def get_yvals(self, x):
if self.cumulative:
raise ValueError("dont' support getting y vals for cumulative")
if self.isfunc:
return self.pofx(x)
else:
rand = stat.interplin(self.xvals, self.pofx, x)
def get_yvals(self, x):
if self.cumulative:
raise ValueError("dont' support getting y vals for cumulative")
if self.isfunc:
return self.pofx(x)
else:
rand = stat.interplin(self.xvals, self.pofx, x)
def _genrand_accum(self, numrand):
# this returns f8
urand = self.rng.uniform(size=numrand)
# to get randoms from the distribution, we interpolate the x(pcum) at
# the test rand values. Clever!
rand = stat.interplin(self.xvals, self.pcum, urand)
return rand
def _genrand_accum(self, numrand):
# this returns f8
urand = self.rng.uniform(size=numrand)
# to get randoms from the distribution, we interpolate the x(pcum) at
# the test rand values. Clever!
rand = stat.interplin(self.xvals, self.pcum, urand)
return rand
def genrand_accum(self, numrand, seed=None):
if seed is not None:
numpy.random.seed(seed=seed)
# this returns f8
urand = numpy.random.random(numrand)
# to get randoms from the distribution, we interpolate the x(pcum) at
# the test rand values. Clever!
rand = stat.interplin(self.xvals, self.pcum, urand)
return rand
def genrand_accum(self, numrand, seed=None):
if seed is not None:
numpy.random.seed(seed=seed)
# this returns f8
urand = numpy.random.random(numrand)
# to get randoms from the distribution, we interpolate the x(pcum) at
# the test rand values. Clever!
rand = stat.interplin(self.xvals, self.pcum, urand)
return rand
