anthonybudd

import numpy as np;
import tensorflow as tf;
import pylab;

xAxis = []
yAxis = []

def make_data(n):
	np.random.seed(42) # To ensure same data for multiple runs
	x = 2.0 * np.array(range(n))

anthonybudd

<?php

add_filter('show_admin_bar', '__return_false');

anthonybudd

<?php
add_filter('nav_menu_css_class' , 'special_nav_class' , 10 , 2);

function special_nav_class ($classes, $item) {
    if (in_array('current-menu-item', $classes) ){
        $classes[] = 'active ';
    }
    return $classes;
}

anthonybudd

<?php 

$where = [
  'meta_relation' => 'AND',
  [
    'meta_key' => 'color',
    'meta_vale' => 'blue'
  ],
  [
    'meta_key' => 'weight',

anthonybudd

<?php
$product = Product::find(15);
$product->hardDelete();
echo $product->color; // NULL

anthonybudd

<?php
Class Product extends WP_Model
{
    ...
    public $virtual = [
        'humanWeight'
    ];

    public $serialize = [
        'humanWeight',

anthonybudd

<?php
Class Product extends WP_Model
{
    ...
    public function _finderHeavy($agrs){
        ...
    }

    public function _postFinderHeavy($results, $args){
        return array_map(function($model){

anthonybudd

<?php
Class Product extends WP_Model
{
    ...
    public function _finderHeavy($agrs){
        return [
            'posts_per_page'  => '50',
            'meta_query'	  => [
                [
                    'key'       => 'weight',

anthonybudd

<?php
Class Product extends WP_Model
{
    ...
    public function _finderHeavy(){
        return [
            'posts_per_page'  => '50',
            'meta_query'	  => [
                [
                    'key'       => 'weight',

anthonybudd

<?php
Class Product extends WP_Model
{
    ...
    public $filter = [
        'weight',
    ];
    
    public function _filterWeight($value){
        return intVal($value);