def corr(x, y=None, method=None):
        """
        Compute the correlation (matrix) for the input RDD(s) using the
        specified method.
        Methods currently supported: I{pearson (default), spearman}.

        If a single RDD of Vectors is passed in, a correlation matrix
        comparing the columns in the input RDD is returned. Use C{method=}
        to specify the method to be used for single RDD inout.
        If two RDDs of floats are passed in, a single float is returned.

        :param x: an RDD of vector for which the correlation matrix is to be computed,
                  or an RDD of float of the same cardinality as y when y is specified.
        :param y: an RDD of float of the same cardinality as x.
        :param method: String specifying the method to use for computing correlation.
                       Supported: `pearson` (default), `spearman`
        :return: Correlation matrix comparing columns in x.

        >>> x = sc.parallelize([1.0, 0.0, -2.0], 2)
        >>> y = sc.parallelize([4.0, 5.0, 3.0], 2)
        >>> zeros = sc.parallelize([0.0, 0.0, 0.0], 2)
        >>> abs(Statistics.corr(x, y) - 0.6546537) < 1e-7
        True
        >>> Statistics.corr(x, y) == Statistics.corr(x, y, "pearson")
        True
        >>> Statistics.corr(x, y, "spearman")
        0.5
        >>> from math import isnan
        >>> isnan(Statistics.corr(x, zeros))
        True
        >>> from pyspark.mllib.linalg import Vectors
        >>> rdd = sc.parallelize([Vectors.dense([1, 0, 0, -2]), Vectors.dense([4, 5, 0, 3]),
        ...                       Vectors.dense([6, 7, 0,  8]), Vectors.dense([9, 0, 0, 1])])
        >>> pearsonCorr = Statistics.corr(rdd)
        >>> print(str(pearsonCorr).replace('nan', 'NaN'))
        [[ 1.          0.05564149         NaN  0.40047142]
         [ 0.05564149  1.                 NaN  0.91359586]
         [        NaN         NaN  1.                 NaN]
         [ 0.40047142  0.91359586         NaN  1.        ]]
        >>> spearmanCorr = Statistics.corr(rdd, method="spearman")
        >>> print(str(spearmanCorr).replace('nan', 'NaN'))
        [[ 1.          0.10540926         NaN  0.4       ]
         [ 0.10540926  1.                 NaN  0.9486833 ]
         [        NaN         NaN  1.                 NaN]
         [ 0.4         0.9486833          NaN  1.        ]]
        >>> try:
        ...     Statistics.corr(rdd, "spearman")
        ...     print("Method name as second argument without 'method=' shouldn't be allowed.")
        ... except TypeError:
        ...     pass
        """
        # Check inputs to determine whether a single value or a matrix is needed for output.
        # Since it's legal for users to use the method name as the second argument, we need to
        # check if y is used to specify the method name instead.
        if type(y) == str:
            raise TypeError("Use 'method=' to specify method name.")

        if not y:
            return callMLlibFunc("corr", x.map(_convert_to_vector), method).toArray()
        else:
            return callMLlibFunc("corr", x.map(float), y.map(float), method)
Compute the correlation (matrix) for the input RDD(s) using the
        specified method.
        Methods currently supported: I{pearson (default), spearman}.

        If a single RDD of Vectors is passed in, a correlation matrix
        comparing the columns in the input RDD is returned. Use C{method=}
        to specify the method to be used for single RDD inout.
        If two RDDs of floats are passed in, a single float is returned.

        :param x: an RDD of vector for which the correlation matrix is to be computed,
                  or an RDD of float of the same cardinality as y when y is specified.
        :param y: an RDD of float of the same cardinality as x.
        :param method: String specifying the method to use for computing correlation.
                       Supported: `pearson` (default), `spearman`
        :return: Correlation matrix comparing columns in x.

        >>> x = sc.parallelize([1.0, 0.0, -2.0], 2)
        >>> y = sc.parallelize([4.0, 5.0, 3.0], 2)
        >>> zeros = sc.parallelize([0.0, 0.0, 0.0], 2)
        >>> abs(Statistics.corr(x, y) - 0.6546537) < 1e-7
        True
        >>> Statistics.corr(x, y) == Statistics.corr(x, y, "pearson")
        True
        >>> Statistics.corr(x, y, "spearman")
        0.5
        >>> from math import isnan
        >>> isnan(Statistics.corr(x, zeros))
        True
        >>> from pyspark.mllib.linalg import Vectors
        >>> rdd = sc.parallelize([Vectors.dense([1, 0, 0, -2]), Vectors.dense([4, 5, 0, 3]),
        ...                       Vectors.dense([6, 7, 0,  8]), Vectors.dense([9, 0, 0, 1])])
        >>> pearsonCorr = Statistics.corr(rdd)
        >>> print(str(pearsonCorr).replace('nan', 'NaN'))
        [[ 1.          0.05564149         NaN  0.40047142]
         [ 0.05564149  1.                 NaN  0.91359586]
         [        NaN         NaN  1.                 NaN]
         [ 0.40047142  0.91359586         NaN  1.        ]]
        >>> spearmanCorr = Statistics.corr(rdd, method="spearman")
        >>> print(str(spearmanCorr).replace('nan', 'NaN'))
        [[ 1.          0.10540926         NaN  0.4       ]
         [ 0.10540926  1.                 NaN  0.9486833 ]
         [        NaN         NaN  1.                 NaN]
         [ 0.4         0.9486833          NaN  1.        ]]
        >>> try:
        ...     Statistics.corr(rdd, "spearman")
        ...     print("Method name as second argument without 'method=' shouldn't be allowed.")
        ... except TypeError:
        ...     pass