mhlr

mhlr

mhlr

from proto import Proto, ProtoMeta
__metaclass__ = ProtoMeta

class A:
  x = "This is X"
  def getY(self):
    return self._y

class B:
  _y = "This is Y"

mhlr

from pylab import *
import scipy
from scipy import stats
from numba import vectorize, guvectorize, float64, jit, njit


@guvectorize(['void(float64[:], float64[:], float64[:])'],
             '(i),(i)->()',
             nopython=True
)

mhlr

# gnerate Zipf distributed data as described in
#  http://arxiv.org/abs/1407.7135
#  Zipf's law arises naturally in structured, high-dimensional data
#  Laurence Aitchison, Nicola Corradi, Peter E. Latham

n <- 2**22
k <- 20
b <- rnorm(k, 1, 0.2)
data <- replicate(n, {z <- runif(1)
                      p <- (z^b) / (z^b + (1-z)^b)

mhlr

(if window-system
    (tool-bar-mode -1)
  (menu-bar-mode -1))
(when (eq window-system 'ns)
  (add-to-list 'default-frame-alist '(height . 80))
  (add-to-list 'default-frame-alist '(width . 132)))

(require 'package)
(setq package-enable-at-startup nil)
(add-to-list 'package-archives '("melpa" . "https://melpa.org/packages/"))