Python JavaModelWrapper.call方法代码示例

# 需要导入模块: from pyspark.mllib.common import JavaModelWrapper [as 别名]
# 或者: from pyspark.mllib.common.JavaModelWrapper import call [as 别名]
    def tallSkinnyQR(self, computeQ=False):
        """
        Compute the QR decomposition of this RowMatrix.

        The implementation is designed to optimize the QR decomposition
        (factorization) for the RowMatrix of a tall and skinny shape.

        Reference:
         Paul G. Constantine, David F. Gleich. "Tall and skinny QR
         factorizations in MapReduce architectures"
         ([[http://dx.doi.org/10.1145/1996092.1996103]])

        :param: computeQ: whether to computeQ
        :return: QRDecomposition(Q: RowMatrix, R: Matrix), where
                 Q = None if computeQ = false.

        >>> rows = sc.parallelize([[3, -6], [4, -8], [0, 1]])
        >>> mat = RowMatrix(rows)
        >>> decomp = mat.tallSkinnyQR(True)
        >>> Q = decomp.Q
        >>> R = decomp.R

        >>> # Test with absolute values
        >>> absQRows = Q.rows.map(lambda row: abs(row.toArray()).tolist())
        >>> absQRows.collect()
        [[0.6..., 0.0], [0.8..., 0.0], [0.0, 1.0]]

        >>> # Test with absolute values
        >>> abs(R.toArray()).tolist()
        [[5.0, 10.0], [0.0, 1.0]]
        """
        decomp = JavaModelWrapper(self._java_matrix_wrapper.call("tallSkinnyQR", computeQ))
        if computeQ:
            java_Q = decomp.call("Q")
            Q = RowMatrix(java_Q)
        else:
            Q = None
        R = decomp.call("R")
        return QRDecomposition(Q, R)

# 需要导入模块: from pyspark.mllib.common import JavaModelWrapper [as 别名]
# 或者: from pyspark.mllib.common.JavaModelWrapper import call [as 别名]
class BlockMatrix(DistributedMatrix):
    """
    Represents a distributed matrix in blocks of local matrices.

    :param blocks: An RDD of sub-matrix blocks
                   ((blockRowIndex, blockColIndex), sub-matrix) that
                   form this distributed matrix. If multiple blocks
                   with the same index exist, the results for
                   operations like add and multiply will be
                   unpredictable.
    :param rowsPerBlock: Number of rows that make up each block.
                         The blocks forming the final rows are not
                         required to have the given number of rows.
    :param colsPerBlock: Number of columns that make up each block.
                         The blocks forming the final columns are not
                         required to have the given number of columns.
    :param numRows: Number of rows of this matrix. If the supplied
                    value is less than or equal to zero, the number
                    of rows will be calculated when `numRows` is
                    invoked.
    :param numCols: Number of columns of this matrix. If the supplied
                    value is less than or equal to zero, the number
                    of columns will be calculated when `numCols` is
                    invoked.
    """
    def __init__(self, blocks, rowsPerBlock, colsPerBlock, numRows=0, numCols=0):
        """
        Note: This docstring is not shown publicly.

        Create a wrapper over a Java BlockMatrix.

        Publicly, we require that `blocks` be an RDD.  However, for
        internal usage, `blocks` can also be a Java BlockMatrix
        object, in which case we can wrap it directly.  This
        assists in clean matrix conversions.

        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
        >>> mat = BlockMatrix(blocks, 3, 2)

        >>> mat_diff = BlockMatrix(blocks, 3, 2)
        >>> (mat_diff._java_matrix_wrapper._java_model ==
        ...  mat._java_matrix_wrapper._java_model)
        False

        >>> mat_same = BlockMatrix(mat._java_matrix_wrapper._java_model, 3, 2)
        >>> (mat_same._java_matrix_wrapper._java_model ==
        ...  mat._java_matrix_wrapper._java_model)
        True
        """
        if isinstance(blocks, RDD):
            blocks = blocks.map(_convert_to_matrix_block_tuple)
            # We use DataFrames for serialization of sub-matrix blocks
            # from Python, so first convert the RDD to a DataFrame on
            # this side. This will convert each sub-matrix block
            # tuple to a Row containing the 'blockRowIndex',
            # 'blockColIndex', and 'subMatrix' values, which can
            # each be easily serialized.  We will convert back to
            # ((blockRowIndex, blockColIndex), sub-matrix) tuples on
            # the Scala side.
            java_matrix = callMLlibFunc("createBlockMatrix", blocks.toDF(),
                                        int(rowsPerBlock), int(colsPerBlock),
                                        long(numRows), long(numCols))
        elif (isinstance(blocks, JavaObject)
              and blocks.getClass().getSimpleName() == "BlockMatrix"):
            java_matrix = blocks
        else:
            raise TypeError("blocks should be an RDD of sub-matrix blocks as "
                            "((int, int), matrix) tuples, got %s" % type(blocks))

        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)

    @property
    def blocks(self):
        """
        The RDD of sub-matrix blocks
        ((blockRowIndex, blockColIndex), sub-matrix) that form this
        distributed matrix.

        >>> mat = BlockMatrix(
        ...     sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
        ...                     ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]), 3, 2)
        >>> blocks = mat.blocks
        >>> blocks.first()
        ((0, 0), DenseMatrix(3, 2, [1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 0))

        """
        # We use DataFrames for serialization of sub-matrix blocks
        # from Java, so we first convert the RDD of blocks to a
        # DataFrame on the Scala/Java side. Then we map each Row in
        # the DataFrame back to a sub-matrix block on this side.
        blocks_df = callMLlibFunc("getMatrixBlocks", self._java_matrix_wrapper._java_model)
        blocks = blocks_df.rdd.map(lambda row: ((row[0][0], row[0][1]), row[1]))
        return blocks

    @property
    def rowsPerBlock(self):
        """
        Number of rows that make up each block.

#.........这里部分代码省略.........

# 需要导入模块: from pyspark.mllib.common import JavaModelWrapper [as 别名]
# 或者: from pyspark.mllib.common.JavaModelWrapper import call [as 别名]
class CoordinateMatrix(DistributedMatrix):
    """
    Represents a matrix in coordinate format.

    :param entries: An RDD of MatrixEntry inputs or
                    (long, long, float) tuples.
    :param numRows: Number of rows in the matrix. A non-positive
                    value means unknown, at which point the number
                    of rows will be determined by the max row
                    index plus one.
    :param numCols: Number of columns in the matrix. A non-positive
                    value means unknown, at which point the number
                    of columns will be determined by the max row
                    index plus one.
    """
    def __init__(self, entries, numRows=0, numCols=0):
        """
        Note: This docstring is not shown publicly.

        Create a wrapper over a Java CoordinateMatrix.

        Publicly, we require that `rows` be an RDD.  However, for
        internal usage, `rows` can also be a Java CoordinateMatrix
        object, in which case we can wrap it directly.  This
        assists in clean matrix conversions.

        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
        ...                           MatrixEntry(6, 4, 2.1)])
        >>> mat = CoordinateMatrix(entries)

        >>> mat_diff = CoordinateMatrix(entries)
        >>> (mat_diff._java_matrix_wrapper._java_model ==
        ...  mat._java_matrix_wrapper._java_model)
        False

        >>> mat_same = CoordinateMatrix(mat._java_matrix_wrapper._java_model)
        >>> (mat_same._java_matrix_wrapper._java_model ==
        ...  mat._java_matrix_wrapper._java_model)
        True
        """
        if isinstance(entries, RDD):
            entries = entries.map(_convert_to_matrix_entry)
            # We use DataFrames for serialization of MatrixEntry entries
            # from Python, so first convert the RDD to a DataFrame on
            # this side. This will convert each MatrixEntry to a Row
            # containing the 'i', 'j', and 'value' values, which can
            # each be easily serialized. We will convert back to
            # MatrixEntry inputs on the Scala side.
            java_matrix = callMLlibFunc("createCoordinateMatrix", entries.toDF(),
                                        long(numRows), long(numCols))
        elif (isinstance(entries, JavaObject)
              and entries.getClass().getSimpleName() == "CoordinateMatrix"):
            java_matrix = entries
        else:
            raise TypeError("entries should be an RDD of MatrixEntry entries or "
                            "(long, long, float) tuples, got %s" % type(entries))

        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)

    @property
    def entries(self):
        """
        Entries of the CoordinateMatrix stored as an RDD of
        MatrixEntries.

        >>> mat = CoordinateMatrix(sc.parallelize([MatrixEntry(0, 0, 1.2),
        ...                                        MatrixEntry(6, 4, 2.1)]))
        >>> entries = mat.entries
        >>> entries.first()
        MatrixEntry(0, 0, 1.2)
        """
        # We use DataFrames for serialization of MatrixEntry entries
        # from Java, so we first convert the RDD of entries to a
        # DataFrame on the Scala/Java side. Then we map each Row in
        # the DataFrame back to a MatrixEntry on this side.
        entries_df = callMLlibFunc("getMatrixEntries", self._java_matrix_wrapper._java_model)
        entries = entries_df.rdd.map(lambda row: MatrixEntry(row[0], row[1], row[2]))
        return entries

    def numRows(self):
        """
        Get or compute the number of rows.

        >>> entries = sc.parallelize([MatrixEntry(0, 0, 1.2),
        ...                           MatrixEntry(1, 0, 2),
        ...                           MatrixEntry(2, 1, 3.7)])

        >>> mat = CoordinateMatrix(entries)
        >>> print(mat.numRows())
        3

        >>> mat = CoordinateMatrix(entries, 7, 6)
        >>> print(mat.numRows())
        7
        """
        return self._java_matrix_wrapper.call("numRows")

    def numCols(self):
        """
        Get or compute the number of cols.
#.........这里部分代码省略.........

# 需要导入模块: from pyspark.mllib.common import JavaModelWrapper [as 别名]
# 或者: from pyspark.mllib.common.JavaModelWrapper import call [as 别名]
class RowMatrix(DistributedMatrix):
    """
    Represents a row-oriented distributed Matrix with no meaningful
    row indices.

    :param rows: An RDD of vectors.
    :param numRows: Number of rows in the matrix. A non-positive
                    value means unknown, at which point the number
                    of rows will be determined by the number of
                    records in the `rows` RDD.
    :param numCols: Number of columns in the matrix. A non-positive
                    value means unknown, at which point the number
                    of columns will be determined by the size of
                    the first row.
    """
    def __init__(self, rows, numRows=0, numCols=0):
        """
        Note: This docstring is not shown publicly.

        Create a wrapper over a Java RowMatrix.

        Publicly, we require that `rows` be an RDD.  However, for
        internal usage, `rows` can also be a Java RowMatrix
        object, in which case we can wrap it directly.  This
        assists in clean matrix conversions.

        >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6]])
        >>> mat = RowMatrix(rows)

        >>> mat_diff = RowMatrix(rows)
        >>> (mat_diff._java_matrix_wrapper._java_model ==
        ...  mat._java_matrix_wrapper._java_model)
        False

        >>> mat_same = RowMatrix(mat._java_matrix_wrapper._java_model)
        >>> (mat_same._java_matrix_wrapper._java_model ==
        ...  mat._java_matrix_wrapper._java_model)
        True
        """
        if isinstance(rows, RDD):
            rows = rows.map(_convert_to_vector)
            java_matrix = callMLlibFunc("createRowMatrix", rows, long(numRows), int(numCols))
        elif (isinstance(rows, JavaObject)
              and rows.getClass().getSimpleName() == "RowMatrix"):
            java_matrix = rows
        else:
            raise TypeError("rows should be an RDD of vectors, got %s" % type(rows))

        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)

    @property
    def rows(self):
        """
        Rows of the RowMatrix stored as an RDD of vectors.

        >>> mat = RowMatrix(sc.parallelize([[1, 2, 3], [4, 5, 6]]))
        >>> rows = mat.rows
        >>> rows.first()
        DenseVector([1.0, 2.0, 3.0])
        """
        return self._java_matrix_wrapper.call("rows")

    def numRows(self):
        """
        Get or compute the number of rows.

        >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6],
        ...                        [7, 8, 9], [10, 11, 12]])

        >>> mat = RowMatrix(rows)
        >>> print(mat.numRows())
        4

        >>> mat = RowMatrix(rows, 7, 6)
        >>> print(mat.numRows())
        7
        """
        return self._java_matrix_wrapper.call("numRows")

    def numCols(self):
        """
        Get or compute the number of cols.

        >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6],
        ...                        [7, 8, 9], [10, 11, 12]])

        >>> mat = RowMatrix(rows)
        >>> print(mat.numCols())
        3

        >>> mat = RowMatrix(rows, 7, 6)
        >>> print(mat.numCols())
        6
        """
        return self._java_matrix_wrapper.call("numCols")

    @since('2.0.0')
    def computeColumnSummaryStatistics(self):
        """
        Computes column-wise summary statistics.
#.........这里部分代码省略.........

# 需要导入模块: from pyspark.mllib.common import JavaModelWrapper [as 别名]
# 或者: from pyspark.mllib.common.JavaModelWrapper import call [as 别名]
class IndexedRowMatrix(DistributedMatrix):
    """
    Represents a row-oriented distributed Matrix with indexed rows.

    :param rows: An RDD of IndexedRows or (long, vector) tuples.
    :param numRows: Number of rows in the matrix. A non-positive
                    value means unknown, at which point the number
                    of rows will be determined by the max row
                    index plus one.
    :param numCols: Number of columns in the matrix. A non-positive
                    value means unknown, at which point the number
                    of columns will be determined by the size of
                    the first row.
    """
    def __init__(self, rows, numRows=0, numCols=0):
        """
        Note: This docstring is not shown publicly.

        Create a wrapper over a Java IndexedRowMatrix.

        Publicly, we require that `rows` be an RDD.  However, for
        internal usage, `rows` can also be a Java IndexedRowMatrix
        object, in which case we can wrap it directly.  This
        assists in clean matrix conversions.

        >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
        ...                        IndexedRow(1, [4, 5, 6])])
        >>> mat = IndexedRowMatrix(rows)

        >>> mat_diff = IndexedRowMatrix(rows)
        >>> (mat_diff._java_matrix_wrapper._java_model ==
        ...  mat._java_matrix_wrapper._java_model)
        False

        >>> mat_same = IndexedRowMatrix(mat._java_matrix_wrapper._java_model)
        >>> (mat_same._java_matrix_wrapper._java_model ==
        ...  mat._java_matrix_wrapper._java_model)
        True
        """
        if isinstance(rows, RDD):
            rows = rows.map(_convert_to_indexed_row)
            # We use DataFrames for serialization of IndexedRows from
            # Python, so first convert the RDD to a DataFrame on this
            # side. This will convert each IndexedRow to a Row
            # containing the 'index' and 'vector' values, which can
            # both be easily serialized.  We will convert back to
            # IndexedRows on the Scala side.
            java_matrix = callMLlibFunc("createIndexedRowMatrix", rows.toDF(),
                                        long(numRows), int(numCols))
        elif (isinstance(rows, JavaObject)
              and rows.getClass().getSimpleName() == "IndexedRowMatrix"):
            java_matrix = rows
        else:
            raise TypeError("rows should be an RDD of IndexedRows or (long, vector) tuples, "
                            "got %s" % type(rows))

        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)

    @property
    def rows(self):
        """
        Rows of the IndexedRowMatrix stored as an RDD of IndexedRows.

        >>> mat = IndexedRowMatrix(sc.parallelize([IndexedRow(0, [1, 2, 3]),
        ...                                        IndexedRow(1, [4, 5, 6])]))
        >>> rows = mat.rows
        >>> rows.first()
        IndexedRow(0, [1.0,2.0,3.0])
        """
        # We use DataFrames for serialization of IndexedRows from
        # Java, so we first convert the RDD of rows to a DataFrame
        # on the Scala/Java side. Then we map each Row in the
        # DataFrame back to an IndexedRow on this side.
        rows_df = callMLlibFunc("getIndexedRows", self._java_matrix_wrapper._java_model)
        rows = rows_df.rdd.map(lambda row: IndexedRow(row[0], row[1]))
        return rows

    def numRows(self):
        """
        Get or compute the number of rows.

        >>> rows = sc.parallelize([IndexedRow(0, [1, 2, 3]),
        ...                        IndexedRow(1, [4, 5, 6]),
        ...                        IndexedRow(2, [7, 8, 9]),
        ...                        IndexedRow(3, [10, 11, 12])])

        >>> mat = IndexedRowMatrix(rows)
        >>> print(mat.numRows())
        4

        >>> mat = IndexedRowMatrix(rows, 7, 6)
        >>> print(mat.numRows())
        7
        """
        return self._java_matrix_wrapper.call("numRows")

    def numCols(self):
        """
        Get or compute the number of cols.

#.........这里部分代码省略.........

# 需要导入模块: from pyspark.mllib.common import JavaModelWrapper [as 别名]
# 或者: from pyspark.mllib.common.JavaModelWrapper import call [as 别名]
class RowMatrix(DistributedMatrix):
    """
    .. note:: Experimental

    Represents a row-oriented distributed Matrix with no meaningful
    row indices.

    :param rows: An RDD of vectors.
    :param numRows: Number of rows in the matrix. A non-positive
                    value means unknown, at which point the number
                    of rows will be determined by the number of
                    records in the `rows` RDD.
    :param numCols: Number of columns in the matrix. A non-positive
                    value means unknown, at which point the number
                    of columns will be determined by the size of
                    the first row.
    """
    def __init__(self, rows, numRows=0, numCols=0):
        """
        Note: This docstring is not shown publicly.

        Create a wrapper over a Java RowMatrix.

        Publicly, we require that `rows` be an RDD.  However, for
        internal usage, `rows` can also be a Java RowMatrix
        object, in which case we can wrap it directly.  This
        assists in clean matrix conversions.

        >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6]])
        >>> mat = RowMatrix(rows)

        >>> mat_diff = RowMatrix(rows)
        >>> (mat_diff._java_matrix_wrapper._java_model ==
        ...  mat._java_matrix_wrapper._java_model)
        False

        >>> mat_same = RowMatrix(mat._java_matrix_wrapper._java_model)
        >>> (mat_same._java_matrix_wrapper._java_model ==
        ...  mat._java_matrix_wrapper._java_model)
        True
        """
        if isinstance(rows, RDD):
            rows = rows.map(_convert_to_vector)
            java_matrix = callMLlibFunc("createRowMatrix", rows, long(numRows), int(numCols))
        elif (isinstance(rows, JavaObject)
              and rows.getClass().getSimpleName() == "RowMatrix"):
            java_matrix = rows
        else:
            raise TypeError("rows should be an RDD of vectors, got %s" % type(rows))

        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)

    @property
    def rows(self):
        """
        Rows of the RowMatrix stored as an RDD of vectors.

        >>> mat = RowMatrix(sc.parallelize([[1, 2, 3], [4, 5, 6]]))
        >>> rows = mat.rows
        >>> rows.first()
        DenseVector([1.0, 2.0, 3.0])
        """
        return self._java_matrix_wrapper.call("rows")

    def numRows(self):
        """
        Get or compute the number of rows.

        >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6],
        ...                        [7, 8, 9], [10, 11, 12]])

        >>> mat = RowMatrix(rows)
        >>> print(mat.numRows())
        4

        >>> mat = RowMatrix(rows, 7, 6)
        >>> print(mat.numRows())
        7
        """
        return self._java_matrix_wrapper.call("numRows")

    def numCols(self):
        """
        Get or compute the number of cols.

        >>> rows = sc.parallelize([[1, 2, 3], [4, 5, 6],
        ...                        [7, 8, 9], [10, 11, 12]])

        >>> mat = RowMatrix(rows)
        >>> print(mat.numCols())
        3

        >>> mat = RowMatrix(rows, 7, 6)
        >>> print(mat.numCols())
        6
        """
        return self._java_matrix_wrapper.call("numCols")

# 需要导入模块: from pyspark.mllib.common import JavaModelWrapper [as 别名]
# 或者: from pyspark.mllib.common.JavaModelWrapper import call [as 别名]
class BlockMatrix(DistributedMatrix):
    """
    .. note:: Experimental

    Represents a distributed matrix in blocks of local matrices.

    :param blocks: An RDD of sub-matrix blocks
                   ((blockRowIndex, blockColIndex), sub-matrix) that
                   form this distributed matrix. If multiple blocks
                   with the same index exist, the results for
                   operations like add and multiply will be
                   unpredictable.
    :param rowsPerBlock: Number of rows that make up each block.
                         The blocks forming the final rows are not
                         required to have the given number of rows.
    :param colsPerBlock: Number of columns that make up each block.
                         The blocks forming the final columns are not
                         required to have the given number of columns.
    :param numRows: Number of rows of this matrix. If the supplied
                    value is less than or equal to zero, the number
                    of rows will be calculated when `numRows` is
                    invoked.
    :param numCols: Number of columns of this matrix. If the supplied
                    value is less than or equal to zero, the number
                    of columns will be calculated when `numCols` is
                    invoked.
    """
    def __init__(self, blocks, rowsPerBlock, colsPerBlock, numRows=0, numCols=0):
        """
        Note: This docstring is not shown publicly.

        Create a wrapper over a Java BlockMatrix.

        Publicly, we require that `blocks` be an RDD.  However, for
        internal usage, `blocks` can also be a Java BlockMatrix
        object, in which case we can wrap it directly.  This
        assists in clean matrix conversions.

        >>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
        ...                          ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))])
        >>> mat = BlockMatrix(blocks, 3, 2)

        >>> mat_diff = BlockMatrix(blocks, 3, 2)
        >>> (mat_diff._java_matrix_wrapper._java_model ==
        ...  mat._java_matrix_wrapper._java_model)
        False

        >>> mat_same = BlockMatrix(mat._java_matrix_wrapper._java_model, 3, 2)
        >>> (mat_same._java_matrix_wrapper._java_model ==
        ...  mat._java_matrix_wrapper._java_model)
        True
        """
        if isinstance(blocks, RDD):
            blocks = blocks.map(_convert_to_matrix_block_tuple)
            # We use DataFrames for serialization of sub-matrix blocks
            # from Python, so first convert the RDD to a DataFrame on
            # this side. This will convert each sub-matrix block
            # tuple to a Row containing the 'blockRowIndex',
            # 'blockColIndex', and 'subMatrix' values, which can
            # each be easily serialized.  We will convert back to
            # ((blockRowIndex, blockColIndex), sub-matrix) tuples on
            # the Scala side.
            java_matrix = callMLlibFunc("createBlockMatrix", blocks.toDF(),
                                        int(rowsPerBlock), int(colsPerBlock),
                                        long(numRows), long(numCols))
        elif (isinstance(blocks, JavaObject)
              and blocks.getClass().getSimpleName() == "BlockMatrix"):
            java_matrix = blocks
        else:
            raise TypeError("blocks should be an RDD of sub-matrix blocks as "
                            "((int, int), matrix) tuples, got %s" % type(blocks))

        self._java_matrix_wrapper = JavaModelWrapper(java_matrix)

    @property
    def blocks(self):
        """
        The RDD of sub-matrix blocks
        ((blockRowIndex, blockColIndex), sub-matrix) that form this
        distributed matrix.

        >>> mat = BlockMatrix(
        ...     sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
        ...                     ((1, 0), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]), 3, 2)
        >>> blocks = mat.blocks
        >>> blocks.first()
        ((0, 0), DenseMatrix(3, 2, [1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 0))

        """
        # We use DataFrames for serialization of sub-matrix blocks
        # from Java, so we first convert the RDD of blocks to a
        # DataFrame on the Scala/Java side. Then we map each Row in
        # the DataFrame back to a sub-matrix block on this side.
        blocks_df = callMLlibFunc("getMatrixBlocks", self._java_matrix_wrapper._java_model)
        blocks = blocks_df.map(lambda row: ((row[0][0], row[0][1]), row[1]))
        return blocks

    @property
    def rowsPerBlock(self):
        """
#.........这里部分代码省略.........