domluna

blahhing away

domluna

package main

import (
	"bytes"
	"fmt"
	"io/ioutil"
	"net"
	"os/user"
	"time"

domluna

Resize Linux partition without any disks/cds/usb things!

Works on Ubuntu 14.04, probably fine on others

1.
sudo losetup /dev/loop0

2. /path/to/file is found with 1., xxx is the number of MiBs to allocate, note: 1MiB ~ 1MB
sudo dd if=/dev/zero bs=1MiB of=/path/to/file conv=notrunc oflag=append count=xxx

domluna

// Pretend this is an actual big struct in which it would be
// expensive to copy by value.
struct BigStruct {
    one: int,
    two: int,
    // etc
    one_hundred: int,
}

// An antipattern would be for the return type to be

domluna

#![feature(macro_rules)]

macro_rules! loop_x {
    ($e: expr) => {
        // $e will not interact with this 'x
        // refers to the 'x in the outside loop
        'x: loop {
            println!("Hello!");
            $e
        }

domluna

#!/bin/sh
# Copyright 2012 The Go Authors. All rights reserved.
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.

# git gofmt pre-commit hook
#
# To use, store as .git/hooks/pre-commit inside your repository and make sure
# it has execute permissions.
#

domluna

def match(s, pairs):
    stack = []
    closers = set(pairs.values())

    for c in s:
        if c in pairs:
            stack.append(pairs[c])
        elif c in closers:
            # we can end early if either is true
            if not stack or c != stack.pop():

domluna

def longest_common_substring(s1, s2):
    """
    lcs[i,j] = if match lcs[i-1, j-1] + 1
    lcs[i,j] = if not match 0
    """
    m = len(s1)
    n = len(s2)

    # Hash table not be more memory efficient. Here we have n*m
    # entries guaranteed. In a hash table all entries that are 0

domluna

def spiral(M):
    """
    Given a matrix M (mxn) print out the elements in spiral order

    Ex:

    30 42 30 55 31
    21 30 60 24 38
    11 22 33 44 55

domluna

from __future__ import print_function, absolute_import
import cgt
from cgt import nn
from cgt.distributions import categorical
import numpy as np
from load import load_mnist
import time

epochs = 10
batch_size = 128