11-rbf-iris-2.nb

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Nejd\[RHacek]\[IAcute]ve na\[CHacek]teme knihovnu neuronov\[YAcute]ch s\
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Nyn\[IAcute] je\[SHacek]t\[EHacek] rozd\[EHacek]l\[IAcute]me tr\[EAcute]novac\
\[IAcute] data na u\[CHacek]\[IAcute]c\[IAcute] a valida\[CHacek]n\[IAcute] \
mno\[ZHacek]inu, v pom\[EHacek]ru 70% u\[CHacek]ic\[IAcute] data a 30% valida\
\[CHacek]n\[IAcute] data. Na u\[CHacek]\[IAcute]c\[IAcute]ch datech budeme s\
\[IAcute]\[THacek] u\[CHacek]it a na valida\[CHacek]n\[IAcute]ch datech \
budeme kontrolovat zda se na nich klasifikace zlep\[SHacek]uje. Pokud by se v\
\[YAcute]sledky s\[IAcute]t\[EHacek] na valida\[CHacek]n\[IAcute]ch datech za\
\[CHacek]aly zhor\[SHacek]ovat, p\[RHacek]erus\[IAcute]me u\[CHacek]en\
\[IAcute] s\[IAcute]t\[EHacek]. T\[IAcute]m zabr\[AAcute]n\[IAcute]me jej\
\[IAcute]mu p\[RHacek]eu\[CHacek]en\[IAcute] (p\[RHacek]\[IAcute]li\[SHacek]n\
\[EAcute]mu p\[RHacek]izp\[URing]soben\[IAcute] se tr\[EAcute]novac\[IAcute]m \
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Nyn\[IAcute] m\[AAcute]me p\[RHacek]ipraveny data k u\[CHacek]en\[IAcute] s\
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Cell["\<\
Proto\[ZHacek]e nev\[IAcute]me p\[RHacek]esn\[EHacek] kolik neuron\[URing] v \
s\[IAcute]ti bude ide\[AAcute]ln\[IAcute] a tipovat by bylo \
riskantn\[IAcute], pou\[ZHacek]ijeme k\[RHacek]\[IAcute]\[ZHacek]ovou \
validaci (podrobn\[EHacek]ji pops\[AAcute]na v samostatn\[EAcute] kapitole), \
kter\[AAcute] n\[AAcute]m ur\[CHacek]\[IAcute] jak\[AAcute] z p\[RHacek]edlo\
\[ZHacek]en\[YAcute]ch s\[IAcute]t\[IAcute] pod\[AAcute]v\[AAcute] nejlep\
\[SHacek]\[IAcute] v\[YAcute]sledky. Vytvo\[RHacek]\[IAcute]me tedy \
u\[ZHacek]\[SHacek]\[IAcute] v\[YAcute]b\[EHacek]r t\[RHacek]\[IAcute] s\
\[IAcute]t\[IAcute] a ty budeme testovat pomoc\[IAcute] k\[RHacek]\[IAcute]\
\[ZHacek]ov\[EAcute] validace. Obecn\[EHacek] se \
k\[RHacek]\[IAcute]\[ZHacek]ov\[AAcute] validace pou\[ZHacek]\[IAcute]v\
\[AAcute] k nastaven\[IAcute] nejlep\[SHacek]\[IAcute]ch parametr\[URing] \
algoritmu (nap\[RHacek]. po\[CHacek]et neuron\[URing]), zde tedy k zji\
\[SHacek]t\[EHacek]n\[IAcute] struktury s\[IAcute]t\[EHacek].\
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Zavedeme si funkci crossvalidate, j\[IAcute]\[ZHacek] d\[AAcute]me jako \
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data a po\[CHacek]et fold\[URing]. Funkci je potom mo\[ZHacek]n\[EAcute] \
spustit p\[RHacek]\[IAcute]kazem crossvalidate[parametry]. Funkce vrac\
\[IAcute] seznam v\[YAcute]sledk\[URing] s\[IAcute]t\[EHacek] pro jednotliv\
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Z grafu je vid\[EHacek]t, \[ZHacek]e s\[IAcute]t\[EHacek] pod\[AAcute]vaj\
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Nau\[CHacek]\[IAcute]me tuto s\[IAcute]\[THacek] na u\[CHacek]ic\[IAcute]ch \
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Nyn\[IAcute] m\[AAcute]me nau\[CHacek]enou s\[IAcute]\[THacek], zb\[YAcute]v\
\[AAcute] n\[AAcute]m zjistit jej\[IAcute] skute\[CHacek]nou chybu. Hodnota \
RMSE, kter\[AAcute] n\[AAcute]m reprezentuje chybovost s\[IAcute]t\[EHacek] b\
\[EHacek]hem u\[CHacek]en\[IAcute], nen\[IAcute] pro \[CHacek]lov\[EHacek]ka \
tak snadno uchopiteln\[AAcute] jako hodnota \
\[UAcute]sp\[EHacek]\[SHacek]nosti v procentech. Vypo\[CHacek]teme si tedy \
skute\[CHacek]nou chybovost s\[IAcute]t\[EHacek] v procentech. K tomu pou\
\[ZHacek]ijeme testovac\[IAcute] data. Nech\[AAcute]me s\[IAcute]\[THacek] \
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jako parametry neuronovou s\[IAcute]\[THacek], vstupn\[IAcute] testovac\
\[IAcute] data, v\[YAcute]stupn\[IAcute] testovac\[IAcute] data a vrac\
\[IAcute] \[UAcute]sp\[EHacek]\[SHacek]nost s\[IAcute]t\[EHacek] v procentech.\
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Cell["\<\
Pou\[ZHacek]ijeme tuto funkci k ur\[CHacek]en\[IAcute] skute\[CHacek]n\
\[EAcute] \[UAcute]sp\[EHacek]\[SHacek]nosti na\[SHacek]\[IAcute] s\[IAcute]t\
\[EHacek] na testovac\[IAcute]ch datech.\
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