def product(self):
return reduce(mul, self, ht(1))
def median(self):
xs = sorted(self)
midpoint = round(len(xs) / 2)
return xs[midpoint] if len(xs) % 2\
else ht(xs[midpoint] + xs[midpoint-1]) / ht(2)
def variance(self):
return self.Sxx() / (len(self) - ht(1))
def stdev(self):
return self.variance()**ht('0.5')
def __len__(self):
return ht(super(type(self), self).__len__())
def Sxx(self):
return sum(self)**ht(2) - self.mean() * len(self)
def test_rules(self):
data = [[(self.x**2 + 3)**(ht(1)/ht(2)), 'x(x^2 + 3)^(-1/2)']]
for y, s in data:
print y, partial_differential(y, self.x), s
assert str(partial_differential(y, self.x)) == s
def norm(self):
''' Return the Euclidian Norm of a vector '''
square = lambda a: a**2
return sum(map(square, self))**ht('0.5')
def product(self):
return reduce(mul, self, ht(1))
def median(self):
xs = sorted(self)
midpoint = round(len(xs)/2)
return xs[midpoint] if len(xs) % 2\
else ht(xs[midpoint] + xs[midpoint-1]) / ht(2)
def stdev(self):
return self.variance() ** ht('0.5')
def variance(self):
return self.Sxx() / (len(self) - ht(1))
def Sxx(self):
return sum(self)**ht(2) - self.mean() * len(self)
def __len__(self):
return ht(super(type(self), self).__len__())
def norm(self):
''' Return the Euclidian Norm of a vector '''
square = lambda a: a ** 2
return sum(map(square, self))**ht('0.5')