Python numpy.core.multiarray.dot() Examples

def test_dot(self):
        a = np.array([[1, 0], [0, 1]])
        b = np.array([[0, 1], [1, 0]])
        c = np.array([[9, 1], [1, -9]])

        assert_equal(np.dot(a, b), a.dot(b))
        assert_equal(np.dot(np.dot(a, b), c), a.dot(b).dot(c))

        # test passing in an output array
        c = np.zeros_like(a)
        a.dot(b, c)
        assert_equal(c, np.dot(a, b))

        # test keyword args
        c = np.zeros_like(a)
        a.dot(b=b, out=c)
        assert_equal(c, np.dot(a, b)) 

def restoredot():
    """
    Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS
    implementations.

    Typically, the user will only need to call this when troubleshooting
    and installation problem, reproducing the conditions of a build without
    an accelerated BLAS, or when being very careful about benchmarking
    linear algebra operations.

    .. note:: Deprecated in Numpy 1.10
              The cblas functions have been integrated into the multarray
              module and restoredot now longer does anything. It will be
              removed in Numpy 1.11.0.

    See Also
    --------
    alterdot : `restoredot` undoes the effects of `alterdot`.

    """
    # 2014-08-13, 1.10
    warnings.warn("restoredot no longer does anything.", DeprecationWarning) 

def test_dot_3args(self):
        from numpy.core.multiarray import dot

        np.random.seed(22)
        f = np.random.random_sample((1024, 16))
        v = np.random.random_sample((16, 32))

        r = np.empty((1024, 32))
        for i in range(12):
            dot(f, v, r)
        assert_equal(sys.getrefcount(r), 2)
        r2 = dot(f, v, out=None)
        assert_array_equal(r2, r)
        assert_(r is dot(f, v, out=r))

        v = v[:, 0].copy() # v.shape == (16,)
        r = r[:, 0].copy() # r.shape == (1024,)
        r2 = dot(f, v)
        assert_(r is dot(f, v, r))
        assert_array_equal(r2, r) 

def test_dot_override(self):
        class A(object):
            def __numpy_ufunc__(self, ufunc, method, pos, inputs, **kwargs):
                return "A"

        class B(object):
            def __numpy_ufunc__(self, ufunc, method, pos, inputs, **kwargs):
                return NotImplemented

        a = A()
        b = B()
        c = np.array([[1]])

        assert_equal(np.dot(a, b), "A")
        assert_equal(c.dot(a), "A")
        assert_raises(TypeError, np.dot, b, c)
        assert_raises(TypeError, c.dot, b) 

def restoredot():
    """
    Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS
    implementations.

    Typically, the user will only need to call this when troubleshooting
    and installation problem, reproducing the conditions of a build without
    an accelerated BLAS, or when being very careful about benchmarking
    linear algebra operations.

    .. note:: Deprecated in Numpy 1.10
              The cblas functions have been integrated into the multarray
              module and restoredot now longer does anything. It will be
              removed in Numpy 1.11.0.

    See Also
    --------
    alterdot : `restoredot` undoes the effects of `alterdot`.

    """
    # 2014-08-13, 1.10
    warnings.warn("restoredot no longer does anything.", DeprecationWarning) 

def test_dot(self):
        a = np.array([[1, 0], [0, 1]])
        b = np.array([[0, 1], [1, 0]])
        c = np.array([[9, 1], [1, -9]])

        assert_equal(np.dot(a, b), a.dot(b))
        assert_equal(np.dot(np.dot(a, b), c), a.dot(b).dot(c))

        # test passing in an output array
        c = np.zeros_like(a)
        a.dot(b, c)
        assert_equal(c, np.dot(a, b))

        # test keyword args
        c = np.zeros_like(a)
        a.dot(b=b, out=c)
        assert_equal(c, np.dot(a, b)) 

def test_dot_3args(self):
        from numpy.core.multiarray import dot

        np.random.seed(22)
        f = np.random.random_sample((1024, 16))
        v = np.random.random_sample((16, 32))

        r = np.empty((1024, 32))
        for i in range(12):
            dot(f, v, r)
        assert_equal(sys.getrefcount(r), 2)
        r2 = dot(f, v, out=None)
        assert_array_equal(r2, r)
        assert_(r is dot(f, v, out=r))

        v = v[:, 0].copy() # v.shape == (16,)
        r = r[:, 0].copy() # r.shape == (1024,)
        r2 = dot(f, v)
        assert_(r is dot(f, v, r))
        assert_array_equal(r2, r) 

def test_all(self):
        dims = [(), (1,), (1, 1)]
        for dim1 in dims:
            for dim2 in dims:
                arg1 = rand(*dim1)
                arg2 = rand(*dim2)
                c1 = dot(arg1, arg2)
                c2 = dot_(arg1, arg2)
                assert_(c1.shape == c2.shape)
                assert_almost_equal(c1, c2, decimal=self.N) 

def test_vecscalar(self):
        b1 = rand(1, 1)
        b2 = rand(1, 8)
        c1 = dot(b1, b2)
        c2 = dot_(b1, b2)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_columnvect2(self):
        b1 = ones((3, 1)).transpose()
        b2 = [6.2]
        c1 = dot(b2, b1)
        c2 = dot_(b2, b1)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_columnvect1(self):
        b1 = ones((3, 1))
        b2 = [5.3]
        c1 = dot(b1, b2)
        c2 = dot_(b1, b2)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_vecvecinner(self):
        b1, b3 = self.b1, self.b3
        c1 = dot(b3, b1)
        c2 = dot_(b3, b1)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_vecvecouter(self):
        b1, b3 = self.b1, self.b3
        c1 = dot(b1, b3)
        c2 = dot_(b1, b3)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_vecmat2(self):
        b3, A = self.b3, self.A
        c1 = dot(b3, A.transpose())
        c2 = dot_(b3, A.transpose())
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_vecmat(self):
        A, b4 = self.A, self.b4
        c1 = dot(b4, A)
        c2 = dot_(b4, A)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_matvec2(self):
        A, b2 = self.A, self.b2
        c1 = dot(A, b2)
        c2 = dot_(A, b2)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_matvec(self):
        A, b1 = self.A, self.b1
        c1 = dot(A, b1)
        c2 = dot_(A, b1)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_matmat(self):
        A = self.A
        c1 = dot(A.transpose(), A)
        c2 = dot_(A.transpose(), A)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_dot_3args_errors(self):
        from numpy.core.multiarray import dot

        np.random.seed(22)
        f = np.random.random_sample((1024, 16))
        v = np.random.random_sample((16, 32))

        r = np.empty((1024, 31))
        assert_raises(ValueError, dot, f, v, r)

        r = np.empty((1024,))
        assert_raises(ValueError, dot, f, v, r)

        r = np.empty((32,))
        assert_raises(ValueError, dot, f, v, r)

        r = np.empty((32, 1024))
        assert_raises(ValueError, dot, f, v, r)
        assert_raises(ValueError, dot, f, v, r.T)

        r = np.empty((1024, 64))
        assert_raises(ValueError, dot, f, v, r[:, ::2])
        assert_raises(ValueError, dot, f, v, r[:, :32])

        r = np.empty((1024, 32), dtype=np.float32)
        assert_raises(ValueError, dot, f, v, r)

        r = np.empty((1024, 32), dtype=int)
        assert_raises(ValueError, dot, f, v, r) 

def alterdot():
    """
    Change `dot`, `vdot`, and `inner` to use accelerated BLAS functions.

    Typically, as a user of Numpy, you do not explicitly call this
    function. If Numpy is built with an accelerated BLAS, this function is
    automatically called when Numpy is imported.

    When Numpy is built with an accelerated BLAS like ATLAS, these
    functions are replaced to make use of the faster implementations.  The
    faster implementations only affect float32, float64, complex64, and
    complex128 arrays. Furthermore, the BLAS API only includes
    matrix-matrix, matrix-vector, and vector-vector products. Products of
    arrays with larger dimensionalities use the built in functions and are
    not accelerated.

    .. note:: Deprecated in Numpy 1.10
              The cblas functions have been integrated into the multarray
              module and alterdot now longer does anything. It will be
              removed in Numpy 1.11.0.

    See Also
    --------
    restoredot : `restoredot` undoes the effects of `alterdot`.

    """
    # 2014-08-13, 1.10
    warnings.warn("alterdot no longer does anything.", DeprecationWarning) 

def test_dot_2args(self):
        from numpy.core.multiarray import dot

        a = np.array([[1, 2], [3, 4]], dtype=float)
        b = np.array([[1, 0], [1, 1]], dtype=float)
        c = np.array([[3, 2], [7, 4]], dtype=float)

        d = dot(a, b)
        assert_allclose(c, d) 

def test_dot_2args(self):
        from numpy.core.multiarray import dot

        a = np.array([[1, 2], [3, 4]], dtype=float)
        b = np.array([[1, 0], [1, 1]], dtype=float)
        c = np.array([[3, 2], [7, 4]], dtype=float)

        d = dot(a, b)
        assert_allclose(c, d) 

def alterdot():
    """
    Change `dot`, `vdot`, and `inner` to use accelerated BLAS functions.

    Typically, as a user of Numpy, you do not explicitly call this
    function. If Numpy is built with an accelerated BLAS, this function is
    automatically called when Numpy is imported.

    When Numpy is built with an accelerated BLAS like ATLAS, these
    functions are replaced to make use of the faster implementations.  The
    faster implementations only affect float32, float64, complex64, and
    complex128 arrays. Furthermore, the BLAS API only includes
    matrix-matrix, matrix-vector, and vector-vector products. Products of
    arrays with larger dimensionalities use the built in functions and are
    not accelerated.

    .. note:: Deprecated in Numpy 1.10
              The cblas functions have been integrated into the multarray
              module and alterdot now longer does anything. It will be
              removed in Numpy 1.11.0.

    See Also
    --------
    restoredot : `restoredot` undoes the effects of `alterdot`.

    """
    # 2014-08-13, 1.10
    warnings.warn("alterdot no longer does anything.", DeprecationWarning) 

def test_all(self):
        dims = [(), (1,), (1, 1)]
        for dim1 in dims:
            for dim2 in dims:
                arg1 = rand(*dim1)
                arg2 = rand(*dim2)
                c1 = dot(arg1, arg2)
                c2 = dot_(arg1, arg2)
                assert_(c1.shape == c2.shape)
                assert_almost_equal(c1, c2, decimal=self.N) 

def test_vecscalar2(self):
        b1 = rand(8, 1)
        b2 = rand(1, 1)
        c1 = dot(b1, b2)
        c2 = dot_(b1, b2)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_vecscalar(self):
        b1 = rand(1, 1)
        b2 = rand(1, 8)
        c1 = dot(b1, b2)
        c2 = dot_(b1, b2)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_columnvect2(self):
        b1 = ones((3, 1)).transpose()
        b2 = [6.2]
        c1 = dot(b2, b1)
        c2 = dot_(b2, b1)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_columnvect1(self):
        b1 = ones((3, 1))
        b2 = [5.3]
        c1 = dot(b1, b2)
        c2 = dot_(b1, b2)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_vecvecouter(self):
        b1, b3 = self.b1, self.b3
        c1 = dot(b1, b3)
        c2 = dot_(b1, b3)
        assert_almost_equal(c1, c2, decimal=self.N) 

def test_vecmat3(self):
        A, b4 = self.A, self.b4
        c1 = dot(A.transpose(), b4)
        c2 = dot_(A.transpose(), b4)
        assert_almost_equal(c1, c2, decimal=self.N)