diff DEPENDENCIES/mingw32/Python27/Lib/site-packages/numpy/matrixlib/tests/test_defmatrix.py @ 87:2a2c65a20a8b

Add Python libs and headers

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/DEPENDENCIES/mingw32/Python27/Lib/site-packages/numpy/matrixlib/tests/test_defmatrix.py	Wed Feb 25 14:05:22 2015 +0000
@@ -0,0 +1,400 @@
+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]])
+
+if __name__ == "__main__":
+    run_module_suite()