Variable Trigonometric Threshold

A linear classifier to be used in conjunction with the Scikit Learn python package.

class vtt.VTT(weights=None, bias=None, *args, **kwargs)

The Variable Trigonometric Threshold (VTT) linear classifier class

coef_

array-like

Feature weights. Also known as the coefficients.

intercept

array-like

This is the classifier bias. For a linear classifier also known as the intercept.

fit(X, y)

Fit the VTT classifier model

Parameters:

• X (sparse matrix, shape = [n_samples, n_features]) – Training data
• y (array-like, shape = [n_samples]) – Target values

set_params(**params)

Set the parameters of the estimator.

Parameters:

• bias (NER) – bias of the estimator. Also known as the intercept
• weights (array-like) – weights of the features. Also known as coeficients.
• bias – NER entities infering column position on X and bias value. Ex: b_4=10, b_5=6.

Example

>>> cls = VTT()
>>> cls.set_params(b_4=10, b_5=6, b_6=8)

get_params(deep=True)

Get parameters for the estimator.

Parameters:deep (boolean, optional) – If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns:mapping of string to any contained subobjects that are estimators. Return type:params

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:

• X (numpy array of shape [n_samples, n_features]) – Training set.
• y (numpy array of shape [n_samples]) – Target values.

Returns:

X_new – Transformed array.

Return type:

numpy array of shape [n_samples, n_features_new]