Hakan Serce hakanserce

#How Elasticsearch handles upsert scripts (v 1.7)

Copied code over from https://github.com/elastic/elasticsearch/blob/1.7/src/main/java/org/elasticsearch/action/update/UpdateHelper.java for reference...

public Result prepare(UpdateRequest request, IndexShard indexShard) {
      long getDateNS = System.nanoTime();
      final GetResult getResult = indexShard.getService().get(request.type(), request.id(),
              new String[]{RoutingFieldMapper.NAME, ParentFieldMapper.NAME, TTLFieldMapper.NAME, TimestampFieldMapper.NAME},
              true, request.version(), request.versionType(), FetchSourceContext.FETCH_SOURCE, false);

	tell application "Todoist"
	activate
	tell application "System Events" to keystroke "q"
	delay 0.2
	tell application "System Events" to keystroke "test task from AppleScript"
	delay 0.2
	tell application "System Events" to key code 76 -- press enter
	end tell

	require 'net/imap'

	def get_exchange_password()
	pass = %x(/usr/bin/security find-generic-password -w -s Exchange ~/Library/Keychains/login.keychain)
	return pass.strip
	end

	imap = Net::IMAP.new("<IMAP SERVER>", <PORT>, true)

	imap.login(<USERNAME>, get_exchange_password)

	on open location this_URL
	set the messageId to text 11 thru -1 of this_URL
	tell application "Microsoft Outlook"
	activate
	open message id messageId
	end tell
	end open location

	function getCurrentApp() {
	app = Application.currentApplication();
	app.includeStandardAdditions = true;
	return app;
	}

	function copyToClipboard(text) {
	getCurrentApp().setTheClipboardTo(text)
	}

	import javax.swing.JFrame;
	import javax.swing.JPanel;
	import java.awt.Graphics;
	import java.awt.Graphics2D;
	import java.awt.BasicStroke;
	import java.awt.Color;

	class TreePanel extends JPanel {

	private float innerAngle;

	# Calculate polynomial features
	# Adapted from https://stackoverflow.com/questions/11723779/2d-numpy-power-for-polynomial-expansion
	def polynomial_features(x, order):
	x = np.asarray(x).T[np.newaxis]
	n = x.shape[1]
	power_matrix = np.tile(np.arange(order + 1), (n, 1)).T[..., np.newaxis]
	X = np.power(x, power_matrix)
	I = np.indices((order + 1, ) * n).reshape((n, (order + 1) ** n)).T

	# @hakanserce - my changes start

	# This gist uses RBF Kernel based Ridge Regression: https://gist.github.com/hakanserce/fdd571132ef44a6a8f7ddd2eb41aba84



	kf = KFold(y_train.size, n_folds=5)
	X_all = np.hstack((X_train, X_test))
	y_all = np.hstack((y_train, y_test))

	def get_cross_validated_mses(rbf_lambda, d2):
	for train, test in kf:

	import matplotlib.pyplot as plt
	import numpy as np


	# A Ridge Regression implementation which allows using arbitrary basis functions.
	class BasisFunctionRidgeRegression(object):

	def __init__(self, basis_transform, d2):
	self.theta = None
	self.basis_transform = basis_transform

	grammar Json;

	struct: ('{' '}') \| ('{' attribute (',' attribute)* '}');
	attribute: STRING ':' value;
	value: NUMBER \| BOOLEAN \| STRING \| struct \| list;
	list: ('[' ']') \| ('[' value (',' value)* ']');

	NUMBER: ('+'\|'-')? ('0'..'9')+ ('.' ('0'..'9')+)?;
	BOOLEAN: 'true' \| 'false';
	STRING: '"' ~('"'\|'\\')* '"';