bebraw

import pyeliza

class Eliza:
    aliases = 'eliza'
    description = 'Virtual therapist'
    _therapist = pyeliza.eliza()

    def execute(self, expression, context):
        '''
        >>> from mock import Mock

jweissbock

import pygame, sys, random, math
from pygame.locals import *

# set up game		
pygame.init()
mainClock = pygame.time.Clock()		

# create window
WINDOWWIDTH = 600
WINDOWHEIGHT = 600

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

pv

import numpy as np
import scipy.linalg.blas

cdef extern from "f2pyptr.h":
    void *f2py_pointer(object) except NULL

ctypedef int dgemm_t(
	char *transa, char *transb,
	int *m, int *n, int *k,
	double *alpha,

syllog1sm

"""A simple implementation of a greedy transition-based parser. Released under BSD license."""
from os import path
import os
import sys
from collections import defaultdict
import random
import time
import pickle

SHIFT = 0; RIGHT = 1; LEFT = 2;

eliangcs

Cheatsheet: pyenv, virtualenvwrapper, and pip

Installation (for Mac OS)

Install pyenv with brew

brew update
brew install pyenv

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

greydanus

""" Trains an MNIST classifier using Synthetic Gradients. See Google DeepMind paper @ arxiv.org/abs/1608.05343. """
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from tensorflow.examples.tutorials.mnist import input_data # just use tensorflow's mnist api
mnist = input_data.read_data_sets('MNIST_data', one_hot=False)

# hyperparameters
global_step = 0
batch_size = 10

yaroslavvb

import numpy as np
import tensorflow as tf
import scipy
from tensorflow.contrib.eager.python import tfe
tfe.enable_eager_execution()

# manual numpy example
# X = np.array(([[0., 1], [2, 3]]))
# W0 = X
# W1 = np.array(([[0., 1], [2, 3]]))/10

johnhw

### JHW 2018
import numpy as np
import umap



# This code from the excellent module at:
# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module
    
import random