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/dports/math/R-cran-mvtnorm/mvtnorm/tests/
H A Dtest-getInt.R5 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",
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/dports/finance/quantlib/QuantLib-1.20/test-suite/
H A Driskstats.cpp43 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()
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/dports/graphics/py-scikit-image/scikit-image-0.19.0/skimage/filters/
H A Dridges.py84 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):
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/dports/science/InsightToolkit/ITK-5.0.1/Modules/Filtering/ImageGradient/test/
H A DitkGradientRecursiveGaussianFilterTest4.cxx74 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()
/dports/math/stanmath/math-4.2.0/test/unit/math/prim/prob/
H A Dskew_double_exponential_ccdf_log_test.cpp35 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
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H A Dskew_double_exponential_cdf_log_test.cpp34 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
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/dports/biology/gmap/gmap-2020-09-12/src/
H A Diit-write-univ.c119 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
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H A Diit-write.c129 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()
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/dports/math/R-cran-nloptr/nloptr/src/nlopt_src/isres/
H A Disres.c109 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()
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/dports/math/nlopt/nlopt-2.7.1/src/algs/isres/
H A Disres.c114 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()
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/dports/cad/repsnapper/repsnapper-2.5a4/libraries/vmmlib/include/vmmlib/
H A Dmatrix_pseudoinverse.hpp47 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()
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/dports/graphics/py-scikit-image/scikit-image-0.19.0/skimage/feature/
H A D_daisy.py15 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)
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/dports/science/pybrain/pybrain-0.3.3/pybrain/optimization/distributionbased/
H A Dfem.py60 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]
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/dports/science/clhep/2.4.1.0/CLHEP/RandomObjects/src/
H A DRandMultiGauss.cc134 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()
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/dports/math/py-optuna/optuna-2.10.0/optuna/samplers/_tpe/
H A Dparzen_estimator.py85 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))
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/dports/science/py-phono3py/phono3py-1.22.3/test/phonon3/
H A Dtest_kappa_RTA.py25 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
/dports/misc/openmvg/openMVG-2.0/src/openMVG/features/sift/
H A Dhierarchical_gaussian_scale_space.hpp27 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()
/dports/science/py-dipy/dipy-1.4.1/dipy/align/
H A Dscalespace.py67 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)
/dports/science/qwalk/mainline-1.0.1-300-g1b7e381/tests/h/
H A Drun_test.py27 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)
/dports/www/moodle310/moodle/lib/mlbackend/php/phpml/src/Phpml/NeuralNetwork/Training/
H A DBackpropagation.php20 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);
/dports/www/moodle311/moodle/lib/mlbackend/php/phpml/src/Phpml/NeuralNetwork/Training/
H A DBackpropagation.php20 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);
/dports/www/moodle39/moodle/lib/mlbackend/php/phpml/src/Phpml/NeuralNetwork/Training/
H A DBackpropagation.php20 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);
/dports/science/py-scipy/scipy-1.7.1/scipy/ndimage/
H A Dfourier.py122 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)
/dports/science/py-dipy/dipy-1.4.1/doc/examples/
H A Daffine_registration_3d.py153 sigmas = [3.0, 1.0, 0.0] variable
173 sigmas=sigmas,
323 sigmas=sigmas,
366 sigmas=sigmas,
/dports/security/vault/vault-1.8.2/vendor/honnef.co/go/tools/ir/
H A Dlift.go401 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 {
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