Python umath.exp函数代码示例

def model_two_basis_functions():
    """ A test function that returns a model similar to model(), except
    that it uses the shapelet basis functions as the surface brightness
    and does not normalize.
    """
    data = empty((nepochs, grid_size, grid_size))
    beta2 = beta ** 2
    for t, z in star_track(nepochs):

        if t == 0:
            x = raytrace()
        else:
            x = raytrace(rE_true, z)

        n = 0
        K1 = 1.0 / sqrt(2 ** n * sqrt(pi) * factorial(n, 1) * beta)
        H1 = hermite(n)

        data[t] = (K1 * H1(x.real / beta) * exp(-x.real ** 2 / (2 * beta2))) * (
            K1 * H1(x.imag / beta) * exp(-x.imag ** 2 / (2 * beta2))
        )
    #        data[t] *= 100

    #        n  = 1
    #        K1 = 1.0/sqrt(2**n * sqrt(pi) * factorial(n,1) * beta)
    #        H1 = hermite(n)
    #
    #        data[t] += (K1 * H1(x.real/beta) * exp(-x.real**2/(2*beta2))) * \
    #                   (K1 * H1(x.imag/beta) * exp(-x.imag**2/(2*beta2)))

    return data

def save_bessel_functions(N):
    """Generate N 2D shapelets and plot."""

    beta2 = beta ** 2
    B = empty((grid_size, grid_size))  # Don't want matrix behaviour here

    # ---------------------------------------------------------------------------
    # Basis function constants, and hermite polynomials
    # ---------------------------------------------------------------------------

    vals = [[n, 1.0 / sqrt((2 ** n) * sqrt(pi) * factorial(n, 1) * beta), 0, 0, 0] for n in xrange(N)]
    expreal = exp(-theta.real ** 2 / (2 * beta2))
    expimag = exp(-theta.imag ** 2 / (2 * beta2))
    for n, K, H, _, _ in vals:
        vals[n][3] = K * jn(n, theta.real) * expreal
        vals[n][4] = K * jn(n, theta.imag) * expimag

    pylab.figure()
    l = 0
    for v1 in vals:
        for v2 in vals:
            B = v1[3] * v2[4]
            pylab.subplot(N, N, l + 1)
            pylab.axis("off")
            pylab.imshow(B.T)
            l += 1
    pylab.suptitle("Shapelets N=%i Beta=%.4f" % (N, beta))
    # pylab.savefig("B%i.png" % N)
    pylab.show()

 def integrand(bt, x):
     global a, st, G, lbd, ym, kp, c, var
     fun=-1
     if var==1:
         fun=polint(bt)*cos(bt*x)*(lbd+2*G)*(exp(bt*b)*(kp**2-1-2*bt*b+2*kp*bt*b)+exp(-bt*b)*(kp**2-1+2*bt*b-2*kp*bt*b))/((kp-1)*(lbd*(kp*exp(2*bt*b)+kp*exp(-2*bt*b)+2)+G*kp*(exp(2*bt*b)+exp(-2*bt*b)))+2*G*(kp**2+kp+4*bt**2 * b **2 -2))
     elif var==2:
         fun=polint(bt)*cos(bt*x)*(G*(exp(bt*b)*(kp**2-kp+2*kp*bt*b)-exp(-bt*b)*(kp**2-kp-2*kp*bt*b))+lbd*(kp**2-kp)*(exp(bt*b)-exp(-bt*b)))/((kp**2-kp)*(lbd+G)*sinh(2*bt*b)+4*kp*G*b*bt)
     return fun

def sigmoid(inX):
    '''
    Sigmoid函数
    :param inX:
    :return: 参数
    '''
    return 1.0 / (1 + exp(-inX))

def ridgeTest(xArr, yArr):
    '''
    计算30不同的参数lam 所对应的回归系数
    数据标准化处理：所有的特征都减去各自的均值并除以方差；
    :param xArr: 输入值
    :param yArr: 真实值
    :return:
    '''
    xMat = mat(xArr)
    yMat = mat(yArr).T

    # mean()计算均值
    xMean = mean(xMat, 0)
    yMean = mean(yMat, 0)

    xVar = var(xMat, 0)  # var() 计算方差
    xMat = (xMat - xMean) / xVar
    yMat = yMat - yMean

    numTestPts = 30  # 迭代次数
    wMat = zeros((numTestPts, shape(xMat)[1]))  # 返回矩阵

    for i in range(numTestPts):
        ws = ridgeRegres(xMat, yMat, exp(i - 10))  # 计算回归系数，指数级
        wMat[i, :] = ws.T

    return wMat

def lwlr(testPoint, xArr, yArr, k=1.0):
    '''
    局部加权线性回归，
    回归系数计算公式：w = (X^TWX)^(-1)X^TWy
    高斯核计算公式：w(i, i) = exp{[x^(i) - x] / (-2 * k^2)}
    :param testPoint: 坐标点
    :param xArr: 输入值
    :param yArr: 真实值
    :param k: 高斯核参数，用户自定义
    :return:
    '''
    xMat = mat(xArr)
    yMat = mat(yArr).T
    m = shape(xMat)[0]
    weights = mat(eye((m)))  # 初始化权重矩阵

    for j in range(m):
        diffMat = testPoint - xMat[j, :]
        weights[j, j] = exp(diffMat * diffMat.T / (-2.0 * k ** 2))  # 高斯核

    xTx = xMat.T * (weights * xMat)

    if linalg.det(xTx) == 0.0:  # 判断矩阵是否可逆
        print "This matrix is singular, cannot do inverse"
        return

    ws = xTx.I * (xMat.T * (weights * yMat))

    return testPoint * ws

def calculate_ideogram(ages, errors, n=500):
    from numpy import array, linspace, zeros, ones
    from numpy.core.umath import exp
    from math import pi

    ages, errors = array(ages), array(errors)
    lages = ages - errors * 2
    uages = ages + errors * 2

    xmax, xmin = uages.max(), lages.min()

    spread = xmax - xmin
    xmax += spread * 0.1
    xmin -= spread * 0.1

    bins = linspace(xmin, xmax, n)
    probs = zeros(n)

    for ai, ei in zip(ages, errors):
        if abs(ai) < 1e-10 or abs(ei) < 1e-10:
            continue

        # calculate probability curve for ai+/-ei
        # p=1/(2*pi*sigma2) *exp (-(x-u)**2)/(2*sigma2)
        # see http://en.wikipedia.org/wiki/Normal_distribution
        ds = (ones(n) * ai - bins) ** 2
        es = ones(n) * ei
        es2 = 2 * es * es
        gs = (es2 * pi) ** -0.5 * exp(-ds / es2)

        # cumulate probabilities
        # numpy element_wise addition
        probs += gs

    return tuple(bins), tuple(probs), xmin, xmax

def profile_exponential(R, Ie=0.40, Rd=0.50):
    return Ie * exp(-abs(R) / Rd)

    beta = 0.20  # arcsec - Basis function normalization
    Nbases = 25  # sqrt(Number of basis functions)
    grid_phys = 2.0  # arcsec - Physical size across grid
    grid_radius = 71  # pixels
    grid_size = 2 * grid_radius + 1  # pixels
    cell_size = grid_phys / grid_size  # arcsec/pixel

def pseudo_peak(center, start, stop, step, magnitude=500, peak_width=0.004, channels=1):
    x = linspace(start, stop, step)
    gaussian = lambda x: magnitude * exp(-((center - x) / peak_width) ** 2)

    for i, d in enumerate(gaussian(x)):
        if abs(center - x[i]) < peak_width:
            #            d = magnitude
            # for j in xrange(channels):
            d = magnitude + magnitude / 50.0 * random.random()

        yield [d * (j + 1) for j in range(channels)]

    def test_priority(self):
        class A(object):
            def __array__(self):
                return np.zeros(1)

            def __array_wrap__(self, arr, context):
                r = type(self)()
                r.arr = arr
                r.context = context
                return r

        class B(A):
            __array_priority__ = 20.0

        class C(A):
            __array_priority__ = 40.0

        x = np.zeros(1)
        a = A()
        b = B()
        c = C()
        f = ncu.minimum
        self.assertTrue(type(f(x, x)) is np.ndarray)
        self.assertTrue(type(f(x, a)) is A)
        self.assertTrue(type(f(x, b)) is B)
        self.assertTrue(type(f(x, c)) is C)
        self.assertTrue(type(f(a, x)) is A)
        self.assertTrue(type(f(b, x)) is B)
        self.assertTrue(type(f(c, x)) is C)

        self.assertTrue(type(f(a, a)) is A)
        self.assertTrue(type(f(a, b)) is B)
        self.assertTrue(type(f(b, a)) is B)
        self.assertTrue(type(f(b, b)) is B)
        self.assertTrue(type(f(b, c)) is C)
        self.assertTrue(type(f(c, b)) is C)
        self.assertTrue(type(f(c, c)) is C)

        self.assertTrue(type(ncu.exp(a) is A))
        self.assertTrue(type(ncu.exp(b) is B))
        self.assertTrue(type(ncu.exp(c) is C))

    def check_priority(self):
        class A(object):
            def __array__(self):
                return zeros(1)

            def __array_wrap__(self, arr, context):
                r = type(self)()
                r.arr = arr
                r.context = context
                return r

        class B(A):
            __array_priority__ = 20.0

        class C(A):
            __array_priority__ = 40.0

        x = zeros(1)
        a = A()
        b = B()
        c = C()
        f = minimum
        self.failUnless(type(f(x, x)) is ndarray)
        self.failUnless(type(f(x, a)) is A)
        self.failUnless(type(f(x, b)) is B)
        self.failUnless(type(f(x, c)) is C)
        self.failUnless(type(f(a, x)) is A)
        self.failUnless(type(f(b, x)) is B)
        self.failUnless(type(f(c, x)) is C)

        self.failUnless(type(f(a, a)) is A)
        self.failUnless(type(f(a, b)) is B)
        self.failUnless(type(f(b, a)) is B)
        self.failUnless(type(f(b, b)) is B)
        self.failUnless(type(f(b, c)) is C)
        self.failUnless(type(f(c, b)) is C)
        self.failUnless(type(f(c, c)) is C)

        self.failUnless(type(exp(a) is A))
        self.failUnless(type(exp(b) is B))
        self.failUnless(type(exp(c) is C))

def gauss_spline(x, n):
    """Gaussian approximation to B-spline basis function of order n.

    Parameters
    ----------
    n : int
        The order of the spline. Must be nonnegative, i.e. n >= 0

    References
    ----------
    .. [1] Bouma H., Vilanova A., Bescos J.O., ter Haar Romeny B.M., Gerritsen
       F.A. (2007) Fast and Accurate Gaussian Derivatives Based on B-Splines. In:
       Sgallari F., Murli A., Paragios N. (eds) Scale Space and Variational
       Methods in Computer Vision. SSVM 2007. Lecture Notes in Computer
       Science, vol 4485. Springer, Berlin, Heidelberg
   """
    signsq = (n + 1) / 12.0
    return 1 / sqrt(2 * pi * signsq) * exp(-x ** 2 / 2 / signsq)

def adaBoostTrainDS(dataArr, classLabels, numIt=40):
    '''
    基于单层决策树的AdaBoost训练过程
    :param dataArr: 数据集
    :param classLabels: 类标签
    :param numIt: 迭代次数， 用户自定义指定
    :return: weakClassArr， 弱分类器集合；aggClassEst，每个数据点的类别估计累计值
    '''
    # 初始化
    weakClassArr = []
    m = shape(dataArr)[0]
    D = mat(ones((m, 1)) / m)  # 初始化概率分布向量，其元素之和为 1
    aggClassEst = mat(zeros((m, 1)))

    for i in range(numIt):
        # 构建单层决策树
        bestStump, error, classEst = buildStump(dataArr, classLabels, D)
        print "D:", D.T

        # alpha每个分类器配备的权重值， 计算公式：alpha = (1/2) * ln[(1-e) / e]
        alpha = float(0.5 * log((1.0 - error) / max(error, 1e-16)))
        bestStump['alpha'] = alpha
        weakClassArr.append(bestStump)  # 存储最佳决策树
        print "classEst: ", classEst.T

        # 更新权重向量D
        # 若正确分类，D[t + 1] = [D[t]*exp(-a) / sum(D)]
        # 若错误分类，D[t + 1] = [D[t]*exp(+a) / sum(D)]
        expon = multiply(-1 * alpha * mat(classLabels).T, classEst)
        D = multiply(D, exp(expon))  # Calc New D for next iteration
        D = D / D.sum()

        aggClassEst += alpha * classEst  # 更新累计类别估计值
        print "aggClassEst: ", aggClassEst.T
        aggErrors = multiply(sign(aggClassEst) != mat(classLabels).T, ones((m, 1)))
        errorRate = aggErrors.sum() / m  # 计算错误率
        print "total error: ", errorRate

        if errorRate == 0.0:
            break  # 为0， 退出循环

    return weakClassArr, aggClassEst

    def _cumulative_probability(self, ages, errors, xmi, xma):
        bins = linspace(xmi, xma, N)
        probs = zeros(N)

        for ai, ei in zip(ages, errors):
            if abs(ai) < 1e-10 or abs(ei) < 1e-10:
                continue

            # calculate probability curve for ai+/-ei
            # p=1/(2*pi*sigma2) *exp (-(x-u)**2)/(2*sigma2)
            # see http://en.wikipedia.org/wiki/Normal_distribution
            ds = (ones(N) * ai - bins) ** 2
            es = ones(N) * ei
            es2 = 2 * es * es
            gs = (es2 * pi) ** -0.5 * exp(-ds / es2)

            # cumulate probabilities
            # numpy element_wise addition
            probs += gs

        return bins, probs

def kernelTrans(X, A, kTup):
    '''
    高斯径向基核函数，将数据从一个低维特征空间转换到高维特征空间
    K(x, y) = exp{[-(||x - y||)^2] / (2 * &^2}
    :param X: 数值型变量1
    :param A: 数值型变量2
    :param kTup: 元组
    :return:
    '''
    m, n = shape(X)
    K = mat(zeros((m, 1)))
    if kTup[0] == 'lin':
        K = X * A.T  #
    elif kTup[0] == 'rbf':
        for j in range(m):
            deltaRow = X[j, :] - A
            K[j] = deltaRow * deltaRow.T
        K = exp(K / (-1 * kTup[1] ** 2))  #
    else:
        raise NameError('Houston We Have a Problem -- \
    That Kernel is not recognized')

    return K