random.paretovariate

All Samples(12) | Call(12) | Derive(0) | Import(0)

Pareto distribution.  alpha is the shape parameter.

src/m/r/mrv-1-1.0.0-preview2/ext/networkx/networkx/utils.py MRV(Download)

def pareto_sequence(n,exponent=1.0):
    """
    Return sample sequence of length n from a Pareto distribution.
    """
    return [random.paretovariate(exponent) for i in xrange(n)]
 
 
def powerlaw_sequence(n,exponent=2.0):
    """
    Return sample sequence of length n from a power law distribution.
    """
    return [random.paretovariate(exponent-1) for i in xrange(n)]

src/n/e/networkx-1.3/networkx/utils.py networkx(Download)

def pareto_sequence(n,exponent=1.0):
    """
    Return sample sequence of length n from a Pareto distribution.
    """
    return [random.paretovariate(exponent) for i in range(n)]
 
 
def powerlaw_sequence(n,exponent=2.0):
    """
    Return sample sequence of length n from a power law distribution.
    """
    return [random.paretovariate(exponent-1) for i in range(n)]

src/s/i/simforge-HEAD/forgemodel/SimForge.py simforge(Download)

                self.featureprefs[f] = self.model.get_universal_preference(f)
                universal = " just like everyone else!"
            else:
                self.featureprefs[f] = random.paretovariate(float(self.model.get_parameter('feature_preferences_pareto_factor')))
 
            self.model.log("i like feature " + str(f) + " about " + str(self.featureprefs[f])+ universal)
            pref_sum = pref_sum + self.featureprefs[f]

    def get_universal_preference(self, feature):
        if not self.universal_preferences.has_key(feature):
            self.universal_preferences[feature] = random.paretovariate(float(self.get_parameter('feature_preferences_pareto_factor')))
        return self.universal_preferences[feature]
 
    def add_software(self, s):
        self.softwares.append(s)

src/d/d/ddsweet-HEAD/trunk/Probability/Src/uns_random.py ddsweet(Download)

            # b is minial value of x, means x >= b.
            self.b = 1
        # cvalue is current value.
        self.__cvalue = random.paretovariate(self.alpha)
        self.__cvalueb = 0
 
    def getcvalue(self):
        self.__cvalue = random.paretovariate(self.alpha)

src/d/d/ddsweet-HEAD/Probability/Src/uns_random.py ddsweet(Download)

            # b is minial value of x, means x >= b.
            self.b = 1
        # cvalue is current value.
        self.__cvalue = random.paretovariate(self.alpha)
        self.__cvalueb = 0
 
    def getcvalue(self):
        self.__cvalue = random.paretovariate(self.alpha)

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

    print "%.8f" % random.gammavariate(20.0, 1.0)
    print "%.8f" % random.gauss(0.0, 1.0)
    print "%.8f" % random.betavariate(3.0, 3.0)
    print "%.8f" % random.paretovariate(1.0)
    print "%.8f" % random.weibullvariate(1.0, 1.0)
    #print "%.8f" % random.stdgamma(1.0,1.0,1.0,1.0) # deprecated in CPython
    #print "%.8f" % random.cunifvariate(0.0,1.0)     # deprecated in CPython

src/a/l/allmydata-tahoe-1.8.0/contrib/fuse/runtests.py allmydata-tahoe(Download)

        def rsize(sz=sz):
            return min(int(random.paretovariate(.25)), sz/12)
 
        # first chop up whole file into random sized chunks
        slices = []
        posn = 0
        while posn < sz: