algorithmnn.java,v

包含了模式识别中常用的一些分类器设计算法

	error = ((double)incorrect / (double)samples) * 100.0;
	
	text =
	    new String(
		       "       Overall results:\n"
		       + "          Total number of samples: "
		       + samples
		       + "\n"
		       + "          Misclassified samples: "
		       + incorrect
		       + "\n"
		       + "          Classification error: "
		       + MathUtil.setDecimal(error, 2)
		       + "%");
	
	pro_box_d.appendMessage(text);
    }   
}
@


1.5
log
@Fixed minor javadoc errors.
@
text
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    Vector support_vectors_d;
    Vector decision_regions_d;
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	point_means_d = new Vector();
	decision_regions_d = new Vector();
	support_vectors_d = new Vector();
	description_d = new Vector();
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	set1_d = (Vector)data_points_d.dset1.clone();
	set2_d = (Vector)data_points_d.dset2.clone();
	set3_d = (Vector)data_points_d.dset3.clone();
	set4_d = (Vector)data_points_d.dset4.clone();
@


1.4
log
@minor modifications on commenting.
@
text
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     * @@return   boolean
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     * @@return   boolean
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     * @@return   boolean
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     * @@return   boolean
@


1.3
log
@Comments changed to comply with Java Documentation.
Alignment for If condition, for loops done.
@
text
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* AlgorithmNN.java
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* Created: 7/15/03
*
* Author: Phil Trasatti
* 
* Last Edited by: Daniel May
*
* Class: AlgorithmNN
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* Description: Nearest Neighbor algorithm. Determines the line of 
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     * method: initialize
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     * arguments: none
     * return   : boolean
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     * description:  
     * implements the initialize() method in the base class.  initializes
     * member data and prepares for execution of first step.  this method
     * "resets" the algorithm.
     */
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	//System.out.println(algo_id + ": initialize()");
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     * method: step1
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     * arguments: none
     * return   : boolean
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     * description:  
     * displays data sets from input box in output box.
     * 
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	//System.out.println(algo_id + ": step1()");
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     * method: step2
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     * arguments: none
     * return   : boolean
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     * description:  
     * computes the means of each data set and displays the means graphically
     * and numerically
     * 
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	//System.out.println(algo_id + ": step2()");
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	//----
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	//----
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     * method: step3
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     * arguments: none
     * return   : boolean
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     * description:   
     * Computes the Decision Regions and the associated errors. 
     * 
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	//System.out.println(algo_id + ": step3()");
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	//----
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	//----
	output_panel_d.addOutput( decision_regions_d, Classify.PTYPE_INPUT, new Color(255, 200, 0));
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	//  // method dispalyMatrices
	//  //
	//  // arguments:
	//  //    Data d: input data point
	//  //
	//  // return   : none
	//  //
	//  // display two matrices
	//  //
	//  public void displayMatrices()
	//  {
	//
	//    // declare local variables
	//    //
	//    double a11 = 0.0;
	//    double a12 = 0.0;
	//    double a21 = 0.0;
	//    double a22 = 0.0;
	//
	//    String text;
	//
	//    a11 = MathUtil.setDecimal(cov_matrix_d.Elem[0][0], 2);
	//    a12 = MathUtil.setDecimal(cov_matrix_d.Elem[0][1], 2);
	//    a21 = MathUtil.setDecimal(cov_matrix_d.Elem[1][0], 2);
	//    a22 = MathUtil.setDecimal(cov_matrix_d.Elem[1][1], 2);
	//
	//    text =
	//      new String(
	//        "      Covariance matrix:\n"
	//          + "         "
	//          + a11
	//          + "    "
	//          + a12
	//          + "\n"
	//          + "         "
	//          + a21
	//          + "    "
	//          + a22);
	//
	//    // append message to process box
	//    //
	//    pro_box_d.appendMessage(text);
	//
	//    a11 = MathUtil.setDecimal(trans_matrix_d.Elem[0][0], 2);
	//    a12 = MathUtil.setDecimal(trans_matrix_d.Elem[0][1], 2);
	//    a21 = MathUtil.setDecimal(trans_matrix_d.Elem[1][0], 2);
	//    a22 = MathUtil.setDecimal(trans_matrix_d.Elem[1][1], 2);
	//
	//    text =
	//      new String(
	//        "      Transformation matrix:\n"
	//          + "         "
	//          + a11
	//          + "    "
	//          + a12
	//          + "\n"
	//          + "         "
	//          + a21
	//          + "    "
	//          + a22);
	//
	//    // append message to process box
	//    //
	//    pro_box_d.appendMessage(text);
	//
	//  }
	
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     * method: run
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     * arguments: none
     * return   : none
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     * description: 
     * implementation of the run function from the Runnable interface.
     * determines what the current step is and calls the appropriate method.
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	//System.out.println(algo_id + " : run()");
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     * method: computeDecisionRegions
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     * arguments: none
     * return   : none
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     * description:
     * compute the line of discrimination for nearest neighbor 
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	//System.out.println(algo_id +": computeDecisionRegions()");
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	double incrementY = (scale.ymax-scale.ymin)/outputHeight;
	double incrementX = (scale.xmax-scale.xmin)/outputWidth;
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		    dist = MathUtil.distance(pixel.x, pixel.y, point.x, point.y);
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		    dist = MathUtil.distance(pixel.x, pixel.y, point.x, point.y);
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		    dist = MathUtil.distance(pixel.x, pixel.y, point.x, point.y);
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		    dist = MathUtil.distance(pixel.x, pixel.y, point.x, point.y);
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		    if (associated != output_canvas_d[i][j - 1] || associated != output_canvas_d[i - 1][j])
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     * method: computeErrors
     *
     * arguments: none
     * return   : none
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     * description:
     * computes and displays the classification errors for each set   
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	//System.out.println(algo_id +": computeErrors()");
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	double incrementY = (scale.ymax-scale.ymin)/outputHeight;
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	double incrementX = (scale.xmax-scale.xmin)/outputWidth;
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		if (output_canvas_d[(int)((point.x-scale.xmin)/incrementX)][(int)((point.y-scale.ymin)/incrementY)] != 0)
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		if (output_canvas_d[(int)((point.x-scale.xmin)/incrementX)][(int)((point.y-scale.ymin)/incrementY)] != 1)
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		if (output_canvas_d[(int)((point.x-scale.xmin)/incrementX)][(int)((point.y-scale.ymin)/incrementY)] != 2)
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		if (output_canvas_d[(int)((point.x-scale.xmin)/incrementX)][(int)((point.y-scale.ymin)/incrementY)] != 3)
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    }    
    
@


1.2
log
@Commented out debug statement out.
@
text
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//----------------------------------------------------------------------
// AlgorithmNN.java
//
// Created: 7/15/03
//
// Author: Phil Trasatti
// 
// Last Edited by: Daniel May
//
// Class: AlgorithmNN
// 
// Description: Nearest Neighbor algorithm. Determines the line of 
// discrimination between data sets based on the points that are equal 
// in distance from the nearest point of each data set.
//---------------------------------------------------------------------
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    //---------------------------------------------------------------
    // method: initialize
    //
    // arguments: none
    // return   : boolean
    //
    // description:  
    // implements the initialize() method in the base class.  initializes
    // member data and prepares for execution of first step.  this method
    // "resets" the algorithm.
    //---------------------------------------------------------------
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	    {
		String str = new String("   0. Initialize the original data.");
		description_d.addElement(str);
		
		str = new String("   1. Displaying the original data.");
		description_d.addElement(str);
		
		str = new String("   2. Computing the means for each class.");
		description_d.addElement(str);
		
		str = new String("   3. Computing the decision regions based on the Nearest Neighbor algorithm.");
		description_d.addElement(str);
	    }
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    //---------------------------------------------------------------
    // method: step1
    //
    // arguments: none
    // return   : boolean
    //
    // description:  
    // displays data sets from input box in output box.
    // 
    //---------------------------------------------------------------
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    //---------------------------------------------------------------
    // method: step2
    //
    // arguments: none
    // return   : boolean
    //
    // description:  
    // computes the means of each data set and displays the means graphically
    // and numerically
    // 
    //---------------------------------------------------------------
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    //---------------------------------------------------------------
    // method: step3
    //
    // arguments: none
    // return   : boolean
    //
    // description:   
    // Computes the Decision Regions and the associated errors. 
    // 
    //---------------------------------------------------------------    
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    //---------------------------------------------------------------
    // method: run
    //
    // arguments: none
    // return   : none
    //
    // description: 
    // implementation of the run function from the Runnable interface.
    // determines what the current step is and calls the appropriate method.
    //--------------------------------------------------------------- 
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	    {
		disableControl();
		step1();
		enableControl();
	    }
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	    {
		disableControl();
		step2();
		enableControl(); 
	    }
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	    {
		disableControl();
		step3(); 
		enableControl(); 
		pro_box_d.appendMessage("   Algorithm Complete");
	    }
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    // -------------------------------------------------------------
    // method: computeDecisionRegions
    //
    // arguments: none
    // return   : none
    //
    // description:
    // compute the line of discrimination for nearest neighbor 
    //--------------------------------------------------------------
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    //--------------------------------------------------------------
    // method: computeErrors
    //
    // arguments: none
    // return   : none
    //
    // description:
    // computes and displays the classification errors for each set   
    //
    //--------------------------------------------------------------   
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		MyPoint point = (MyPoint)set1_d.elementAt(i);
		samples1++;
		if ((point.x > scale.xmin && point.x < scale.xmax)
		    && (point.y > scale.ymin && point.y < scale.ymax))
		    {
			if (output_canvas_d[(int)((point.x-scale.xmin)/incrementX)][(int)((point.y-scale.ymin)/incrementY)] != 0)
			    {
				incorrect1++;
			    }
		    }
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	    {
		
		error = ((double)incorrect1 / (double)samples1) * 100.0;
		
		text =
		    new String(
			       "       Results for class 0:\n"
			       + "          Total number of samples: "
			       + samples1
			       + "\n"
			       + "          Misclassified samples: "
			       + incorrect1
			       + "\n"
			       + "          Classification error: "
			       + MathUtil.setDecimal(error, 2)
			       + "%");
		
		pro_box_d.appendMessage(text);
	    }
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	    {
		
		MyPoint point = (MyPoint)set2_d.elementAt(i);
		samples2++;
		if ((point.x > scale.xmin && point.x < scale.xmax)
		    && (point.y > scale.ymin && point.y < scale.ymax))
		    {
			if (output_canvas_d[(int)((point.x-scale.xmin)/incrementX)][(int)((point.y-scale.ymin)/incrementY)] != 1)
			    {
				incorrect2++;
			    }
		    }
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	    {
		
		error = ((double)incorrect2 / (double)samples2) * 100.0;
		
		text =
		    new String(
			       "       Results for class 1:\n"
			       + "          Total number of samples: "
			       + samples2
			       + "\n"
			       + "          Misclassified samples: "
			       + incorrect2
			       + "\n"
			       + "          Classification error: "
			       + MathUtil.setDecimal(error, 2)
			       + "%");
		
		pro_box_d.appendMessage(text);
	    }
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		MyPoint point = (MyPoint)set3_d.elementAt(i);
		samples3++;
		if ((point.x > scale.xmin && point.x < scale.xmax)
		    && (point.y > scale.ymin && point.y < scale.ymax))
		    {
			if (output_canvas_d[(int)((point.x-scale.xmin)/incrementX)][(int)((point.y-scale.ymin)/incrementY)] != 2)
			    {
				incorrect3++;
			    }
		    }
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	    {
		
		error = ((double)incorrect3 / (double)samples3) * 100.0;
		
		text =
		    new String(
			       "       Results for class 2:\n"
			       + "          Total number of samples: "
			       + samples3
			       + "\n"
			       + "          Misclassified samples: "
			       + incorrect3
			       + "\n"
			       + "          Classification error: "
			       + MathUtil.setDecimal(error, 2)
			       + "%");
		
		pro_box_d.appendMessage(text);
	    }
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		MyPoint point = (MyPoint)set4_d.elementAt(i);
		samples4++;
		if ((point.x > scale.xmin && point.x < scale.xmax)
		    && (point.y > scale.ymin && point.y < scale.ymax))
		    {
			if (output_canvas_d[(int)((point.x-scale.xmin)/incrementX)][(int)((point.y-scale.ymin)/incrementY)] != 3)
			    {
				incorrect4++;
			    }
		    }
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	    {
		
		error = ((double)incorrect4 / (double)samples4) * 100.0;
		
		text =
		    new String(
			       "       Results for class 3:\n"
			       + "          Total number of samples: "
			       + samples4
			       + "\n"
			       + "          Misclassified samples: "
			       + incorrect4
			       + "\n"
			       + "          Classification error: "
			       + MathUtil.setDecimal(error, 2)
			       + "%");
		
		pro_box_d.appendMessage(text);
	    }
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@


1.1
log
@Initial revision
@
text
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	System.out.println(algo_id + ": initialize()");
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	System.out.println(algo_id + ": step1()");
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	System.out.println(algo_id + ": step2()");
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	System.out.println(algo_id + ": step3()");
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	System.out.println(algo_id + " : run()");
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		pro_box_d.appendMessage("   Step Sequence Complete");
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	System.out.println(algo_id +": computeDecisionRegions()");
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	System.out.println(algo_id +": computeErrors()");
@