slearn.symbolicML

class slearn.symbolicML(classifier_name='MLPClassifier', ws=3, random_seed=0, verbose=0)[source]

Classifier for symbolic sequences.

classifier_name - str, default=MLPClassifier,

optional choices = {“KNeighborsClassifier”, “GaussianProcessClassifier” “QuadraticDiscriminantAnalysis”, “DecisionTreeClassifier”, “LogisticRegression”, “AdaBoostClassifier”, “RandomForestClassifier”, “GaussianNB”, “DeepForest”, “LGBM”, “SVC”, “RBF”}:

The classifier you specify for symbols prediction.

ws - int, default=3:

The windows size for symbols to be the features, i.e, the dimensions of features. The larger the window, the more information about time series can be taken into account.

random_seed - int, default=0:

The random state fixed for classifers in scikit-learn.

verbose - int, default=0:

Whether to print progress messages to stdout.

__init__(classifier_name='MLPClassifier', ws=3, random_seed=0, verbose=0)[source]

Methods

__init__([classifier_name, ws, random_seed, ...])

construct_train(series)

Construct features and target labels for symbols.

encode(string)

Construct features and target labels for symbols and encode to numerical values.

forecast(x, y[, step, inversehash, centers])

init_classifier()