PNN Neural Network Class
Info
The PNN Neural Network Class is a Library for MetaTrader 5 that the cnetpnn class implements a probabilistic neural network (pnn). The network is created using the class parametric constructor.
Usage
This tool is typically used for enhancing chart analysis and decision making.
Platform
This Library works exclusively on MetaTrader 5 (both build 600+ and newer versions).
Setup
Place the downloaded file in MQL5/Libraries folder via File ? Open Data Folder in MetaTrader 5.
How to Install and Use PNN Neural Network Class
1. Storage: Place library files in the MQL/Libraries directory to ensure they are accessible to your projects.
2. Implementation: Include the library in your code using the #import directive, ensuring you match the exact function names and parameters.
3. Compilation: Ensure the library is present in the directory before you compile your main EA or script, as the compiler links them during this phase.
4. Management: Keep libraries organized in sub-folders if you manage many custom functions to maintain a clean project structure.
Frequently Asked Questions
Q: What is a library file used for? A: Libraries store reusable code modules, allowing you to centralize common logic used by multiple EAs or indicators.
Q: Is a library executable? A: No, libraries are non-executable files containing functions; they must be imported into an EA, indicator, or script to function.
Q: Can I update a library while the platform is running? A: You should compile your EA or script after updating a library to ensure the latest code changes are integrated.
What this tool does
The cnetpnn class implements a Probabilistic Neural Network (PNN).
Typical Use Case
This Library excels in automated trading and technical analysis on MetaTrader 5.
Compatible Platform & Setup
This Library works on MetaTrader 5. Place the file in the MQL5/Libraries folder and restart the terminal.
Description & Settings
Related: as q neural net pure mq l5 neural network library - another powerful library for MetaTrader 5 traders.
The cnetpnn class implements a Probabilistic Neural Network (PNN).Also recommended: cexecutionsafety: network latency guard for metatrader 5 expert advisors - similar library with strong performance on MetaTrader 5.
The network is created using the class parametric constructor.
Class numbers (classification targets) start from zero and must be consecutive. For instance, if you set 3 classes, the class numbers should be: 0, 1, 2.
Training the network is done by calling the learn method, which takes the following parameters: the number of learning patterns, input data array, output data array, number of learning cycles, and maximum learning error.
Input and output training data are organized in one-dimensional arrays, vector by vector. Each input learning vector must include the class number in the input data. The training process is limited by either the number of epochs or the acceptable error.
The learn method returns these values:
- 0: Network training is successful, and the result can be evaluated through the class variables: mse (learning error) and epoch (number of completed learning cycles).
- -1: Invalid class in the input learning data.
- -4: Insufficient memory.
To obtain the network's response, use the calculate method with an input vector array. This method returns the class number corresponding to the input vector or -1 if the network has not been trained.
The save and load methods facilitate network storage and retrieval from files. Network topology, learning errors, and weight arrays are saved to the file. If the loaded network topology differs from the established network topology, the network won't be loaded, and the load method will return false.
A sample usage of the class is provided in the included test_pnn_xor, which demonstrates training the network for the 'exclusive or' function.
You may also like: MLP Neural Network Class for MetaTrader - excellent alternative for library users on MetaTrader 5.
Source Code
#include <class_pnn.mqh>
double ivect[2]; // input vector
double inpps[]= {1,1,1,0,0,1,0,0}; // input teaching data array
double tchs[]= {0,1,1,0}; // output teaching data array
void onstart()
{
cnetpnn* net=new cnetpnn(2,2);
net.learn(4,inpps,tchs,100,1.0e-8);
print("mse=",net.mse);
int h=fileopen("primer.net",file_bin|file_write);.......
⚠ Limitations & Risk Warning
- This tool is provided for educational and testing purposes only.
- Past performance does not guarantee future results.
- Trading involves substantial risk of loss. Use on a demo account first.
- Results may vary depending on market conditions, broker, and settings.
- We recommend thorough backtesting and forward testing before using with real funds.