searchengine - vectorcompare

class VectorCompare:
    # return the frequency map of words in a document
    def concordance(self, document):
        if type(document) != str:
            raise ValueError('Supplied argument should be of type string')
        con = {}
        for word in document.split(' '):
            if word in con:
                con[word] = con[word] + 1
            else:
                con[word] = 1
        return con

    # calculate the Euclidean norm of the vector (it's length, by the Pythagorean theorem)
    def magnitude(self, concordance):
        if type(concordance) != dict:
            raise ValueError('Supplied argument should be of type dict')
        total = 0
        for word, count in concordance.items():
            total += count ** 2
        return math.sqrt(total)

    def relation(self, concordance1, concordance2):
        if type(concordance1) != dict:
            raise ValueError('Supplied Argument 1 should be of type dict')
        if type(concordance2) != dict:
            raise ValueError('Supplied Argument 2 should be of type dict')
        relevance = 0
        # calculate topvalue = max{count1*count2}
        topvalue = 0
        for word, count in concordance1.items():
            if word in concordance2:
                topvalue += count * concordance2[word]
        # return topvalue / (|v1||v2|) which is a measurement of how relevant the two
        # vectors are to each other
        if (self.magnitude(concordance1) * self.magnitude(concordance2)) != 0:
            return topvalue / (self.magnitude(concordance1) * self.magnitude(concordance2))
        else:
            return 0