# Bayes 1


data  = [['i100', 'both', 'sedentary', 'moderate', 'yes'],
         ['i100', 'both', 'sedentary', 'moderate', 'no'],
         ['i500', 'health', 'sedentary', 'moderate', 'yes'],
         ['i500', 'appearance', 'active', 'moderate', 'yes'],
         ['i500', 'appearance', 'moderate', 'aggressive', 'yes'],
         ['i100', 'appearance', 'moderate', 'aggressive', 'no'],
         ['i500', 'health', 'moderate', 'aggressive', 'no'],
         ['i100', 'both', 'active', 'moderate', 'yes'],
         ['i500', 'both', 'moderate', 'aggressive', 'yes'],
         ['i500', 'appearance', 'active', 'aggressive', 'yes'],
         ['i500', 'both', 'active', 'aggressive', 'no'],
         ['i500', 'health', 'active', 'moderate', 'no'],
         ['i500', 'health', 'sedentary', 'aggressive', 'yes'],
         ['i100', 'appearance', 'active', 'moderate', 'no'],
         ['i100', 'health', 'sedentary', 'moderate', 'no']]


class Bayes:
    

    def __init__(self, data):
        # here I am assuming the first column of the data is the class.
        self.data = data
        self.prior = {}
        self.conditional = {}



    def train(self):
         """train the Bayes Classifier
         basically a lot of counting"""
         total = 0
         classes = {}
         counts = {}
         # determine size of a training vector
         size = len(self.data[0])
         #
         #   iterate through training instances
         for instance in self.data:
             total += 1
             category = instance[0]
             classes.setdefault(category, 0)
             counts.setdefault(category, {})
             classes[category] += 1
             # now process each column in instance
             col = 0
             for columnValue in instance[1:]:
                 col += 1
                 tmp = {}
                 if col in counts[category]:
                     tmp = counts[category][col]
                 if columnValue in tmp:
                     tmp[columnValue] += 1
                 else:
                    tmp[columnValue] = 1
                 counts[category][col] = tmp
         # ok. done counting. now compute probabilities
         #
         # first prior probabilities
         #
         for (category, count) in classes.items():
             self.prior[category] = count / total
         # now compute conditional probabilities
         for (category, columns) in counts.items():
             tmp = {}
             for (col, valueCounts) in columns.items():
                 tmp2 = {}
                 for (value, count) in valueCounts.items():
                     tmp2[value] = count / classes[category]
                 tmp[col] = tmp2
             #convert tmp to vector
             tmp3 = []
             for i in range(1, size):
                 tmp3.append(tmp[i])
             self.conditional[category] = tmp3
                 

    def classify(self, instance):
        categories = {}
        for (category, vector) in self.conditional.items():
            prob = 1
            for i in range(len(vector)):
                colProbability = .0000001
                if instance[i] in vector[i]:
                    # get the probability for that column value
                    colProbability = vector[i][instance[i]]
                prob = prob * colProbability
            prob = prob * self.prior[category]
            categories[category]  = prob
        cat = list(categories.items())
        cat.sort(key=lambda catTuple: catTuple[1], reverse = True)
        return(cat[0])