Doing everything in wrong manner

Here is a script to replace all sizes in template project to twice smaller. It is bad idea to use regular expressions for html and it is uncommon to change text on the fly in C# but it works.


public static void Main(string[] args)
        {
            string path = @"email\src";
            Regex sizeRegEx = new Regex("size=\".+\"");
            Regex valueRegEx = new Regex(@"\d+");
          
            foreach(string file in Directory.GetFiles(path, "*.html", SearchOption.AllDirectories))
            {
                string text = File.ReadAllText(file);
                var aStringBuilder = new StringBuilder(text);
              
                foreach(Match m in sizeRegEx.Matches(text))
                {
                    Console.WriteLine(m.Value);
                    Match valueMath = valueRegEx.Match(m.Value);
                    Console.WriteLine(valueMath.Value);
                    aStringBuilder.Remove(m.Index + valueMath.Index, valueMath.Value.Length);
                    int size = int.Parse(valueRegEx.Match(m.Value).Value);
                    string newValue = (size / 2).ToString();
                    aStringBuilder.Insert(m.Index + valueMath.Index, newValue);
                    if(valueMath.Value.Length == 2 && newValue.Length == 1)
                    {
                        aStringBuilder.Insert(m.Index + valueMath.Index + 1, " ");
                    }
                }
                File.WriteAllText(file, aStringBuilder.ToString());
            }
        }
      
        public static string DeleteLines(string input, int linesToSkip)
        {
            int startIndex = 0;
            for (int i = 0; i < linesToSkip; ++i)
                startIndex = input.IndexOf('\n', startIndex) + 1;
            return input.Substring(startIndex);
        }

OneNote as heap of theses

Within a year I collecting ideas and theses for my Post AI book in one great service I know for me. This service is a part of Microsoft Office 365 known as Microsoft Onenote. It is available for all my devices as same as Windows and Linux desktop. It is possible to add named notes that i can append on every day traffic jam. And this is free sync point within my devices so that I have one complete story as result.

OpenCL 2.1 NEO

Here my test of integrated OpenCL kernels. I compile CapsBasic-beignet and it shows me that I have new NEO driver that replace beignet.

Number of available platforms: 2
Platform names:
    [0] Intel(R) OpenCL HD Graphics [Selected]
    [1] Intel Gen OCL Driver
Number of devices available for each type:
    CL_DEVICE_TYPE_CPU: 0
    CL_DEVICE_TYPE_GPU: 1
    CL_DEVICE_TYPE_ACCELERATOR: 0

*** Detailed information for each device ***

CL_DEVICE_TYPE_GPU[0]
    CL_DEVICE_NAME: Intel(R) Gen9 HD Graphics NEO
    CL_DEVICE_AVAILABLE: 1
    CL_DEVICE_VENDOR: Intel(R) Corporation
    CL_DEVICE_PROFILE: FULL_PROFILE
    CL_DEVICE_VERSION: OpenCL 2.1 NEO
    CL_DRIVER_VERSION: 19.03.12192
    CL_DEVICE_OPENCL_C_VERSION: OpenCL C 2.0
    CL_DEVICE_MAX_COMPUTE_UNITS: 23
    CL_DEVICE_MAX_CLOCK_FREQUENCY: 1100
    CL_DEVICE_MAX_WORK_GROUP_SIZE: 256
    CL_DEVICE_ADDRESS_BITS: 64
    CL_DEVICE_MEM_BASE_ADDR_ALIGN: 1024
    CL_DEVICE_MAX_MEM_ALLOC_SIZE: 1595310080
    CL_DEVICE_GLOBAL_MEM_SIZE: 3190620160
    CL_DEVICE_MAX_CONSTANT_BUFFER_SIZE: 1595310080
    CL_DEVICE_GLOBAL_MEM_CACHE_SIZE: 524288
    CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE: 64
    CL_DEVICE_LOCAL_MEM_SIZE: 65536
    CL_DEVICE_PROFILING_TIMER_RESOLUTION: 83
    CL_DEVICE_IMAGE_SUPPORT: 1
    CL_DEVICE_ERROR_CORRECTION_SUPPORT: 0
    CL_DEVICE_HOST_UNIFIED_MEMORY: 1
    CL_DEVICE_EXTENSIONS: cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_fp16 cl_khr_depth_images cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_icd cl_khr_image2d_from_buffer cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_intel_subgroups cl_intel_required_subgroup_size cl_intel_subgroups_short cl_khr_spir cl_intel_accelerator cl_intel_media_block_io cl_intel_driver_diagnostics cl_intel_device_side_avc_motion_estimation cl_khr_priority_hints cl_khr_throttle_hints cl_khr_create_command_queue cl_khr_fp64 cl_khr_subgroups cl_khr_il_program cl_intel_spirv_device_side_avc_motion_estimation cl_intel_spirv_media_block_io cl_intel_spirv_subgroups cl_khr_mipmap_image cl_khr_mipmap_image_writes cl_intel_planar_yuv cl_intel_packed_yuv cl_intel_motion_estimation cl_intel_advanced_motion_estimation cl_intel_va_api_media_sharing
    CL_DEVICE_PREFERRED_VECTOR_WIDTH_INT: 4
    CL_DEVICE_PREFERRED_VECTOR_WIDTH_LONG: 1
    CL_DEVICE_PREFERRED_VECTOR_WIDTH_FLOAT: 1
    CL_DEVICE_PREFERRED_VECTOR_WIDTH_DOUBLE: 1
    CL_DEVICE_NATIVE_VECTOR_WIDTH_INT: 4
    CL_DEVICE_NATIVE_VECTOR_WIDTH_LONG: 1
    CL_DEVICE_NATIVE_VECTOR_WIDTH_FLOAT: 1
    CL_DEVICE_NATIVE_VECTOR_WIDTH_DOUBLE: 1

Then I tested Intel(R) OpenCL HD Graphics with fractal-gen-opencl but results for existing implementation is not optimistic because it cannot perform
10000000 iterations to use on each pixel to determine its set membership of mandelbrot fractal. I was waiting a day but it still calculate pixels. 1000000 iterations works very fast ~8sec to create 768x786 fractal.

 

mandelbrot

 

burning-ship

OpenCL runtime on my computer

A year as I installed New Inlel CPU to my computer. It's quite cheap unit from 8th generation with GPU on chip and with opencl cores to perform parallel calculations. I have an idea to try wrapper to opencllib in .net core and start learning opencl with my CUDA background. I just found Ubuntu 18.04 already has runtime. Here is my clinfo. As you can see Beignet is us used for check device, but Neo is going to replace it soon.

clinfo

beignet-opencl-icd: no supported GPU found, this is probably the wrong opencl-icd package for this hardware
(If you have multiple ICDs installed and OpenCL works, you can ignore this message)
beignet-opencl-icd: no supported GPU found, this is probably the wrong opencl-icd package for this hardware
(If you have multiple ICDs installed and OpenCL works, you can ignore this message)
Number of platforms                               2
  Platform Name                                   Intel(R) OpenCL HD Graphics
  Platform Vendor                                 Intel(R) Corporation
  Platform Version                                OpenCL 2.1
  Platform Profile                                FULL_PROFILE
  Platform Extensions                             cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_fp16 cl_khr_depth_images cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_icd cl_khr_image2d_from_buffer cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_intel_subgroups cl_intel_required_subgroup_size cl_intel_subgroups_short cl_khr_spir cl_intel_accelerator cl_intel_media_block_io cl_intel_driver_diagnostics cl_intel_device_side_avc_motion_estimation cl_khr_priority_hints cl_khr_throttle_hints cl_khr_create_command_queue cl_khr_fp64 cl_khr_subgroups cl_khr_il_program cl_intel_spirv_device_side_avc_motion_estimation cl_intel_spirv_media_block_io cl_intel_spirv_subgroups cl_khr_mipmap_image cl_khr_mipmap_image_writes cl_intel_planar_yuv cl_intel_packed_yuv cl_intel_motion_estimation cl_intel_advanced_motion_estimation cl_intel_va_api_media_sharing
  Platform Host timer resolution                  1ns
  Platform Extensions function suffix             INTEL

  Platform Name                                   Intel Gen OCL Driver
  Platform Vendor                                 Intel
  Platform Version                                OpenCL 2.0 beignet 1.3
  Platform Profile                                FULL_PROFILE
  Platform Extensions                             cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_byte_addressable_store cl_khr_3d_image_writes cl_khr_image2d_from_buffer cl_khr_depth_images cl_khr_spir cl_khr_icd cl_intel_accelerator cl_intel_subgroups cl_intel_subgroups_short cl_khr_gl_sharing
  Platform Extensions function suffix             Intel
beignet-opencl-icd: no supported GPU found, this is probably the wrong opencl-icd package for this hardware
(If you have multiple ICDs installed and OpenCL works, you can ignore this message)

  Platform Name                                   Intel(R) OpenCL HD Graphics
Number of devices                                 1
  Device Name                                     Intel(R) Gen9 HD Graphics NEO
  Device Vendor                                   Intel(R) Corporation
  Device Vendor ID                                0x8086
  Device Version                                  OpenCL 2.1
  Driver Version                                  19.03.12192
  Device OpenCL C Version                         OpenCL C 2.0
  Device Type                                     GPU
  Device Profile                                  FULL_PROFILE
  Device Available                                Yes
  Compiler Available                              Yes
  Linker Available                                Yes
  Max compute units                               23
  Max clock frequency                             1100MHz
  Device Partition                                (core)
    Max number of sub-devices                     0
    Supported partition types                     None
  Max work item dimensions                        3
  Max work item sizes                             256x256x256
  Max work group size                             256
  Preferred work group size multiple              32
  Max sub-groups per work group                   32
  Sub-group sizes (Intel)                         8, 16, 32
  Preferred / native vector sizes                
    char                                                16 / 16     
    short                                                8 / 8      
    int                                                  4 / 4      
    long                                                 1 / 1      
    half                                                 8 / 8        (cl_khr_fp16)
    float                                                1 / 1      
    double                                               1 / 1        (cl_khr_fp64)
  Half-precision Floating-point support           (cl_khr_fp16)
    Denormals                                     Yes
    Infinity and NANs                             Yes
    Round to nearest                              Yes
    Round to zero                                 Yes
    Round to infinity                             Yes
    IEEE754-2008 fused multiply-add               Yes
    Support is emulated in software               No
  Single-precision Floating-point support         (core)
    Denormals                                     Yes
    Infinity and NANs                             Yes
    Round to nearest                              Yes
    Round to zero                                 Yes
    Round to infinity                             Yes
    IEEE754-2008 fused multiply-add               Yes
    Support is emulated in software               No
    Correctly-rounded divide and sqrt operations  Yes
  Double-precision Floating-point support         (cl_khr_fp64)
    Denormals                                     Yes
    Infinity and NANs                             Yes
    Round to nearest                              Yes
    Round to zero                                 Yes
    Round to infinity                             Yes
    IEEE754-2008 fused multiply-add               Yes
    Support is emulated in software               No
  Address bits                                    64, Little-Endian
  Global memory size                              3190620160 (2.971GiB)
  Error Correction support                        No
  Max memory allocation                           1595310080 (1.486GiB)
  Unified memory for Host and Device              Yes
  Shared Virtual Memory (SVM) capabilities        (core)
    Coarse-grained buffer sharing                 Yes
    Fine-grained buffer sharing                   No
    Fine-grained system sharing                   No
    Atomics                                       No
  Minimum alignment for any data type             128 bytes
  Alignment of base address                       1024 bits (128 bytes)
  Preferred alignment for atomics                
    SVM                                           64 bytes
    Global                                        64 bytes
    Local                                         64 bytes
  Max size for global variable                    65536 (64KiB)
  Preferred total size of global vars             1595310080 (1.486GiB)
  Global Memory cache type                        Read/Write
  Global Memory cache size                        524288 (512KiB)
  Global Memory cache line size                   64 bytes
  Image support                                   Yes
    Max number of samplers per kernel             16
    Max size for 1D images from buffer            99706880 pixels
    Max 1D or 2D image array size                 2048 images
    Base address alignment for 2D image buffers   4 bytes
    Pitch alignment for 2D image buffers          4 pixels
    Max 2D image size                             16384x16384 pixels
    Max planar YUV image size                     16384x16352 pixels
    Max 3D image size                             16384x16384x2048 pixels
    Max number of read image args                 128
    Max number of write image args                128
    Max number of read/write image args           128
  Max number of pipe args                         16
  Max active pipe reservations                    1
  Max pipe packet size                            1024
  Local memory type                               Local
  Local memory size                               65536 (64KiB)
  Max number of constant args                     8
  Max constant buffer size                        1595310080 (1.486GiB)
  Max size of kernel argument                     1024
  Queue properties (on host)                     
    Out-of-order execution                        Yes
    Profiling                                     Yes
  Queue properties (on device)                   
    Out-of-order execution                        Yes
    Profiling                                     Yes
    Preferred size                                131072 (128KiB)
    Max size                                      67108864 (64MiB)
  Max queues on device                            1
  Max events on device                            1024
  Prefer user sync for interop                    Yes
  Profiling timer resolution                      83ns
  Execution capabilities                         
    Run OpenCL kernels                            Yes
    Run native kernels                            No
    Sub-group independent forward progress        Yes
    IL version                                    SPIR-V_1.0
    SPIR versions                                 1.2
  printf() buffer size                            4194304 (4MiB)
  Built-in kernels                                block_motion_estimate_intel;block_advanced_motion_estimate_check_intel;block_advanced_motion_estimate_bidirectional_check_intel;
  Motion Estimation accelerator version (Intel)   2
    Device-side AVC Motion Estimation version     1
      Supports texture sampler use                Yes
      Supports preemption                         No
  Device Extensions                               cl_khr_3d_image_writes cl_khr_byte_addressable_store cl_khr_fp16 cl_khr_depth_images cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_icd cl_khr_image2d_from_buffer cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_intel_subgroups cl_intel_required_subgroup_size cl_intel_subgroups_short cl_khr_spir cl_intel_accelerator cl_intel_media_block_io cl_intel_driver_diagnostics cl_intel_device_side_avc_motion_estimation cl_khr_priority_hints cl_khr_throttle_hints cl_khr_create_command_queue cl_khr_fp64 cl_khr_subgroups cl_khr_il_program cl_intel_spirv_device_side_avc_motion_estimation cl_intel_spirv_media_block_io cl_intel_spirv_subgroups cl_khr_mipmap_image cl_khr_mipmap_image_writes cl_intel_planar_yuv cl_intel_packed_yuv cl_intel_motion_estimation cl_intel_advanced_motion_estimation cl_intel_va_api_media_sharing

  Platform Name                                   Intel Gen OCL Driver
Number of devices                                 0

NULL platform behavior
  clGetPlatformInfo(NULL, CL_PLATFORM_NAME, ...)  Intel(R) OpenCL HD Graphics
  clGetDeviceIDs(NULL, CL_DEVICE_TYPE_ALL, ...)   Success [INTEL]
  clCreateContext(NULL, ...) [default]            Success [INTEL]
  clCreateContext(NULL, ...) [other]              <error: no devices in non-default plaforms>
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_DEFAULT)  Success (1)
    Platform Name                                 Intel(R) OpenCL HD Graphics
    Device Name                                   Intel(R) Gen9 HD Graphics NEO
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_CPU)  No devices found in platform
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_GPU)  Success (1)
    Platform Name                                 Intel(R) OpenCL HD Graphics
    Device Name                                   Intel(R) Gen9 HD Graphics NEO
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_ACCELERATOR)  No devices found in platform
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_CUSTOM)  No devices found in platform
  clCreateContextFromType(NULL, CL_DEVICE_TYPE_ALL)  Success (1)
    Platform Name                                 Intel(R) OpenCL HD Graphics
    Device Name                                   Intel(R) Gen9 HD Graphics NEO

ICD loader properties
  ICD loader Name                                 OpenCL ICD Loader
  ICD loader Vendor                               OCL Icd free software
  ICD loader Version                              2.2.11
  ICD loader Profile                              OpenCL 2.1Ni

It's path to Zen

I want to tell about simple strategy to use multiple code editors alongside to maintain huge code open source code. For example, you need to maintain code that can be debugged only in certain remote environment and then, you have to develop new module for big plugin architecture.

 It is better to study code on the fly with tiny editor without code completion and refactoring. This editor also will be connected to remote host with rsync. I prefer Zed Code editor available as chrome plugin.

Zed Code Editor

Zen is lightweight to work with small projects from chromebook and you can start with cheap environment. But in case you work with new plugins, then you need to learn architecture and decisions contains in solution code. It is better to use code analysis tools and smart navigation in solution. Alongside with Zen i have used Visual Code for me.

Visual Code

Stage and production environment have no advantages to rapid development, but it allow to test real word responses.

Slow remote

I grow reading books

React.Js быстрый старт. Стоян Стефанов. Издательство "Питер"

Scalability rules. 50 principles for scaling web sites.Martin L. Abbot, Michael T. Fishero

A book to make complete web solutions really robust and stable for work with huge amount customers or consumers. I can recommend it for maintenance as same to make leap in quality after acquire code base. Tips is not based on any framework so it might be architect challenge to apply book recommendations to modern infrastructure.

The Open CL specification. Khronos Open CL workging group.

this book have not included kernel language i could looked for. This is a reference to functions provided with open cl library. I was found it similar to CUDA library I studied before. it's also describes how to manage device context with c# and java wrappers. This is low level access but it's the fastest.

Introducing Bootstrap 4. Jorg Krause

Entity Framework 6 recipes. Barin Driscoll. Nitin Gupta, Robert Vettor, Zeeshman Hirani, and Latty Tenny

This excellent book for those who really want to know all aspects of ORM and database. It covers multiple non trivial examples and advanced designs. You can ask yourself about ESQL using or complex tapes in case of maintainability of your code. Then you will do the choice to add this complexity to solution or not.

97 things every software architect should know

CL via C# 4th edition

Deploying Chromebooks in the classroom, Guy Hart-Davis

Fireworks algorithm. Ying Tan.

The CUDA handbook, Nicholas Wilt

Twitter Bootstrap succinctly, by Peter  Shaw

Selected theoretical publications on GA clustering and source code isomorphism.

Data Clustering using Genetic Algorithm. Zheyung Feng.

Работа написана в 2012 и обладает максимально возможной , полнотой описания операторов

генетического алгоритма и их воздействие на производительность и определение глобального

минимума. Показывается добавление шагов k-mean к каждой итерации генетического алгоритма,

что приводит к заметному уменьшению шагов решения. Так же определено, что предварительная

сортировка данных приводит к увеличению вероятности нахождения локального минимума, но

операторы генетического алгоритма могут быть подобраны так, что исправляют эту возможную

ситуацию. Так же затронут вопрос неевклидовых кластеров вытянутой или серповидной формы, за

их нахождение отвечают операторы селекции, объединяющие и удаляющие кластера. Статья

является наглядным пособием по созданию инструментов классификации на основе GA.

Implementation and Analysis of Graph Algorithm Used for Software Plagiatism Identification. Dr. Carlos Rivero

То , что среди статей об классификации данных освещаю и статьи по поиску плагиата в

программах, говорит о моей цели объединить два механизма определения схожести.

Классификатор и анализ изоморфизма связаны между собой в том , что классификация кода не

может быть представлена евклидовом пространством, вместо этого определением разницы в коде

может заниматься изоморфный анализ графов активности, полученных ANTLR.

В статье не только рассматривается способ сохранения AST ,  в базе данных графов Neo4j , но и

алгоритм нахождения подобных поддеревьев в полученной базе данных. К сожалению, средства

нахождения изоморфизма в работе не указан, но они ссылаются на  GraphQL и Nema.

Parallel programing with Inductive Synthesis. Shaon Barman, Rastislav Bodik, Sagar Jain, Yewen Pu, Saurabh Srivastava, and Nicholas Tung.

Эта работа может показаться сугубо математической, так как в ней обсуждается нахождение

коэффициентов факторизованного представления полинома, но в целом она рассказывает более

общий принцип адаптации выбранного алгоритма к необходимому математическому решению с

помощью gpu. Если представить, что генетический алгоритм действительно найдет подходящий

для решения проблем программный код, то параметры взаимодействия с этим кодом, как и

внутренние коэффициенты нужно будет вычислить. При этом можно применять индуктивный

подход. Такая операция перебора и определения коэффициентов возможна с помощью gpu.

Abstract Syntax Tree Generation using Modified Grammar for Source Code Plagiatism Detection. Resmi N. G. Soman K. P.

 В статье от Resmi и Soman обсуждается каким образом пробовать делать грамматику для ANTLR ,

инвариантной к стилю написания кода. Затрагивается проблема вариаций в синтаксисе и

адаптация к комментариям и другим не связанным с синтаксисом элементам. Но к сожалению , не

решена инвариантность синтаксического сахара. Метод фильтрации кода  на уровне грамматики ,

в принципе можно использовать как базовый при внесении AST в базу данных ArangoDB, но

парсинг полноценной грамматики и вычисление разницы между графами может пополнить

метаданные вершин графа, оставленными автором комментариями.

Genetic Algorithm for Clustering with an Ordered Representation. Jay Bhuyan

Работа представлена в 1991 году и отражает использование генетического алгоритма в

кластеризации данных, как нового метода, позволяющего обходить локальные минимумы в

решении. В примере рассматривается евклидово пространство и два вида жадных алгоритмов.

Алгоритм называется жадным , если он принимает во внимание оптимальное решение на каждом

шаге алгоритма.

Если данные заранее отсортированы , с применением эвристического сравнения данных между

собой, то генетический алгоритм получает схожесть.

Автор показывает , что применение эвристических правил в генетическом алгоритме позволяет

балансировать между производительностью и точностью. Также в эвристике автор предлагает

использовать противоположные суждения, для того чтобы найти глобальный оптимум. И главной

идеей является утверждение в том , что подготовленные данные лучше поддаются

классификации, и в этом могут помочь сортировки больших данных, которые я описал в  блоге .

.NET Portability Analyzer

Когда то использовал автоматический анализатор кода для проверки совместимости .NET приложений с Xamarin платформой. Мое приложение тогда показало 100% совместимость, все таки Mono+GtkSharp. Теперь портируя приложения на .Net Core тоже использую подобный анализатор.

Собрать можно с помощью скрипта

.\build.ps1 -Configuration Debug -Platform AnyCPU

после этого посмотреть установленные на компьютере платформы

.\ApiPort.exe listTargets

Проверить, что требует портирования, задав платформы:

.\ApiPort.exe analyze -f "my.exe" -t ".NET Framework, Version=4.6.2" -t ".Net Core, Version=2.1" -r HTML -o AnalysisReport.html

Еще, есть UI, тем более что WPF уже будет в .NET Core 3.0

Вот какого плана создается отчет

Target type	.Net Core,Version=v2.1	.NET Framework,Version=v4.6.2	Recommended changes
Microsoft.AspNetCore.StaticFiles.FileExtensionContentTypeProvider
#ctor
get_Mappings
TryGetContentType(System.String,System.String@)
Microsoft.VisualBasic.Devices.ComputerInfo
#ctor
get_AvailablePhysicalMemory
get_OSFullName
get_TotalPhysicalMemory
System.Configuration.ConfigurationElement			Use configuration appropriate for your application model. For portable Framework Components, expose API for configuration on type.
#ctor			Use configuration appropriate for your application model. For portable Framework Components, expose API for configuration on type.
get_Item(System.String)			Use configuration appropriate for your application model. For portable Framework Components, expose API for configuration on type.
set_Item(System.String,System.Object)			Use configuration appropriate for your application model. For portable Framework Components, expose API for configuration on type.
System.Configuration.ConfigurationManager			Use configuration appropriate for your application model. For portable Framework Components, expose API for configuration on type.
get_AppSettings			Use configuration appropriate for your application model. For portable Framework Components, expose API for configuration on type.
GetSection(System.String)			Use configuration appropriate for your application model. For portable Framework Components, expose API for configuration on type.
System.Configuration.ConfigurationPropertyAttribute			Use configuration appropriate for your application model. For portable Framework Components, expose API for configuration on type.
#ctor(System.String)			Use configuration appropriate for your application model. For portable Framework Components, expose API for configuration on type.
System.Configuration.ConfigurationSection			Use configuration appropriate for your application model. For portable Framework Components, expose API for configuration on type.
#ctor			Use configuration appropriate for your application model. For portable Framework Components, expose API for configuration on type.