def classify_naive_bayes(params, label_probs, features):
    #create an array with zeros in the size of test (200)
    laux = np.zeros((np.size(features, axis=0), 1))
    labels = np.squeeze(np.asarray(laux)) 

    # find conditional proba. for each class
    # find most likely label for each instance
    for i in range(np.size(features, axis=0)):
    	cp1 = features[i,:]*params[0]+label_probs[0]
  	cp2 = features[i,:]*params[1]+label_probs[1]
        if cp1 > cp2:
        	j = 0
	else:
		j = 1
 
	labels[i]=j
    return labels
compute the naive bayes classifier
dataset/ETHPy150Open bt3gl/Advanced-Machine-Learning/naive_bayes_vs_logistic_regression/nb.py/classify_naive_bayes