Bayes.py

import numpy as np

class Bayes:
    def fit(self,X,y):
        n_samples , n_features = X.shape;
        self._classes = np.unique(y);
        n_classes = len(self._classes);

        #calculate mean,variance,prior for each class
        self._mean = np.zeros((n_classes,n_features), dtype=np.float64);
        self._var = np.zeros((n_classes,n_features), dtype=np.float64);
        self._prior = np.zeros((n_classes), dtype=np.float64);

        for idx,c in enumerate(self._classes):
            X_c = X[y==c];
            self._mean[idx,:] = X_c.mean(axis=0);  
            self._var[idx,:] = X_c.var(axis=0);  
            self._prior[idx] = X_c.shape[0]/float(n_samples);
        
    def predict(self,X):
        y_pred = [self._predict(x) for x in X];
        return np.array(y_pred);

    def _predict(self,X):
        posteriors =[];


        #Calculate posterior probability for each class
        for idx,c in enumerate(self._classes):
            prior = np.log(self._prior[idx]);
            posterior = np.sum(np.log(self._pdf(idx,c)));
            posterior = prior + posterior
            posteriors.append(posterior);
        
        #Return class with highest probability
        return self._classes[np.argmax(posteriors)];

    def _pdf(self,class_idx,x):
        mean = self._mean[class_idx];
        var = self.var[class_idx];
        numerator = np.exp(-((x-mean)**2)/(2*var));
        denominator = np.sqrt(2*np.pi*var);
        return numerator/denominator;