euclidean_classifier.py

from sklearn.base import BaseEstimator, ClassifierMixin

class EuclideanClassifier(BaseEstimator, ClassifierMixin):  
    """Classify samples based on the distance from the mean feature value"""

    def __init__(self):
        self.X_mean_ = None


    def fit(self, X, y):
        """
        This should fit classifier. All the "work" should be done here.
        
        Calculates self.X_mean_ based on the mean 
        feature values in X for each class.
        
        self.X_mean_ becomes a numpy.ndarray of shape 
        (n_classes, n_features)
        
        fit always returns self.
        """
        raise NotImplementedError
        #return self


    def predict(self, X):
        """
        Make predictions for X based on the
        euclidean distance from self.X_mean_
        """
        raise NotImplementedError
    
    def score(self, X, y):
        """
        Return accuracy score on the predictions
        for X based on ground truth y
        """
        raise NotImplementedError