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/dports/devel/tcllib/tcllib-1.20/modules/math/
H A Dstat_logit.tcl68 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}]
/dports/devel/tcllibc/tcllib-1.20/modules/math/
H A Dstat_logit.tcl68 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}]
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/examples/
H A Dex_generic_mle.py17 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.)
H A Dex_generic_mle_t.py43 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)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/base/
H A D_penalized.py68 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)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tsa/statespace/tests/
H A Dtest_concentrated.py57 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 …]
/dports/math/R-cran-MCMCpack/MCMCpack/R/
H A DBayesFactors.R44 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)
/dports/math/R-cran-LearnBayes/LearnBayes/R/
H A Dpoisson.gamma.mix.R8 loglike=0 functionVar
10 loglike=loglike+dpois(y[j],L*t[j],log=TRUE)
12 m.prob=exp(loglike+
H A Dlogpoissnormal.R6 loglike=log(dgamma(lambda,shape=sum(y)+1,scale=1/length(y))) functionVar
8 return(loglike+logprior)
H A Dlogpoissgamma.R6 loglike=log(dgamma(lambda,shape=sum(y)+1,rate=length(y))) functionVar
8 return(loglike+logprior)
H A Dbayes.model.selection.R18 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)
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/tools/tests/
H A Dtest_numdiff.py52 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 …]
/dports/astro/py-astropy/astropy-5.0/astropy/timeseries/periodograms/bls/
H A Dmethods.py126 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)
/dports/math/R-cran-MCMCpack/MCMCpack/src/
H A DMCMCmnl.h47 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()
H A DMCMCnbutil.cc322 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()
H A DcMCMCprobit.cc126 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()
H A DcMCMCprobitres.cc135 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()
H A DcMCMCregressChange.cc49 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()
/dports/math/R-cran-survey/survey/man/
H A Dsvymle.Rd16 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 …]
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/tsa/examples/
H A Dex_mle_arma.py41 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))
/dports/math/py-statsmodels/statsmodels-0.13.1/statsmodels/sandbox/regression/
H A Drunmnl.py91 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)
/dports/science/R-cran-bayesm/bayesm/src/
H A DrmnlIndepMetrop_rcpp_loop.cpp16 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()
/dports/math/py-spglm/spglm-1.0.8/spglm/
H A Dfamily.py197 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
/dports/misc/mxnet/incubator-mxnet-1.9.0/3rdparty/ctc_include/detail/
H A Dgpu_ctc_kernels.h219 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()
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/3rdparty/ctc_include/detail/
H A Dgpu_ctc_kernels.h219 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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