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/dports/math/py-iohexperimenter/IOHexperimenter-0.2.9.2/src/
H A Dcoco_transformation_objs.hpp18 double log_y; in transform_obj_oscillate_evaluate() local
19 log_y = log(fabs(y[i])) / factor; in transform_obj_oscillate_evaluate()
21 y[i] = pow(exp(log_y + 0.49 * (sin(log_y) + sin(0.79 * log_y))), factor); in transform_obj_oscillate_evaluate()
23 y[i] = -pow(exp(log_y + 0.49 * (sin(0.55 * log_y) + sin(0.31 * log_y))), factor); in transform_obj_oscillate_evaluate()
/dports/science/py-chainer/chainer-7.8.0/chainer/functions/loss/
H A Dsoftmax_cross_entropy.py148 log_y = log_softmax._log_softmax(x)
150 self.y = numpy.exp(log_y)
158 log_yd = numpy.rollaxis(log_y, 1)
200 log_y = log_softmax._log_softmax(x)
202 self.y = cupy.exp(log_y)
211 log_y = cupy.rollaxis(log_y, 1, log_y.ndim)
231 )(t, log_y.reduced_view(), log_y.shape[-1],
233 ret = ret.astype(log_y.dtype, copy=False)
245 )(t, log_y.reduced_view(), log_y.shape[-1], self.ignore_label)
249 def _soft_target_loss(self, xp, x, t, log_y): argument
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/dports/math/stanmath/math-4.2.0/stan/math/prim/prob/
H A Ddirichlet_rng.hpp52 VectorXd log_y(alpha.size()); in dirichlet_rng() local
57 log_y(i) = log(gamma_rng()) + log_u / alpha(i); in dirichlet_rng()
59 double log_sum_y = log_sum_exp(log_y); in dirichlet_rng()
62 theta(i) = exp(log_y(i) - log_sum_y); in dirichlet_rng()
H A Dchi_square_lpdf.hpp72 const auto& log_y = to_ref_if<!is_constant_all<T_dof>::value>(log(y_val)); in chi_square_lpdf() local
79 logp += sum((half_nu - 1.0) * log_y); in chi_square_lpdf()
92 (log_y - digamma(half_nu)) * 0.5 - HALF_LOG_TWO); in chi_square_lpdf()
95 = sum(log_y - digamma(half_nu)) * 0.5 - HALF_LOG_TWO * N; in chi_square_lpdf()
H A Dpareto_lpdf.hpp61 const auto& log_y = to_ref_if<!is_constant_all<T_shape>::value>(log(y_val)); in pareto_lpdf() local
69 logp -= sum(alpha_val * log_y + log_y) * N / max_size(alpha, y); in pareto_lpdf()
88 ops_partials.edge3_.partials_ = inv(alpha_val) + log_y_min - log_y; in pareto_lpdf()
H A Dbeta_proportion_lpdf.hpp83 const auto& log_y in beta_proportion_lpdf() local
98 logp += sum((mukappa - 1) * log_y + (kappa_val - mukappa - 1) * log1m_y); in beta_proportion_lpdf()
116 * (digamma_kappa_mukappa - digamma_mukappa + log_y - log1m_y); in beta_proportion_lpdf()
120 = digamma(kappa_val) + mu_val * (log_y - digamma_mukappa) in beta_proportion_lpdf()
H A Dinv_chi_square_lpdf.hpp79 const auto& log_y = to_ref_if<!is_constant_all<T_dof>::value>(log(y_val)); in inv_chi_square_lpdf() local
83 T_partials_return logp = -sum((half_nu + 1.0) * log_y); in inv_chi_square_lpdf()
97 = -HALF_LOG_TWO - (digamma(half_nu) + log_y) * 0.5; in inv_chi_square_lpdf()
H A Dfrechet_lpdf.hpp64 const auto& log_y in frechet_lpdf() local
78 logp -= sum((alpha_val + 1.0) * log_y) * N / max_size(y, alpha); in frechet_lpdf()
86 = inv(alpha_val) + (1 - sigma_div_y_pow_alpha) * (log_sigma - log_y); in frechet_lpdf()
/dports/math/py-pystan/pystan-2.19.0.0/pystan/stan/lib/stan_math/stan/math/prim/mat/prob/
H A Ddirichlet_rng.hpp57 VectorXd log_y(alpha.size()); in dirichlet_rng() local
62 log_y(i) = log(gamma_rng()) + log_u / alpha(i); in dirichlet_rng()
64 double log_sum_y = log_sum_exp(log_y); in dirichlet_rng()
67 theta(i) = exp(log_y(i) - log_sum_y); in dirichlet_rng()
/dports/math/py-pystan/pystan-2.19.0.0/pystan/stan/lib/stan_math/stan/math/prim/scal/prob/
H A Dpareto_lpdf.hpp65 log_y(length(y)); in pareto_lpdf() local
68 log_y[n] = log(value_of(y_vec[n])); in pareto_lpdf()
102 logp -= alpha_dbl * log_y[n] + log_y[n]; in pareto_lpdf()
110 += 1 / alpha_dbl + log_y_min[n] - log_y[n]; in pareto_lpdf()
H A Dinv_chi_square_lpdf.hpp79 log_y(length(y)); in inv_chi_square_lpdf() local
82 log_y[i] = log(value_of(y_vec[i])); in inv_chi_square_lpdf()
110 logp -= (half_nu + 1.0) * log_y[n]; in inv_chi_square_lpdf()
121 - 0.5 * log_y[n]; in inv_chi_square_lpdf()
H A Dchi_square_lpdf.hpp81 log_y(length(y)); in chi_square_lpdf() local
84 log_y[i] = log(value_of(y_vec[i])); in chi_square_lpdf()
115 logp += (half_nu - 1.0) * log_y[n]; in chi_square_lpdf()
125 + log_y[n] * 0.5; in chi_square_lpdf()
H A Dbeta_proportion_lpdf.hpp92 log_y(length(y)); in beta_proportion_lpdf() local
99 log_y[n] = log(value_of(y_vec[n])); in beta_proportion_lpdf()
159 logp += (mukappa_dbl - 1) * log_y[n] in beta_proportion_lpdf()
172 * (digamma_kappa_mukappa[n] - digamma_mukappa[n] + log_y[n] in beta_proportion_lpdf()
176 += digamma_kappa[n] + mu_dbl * (log_y[n] - digamma_mukappa[n]) in beta_proportion_lpdf()
H A Dgamma_lpdf.hpp92 log_y(length(y)); in gamma_lpdf() local
96 log_y[n] = log(value_of(y_vec[n])); in gamma_lpdf()
130 logp += (alpha_dbl - 1.0) * log_y[n]; in gamma_lpdf()
138 += -digamma_alpha[n] + log_beta[n] + log_y[n]; in gamma_lpdf()
H A Dinv_gamma_lpdf.hpp88 log_y(length(y)); in inv_gamma_lpdf() local
95 log_y[n] = log(value_of(y_vec[n])); in inv_gamma_lpdf()
129 logp -= (alpha_dbl + 1.0) * log_y[n]; in inv_gamma_lpdf()
138 += -digamma_alpha[n] + log_beta[n] - log_y[n]; in inv_gamma_lpdf()
H A Dfrechet_lpdf.hpp67 log_y(length(y)); in frechet_lpdf() local
70 log_y[i] = log(value_of(y_vec[i])); in frechet_lpdf()
102 logp -= (alpha_dbl + 1.0) * log_y[n]; in frechet_lpdf()
117 + (1.0 - sigma_div_y_pow_alpha[n]) * (log_sigma[n] - log_y[n]); in frechet_lpdf()
H A Dweibull_lpdf.hpp83 log_y(length(y)); in weibull_lpdf() local
86 log_y[i] = log(value_of(y_vec[i])); in weibull_lpdf()
118 logp += (alpha_dbl - 1.0) * log_y[n]; in weibull_lpdf()
133 + (1.0 - y_div_sigma_pow_alpha[n]) * (log_y[n] - log_sigma[n]); in weibull_lpdf()
H A Dlognormal_lpdf.hpp88 log_y(length(y)); in lognormal_lpdf() local
91 log_y[n] = log(value_of(y_vec[n])); in lognormal_lpdf()
109 logy_m_mu = log_y[n] - mu_dbl; in lognormal_lpdf()
119 logp -= log_y[n]; in lognormal_lpdf()
H A Dscaled_inv_chi_square_lpdf.hpp92 log_y(length(y)); in scaled_inv_chi_square_lpdf() local
95 log_y[i] = log(value_of(y_vec[i])); in scaled_inv_chi_square_lpdf()
135 logp -= (half_nu[n] + 1.0) * log_y[n]; in scaled_inv_chi_square_lpdf()
147 - 0.5 * log_y[n] - 0.5 * s_dbl * s_dbl * inv_y[n]; in scaled_inv_chi_square_lpdf()
/dports/math/stanmath/math-4.2.0/stan/math/opencl/prim/
H A Dpareto_lpdf.hpp74 auto log_y = log(y_val); in pareto_lpdf() local
81 logp1 - elt_multiply(alpha_val, log_y) - log_y, logp1); in pareto_lpdf()
88 auto alpha_deriv = elt_divide(1.0, alpha_val) + log_y_min - log_y; in pareto_lpdf()
H A Dinv_chi_square_lpdf.hpp78 auto log_y = log(y_val); in inv_chi_square_lpdf() local
82 auto logp1 = -elt_multiply(half_nu + 1.0, log_y); in inv_chi_square_lpdf()
90 auto nu_deriv = -HALF_LOG_TWO - (digamma(half_nu) + log_y) * 0.5; in inv_chi_square_lpdf()
/dports/graphics/py-plotly/plotly-4.14.3/plotly/express/
H A D_chart_types.py51 log_y=False,
95 log_y=False,
164 log_y=False,
238 log_y=False,
282 log_y=False,
338 log_y=False,
433 log_y=False,
496 log_y=False, argument
545 log_y=False, argument
597 log_y=False, argument
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/dports/games/uqm/uqm-0.8.0/src/uqm/
H A Dhyper.c86 GLOBAL_SIS (log_y) -= new_dy; in MoveSIS()
87 if (GLOBAL_SIS (log_y) < 0) in MoveSIS()
89 new_dy += (SIZE)GLOBAL_SIS (log_y); in MoveSIS()
90 GLOBAL_SIS (log_y) = 0; in MoveSIS()
92 else if (GLOBAL_SIS (log_y) > MAX_Y_LOGICAL) in MoveSIS()
95 GLOBAL_SIS (log_y) = MAX_Y_LOGICAL; in MoveSIS()
401 SDWORD log_x, log_y; in ElementToUniverse() local
405 log_y = GLOBAL_SIS (log_y) in ElementToUniverse()
408 pPt->y = LOGY_TO_UNIVERSE (log_y); in ElementToUniverse()
1237 EncounterPtr->log_y -= delta_y; in ProcessEncounter()
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/dports/math/octave-forge-statistics/statistics-1.4.3/inst/
H A Diwishpdf.m10 ## @deftypefn {Function File} {} @var{y} = iwishpdf (@var{W}, @var{Tau}, @var{df}, @var{log_y}=fal…
14 ## If the flag @var{log_y} is set, return the log probability density -- this helps avoid underflow…
24 function [y] = iwishpdf(W, Tau, df, log_y=false)
50 if ~log_y
H A Dwishpdf.m10 ## @deftypefn {Function File} {} @var{y} = wishpdf (@var{W}, @var{Sigma}, @var{df}, @var{log_y}=fa…
14 ## If the flag @var{log_y} is set, return the log probability density -- this helps avoid underflow…
24 function [y] = wishpdf(W, Sigma, df, log_y=false)
50 if ~log_y

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