brydavis

# coding: utf-8

# In[70]:

import re

ssn_data = [
    ["000115464","000135464"], # 4
    ["763415464", "165415464"], #6

brydavis

local MAX_ACCOUNT_ID = 1

function withdraw(self, v)
	self.balance = self.balance - v
end

function deposit (self, v)
 	self.balance = self.balance + v
end

brydavis

CSV = {}

function CSV:load (filename, headers)
	headers = headers or false
	-- if not headers then
		file = io.open(filename, "r") -- "test.csv"
		linecount = 0 

		panel = {}
		setmetatable(panel, self)

brydavis

from __future__ import division

import math


def future_value(V, R, n, m=1):
    return V * (1+(R/m))**(n*m)

def present_value(FV, R, n):
    return FV / ((1+R)**n)

brydavis

       0000000                         000        0000000
     111111111      11111111100          000      111111111
     00000        111111111111111111      00000      000000
     000        1111111111111111111111111100000         000
     000        1111       1111111111111111100          000
     000         11       0     1111111100              000
     000          1      00             1               000
     000               00      00       1               000
     000             000    00000       1               000
  00000            0000  00000000       1                00000

brydavis

package main

import (
	"container/ring"
	"fmt"
	"os"
	"time"
)

func main() {

brydavis

package main

import "fmt"

func main() {
	a := A{}
	a.N = "hello"
	fmt.Println(a.Method.O("world"))
	fmt.Println(a.Method.I("whatup!"))
	fmt.Println(a.Method.F("wtf?"))

brydavis

# coding: utf-8

# In[39]:

import re
import pymysql
import pandas as pd

original_headers = """NPI","Entity Type Code","Replacement NPI","Employer Identification Number (EIN)","Provider Organization Name (Legal Business Name)","Provider Last Name (Legal Name)","Provider First Name","Provider Middle Name","Provider Name Prefix Text","Provider Name Suffix Text","Provider Credential Text","Provider Other Organization Name","Provider Other Organization Name Type Code","Provider Other Last Name","Provider Other First Name","Provider Other Middle Name","Provider Other Name Prefix Text","Provider Other Name Suffix Text","Provider Other Credential Text","Provider Other Last Name Type Code","Provider First Line Business Mailing Address","Provider Second Line Business Mailing Address","Provider Business Mailing Address City Name","Provider Business Mailing Address State Name","Provider Business Mailing Address Postal Code","Provider Business Mailing Address Country Code (If outside U.S.)","Provider Business Mailing A

brydavis

# http://machinelearningmastery.com/naive-bayes-classifier-scratch-python/
    
import pandas as pd
import random

pima = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data", header=None)
pima.columns = [
    "num_pregnant",
    "plasma_glucose",
    "diastolic_bp",

brydavis

def fac(n):
    return int(n * fac(n-1)) if (n-1) > 0 else n

def perm(n,r): # n! / (n-r)!
    return int(fac(n) / fac(n-r))

def comb(n,r): # n! / r!(n-r)!
    return int(fac(n) / (fac(r)*fac(n-r)))