random.lognormvariate

All Samples(26) | Call(26) | Derive(0) | Import(0)

Log normal distribution.

If you take the natural logarithm of this distribution, you'll get a
normal distribution with mean mu and standard deviation sigma.
mu can have any value, and sigma must be greater than zero.

src/s/p/speechresearch-HEAD/trunk/gmisclib/mcmc.py speechresearch(Download)

		ergodicity simpler.
		"""
		assert type(self.vscale)==types.FloatType
		return random.lognormvariate(0.0, self.TOL/2.0)*self.vscale
 
 
	def status(self):

src/s/p/speechresearch-HEAD/gmisclib/mcmc.py speechresearch(Download)

		ergodicity simpler.
		"""
		assert type(self.vscale)==types.FloatType
		return random.lognormvariate(0.0, self.TOL/2.0)*self.vscale
 
 
	def status(self):

src/g/m/gmisclib-0.64.9/mcmc.py gmisclib(Download)

		ergodicity simpler.
		"""
		assert type(self.vscale)==types.FloatType
		return random.lognormvariate(0.0, self.TOL/2.0)*self.vscale
 
 
	def status(self):

src/b/i/biskit-HEAD/trunk/Biskit/Statistics/lognormal.py biskit(Download)

            beta = .7
            x = 10.
 
            R = [ random.lognormvariate( alpha, beta ) for j in range( 10 ) ]
 
            cr += [ logConfidence( x, R )[0] ]

src/b/i/biskit-HEAD/trunk/Biskit/Statistics/Density.py biskit(Download)

	alpha = 2.
	beta = 0.6
 
	self.R = [ random.lognormvariate( alpha, beta )
		   for i in range( 10000 )]
 
	p = logConfidence( 6.0, self.R )[0]#, area(6.0, alpha, beta)

src/b/i/biskit-HEAD/Biskit/Statistics/lognormal.py biskit(Download)

            beta = .7
            x = 10.
 
            R = [ random.lognormvariate( alpha, beta ) for j in range( 10 ) ]
 
            cr += [ logConfidence( x, R )[0] ]

src/b/i/biskit-HEAD/Biskit/Statistics/Density.py biskit(Download)

	alpha = 2.
	beta = 0.6
 
	self.R = [ random.lognormvariate( alpha, beta )
		   for i in range( 10000 )]
 
	p = logConfidence( 6.0, self.R )[0]#, area(6.0, alpha, beta)

src/s/h/shedskin-HEAD/tests/172.py shedskin(Download)

    print nums
    print "%.8f" % random.uniform(-0.5,0.5)
    print "%.8f" % random.normalvariate(0.0, 1.0)
    print "%.8f" % random.lognormvariate(0.0, 1.0)
    print "%.8f" % random.expovariate(1.0)
    print "%.8f" % random.vonmisesvariate(0.0, 1.0)
    print "%.8f" % random.gammavariate(20.0, 1.0)

src/i/r/ironruby-HEAD/External.LCA_RESTRICTED/Languages/IronPython/27/Lib/test/test_multiprocessing.py ironruby(Download)

 
        # create and destroy lots of blocks of different sizes
        for i in xrange(iterations):
            size = int(random.lognormvariate(0, 1) * 1000)
            b = multiprocessing.heap.BufferWrapper(size)
            blocks.append(b)
            if len(blocks) > maxblocks:

src/i/r/ironruby-HEAD/External.LCA_RESTRICTED/Languages/CPython/27/Lib/test/test_multiprocessing.py ironruby(Download)

 
        # create and destroy lots of blocks of different sizes
        for i in xrange(iterations):
            size = int(random.lognormvariate(0, 1) * 1000)
            b = multiprocessing.heap.BufferWrapper(size)
            blocks.append(b)
            if len(blocks) > maxblocks:

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