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Test training params on subsets of the training data 11910: « SVM::__construct SVM::getOptions » PHP Manual SVM Test training params on subsets of the training data 検索 SVM::crossvalidate (PECL svm >= 0.1.0) SVM::crossvalidate — Test training params on subsets of the training data 説明 public funct ... ion svm::crossvalidate ( array $problem , int $number_of_folds ): float Crossvalidate can be used to test the effectiveness of the current parameter set on a subs ... et of the training data. Given a problem set and a n "folds", it separates the problem set into n subsets, and the repeatedly trains on one subset and tests on ano ... e accuracy score returned should be relatively useful, so it can be used to test different training parameters. パラメータ problem The problem data. This can either be in the form of an array, the URL of an SVMLight formatted file, or a stream to an ope ...; https://man.mtool.work/php/8.3/svm.crossvalidate.html

Test training params on subsets of the training data 11910: « SVM::__construct SVM::getOptions » PHP Manual SVM Test training params on subsets of the training data 検索 SVM::crossvalidate (PECL svm >= 0.1.0) SVM::crossvalidate — Test training params on subsets of the training data 説明 public funct ... ion svm::crossvalidate ( array $problem , int $number_of_folds ): float Crossvalidate can be used to test the effectiveness of the current parameter set on a subs ... et of the training data. Given a problem set and a n "folds", it separates the problem set into n subsets, and the repeatedly trains on one subset and tests on ano ... e accuracy score returned should be relatively useful, so it can be used to test different training parameters. パラメータ problem The problem data. This can either be in the form of an array, the URL of an SVMLight formatted file, or a stream to an ope ...; https://man.mtool.work/php/8.3/svm.crossvalidate.html

Create a SVMModel based on training data 11049: « SVM::setOptions SVMModel » PHP Manual SVM Create a SVMModel based on training data 検索 SVM::train (PECL svm >= 0.1.0) SVM::train — Create a SVMModel based on training data 説明 public function svm::train ( array $problem , array $weights ... = ? ): SVMModel Train a support vector machine based on the supplied training data. パラメータ problem The problem can be provided in three different ways. An array, where the data should start with the class label (usually 1 ... or -1) then followed by a sparse data set of dimension => data pairs. A URL to a file containing a SVM Light formatted problem, with the each line being a new training example, the start of each line containing the class (1, -1) then a series of tab ... can be used to classify previously unseen data. Throws SVMException on error 関連キーワード: data SVMModel training based problem Create train array example svm weights different start class set file containing formatted line Weights ...; https://man.mtool.work/php/8.3/svm.train.html

Create a SVMModel based on training data 11049: « SVM::setOptions SVMModel » PHP Manual SVM Create a SVMModel based on training data 検索 SVM::train (PECL svm >= 0.1.0) SVM::train — Create a SVMModel based on training data 説明 public function svm::train ( array $problem , array $weights ... = ? ): SVMModel Train a support vector machine based on the supplied training data. パラメータ problem The problem can be provided in three different ways. An array, where the data should start with the class label (usually 1 ... or -1) then followed by a sparse data set of dimension => data pairs. A URL to a file containing a SVM Light formatted problem, with the each line being a new training example, the start of each line containing the class (1, -1) then a series of tab ... that can be used to classify previously unseen data. Throws SVMException on error 関連キーワード: SVMModel training based problem Create train example weights different start containing formatted line Weights classes setOptions Train support vector mac ...; https://man.mtool.work/php/8.3/svm.train.html

定義済み定数 10819: « インストール手順 Simdjson 関数 » PHP Manual Simdjson 定義済み定数検索定義済み定数以下の定数が定義されています。この関数の拡張モジュールが PHP 組み込みでコンパイルされているか、実行時に動的にロードされている場合のみ使用可能です。 SIMD ... _DEPTH_ERROR ( int ) The JSON document was too deep (too many nested objects and arrays) SIMDJSON_ERR_STRING_ERROR ( int ) Problem while parsing a string SIMDJSON_ERR_T_ATOM_ERROR ( int ) Problem while parsing an atom starting with the letter 't' SIMDJS ... ON_ERR_F_ATOM_ERROR ( int ) Problem while parsing an atom starting with the letter 'f' SIMDJSON_ERR_N_ATOM_ERROR ( int ) Problem while parsing an atom startin ... g with the letter 'n' SIMDJSON_ERR_NUMBER_ERROR ( int ) Problem while parsing a number SIMDJSON_ERR_UTF8_ERROR ( int ) The input is not valid UTF-8 SIMDJSON_ERR_UNINITIALIZED ( int ) The ...; https://man.mtool.work/php/8.3/simdjson.constants.html

定義済み定数 10742: « インストール手順 Simdjson 関数 » PHP Manual Simdjson 定義済み定数検索定義済み定数以下の定数が定義されています。この関数の拡張モジュールが PHP 組み込みでコンパイルされているか、実行時に動的にロードされている場合のみ使用可能です。 SIMD ... _DEPTH_ERROR ( int ) The JSON document was too deep (too many nested objects and arrays) SIMDJSON_ERR_STRING_ERROR ( int ) Problem while parsing a string SIMDJSON_ERR_T_ATOM_ERROR ( int ) Problem while parsing an atom starting with the letter 't' SIMDJS ... ON_ERR_F_ATOM_ERROR ( int ) Problem while parsing an atom starting with the letter 'f' SIMDJSON_ERR_N_ATOM_ERROR ( int ) Problem while parsing an atom startin ... g with the letter 'n' SIMDJSON_ERR_NUMBER_ERROR ( int ) Problem while parsing a number SIMDJSON_ERR_UTF8_ERROR ( int ) The input is not valid UTF-8 SIMDJSON_ERR_UNINITIALIZED ( int ) The ...; https://man.mtool.work/php/8.3/simdjson.constants.html

addcslashes でクォートされた文字列をアンクォートする 9174: « strip_tags stripos » PHP Manual String 関数 addcslashes でクォートされた文字列をアンクォートする検索 stripcslashes (PHP 4, PHP 5, PHP 7, PHP 8) stripcslashes — addcslashes() でクォートされた文字列をアンクォートする説明 function stripcsl ... 戻り値元に戻した文字列を返します。例例1 stripcslashes() の例 <?php var_dump ( stripcslashes ( 'I\'d have a coffee.\nNot a problem.' ) === "I'd have a coffee. Not a problem." ); // true ?> 参考 addcslashes() - C 言語と同様にスラッシュで文字列をクォートす ... る関連キーワード: stripcslashes addcslashes クォート string アンクォート言語表現 coffee problem strip tags Search stripos String function バックスラッシュ認識パラメータ戻り値 php ...; https://man.mtool.work/php/8.3/function.stripcslashes.html

addcslashes でクォートされた文字列をアンクォートする 9174: « strip_tags stripos » PHP Manual String 関数 addcslashes でクォートされた文字列をアンクォートする検索 stripcslashes (PHP 4, PHP 5, PHP 7, PHP 8) stripcslashes — addcslashes() でクォートされた文字列をアンクォートする説明 function stripcsl ... 戻り値元に戻した文字列を返します。例例1 stripcslashes() の例 <?php var_dump ( stripcslashes ( 'I\'d have a coffee.\nNot a problem.' ) === "I'd have a coffee. Not a problem." ); // true ?> 参考 addcslashes() - C 言語と同様にスラッシュで文字列をクォートす ... る関連キーワード: stripcslashes addcslashes クォートアンクォート言語表現 coffee problem strip tags stripos バックスラッシュ認識 dump nNot 参考スラッシュ ...; https://man.mtool.work/php/8.3/function.stripcslashes.html

Return the candidate limit 8314: « fann_get_cascade_candidate_change_fraction fann_get_cascade_candidate_stagnation_epochs » PHP Manual Fann 関数 Return the candidate limit 検索 fann_get_cascade_candidate_limit (PECL fann >= 1.0.0) fann_get_cascade_candidate_limit — Return ... etween the MSE and candidate score. Set this to a lower value to avoid overfitting and to a higher if overfitting is not a problem. The default candidate limit is 1000.0. パラメータ ann ニューラルネットワークリソース。戻り値 The candidate limit, or fal ... ting change fraction stagnation epochs Fann resource float much neuron trained proportion between score lower avoid higher problem default ニューラル ...; https://man.mtool.work/php/8.3/function.fann-get-cascade-candidate-limit.html

The SVM class 8237: « 例 SVM::__construct » PHP Manual SVM The SVM class 検索 The SVM class (PECL svm >= 0.1.0) はじめにクラス概要 class SVM { /* 定数 */ const int C_SVC = 0 ; const int NU_SVC = 1 ; const int ONE_CLASS = 2 ; const int EPSILON_SVR = 3 ; const ... const int OPT_CACHE_SIZE = 207 ; /* メソッド */ public function __construct () public function svm::crossvalidate ( array $problem , int $number_of_folds ): float public function getOptions (): array public function setOptions ( array $params ): bool pu ... blic function svm::train ( array $problem , array $weights = ? ): SVMModel } 定義済み定数 SVM Constants SVM::C_SVC The basic C_SVC SVM type. The default, and a good ... SVR A NU style SVM regression type SVM::KERNEL_LINEAR A very simple kernel, can work well on large document classification problems SVM::KERNEL_POLY A polynomial kernel SVM::KERNEL_RBF The common Gaussian RBD kernel. Handles non-linear problems well and ...; https://man.mtool.work/php/8.3/class.svm.html

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