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#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import torch.nn as nn
import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.autograd import Variable, grad
import torch.optim as optim
import torchvision.utils as vutils

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-

# implementation of backpropogation algorithm based on
# http://neuralnetworksanddeeplearning.com/chap1.html
#
# note that mini-batches are calculated in matrix form

import numpy as np
import random

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-

from sklearn.datasets import load_iris
import numpy as np
import math
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.optimize import fmin_bfgs

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import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import math

#==============================================================================
# Try to verify the cost function is non-convex

xs = np.random.rand(30)
ys = np.random.rand(30)

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import math
class DecisionTree(object):
    def __init__(self, data, method="naive"):
        """Learn a decision tree from data and label

        :data: List[List[val]], a list contains M sample, each sample is represented by a List
               The last column of the sample is the label

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def generate(c1,c2,bitlen):
    a = c1 & ~(1<<bitlen)
    b = c2 & ~(1<<bitlen)
    c = c1 >> 1
    d = c2 >> 1
    return (a&~b&~c&~d) | (~a&b&~c&~d) | (~a&~b&c&~d) | (~a&~b&~c&d)

from collections import defaultdict
def build_map(n, nums):
    mapping = defaultdict(set)

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-

import subprocess
import re
import json
import sys
from math import ceil

TMUX_BUFFER_NAME = 'dwm'

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; Sieve of Eratosthenes
; Checkout https://www.cs.hmc.edu/~oneill/papers/Sieve-JFP.pdf
(def primes
  (concat
    [2]
    (lazy-seq
      (let [prime-inner
            (fn prime-inner [x table]
              (if (table x)
                (prime-inner (inc x)

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set autoupdategist
set smoothscroll
let scrollstep = 90
let scrollduration = 30
let barposition = "bottom"

" vimperator style follow link
let numerichints = 1
let typelinkhints = 1

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#!/usr/bin/env python2
# -*- coding: utf-8 -*-

from PIL import Image

def chunks(l, n):
    """Yield successive n-sized chunks from l."""
    for i in range(0, len(l), n):
        yield l[i:i + n]