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  # this work for additional information regarding copyright ownership.
  # The ASF licenses this file to You under the Apache License, Version 2.0
  # (the "License"); you may not use this file except in compliance with
  # the License.  You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  import unittest
  import array as pyarray
  
  from numpy import arange, array, array_equal, inf, ones, tile, zeros
  
  from pyspark.serializers import PickleSerializer
  from pyspark.ml.linalg import DenseMatrix, DenseVector, MatrixUDT, SparseMatrix, SparseVector, \
      Vector, VectorUDT, Vectors
  from pyspark.testing.mllibutils import MLlibTestCase
  from pyspark.sql import Row
  
  
  class VectorTests(MLlibTestCase):
  
      def _test_serialize(self, v):
          ser = PickleSerializer()
          self.assertEqual(v, ser.loads(ser.dumps(v)))
          jvec = self.sc._jvm.org.apache.spark.ml.python.MLSerDe.loads(bytearray(ser.dumps(v)))
          nv = ser.loads(bytes(self.sc._jvm.org.apache.spark.ml.python.MLSerDe.dumps(jvec)))
          self.assertEqual(v, nv)
          vs = [v] * 100
          jvecs = self.sc._jvm.org.apache.spark.ml.python.MLSerDe.loads(bytearray(ser.dumps(vs)))
          nvs = ser.loads(bytes(self.sc._jvm.org.apache.spark.ml.python.MLSerDe.dumps(jvecs)))
          self.assertEqual(vs, nvs)
  
      def test_serialize(self):
          self._test_serialize(DenseVector(range(10)))
          self._test_serialize(DenseVector(array([1., 2., 3., 4.])))
          self._test_serialize(DenseVector(pyarray.array('d', range(10))))
          self._test_serialize(SparseVector(4, {1: 1, 3: 2}))
          self._test_serialize(SparseVector(3, {}))
          self._test_serialize(DenseMatrix(2, 3, range(6)))
          sm1 = SparseMatrix(
              3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0])
          self._test_serialize(sm1)
  
      def test_dot(self):
          sv = SparseVector(4, {1: 1, 3: 2})
          dv = DenseVector(array([1., 2., 3., 4.]))
          lst = DenseVector([1, 2, 3, 4])
          mat = array([[1., 2., 3., 4.],
                       [1., 2., 3., 4.],
                       [1., 2., 3., 4.],
                       [1., 2., 3., 4.]])
          arr = pyarray.array('d', [0, 1, 2, 3])
          self.assertEqual(10.0, sv.dot(dv))
          self.assertTrue(array_equal(array([3., 6., 9., 12.]), sv.dot(mat)))
          self.assertEqual(30.0, dv.dot(dv))
          self.assertTrue(array_equal(array([10., 20., 30., 40.]), dv.dot(mat)))
          self.assertEqual(30.0, lst.dot(dv))
          self.assertTrue(array_equal(array([10., 20., 30., 40.]), lst.dot(mat)))
          self.assertEqual(7.0, sv.dot(arr))
  
      def test_squared_distance(self):
          def squared_distance(a, b):
              if isinstance(a, Vector):
                  return a.squared_distance(b)
              else:
                  return b.squared_distance(a)
  
          sv = SparseVector(4, {1: 1, 3: 2})
          dv = DenseVector(array([1., 2., 3., 4.]))
          lst = DenseVector([4, 3, 2, 1])
          lst1 = [4, 3, 2, 1]
          arr = pyarray.array('d', [0, 2, 1, 3])
          narr = array([0, 2, 1, 3])
          self.assertEqual(15.0, squared_distance(sv, dv))
          self.assertEqual(25.0, squared_distance(sv, lst))
          self.assertEqual(20.0, squared_distance(dv, lst))
          self.assertEqual(15.0, squared_distance(dv, sv))
          self.assertEqual(25.0, squared_distance(lst, sv))
          self.assertEqual(20.0, squared_distance(lst, dv))
          self.assertEqual(0.0, squared_distance(sv, sv))
          self.assertEqual(0.0, squared_distance(dv, dv))
          self.assertEqual(0.0, squared_distance(lst, lst))
          self.assertEqual(25.0, squared_distance(sv, lst1))
          self.assertEqual(3.0, squared_distance(sv, arr))
          self.assertEqual(3.0, squared_distance(sv, narr))
  
      def test_hash(self):
          v1 = DenseVector([0.0, 1.0, 0.0, 5.5])
          v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
          v3 = DenseVector([0.0, 1.0, 0.0, 5.5])
          v4 = SparseVector(4, [(1, 1.0), (3, 2.5)])
          self.assertEqual(hash(v1), hash(v2))
          self.assertEqual(hash(v1), hash(v3))
          self.assertEqual(hash(v2), hash(v3))
          self.assertFalse(hash(v1) == hash(v4))
          self.assertFalse(hash(v2) == hash(v4))
  
      def test_eq(self):
          v1 = DenseVector([0.0, 1.0, 0.0, 5.5])
          v2 = SparseVector(4, [(1, 1.0), (3, 5.5)])
          v3 = DenseVector([0.0, 1.0, 0.0, 5.5])
          v4 = SparseVector(6, [(1, 1.0), (3, 5.5)])
          v5 = DenseVector([0.0, 1.0, 0.0, 2.5])
          v6 = SparseVector(4, [(1, 1.0), (3, 2.5)])
          self.assertEqual(v1, v2)
          self.assertEqual(v1, v3)
          self.assertFalse(v2 == v4)
          self.assertFalse(v1 == v5)
          self.assertFalse(v1 == v6)
  
      def test_equals(self):
          indices = [1, 2, 4]
          values = [1., 3., 2.]
          self.assertTrue(Vectors._equals(indices, values, list(range(5)), [0., 1., 3., 0., 2.]))
          self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 3., 1., 0., 2.]))
          self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 3., 0., 2.]))
          self.assertFalse(Vectors._equals(indices, values, list(range(5)), [0., 1., 3., 2., 2.]))
  
      def test_conversion(self):
          # numpy arrays should be automatically upcast to float64
          # tests for fix of [SPARK-5089]
          v = array([1, 2, 3, 4], dtype='float64')
          dv = DenseVector(v)
          self.assertTrue(dv.array.dtype == 'float64')
          v = array([1, 2, 3, 4], dtype='float32')
          dv = DenseVector(v)
          self.assertTrue(dv.array.dtype == 'float64')
  
      def test_sparse_vector_indexing(self):
          sv = SparseVector(5, {1: 1, 3: 2})
          self.assertEqual(sv[0], 0.)
          self.assertEqual(sv[3], 2.)
          self.assertEqual(sv[1], 1.)
          self.assertEqual(sv[2], 0.)
          self.assertEqual(sv[4], 0.)
          self.assertEqual(sv[-1], 0.)
          self.assertEqual(sv[-2], 2.)
          self.assertEqual(sv[-3], 0.)
          self.assertEqual(sv[-5], 0.)
          for ind in [5, -6]:
              self.assertRaises(IndexError, sv.__getitem__, ind)
          for ind in [7.8, '1']:
              self.assertRaises(TypeError, sv.__getitem__, ind)
  
          zeros = SparseVector(4, {})
          self.assertEqual(zeros[0], 0.0)
          self.assertEqual(zeros[3], 0.0)
          for ind in [4, -5]:
              self.assertRaises(IndexError, zeros.__getitem__, ind)
  
          empty = SparseVector(0, {})
          for ind in [-1, 0, 1]:
              self.assertRaises(IndexError, empty.__getitem__, ind)
  
      def test_sparse_vector_iteration(self):
          self.assertListEqual(list(SparseVector(3, [], [])), [0.0, 0.0, 0.0])
          self.assertListEqual(list(SparseVector(5, [0, 3], [1.0, 2.0])), [1.0, 0.0, 0.0, 2.0, 0.0])
  
      def test_matrix_indexing(self):
          mat = DenseMatrix(3, 2, [0, 1, 4, 6, 8, 10])
          expected = [[0, 6], [1, 8], [4, 10]]
          for i in range(3):
              for j in range(2):
                  self.assertEqual(mat[i, j], expected[i][j])
  
          for i, j in [(-1, 0), (4, 1), (3, 4)]:
              self.assertRaises(IndexError, mat.__getitem__, (i, j))
  
      def test_repr_dense_matrix(self):
          mat = DenseMatrix(3, 2, [0, 1, 4, 6, 8, 10])
          self.assertTrue(
              repr(mat),
              'DenseMatrix(3, 2, [0.0, 1.0, 4.0, 6.0, 8.0, 10.0], False)')
  
          mat = DenseMatrix(3, 2, [0, 1, 4, 6, 8, 10], True)
          self.assertTrue(
              repr(mat),
              'DenseMatrix(3, 2, [0.0, 1.0, 4.0, 6.0, 8.0, 10.0], False)')
  
          mat = DenseMatrix(6, 3, zeros(18))
          self.assertTrue(
              repr(mat),
              'DenseMatrix(6, 3, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ..., \
                  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], False)')
  
      def test_repr_sparse_matrix(self):
          sm1t = SparseMatrix(
              3, 4, [0, 2, 3, 5], [0, 1, 2, 0, 2], [3.0, 2.0, 4.0, 9.0, 8.0],
              isTransposed=True)
          self.assertTrue(
              repr(sm1t),
              'SparseMatrix(3, 4, [0, 2, 3, 5], [0, 1, 2, 0, 2], [3.0, 2.0, 4.0, 9.0, 8.0], True)')
  
          indices = tile(arange(6), 3)
          values = ones(18)
          sm = SparseMatrix(6, 3, [0, 6, 12, 18], indices, values)
          self.assertTrue(
              repr(sm), "SparseMatrix(6, 3, [0, 6, 12, 18], \
                  [0, 1, 2, 3, 4, 5, 0, 1, ..., 4, 5, 0, 1, 2, 3, 4, 5], \
                  [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, ..., \
                  1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], False)")
  
          self.assertTrue(
              str(sm),
              "6 X 3 CSCMatrix\n\
              (0,0) 1.0\n(1,0) 1.0\n(2,0) 1.0\n(3,0) 1.0\n(4,0) 1.0\n(5,0) 1.0\n\
              (0,1) 1.0\n(1,1) 1.0\n(2,1) 1.0\n(3,1) 1.0\n(4,1) 1.0\n(5,1) 1.0\n\
              (0,2) 1.0\n(1,2) 1.0\n(2,2) 1.0\n(3,2) 1.0\n..\n..")
  
          sm = SparseMatrix(1, 18, zeros(19), [], [])
          self.assertTrue(
              repr(sm),
              'SparseMatrix(1, 18, \
                  [0, 0, 0, 0, 0, 0, 0, 0, ..., 0, 0, 0, 0, 0, 0, 0, 0], [], [], False)')
  
      def test_sparse_matrix(self):
          # Test sparse matrix creation.
          sm1 = SparseMatrix(
              3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0])
          self.assertEqual(sm1.numRows, 3)
          self.assertEqual(sm1.numCols, 4)
          self.assertEqual(sm1.colPtrs.tolist(), [0, 2, 2, 4, 4])
          self.assertEqual(sm1.rowIndices.tolist(), [1, 2, 1, 2])
          self.assertEqual(sm1.values.tolist(), [1.0, 2.0, 4.0, 5.0])
          self.assertTrue(
              repr(sm1),
              'SparseMatrix(3, 4, [0, 2, 2, 4, 4], [1, 2, 1, 2], [1.0, 2.0, 4.0, 5.0], False)')
  
          # Test indexing
          expected = [
              [0, 0, 0, 0],
              [1, 0, 4, 0],
              [2, 0, 5, 0]]
  
          for i in range(3):
              for j in range(4):
                  self.assertEqual(expected[i][j], sm1[i, j])
          self.assertTrue(array_equal(sm1.toArray(), expected))
  
          for i, j in [(-1, 1), (4, 3), (3, 5)]:
              self.assertRaises(IndexError, sm1.__getitem__, (i, j))
  
          # Test conversion to dense and sparse.
          smnew = sm1.toDense().toSparse()
          self.assertEqual(sm1.numRows, smnew.numRows)
          self.assertEqual(sm1.numCols, smnew.numCols)
          self.assertTrue(array_equal(sm1.colPtrs, smnew.colPtrs))
          self.assertTrue(array_equal(sm1.rowIndices, smnew.rowIndices))
          self.assertTrue(array_equal(sm1.values, smnew.values))
  
          sm1t = SparseMatrix(
              3, 4, [0, 2, 3, 5], [0, 1, 2, 0, 2], [3.0, 2.0, 4.0, 9.0, 8.0],
              isTransposed=True)
          self.assertEqual(sm1t.numRows, 3)
          self.assertEqual(sm1t.numCols, 4)
          self.assertEqual(sm1t.colPtrs.tolist(), [0, 2, 3, 5])
          self.assertEqual(sm1t.rowIndices.tolist(), [0, 1, 2, 0, 2])
          self.assertEqual(sm1t.values.tolist(), [3.0, 2.0, 4.0, 9.0, 8.0])
  
          expected = [
              [3, 2, 0, 0],
              [0, 0, 4, 0],
              [9, 0, 8, 0]]
  
          for i in range(3):
              for j in range(4):
                  self.assertEqual(expected[i][j], sm1t[i, j])
          self.assertTrue(array_equal(sm1t.toArray(), expected))
  
      def test_dense_matrix_is_transposed(self):
          mat1 = DenseMatrix(3, 2, [0, 4, 1, 6, 3, 9], isTransposed=True)
          mat = DenseMatrix(3, 2, [0, 1, 3, 4, 6, 9])
          self.assertEqual(mat1, mat)
  
          expected = [[0, 4], [1, 6], [3, 9]]
          for i in range(3):
              for j in range(2):
                  self.assertEqual(mat1[i, j], expected[i][j])
          self.assertTrue(array_equal(mat1.toArray(), expected))
  
          sm = mat1.toSparse()
          self.assertTrue(array_equal(sm.rowIndices, [1, 2, 0, 1, 2]))
          self.assertTrue(array_equal(sm.colPtrs, [0, 2, 5]))
          self.assertTrue(array_equal(sm.values, [1, 3, 4, 6, 9]))
  
      def test_norms(self):
          a = DenseVector([0, 2, 3, -1])
          self.assertAlmostEqual(a.norm(2), 3.742, 3)
          self.assertTrue(a.norm(1), 6)
          self.assertTrue(a.norm(inf), 3)
          a = SparseVector(4, [0, 2], [3, -4])
          self.assertAlmostEqual(a.norm(2), 5)
          self.assertTrue(a.norm(1), 7)
          self.assertTrue(a.norm(inf), 4)
  
          tmp = SparseVector(4, [0, 2], [3, 0])
          self.assertEqual(tmp.numNonzeros(), 1)
  
  
  class VectorUDTTests(MLlibTestCase):
  
      dv0 = DenseVector([])
      dv1 = DenseVector([1.0, 2.0])
      sv0 = SparseVector(2, [], [])
      sv1 = SparseVector(2, [1], [2.0])
      udt = VectorUDT()
  
      def test_json_schema(self):
          self.assertEqual(VectorUDT.fromJson(self.udt.jsonValue()), self.udt)
  
      def test_serialization(self):
          for v in [self.dv0, self.dv1, self.sv0, self.sv1]:
              self.assertEqual(v, self.udt.deserialize(self.udt.serialize(v)))
  
      def test_infer_schema(self):
          rdd = self.sc.parallelize([Row(label=1.0, features=self.dv1),
                                     Row(label=0.0, features=self.sv1)])
          df = rdd.toDF()
          schema = df.schema
          field = [f for f in schema.fields if f.name == "features"][0]
          self.assertEqual(field.dataType, self.udt)
          vectors = df.rdd.map(lambda p: p.features).collect()
          self.assertEqual(len(vectors), 2)
          for v in vectors:
              if isinstance(v, SparseVector):
                  self.assertEqual(v, self.sv1)
              elif isinstance(v, DenseVector):
                  self.assertEqual(v, self.dv1)
              else:
                  raise TypeError("expecting a vector but got %r of type %r" % (v, type(v)))
  
  
  class MatrixUDTTests(MLlibTestCase):
  
      dm1 = DenseMatrix(3, 2, [0, 1, 4, 5, 9, 10])
      dm2 = DenseMatrix(3, 2, [0, 1, 4, 5, 9, 10], isTransposed=True)
      sm1 = SparseMatrix(1, 1, [0, 1], [0], [2.0])
      sm2 = SparseMatrix(2, 1, [0, 0, 1], [0], [5.0], isTransposed=True)
      udt = MatrixUDT()
  
      def test_json_schema(self):
          self.assertEqual(MatrixUDT.fromJson(self.udt.jsonValue()), self.udt)
  
      def test_serialization(self):
          for m in [self.dm1, self.dm2, self.sm1, self.sm2]:
              self.assertEqual(m, self.udt.deserialize(self.udt.serialize(m)))
  
      def test_infer_schema(self):
          rdd = self.sc.parallelize([("dense", self.dm1), ("sparse", self.sm1)])
          df = rdd.toDF()
          schema = df.schema
          self.assertTrue(schema.fields[1].dataType, self.udt)
          matrices = df.rdd.map(lambda x: x._2).collect()
          self.assertEqual(len(matrices), 2)
          for m in matrices:
              if isinstance(m, DenseMatrix):
                  self.assertTrue(m, self.dm1)
              elif isinstance(m, SparseMatrix):
                  self.assertTrue(m, self.sm1)
              else:
                  raise ValueError("Expected a matrix but got type %r" % type(m))
  
  
  if __name__ == "__main__":
      from pyspark.ml.tests.test_linalg import *
  
      try:
          import xmlrunner
          testRunner = xmlrunner.XMLTestRunner(output='target/test-reports')
      except ImportError:
          testRunner = None
      unittest.main(testRunner=testRunner, verbosity=2)
  