naive_bayes_predict.py

def predict(self, X):
    y_pred = [self.__predict__(x) for x in X]
    return y_pred


def __predict__(self, x):  # Helper Function for the function 'predict'
    posteriors = []
    for idx, c in enumerate(self.classes):
        prior = self.priors[idx]  # P(yi)

        P_X_yi = self.__gauss_pdf__(idx, x)  # P(X | yi) - Likelihood

        # Equation 4.6
        posterior = 0
        for P_x_yi in P_X_yi:
            posterior += np.log(P_x_yi)  # P(x1 | yi) + ... + P(xn | yi)
        posterior = posterior + np.log(prior)

        posteriors.append(posterior)

    return self.classes[np.argmax(posteriors)]  # Select class with highest Posterior


def __gauss_pdf__(self, idx, x):
    mu = self.means[idx]
    std = self.stds[idx]

    return np.exp(-(x - mu) ** 2 / (2 * std ** 2)) / np.sqrt(2 * np.pi * std ** 2)  # Equation 4.7