arpitbbhayani

def area(l, b):
  return l * b

>>> namespace = Namespace.get_instance()
>>> func = namespace.register(area)
>>> func(3, 4)
12

arpitbbhayani

class Namespace(object):
  """Namespace is the singleton class that is responsible
  for holding all the functions.
  """

  __instance = None
  def __init__(self):
    if self.__instance is None:
      self.function_map = dict()
      Namespace.__instance = self

arpitbbhayani

def area(l, b):
  return l * b

>>> func = Function(area)

>>> func.key()
('__main__', <class 'function'>, 'area', 2)

>>> func(3, 4)
12

arpitbbhayani

from inspect import getfullargspec

class Function(object):
  """Function is a wrap over standard python function.
  """

  def __init__(self, fn):
    self.fn = fn

  def __call__(self, *args, **kwargs):

arpitbbhayani

def area(radius):
  return 3.14 * radius ** 2

>>> locals()
{
  ...
  'area': <function area at 0x10476a440>,
  ...
}

arpitbbhayani

int area(int length, int breadth) {
  return length * breadth;
}

float area(int radius) {
  return 3.14 * radius * radius;
}

arpitbbhayani

def get_path_length(x, T, e):
  """The function returns the path length h(x) of an instance
  x in tree `T`.
  here e is the number of edges traversed from the root till the current
  subtree T.
  """
  if is_external_node(T):
    # when T is the root of an external node subtree
    # we estimate path length and return.
    # here c is the function which estimates the average path length

arpitbbhayani

def construct_forest(X, trees_count, subsample_count):
  """The function constructs a forest from given inputs/data points X.
  """
  forest = []
  for i in range(0, trees_count):
    # max_height is in fact the average height of the tree that would be
    # constructed from given points. This acts as max_height for the
    # construction because we are only interested in data points that have
    # shorter-than-average path lengths, as those points are more likely
    # to be anomalies.

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def construct_tree(X, current_height, max_height):
  """The function constructs a tree/sub-tree on points X.
  current_height: represents the height of the current tree to
    the root of the decision tree.
  max_height: the max height of the tree that should be constructed.
  The current_height and max_height only exists to make the algorithm efficient
  as we assume that no anomalies exist at depth >= max_height.
  """
  if current_height >= max_height:
    # here we are sure that no anomalies exist hence we

arpitbbhayani

...
    for (i = 0; i < size_b; ++i) {
        borrow = a->ob_digit[i] - b->ob_digit[i] - borrow;
        z->ob_digit[i] = borrow & PyLong_MASK;
        borrow >>= PyLong_SHIFT;
        borrow &= 1; /* Keep only one sign bit */
    }
    for (; i < size_a; ++i) {
        borrow = a->ob_digit[i] - borrow;
        z->ob_digit[i] = borrow & PyLong_MASK;