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tensoreflusso:: ops:: Gamma casuale
#include <random_ops.h>
Restituisce valori casuali dalle distribuzioni Gamma descritte da alfa.
Riepilogo
Questa operazione utilizza l'algoritmo di Marsaglia et al. acquisire campioni tramite trasformazione-rifiuto da coppie di variabili casuali uniformi e normali. Vedere http://dl.acm.org/citation.cfm?id=358414
Argomenti:
- scope: un oggetto Scope
- forma: tensore intero 1-D. Forma dei campioni indipendenti da trarre da ciascuna distribuzione descritta dai parametri di forma indicati in alfa.
- alfa: un tensore in cui ogni scalare è un parametro di "forma" che descrive la distribuzione gamma associata.
Attributi facoltativi (vedi Attrs
):
- seme: se
seed
o seed2
sono impostati su un valore diverso da zero, il generatore di numeri casuali viene seminato dal seme specificato. Altrimenti, viene seminato da un seme casuale. - seed2: un secondo seme per evitare la collisione del seme.
Resi:
-
Output
: un tensore con forma forma shape + shape(alpha)
. Ogni sezione [:, ..., :, i0, i1, ...iN]
contiene i campioni estratti per alpha[i0, i1, ...iN]
. Il dtype dell'output corrisponde al dtype di alpha.
Funzioni pubbliche statiche |
---|
Seed (int64 x) | |
Seed2 (int64 x) | |
Attributi pubblici
Funzioni pubbliche
nodo
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operatore::tensorflow::Output
operator::tensorflow::Output() const
Funzioni pubbliche statiche
Seme
Attrs Seed(
int64 x
)
Seme2
Attrs Seed2(
int64 x
)
Salvo quando diversamente specificato, i contenuti di questa pagina sono concessi in base alla licenza Creative Commons Attribution 4.0, mentre gli esempi di codice sono concessi in base alla licenza Apache 2.0. Per ulteriori dettagli, consulta le norme del sito di Google Developers. Java è un marchio registrato di Oracle e/o delle sue consociate.
Ultimo aggiornamento 2025-07-26 UTC.
[null,null,["Ultimo aggiornamento 2025-07-26 UTC."],[],[],null,["# tensorflow::ops::RandomGamma Class Reference\n\ntensorflow::ops::RandomGamma\n============================\n\n`#include \u003crandom_ops.h\u003e`\n\nOutputs random values from the Gamma distribution(s) described by alpha.\n\nSummary\n-------\n\nThis op uses the algorithm by Marsaglia et al. to acquire samples via transformation-rejection from pairs of uniform and normal random variables. See \u003chttp://dl.acm.org/citation.cfm?id=358414\u003e\n\nArguments:\n\n- scope: A [Scope](/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope) object\n- shape: 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in alpha.\n- alpha: A tensor in which each scalar is a \"shape\" parameter describing the associated gamma distribution.\n\n\u003cbr /\u003e\n\nOptional attributes (see [Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/random-gamma/attrs#structtensorflow_1_1ops_1_1_random_gamma_1_1_attrs)):\n\n- seed: If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.\n- seed2: A second seed to avoid seed collision.\n\n\u003cbr /\u003e\n\nReturns:\n\n- [Output](/versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output): A tensor with shape `shape + shape(alpha)`. Each slice `[:, ..., :, i0, i1, ...iN]` contains the samples drawn for `alpha[i0, i1, ...iN]`. The dtype of the output matches the dtype of alpha.\n\n\u003cbr /\u003e\n\n| ### Constructors and Destructors ||\n|---|---|\n| [RandomGamma](#classtensorflow_1_1ops_1_1_random_gamma_1a54b3819de158eaa8e1f4dd2e09c38350)`(const ::`[tensorflow::Scope](/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` shape, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` alpha)` ||\n| [RandomGamma](#classtensorflow_1_1ops_1_1_random_gamma_1afb5a4dcc9f3b7849c9ccf8e49233c658)`(const ::`[tensorflow::Scope](/versions/r1.15/api_docs/cc/class/tensorflow/scope#classtensorflow_1_1_scope)` & scope, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` shape, ::`[tensorflow::Input](/versions/r1.15/api_docs/cc/class/tensorflow/input#classtensorflow_1_1_input)` alpha, const `[RandomGamma::Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/random-gamma/attrs#structtensorflow_1_1ops_1_1_random_gamma_1_1_attrs)` & attrs)` ||\n\n| ### Public attributes ||\n|------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|\n| [operation](#classtensorflow_1_1ops_1_1_random_gamma_1a3442325c98888cd41398f85c8dc7215d) | [Operation](/versions/r1.15/api_docs/cc/class/tensorflow/operation#classtensorflow_1_1_operation) |\n| [output](#classtensorflow_1_1ops_1_1_random_gamma_1ae108904c41339fe8cced748589ef2622) | `::`[tensorflow::Output](/versions/r1.15/api_docs/cc/class/tensorflow/output#classtensorflow_1_1_output) |\n\n| ### Public functions ||\n|------------------------------------------------------------------------------------------------------------------------|------------------------|\n| [node](#classtensorflow_1_1ops_1_1_random_gamma_1a0a8429580ed9eda5d1b850c9fc9cd7c6)`() const ` | `::tensorflow::Node *` |\n| [operator::tensorflow::Input](#classtensorflow_1_1ops_1_1_random_gamma_1ad5e60091b7438c54f6d2457fccba06ed)`() const ` | ` ` ` ` |\n| [operator::tensorflow::Output](#classtensorflow_1_1ops_1_1_random_gamma_1a20b55a813e49ae84f48cd79c87285409)`() const ` | ` ` ` ` |\n\n| ### Public static functions ||\n|-------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------|\n| [Seed](#classtensorflow_1_1ops_1_1_random_gamma_1a62800c601cb18e766b0f41f18f86f335)`(int64 x)` | [Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/random-gamma/attrs#structtensorflow_1_1ops_1_1_random_gamma_1_1_attrs) |\n| [Seed2](#classtensorflow_1_1ops_1_1_random_gamma_1a42984b9ff3911c8867903be5bcd97ac7)`(int64 x)` | [Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/random-gamma/attrs#structtensorflow_1_1ops_1_1_random_gamma_1_1_attrs) |\n\n| ### Structs ||\n|-------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [tensorflow::ops::RandomGamma::Attrs](/versions/r1.15/api_docs/cc/struct/tensorflow/ops/random-gamma/attrs) | Optional attribute setters for [RandomGamma](/versions/r1.15/api_docs/cc/class/tensorflow/ops/random-gamma#classtensorflow_1_1ops_1_1_random_gamma). |\n\nPublic attributes\n-----------------\n\n### operation\n\n```text\nOperation operation\n``` \n\n### output\n\n```text\n::tensorflow::Output output\n``` \n\nPublic functions\n----------------\n\n### RandomGamma\n\n```gdscript\n RandomGamma(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input shape,\n ::tensorflow::Input alpha\n)\n``` \n\n### RandomGamma\n\n```gdscript\n RandomGamma(\n const ::tensorflow::Scope & scope,\n ::tensorflow::Input shape,\n ::tensorflow::Input alpha,\n const RandomGamma::Attrs & attrs\n)\n``` \n\n### node\n\n```gdscript\n::tensorflow::Node * node() const \n``` \n\n### operator::tensorflow::Input\n\n```gdscript\n operator::tensorflow::Input() const \n``` \n\n### operator::tensorflow::Output\n\n```gdscript\n operator::tensorflow::Output() const \n``` \n\nPublic static functions\n-----------------------\n\n### Seed\n\n```text\nAttrs Seed(\n int64 x\n)\n``` \n\n### Seed2\n\n```text\nAttrs Seed2(\n int64 x\n)\n```"]]