def test_basic(self):
     dts = list(zip(['f2', 'f4', 'f8', 'c8', 'c16'],
                    [np.float16, np.float32, np.float64, np.complex64,
                     np.complex128]))
     for dt1, dt2 in dts:
         for attr in ('bits', 'eps', 'epsneg', 'iexp', 'machar', 'machep',
                      'max', 'maxexp', 'min', 'minexp', 'negep', 'nexp',
                      'nmant', 'precision', 'resolution', 'tiny'):
             assert_equal(getattr(finfo(dt1), attr),
                          getattr(finfo(dt2), attr), attr)
     assert_raises(ValueError, finfo, 'i4')

#.........这里部分代码省略.........
    values can add numerical noise to the result.

    Note that fitting polynomial coefficients is inherently badly conditioned
    when the degree of the polynomial is large or the interval of sample points
    is badly centered. The quality of the fit should always be checked in these
    cases. When polynomial fits are not satisfactory, splines may be a good
    alternative.

    References
    ----------
    .. [1] Wikipedia, "Curve fitting",
           http://en.wikipedia.org/wiki/Curve_fitting
    .. [2] Wikipedia, "Polynomial interpolation",
           http://en.wikipedia.org/wiki/Polynomial_interpolation

    Examples
    --------
    >>> x = np.array([0.0, 1.0, 2.0, 3.0,  4.0,  5.0])
    >>> y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
    >>> z = np.polyfit(x, y, 3)
    >>> z
    array([ 0.08703704, -0.81349206,  1.69312169, -0.03968254])

    It is convenient to use `poly1d` objects for dealing with polynomials:

    >>> p = np.poly1d(z)
    >>> p(0.5)
    0.6143849206349179
    >>> p(3.5)
    -0.34732142857143039
    >>> p(10)
    22.579365079365115

    High-order polynomials may oscillate wildly:

    >>> p30 = np.poly1d(np.polyfit(x, y, 30))
    /... RankWarning: Polyfit may be poorly conditioned...
    >>> p30(4)
    -0.80000000000000204
    >>> p30(5)
    -0.99999999999999445
    >>> p30(4.5)
    -0.10547061179440398

    Illustration:

    >>> import matplotlib.pyplot as plt
    >>> xp = np.linspace(-2, 6, 100)
    >>> plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')
    [<matplotlib.lines.Line2D object at 0x...>, <matplotlib.lines.Line2D object at 0x...>, <matplotlib.lines.Line2D object at 0x...>]
    >>> plt.ylim(-2,2)
    (-2, 2)
    >>> plt.show()

    """
    order = int(deg) + 1
    x = NX.asarray(x) + 0.0
    y = NX.asarray(y) + 0.0

    # check arguments.
    if deg < 0 :
        raise ValueError("expected deg >= 0")
    if x.ndim != 1:
        raise TypeError("expected 1D vector for x")
    if x.size == 0:
        raise TypeError("expected non-empty vector for x")
    if y.ndim < 1 or y.ndim > 2 :
        raise TypeError("expected 1D or 2D array for y")
    if x.shape[0] != y.shape[0] :
        raise TypeError("expected x and y to have same length")

    # set rcond
    if rcond is None :
        rcond = len(x)*finfo(x.dtype).eps

    # scale x to improve condition number
    scale = abs(x).max()
    if scale != 0 :
        x /= scale

    # solve least squares equation for powers of x
    v = vander(x, order)
    c, resids, rank, s = lstsq(v, y, rcond)

    # warn on rank reduction, which indicates an ill conditioned matrix
    if rank != order and not full:
        msg = "Polyfit may be poorly conditioned"
        warnings.warn(msg, RankWarning)

    # scale returned coefficients
    if scale != 0 :
        if c.ndim == 1 :
            c /= vander([scale], order)[0]
        else :
            c /= vander([scale], order).T

    if full :
        return c, resids, rank, s, rcond
    else :
        return c

def test_instances():
    iinfo(10)
    finfo(3.0)

 def test_singleton(self,level=2):
     ftype = finfo(longdouble)
     ftype2 = finfo(longdouble)
     assert_equal(id(ftype), id(ftype2))

#.........这里部分代码省略.........
    >>> y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
    >>> z = np.polyfit(x, y, 3)
    >>> z
    array([ 0.08703704, -0.81349206,  1.69312169, -0.03968254])

    It is convenient to use `poly1d` objects for dealing with polynomials:

    >>> p = np.poly1d(z)
    >>> p(0.5)
    0.6143849206349179
    >>> p(3.5)
    -0.34732142857143039
    >>> p(10)
    22.579365079365115

    High-order polynomials may oscillate wildly:

    >>> p30 = np.poly1d(np.polyfit(x, y, 30))
    /... RankWarning: Polyfit may be poorly conditioned...
    >>> p30(4)
    -0.80000000000000204
    >>> p30(5)
    -0.99999999999999445
    >>> p30(4.5)
    -0.10547061179440398

    Illustration:

    >>> import matplotlib.pyplot as plt
    >>> xp = np.linspace(-2, 6, 100)
    >>> _ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')
    >>> plt.ylim(-2,2)
    (-2, 2)
    >>> plt.show()

    """
    order = int(deg) + 1
    x = NX.asarray(x) + 0.0
    y = NX.asarray(y) + 0.0

    # check arguments.
    if deg < 0:
        raise ValueError("expected deg >= 0")
    if x.ndim != 1:
        raise TypeError("expected 1D vector for x")
    if x.size == 0:
        raise TypeError("expected non-empty vector for x")
    if y.ndim < 1 or y.ndim > 2:
        raise TypeError("expected 1D or 2D array for y")
    if x.shape[0] != y.shape[0]:
        raise TypeError("expected x and y to have same length")

    # set rcond
    if rcond is None:
        rcond = len(x)*finfo(x.dtype).eps

    # set up least squares equation for powers of x
    lhs = vander(x, order)
    rhs = y

    # apply weighting
    if w is not None:
        w = NX.asarray(w) + 0.0
        if w.ndim != 1:
            raise TypeError("expected a 1-d array for weights")
        if w.shape[0] != y.shape[0]:
            raise TypeError("expected w and y to have the same length")
        lhs *= w[:, NX.newaxis]
        if rhs.ndim == 2:
            rhs *= w[:, NX.newaxis]
        else:
            rhs *= w

    # scale lhs to improve condition number and solve
    scale = NX.sqrt((lhs*lhs).sum(axis=0))
    lhs /= scale
    c, resids, rank, s = lstsq(lhs, rhs, rcond)
    c = (c.T/scale).T  # broadcast scale coefficients

    # warn on rank reduction, which indicates an ill conditioned matrix
    if rank != order and not full:
        msg = "Polyfit may be poorly conditioned"
        warnings.warn(msg, RankWarning)

    if full:
        return c, resids, rank, s, rcond
    elif cov:
        Vbase = inv(dot(lhs.T, lhs))
        Vbase /= NX.outer(scale, scale)
        # Some literature ignores the extra -2.0 factor in the denominator, but
        #  it is included here because the covariance of Multivariate Student-T
        #  (which is implied by a Bayesian uncertainty analysis) includes it.
        #  Plus, it gives a slightly more conservative estimate of uncertainty.
        fac = resids / (len(x) - order - 2.0)
        if y.ndim == 1:
            return c, Vbase * fac
        else:
            return c, Vbase[:,:, NX.newaxis] * fac
    else:
        return c

def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):

    import numpy.core.numeric as NX
    from numpy.core import isscalar, abs, dot
    from numpy.lib.twodim_base import diag, vander
    from numpy.linalg import eigvals, lstsq, inv
    try:
        from numpy.core import finfo # 1.7
    except:
        from numpy.lib.getlimits import finfo # 1.3 support for cluster

    order = int(deg) + 1
    x = NX.asarray(x) + 0.0
    y = NX.asarray(y) + 0.0

    # check arguments.
    if deg < 0 :
        raise ValueError("expected deg >= 0")
    if x.ndim != 1:
        raise TypeError("expected 1D vector for x")
    if x.size == 0:
        raise TypeError("expected non-empty vector for x")
    if y.ndim < 1 or y.ndim > 2 :
        raise TypeError("expected 1D or 2D array for y")
    if x.shape[0] != y.shape[0] :
        raise TypeError("expected x and y to have same length")

    # set rcond
    if rcond is None :
        rcond = len(x)*finfo(x.dtype).eps

    # set up least squares equation for powers of x
    lhs = vander(x, order)
    rhs = y

    # apply weighting
    if w is not None:
        w = NX.asarray(w) + 0.0
        if w.ndim != 1:
            raise TypeError, "expected a 1-d array for weights"
        if w.shape[0] != y.shape[0] :
            raise TypeError, "expected w and y to have the same length"
        lhs *= w[:, NX.newaxis]
        if rhs.ndim == 2:
            rhs *= w[:, NX.newaxis]
        else:
            rhs *= w

    # scale lhs to improve condition number and solve
    scale = NX.sqrt((lhs*lhs).sum(axis=0))
    lhs /= scale
    c, resids, rank, s = lstsq(lhs, rhs, rcond)
    c = (c.T/scale).T  # broadcast scale coefficients

    # warn on rank reduction, which indicates an ill conditioned matrix
    if rank != order and not full:
        msg = "Polyfit may be poorly conditioned"
        warnings.warn(msg, RankWarning)

    if full :
        return c, resids, rank, s, rcond
    elif cov :
        Vbase = inv(dot(lhs.T,lhs))
        Vbase /= NX.outer(scale, scale)
        # Some literature ignores the extra -2.0 factor in the denominator, but
        #  it is included here because the covariance of Multivariate Student-T
        #  (which is implied by a Bayesian uncertainty analysis) includes it.
        #  Plus, it gives a slightly more conservative estimate of uncertainty.
        fac = resids / (len(x) - order - 2.0)
        if y.ndim == 1:
            return c, Vbase * fac
        else:
            return c, Vbase[:,:,NX.newaxis] * fac
    else :
        return c