#Naive Bayes Classifier using Numpy 
import numpy as np

class NaiveBayes:

    def fit(self, X, y):
    """Function to establish relation between dependent and independent variable
    Args:
        X: Independent variable
        y: Dependent variable
    Returns:
        None
    """
        n_samples, n_features = X.shape
        self._classes = np.unique(y)
        n_classes = len(self._classes)

        self._mean = np.zeros((n_classes, n_features), dtype= np.float64)
        self._var = np.zeros((n_classes, n_features), dtype= np.float64)
        self._priors = np.zeros(n_classes, dtype = np.float64)

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

    def _predict(self, x):
        posteriors = []
        
        for idx, c in enumerate(self._classes):
            prior = np.log(self._priors[idx])
            class_conditional = np.sum(np.log(self._pdf(idx, x)))
            posterior = prior + class_conditional
            posteriors.append(posterior)

        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)
        
