cri1~ddlZddlmZddlmZddlZddlmZddlmZddl m Z ddl m Z dgZ Gd dZdS) N)Optional) deprecated)Tensor) constraints) lazy_property)_size Distributionc 6eZdZdZdZdZdZededdfdZ e j e j dfde j d e j d e eddffd Z d'defd Zede j fd Zede j fdZedeeejffdZede ejfdZedefdZedefdZedefdZedefdZe j fdedefdZe j fdedefdZe de!de"defdZ#dedefdZ$dedefdZ%dedefdZ&d(dedefd Z'defd!Z(defd"Z)e j fdede j fd#Z*deddfd$Z+d'd%Z,defd&Z-xZ.S))r aS Distribution is the abstract base class for probability distributions. Args: batch_shape (torch.Size): The shape over which parameters are batched. event_shape (torch.Size): The shape of a single sample (without batching). validate_args (bool, optional): Whether to validate arguments. Default: None. FTvaluereturnNc4|dvrt|t_dS)a Sets whether validation is enabled or disabled. The default behavior mimics Python's ``assert`` statement: validation is on by default, but is disabled if Python is run in optimized mode (via ``python -O``). Validation may be expensive, so you may want to disable it once a model is working. Args: value (bool): Whether to enable validation. )TFN) ValueErrorr _validate_args)r s x/var/www/html/bestrading.cuttalo.com/models/btc_v9/venv/lib/python3.11/site-packages/torch/distributions/distribution.pyset_default_validate_argsz&Distribution.set_default_validate_argss#  % % &+ ### batch_shape event_shape validate_argsc||_||_|||_|jr@ |j}n6#t$r)i}t j|jddzdzdYnwxYw|D]\}}tj |r||j vr1ttt||trTt||}||}t#j|s_t'd|dt|jdt+|jd t/|d t/|d | t1dS) Nz$ does not define `arg_constraints`. zAPlease set `arg_constraints = {}` or initialize the distribution z2with `validate_args=False` to turn off validation. stacklevelzExpected parameter z ( of shape z) of distribution z to satisfy the constraint , but found invalid values: ) _batch_shape _event_shaperarg_constraintsNotImplementedErrorwarningswarn __class__itemsr is_dependent__dict__ isinstancegetattrtyperchecktorch _is_all_truer__name__tupleshapereprsuper__init__) selfrrrrparam constraintr validr"s rr1zDistribution.__init__/s ('  $"/D     "&"6&   "$ ~KKKYZJK !  &5%:%:%<%<  !z+J77 --*DJJ.. 33-e,,"((//)%00$>e>> KK0>>>+/::>>6:*5E5E>>7< >> s*0AAct)a/ Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to `batch_shape`. This method calls :class:`~torch.Tensor.expand` on the distribution's parameters. As such, this does not allocate new memory for the expanded distribution instance. Additionally, this does not repeat any args checking or parameter broadcasting in `__init__.py`, when an instance is first created. Args: batch_shape (torch.Size): the desired expanded size. _instance: new instance provided by subclasses that need to override `.expand`. Returns: New distribution instance with batch dimensions expanded to `batch_size`. r)r2r _instances rexpandzDistribution.expandWs &"!rc|jS)zF Returns the shape over which parameters are batched. )rr2s rrzDistribution.batch_shapel   rc|jS)zJ Returns the shape of a single sample (without batching). )rr;s rrzDistribution.event_shapesr<rct)a  Returns a dictionary from argument names to :class:`~torch.distributions.constraints.Constraint` objects that should be satisfied by each argument of this distribution. Args that are not tensors need not appear in this dict. r7r;s rrzDistribution.arg_constraintsz "!rct)z Returns a :class:`~torch.distributions.constraints.Constraint` object representing this distribution's support. r7r;s rsupportzDistribution.support "!rct)z7 Returns the mean of the distribution. r7r;s rmeanzDistribution.mean "!rc0t|jd)z7 Returns the mode of the distribution. z does not implement mode)rr"r;s rmodezDistribution.modes "T^"M"M"MNNNrct)z; Returns the variance of the distribution. r7r;s rvariancezDistribution.variancerErc4|jS)zE Returns the standard deviation of the distribution. )rIsqrtr;s rstddevzDistribution.stddevs }!!###r sample_shapectj5||cdddS#1swxYwYdS)z Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched. N)r*no_gradrsampler2rMs rsamplezDistribution.samples ]__ . .<< -- . . . . . . . . . . . . . . . . . .s 6::ct)z Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched. r7rQs rrPzDistribution.rsamplerBrz=`sample_n(n)` will be deprecated. Use `sample((n,))` instead.)categoryncR|tj|fS)zq Generates n samples or n batches of samples if the distribution parameters are batched. )rRr*Size)r2rUs rsample_nzDistribution.sample_ns"{{5:qd++,,,rct)z Returns the log of the probability density/mass function evaluated at `value`. Args: value (Tensor): r7r2r s rlog_probzDistribution.log_probr?rct)z Returns the cumulative density/mass function evaluated at `value`. Args: value (Tensor): r7rZs rcdfzDistribution.cdfr?rct)z Returns the inverse cumulative density/mass function evaluated at `value`. Args: value (Tensor): r7rZs ricdfzDistribution.icdfr?rr9ct)ar Returns tensor containing all values supported by a discrete distribution. The result will enumerate over dimension 0, so the shape of the result will be `(cardinality,) + batch_shape + event_shape` (where `event_shape = ()` for univariate distributions). Note that this enumerates over all batched tensors in lock-step `[[0, 0], [1, 1], ...]`. With `expand=False`, enumeration happens along dim 0, but with the remaining batch dimensions being singleton dimensions, `[[0], [1], ..`. To iterate over the full Cartesian product use `itertools.product(m.enumerate_support())`. Args: expand (bool): whether to expand the support over the batch dims to match the distribution's `batch_shape`. Returns: Tensor iterating over dimension 0. r7)r2r9s renumerate_supportzDistribution.enumerate_supports ,"!rct)z Returns entropy of distribution, batched over batch_shape. Returns: Tensor of shape batch_shape. r7r;s rentropyzDistribution.entropys "!rcNtj|S)z Returns perplexity of distribution, batched over batch_shape. Returns: Tensor of shape batch_shape. )r*exprcr;s r perplexityzDistribution.perplexitysy(((rct|tjstj|}tj||jz|jzS)ax Returns the size of the sample returned by the distribution, given a `sample_shape`. Note, that the batch and event shapes of a distribution instance are fixed at the time of construction. If this is empty, the returned shape is upcast to (1,). Args: sample_shape (torch.Size): the size of the sample to be drawn. )r&r*rWrrrQs r_extended_shapezDistribution._extended_shape sG, 33 4 :l33Lz,)::T=NNOOOrc t|tjstdt |t |jz }||d|jkr-td|d|jd|}|j|jz}tt|t|D]-\}}|dkr"|dkr||krtd|d|d. |j }n5#t$r(tj |jdd zd zd YdSwxYw|J||}tj|s\td t#|jdt'|jdt+|dt+|d| dS)a Argument validation for distribution methods such as `log_prob`, `cdf` and `icdf`. The rightmost dimensions of a value to be scored via these methods must agree with the distribution's batch and event shapes. Args: value (Tensor): the tensor whose log probability is to be computed by the `log_prob` method. Raises ValueError: when the rightmost dimensions of `value` do not match the distribution's batch and event shapes. z/The value argument to log_prob must be a TensorNz5The right-most size of value must match event_shape: z vs .z9Value is not broadcastable with batch_shape+event_shape: z% does not define `support` to enable z;sample validation. Please initialize the distribution with z-`validate_args=False` to turn off validation.rrzExpected value argument (rz) to be within the support (z) of the distribution r)r&r*rrlensizerrzipreversedrArr r!r"r)r+r(r,r-r.r/) r2r event_dim_start actual_shapeexpected_shapeijrAr5s r_validate_samplezDistribution._validate_samplesY%.. PNOO Oejjll++c$2C.D.DD ::<<(( )T-> > >n nnZ^Zknnn zz|| *T->>..0H0HII  DAqAvv!q&&Q!VV sP\ssbpsss lGG"    M>HHHOPAB      FF """ e$$!%(( 6KK(6649%+4F4F66-1']]66(,Dzz66/4 66   s.D66.E('E(c|Bt|j|jkr%td|jjd|jd|"|t|n|S)Nz Subclass z of zR that defines a custom __init__ method must also define a custom .expand() method.)r(r1rr"r,__new__)r2clsr8s r_get_checked_instancez"Distribution._get_checked_instanceLs|  d!4 !D!D%>DN3>>>>> ,5+z)Distribution.__repr__..Us)XXXTQQ$-EWEWqEWEWEWrz, cg|]V}|dj|dkr j|nj|WS)z: rk)r%numelrm)r}pr2s rrz)Distribution.__repr__..Wst   hhDM!,<,B,B,D,D,I,I a((t}]^O_OdOdOfOfhh   r())rr#joinr"r,)r2 param_names args_strings` r__repr__zDistribution.__repr__TsXXXXT%9%?%?%A%AXXX ii    $     ~&,{:S@@r)N)T)/r, __module__ __qualname____doc__ has_rsamplehas_enumerate_supportr staticmethodboolrr*rWrr1rr9propertyrrdictstrr ConstraintrrArrDrGrIrLrRrPr FutureWarningintrXr[r]r_rarcrfrhruryr __classcell__)r"s@rr r sYK!N,,$,,,\,$#-%*,,",%*,,(, &&Z&Z& ~ &  &&&&&&P""%""""*!UZ!!!X! !UZ!!!X! "c;+A&A!B"""X""+"89"""X""f"""X" OfOOOXO "&"""X" $$$$X$ ,65:<<..5.F....-7EJLL""E"V""""ZG-#-&--- -"f"""""""F"""""&"V"""""""""""0""""")F))))5?EJLL P PE PUZ P P P P1f11111fLLLLA#AAAAAAAAr)r typingrtyping_extensionsrr*rtorch.distributionsrtorch.distributions.utilsr torch.typesr__all__r r|rrrs(((((( ++++++333333  LALALALALALALALALALAr