text: from __future__ import division, absolute_import, print_function
  
  from numpy.testing import *
  from numpy.core import *
  from numpy import matrix, asmatrix, bmat
  from numpy.matrixlib.defmatrix import matrix_power
  from numpy.matrixlib import mat
  import numpy as np
  import collections
  
  class TestCtor(TestCase):
      def test_basic(self):
          A = array([[1, 2], [3, 4]])
          mA = matrix(A)
          assert_(all(mA.A == A))
  
          B = bmat("A,A;A,A")
          C = bmat([[A, A], [A, A]])
          D = array([[1, 2, 1, 2],
                     [3, 4, 3, 4],
                     [1, 2, 1, 2],
                     [3, 4, 3, 4]])
          assert_(all(B.A == D))
          assert_(all(C.A == D))
  
          E = array([[5, 6], [7, 8]])
          AEresult = matrix([[1, 2, 5, 6], [3, 4, 7, 8]])
          assert_(all(bmat([A, E]) == AEresult))
  
          vec = arange(5)
          mvec = matrix(vec)
          assert_(mvec.shape == (1, 5))
  
      def test_exceptions(self):
          # Check for TypeError when called with invalid string data.
          assert_raises(TypeError, matrix, "invalid")
  
      def test_bmat_nondefault_str(self):
          A = array([[1, 2], [3, 4]])
          B = array([[5, 6], [7, 8]])
          Aresult = array([[1, 2, 1, 2],
                           [3, 4, 3, 4],
                           [1, 2, 1, 2],
                           [3, 4, 3, 4]])
          Bresult = array([[5, 6, 5, 6],
                           [7, 8, 7, 8],
                           [5, 6, 5, 6],
                           [7, 8, 7, 8]])
          mixresult = array([[1, 2, 5, 6],
                             [3, 4, 7, 8],
                             [5, 6, 1, 2],
                             [7, 8, 3, 4]])
          assert_(all(bmat("A,A;A,A") == Aresult))
          assert_(all(bmat("A,A;A,A", ldict={'A':B}) == Aresult))
          assert_raises(TypeError, bmat, "A,A;A,A", gdict={'A':B})
          assert_(all(bmat("A,A;A,A", ldict={'A':A}, gdict={'A':B}) == Aresult))
          b2 = bmat("A,B;C,D", ldict={'A':A,'B':B}, gdict={'C':B,'D':A})
          assert_(all(b2 == mixresult))
  
  
  class TestProperties(TestCase):
      def test_sum(self):
          """Test whether matrix.sum(axis=1) preserves orientation.
          Fails in NumPy <= 0.9.6.2127.
          """
          M = matrix([[1, 2, 0, 0],
                     [3, 4, 0, 0],
                     [1, 2, 1, 2],
                     [3, 4, 3, 4]])
          sum0 = matrix([8, 12, 4, 6])
          sum1 = matrix([3, 7, 6, 14]).T
          sumall = 30
          assert_array_equal(sum0, M.sum(axis=0))
          assert_array_equal(sum1, M.sum(axis=1))
          assert_equal(sumall, M.sum())
  
          assert_array_equal(sum0, np.sum(M, axis=0))
          assert_array_equal(sum1, np.sum(M, axis=1))
          assert_equal(sumall, np.sum(M))
  
  
      def test_prod(self):
          x = matrix([[1, 2, 3], [4, 5, 6]])
          assert_equal(x.prod(), 720)
          assert_equal(x.prod(0), matrix([[4, 10, 18]]))
          assert_equal(x.prod(1), matrix([[6], [120]]))
  
          assert_equal(np.prod(x), 720)
          assert_equal(np.prod(x, axis=0), matrix([[4, 10, 18]]))
          assert_equal(np.prod(x, axis=1), matrix([[6], [120]]))
  
          y = matrix([0, 1, 3])
          assert_(y.prod() == 0)
  
      def test_max(self):
          x = matrix([[1, 2, 3], [4, 5, 6]])
          assert_equal(x.max(), 6)
          assert_equal(x.max(0), matrix([[4, 5, 6]]))
          assert_equal(x.max(1), matrix([[3], [6]]))
  
          assert_equal(np.max(x), 6)
          assert_equal(np.max(x, axis=0), matrix([[4, 5, 6]]))
          assert_equal(np.max(x, axis=1), matrix([[3], [6]]))
  
      def test_min(self):
          x = matrix([[1, 2, 3], [4, 5, 6]])
          assert_equal(x.min(), 1)
          assert_equal(x.min(0), matrix([[1, 2, 3]]))
          assert_equal(x.min(1), matrix([[1], [4]]))
  
          assert_equal(np.min(x), 1)
          assert_equal(np.min(x, axis=0), matrix([[1, 2, 3]]))
          assert_equal(np.min(x, axis=1), matrix([[1], [4]]))
  
      def test_ptp(self):
          x = np.arange(4).reshape((2, 2))
          assert_(x.ptp() == 3)
          assert_(all(x.ptp(0) == array([2, 2])))
          assert_(all(x.ptp(1) == array([1, 1])))
  
      def test_var(self):
          x = np.arange(9).reshape((3, 3))
          mx = x.view(np.matrix)
          assert_equal(x.var(ddof=0), mx.var(ddof=0))
          assert_equal(x.var(ddof=1), mx.var(ddof=1))
  
      def test_basic(self):
          import numpy.linalg as linalg
  
          A = array([[1., 2.],
                     [3., 4.]])
          mA = matrix(A)
          assert_(allclose(linalg.inv(A), mA.I))
          assert_(all(array(transpose(A) == mA.T)))
          assert_(all(array(transpose(A) == mA.H)))
          assert_(all(A == mA.A))
  
          B = A + 2j*A
          mB = matrix(B)
          assert_(allclose(linalg.inv(B), mB.I))
          assert_(all(array(transpose(B) == mB.T)))
          assert_(all(array(conjugate(transpose(B)) == mB.H)))
  
      def test_pinv(self):
          x = matrix(arange(6).reshape(2, 3))
          xpinv = matrix([[-0.77777778,  0.27777778],
                          [-0.11111111,  0.11111111],
                          [ 0.55555556, -0.05555556]])
          assert_almost_equal(x.I, xpinv)
  
      def test_comparisons(self):
          A = arange(100).reshape(10, 10)
          mA = matrix(A)
          mB = matrix(A) + 0.1
          assert_(all(mB == A+0.1))
          assert_(all(mB == matrix(A+0.1)))
          assert_(not any(mB == matrix(A-0.1)))
          assert_(all(mA < mB))
          assert_(all(mA <= mB))
          assert_(all(mA <= mA))
          assert_(not any(mA < mA))
  
          assert_(not any(mB < mA))
          assert_(all(mB >= mA))
          assert_(all(mB >= mB))
          assert_(not any(mB > mB))
  
          assert_(all(mA == mA))
          assert_(not any(mA == mB))
          assert_(all(mB != mA))
  
          assert_(not all(abs(mA) > 0))
          assert_(all(abs(mB > 0)))
  
      def test_asmatrix(self):
          A = arange(100).reshape(10, 10)
          mA = asmatrix(A)
          A[0, 0] = -10
          assert_(A[0, 0] == mA[0, 0])
  
      def test_noaxis(self):
          A = matrix([[1, 0], [0, 1]])
          assert_(A.sum() == matrix(2))
          assert_(A.mean() == matrix(0.5))
  
      def test_repr(self):
          A = matrix([[1, 0], [0, 1]])
          assert_(repr(A) == "matrix([[1, 0],\n        [0, 1]])")
  
  class TestCasting(TestCase):
      def test_basic(self):
          A = arange(100).reshape(10, 10)
          mA = matrix(A)
  
          mB = mA.copy()
          O = ones((10, 10), float64) * 0.1
          mB = mB + O
          assert_(mB.dtype.type == float64)
          assert_(all(mA != mB))
          assert_(all(mB == mA+0.1))
  
          mC = mA.copy()
          O = ones((10, 10), complex128)
          mC = mC * O
          assert_(mC.dtype.type == complex128)
          assert_(all(mA != mB))
  
  
  class TestAlgebra(TestCase):
      def test_basic(self):
          import numpy.linalg as linalg
  
          A = array([[1., 2.],
                     [3., 4.]])
          mA = matrix(A)
  
          B = identity(2)
          for i in range(6):
              assert_(allclose((mA ** i).A, B))
              B = dot(B, A)
  
          Ainv = linalg.inv(A)
          B = identity(2)
          for i in range(6):
              assert_(allclose((mA ** -i).A, B))
              B = dot(B, Ainv)
  
          assert_(allclose((mA * mA).A, dot(A, A)))
          assert_(allclose((mA + mA).A, (A + A)))
          assert_(allclose((3*mA).A, (3*A)))
  
          mA2 = matrix(A)
          mA2 *= 3
          assert_(allclose(mA2.A, 3*A))
  
      def test_pow(self):
          """Test raising a matrix to an integer power works as expected."""
          m = matrix("1. 2.; 3. 4.")
          m2 = m.copy()
          m2 **= 2
          mi = m.copy()
          mi **= -1
          m4 = m2.copy()
          m4 **= 2
          assert_array_almost_equal(m2, m**2)
          assert_array_almost_equal(m4, np.dot(m2, m2))
          assert_array_almost_equal(np.dot(mi, m), np.eye(2))
  
      def test_notimplemented(self):
          '''Check that 'not implemented' operations produce a failure.'''
          A = matrix([[1., 2.],
                      [3., 4.]])
  
          # __rpow__
          try:
              1.0**A
          except TypeError:
              pass
          else:
              self.fail("matrix.__rpow__ doesn't raise a TypeError")
  
          # __mul__ with something not a list, ndarray, tuple, or scalar
          try:
              A*object()
          except TypeError:
              pass
          else:
              self.fail("matrix.__mul__ with non-numeric object doesn't raise"
                        "a TypeError")
  
  class TestMatrixReturn(TestCase):
      def test_instance_methods(self):
          a = matrix([1.0], dtype='f8')
          methodargs = {
              'astype': ('intc',),
              'clip': (0.0, 1.0),
              'compress': ([1],),
              'repeat': (1,),
              'reshape': (1,),
              'swapaxes': (0, 0),
              'dot': np.array([1.0]),
              }
          excluded_methods = [
              'argmin', 'choose', 'dump', 'dumps', 'fill', 'getfield',
              'getA', 'getA1', 'item', 'nonzero', 'put', 'putmask', 'resize',
              'searchsorted', 'setflags', 'setfield', 'sort',
              'partition', 'argpartition',
              'take', 'tofile', 'tolist', 'tostring', 'tobytes', 'all', 'any',
              'sum', 'argmax', 'argmin', 'min', 'max', 'mean', 'var', 'ptp',
              'prod', 'std', 'ctypes', 'itemset', 'setasflat'
              ]
          for attrib in dir(a):
              if attrib.startswith('_') or attrib in excluded_methods:
                  continue
              f = getattr(a, attrib)
              if isinstance(f, collections.Callable):
                  # reset contents of a
                  a.astype('f8')
                  a.fill(1.0)
                  if attrib in methodargs:
                      args = methodargs[attrib]
                  else:
                      args = ()
                  b = f(*args)
                  assert_(type(b) is matrix, "%s" % attrib)
          assert_(type(a.real) is matrix)
          assert_(type(a.imag) is matrix)
          c, d = matrix([0.0]).nonzero()
          assert_(type(c) is matrix)
          assert_(type(d) is matrix)
  
  
  class TestIndexing(TestCase):
      def test_basic(self):
          x = asmatrix(zeros((3, 2), float))
          y = zeros((3, 1), float)
          y[:, 0] = [0.8, 0.2, 0.3]
          x[:, 1] = y>0.5
          assert_equal(x, [[0, 1], [0, 0], [0, 0]])
  
  
  class TestNewScalarIndexing(TestCase):
      def setUp(self):
          self.a = matrix([[1, 2], [3, 4]])
  
      def test_dimesions(self):
          a = self.a
          x = a[0]
          assert_equal(x.ndim, 2)
  
      def test_array_from_matrix_list(self):
          a = self.a
          x = array([a, a])
          assert_equal(x.shape, [2, 2, 2])
  
      def test_array_to_list(self):
          a = self.a
          assert_equal(a.tolist(), [[1, 2], [3, 4]])
  
      def test_fancy_indexing(self):
          a = self.a
          x = a[1, [0, 1, 0]]
          assert_(isinstance(x, matrix))
          assert_equal(x, matrix([[3,  4,  3]]))
          x = a[[1, 0]]
          assert_(isinstance(x, matrix))
          assert_equal(x, matrix([[3,  4], [1, 2]]))
          x = a[[[1], [0]], [[1, 0], [0, 1]]]
          assert_(isinstance(x, matrix))
          assert_equal(x, matrix([[4,  3], [1,  2]]))
  
      def test_matrix_element(self):
          x = matrix([[1, 2, 3], [4, 5, 6]])
          assert_equal(x[0][0], matrix([[1, 2, 3]]))
          assert_equal(x[0][0].shape, (1, 3))
          assert_equal(x[0].shape, (1, 3))
          assert_equal(x[:, 0].shape, (2, 1))
  
          x = matrix(0)
          assert_equal(x[0, 0], 0)
          assert_equal(x[0], 0)
          assert_equal(x[:, 0].shape, x.shape)
  
      def test_scalar_indexing(self):
          x = asmatrix(zeros((3, 2), float))
          assert_equal(x[0, 0], x[0][0])
  
      def test_row_column_indexing(self):
          x = asmatrix(np.eye(2))
          assert_array_equal(x[0,:], [[1, 0]])
          assert_array_equal(x[1,:], [[0, 1]])
          assert_array_equal(x[:, 0], [[1], [0]])
          assert_array_equal(x[:, 1], [[0], [1]])
  
      def test_boolean_indexing(self):
          A = arange(6)
          A.shape = (3, 2)
          x = asmatrix(A)
          assert_array_equal(x[:, array([True, False])], x[:, 0])
          assert_array_equal(x[array([True, False, False]),:], x[0,:])
  
      def test_list_indexing(self):
          A = arange(6)
          A.shape = (3, 2)
          x = asmatrix(A)
          assert_array_equal(x[:, [1, 0]], x[:, ::-1])
          assert_array_equal(x[[2, 1, 0],:], x[::-1,:])
  
  
  class TestPower(TestCase):
      def test_returntype(self):
          a = array([[0, 1], [0, 0]])
          assert_(type(matrix_power(a, 2)) is ndarray)
          a = mat(a)
          assert_(type(matrix_power(a, 2)) is matrix)
  
      def test_list(self):
          assert_array_equal(matrix_power([[0, 1], [0, 0]], 2), [[0, 0], [0, 0]])
  
  
  class TestShape(TestCase):
      def setUp(self):
          self.a = array([[1], [2]])
          self.m = matrix([[1], [2]])
  
      def test_shape(self):
          assert_equal(self.a.shape, (2, 1))
          assert_equal(self.m.shape, (2, 1))
  
      def test_numpy_ravel(self):
          assert_equal(np.ravel(self.a).shape, (2,))
          assert_equal(np.ravel(self.m).shape, (2,))
  
      def test_member_ravel(self):
          assert_equal(self.a.ravel().shape, (2,))
          assert_equal(self.m.ravel().shape, (1, 2))
  
      def test_member_flatten(self):
          assert_equal(self.a.flatten().shape, (2,))
          assert_equal(self.m.flatten().shape, (1, 2))
  
      def test_numpy_ravel_order(self):
          for t in array, matrix:
              x = t([[1, 2, 3], [4, 5, 6]])
              assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
              assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
              assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
              assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
  
      def test_matrix_ravel_order(self):
          x = matrix([[1, 2, 3], [4, 5, 6]])
          assert_equal(x.ravel(), [[1, 2, 3, 4, 5, 6]])
          assert_equal(x.ravel(order='F'), [[1, 4, 2, 5, 3, 6]])
          assert_equal(x.T.ravel(), [[1, 4, 2, 5, 3, 6]])
          assert_equal(x.T.ravel(order='A'), [[1, 2, 3, 4, 5, 6]])
  
      def test_array_memory_sharing(self):
          assert_(np.may_share_memory(self.a, self.a.ravel()))
          assert_(not np.may_share_memory(self.a, self.a.flatten()))
  
      def test_matrix_memory_sharing(self):
          assert_(np.may_share_memory(self.m, self.m.ravel()))
          assert_(not np.may_share_memory(self.m, self.m.flatten()))
  
  
  if __name__ == "__main__":
      run_module_suite()
  