robertmaxwilliams

#! /usr/bin/python
# -*- coding: utf8 -*-

""" GAN-CLS """
import tensorflow as tf
import tensorlayer as tl
from tensorlayer.layers import *
from tensorlayer.prepro import *
from tensorlayer.cost import *
import numpy as np

robertmaxwilliams

#include <stdio.h>
/* Useful vidoes:

1d kernel for data processing:
	https://www.youtube.com/watch?v=D0F-NPerCIE
numberphile on image bluring (same thing in 2d):
	https://www.youtube.com/watch?v=C_zFhWdM4ic
excellent explanations in there. It introduced me to the concept.

*/

robertmaxwilliams

# copy filter from here and paste into stdin
# http://dev.theomader.com/gaussian-kernel-calculator/ 

input_string = input("paste in filter, it won't have spaces but that's okay: ")
float_filter = [float('0.'+x) for x in input_string.split('0.')][1:]
smallest = min(float_filter)
discrete_filter = [round(x/smallest) for x in float_filter]
print(discrete_filter)

robertmaxwilliams

from itertools import combinations
from copy import copy
import pdb

def grabpoints(size, mod, pointnums):
    """ returns nums under size
        that are divisible by mod,
        duplicated on pointnums
    """
    box = {n for n in range(size) if n%mod == 0}

robertmaxwilliams

import random
import subprocess
import shlex

cows = ['apt', 'beavis.zen', 'bong', 'bud-frogs', 'bunny', 'calvin', 'cheese', 'cock', 'cower',
        'daemon', 'default', 'dragon', 'dragon-and-cow', 'duck', 'elephant', 'elephant-in-snake',
        'eyes', 'flaming-sheep', 'ghostbusters', 'gnu', 'head-in', 'hellokitty', 'kiss', 'kitty',
        'koala', 'kosh', 'luke-koala', 'mech-and-cow', 'meow', 'milk', 'moofasa', 'moose',
        'mutilated', 'pony', 'pony-smaller', 'ren', 'sheep', 'skeleton', 'snowman',
        'sodomized-sheep', 'stegosaurus', 'stimpy', 'suse', 'three-eyes', 'turkey', 'turtle', 'tux',

robertmaxwilliams

How to Classify a sklearn classifier

from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, AdaBoostRegressor, ExtraTreesClassifier, RandomForestClassifier, AdaBoostClassifier
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.kernel_ridge import KernelRidge
from sklearn.linear_model import LinearRegression, Lasso, LogisticRegression
from sklearn.neural_network import MLPRegressor, MLPClassifier
from sklearn.svm import SVC, SVR
from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier

robertmaxwilliams

robertmaxwilliams

$ cowsay 'hello world' | cowsay -n | cowsay -n | cowsay -n | cowsay -n
 __________________________________________
/  ______________________________________  \
| /  __________________________________  \ |
| | /  ______________________________  \ | |
| | | /  _____________               \ | | |
| | | | < hello world >              | | | |
| | | |  -------------               | | | |
| | | |         \   ^__^             | | | |
| | | |          \  (oo)\_______     | | | |

robertmaxwilliams

import os
import os.path as p
import random
import sys
# TODO: DO TO
PACKAGE_NAME = "mastml" #TODO

#pure_print = print
#def make_myprint(leader):
#    def myprint(*args):

robertmaxwilliams

(defun inverse-collatz (n)
  "returns a list with one or two values that collatz to n"
  (cons (* 2 n)
	(let ((other-result (/ (- n 1) 3)))
	  (if (integerp other-result)
	      (list other-result)
	      nil))))


;; use lambda closure to access lexically scoped variable "starting-list"