Python numpy.outer方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def map_fit(interface, state, label, inp):
    """
    Function calculates matrices ete and etde for every sample, aggregates and output them.
    """
    import numpy as np
    ete, etde = 0, 0
    out = interface.output(0)

    for row in inp:
        row = row.strip().split(state["delimiter"])  # split row
        if len(row) > 1:  # check if row is empty
            # intercept term is added to every sample
            x = np.array([(0 if v in state["missing_vals"] else float(v)) for i, v in enumerate(row) if
                          i in state["X_indices"]] + [-1])
            # map label value to 1 or -1. If label does not match set error
            y = 1 if state["y_map"][0] == row[state["y_index"]] else -1 if state["y_map"][1] == row[
                state["y_index"]] else "Error"
            ete += np.outer(x, x)
            etde += x * y
    out.add("etde", etde)
    for i, row in enumerate(ete):
        out.add(i, row) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def map_fit(interface, state, label, inp):
    import numpy as np
    combiner = dict([(k, [0, 0]) for k in state["samples"].keys()])
    out = interface.output(0)

    for row in inp:
        row = row.strip().split(state["delimiter"])
        if len(row) > 1:
            x = np.array([1] + [(0 if v in state["missing_vals"] else float(v)) for i, v in enumerate(row) if
                                i in state["X_indices"]])
            y = float(row[state["y_index"]])

            for test_id, x1 in state["samples"].iteritems():
                w = np.exp(np.dot(-(x1 - x).transpose(), x1 - x) / state["tau"])
                combiner[test_id][0] += w * np.outer(x, x)
                combiner[test_id][1] += w * x * y

    for k, v in combiner.iteritems():
        out.add(k + state["delimiter"] + "b", v[1])
        for i in range(len(v[0])):
            out.add(k + state["delimiter"] + "A" + state["delimiter"] + str(i), v[0][i]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def map_fit(interface, state, label, inp):
    import numpy as np
    a, b = 0, 0
    out = interface.output(0)

    for row in inp:
        row = row.strip().split(state["delimiter"])
        if len(row) > 1:
            x = np.array([1] + [(0 if v in state["missing_vals"] else float(v)) for i, v in enumerate(row) if
                                i in state["X_indices"]])
            y = float(row[state["y_index"]])
            a += np.outer(x, x)
            b += x * y
    out.add("b", b)
    for i, row in enumerate(a):
        out.add(i, row) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def _eigen_components(self):
        components = [(0, np.diag([1, 1, 1, 0, 1, 0, 0, 1]))]
        nontrivial_part = np.zeros((3, 3), dtype=np.complex128)
        for ij, w in zip([(1, 2), (0, 2), (0, 1)], self.weights):
            nontrivial_part[ij] = w
            nontrivial_part[ij[::-1]] = w.conjugate()
        assert np.allclose(nontrivial_part, nontrivial_part.conj().T)
        eig_vals, eig_vecs = np.linalg.eigh(nontrivial_part)
        for eig_val, eig_vec in zip(eig_vals, eig_vecs.T):
            exp_factor = -eig_val / np.pi
            proj = np.zeros((8, 8), dtype=np.complex128)
            nontrivial_indices = np.array([3, 5, 6], dtype=np.intp)
            proj[nontrivial_indices[:, np.newaxis], nontrivial_indices] = (
                np.outer(eig_vec.conjugate(), eig_vec))
            components.append((exp_factor, proj))
        return components 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def quat2mat(quaternion):
    """
    Converts given quaternion (x, y, z, w) to matrix.

    Args:
        quaternion: vec4 float angles

    Returns:
        3x3 rotation matrix
    """
    q = np.array(quaternion, dtype=np.float32, copy=True)[[3, 0, 1, 2]]
    n = np.dot(q, q)
    if n < EPS:
        return np.identity(3)
    q *= math.sqrt(2.0 / n)
    q = np.outer(q, q)
    return np.array(
        [
            [1.0 - q[2, 2] - q[3, 3], q[1, 2] - q[3, 0], q[1, 3] + q[2, 0]],
            [q[1, 2] + q[3, 0], 1.0 - q[1, 1] - q[3, 3], q[2, 3] - q[1, 0]],
            [q[1, 3] - q[2, 0], q[2, 3] + q[1, 0], 1.0 - q[1, 1] - q[2, 2]],
        ]
    ) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def quintic_spline(dx, dtype=np.float64):
    def inner(x):
        return 1 + x ** 3 / 12 * (-95 + 138 * x - 55 * x ** 2)

    def middle(x):
        return (x - 1) * (x - 2) / 24 * (-138 + 348 * x - 249 * x ** 2 + 55 * x ** 3)

    def outer(x):
        return (x - 2) * (x - 3) ** 2 / 24 * (-54 + 50 * x - 11 * x ** 2)

    window = np.arange(-3, 4)
    x = np.abs(dx - window)
    result = np.piecewise(
        x,
        [x <= 1, (x > 1) & (x <= 2), (x > 2) & (x <= 3)],
        [lambda x: inner(x), lambda x: middle(x), lambda x: outer(x)],
    )
    return result, window 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def tdhf_frozen_mask(eri, kind="ov"):
    if isinstance(eri.nocc, int):
        nocc = int(eri.model.mo_occ.sum() // 2)
        mask = eri.space
    else:
        nocc = numpy.array(tuple(int(i.sum() // 2) for i in eri.model.mo_occ))
        assert numpy.all(nocc == nocc[0])
        assert numpy.all(eri.space == eri.space[0, numpy.newaxis, :])
        nocc = nocc[0]
        mask = eri.space[0]
    mask_o = mask[:nocc]
    mask_v = mask[nocc:]
    if kind == "ov":
        mask_ov = numpy.outer(mask_o, mask_v).reshape(-1)
        return numpy.tile(mask_ov, 2)
    elif kind == "1ov":
        return numpy.outer(mask_o, mask_v).reshape(-1)
    elif kind == "sov":
        mask_ov = numpy.outer(mask_o, mask_v).reshape(-1)
        nk = len(eri.model.mo_occ)
        return numpy.tile(mask_ov, 2 * nk ** 2)
    elif kind == "o,v":
        return mask_o, mask_v 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def test_b2q():
    # conversion of b matrix to q
    q = np.array([1,2,3])
    s = np.sqrt(np.sum(q * q)) # vector norm
    B = np.outer(q, q)
    assert_array_almost_equal(q*s, B2q(B))
    q = np.array([1,2,3])
    # check that the sign of the vector as positive x convention
    B = np.outer(-q, -q)
    assert_array_almost_equal(q*s, B2q(B))
    q = np.array([-1, 2, 3])
    B = np.outer(q, q)
    assert_array_almost_equal(-q*s, B2q(B))
    B = np.eye(3) * -1
    assert_raises(ValueError, B2q, B)
    # no error if we up the tolerance
    q = B2q(B, tol=1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def optimization_variables_avg_QCQP():
    l = np.array([4, 1, 1, -5, 3], dtype=float)
    A = np.array([[1, 2, 3, 4],
                  [3, 1, 2, 2],
                  [2, 3, 1, 1],
                  [1, 3, 5, 1]], dtype=float)
    A = np.vstack([-np.mean(A, axis=1), A - np.mean(A, axis=1)])
    Q = A.dot(A.T)
    A_in = np.array([[1, 5, 2, 4],
                     [5, 2, 1, 2],
                     [0, 1, 1, 1],
                     [2, 4, 3, 5]], dtype=float)
    A_in = np.vstack([-np.mean(A_in, axis=1), A_in - np.mean(A_in, axis=1)])
    Q_in = A_in.dot(A_in.T)
    Q_in += np.outer(l, l)
    return l, Q, np.ones(5), Q_in 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def test_both_limit_angle_Q_iteration(self, optimization_variables_avg_QCQP):
        l, Q, A, Q_in = optimization_variables_avg_QCQP
        # l, Q, A = optimization_variables_avg
        # Q_in = 6 * np.eye(len(l)) + np.outer(l, l)
        max_angle = 20
        m = 2e-3
        m1 = 4e-3
        f = .01
        x = optimization_methods.optimize_focality(l, Q, f,
                                                   max_el_current=m,
                                                   max_total_current=m1,
                                                   Qin=Q_in,
                                                   max_angle=max_angle)
        x_sp = optimize_focality(
            l, Q, f, max_el_current=m, max_total_current=m1,
            Qin=Q_in, max_angle=max_angle)
        assert np.linalg.norm(x, 1) <= 2 * m1 + 1e-4
        assert np.all(np.abs(x) <= m + 1e-4)
        assert np.isclose(np.sum(x), 0)
        assert np.isclose(l.dot(x), f)
        assert np.arccos(l.dot(x) / np.sqrt(x.dot(Q_in).dot(x))) <= np.deg2rad(max_angle)
        assert np.allclose(x.dot(Q.dot(x)), x_sp.dot(Q.dot(x_sp)), rtol=1e-4, atol=1e-5) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def mutcoh(A):
    """ mutcoh calculates the mutual coherence of a matrix A, i.e. the cosine
        of the smallest angle between two columns
      
        mutual_coherence = mutcoh(A)
 
        Input:
            A ... m x n matrix
 
        Output:
            mutual_coherence """

    T = np.dot(A.conj().T,A)
    s = np.sqrt(np.diag(T))    
    S = np.diag(s)
    mutual_coherence = np.max(1.0*(T-S)/np.outer(s,s))
    
    return mutual_coherence 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def reflection_matrix(point, normal):
    """Return matrix to mirror at plane defined by point and normal vector.
    >>> v0 = numpy.random.random(4) - 0.5
    >>> v0[3] = 1.0
    >>> v1 = numpy.random.random(3) - 0.5
    >>> R = reflection_matrix(v0, v1)
    >>> numpy.allclose(2., numpy.trace(R))
    True
    >>> numpy.allclose(v0, numpy.dot(R, v0))
    True
    >>> v2 = v0.copy()
    >>> v2[:3] += v1
    >>> v3 = v0.copy()
    >>> v2[:3] -= v1
    >>> numpy.allclose(v2, numpy.dot(R, v3))
    True
    """
    normal = unit_vector(normal[:3])
    M = numpy.identity(4)
    M[:3, :3] -= 2.0 * numpy.outer(normal, normal)
    M[:3, 3] = (2.0 * numpy.dot(point[:3], normal)) * normal
    return M 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def shear_matrix(angle, direction, point, normal):
    """Return matrix to shear by angle along direction vector on shear plane.
    The shear plane is defined by a point and normal vector. The direction
    vector must be orthogonal to the plane's normal vector.
    A point P is transformed by the shear matrix into P" such that
    the vector P-P" is parallel to the direction vector and its extent is
    given by the angle of P-P'-P", where P' is the orthogonal projection
    of P onto the shear plane.
    >>> angle = (random.random() - 0.5) * 4*math.pi
    >>> direct = numpy.random.random(3) - 0.5
    >>> point = numpy.random.random(3) - 0.5
    >>> normal = numpy.cross(direct, numpy.random.random(3))
    >>> S = shear_matrix(angle, direct, point, normal)
    >>> numpy.allclose(1.0, numpy.linalg.det(S))
    True
    """
    normal = unit_vector(normal[:3])
    direction = unit_vector(direction[:3])
    if abs(numpy.dot(normal, direction)) > 1e-6:
        raise ValueError("direction and normal vectors are not orthogonal")
    angle = math.tan(angle)
    M = numpy.identity(4)
    M[:3, :3] += angle * numpy.outer(direction, normal)
    M[:3, 3] = -angle * numpy.dot(point[:3], normal) * direction
    return M 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def test_4(self):
        """
        Test of take, transpose, inner, outer products.

        """
        x = self.arange(24)
        y = np.arange(24)
        x[5:6] = self.masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert self.allequal(np.transpose(y, (2, 0, 1)), self.transpose(x, (2, 0, 1)))
        assert self.allequal(np.take(y, (2, 0, 1), 1), self.take(x, (2, 0, 1), 1))
        assert self.allequal(np.inner(self.filled(x, 0), self.filled(y, 0)),
                            self.inner(x, y))
        assert self.allequal(np.outer(self.filled(x, 0), self.filled(y, 0)),
                            self.outer(x, y))
        y = self.array(['abc', 1, 'def', 2, 3], object)
        y[2] = self.masked
        t = self.take(y, [0, 3, 4])
        assert t[0] == 'abc'
        assert t[1] == 2
        assert t[2] == 3 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import outer [as 別名]
def test_testTakeTransposeInnerOuter(self):
        # Test of take, transpose, inner, outer products
        x = arange(24)
        y = np.arange(24)
        x[5:6] = masked
        x = x.reshape(2, 3, 4)
        y = y.reshape(2, 3, 4)
        assert_(eq(np.transpose(y, (2, 0, 1)), transpose(x, (2, 0, 1))))
        assert_(eq(np.take(y, (2, 0, 1), 1), take(x, (2, 0, 1), 1)))
        assert_(eq(np.inner(filled(x, 0), filled(y, 0)),
                   inner(x, y)))
        assert_(eq(np.outer(filled(x, 0), filled(y, 0)),
                   outer(x, y)))
        y = array(['abc', 1, 'def', 2, 3], object)
        y[2] = masked
        t = take(y, [0, 3, 4])
        assert_(t[0] == 'abc')
        assert_(t[1] == 2)
        assert_(t[2] == 3)