QiMata

enum Channel : byte
{
		UnknownChannel = 0,
		Email = 1,
		CallCenter = 2,
		BrokerHelpDesk = 3,
		BrokerPortal = 4
}

QiMata

CREATE TRIGGER address_versioning_trigger
BEFORE UPDATE OR DELETE ON location.address
FOR EACH ROW EXECUTE PROCEDURE address_trigger_function();

QiMata

create function address_trigger_function()
    returns trigger as $$
    BEGIN
        insert into logging_address_address (address_id, type, street1, street2, street3, street4, city, po_box_code, phone_number, date_created, operation)
        values (old.address_id, old.type, old.street1,old.street2,old.street3,old.street4,old.city,old.po_box_code,old.phone_number, old.date_created,TG_OP);

        RETURN NEW;
    end;
    $$ LANGUAGE plpgsql;

QiMata

create table logging_address
(
    like address EXCLUDING CONSTRAINTS,
    operation char(10) not null,
    date_operated timestamp with time zone not null default current_timestamp
);

QiMata

create table address
(
    address_id             integer                  not null,
    type                   varchar(100),
    street1                varchar(120)             not null,
    street2                varchar(120),
    street3                varchar(120),
    street4                varchar(120),
    city                   varchar(80),
    po_box_code            varchar(20)              not null,

QiMata

public class ClassGenerator
{
    private readonly ModuleBuilder _moduleBuilder;

    public ClassGenerator()
    {
        var an = new AssemblyName("DynamicProtoAssembly");
        AssemblyBuilder assemblyBuilder = AssemblyBuilder.DefineDynamicAssembly(an,AssemblyBuilderAccess.Run);
        _moduleBuilder = assemblyBuilder.DefineDynamicModule("DynamicProtoModule");

QiMata

from PIL import Image
import numpy as np
import cv2

def convert_to_opencv(image):
    # RGB -> BGR conversion is performed as well.
    image = image.convert('RGB')
    r,g,b = np.array(image).T
    opencv_image = np.array([b,g,r]).transpose()
    return opencv_image

QiMata

import tensorflow as tf
import numpy as np


# The category name and probability percentage
class CategoryScore:
    def __init__(self, category, probability: float):
        self.category = category
        self.probability = probability

QiMata

using System;

public delegate void HandleMessage(byte[] message);

internal delegate void InternalHandleMessage(IntPtr array, int length);
public interface ISharedMemoryConsumer
{
    long Size();

    void SetCallback(HandleMessage handler);

QiMata

extern "C" ImageNetCoreSharedMemoryConsumer* GetImageConsumer(char * name)
{
	return new ImageNetCoreSharedMemoryConsumer(std::string(name));
}

extern "C" void DeleteImageNetCoreSharedMemoryConsumer(ImageNetCoreSharedMemoryConsumer* consumer)
{
	delete consumer;
}