arpitbbhayani

case TARGET(BINARY_ADD): {
    PyObject *right = POP();
    PyObject *left = TOP();
    PyObject *result;
    if (PyUnicode_CheckExact(left) &&
             PyUnicode_CheckExact(right)) {
        result = unicode_concatenate(tstate, left, right, f, next_instr);
    }
    else {
        // Do this operation only when both the operands are numbers and

arpitbbhayani

#include <stdio.h>
#include <math.h>
int main(void) {
  printf("%Lf\n", powl(2, 20000));
  return 0;
}

$ ./a.out
inf

arpitbbhayani

>>> 2 ** 20000
39802768403379665923543072061912024537047727804924259387134 ...
...
... 6021 digits long ...
...
6309376

arpitbbhayani

struct _longobject {
    PyObject_VAR_HEAD
    digit ob_digit[1];
};

arpitbbhayani

typedef struct {
    PyObject ob_base;
    Py_ssize_t ob_size; /* Number of items in variable part */
} PyVarObject;

arpitbbhayani

struct _longobject {
    PyObject ob_base;
    Py_ssize_t ob_size; /* Number of items in variable part */
    digit ob_digit[1];
};

arpitbbhayani

...
    for (i = 0; i < size_b; ++i) {
        carry += a->ob_digit[i] + b->ob_digit[i];
        z->ob_digit[i] = carry & PyLong_MASK;
        carry >>= PyLong_SHIFT;
    }
    for (; i < size_a; ++i) {
        carry += a->ob_digit[i];
        z->ob_digit[i] = carry & PyLong_MASK;
        carry >>= PyLong_SHIFT;

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;

arpitbbhayani

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

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.