naiveBayes.py

'''
    TEMPLATE FOR MACHINE LEARNING HOMEWORK
    AUTHOR Eric Eaton
'''

import numpy as np

class NaiveBayes:

    def __init__(self, useLaplaceSmoothing=True):
        '''
        Constructor
        '''
      

    def fit(self, X, y):
        '''
        Trains the model
        Arguments:
            X is a n-by-d numpy array
            y is an n-dimensional numpy array
        '''


    def predict(self, X):
        '''
        Used the model to predict values for each instance in X
        Arguments:
            X is a n-by-d numpy array
        Returns:
            an n-dimensional numpy array of the predictions
        '''

    
    def predictProbs(self, X):
        '''
        Used the model to predict a vector of class probabilities for each instance in X
        Arguments:
            X is a n-by-d numpy array
        Returns:
            an n-by-K numpy array of the predicted class probabilities (for K classes)
        '''
        
        
        
        
class OnlineNaiveBayes:

    def __init__(self, useLaplaceSmoothing=True):
        '''
        Constructor
        '''
      

    def fit(self, X, y):
        '''
        Trains the model
        Arguments:
            X is a n-by-d numpy array
            y is an n-dimensional numpy array
        '''


    def predict(self, X):
        '''
        Used the model to predict values for each instance in X
        Arguments:
            X is a n-by-d numpy array
        Returns:
            an n-dimensional numpy array of the predictions
        '''
    
    
    def predictProbs(self, X):
        '''
        Used the model to predict a vector of class probabilities for each instance in X
        Arguments:
            X is a n-by-d numpy array
        Returns:
            an n-by-K numpy array of the predicted class probabilities (for K classes)
        '''