mchirico

#!/bin/bash
grep -h 'blog' httpd/data/access*|grep -v '^76.124.247.181'|grep -v 'www.google.com'|grep -v 'spider'|
awk '{
  split($0, a, "\"")
  $6 = a[2]
  $7 = $(NF - 1)
  $8 = $NF
  printf("%s %s,%s,\"%s\"\n",$4,$5,$1,$6)
}'

mchirico

# Iptables works from top down. So you want to put your accepts last
#
*filter
:INPUT ACCEPT [0:0]
:FORWARD ACCEPT [0:0]
:OUTPUT ACCEPT [0:0]
-A INPUT -m state --state RELATED,ESTABLISHED -j ACCEPT
-A INPUT -p icmp -j ACCEPT
-A INPUT -i lo -j ACCEPT
# Amazon AWS can block port 22, so keep open.

mchirico

#!/usr/bin/env python
"""
Created by mchirico@gmail.com


"""
import os,time,sys,signal,getopt,popen2,re,curses,datetime,random

import thread,threading
lock = threading.Lock()

mchirico

echo "<callDate>"|awk 'BEGIN{cmd="date +%Y-%m-%d_%H:%M:%S"}  /<callDate>/{cmd|getline D; close(cmd); print D,$1}'

mchirico

df.dropna(how='all')     #drop only if ALL columns are NaN

df.dropna()     #drop all rows that have any NaN values

df.dropna(thresh=2)   #Drop row if it does not have at least two values that are **not** NaN

df.dropna(subset=[1])   #Drop only if NaN in specific column

mchirico

    
    """
    Some commands in Python Pandas to do the following:
    1. Read in a file
    2. Get a list of the headers
    3. Change a header (rename)
    4. Drop one of the headers (just happends to be the one we renamed)
    5. Apply a function to each one of the rows
    6. Merge it with another DataFrame (pm)
    7. Assuming it has a datetime index, write out

mchirico

# Kaggle knowledge 
# Using Google maps
#  Adapted from Ben Hamner's code
#  Ref https://www.kaggle.com/benhamner/sf-crime/san-francisco-top-crimes-map?scriptVersionId=11462
#
#

library(dplyr)
library(ggmap)
library(ggplot2)

mchirico

from keras.layers.core import Dense, Activation

model.add(Dense(input_dim=100, output_dim=64, init="uniform"))
model.add(Activation("relu"))

model.add(Dense(input_dim=64, output_dim=64, init="uniform"))
model.add(Activation("softmax"))

model.add(Dense(input_dim=64, output_dim=10, init="uniform"))
model.add(PReLU((hn,)))

mchirico

/*
The Dropbox Developer  blogs example has an error in the code

https://blogs.dropbox.com/developers/2015/05/try-out-swiftydropbox-the-new-swift-sdk-for-dropbox-api-v2/#.VYh2ane5W_4.twitter
*/

// AppDelegate.swift  
func application(application: UIApplication, openURL url: NSURL, sourceApplication: String?, annotation: AnyObject?) -> Bool {
     
    if let authResult = Dropbox.handleRedirectURL(url) {

mchirico

#!/bin/bash

# First, just build the training set.
echo "1 'example1 | a b c" > train.vw
echo "2 'example2 | x y z" >> train.vw
echo "3 'example3 | a x c" >> train.vw
echo "4 'example4 | x x a" >> train.vw

echo 'Our training set  train.vw'
cat train.vw