GIF89a=( õ' 7IAXKgNgYvYx\%wh…hŽth%ˆs%—x¨}9®Œ©€&©‰%¶†(¹–.¹5·œD¹&Çš)ÇŸ5ǘ;Í£*È¡&Õ²)ׯ7×µ<Ñ»4ï°3ø‘HÖ§KͯT÷¨Yÿšqÿ»qÿÔFØ !ù ' !ÿ NETSCAPE2.0 , =( þÀ“pH,È¤rÉl:ŸÐ¨tJ­Z¯Ø¬vËíz¿à°xL.›Ïè´zÍn»ßð¸|N¯Ûïø¼~Ïïûÿ€‚ƒ„…†‡ˆ‰Š‹ŒŽ‘’“”•–—˜™š›œžŸ ¡¢£¤¥¦§gª«ªE¯°¨¬ª±²Œ¹º¹E¾­”´ÂB¶¯ §Åȸ»ÑD¾¿Á•ÄÅ®° ÝH¾ÒLÀÆDÙ«D¶BÝïðÀ¾DÑÑÔTÌÍíH òGö¨A RÎڐ |¥ ٭&ºìE8œ¹kGÔAÞpx­a¶­ã R2XB®åE8I€Õ6Xî:vT)äžþÀq¦è³¥ì仕F~%xñ  4#ZÔ‰O|-4Bs‘X:= QÉ œš lºÒyXJŠGȦ|s hÏíK–3l7·B|¥$'7Jީܪ‰‡àá”Dæn=Pƒ ¤Òëí‰`䌨ljóá¯Éüv>á–Á¼5 ½.69ûϸd«­ºÀûnlv©‹ªîf{¬ÜãPbŸ  l5‘Ž¯pß ´ ˜3aÅùäI«O’ý·‘áÞ‡˜¾Æ‚ÙÏiÇÿ‹Àƒ #öó)pâš Þ½ ‘Ý{ó)vmÞü%D~ 6f s}ŃƒDØW Eþ`‡þ À…L8xá†ç˜{)x`X/> Ì}mø‚–RØ‘*|`D=‚Ø_ ^ð5 !_…'aä“OÚ—7âcð`D”Cx`ÝÂ¥ä‹éY¹—F¼¤¥Š?¡Õ™ n@`} lď’ÄÉ@4>ñd œ à‘vÒxNÃ×™@žd=ˆgsžG±æ ´²æud &p8Qñ)ˆ«lXD©øÜéAžHìySun jª×k*D¤LH] †¦§C™Jä–´Xb~ʪwStŽ6K,°£qÁœ:9ت:¨þªl¨@¡`‚ûÚ ».Û¬¯t‹ÆSÉ[:°=Š‹„‘Nåû”Ìî{¿ÂA ‡Rà›ÀÙ6úë°Ÿð0Ä_ ½;ÃϱîÉì^ÇÛÇ#Ëë¼ôº!±Ä˜íUîÅÇ;0L1óÁµö«p% AÀºU̬ݵ¼á%霼€‡¯Á~`ÏG¯»À× ­²± =4ªnpð3¾¤³¯­ü¾¦îuÙuµÙ®|%2ÊIÿür¦#0·ÔJ``8È@S@5ê¢ ö×Þ^`8EÜ]ý.뜃Âç 7 ú ȉÞj œ½Dç zý¸iþœÑÙûÄë!ˆÞÀl§Ïw‹*DçI€nEX¯¬¼ &A¬Go¼QföõFç°¯;é¦÷îŽêJ°îúôF5¡ÌQ|îúöXªæ»TÁÏyñêï]ê² o óÎC=öõ›ÒÓPB@ D×½œä(>èCÂxŽ`±«Ÿ–JЀ»Û á¤±p+eE0`ëŽ`A Ú/NE€Ø†À9‚@¤à H½7”à‡%B‰`Àl*ƒó‘–‡8 2ñ%¸ —€:Ù1Á‰E¸àux%nP1ð!‘ðC)¾P81lÑɸF#ˆ€{´âé°ÈB„0>±û °b¡Š´±O‚3È–Ù()yRpbµ¨E.Z‘D8ÊH@% òŒx+%Ù˜Æcü »¸˜fõ¬b·d`Fê™8èXH"ÉÈ-±|1Ô6iI, 2““¬$+](A*jÐ QTÂo‰.ÛU슬Œã„Ž`¯SN¡–¶Äåyše¯ª’­¬‚´b¦Éož œ)åyâ@Ì®3 ÎtT̉°&Ø+žLÀf"Ø-|žçÔ>‡Ðv¦Ðžì\‚ Q1)Ž@Žh#aP72”ˆ™¨$‚ !ù " , =( …7IAXG]KgNgYvYxR"k\%w]'}hŽth%ˆg+ˆs%—r.—m3šx3˜x¨}9®€&©€+¨‡7§‰%¶†(¹–.¹œD¹&ǘ;Í•&ײ)×»4ïÌ6ò§KÍ þ@‘pH,È¤rÉl:ŸÐ¨tJ­Z¯Ø¬vËíz¿à°xL.›Ïè´zÍn»ßð¸|N¯Ûïø¼~Ïïûÿ€‚ƒ„…†‡ˆ‰Š‹ŒŽ‘’“”•–—˜™š›œžŸ ¡¢£¤¥¦§g «¬ E ±± ¨­¶°ººE Á´”·®C¬²§Ç¶Œ»ÓDÃÕƷ¯Ê±H½ºM×ÁGÚ¬D¶BËÁ½î½DÓôTÏÛßîG»ôõC×CÌ l&âž:'òtU³6ɹ#·Ø)€'Ü.6±&ëÍÈ» K(8p0N?!æ2"ÛˆNIJX>R¼ÐO‚M '¡¨2¸*Ÿþ>#n↠å@‚<[:¡Iïf’ ¤TÚ˘CdbÜÙ“[«ŽEú5MBo¤×@€`@„€Êt W-3 ¶Ÿ¡BíêäjIÝ…Eò9[T…$íêﯧ„…•s»Óȳ¹€ÅÚdc®UUρ#±Ùïldj?´í¼²`\ŽÁðÞu|3'ÖŒ]ë6 ¶S#²‡˜FKLÈ *N E´‘áäŠ$˜›eÄYD„ºq«.è촁ƒs \-ÔjA 9²õ÷å- üúM[Âx(ís÷ì®x€|í¡Ù’p¦‚ ŽkÛTÇDpE@WÜ ²Ç]kŠ1¨ þ€·Yb ÓÁ‰l°*n0 ç™—žzBdОu¾7ĉBl€â‰-ºx~|UåU‰  h*Hœ|e"#"?vpÄiŠe6^ˆ„+qâŠm8 #VÇá ‘å–ÄV„œ|Šè•m"сœn|@›U¶ÆΞ—Špb¥G¨ED”€±Úê2FÌIç? >Éxå Œ± ¡¤„%‘žjŸ‘ꄯ<Ìaà9ijÐ2˜D¦È&›†Z`‚å]wþ¼Â:ç6àB¤7eFJ|õÒ§Õ,¨äàFÇ®cS·Ê¶+B°,‘Þ˜ºNûãØ>PADÌHD¹æž«ÄÀnÌ¥}­#Ë’ë QÀÉSÌÂÇ2ÌXÀ{æk²lQÁ2«ÊðÀ¯w|2Í h‹ÄÂG€,m¾¶ë3ÐÙ6-´ÅE¬L°ÆIij*K½ÀÇqï`DwVÍQXœÚÔpeœ±¬Ñ q˜§Tœ½µƒ°Œìu Â<¶aØ*At¯lmEØ ü ôÛN[P1ÔÛ¦­±$ÜÆ@`ùåDpy¶yXvCAyåB`ŽD¶ 0QwG#¯ æš[^Äþ $ÀÓÝǦ{„L™[±úKÄgÌ;ï£S~¹ìGX.ôgoT.»åˆ°ùŸûù¡?1zö¦Ÿž:ÅgÁ|ìL¹ „®£œŠ‚à0œ]PÁ^p F<"•ç?!,ñ‡N4—…PÄ Á„ö¨Û:Tè@hÀ‹%táÿ:ø-žI<`þ‹p I….)^ 40D#p@ƒj4–؀:²‰1Øâr˜¼F2oW¼#Z†;$Q q” ‘ ÂK¦ñNl#29 !’F@¥Bh·ᏀL!—XFóLH‘Kh¤.«hE&JòG¨¥<™WN!€ÑÙÚˆY„@†>Œž19J" 2,/ &.GXB%ÌRÈ9B6¹W]’î×ÔW¥’IÎ$ ñ‹ÓŒE8YÆ ¼³™ñA5“à®Q.aŸB€&Ø©³ JÁ—! ¦t)K%tœ-¦JF bòNMxLôþ)ÐR¸Ð™‘ èÝ6‘O!THÌ„HÛ ‰ !ù ) , =( …AXKgNgYvYxR"k\%wh…hŽh%ˆg+ˆs%—r.—x3˜x¨}9®€&©€+¨Œ,©‡7§‰%¶†(¹–.¹5·&Çš)ǘ;Í•&×£*Ȳ)ׯ7×»4ï°3øÌ6ò‘HÖ§KÍ»Hó¯T÷¨Yÿ»qÿÇhÿ þÀ”pH,È¤rÉl:ŸÐ¨tJ­Z¯Ø¬vËíz¿à°xL.›Ïè´zÍn»ßð¸|N¯Ûïø¼~Ïïûÿ€‚ƒ„…†‡ˆ‰Š‹ŒŽ‘’“”•–—˜™š›œžŸ ¡¢£¤¥¦§g ª« E$±²¨ª­ · °²½$E$ÂÕ««D· Í ¿¦Ç¶¸ÌŒ¾³CÃÅÆ E ééH½MÛÂGâªD­ çBêêϾD²ÒaÀà€Š1r­ðÓ¤ ÔožzU!L˜C'¾yW½UGtäÇïÙllê0×àÂuGþ)AÀs[þ·xì ÁxO%ƒûX2ó—  P£n›R/¡ÑšHše+êDm?# —‘Ç£6¡8íJ¡ŸâDiäªM¥Ö„ôj“¬¹£5oQ7°- <‡ *´lãÓŒ2r/a!l)dÈ A™ÈE¢ôÔ͆…ð ;Ö˜c ¡%ß‚’Ùˆâ¸b½—pe~C"BíëÚHïeF2§æŠ8qb t_`urŠeü wÅu3êæPv§h•"ß`íÍxçLĹÜÖ3á  ~Öº“®›¸ÏMDfJÙ °„ÛµáWõ%§œ‚à©–‚X Ó؁)@®Ñ›Eþ´wëuÅSxb8y\mÖzœ¥§ZbºE—ÂLªÌw!y(>¡™wú=Ç|ÅÝs¢d €CÁW)HÜcC$€L Ä7„r.á\{)@ð` @ äXÈ$PD” `šaG:§æˆOˆ72EÐamn]ù"ŒcÊxÑŒ° &dR8`g«iÙŸLR!¦P …d’ä¡“¦ðÎTƒ¦ià|À _ ¥ Qi#¦Šg›Æ ›noMµ ›V ã£)p ç£ÎW…š=Âeªk§†j„ ´®1ß²sÉxéW«jšl|0¯B0Û, \jÛ´›6±¬¶C ÛíWþï|ëÙ‹¸ñzĸV {ì;Ýñn¼òVˆm³I¼³.Ðã¤PN¥ ²µ¼„µCã+¹ÍByî£Ñ¾HŸ›ëê 7ìYÆFTk¨SaoaY$Dµœìï¿Ã29RÈkt Çïfñ ÇÒ:ÀÐSp¹3ÇI¨â¥DZÄ ü9Ïýögñ½­uÔ*3)O‘˜Ö[_hv ,àî×Et Ÿé¶BH€ Õ[ü±64M@ÔSÌM7dÐl5-ÄÙU܍´©zߌ3Ô€3ž„ „ ¶ÛPô½5×g› êÚ˜kN„Ý…0Îj4€Ìë°“#{þÕ3S2çKÜ'ợlø¼Ú2K{° {Û¶?žm𸧠ËI¼nEò='êüóºè^üæÃ_Û=°óž‚ì#Oý¿Í'¡½áo..ÏYìnüñCœO±Áa¿¢Kô½o,üÄËbö²çºíï{ËC Ú— "”Ï{ËK ÍÒw„õ±Oz dÕ¨à:$ ƒô—«v»] A#ð «€¿šéz)Rx׿ˆ¥‚d``èw-îyÏf×K!ð€þ­Ð|ìPľ„=Ì`ý(f” 'Pa ¥ÐBJa%Ðâf§„%Š¡}FàáÝ×6>ÉäŠG"éŽè=ø!oŠ°^FP¼Ø©Q„ÀCÙÁ`(Ž\ÄÝ® ©Â$<n@dÄ E#ììUÒI! ‚#lù‹`k¦ÐÇ'Rró’ZýNBÈMF Í[¤+‹ðɈ-áwj¨¥þ8¾rá ,VÂh„"|½œ=×G_¦Ñ™EØ 0i*%̲˜Æda0mV‚k¾)›;„&6 p>ÓjK “¦Ç# âDÂ:ûc?:R Ó¬fÞéI-Ì“•Ã<ä=™Ï7˜3œ¨˜c2ŒW ,ˆ”8(T™P‰F¡Jhç"‚ ; 403WebShell
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Current File : /opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/random/mtrand.pyi
from collections.abc import Callable
from typing import Any, Union, overload, Literal

from numpy import (
    bool_,
    dtype,
    float32,
    float64,
    int8,
    int16,
    int32,
    int64,
    int_,
    ndarray,
    uint,
    uint8,
    uint16,
    uint32,
    uint64,
)
from numpy.random.bit_generator import BitGenerator
from numpy._typing import (
    ArrayLike,
    _ArrayLikeFloat_co,
    _ArrayLikeInt_co,
    _DoubleCodes,
    _DTypeLikeBool,
    _DTypeLikeInt,
    _DTypeLikeUInt,
    _Float32Codes,
    _Float64Codes,
    _Int8Codes,
    _Int16Codes,
    _Int32Codes,
    _Int64Codes,
    _IntCodes,
    _ShapeLike,
    _SingleCodes,
    _SupportsDType,
    _UInt8Codes,
    _UInt16Codes,
    _UInt32Codes,
    _UInt64Codes,
    _UIntCodes,
)

_DTypeLikeFloat32 = Union[
    dtype[float32],
    _SupportsDType[dtype[float32]],
    type[float32],
    _Float32Codes,
    _SingleCodes,
]

_DTypeLikeFloat64 = Union[
    dtype[float64],
    _SupportsDType[dtype[float64]],
    type[float],
    type[float64],
    _Float64Codes,
    _DoubleCodes,
]

class RandomState:
    _bit_generator: BitGenerator
    def __init__(self, seed: None | _ArrayLikeInt_co | BitGenerator = ...) -> None: ...
    def __repr__(self) -> str: ...
    def __str__(self) -> str: ...
    def __getstate__(self) -> dict[str, Any]: ...
    def __setstate__(self, state: dict[str, Any]) -> None: ...
    def __reduce__(self) -> tuple[Callable[[str], RandomState], tuple[str], dict[str, Any]]: ...
    def seed(self, seed: None | _ArrayLikeFloat_co = ...) -> None: ...
    @overload
    def get_state(self, legacy: Literal[False] = ...) -> dict[str, Any]: ...
    @overload
    def get_state(
        self, legacy: Literal[True] = ...
    ) -> dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]: ...
    def set_state(
        self, state: dict[str, Any] | tuple[str, ndarray[Any, dtype[uint32]], int, int, float]
    ) -> None: ...
    @overload
    def random_sample(self, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def random_sample(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def random(self, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def random(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def beta(self, a: float, b: float, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def beta(
        self, a: _ArrayLikeFloat_co, b: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def exponential(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def exponential(
        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def standard_exponential(self, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def standard_exponential(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def tomaxint(self, size: None = ...) -> int: ...  # type: ignore[misc]
    @overload
    def tomaxint(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[int_]]: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: int,
        high: None | int = ...,
    ) -> int: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: int,
        high: None | int = ...,
        size: None = ...,
        dtype: _DTypeLikeBool = ...,
    ) -> bool: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: int,
        high: None | int = ...,
        size: None = ...,
        dtype: _DTypeLikeInt | _DTypeLikeUInt = ...,
    ) -> int: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[int_]]: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
        dtype: _DTypeLikeBool = ...,
    ) -> ndarray[Any, dtype[bool_]]: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
        dtype: dtype[int8] | type[int8] | _Int8Codes | _SupportsDType[dtype[int8]] = ...,
    ) -> ndarray[Any, dtype[int8]]: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
        dtype: dtype[int16] | type[int16] | _Int16Codes | _SupportsDType[dtype[int16]] = ...,
    ) -> ndarray[Any, dtype[int16]]: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
        dtype: dtype[int32] | type[int32] | _Int32Codes | _SupportsDType[dtype[int32]] = ...,
    ) -> ndarray[Any, dtype[int32]]: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
        dtype: None | dtype[int64] | type[int64] | _Int64Codes | _SupportsDType[dtype[int64]] = ...,
    ) -> ndarray[Any, dtype[int64]]: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
        dtype: dtype[uint8] | type[uint8] | _UInt8Codes | _SupportsDType[dtype[uint8]] = ...,
    ) -> ndarray[Any, dtype[uint8]]: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
        dtype: dtype[uint16] | type[uint16] | _UInt16Codes | _SupportsDType[dtype[uint16]] = ...,
    ) -> ndarray[Any, dtype[uint16]]: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
        dtype: dtype[uint32] | type[uint32] | _UInt32Codes | _SupportsDType[dtype[uint32]] = ...,
    ) -> ndarray[Any, dtype[uint32]]: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
        dtype: dtype[uint64] | type[uint64] | _UInt64Codes | _SupportsDType[dtype[uint64]] = ...,
    ) -> ndarray[Any, dtype[uint64]]: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
        dtype: dtype[int_] | type[int] | type[int_] | _IntCodes | _SupportsDType[dtype[int_]] = ...,
    ) -> ndarray[Any, dtype[int_]]: ...
    @overload
    def randint(  # type: ignore[misc]
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
        dtype: dtype[uint] | type[uint] | _UIntCodes | _SupportsDType[dtype[uint]] = ...,
    ) -> ndarray[Any, dtype[uint]]: ...
    def bytes(self, length: int) -> bytes: ...
    @overload
    def choice(
        self,
        a: int,
        size: None = ...,
        replace: bool = ...,
        p: None | _ArrayLikeFloat_co = ...,
    ) -> int: ...
    @overload
    def choice(
        self,
        a: int,
        size: _ShapeLike = ...,
        replace: bool = ...,
        p: None | _ArrayLikeFloat_co = ...,
    ) -> ndarray[Any, dtype[int_]]: ...
    @overload
    def choice(
        self,
        a: ArrayLike,
        size: None = ...,
        replace: bool = ...,
        p: None | _ArrayLikeFloat_co = ...,
    ) -> Any: ...
    @overload
    def choice(
        self,
        a: ArrayLike,
        size: _ShapeLike = ...,
        replace: bool = ...,
        p: None | _ArrayLikeFloat_co = ...,
    ) -> ndarray[Any, Any]: ...
    @overload
    def uniform(self, low: float = ..., high: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def uniform(
        self,
        low: _ArrayLikeFloat_co = ...,
        high: _ArrayLikeFloat_co = ...,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def rand(self) -> float: ...
    @overload
    def rand(self, *args: int) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def randn(self) -> float: ...
    @overload
    def randn(self, *args: int) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def random_integers(self, low: int, high: None | int = ..., size: None = ...) -> int: ...  # type: ignore[misc]
    @overload
    def random_integers(
        self,
        low: _ArrayLikeInt_co,
        high: None | _ArrayLikeInt_co = ...,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[int_]]: ...
    @overload
    def standard_normal(self, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def standard_normal(  # type: ignore[misc]
        self, size: _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def normal(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def normal(
        self,
        loc: _ArrayLikeFloat_co = ...,
        scale: _ArrayLikeFloat_co = ...,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def standard_gamma(  # type: ignore[misc]
        self,
        shape: float,
        size: None = ...,
    ) -> float: ...
    @overload
    def standard_gamma(
        self,
        shape: _ArrayLikeFloat_co,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def gamma(self, shape: float, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def gamma(
        self,
        shape: _ArrayLikeFloat_co,
        scale: _ArrayLikeFloat_co = ...,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def f(self, dfnum: float, dfden: float, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def f(
        self, dfnum: _ArrayLikeFloat_co, dfden: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def noncentral_f(self, dfnum: float, dfden: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def noncentral_f(
        self,
        dfnum: _ArrayLikeFloat_co,
        dfden: _ArrayLikeFloat_co,
        nonc: _ArrayLikeFloat_co,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def chisquare(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def chisquare(
        self, df: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def noncentral_chisquare(self, df: float, nonc: float, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def noncentral_chisquare(
        self, df: _ArrayLikeFloat_co, nonc: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def standard_t(self, df: float, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def standard_t(
        self, df: _ArrayLikeFloat_co, size: None = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def standard_t(
        self, df: _ArrayLikeFloat_co, size: _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def vonmises(self, mu: float, kappa: float, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def vonmises(
        self, mu: _ArrayLikeFloat_co, kappa: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def pareto(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def pareto(
        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def weibull(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def weibull(
        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def power(self, a: float, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def power(
        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def standard_cauchy(self, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def standard_cauchy(self, size: _ShapeLike = ...) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def laplace(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def laplace(
        self,
        loc: _ArrayLikeFloat_co = ...,
        scale: _ArrayLikeFloat_co = ...,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def gumbel(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def gumbel(
        self,
        loc: _ArrayLikeFloat_co = ...,
        scale: _ArrayLikeFloat_co = ...,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def logistic(self, loc: float = ..., scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def logistic(
        self,
        loc: _ArrayLikeFloat_co = ...,
        scale: _ArrayLikeFloat_co = ...,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def lognormal(self, mean: float = ..., sigma: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def lognormal(
        self,
        mean: _ArrayLikeFloat_co = ...,
        sigma: _ArrayLikeFloat_co = ...,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def rayleigh(self, scale: float = ..., size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def rayleigh(
        self, scale: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def wald(self, mean: float, scale: float, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def wald(
        self, mean: _ArrayLikeFloat_co, scale: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def triangular(self, left: float, mode: float, right: float, size: None = ...) -> float: ...  # type: ignore[misc]
    @overload
    def triangular(
        self,
        left: _ArrayLikeFloat_co,
        mode: _ArrayLikeFloat_co,
        right: _ArrayLikeFloat_co,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[float64]]: ...
    @overload
    def binomial(self, n: int, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
    @overload
    def binomial(
        self, n: _ArrayLikeInt_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[int_]]: ...
    @overload
    def negative_binomial(self, n: float, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
    @overload
    def negative_binomial(
        self, n: _ArrayLikeFloat_co, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[int_]]: ...
    @overload
    def poisson(self, lam: float = ..., size: None = ...) -> int: ...  # type: ignore[misc]
    @overload
    def poisson(
        self, lam: _ArrayLikeFloat_co = ..., size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[int_]]: ...
    @overload
    def zipf(self, a: float, size: None = ...) -> int: ...  # type: ignore[misc]
    @overload
    def zipf(
        self, a: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[int_]]: ...
    @overload
    def geometric(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
    @overload
    def geometric(
        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[int_]]: ...
    @overload
    def hypergeometric(self, ngood: int, nbad: int, nsample: int, size: None = ...) -> int: ...  # type: ignore[misc]
    @overload
    def hypergeometric(
        self,
        ngood: _ArrayLikeInt_co,
        nbad: _ArrayLikeInt_co,
        nsample: _ArrayLikeInt_co,
        size: None | _ShapeLike = ...,
    ) -> ndarray[Any, dtype[int_]]: ...
    @overload
    def logseries(self, p: float, size: None = ...) -> int: ...  # type: ignore[misc]
    @overload
    def logseries(
        self, p: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[int_]]: ...
    def multivariate_normal(
        self,
        mean: _ArrayLikeFloat_co,
        cov: _ArrayLikeFloat_co,
        size: None | _ShapeLike = ...,
        check_valid: Literal["warn", "raise", "ignore"] = ...,
        tol: float = ...,
    ) -> ndarray[Any, dtype[float64]]: ...
    def multinomial(
        self, n: _ArrayLikeInt_co, pvals: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[int_]]: ...
    def dirichlet(
        self, alpha: _ArrayLikeFloat_co, size: None | _ShapeLike = ...
    ) -> ndarray[Any, dtype[float64]]: ...
    def shuffle(self, x: ArrayLike) -> None: ...
    @overload
    def permutation(self, x: int) -> ndarray[Any, dtype[int_]]: ...
    @overload
    def permutation(self, x: ArrayLike) -> ndarray[Any, Any]: ...

_rand: RandomState

beta = _rand.beta
binomial = _rand.binomial
bytes = _rand.bytes
chisquare = _rand.chisquare
choice = _rand.choice
dirichlet = _rand.dirichlet
exponential = _rand.exponential
f = _rand.f
gamma = _rand.gamma
get_state = _rand.get_state
geometric = _rand.geometric
gumbel = _rand.gumbel
hypergeometric = _rand.hypergeometric
laplace = _rand.laplace
logistic = _rand.logistic
lognormal = _rand.lognormal
logseries = _rand.logseries
multinomial = _rand.multinomial
multivariate_normal = _rand.multivariate_normal
negative_binomial = _rand.negative_binomial
noncentral_chisquare = _rand.noncentral_chisquare
noncentral_f = _rand.noncentral_f
normal = _rand.normal
pareto = _rand.pareto
permutation = _rand.permutation
poisson = _rand.poisson
power = _rand.power
rand = _rand.rand
randint = _rand.randint
randn = _rand.randn
random = _rand.random
random_integers = _rand.random_integers
random_sample = _rand.random_sample
rayleigh = _rand.rayleigh
seed = _rand.seed
set_state = _rand.set_state
shuffle = _rand.shuffle
standard_cauchy = _rand.standard_cauchy
standard_exponential = _rand.standard_exponential
standard_gamma = _rand.standard_gamma
standard_normal = _rand.standard_normal
standard_t = _rand.standard_t
triangular = _rand.triangular
uniform = _rand.uniform
vonmises = _rand.vonmises
wald = _rand.wald
weibull = _rand.weibull
zipf = _rand.zipf
# Two legacy that are trivial wrappers around random_sample
sample = _rand.random_sample
ranf = _rand.random_sample

def set_bit_generator(bitgen: BitGenerator) -> None:
    ...

def get_bit_generator() -> BitGenerator:
    ...

Youez - 2016 - github.com/yon3zu
LinuXploit