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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def columnSimilarities(self, threshold=0.0):
 """ Compute similarities between columns of this matrix. The threshold parameter is a trade-off knob between estimate quality and computational cost. The default threshold setting of 0 guarantees deterministically correct results, but uses the brute-force approach of computing normalized dot products. Setting the threshold to positive values uses a sampling approach and incurs strictly less computational cost than the brute-force approach. However the similarities computed will be estimates. The sampling guarantees relative-error correctness for those pairs of columns that have similarity greater than the given similarity threshold. To describe the guarantee, we set some notation: * Let A be the smallest in magnitude non-zero element of this matrix. * Let B be the largest in magnitude non-zero element of this matrix. * Let L be the maximum number of non-zeros per row. For example, for {0,1} matrices: A=B=1. Another example, for the Netflix matrix: A=1, B=5 For those column pairs that are above the threshold, the computed similarity is correct to within 20% relative error with probability at least 1 - (0.981)^10/B^ The shuffle size is bounded by the *smaller* of the following two expressions: * O(n log(n) L / (threshold * A)) * O(m L^2^) The latter is the cost of the brute-force approach, so for non-zero thresholds, the cost is always cheaper than the brute-force approach. :param: threshold: Set to 0 for deterministic guaranteed correctness. Similarities above this threshold are estimated with the cost vs estimate quality trade-off described above. :return: An n x n sparse upper-triangular CoordinateMatrix of cosine similarities between columns of this matrix. """

        java_sims_mat = self._java_matrix_wrapper.call("columnSimilarities", float(threshold))
        return CoordinateMatrix(java_sims_mat)