/dports/devel/tcllib/tcllib-1.20/modules/math/ |
H A D | stat_logit.tcl | 68 set loglike 0.0 78 set loglike [expr {$loglike - log(1.0 + $exp)}] 80 set loglike [expr {$loglike - $sum - log(1.0 + $exp)}] 83 return [expr {-$loglike}]
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/dports/devel/tcllibc/tcllib-1.20/modules/math/ |
H A D | stat_logit.tcl | 68 set loglike 0.0 78 set loglike [expr {$loglike - log(1.0 + $exp)}] 80 set loglike [expr {$loglike - $sum - log(1.0 + $exp)}] 83 return [expr {-$loglike}]
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/examples/ |
H A D | ex_generic_mle.py | 17 loglike = probit_mod.loglike variable 19 mod = GenericLikelihoodModel(data.endog, data.exog*2, loglike, score) 32 mod = GenericLikelihoodModel(data.endog, data.exog, loglike=model_loglike) 85 def loglike(self, params): member in MygMLE 122 hb=-approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4) 123 hf=-approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=1e-4) 127 grad = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4) 129 gradb = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4) 130 gradf = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=1e-4) 135 mod_norm2.loglike(start_params/2.)
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H A D | ex_generic_mle_t.py | 43 def loglike(self, params): member in MyT 96 hb=-approx_hess(modp.start_value, modp.loglike, epsilon=-1e-4) 97 tmp = modp.loglike(modp.start_value)
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/base/ |
H A D | _penalized.py | 68 def loglike(self, params, pen_weight=None, **kwds): member in PenalizedMixin 75 llf = super(PenalizedMixin, self).loglike(params, **kwds) 104 loglike = lambda p: self.loglike(p, pen_weight=pen_weight, **kwds) function 107 return approx_fprime_cs(params, loglike) 109 return approx_fprime(params, loglike, centered=True) 147 loglike = lambda p: self.loglike(p, pen_weight=pen_weight, **kwds) function 150 return approx_hess(params, loglike)
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/tests/ |
H A D | test_concentrated.py | 57 assert_allclose(out.mod_conc.loglike(out.params_conc), 58 out.mod_orig.loglike(out.params_orig)) 67 assert_allclose(out.mod_conc.loglike(out.params_conc), 68 out.mod_orig.loglike(out.params_orig)) 81 assert_allclose(out.mod_conc.loglike(out.params_conc), 82 out.mod_orig.loglike(out.params_orig)) 93 assert_allclose(out.mod_conc.loglike(out.params_conc), 94 out.mod_orig.loglike(out.params_orig)) 138 mod_conc.loglike(params[:-1]), mod_orig.loglike(params)) 143 llf1 = mod_conc.loglike(params[:-1]) [all …]
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/dports/math/R-cran-MCMCpack/MCMCpack/R/ |
H A D | BayesFactors.R | 44 loglike <- sum(dnorm(y, X%*%beta, sqrt(sigma2), log=TRUE)) functionVar 50 return (loglike + logprior) 63 loglike <- sum( y * log(p) + (1-y)*log(1-p) ) functionVar 66 return (loglike + logprior) 79 loglike <- sum( y * log(p) + (1-y)*log(1-p) ) functionVar 82 return (loglike + logprior) 94 loglike <- sum( y * log(p) + (1-y)*log(1-p) ) functionVar 102 return (loglike + logprior) 116 loglike <- sum(dpois(y, lambda, log=TRUE)) functionVar 119 return (loglike + logprior)
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/dports/math/R-cran-LearnBayes/LearnBayes/R/ |
H A D | poisson.gamma.mix.R | 8 loglike=0 functionVar 10 loglike=loglike+dpois(y[j],L*t[j],log=TRUE) 12 m.prob=exp(loglike+
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H A D | logpoissnormal.R | 6 loglike=log(dgamma(lambda,shape=sum(y)+1,scale=1/length(y))) functionVar 8 return(loglike+logprior)
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H A D | logpoissgamma.R | 6 loglike=log(dgamma(lambda,shape=sum(y)+1,rate=length(y))) functionVar 8 return(loglike+logprior)
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H A D | bayes.model.selection.R | 18 loglike = sum(dnorm(y, mean = X %*% as.vector(beta), functionVar 20 else loglike = sum(dnorm(y, mean = X * beta, sd = sigma, 24 return(loglike + logprior)
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tools/tests/ |
H A D | test_numdiff.py | 52 self.mod.loglike) 56 self.mod.loglike) 79 self.mod.loglike) 85 hecs, gradcs = numdiff.approx_hess1(test_params, self.mod.loglike, 93 hecs = numdiff.approx_hess3(test_params, self.mod.loglike, 1e-5) 137 hecs = numdiff.approx_hess_cs(test_params, self.mod.loglike) 144 hecs = numdiff.approx_hess3(test_params, self.mod.loglike, 1e-4) 374 loglike = mod.loglike variable 382 print('fd', numdiff.approx_fprime(test_params,loglike,epsilon)) 383 print('cs', numdiff.approx_fprime_cs(test_params,loglike)) [all …]
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/dports/astro/py-astropy/astropy-5.0/astropy/timeseries/periodograms/bls/ |
H A D | methods.py | 126 loglike = -0.5*np.sum((y_in - y[m_in])**2 * ivar[m_in]) 127 loglike += 0.5*np.sum((y_out - y[m_in])**2 * ivar[m_in]) 131 objective = loglike 140 snr, loglike)
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/dports/math/R-cran-MCMCpack/MCMCpack/src/ |
H A D | MCMCmnl.h | 47 double loglike = 0.0; in mnl_logpost() local 63 loglike += std::log(numer(i,j) / denom[i]); in mnl_logpost() 83 return (loglike + logprior); in mnl_logpost()
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H A D | MCMCnbutil.cc | 322 double loglike = 0; in rho_conditional_log_density() local 328 loglike += lngammafn(rho + y[t]) - lngammafn(rho) - lngammafn(y[t] +1); in rho_conditional_log_density() 329 loglike += rho * log(rho) + y[t]*log(lambda[t])- (rho + y[t]) * log(rho + lambda[t]); in rho_conditional_log_density() 332 double lpost = logprior + loglike; in rho_conditional_log_density()
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H A D | cMCMCprobit.cc | 126 double loglike = 0.0; in MCMCprobit_impl() local 130 loglike += log(dbinom(Y(i), 1, phi)); in MCMCprobit_impl() 143 logmarglike = loglike + logprior - logbeta; in MCMCprobit_impl() 146 Rprintf("loglike = %10.5f\n", loglike); in MCMCprobit_impl()
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H A D | cMCMCprobitres.cc | 135 double loglike = 0.0; in MCMCprobitres_impl() local 139 loglike += log(dbinom(Y(i), 1, phi)); in MCMCprobitres_impl() 145 logmarglike = loglike + logprior - logbeta; in MCMCprobitres_impl() 148 Rprintf("\n loglike %10.5f", loglike, "\n"); in MCMCprobitres_impl()
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H A D | cMCMCregressChange.cc | 49 Matrix<double> loglike(n, 1); in loglike_fn() local 70 loglike[t] = log(sum(unnorm_pstyt)); in loglike_fn() 74 return loglike; in loglike_fn() 182 double& loglike) in MCMCregressChange_impl() argument 428 loglike = sum(loglike_fn(m, Y, X, beta_st, Sigma_st, P_st)); in MCMCregressChange_impl() 458 logmarglike = (loglike + logprior) - (pdf_beta + pdf_Sigma + pdf_P); in MCMCregressChange_impl() 461 Rprintf("loglike = %10.5f\n", loglike); in MCMCregressChange_impl() 509 double loglike; in cMCMCregressChange() local 528 logmarglike, loglike); in cMCMCregressChange() 530 loglikeholder[0] = loglike; in cMCMCregressChange()
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/dports/math/R-cran-survey/survey/man/ |
H A D | svymle.Rd | 16 svymle(loglike, gradient = NULL, design, formulas, start = NULL, control 22 \item{loglike}{vectorised loglikelihood function} 23 …\item{gradient}{Derivative of \code{loglike}. Required for variance computation and helpful for fi… 35 \item{\dots}{Arguments to \code{loglike} and \code{gradient} that are 49 a linear predictor for each freely varying argument of \code{loglike}. 97 m1 <- svymle(loglike=dnorm,gradient=NULL, design=dstrat, 106 m2 <- svymle(loglike=dnorm,gradient=gr, design=dstrat, 115 m3 <- svymle(loglike=dnorm,gradient=gr, design=dstrat, 122 m3 <- svymle(loglike=dnorm,gradient=gr, design=dstrat, 160 m<-svymle(loglike=lcens, gradient=gcens, design=dpbc, method="newuoa", [all …]
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/tsa/examples/ |
H A D | ex_mle_arma.py | 41 print(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params)) 71 print(-np.linalg.inv(ndt.Hessian(arma1.loglike, stepMax=1e-2)(res2.params))[:2,:2]) 86 pcov = -np.linalg.inv(ndt.Hessian(arma4.loglike, stepMax=1e-2)(res4.params)) 117 pcov = -np.linalg.inv(ndt.Hessian(arma4.loglike, stepMax=1e-2)(res4.params))
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/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/regression/ |
H A D | runmnl.py | 91 def loglike(self, params): member in TryCLogit 97 loglike = (self.endog * np.log(probs)).sum(1) 101 return -loglike.sum() #return sum for now not for each observation 106 return optimize.fmin(self.loglike, start_params, maxfun=10000) 317 res = optimize.fmin(clogit.loglike, np.ones(6)) 321 res2 = optimize.fmin(clogit.loglike, tab2324) 323 res3 = optimize.fmin(clogit.loglike, np.zeros(6),maxfun=10000)
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/dports/science/R-cran-bayesm/bayesm/src/ |
H A D | rmnlIndepMetrop_rcpp_loop.cpp | 16 vec loglike(R/keep); in rmnlIndepMetrop_rcpp_loop() local 53 loglike[mkeep-1] = oldloglike; in rmnlIndepMetrop_rcpp_loop() 61 Named("loglike") = loglike, in rmnlIndepMetrop_rcpp_loop()
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/dports/math/py-spglm/spglm-1.0.8/spglm/ |
H A D | family.py | 197 def loglike(self, endog, mu, freq_weights=1., scale=1.): member in Family 315 def loglike(self, endog, mu, freq_weights=1., scale=1.): member in Poisson 337 loglike = np.sum(freq_weights * (endog * np.log(mu) - mu - 339 return scale * loglike 443 def loglike(self, endog, mu, freq_weights=1., scale=1.): member in QuasiPoisson 547 def loglike(self, endog, mu, freq_weights=1., scale=1.): member in Gaussian 681 def loglike(self, endog, mu, freq_weights=1., scale=1.): member in Gamma 866 def loglike(self, endog, mu, freq_weights=1, scale=1.): member in Binomial
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/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/ctc_include/detail/ |
H A D | gpu_ctc_kernels.h | 219 ProbT loglike = ctc_helper::neg_inf<ProbT>(); in compute_alpha_kernel() local 228 loglike = log_plus_f(loglike, alpha[i + (T - 1) * S]); in compute_alpha_kernel() 230 nll_forward[blockIdx.x] = -loglike; in compute_alpha_kernel() 454 ProbT loglike = ctc_helper::neg_inf<ProbT>(); in compute_betas_and_grad_kernel() local 463 loglike = log_plus_f(loglike, temp_buffer.beta[i]); in compute_betas_and_grad_kernel() 465 nll_backward[blockIdx.x] = -loglike; in compute_betas_and_grad_kernel()
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/dports/misc/py-mxnet/incubator-mxnet-1.9.0/3rdparty/ctc_include/detail/ |
H A D | gpu_ctc_kernels.h | 219 ProbT loglike = ctc_helper::neg_inf<ProbT>(); in compute_alpha_kernel() local 228 loglike = log_plus_f(loglike, alpha[i + (T - 1) * S]); in compute_alpha_kernel() 230 nll_forward[blockIdx.x] = -loglike; in compute_alpha_kernel() 454 ProbT loglike = ctc_helper::neg_inf<ProbT>(); in compute_betas_and_grad_kernel() local 463 loglike = log_plus_f(loglike, temp_buffer.beta[i]); in compute_betas_and_grad_kernel() 465 nll_backward[blockIdx.x] = -loglike; in compute_betas_and_grad_kernel()
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