Python numpy.spacing方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def validate_cov_matrix(M):
    M = (M + M.T) * 0.5
    k = 0
    I = np.eye(M.shape[0])
    while True:
        try:
            _ = np.linalg.cholesky(M)
            break
        except np.linalg.LinAlgError:
            # Find the nearest positive definite matrix for M. Modified from
            # http://www.mathworks.com/matlabcentral/fileexchange/42885-nearestspd
            # Might take several minutes
            k += 1
            w, v = np.linalg.eig(M)
            min_eig = v.min()
            M += (-min_eig * k * k + np.spacing(min_eig)) * I
    return M 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def MeanPixelAccuracy(pred, label):
    """
    Function to compute the mean pixel accuracy for semantic segmentation between mini-batch tensors
    :param pred: Tensor of predictions
    :param label: Tensor of ground-truth
    :return: Mean pixel accuracy for all the mini-bath
    """
    # Convert tensors to numpy arrays
    imPred = np.asarray(torch.squeeze(pred))
    imLab = np.asarray(torch.squeeze(label))

    # Create empty numpy arrays
    pixel_accuracy = np.empty(imLab.shape[0])
    pixel_correct = np.empty(imLab.shape[0])
    pixel_labeled = np.empty(imLab.shape[0])

    # Compute pixel accuracy for each pair of images in the batch
    for i in range(imLab.shape[0]):
        pixel_accuracy[i], pixel_correct[i], pixel_labeled[i] = pixelAccuracy(imPred[i], imLab[i])

    # Compute the final accuracy for the batch
    acc = 100.0 * np.sum(pixel_correct) / (np.spacing(1) + np.sum(pixel_labeled))

    return acc 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def semanticIoU(pred, label):
    """
    Computes the mean Intersection over Union for all the classes between two mini-batch tensors of semantic
    segmentation
    :param pred: Tensor of predictions
    :param label: Tensor of ground-truth
    :return: Mean semantic intersection over Union for all the classes
    """
    imPred = np.asarray(torch.squeeze(pred))
    imLab = np.asarray(torch.squeeze(label))

    area_intersection = []
    area_union = []

    for i in range(imLab.shape[0]):
        intersection, union = intersectionAndUnion(imPred[i], imLab[i])
        area_intersection.append(intersection)
        area_union.append(union)

    IoU = 1.0 * np.sum(area_intersection, axis=0) / np.sum(np.spacing(1)+area_union, axis=0)

    return np.mean(IoU) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def oks_iou(g, d, a_g, a_d, sigmas=None, in_vis_thre=None):
    if not isinstance(sigmas, np.ndarray):
        sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89]) / 10.0
    vars = (sigmas * 2) ** 2
    xg = g[0::3]
    yg = g[1::3]
    vg = g[2::3]
    ious = np.zeros((d.shape[0]))
    for n_d in range(0, d.shape[0]):
        xd = d[n_d, 0::3]
        yd = d[n_d, 1::3]
        vd = d[n_d, 2::3]
        dx = xd - xg
        dy = yd - yg
        e = (dx ** 2 + dy ** 2) / vars / ((a_g + a_d[n_d]) / 2 + np.spacing(1)) / 2
        if in_vis_thre is not None:
            ind = list(vg > in_vis_thre) and list(vd > in_vis_thre)
            e = e[ind]
        ious[n_d] = np.sum(np.exp(-e)) / e.shape[0] if e.shape[0] != 0 else 0.0
    return ious 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def test_spacing_nextafter(self):
        """Test np.spacing and np.nextafter"""
        # All non-negative finite #'s
        a = np.arange(0x7c00, dtype=uint16)
        hinf = np.array((np.inf,), dtype=float16)
        a_f16 = a.view(dtype=float16)

        assert_equal(np.spacing(a_f16[:-1]), a_f16[1:]-a_f16[:-1])

        assert_equal(np.nextafter(a_f16[:-1], hinf), a_f16[1:])
        assert_equal(np.nextafter(a_f16[0], -hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], -hinf), a_f16[:-1])

        # switch to negatives
        a |= 0x8000

        assert_equal(np.spacing(a_f16[0]), np.spacing(a_f16[1]))
        assert_equal(np.spacing(a_f16[1:]), a_f16[:-1]-a_f16[1:])

        assert_equal(np.nextafter(a_f16[0], hinf), -a_f16[1])
        assert_equal(np.nextafter(a_f16[1:], hinf), a_f16[:-1])
        assert_equal(np.nextafter(a_f16[:-1], -hinf), a_f16[1:]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def intersectionAndUnion(imPred, imLab, numClass):
    """
    This function takes the prediction and label of a single image,
    returns intersection and union areas for each class
    To compute over many images do:
    for i in range(Nimages):
        (area_intersection[:,i], area_union[:,i]) = intersectionAndUnion(imPred[i], imLab[i])
    IoU = 1.0 * np.sum(area_intersection, axis=1) / np.sum(np.spacing(1)+area_union, axis=1)
    """
    # Remove classes from unlabeled pixels in gt image.
    # We should not penalize detections in unlabeled portions of the image.
    imPred = imPred * (imLab >= 0)

    # Compute area intersection:
    intersection = imPred * (imPred == imLab)
    (area_intersection, _) = np.histogram(intersection, bins=numClass, range=(1, numClass))

    # Compute area union:
    (area_pred, _) = np.histogram(imPred, bins=numClass, range=(1, numClass))
    (area_lab, _) = np.histogram(imLab, bins=numClass, range=(1, numClass))
    area_union = area_pred + area_lab - area_intersection
    return (area_intersection, area_union) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def laplacian(W, normalized=True):
    """Return graph Laplacian"""

    # Degree matrix.
    d = W.sum(axis=0)

    # Laplacian matrix.
    if not normalized:
        D = scipy.sparse.diags(d.A.squeeze(), 0)
        L = D - W
    else:
        d += np.spacing(np.array(0, W.dtype))
        d = 1 / np.sqrt(d)
        D = scipy.sparse.diags(d.A.squeeze(), 0)
        I = scipy.sparse.identity(d.size, dtype=W.dtype)
        L = I - D * W * D

    assert np.abs(L - L.T).mean() < 1e-9
    assert type(L) is scipy.sparse.csr.csr_matrix
    return L 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def compute_oks(src_keypoints, src_roi, dst_keypoints, dst_roi):
    """Compute OKS for predicted keypoints wrt gt_keypoints.
    src_keypoints: 4xK
    src_roi: 4x1
    dst_keypoints: Nx4xK
    dst_roi: Nx4
    """

    sigmas = np.array([
        .26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87,
        .87, .89, .89]) / 10.0
    vars = (sigmas * 2)**2

    # area
    src_area = (src_roi[2] - src_roi[0] + 1) * (src_roi[3] - src_roi[1] + 1)

    # measure the per-keypoint distance if keypoints visible
    dx = dst_keypoints[:, 0, :] - src_keypoints[0, :]
    dy = dst_keypoints[:, 1, :] - src_keypoints[1, :]

    e = (dx**2 + dy**2) / vars / (src_area + np.spacing(1)) / 2
    e = np.sum(np.exp(-e), axis=1) / e.shape[1]

    return e 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def intersectionAndUnion(imPred, imLab, numClass):
    """
    This function takes the prediction and label of a single image,
    returns intersection and union areas for each class
    To compute over many images do:
    for i in range(Nimages):
        (area_intersection[:,i], area_union[:,i]) = intersectionAndUnion(imPred[i], imLab[i])
    IoU = 1.0 * np.sum(area_intersection, axis=1) / np.sum(np.spacing(1)+area_union, axis=1)
    """
    # Remove classes from unlabeled pixels in gt image.
    # We should not penalize detections in unlabeled portions of the image.
    imPred = imPred * (imLab > 0)

    # Compute area intersection:
    intersection = imPred * (imPred == imLab)
    (area_intersection, _) = np.histogram(intersection, bins=numClass, range=(1, numClass))

    # Compute area union:
    (area_pred, _) = np.histogram(imPred, bins=numClass, range=(1, numClass))
    (area_lab, _) = np.histogram(imLab, bins=numClass, range=(1, numClass))
    area_union = area_pred + area_lab - area_intersection
    return (area_intersection, area_union) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def _yeo_johnson_inverse_transform(self, x, lmbda):
        """Return inverse-transformed input x following Yeo-Johnson inverse
        transform with parameter lambda.
        """
        x_inv = np.zeros_like(x)
        pos = x >= 0

        # when x >= 0
        if abs(lmbda) < np.spacing(1.):
            x_inv[pos] = np.exp(x[pos]) - 1
        else:  # lmbda != 0
            x_inv[pos] = np.power(x[pos] * lmbda + 1, 1 / lmbda) - 1

        # when x < 0
        if abs(lmbda - 2) > np.spacing(1.):
            x_inv[~pos] = 1 - np.power(-(2 - lmbda) * x[~pos] + 1,
                                       1 / (2 - lmbda))
        else:  # lmbda == 2
            x_inv[~pos] = 1 - np.exp(-x[~pos])

        return x_inv 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def _yeo_johnson_transform(self, x, lmbda):
        """Return transformed input x following Yeo-Johnson transform with
        parameter lambda.
        """

        out = np.zeros_like(x)
        pos = x >= 0  # binary mask

        # when x >= 0
        if abs(lmbda) < np.spacing(1.):
            out[pos] = np.log1p(x[pos])
        else:  # lmbda != 0
            out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda

        # when x < 0
        if abs(lmbda - 2) > np.spacing(1.):
            out[~pos] = -(np.power(-x[~pos] + 1, 2 - lmbda) - 1) / (2 - lmbda)
        else:  # lmbda == 2
            out[~pos] = -np.log1p(-x[~pos])

        return out 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def laplacian(W, normalized=True):
    """Return the Laplacian of the weigth matrix."""

    # Degree matrix.
    d = W.sum(axis=0)

    # Laplacian matrix.
    if not normalized:
        D = scipy.sparse.diags(d.A.squeeze(), 0)
        L = D - W
    else:
        d += np.spacing(np.array(0, W.dtype))
        d = 1 / np.sqrt(d)
        D = scipy.sparse.diags(d.A.squeeze(), 0)
        I = scipy.sparse.identity(d.size, dtype=W.dtype)
        L = I - D * W * D

    # assert np.abs(L - L.T).mean() < 1e-9
    assert type(L) is scipy.sparse.csr.csr_matrix
    return L 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def __rtruediv__(self, other):
        """ Support for division .../G

        """
        if not np.equal(*self._shape):
            raise ValueError('Nonsquare systems cannot be inverted')

        a, b, c, d = self._a, self._b, self._c, self._d

        if np.any(svdvals(d) < np.spacing(1.)):
            raise LinAlgError('The feedthrough term of the system is not'
                              ' invertible.')
        else:
            # A-BD^{-1}C | BD^{-1}
            # -----------|--------
            # -D^{-1}C   | D^{-1}
            if self._isgain:
                ai, bi, ci = None, None, None
            else:
                ai = a - b @ solve(d, c)
                bi = (solve(d.T, b.T)).T
                ci = -solve(d, c)
            di = inv(d)

            return other @ State(ai, bi, ci, di, dt=self._dt) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def contFrac(self, x, a = 5., b = 0., c = 5./2.):
        #initialize
        k = 1 - 2*(a-b)
        l = 2*(c-1)
        d = 4*c*(c-1)
        n = 4*b*(c-a)
        A = np.ones(x.size)
        B = np.ones(x.size)
        G = np.ones(x.size)
        
        Gprev = np.zeros(x.size)+2
        counter = 0
        #loop until convergence of continued fraction
        while (np.max(np.abs(G-Gprev)) > self.epsmult*np.max(np.spacing(G))) and (counter < 1000):
            k = -k
            l = l+2.
            d = d+4.*l
            n = n+(1.+k)*l
            A = d/(d - n*A*x)
            B = (A-1.)*B
            Gprev = G
            G = G + B
            counter += 1
        
        if (counter == 1000):
            raise ValueError('Failed to converge on G, most likely due to divergence in continued fractions.')
        
        return G 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import spacing [as 別名]
def calcSTM(self,dt,j):
        #allocate
        u = 0
        deltaU = 0
        t = 0
        counter = 0
        
        #For elliptic orbits, calculate period effects
        if self.beta[j] >0:
            P = 2*np.pi*self.mu[j]*self.beta[j]**(-3./2.)
            n = np.floor((dt + P/2 - 2*self.nu0[j]/self.beta[j])/P)
            deltaU = 2*np.pi*n*self.beta[j]**(-5./2.)
        
        #loop until convergence of the time array to the time step
        while (np.max(np.abs(t-dt)) > self.epsmult*np.spacing(dt)) and (counter < 1000):
            q = self.beta[j]*u**2./(1+self.beta[j]*u**2.)
            U0w2 = 1. - 2.*q
            U1w2 = 2.*(1.-q)*u
            temp = self.contFrac(q)
            U = 16./15.*U1w2**5.*temp + deltaU
            U0 = 2.*U0w2**2.-1.
            U1 = 2.*U0w2*U1w2
            U2 = 2.*U1w2**2.
            U3 = self.beta[j]*U + U1*U2/3.
            r = self.r0norm[j]*U0 + self.nu0[j]*U1 + self.mu[j]*U2
            t = self.r0norm[j]*U1 + self.nu0[j]*U2 + self.mu[j]*U3
            u = u - (t-dt)/(4.*(1.-q)*r)
            counter += 1
        
        if (counter == 1000):
            raise ValueError('Failed to converge on t: %e/%e'%(np.max(np.abs(t-dt)), self.epsmult*np.spacing(dt)))
        
        #Kepler solution
        f = 1 - self.mu[j]/self.r0norm[j]*U2
        g = self.r0norm[j]*U1 + self.nu0[j]*U2
        F = -self.mu[j]*U1/r/self.r0norm[j]
        G = 1 - self.mu[j]/r*U2
        
        Phi = np.vstack((np.hstack((np.eye(3)*f, np.eye(3)*g)),np.hstack((np.eye(3)*F, np.eye(3)*G))))
       
        return Phi