Xqj/ddlZddlZddlZdZdZdZdZdZdZ dZ dd e d fd Z d Z d ZdZddZddZddZdS)Nc|tj|tj}tj|tj}|jdkr|d}|jd}|jdkr|d}|jddkr|dkr||d}|dddfd}|dddfd}tjd||z |z dzz|tj dtj zzz S)  Gaussian likelihood for batched θ. x: Tensor of shape (N,) or (batch_size, N) theta: Tensor of shape (batch_size, 2) -> [mu, sigma] for each θ Returns: Tensor of shape (batch_size, N) -> likelihood of each x for each θ dtyperN@ torch as_tensorfloat32ndim unsqueezeshapeexpandexpmathsqrtpi)xtheta correlation batch_sizemusigmas O/global/cfs/cdirs/lsst/www/DESC_TD_PUBLIC/users/Brodie/SBI/kestrel/Functions.py DistGaussianr s ///A OE 7 7 7E zQ""QJ v{{ KKNNwqzQ:>> HHZ $ $ qqq!t  q ! !B !!!Q$K ! !! $ $E 9Ta"fe^a// 0 0EDIcDGm [mu0, sigma, m] correlation: Same shape as x Returns: Tensor of shape (batch_size, N) rrrrNr r r r )rrrrmu0rmrs rDistGaussian_EVOLr$'s ///A OE 7 7 7E/+U]CCCK zQ""QJ v{{ KKNNwqzQ:>> HHZ $ $1!++A.. q  Z!^^!((R88 1+   " "C !!!Q$K ! !! $ $E aaad a  A q; B 9Ta"f-!33 4 4 #PTPW-@X@X8X YYr ctj|tj}tj|tj}|jdkr|d}|jd}|jdkr|d}|jddkr|dkr||d}|dddfd}d}d|z }d|z }||z tj||z |zz}|S)u Exponential likelihood for batched θ. x: Tensor of shape (N,) or (batch_size, N) theta: Returns: Tensor of shape (batch_size, N) -> likelihood of each x for each θ rrrrN??r rrrrrrr rrrrtau tau_proposal lambda_targetlambda_proposalweightss rDistExponentialr/Ps ///A OE 7 7 7E zQ""QJ v{{ KKNNwqzQ:>> HHZ $ $ 1+   " "CL#IML(O.%)mo>]<^ab [mu1, sigma1, mu2, sigma2, a] for each θ Returns: Tensor of shape (batch_size, N) -> likelihood of each x for each θ rrrrNr r r r ) rrrrmu1sigma1mu2sigma2aG1G2s rDistDoubleGaussianr<s ///A OE 7 7 7E zQ""QJ v{{ KKNNwqzQ:>> HHZ $ $ 1+   " "C 111a4[ " "1 % %F 1+   " "C 111a4[ " "1 % %F aaad a  A 4AGV+a// 0 0FTYsTW}=U=U4U VB 59Ta#gv-11 2 2 2ftytw?W?W6W XB b5Lr ctj|tj|j}tj|tj}tj|tj|j}|jdkr|d}|jd}|jdkr|d}|jddkr|dkr||d}|jdkr|d}|jddkr|dkr||d}|dddfd}|dddfd}|dddfd}|dtj||zdz zz dz}||z |z }tjd |dzz|tj d tj zzz } | S) z Logistic-shaped likelihood for importance sampling. x: (N,) or (B, N) theta: (B, 3) -> [L, k, sigma] correlation: (N,) or (B, N) Returns: (B, N) likelihood weights )rdevicerrrrNr r r ) r rrr>rrrrrrrr) rrrBLkrmeanzpdfs r DistLogisticrFs u|DDDA OE 7 7 7E/+U]5<XXXK zQ"" AAv{{ KKNNwqzQ1q55 HHQOO1!++A.. q  QUU!((B//  !!!Q$K ! !! $ $A !!!Q$K ! !! $ $A !!!Q$K ! !! $ $E EIa+o2333 4q 8D TUA )D1a4K EDIcDGm,D,D$D EC Jr cBtj|tj}tj|tj}|jdkr|d}|jd}|jdkr|d}|jddkr|dkr||d}|dddfd}|dddfd}d|z }||dz ztj| |zz}|S)u Gamma likelihood for batched θ. x: Tensor of shape (N,) or (batch_size, N) theta: Returns: Tensor of shape (batch_size, N) -> likelihood of each x for each θ rrrrNr'r()rrrrr*gammar,r.s r DistGammarIs ///A OE 7 7 7E zQ""QJ v{{ KKNNwqzQ:>> HHZ $ $ 1+   " "C !!!A#J  # #E#IM%!)nuy=)9A)=>>>G Nr 333333?infctj|tj}tj|tj}|jdkr|d}|jd}|jdkr|d}|jddkr|dkr||d}|dddfd}|dddfd}tjd||z |z dzz|tj dtj zzz }d } ||z |z } ||z |z } | | | | z } tj | d } ||k||kz} tj |}|| | || z || <|S) u Truncated Gaussian likelihood for batched θ. x: Tensor (N,) or (batch_size, N) theta: Tensor (batch_size, 2) -> [mu, sigma] low, high: truncation bounds (can be scalars or tensors) Returns: Tensor (batch_size, N) rrrrNr r r c`ddtj|tjdz zzS)Nr&rr )r erfrr)rDs r normal_cdfz)DistTruncatedGaussian..normal_cdf7s)a%)A ! $455566r g-q=)min)r rrrrrrrrrrclamp zeros_like expand_as)rrrlowhighrrrrErOr9bZmask truncated_pdfs rDistTruncatedGaussianrZs ///A OE 7 7 7E zQ""QJ v{{ KKNNwqzQ:>> HHZ $ $ qqq!t  q ! !B !!!Q$K ! !! $ $E )DQVUNQ.. / /549S47];S;S3S TC777 rUA eA 1 1 %A  A5!!!A Hd #D$S))Md)akk#&6&6t&<rs1  VVV<'Z'Z'ZR$$$L&&&R$$$L***X"""J15#EE%LL2222h   $"""*777           r