives bad error code in compute_inv() 104 … typename vector< T::ROWS, Tinternal >::const_iterator it = sigmas.begin(), it_end = sigmas.end(); in compute_inv() 119 sigmas.reciprocal_safe(); in compute_inv() 127 it = sigmas.begin(); in compute_inv() 154 vec_n_type& sigmas = _work->sigmas; in compute() local 171 … typename vector< T::COLS, Tinternal >::const_iterator it = sigmas.begin(), it_end = sigmas.end(); in compute() 187 sigmas.reciprocal_safe(); in compute() [all …]
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/dports/graphics/py-scikit-image/scikit-image-0.19.0/skimage/feature/ |
H A D | _daisy.py | 15 normalization='l1', sigmas=None, ring_radii=None, visualize=False): argument 108 if sigmas is not None and ring_radii is not None \ 109 and len(sigmas) - 1 != len(ring_radii): 114 if sigmas is not None: 115 rings = len(sigmas) - 1 116 if sigmas is None: 117 sigmas = [radius * (i + 1) / float(2 * rings) for i in range(rings)] 144 sigmas = [sigmas[0]] + sigmas 200 dy = sigmas[0] * bin_size * math.sin(o) 201 dx = sigmas[0] * bin_size * math.cos(o) [all …]
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/dports/science/pybrain/pybrain-0.3.3/pybrain/optimization/distributionbased/ |
H A D | fem.py | 60 self.sigmas = [] 74 self.sigmas.append(dot(eye(xdim), self.initCovariances)) 102 sample = normal(mu, self.sigmas[chosenOne]) 104 sample = multivariate_normal(mu, self.sigmas[chosenOne]) 114 self.bestSigma = self.sigmas[chosenOne].copy() 177 self.sigmas[c] *= (1. - updateSize[c]) 200 self.sigmas[c] = 4.0 * self.sigmas[bestCenter].copy() 201 self.sigmas[bestCenter] *= 0.25 256 self.allsigmas.append(deepcopy(self.sigmas)) 266 self.sigmas = [1.2 * sigma for sigma in self.sigmas] [all …]
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/dports/science/clhep/2.4.1.0/CLHEP/RandomObjects/src/ |
H A D | RandMultiGauss.cc | 134 HepVector & sigmas ) { in prepareUsigmas() argument 144 sigmas(i) = sqrt ( s2 ); in prepareUsigmas() 162 const HepVector & sigmas, in deviates() argument 170 int n = sigmas.num_row(); in deviates() 199 v(i) *= sigmas(i); in deviates() 223 HepVector sigmas; in fire() local 226 prepareUsigmas ( S, U, sigmas ); in fire() 227 return mu + deviates ( U, sigmas, localEngine, set, nextGaussian ); in fire() 263 HepVector sigmas; in fireArray() local 267 prepareUsigmas ( S, U, sigmas ); in fireArray() [all …]
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/dports/math/py-optuna/optuna-2.10.0/optuna/samplers/_tpe/ |
H A D | parzen_estimator.py | 85 mus = sigmas = None 93 self._sigmas[param_name] = sigmas 114 sigmas = self._sigmas[param_name] 118 assert sigmas is not None 133 scale=sigmas[active], 180 assert sigmas is not None 183 p_accept = cdf_func(high, mus, sigmas) - cdf_func(low, mus, sigmas) 399 sigmas = np.empty(n_observations) 403 sigmas[:] = sigmas0 * (high - low) 430 sigmas = np.asarray(np.clip(sigmas, minsigma, maxsigma)) [all …]
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/dports/science/py-phono3py/phono3py-1.22.3/test/phonon3/ |
H A D | test_kappa_RTA.py | 25 si_pbesol.sigmas = [0.1, ] 29 si_pbesol.sigmas = None 33 si_pbesol.sigmas = [0.1, ] 37 si_pbesol.sigmas = None 41 si_pbesol.sigmas = [0.1, ] 45 si_pbesol.sigmas = None
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/dports/misc/openmvg/openMVG-2.0/src/openMVG/features/sift/ |
H A D | hierarchical_gaussian_scale_space.hpp | 27 std::vector<float> sigmas; // sigma values member 154 octave.sigmas.resize(m_nb_slice + m_params.supplementary_levels); in NextOctave() 157 octave.sigmas[s] = in NextOctave() 163 for (int s = 1; s < octave.sigmas.size(); ++s) in NextOctave() 168 const double sig_prev = octave.sigmas[s-1]; in NextOctave() 169 const double sig_next = octave.sigmas[s]; in NextOctave() 190 ImageDecimate(octave.slices[octave.sigmas.size()-index], m_cur_base_octave_image); in NextOctave()
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/dports/science/py-dipy/dipy-1.4.1/dipy/align/ |
H A D | scalespace.py | 67 self.sigmas = [np.zeros(self.dim)] 96 sigmas = sigma_factor * (output_spacing / input_spacing - 1.0) 99 filtered = filters.gaussian_filter(image, sigmas) 112 self.sigmas.append(sigmas) 311 return self._get_attribute(self.sigmas, level) 315 def __init__(self, image, factors, sigmas, argument 352 if len(sigmas) != self.num_levels: 373 self.sigmas = [np.ones(self.dim) * sigmas[self.num_levels - 1]] 408 new_sigmas = np.ones(self.dim) * sigmas[self.num_levels - i - 1] 425 self.sigmas.append(new_sigmas)
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/dports/science/qwalk/mainline-1.0.1-300-g1b7e381/tests/h/ |
H A D | run_test.py | 27 sigmas={} variable 32 sigmas[k]=3.0 45 success=compare_result_ref(ref_data,dat_properties,sigmas) 61 sigmas={'total_energy':3.0} variable 74 success=compare_result_ref(ref_data,dat_properties,sigmas)
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/dports/www/moodle310/moodle/lib/mlbackend/php/phpml/src/Phpml/NeuralNetwork/Training/ |
H A D | Backpropagation.php | 20 private $sigmas = []; variable in Phpml\\NeuralNetwork\\Training\\Backpropagation 46 $this->sigmas = []; 56 $this->prevSigmas = $this->sigmas; 60 $this->sigmas = []; 80 $this->sigmas[] = new Sigma($neuron, $sigma);
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/dports/www/moodle311/moodle/lib/mlbackend/php/phpml/src/Phpml/NeuralNetwork/Training/ |
H A D | Backpropagation.php | 20 private $sigmas = []; variable in Phpml\\NeuralNetwork\\Training\\Backpropagation 46 $this->sigmas = []; 56 $this->prevSigmas = $this->sigmas; 60 $this->sigmas = []; 80 $this->sigmas[] = new Sigma($neuron, $sigma);
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/dports/www/moodle39/moodle/lib/mlbackend/php/phpml/src/Phpml/NeuralNetwork/Training/ |
H A D | Backpropagation.php | 20 private $sigmas = []; variable in Phpml\\NeuralNetwork\\Training\\Backpropagation 46 $this->sigmas = []; 56 $this->prevSigmas = $this->sigmas; 60 $this->sigmas = []; 80 $this->sigmas[] = new Sigma($neuron, $sigma);
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/dports/science/py-scipy/scipy-1.7.1/scipy/ndimage/ |
H A D | fourier.py | 122 sigmas = _ni_support._normalize_sequence(sigma, input.ndim) 123 sigmas = numpy.asarray(sigmas, dtype=numpy.float64) 124 if not sigmas.flags.contiguous: 125 sigmas = sigmas.copy() 127 _nd_image.fourier_filter(input, sigmas, n, axis, output, 0)
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/dports/science/py-dipy/dipy-1.4.1/doc/examples/ |
H A D | affine_registration_3d.py | 153 sigmas = [3.0, 1.0, 0.0] variable 173 sigmas=sigmas, 323 sigmas=sigmas, 366 sigmas=sigmas,
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/dports/security/vault/vault-1.8.2/vendor/honnef.co/go/tools/ir/ |
H A D | lift.go | 401 for _, sigma := range np.sigmas { 667 sigmas []*Sigma member 843 sigmas = append(sigmas, sigma) 845 sigmas = append(sigmas, nil) 1004 for _, sigmas := range newSigmas[u.Index] { 1005 for _, sigma := range sigmas.sigmas { 1025 for _, sigmas := range newSigmas[u.Index] { 1026 if sigmas.alloc == alloc && sigmas.sigmas[succi] != nil { 1027 newval = sigmas.sigmas[succi] 1055 if sigma.sigmas[idx] != nil { [all …]
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