olpy/classifiers/romma.py
def _is_mistake(self, x: np.ndarray, y: int):
        """
        Evaluates whether the prediction using the current weights leads
        to a mistake or not.

        Parameters
        ----------
        x: list or np.ndarray
            Input data point
        y: int 
            Output value. Has value 1 or -1

        Returns
        -------
        bool: True if there is an error, False otherwise
        """
        return np.sign(self.weights.dot(x)) != y

        Evaluates whether the prediction using the current weights leads
        to a mistake or not.

        Parameters
        ----------
        x: list or np.ndarray
            Input data point
        y: int 
            Output value. Has value 1 or -1

        Returns
        -------
        bool: True if there is an error, False otherwise
        
Evaluates whether the prediction using the current weights leads
to a mistake or not.

Parameters

list or np.ndarray
Input data point
y: int
Output value. Has value 1 or -1

Returns

True if there is an error, False otherwise
Evaluates whether the prediction using the current weights leads to a mistake or not . Parameters list or np . ndarray Input data point y : int Output value . Has value 1 or - 1 Returns True if there is an error False otherwise
def _is_mistake(self, x: np.ndarray, y: int):
        return np.sign(self.weights.dot(x)) != y
def _is_mistake ( self , x : np . ndarray , y : int ) : return np . sign ( self . weights . dot ( x ) ) != y
Evaluates whether the prediction using the current weights leads
to a mistake or not.
Evaluates whether the prediction using the current weights leads to a mistake or not .
"""
        Evaluates whether the prediction using the current weights leads
        to a mistake or not.

        Parameters
        ----------
        x: list or np.ndarray
            Input data point
        y: int 
            Output value. Has value 1 or -1

        Returns
        -------
        bool: True if there is an error, False otherwise
        """
self
x
np.ndarray
y
int