//+------------------------------------------------------------------+
//|                                                BayesianRidge.mq5 |
//|                                  Copyright 2023, MetaQuotes Ltd. |
//|                                             https://www.mql5.com |
//+------------------------------------------------------------------+
#property copyright "Copyright 2023, MetaQuotes Ltd."
#property link      "https://www.mql5.com"
#property version   "1.00"

#define   ModelName          "BayesianRidge"
#define   ONNXFilenameFloat  "models\\bayesian_ridge_float.onnx"
#define   ONNXFilenameDouble "models\\bayesian_ridge_double.onnx"

#resource ONNXFilenameFloat  as const uchar ExtModelFloat[];
#resource ONNXFilenameDouble as const uchar ExtModelDouble[];

#define   TestFloatModel  1
#define   TestDoubleModel 2

//+------------------------------------------------------------------+
//| Calculate regression using float values                          |
//+------------------------------------------------------------------+
bool RunModelFloat(long model,vector &input_vector, vector &output_vector)
  {
//--- check number of input samples
   ulong batch_size=input_vector.Size();
   if(batch_size==0)
      return(false);
//--- prepare output array
   output_vector.Resize((int)batch_size);
//--- prepare input tensor
   float input_data[];
   ArrayResize(input_data,(int)batch_size);
//--- set input shape
   ulong input_shape[]= {batch_size, 1};
   OnnxSetInputShape(model,0,input_shape);
//--- copy data to the input tensor
   for(int k=0; k<(int)batch_size; k++)
      input_data[k]=(float)input_vector[k];
//--- prepare output tensor
   float output_data[];
   ArrayResize(output_data,(int)batch_size);
//--- set output shape
   ulong output_shape[]= {batch_size,1};
   OnnxSetOutputShape(model,0,output_shape);
//--- run the model
   bool res=OnnxRun(model,ONNX_DEBUG_LOGS,input_data,output_data);
//--- copy output to vector
   if(res)
     {
      for(int k=0; k<(int)batch_size; k++)
         output_vector[k]=output_data[k];
     }
//---
   return(res);
  }
//+------------------------------------------------------------------+
//| Calculate regression using double values                         |
//+------------------------------------------------------------------+
bool RunModelDouble(long model,vector &input_vector, vector &output_vector)
  {
//--- check number of input samples
   ulong batch_size=input_vector.Size();
   if(batch_size==0)
      return(false);
//--- prepare output array
   output_vector.Resize((int)batch_size);
//--- prepare input tensor
   double input_data[];
   ArrayResize(input_data,(int)batch_size);
//--- set input shape
   ulong input_shape[]= {batch_size, 1};
   OnnxSetInputShape(model,0,input_shape);
//--- copy data to the input tensor
   for(int k=0; k<(int)batch_size; k++)
      input_data[k]=input_vector[k];
//--- prepare output tensor
   double output_data[];
   ArrayResize(output_data,(int)batch_size);
//--- set output shape
   ulong output_shape[]= {batch_size,1};
   OnnxSetOutputShape(model,0,output_shape);
//--- run the model
   bool res=OnnxRun(model,ONNX_DEBUG_LOGS,input_data,output_data);
//--- copy output to vector
   if(res)
     {
      for(int k=0; k<(int)batch_size; k++)
         output_vector[k]=output_data[k];
     }
//---
   return(res);
  }

//+------------------------------------------------------------------+
//| Generate synthetic data                                          |
//+------------------------------------------------------------------+
bool GenerateData(const int n,vector &x,vector &y)
  {
   if(n<=0)
      return(false);
//--- prepare arrays
   x.Resize(n);
   y.Resize(n);
//---
   for(int i=0; i<n; i++)
     {
      x[i]=(double)1.0*i;
      y[i]=(double)(4*x[i] + 10*sin(x[i]*0.5));
     }
//---
   return(true);
  }

//+------------------------------------------------------------------+
//| TestRegressionModel                                              |
//+------------------------------------------------------------------+
bool TestRegressionModel(const string model_name,const int model_type)
  {
//---
   long  model=INVALID_HANDLE;
   ulong flags=ONNX_DEFAULT;

   if(model_type==TestFloatModel)
     {
      PrintFormat("\nTesting ONNX float: %s (%s)",model_name,ONNXFilenameFloat);
      model=OnnxCreateFromBuffer(ExtModelFloat,flags);
     }
   else
      if(model_type==TestDoubleModel)
        {
         PrintFormat("\nTesting ONNX double: %s (%s)",model_name,ONNXFilenameDouble);
         model=OnnxCreateFromBuffer(ExtModelDouble,flags);
        }
      else
        {
         PrintFormat("Model type is not incorrect.");
         return(false);
        }
//--- check
   if(model==INVALID_HANDLE)
     {
      PrintFormat("model_name=%s OnnxCreate error %d",model_name,GetLastError());
      return(false);
     }
//---
   vector x_values= {};
   vector y_true= {};
   vector y_predicted= {};
//---
   int n=100;
   GenerateData(n,x_values,y_true);
//---
   bool run_result=false;
   if(model_type==TestFloatModel)
     {
      run_result=RunModelFloat(model,x_values,y_predicted);
     }
   else
      if(model_type==TestDoubleModel)
        {
         run_result=RunModelDouble(model,x_values,y_predicted);
        }
//---
   if(run_result)
     {
      PrintFormat("MQL5:   R-Squared (Coefficient of determination): %.16f",y_predicted.RegressionMetric(y_true,REGRESSION_R2));
      PrintFormat("MQL5:   Mean Absolute Error: %.16f",y_predicted.RegressionMetric(y_true,REGRESSION_MAE));
      PrintFormat("MQL5:   Mean Squared Error: %.16f",y_predicted.RegressionMetric(y_true,REGRESSION_MSE));
     }
   else
      PrintFormat("Error %d",GetLastError());
//--- release model
   OnnxRelease(model);
//---
   return(true);
  }
//+------------------------------------------------------------------+
//| Script program start function                                    |
//+------------------------------------------------------------------+
int OnStart(void)
  {
//--- test ONNX regression model for float
   TestRegressionModel(ModelName,TestFloatModel);
//--- test ONNX regression model for double
   TestRegressionModel(ModelName,TestDoubleModel);
//---
   return(0);
  }
//+------------------------------------------------------------------+