padjiman

## gist:2cb53fcea28ad384e0bdea9f107caa7f
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [

## vw_blog.ipynb

      
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## roc_curve_blog.ipynb

      
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              March 23, 2018 14:16
            
          
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## gist:1609cb3de16dfcb8b0b9b6f5cace630c
vw train.vw -c --passes 4 -f model.vw --loss_function logistic --interactions ci -b 26

## Tabular2VWGenerator.java
package vw;

import java.io.*;
import java.text.ParseException;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.HashMap;
import java.util.List;
import java.util.stream.Collectors;

## SGD_and_optims.py
def lin(params,x):
    return params[0]*x + params[1]

def linear_gradients(params_to_learn,x,y):
    a = params_to_learn[0]
    b = params_to_learn[1]
    de_da = 2*x*(a*x+b-y)
    de_db = 2*(a*x+b-y)

    return [de_da,de_db]
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 125,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	package vw;

	import java.io.*;
	import java.text.ParseException;
	import java.util.ArrayList;
	import java.util.Arrays;
	import java.util.HashMap;
	import java.util.List;
	import java.util.stream.Collectors;
	def lin(params,x):
	return params[0]*x + params[1]

	def linear_gradients(params_to_learn,x,y):
	a = params_to_learn[0]
	b = params_to_learn[1]
	de_da = 2x(a*x+b-y)
	de_db = 2(ax+b-y)

	return [de_da,de_db]