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@@ -0,0 +1,497 @@
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+#ifdef NICE_USELIB_MEX
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+/**
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+* @file GPHIKRawClassifierMex.cpp
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+* @author Alexander Freytag
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+* @date 21-09-2015 (dd-mm-yyyy)
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+* @brief Matlab-Interface of our GPHIKRawClassifier, allowing for training and classification without more advanced methods.
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+*/
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+
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+// STL includes
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+#include <math.h>
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+#include <matrix.h>
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+#include <mex.h>
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+
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+// NICE-core includes
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+#include <core/basics/Config.h>
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+#include <core/basics/Timer.h>
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+#include <core/vector/MatrixT.h>
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+#include <core/vector/VectorT.h>
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+
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+// gp-hik-core includes
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+#include "gp-hik-core/GPHIKRawClassifier.h"
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+
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+
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+// Interface for conversion between Matlab and C objects
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+#include "gp-hik-core/matlab/classHandleMtoC.h"
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+#include "gp-hik-core/matlab/ConverterMatlabToNICE.h"
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+#include "gp-hik-core/matlab/ConverterNICEToMatlab.h"
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+
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+using namespace std; //C basics
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+using namespace NICE; // nice-core
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+
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+
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+NICE::Config parseParametersGPHIKRawClassifier(const mxArray *prhs[], int nrhs)
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+{
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+ NICE::Config conf;
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+
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+ // if first argument is the filename of an existing config file,
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+ // read the config accordingly
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+
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+ int i_start ( 0 );
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+ std::string variable = MatlabConversion::convertMatlabToString(prhs[i_start]);
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+ if(variable == "conf")
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+ {
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+ conf = NICE::Config ( MatlabConversion::convertMatlabToString( prhs[i_start+1] ) );
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+ i_start = i_start+2;
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+ }
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+
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+ // now run over all given parameter specifications
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+ // and add them to the config
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+ for( int i=i_start; i < nrhs; i+=2 )
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+ {
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+ std::string variable = MatlabConversion::convertMatlabToString(prhs[i]);
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+
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+ /////////////////////////////////////////
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+ // READ STANDARD BOOLEAN VARIABLES
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+ /////////////////////////////////////////
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+ if( (variable == "verbose") ||
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+ (variable == "debug") ||
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+ (variable == "ils_verbose")
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+ )
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+ {
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+ if ( mxIsChar( prhs[i+1] ) )
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+ {
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+ string value = MatlabConversion::convertMatlabToString( prhs[i+1] );
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+ if ( (value != "true") && (value != "false") )
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+ {
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+ std::string errorMsg = "Unexpected parameter value for \'" + variable + "\'. In string modus, \'true\' or \'false\' expected.";
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+ mexErrMsgIdAndTxt( "mexnice:error", errorMsg.c_str() );
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+ }
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+
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+ if( value == "true" )
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+ conf.sB("GPHIKRawClassifier", variable, true);
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+ else
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+ conf.sB("GPHIKRawClassifier", variable, false);
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+ }
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+ else if ( mxIsLogical( prhs[i+1] ) )
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+ {
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+ bool value = MatlabConversion::convertMatlabToBool( prhs[i+1] );
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+ conf.sB("GPHIKRawClassifier", variable, value);
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+ }
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+ else
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+ {
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+ std::string errorMsg = "Unexpected parameter value for \'" + variable + "\'. \'true\', \'false\', or logical expected.";
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+ mexErrMsgIdAndTxt( "mexnice:error", errorMsg.c_str() );
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+ }
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+ }
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+
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+ /////////////////////////////////////////
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+ // READ STANDARD INT VARIABLES
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+ /////////////////////////////////////////
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+
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+ /////////////////////////////////////////
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+ // READ STRICT POSITIVE INT VARIABLES
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+ /////////////////////////////////////////
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+ if ( variable == "ils_max_iterations" )
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+ {
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+ if ( mxIsDouble( prhs[i+1] ) )
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+ {
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+ double value = MatlabConversion::convertMatlabToDouble(prhs[i+1]);
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+ if( value < 1 )
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+ {
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+ std::string errorMsg = "Expected parameter value larger than 0 for \'" + variable + "\'.";
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+ mexErrMsgIdAndTxt( "mexnice:error", errorMsg.c_str() );
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+ }
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+ conf.sI("GPHIKRawClassifier", variable, (int) value);
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+ }
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+ else if ( mxIsInt32( prhs[i+1] ) )
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+ {
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+ int value = MatlabConversion::convertMatlabToInt32(prhs[i+1]);
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+ if( value < 1 )
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+ {
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+ std::string errorMsg = "Expected parameter value larger than 0 for \'" + variable + "\'.";
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+ mexErrMsgIdAndTxt( "mexnice:error", errorMsg.c_str() );
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+ }
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+ conf.sI("GPHIKRawClassifier", variable, value);
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+ }
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+ else
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+ {
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+ std::string errorMsg = "Unexpected parameter value for \'" + variable + "\'. Int32 or Double expected.";
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+ mexErrMsgIdAndTxt( "mexnice:error", errorMsg.c_str() );
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+ }
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+ }
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+
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+ /////////////////////////////////////////
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+ // READ STANDARD DOUBLE VARIABLES
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+ /////////////////////////////////////////
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+
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+
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+ /////////////////////////////////////////
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+ // READ POSITIVE DOUBLE VARIABLES
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+ /////////////////////////////////////////
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+ if ( (variable == "f_tolerance") ||
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+ (variable == "ils_min_delta") ||
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+ (variable == "ils_min_residual") ||
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+ (variable == "noise")
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+ )
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+ {
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+ if ( mxIsDouble( prhs[i+1] ) )
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+ {
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+ double value = MatlabConversion::convertMatlabToDouble(prhs[i+1]);
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+ if( value < 0.0 )
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+ {
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+ std::string errorMsg = "Expected parameter value larger than 0 for \'" + variable + "\'.";
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+ mexErrMsgIdAndTxt( "mexnice:error", errorMsg.c_str() );
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+ }
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+ conf.sD("GPHIKRawClassifier", variable, value);
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+ }
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+ else
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+ {
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+ std::string errorMsg = "Unexpected parameter value for \'" + variable + "\'. Double expected.";
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+ mexErrMsgIdAndTxt( "mexnice:error", errorMsg.c_str() );
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+ }
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+ }
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+
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+ /////////////////////////////////////////
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+ // READ REMAINING SPECIFIC VARIABLES
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+ /////////////////////////////////////////
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+
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+ if(variable == "ils_method")
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+ {
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+ string value = MatlabConversion::convertMatlabToString(prhs[i+1]);
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+ if(value != "CG" && value != "CGL" && value != "SYMMLQ" && value != "MINRES")
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+ mexErrMsgIdAndTxt("mexnice:error","Unexpected parameter value for \'ils_method\'. \'CG\', \'CGL\', \'SYMMLQ\' or \'MINRES\' expected.");
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+ conf.sS("GPHIKRawClassifier", variable, value);
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+ }
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+
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+ }
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+
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+
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+ return conf;
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+}
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+
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+// MAIN MATLAB FUNCTION
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+void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
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+{
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+ // get the command string specifying what to do
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+ if (nrhs < 1)
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+ mexErrMsgTxt("No commands and options passed... Aborting!");
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+
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+ if( !mxIsChar( prhs[0] ) )
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+ mexErrMsgTxt("First argument needs to be the command, ie.e, the class method to call... Aborting!");
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+
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+ std::string cmd = MatlabConversion::convertMatlabToString( prhs[0] );
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+
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+
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+ // create object
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+ if ( !strcmp("new", cmd.c_str() ) )
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+ {
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+ // check output variable
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+ if (nlhs != 1)
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+ mexErrMsgTxt("New: One output expected.");
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+
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+ // read config settings
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+ NICE::Config conf = parseParametersGPHIKRawClassifier(prhs+1,nrhs-1);
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+
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+ // create class instance
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+ NICE::GPHIKRawClassifier * classifier = new NICE::GPHIKRawClassifier ( &conf, "GPHIKRawClassifier" /*sectionName in config*/ );
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+
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+
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+ // handle to the C++ instance
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+ plhs[0] = MatlabConversion::convertPtr2Mat<NICE::GPHIKRawClassifier>( classifier );
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+ return;
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+ }
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+
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+ // in all other cases, there should be a second input,
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+ // which the be the class instance handle
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+ if (nrhs < 2)
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+ mexErrMsgTxt("Second input should be a class instance handle.");
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+
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+ // delete object
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+ if ( !strcmp("delete", cmd.c_str() ) )
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+ {
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+ // Destroy the C++ object
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+ MatlabConversion::destroyObject<NICE::GPHIKRawClassifier>(prhs[1]);
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+ return;
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+ }
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+
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+ // get the class instance pointer from the second input
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+ // every following function needs the classifier object
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+ NICE::GPHIKRawClassifier * classifier = MatlabConversion::convertMat2Ptr<NICE::GPHIKRawClassifier>(prhs[1]);
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+
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+
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+ ////////////////////////////////////////
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+ // Check which class method to call //
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+ ////////////////////////////////////////
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+
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+
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+ // standard train - assumes initialized object
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+ if (!strcmp("train", cmd.c_str() ))
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+ {
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+ // Check parameters
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+ if (nlhs < 0 || nrhs < 4)
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+ {
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+ mexErrMsgTxt("Train: Unexpected arguments.");
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+ }
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+
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+ //------------- read the data --------------
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+
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+ std::vector< const NICE::SparseVector *> examplesTrain;
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+ NICE::Vector yMultiTrain;
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+
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+ if ( mxIsSparse( prhs[2] ) )
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+ {
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+ examplesTrain = MatlabConversion::convertSparseMatrixToNice( prhs[2] );
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+ }
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+ else
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+ {
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+ NICE::Matrix dataTrain;
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+ dataTrain = MatlabConversion::convertDoubleMatrixToNice(prhs[2]);
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+
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+ //----------------- convert data to sparse data structures ---------
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+ examplesTrain.resize( dataTrain.rows() );
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+
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+
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+ std::vector< const NICE::SparseVector *>::iterator exTrainIt = examplesTrain.begin();
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+ for (int i = 0; i < (int)dataTrain.rows(); i++, exTrainIt++)
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+ {
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+ *exTrainIt = new NICE::SparseVector( dataTrain.getRow(i) );
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+ }
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+ }
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+
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+ yMultiTrain = MatlabConversion::convertDoubleVectorToNice(prhs[3]);
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+
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+ //----------------- train our classifier -------------
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+ classifier->train ( examplesTrain , yMultiTrain );
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+
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+ //----------------- clean up -------------
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+ for(int i=0;i<examplesTrain.size();i++)
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+ delete examplesTrain[i];
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+
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+ return;
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+ }
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+
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+
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+ // Classify
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+ if ( !strcmp("classify", cmd.c_str() ) )
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+ {
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+ // Check parameters
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+ if ( (nlhs < 0) || (nrhs < 2) )
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+ {
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+ mexErrMsgTxt("Test: Unexpected arguments.");
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+ }
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+
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+ //------------- read the data --------------
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+
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+ uint result;
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+ NICE::SparseVector scores;
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+
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+ if ( mxIsSparse( prhs[2] ) )
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+ {
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+ NICE::SparseVector * example;
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+ example = new NICE::SparseVector ( MatlabConversion::convertSparseVectorToNice( prhs[2] ) );
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+ classifier->classify ( example, result, scores );
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+
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+ //----------------- clean up -------------
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+ delete example;
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+ }
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+ else
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+ {
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+ NICE::Vector * example;
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+ example = new NICE::Vector ( MatlabConversion::convertDoubleVectorToNice(prhs[2]) );
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+ NICE::SparseVector * svec = new NICE::SparseVector( *example );
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+ delete example;
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+
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+ classifier->classify ( svec, result, scores );
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+
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+ //----------------- clean up -------------
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+ delete svec;
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+
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+ }
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+
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+
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+
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+ // output
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+ plhs[0] = mxCreateDoubleScalar( result );
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+
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+ if(nlhs >= 2)
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+ {
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+ plhs[1] = MatlabConversion::convertSparseVectorFromNice( scores, true /*b_adaptIndex*/);
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+ }
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+ return;
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+ }
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+
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+
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+ // Test - evaluate classifier on whole test set
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+ if ( !strcmp("test", cmd.c_str() ) )
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+ {
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+ // Check parameters
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+ if (nlhs < 0 || nrhs < 4)
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+ mexErrMsgTxt("Test: Unexpected arguments.");
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+ //------------- read the data --------------
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+
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+
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+ bool dataIsSparse ( mxIsSparse( prhs[2] ) );
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+
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+ std::vector< const NICE::SparseVector *> dataTest_sparse;
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+ NICE::Matrix dataTest_dense;
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+
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+ if ( dataIsSparse )
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+ {
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+ dataTest_sparse = MatlabConversion::convertSparseMatrixToNice( prhs[2] );
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+ }
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+ else
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+ {
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+ dataTest_dense = MatlabConversion::convertDoubleMatrixToNice(prhs[2]);
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+ }
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+
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+ NICE::Vector yMultiTest;
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+ yMultiTest = MatlabConversion::convertDoubleVectorToNice(prhs[3]);
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+
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+
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+ // ------------------------------------------
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+ // ------------- PREPARATION --------------
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+ // ------------------------------------------
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+
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+ // determine classes known during training and corresponding mapping
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+ // thereby allow for non-continous class labels
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+ std::set< uint > classesKnownTraining = classifier->getKnownClassNumbers();
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+
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+ uint noClassesKnownTraining ( classesKnownTraining.size() );
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+ std::map< uint, uint > mapClNoToIdxTrain;
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+ std::set< uint >::const_iterator clTrIt = classesKnownTraining.begin();
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+ for ( uint i=0; i < noClassesKnownTraining; i++, clTrIt++ )
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+ mapClNoToIdxTrain.insert ( std::pair< uint, uint > ( *clTrIt, i ) );
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+
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+ // determine classes known during testing and corresponding mapping
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+ // thereby allow for non-continous class labels
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+ std::set< uint > classesKnownTest;
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+ classesKnownTest.clear();
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+
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+
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+ // determine which classes we have in our label vector
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+ // -> MATLAB: myClasses = unique(y);
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+ for ( NICE::Vector::const_iterator it = yMultiTest.begin(); it != yMultiTest.end(); it++ )
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+ {
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+ if ( classesKnownTest.find ( *it ) == classesKnownTest.end() )
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+ {
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+ classesKnownTest.insert ( *it );
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+ }
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+ }
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+
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+ int noClassesKnownTest ( classesKnownTest.size() );
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+ std::map< uint, uint> mapClNoToIdxTest;
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+ std::set< uint >::const_iterator clTestIt = classesKnownTest.begin();
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+ for ( uint i=0; i < noClassesKnownTest; i++, clTestIt++ )
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+ mapClNoToIdxTest.insert ( std::pair< uint, uint > ( *clTestIt, i ) );
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+
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+
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+
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+ int i_numTestSamples;
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+
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+ if ( dataIsSparse )
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+ i_numTestSamples = dataTest_sparse.size();
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+ else
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+ i_numTestSamples = (int) dataTest_dense.rows();
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+
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+ NICE::Matrix confusionMatrix( noClassesKnownTraining, noClassesKnownTest, 0.0);
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+ NICE::Matrix scores( i_numTestSamples, noClassesKnownTraining, 0.0);
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+
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+
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+
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+ // ------------------------------------------
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+ // ------------- CLASSIFICATION --------------
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+ // ------------------------------------------
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+
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+ NICE::Timer t;
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+ double testTime (0.0);
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+
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+
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+
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+ for (int i = 0; i < i_numTestSamples; i++)
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+ {
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+ //----------------- convert data to sparse data structures ---------
|
|
|
+
|
|
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+
|
|
|
+ uint result;
|
|
|
+ NICE::SparseVector exampleScoresSparse;
|
|
|
+
|
|
|
+ if ( dataIsSparse )
|
|
|
+ {
|
|
|
+ // and classify
|
|
|
+ t.start();
|
|
|
+ classifier->classify( dataTest_sparse[ i ], result, exampleScoresSparse );
|
|
|
+ t.stop();
|
|
|
+ testTime += t.getLast();
|
|
|
+ }
|
|
|
+ else
|
|
|
+ {
|
|
|
+ NICE::Vector example ( dataTest_dense.getRow(i) );
|
|
|
+ NICE::SparseVector * svec = new NICE::SparseVector ( example );
|
|
|
+ // and classify
|
|
|
+ t.start();
|
|
|
+ classifier->classify( svec, result, exampleScoresSparse );
|
|
|
+ t.stop();
|
|
|
+ testTime += t.getLast();
|
|
|
+ delete svec;
|
|
|
+ }
|
|
|
+
|
|
|
+ confusionMatrix( mapClNoToIdxTrain.find(result)->second, mapClNoToIdxTest.find(yMultiTest[i])->second ) += 1.0;
|
|
|
+ int scoreCnt ( 0 );
|
|
|
+ for ( NICE::SparseVector::const_iterator scoreIt = exampleScoresSparse.begin(); scoreIt != exampleScoresSparse.end(); scoreIt++, scoreCnt++ )
|
|
|
+ {
|
|
|
+ scores(i,scoreCnt) = scoreIt->second;
|
|
|
+ }
|
|
|
+
|
|
|
+ }
|
|
|
+
|
|
|
+ std::cerr << "Time for testing: " << testTime << std::endl;
|
|
|
+
|
|
|
+ // clean up
|
|
|
+ if ( dataIsSparse )
|
|
|
+ {
|
|
|
+ for ( std::vector<const NICE::SparseVector *>::iterator it = dataTest_sparse.begin(); it != dataTest_sparse.end(); it++)
|
|
|
+ delete *it;
|
|
|
+ }
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+ confusionMatrix.normalizeColumnsL1();
|
|
|
+
|
|
|
+ double recRate = confusionMatrix.trace()/confusionMatrix.cols();
|
|
|
+
|
|
|
+
|
|
|
+ plhs[0] = mxCreateDoubleScalar( recRate );
|
|
|
+
|
|
|
+ if(nlhs >= 2)
|
|
|
+ plhs[1] = MatlabConversion::convertMatrixFromNice(confusionMatrix);
|
|
|
+ if(nlhs >= 3)
|
|
|
+ plhs[2] = MatlabConversion::convertMatrixFromNice(scores);
|
|
|
+
|
|
|
+
|
|
|
+ return;
|
|
|
+ }
|
|
|
+
|
|
|
+ ///////////////////// INTERFACE ONLINE LEARNABLE /////////////////////
|
|
|
+ // interface specific methods for incremental extensions
|
|
|
+ ///////////////////// INTERFACE ONLINE LEARNABLE /////////////////////
|
|
|
+
|
|
|
+ // not supported here
|
|
|
+
|
|
|
+ ///////////////////// INTERFACE PERSISTENT /////////////////////
|
|
|
+ // interface specific methods for store and restore
|
|
|
+ ///////////////////// INTERFACE PERSISTENT /////////////////////
|
|
|
+
|
|
|
+ // not supported here
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+ // Got here, so command not recognized
|
|
|
+
|
|
|
+ std::string errorMsg (cmd.c_str() );
|
|
|
+ errorMsg += " -- command not recognized.";
|
|
|
+ mexErrMsgTxt( errorMsg.c_str() );
|
|
|
+
|
|
|
+}
|
|
|
+#endif
|
|
|
+
|
Erik Rodner