コード例 #1
0
def simple_gaussian_kernel(X):

    # cut off exponent at -500 to prevent overflow errors
    if type(X) == Numeric.ArrayType:
        Numeric.putmask(X, X < -500, -500)
    elif X < -500:
        X = -500

    return exp(X)
コード例 #2
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def gaussian(dist, stddev):
    X = -dist**2 / (2 * stddev**2)

    # cut off exponent at -500 to prevent overflow errors
    if type(X) == Numeric.ArrayType:
        Numeric.putmask(X, X < -500, -500)
    elif X < -500:
        X = -500

    return exp(X)
コード例 #3
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 def leftDrag(self, event):
     """
     Event handler for LMB drag event.
     
     @param event: A Qt mouse event.
     @type  event: U{B{QMouseEvent}<http://doc.trolltech.com/4/qmouseevent.html>}
     """
     dScale = .025  # This works nicely. Mark 2008-01-29.
     delta = self.prevY - event.y()
     self.prevY = event.y()
     factor = exp(dScale * delta)
     #print "y, py =", event.y(), self.prevY, ", delta =", delta, ", factor=", factor
     self.glpane.rescale_around_point(factor)
     self.glpane.gl_update()
     return
コード例 #4
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ファイル: ZoomInOutMode.py プロジェクト: foulowl/nanoengineer
    def leftDrag(self, event):
        """
        Event handler for LMB drag event.

        @param event: A Qt mouse event.
        @type  event: U{B{QMouseEvent}<http://doc.trolltech.com/4/qmouseevent.html>}
        """
        dScale = .025 # This works nicely. Mark 2008-01-29.
        delta = self.prevY - event.y()
        self.prevY = event.y()
        factor = exp(dScale * delta)
        #print "y, py =", event.y(), self.prevY, ", delta =", delta, ", factor=", factor
        self.glpane.rescale_around_point(factor)
        self.glpane.gl_update()
        return
コード例 #5
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ファイル: LeastSquares.py プロジェクト: fxia22/ASM_xf
    def f(param, t):
	return param[0]*exp(-param[1]/t)
コード例 #6
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ファイル: LeastSquares.py プロジェクト: fxia22/ASM_xf
    def f2(param, t):
	return 1e13*exp(-param[0]/t)
コード例 #7
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 def func(self, param, t):
     return self.N * param[0] * (1 / exp(param[0] * t))
コード例 #8
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ファイル: iagaussian.py プロジェクト: crowell/straighten
def iagaussian(s,mu,sigma):
    """  o Purpose
      Generate a 2D Gaussian image.

  o Synopsis
      g = iagaussian(s,mu,sigma)

  o Input
      s: [rows columns]
    mu: Mean vector. 2D point (x;y). Point of maximum value.
    sigma: covariance matrix (square).  [ Sx^2 Sxy; Syx Sy^2]

  o Output
      g: 

  o Description
      A 2D Gaussian image is an image with a Gaussian distribution. It can be used to generate test patterns or Gaussian filters both for spatial and frequency domain. The integral of the gaussian function is 1.0.

  o Examples
      import Numeric
      f = iagaussian([8,4], [3,1], [[1,0],[0,1]])
      print Numeric.array2string(f, precision=4, suppress_small=1)
      g = ianormalize(f, [0,255]).astype(Numeric.UnsignedInt8)
      print g
    f = iagaussian(100, 50, 10*10)
      g = ianormalize(f, [0,1])
      g,d = iaplot(g)
      showfig(g)
    f = iagaussian([50,50], [25,10], [[10*10,0],[0,20*20]])
      g = ianormalize(f, [0,255]).astype(Numeric.UnsignedInt8)
      iashow(g)
"""
    from Numeric import asarray,product,arange,NewAxis,transpose,matrixmultiply,reshape,concatenate,resize,sum,zeros,Float,ravel,pi,sqrt,exp 
    from LinearAlgebra import inverse,determinant 
    
    if type(sigma).__name__ in ['int', 'float', 'complex']: sigma = [sigma]
    s, mu, sigma = asarray(s), asarray(mu), asarray(sigma)
    
    if (product(s) == max(s)):
        x = arange(product(s))
        d = x - mu
        if len(d.shape) == 1:
            tmp1 = d[:,NewAxis]
            tmp3 = d
        else:
            tmp1 = transpose(d)
            tmp3 = tmp1
        if len(sigma) == 1:
            tmp2 = 1./sigma
        else:
            tmp2 = inverse(sigma)
        k = matrixmultiply(tmp1, tmp2) * tmp3
    else:
        aux = arange(product(s))
        x, y = iaind2sub(s, aux)
        xx = reshape(concatenate((x,y)), (2, product(x.shape)))
    
        d = transpose(xx) - resize(reshape(mu,(len(mu),1)), (s[0]*s[1],len(mu)))
    
        if len(sigma) == 1:
            tmp = 1./sigma
        else:
            tmp = inverse(sigma)
        k = matrixmultiply(d, tmp) * d
        k = sum(transpose(k))
    
    g = zeros(s, Float)
    aux = ravel(g)
    if len(sigma) == 1:
        tmp = sigma
    else:
        tmp = determinant(sigma)
    aux[:] = 1./(2*pi*sqrt(tmp)) * exp(-1./2 * k)
    
    return g
コード例 #9
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 def func(self, param, t):
     return param[0] * param[1] * param[1] * t * (1 / exp(param[1] * t))
コード例 #10
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 def func(self, param, t):
     return self.N*param[0]*param[1]*power(param[1]*t, param[0]-1)*\
     (1/exp(power(param[1]*t, param[0])))
コード例 #11
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ファイル: test.py プロジェクト: pombredanne/old-cogent
def combinations(n, k):
    """Returns the number of ways of choosing k items from n.
    """
    return exp(lgam(n+1) - lgam(k+1) - lgam(n-k+1))
コード例 #12
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 def f2(param, t):
     return 1e13 * exp(-param[0] / t)
コード例 #13
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 def f(param, t):
     return param[0] * exp(-param[1] / t)
コード例 #14
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ファイル: vline_demo.py プロジェクト: jtomase/matplotlib
def f(t):
    s1 = sin(2 * pi * t)
    e1 = exp(-t)
    return absolute(multiply(s1, e1)) + .05
コード例 #15
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def f(t):
    s1 = sin(2*pi*t)
    e1 = exp(-t)
    return absolute(multiply(s1,e1))+.05