Computes TF metrics for each sentence (column) in the given matrix.
        You can read more about smoothing parameter at URL below:
        http://nlp.stanford.edu/IR-book/html/htmledition/maximum-tf-normalization-1.html

def _compute_term_frequency(self, matrix, smooth=0.4):
        """
        Computes TF metrics for each sentence (column) in the given matrix.
        You can read more about smoothing parameter at URL below:
        http://nlp.stanford.edu/IR-book/html/htmledition/maximum-tf-normalization-1.html
        """
        assert 0.0 <= smooth < 1.0

        max_word_frequencies = numpy.max(matrix, axis=0)
        rows, cols = matrix.shape
        for row in range(rows):
            for col in range(cols):
                max_word_frequency = max_word_frequencies[col]
                if max_word_frequency != 0:
                    frequency = matrix[row, col]/max_word_frequency
                    matrix[row, col] = smooth + (1.0 - smooth)*frequency

        return matrix