This example uses a database of a complete set of possible board configurations at the end of tic-tac-toe games, where "x" is assumed to have played first. The target concept is "win for x" (i.e., true when "x" has one of 8 possible ways to create a "three-in-a-row").
There are nine nominal input attributes for each square on the tic-tac-toe board and are encoded such that 0=player x has taken, 1=player o has taken, 2=blank.
The predicted atribute is a win or loose at tic-tac-toe. For this example the first 626 observations are a win and the next 332 are loss.
The structure of the network consists of 27 input nodes and three layers, with five perceptrons in the first hidden layer, three perceptrons in the second hidden layer, and one perceptron in the output layer.
There are a total of 162 weights in this network. The activations functions are logistic for all layers. Since the target output is binary classification the logistic activation function must be used in the output layer. Training is conducted using the quasi-newton trainer using the binary entropy error function provided by the BinaryClassification
class.
import com.imsl.datamining.neural.*;
import java.io.*;
import java.util.logging.*;
import com.imsl.math.PrintMatrix;
import com.imsl.math.PrintMatrixFormat;
import com.imsl.stat.Random;
//*****************************************************************************
// Three Layer Feed-Forward Network with 4 inputs, all
// continuous, and 2 classification categories.
//
// new classification training_ex4.c
//
// Three Layer Feed-Forward Network with 4 inputs, all
// continuous, and 2 classification categories.
//
// This database encodes the complete set of possible board configurations
// at the end of tic-tac-toe games, where "x" is assumed to have played
// first. The target concept is "win for x" (i.e., true when "x" has one
// of 8 possible ways to create a "three-in-a-row").
//
// Predicted attribute: win or loose at tic-tac-toe
// First 626 obs are positive (win) and the next 332 are negative (loss)
//
// Input Attributes (10 categorical Attributes)
// Attribute Information: (0=player x has taken, 1=player o has taken, 2=blank)
//
// 1. top-left-square: {x,o,b}
// 2. top-middle-square: {x,o,b}
// 3. top-right-square: {x,o,b}
// 4. middle-left-square: {x,o,b}
// 5. middle-middle-square: {x,o,b}
// 6. middle-right-square: {x,o,b}
// 7. bottom-left-square: {x,o,b}
// 8. bottom-middle-square: {x,o,b}
// 9. bottom-right-square: {x,o,b}
// 10. Class: {positive,negative}
//*****************************************************************************
public class BinaryClassificationEx2 implements Serializable
{
private static int nObs = 958; // number of training patterns
private static int nInputs = 27; // 9 nominal coded as 0=x, 1=O, 2=blank
private static int nCategorical = 27; // seven categorical attributes
private static int nContinuous = 0; // two continuous input attribute
private static int nOutputs = 1; // one continuous output (nClasses=2)
private static int nLayers = 3; // number of perceptron layers
private static int nPerceptrons1 = 5; // perceptrons in 1st hidden layer
private static int nPerceptrons2 = 3; // perceptrons in 2nd hidden layer
private static boolean trace = true; // Turns on/off training log
private static Activation hiddenLayerActivation = Activation.LOGISTIC;
private static Activation outputLayerActivation = Activation.LOGISTIC;
private static int[][] data = {
{0,0,0,0,1,1,0,1,1},{0,0,0,0,1,1,1,0,1},{0,0,0,0,1,1,1,1,0},{0,0,0,0,1,1,1,2,2},
{0,0,0,0,1,1,2,1,2},{0,0,0,0,1,1,2,2,1},{0,0,0,0,1,2,1,1,2},{0,0,0,0,1,2,1,2,1},
{0,0,0,0,1,2,2,1,1},{0,0,0,0,2,1,1,1,2},{0,0,0,0,2,1,1,2,1},{0,0,0,0,2,1,2,1,1},
{0,0,0,1,0,1,0,1,1},{0,0,0,1,0,1,1,0,1},{0,0,0,1,0,1,1,1,0},{0,0,0,1,0,1,1,2,2},
{0,0,0,1,0,1,2,1,2},{0,0,0,1,0,1,2,2,1},{0,0,0,1,0,2,1,1,2},{0,0,0,1,0,2,1,2,1},
{0,0,0,1,0,2,2,1,1},{0,0,0,1,1,0,0,1,1},{0,0,0,1,1,0,1,0,1},{0,0,0,1,1,0,1,1,0},
{0,0,0,1,1,0,1,2,2},{0,0,0,1,1,0,2,1,2},{0,0,0,1,1,0,2,2,1},{0,0,0,1,1,2,0,1,2},
{0,0,0,1,1,2,0,2,1},{0,0,0,1,1,2,1,0,2},{0,0,0,1,1,2,1,2,0},{0,0,0,1,1,2,2,0,1},
{0,0,0,1,1,2,2,1,0},{0,0,0,1,1,2,2,2,2},{0,0,0,1,2,0,1,1,2},{0,0,0,1,2,0,1,2,1},
{0,0,0,1,2,0,2,1,1},{0,0,0,1,2,1,0,1,2},{0,0,0,1,2,1,0,2,1},{0,0,0,1,2,1,1,0,2},
{0,0,0,1,2,1,1,2,0},{0,0,0,1,2,1,2,0,1},{0,0,0,1,2,1,2,1,0},{0,0,0,1,2,1,2,2,2},
{0,0,0,1,2,2,0,1,1},{0,0,0,1,2,2,1,0,1},{0,0,0,1,2,2,1,1,0},{0,0,0,1,2,2,1,2,2},
{0,0,0,1,2,2,2,1,2},{0,0,0,1,2,2,2,2,1},{0,0,0,2,0,1,1,1,2},{0,0,0,2,0,1,1,2,1},
{0,0,0,2,0,1,2,1,1},{0,0,0,2,1,0,1,1,2},{0,0,0,2,1,0,1,2,1},{0,0,0,2,1,0,2,1,1},
{0,0,0,2,1,1,0,1,2},{0,0,0,2,1,1,0,2,1},{0,0,0,2,1,1,1,0,2},{0,0,0,2,1,1,1,2,0},
{0,0,0,2,1,1,2,0,1},{0,0,0,2,1,1,2,1,0},{0,0,0,2,1,1,2,2,2},{0,0,0,2,1,2,0,1,1},
{0,0,0,2,1,2,1,0,1},{0,0,0,2,1,2,1,1,0},{0,0,0,2,1,2,1,2,2},{0,0,0,2,1,2,2,1,2},
{0,0,0,2,1,2,2,2,1},{0,0,0,2,2,1,0,1,1},{0,0,0,2,2,1,1,0,1},{0,0,0,2,2,1,1,1,0},
{0,0,0,2,2,1,1,2,2},{0,0,0,2,2,1,2,1,2},{0,0,0,2,2,1,2,2,1},{0,0,0,2,2,2,1,1,2},
{0,0,0,2,2,2,1,2,1},{0,0,0,2,2,2,2,1,1},{0,0,1,0,0,1,1,1,0},{0,0,1,0,1,0,0,1,1},
{0,0,1,0,1,1,0,1,0},{0,0,1,0,1,1,0,2,2},{0,0,1,0,1,2,0,1,2},{0,0,1,0,1,2,0,2,1},
{0,0,1,0,2,1,0,1,2},{0,0,1,0,2,2,0,1,1},{0,0,1,1,0,0,1,0,1},{0,0,1,1,0,0,1,1,0},
{0,0,1,1,0,1,0,1,0},{0,0,1,1,0,1,1,0,0},{0,0,1,1,0,1,2,0,2},{0,0,1,1,0,1,2,2,0},
{0,0,1,1,0,2,1,0,2},{0,0,1,1,0,2,1,2,0},{0,0,1,1,0,2,2,0,1},{0,0,1,1,0,2,2,1,0},
{0,0,1,2,0,1,1,0,2},{0,0,1,2,0,1,1,2,0},{0,0,1,2,0,1,2,1,0},{0,0,1,2,0,2,1,0,1},
{0,0,1,2,0,2,1,1,0},{0,0,2,0,1,1,0,1,2},{0,0,2,0,1,1,0,2,1},{0,0,2,0,1,2,0,1,1},
{0,0,2,0,2,1,0,1,1},{0,0,2,1,0,1,1,0,2},{0,0,2,1,0,1,1,2,0},{0,0,2,1,0,1,2,0,1},
{0,0,2,1,0,1,2,1,0},{0,0,2,1,0,2,1,0,1},{0,0,2,1,0,2,1,1,0},{0,0,2,2,0,1,1,0,1},
{0,0,2,2,0,1,1,1,0},{0,1,0,0,0,1,0,1,1},{0,1,0,0,0,1,1,1,0},{0,1,0,0,1,1,0,0,1},
{0,1,0,0,1,1,0,2,2},{0,1,0,0,1,2,0,2,1},{0,1,0,0,2,1,0,1,2},{0,1,0,0,2,1,0,2,1},
{0,1,0,0,2,2,0,1,1},{0,1,0,1,0,0,0,1,1},{0,1,0,1,0,0,1,1,0},{0,1,0,1,0,1,0,0,1},
{0,1,0,1,0,1,0,1,0},{0,1,0,1,0,1,0,2,2},{0,1,0,1,0,1,1,0,0},{0,1,0,1,0,1,2,2,0},
{0,1,0,1,0,2,0,1,2},{0,1,0,1,0,2,0,2,1},{0,1,0,1,0,2,1,2,0},{0,1,0,1,0,2,2,1,0},
{0,1,0,1,1,0,1,0,0},{0,1,0,1,1,0,2,2,0},{0,1,0,1,2,0,1,2,0},{0,1,0,1,2,0,2,1,0},
{0,1,0,2,0,1,0,1,2},{0,1,0,2,0,1,0,2,1},{0,1,0,2,0,1,1,2,0},{0,1,0,2,0,1,2,1,0},
{0,1,0,2,0,2,0,1,1},{0,1,0,2,0,2,1,1,0},{0,1,0,2,1,0,1,2,0},{0,1,0,2,2,0,1,1,0},
{0,1,1,0,0,0,0,1,1},{0,1,1,0,0,0,1,0,1},{0,1,1,0,0,0,1,1,0},{0,1,1,0,0,0,1,2,2},
{0,1,1,0,0,0,2,1,2},{0,1,1,0,0,0,2,2,1},{0,1,1,0,0,1,0,1,0},{0,1,1,0,0,1,0,2,2},
{0,1,1,0,0,1,1,0,0},{0,1,1,0,0,1,2,2,0},{0,1,1,0,0,2,0,1,2},{0,1,1,0,0,2,0,2,1},
{0,1,1,0,0,2,1,2,0},{0,1,1,0,0,2,2,1,0},{0,1,1,0,1,0,0,0,1},{0,1,1,0,1,0,0,2,2},
{0,1,1,0,1,1,0,0,0},{0,1,1,0,1,2,0,0,2},{0,1,1,0,1,2,0,2,0},{0,1,1,0,2,0,0,1,2},
{0,1,1,0,2,0,0,2,1},{0,1,1,0,2,1,0,0,2},{0,1,1,0,2,1,0,2,0},{0,1,1,0,2,2,0,0,1},
{0,1,1,0,2,2,0,1,0},{0,1,1,0,2,2,0,2,2},{0,1,1,1,0,0,0,1,0},{0,1,1,1,0,0,1,0,0},
{0,1,1,1,0,0,2,2,0},{0,1,1,1,0,1,0,0,0},{0,1,1,1,0,2,0,2,0},{0,1,1,1,0,2,2,0,0},
{0,1,1,1,1,0,0,0,0},{0,1,1,1,2,2,0,0,0},{0,1,1,2,0,0,1,2,0},{0,1,1,2,0,0,2,1,0},
{0,1,1,2,0,1,0,2,0},{0,1,1,2,0,1,2,0,0},{0,1,1,2,0,2,0,1,0},{0,1,1,2,0,2,1,0,0},
{0,1,1,2,0,2,2,2,0},{0,1,1,2,1,2,0,0,0},{0,1,1,2,2,1,0,0,0},{0,1,2,0,0,0,1,1,2},
{0,1,2,0,0,0,1,2,1},{0,1,2,0,0,0,2,1,1},{0,1,2,0,0,1,0,1,2},{0,1,2,0,0,1,0,2,1},
{0,1,2,0,0,1,1,2,0},{0,1,2,0,0,1,2,1,0},{0,1,2,0,0,2,0,1,1},{0,1,2,0,0,2,1,1,0},
{0,1,2,0,1,0,0,2,1},{0,1,2,0,1,1,0,0,2},{0,1,2,0,1,1,0,2,0},{0,1,2,0,1,2,0,0,1},
{0,1,2,0,1,2,0,2,2},{0,1,2,0,2,0,0,1,1},{0,1,2,0,2,1,0,0,1},{0,1,2,0,2,1,0,1,0},
{0,1,2,0,2,1,0,2,2},{0,1,2,0,2,2,0,1,2},{0,1,2,0,2,2,0,2,1},{0,1,2,1,0,0,1,2,0},
{0,1,2,1,0,0,2,1,0},{0,1,2,1,0,1,0,2,0},{0,1,2,1,0,1,2,0,0},{0,1,2,1,0,2,0,1,0},
{0,1,2,1,0,2,1,0,0},{0,1,2,1,0,2,2,2,0},{0,1,2,1,1,2,0,0,0},{0,1,2,1,2,1,0,0,0},
{0,1,2,2,0,0,1,1,0},{0,1,2,2,0,1,0,1,0},{0,1,2,2,0,1,1,0,0},{0,1,2,2,0,1,2,2,0},
{0,1,2,2,0,2,1,2,0},{0,1,2,2,0,2,2,1,0},{0,1,2,2,1,1,0,0,0},{0,2,0,0,1,1,0,1,2},
{0,2,0,0,1,1,0,2,1},{0,2,0,0,1,2,0,1,1},{0,2,0,0,2,1,0,1,1},{0,2,0,1,0,1,0,1,2},
{0,2,0,1,0,1,0,2,1},{0,2,0,1,0,1,1,2,0},{0,2,0,1,0,1,2,1,0},{0,2,0,1,0,2,0,1,1},
{0,2,0,1,0,2,1,1,0},{0,2,0,1,1,0,1,2,0},{0,2,0,1,1,0,2,1,0},{0,2,0,1,2,0,1,1,0},
{0,2,0,2,0,1,0,1,1},{0,2,0,2,0,1,1,1,0},{0,2,0,2,1,0,1,1,0},{0,2,1,0,0,0,1,1,2},
{0,2,1,0,0,0,1,2,1},{0,2,1,0,0,0,2,1,1},{0,2,1,0,0,1,0,1,2},{0,2,1,0,0,1,1,2,0},
{0,2,1,0,0,1,2,1,0},{0,2,1,0,0,2,0,1,1},{0,2,1,0,0,2,1,1,0},{0,2,1,0,1,0,0,1,2},
{0,2,1,0,1,0,0,2,1},{0,2,1,0,1,1,0,0,2},{0,2,1,0,1,1,0,2,0},{0,2,1,0,1,2,0,0,1},
{0,2,1,0,1,2,0,1,0},{0,2,1,0,1,2,0,2,2},{0,2,1,0,2,0,0,1,1},{0,2,1,0,2,1,0,1,0},
{0,2,1,0,2,1,0,2,2},{0,2,1,0,2,2,0,1,2},{0,2,1,0,2,2,0,2,1},{0,2,1,1,0,0,1,2,0},
{0,2,1,1,0,0,2,1,0},{0,2,1,1,0,1,0,2,0},{0,2,1,1,0,1,2,0,0},{0,2,1,1,0,2,0,1,0},
{0,2,1,1,0,2,1,0,0},{0,2,1,1,0,2,2,2,0},{0,2,1,1,1,2,0,0,0},{0,2,1,1,2,1,0,0,0},
{0,2,1,2,0,0,1,1,0},{0,2,1,2,0,1,0,1,0},{0,2,1,2,0,1,1,0,0},{0,2,1,2,0,1,2,2,0},
{0,2,1,2,0,2,1,2,0},{0,2,1,2,0,2,2,1,0},{0,2,1,2,1,1,0,0,0},{0,2,2,0,0,1,0,1,1},
{0,2,2,0,0,1,1,1,0},{0,2,2,0,1,0,0,1,1},{0,2,2,0,1,1,0,0,1},{0,2,2,0,1,1,0,1,0},
{0,2,2,0,1,1,0,2,2},{0,2,2,0,1,2,0,1,2},{0,2,2,0,1,2,0,2,1},{0,2,2,0,2,1,0,1,2},
{0,2,2,0,2,1,0,2,1},{0,2,2,0,2,2,0,1,1},{0,2,2,1,0,0,1,1,0},{0,2,2,1,0,1,0,1,0},
{0,2,2,1,0,1,1,0,0},{0,2,2,1,0,1,2,2,0},{0,2,2,1,0,2,1,2,0},{0,2,2,1,0,2,2,1,0},
{0,2,2,2,0,1,1,2,0},{0,2,2,2,0,1,2,1,0},{0,2,2,2,0,2,1,1,0},{1,0,0,0,0,1,0,1,1},
{1,0,0,0,0,1,1,0,1},{1,0,0,0,1,0,1,1,0},{1,0,0,1,0,0,0,1,1},{1,0,0,1,0,1,0,0,1},
{1,0,0,1,0,1,0,1,0},{1,0,0,1,0,1,0,2,2},{1,0,0,1,0,1,2,0,2},{1,0,0,1,0,2,0,1,2},
{1,0,0,1,0,2,0,2,1},{1,0,0,1,0,2,2,0,1},{1,0,0,1,1,0,0,1,0},{1,0,0,1,1,0,2,2,0},
{1,0,0,1,2,0,2,1,0},{1,0,0,2,0,1,0,1,2},{1,0,0,2,0,1,0,2,1},{1,0,0,2,0,1,1,0,2},
{1,0,0,2,0,1,2,0,1},{1,0,0,2,0,2,0,1,1},{1,0,0,2,0,2,1,0,1},{1,0,0,2,1,0,1,2,0},
{1,0,0,2,1,0,2,1,0},{1,0,0,2,2,0,1,1,0},{1,0,1,0,0,0,0,1,1},{1,0,1,0,0,0,1,0,1},
{1,0,1,0,0,0,1,1,0},{1,0,1,0,0,0,1,2,2},{1,0,1,0,0,0,2,1,2},{1,0,1,0,0,0,2,2,1},
{1,0,1,0,0,1,1,0,0},{1,0,1,0,0,1,2,0,2},{1,0,1,0,0,2,1,0,2},{1,0,1,0,0,2,2,0,1},
{1,0,1,0,1,1,0,0,0},{1,0,1,1,0,0,0,0,1},{1,0,1,1,0,0,2,0,2},{1,0,1,1,0,1,0,0,0},
{1,0,1,1,0,2,0,0,2},{1,0,1,1,0,2,2,0,0},{1,0,1,1,1,0,0,0,0},{1,0,1,1,2,2,0,0,0},
{1,0,1,2,0,0,1,0,2},{1,0,1,2,0,0,2,0,1},{1,0,1,2,0,1,0,0,2},{1,0,1,2,0,1,2,0,0},
{1,0,1,2,0,2,0,0,1},{1,0,1,2,0,2,1,0,0},{1,0,1,2,0,2,2,0,2},{1,0,1,2,1,2,0,0,0},
{1,0,1,2,2,1,0,0,0},{1,0,2,0,0,0,1,1,2},{1,0,2,0,0,0,1,2,1},{1,0,2,0,0,0,2,1,1},
{1,0,2,0,0,1,1,0,2},{1,0,2,0,0,1,2,0,1},{1,0,2,0,0,2,1,0,1},{1,0,2,1,0,0,2,0,1},
{1,0,2,1,0,1,0,0,2},{1,0,2,1,0,1,2,0,0},{1,0,2,1,0,2,0,0,1},{1,0,2,1,0,2,2,0,2},
{1,0,2,1,1,2,0,0,0},{1,0,2,1,2,1,0,0,0},{1,0,2,2,0,0,1,0,1},{1,0,2,2,0,1,0,0,1},
{1,0,2,2,0,1,1,0,0},{1,0,2,2,0,1,2,0,2},{1,0,2,2,0,2,1,0,2},{1,0,2,2,0,2,2,0,1},
{1,0,2,2,1,1,0,0,0},{1,1,0,0,0,0,0,1,1},{1,1,0,0,0,0,1,0,1},{1,1,0,0,0,0,1,1,0},
{1,1,0,0,0,0,1,2,2},{1,1,0,0,0,0,2,1,2},{1,1,0,0,0,0,2,2,1},{1,1,0,0,0,1,0,0,1},
{1,1,0,0,0,1,0,1,0},{1,1,0,0,0,1,0,2,2},{1,1,0,0,0,2,0,1,2},{1,1,0,0,0,2,0,2,1},
{1,1,0,0,1,0,1,0,0},{1,1,0,0,1,0,2,2,0},{1,1,0,0,1,1,0,0,0},{1,1,0,0,2,0,1,2,0},
{1,1,0,0,2,0,2,1,0},{1,1,0,1,0,0,0,0,1},{1,1,0,1,0,0,0,1,0},{1,1,0,1,0,0,0,2,2},
{1,1,0,1,0,0,2,2,0},{1,1,0,1,0,1,0,0,0},{1,1,0,1,0,2,0,0,2},{1,1,0,1,0,2,0,2,0},
{1,1,0,1,1,0,0,0,0},{1,1,0,1,2,0,0,2,0},{1,1,0,1,2,0,2,0,0},{1,1,0,1,2,2,0,0,0},
{1,1,0,2,0,0,0,1,2},{1,1,0,2,0,0,0,2,1},{1,1,0,2,0,0,1,2,0},{1,1,0,2,0,0,2,1,0},
{1,1,0,2,0,1,0,0,2},{1,1,0,2,0,1,0,2,0},{1,1,0,2,0,2,0,0,1},{1,1,0,2,0,2,0,1,0},
{1,1,0,2,0,2,0,2,2},{1,1,0,2,1,0,0,2,0},{1,1,0,2,1,0,2,0,0},{1,1,0,2,1,2,0,0,0},
{1,1,0,2,2,0,0,1,0},{1,1,0,2,2,0,1,0,0},{1,1,0,2,2,0,2,2,0},{1,1,0,2,2,1,0,0,0},
{1,1,2,0,0,0,0,1,2},{1,1,2,0,0,0,0,2,1},{1,1,2,0,0,0,1,0,2},{1,1,2,0,0,0,1,2,0},
{1,1,2,0,0,0,2,0,1},{1,1,2,0,0,0,2,1,0},{1,1,2,0,0,0,2,2,2},{1,1,2,0,1,2,0,0,0},
{1,1,2,0,2,1,0,0,0},{1,1,2,1,0,2,0,0,0},{1,1,2,1,2,0,0,0,0},{1,1,2,2,0,1,0,0,0},
{1,1,2,2,1,0,0,0,0},{1,1,2,2,2,2,0,0,0},{1,2,0,0,0,0,1,1,2},{1,2,0,0,0,0,1,2,1},
{1,2,0,0,0,0,2,1,1},{1,2,0,0,0,1,0,1,2},{1,2,0,0,0,1,0,2,1},{1,2,0,0,0,2,0,1,1},
{1,2,0,0,1,0,1,2,0},{1,2,0,0,1,0,2,1,0},{1,2,0,0,2,0,1,1,0},{1,2,0,1,0,0,0,1,2},
{1,2,0,1,0,0,0,2,1},{1,2,0,1,0,0,2,1,0},{1,2,0,1,0,1,0,0,2},{1,2,0,1,0,1,0,2,0},
{1,2,0,1,0,2,0,0,1},{1,2,0,1,0,2,0,1,0},{1,2,0,1,0,2,0,2,2},{1,2,0,1,1,0,0,2,0},
{1,2,0,1,1,0,2,0,0},{1,2,0,1,1,2,0,0,0},{1,2,0,1,2,0,0,1,0},{1,2,0,1,2,0,2,2,0},
{1,2,0,1,2,1,0,0,0},{1,2,0,2,0,0,0,1,1},{1,2,0,2,0,0,1,1,0},{1,2,0,2,0,1,0,0,1},
{1,2,0,2,0,1,0,1,0},{1,2,0,2,0,1,0,2,2},{1,2,0,2,0,2,0,1,2},{1,2,0,2,0,2,0,2,1},
{1,2,0,2,1,0,0,1,0},{1,2,0,2,1,0,1,0,0},{1,2,0,2,1,0,2,2,0},{1,2,0,2,1,1,0,0,0},
{1,2,0,2,2,0,1,2,0},{1,2,0,2,2,0,2,1,0},{1,2,1,0,0,0,0,1,2},{1,2,1,0,0,0,0,2,1},
{1,2,1,0,0,0,1,0,2},{1,2,1,0,0,0,1,2,0},{1,2,1,0,0,0,2,0,1},{1,2,1,0,0,0,2,1,0},
{1,2,1,0,0,0,2,2,2},{1,2,1,0,1,2,0,0,0},{1,2,1,0,2,1,0,0,0},{1,2,1,1,0,2,0,0,0},
{1,2,1,1,2,0,0,0,0},{1,2,1,2,0,1,0,0,0},{1,2,1,2,1,0,0,0,0},{1,2,1,2,2,2,0,0,0},
{1,2,2,0,0,0,0,1,1},{1,2,2,0,0,0,1,0,1},{1,2,2,0,0,0,1,1,0},{1,2,2,0,0,0,1,2,2},
{1,2,2,0,0,0,2,1,2},{1,2,2,0,0,0,2,2,1},{1,2,2,0,1,1,0,0,0},{1,2,2,1,0,1,0,0,0},
{1,2,2,1,1,0,0,0,0},{1,2,2,1,2,2,0,0,0},{1,2,2,2,1,2,0,0,0},{1,2,2,2,2,1,0,0,0},
{2,0,0,1,0,1,0,1,2},{2,0,0,1,0,1,0,2,1},{2,0,0,1,0,1,1,0,2},{2,0,0,1,0,1,2,0,1},
{2,0,0,1,0,2,0,1,1},{2,0,0,1,0,2,1,0,1},{2,0,0,1,1,0,1,2,0},{2,0,0,1,1,0,2,1,0},
{2,0,0,1,2,0,1,1,0},{2,0,0,2,0,1,0,1,1},{2,0,0,2,0,1,1,0,1},{2,0,0,2,1,0,1,1,0},
{2,0,1,0,0,0,1,1,2},{2,0,1,0,0,0,1,2,1},{2,0,1,0,0,0,2,1,1},{2,0,1,0,0,1,1,0,2},
{2,0,1,0,0,2,1,0,1},{2,0,1,1,0,0,1,0,2},{2,0,1,1,0,0,2,0,1},{2,0,1,1,0,1,0,0,2},
{2,0,1,1,0,1,2,0,0},{2,0,1,1,0,2,0,0,1},{2,0,1,1,0,2,1,0,0},{2,0,1,1,0,2,2,0,2},
{2,0,1,1,1,2,0,0,0},{2,0,1,1,2,1,0,0,0},{2,0,1,2,0,0,1,0,1},{2,0,1,2,0,1,1,0,0},
{2,0,1,2,0,1,2,0,2},{2,0,1,2,0,2,1,0,2},{2,0,1,2,0,2,2,0,1},{2,0,1,2,1,1,0,0,0},
{2,0,2,0,0,1,1,0,1},{2,0,2,1,0,0,1,0,1},{2,0,2,1,0,1,0,0,1},{2,0,2,1,0,1,1,0,0},
{2,0,2,1,0,1,2,0,2},{2,0,2,1,0,2,1,0,2},{2,0,2,1,0,2,2,0,1},{2,0,2,2,0,1,1,0,2},
{2,0,2,2,0,1,2,0,1},{2,0,2,2,0,2,1,0,1},{2,1,0,0,0,0,1,1,2},{2,1,0,0,0,0,1,2,1},
{2,1,0,0,0,0,2,1,1},{2,1,0,0,0,1,0,1,2},{2,1,0,0,0,1,0,2,1},{2,1,0,0,0,2,0,1,1},
{2,1,0,0,1,0,1,2,0},{2,1,0,0,2,0,1,1,0},{2,1,0,1,0,0,0,1,2},{2,1,0,1,0,0,0,2,1},
{2,1,0,1,0,0,1,2,0},{2,1,0,1,0,0,2,1,0},{2,1,0,1,0,1,0,0,2},{2,1,0,1,0,1,0,2,0},
{2,1,0,1,0,2,0,0,1},{2,1,0,1,0,2,0,1,0},{2,1,0,1,0,2,0,2,2},{2,1,0,1,1,0,0,2,0},
{2,1,0,1,1,0,2,0,0},{2,1,0,1,1,2,0,0,0},{2,1,0,1,2,0,0,1,0},{2,1,0,1,2,0,1,0,0},
{2,1,0,1,2,0,2,2,0},{2,1,0,1,2,1,0,0,0},{2,1,0,2,0,0,0,1,1},{2,1,0,2,0,0,1,1,0},
{2,1,0,2,0,1,0,0,1},{2,1,0,2,0,1,0,1,0},{2,1,0,2,0,1,0,2,2},{2,1,0,2,0,2,0,1,2},
{2,1,0,2,0,2,0,2,1},{2,1,0,2,1,0,1,0,0},{2,1,0,2,1,0,2,2,0},{2,1,0,2,1,1,0,0,0},
{2,1,0,2,2,0,1,2,0},{2,1,0,2,2,0,2,1,0},{2,1,1,0,0,0,0,1,2},{2,1,1,0,0,0,0,2,1},
{2,1,1,0,0,0,1,0,2},{2,1,1,0,0,0,1,2,0},{2,1,1,0,0,0,2,0,1},{2,1,1,0,0,0,2,1,0},
{2,1,1,0,0,0,2,2,2},{2,1,1,0,1,2,0,0,0},{2,1,1,0,2,1,0,0,0},{2,1,1,1,0,2,0,0,0},
{2,1,1,1,2,0,0,0,0},{2,1,1,2,0,1,0,0,0},{2,1,1,2,1,0,0,0,0},{2,1,1,2,2,2,0,0,0},
{2,1,2,0,0,0,0,1,1},{2,1,2,0,0,0,1,0,1},{2,1,2,0,0,0,1,1,0},{2,1,2,0,0,0,1,2,2},
{2,1,2,0,0,0,2,1,2},{2,1,2,0,0,0,2,2,1},{2,1,2,0,1,1,0,0,0},{2,1,2,1,0,1,0,0,0},
{2,1,2,1,1,0,0,0,0},{2,1,2,1,2,2,0,0,0},{2,1,2,2,1,2,0,0,0},{2,1,2,2,2,1,0,0,0},
{2,2,0,0,0,1,0,1,1},{2,2,0,0,1,0,1,1,0},{2,2,0,1,0,0,0,1,1},{2,2,0,1,0,0,1,1,0},
{2,2,0,1,0,1,0,0,1},{2,2,0,1,0,1,0,1,0},{2,2,0,1,0,1,0,2,2},{2,2,0,1,0,2,0,1,2},
{2,2,0,1,0,2,0,2,1},{2,2,0,1,1,0,0,1,0},{2,2,0,1,1,0,1,0,0},{2,2,0,1,1,0,2,2,0},
{2,2,0,1,2,0,1,2,0},{2,2,0,1,2,0,2,1,0},{2,2,0,2,0,1,0,1,2},{2,2,0,2,0,1,0,2,1},
{2,2,0,2,0,2,0,1,1},{2,2,0,2,1,0,1,2,0},{2,2,0,2,1,0,2,1,0},{2,2,0,2,2,0,1,1,0},
{2,2,1,0,0,0,0,1,1},{2,2,1,0,0,0,1,0,1},{2,2,1,0,0,0,1,1,0},{2,2,1,0,0,0,1,2,2},
{2,2,1,0,0,0,2,1,2},{2,2,1,0,0,0,2,2,1},{2,2,1,0,1,1,0,0,0},{2,2,1,1,0,1,0,0,0},
{2,2,1,1,1,0,0,0,0},{2,2,1,1,2,2,0,0,0},{2,2,1,2,1,2,0,0,0},{2,2,1,2,2,1,0,0,0},
{2,2,2,0,0,0,1,1,2},{2,2,2,0,0,0,1,2,1},{2,2,2,0,0,0,2,1,1},{2,2,2,1,1,2,0,0,0},
{2,2,2,1,2,1,0,0,0},{2,2,2,2,1,1,0,0,0},{0,0,1,0,0,1,1,2,1},{0,0,1,0,0,1,2,1,1},
{0,0,1,0,0,2,1,1,1},{0,0,1,0,1,0,1,1,2},{0,0,1,0,1,0,1,2,1},{0,0,1,0,1,1,1,0,2},
{0,0,1,0,1,1,1,2,0},{0,0,1,0,1,1,2,0,1},{0,0,1,0,1,2,1,0,1},{0,0,1,0,1,2,1,1,0},
{0,0,1,0,1,2,1,2,2},{0,0,1,0,2,0,1,1,1},{0,0,1,0,2,1,1,0,1},{0,0,1,0,2,1,2,2,1},
{0,0,1,1,0,1,0,2,1},{0,0,1,1,1,0,1,0,2},{0,0,1,1,1,0,1,2,0},{0,0,1,1,1,1,0,0,2},
{0,0,1,1,1,1,0,2,0},{0,0,1,1,1,1,2,0,0},{0,0,1,1,1,2,1,0,0},{0,0,1,1,2,1,0,0,1},
{0,0,1,2,0,0,1,1,1},{0,0,1,2,0,1,0,1,1},{0,0,1,2,0,1,2,2,1},{0,0,1,2,1,0,1,0,1},
{0,0,1,2,1,0,1,1,0},{0,0,1,2,1,0,1,2,2},{0,0,1,2,1,1,0,0,1},{0,0,1,2,1,1,1,0,0},
{0,0,1,2,1,2,1,0,2},{0,0,1,2,1,2,1,2,0},{0,0,1,2,2,1,0,2,1},{0,0,1,2,2,1,2,0,1},
{0,0,2,0,0,1,1,1,1},{0,0,2,0,1,0,1,1,1},{0,0,2,0,2,2,1,1,1},{0,0,2,1,0,0,1,1,1},
{0,0,2,1,1,1,0,0,1},{0,0,2,1,1,1,0,1,0},{0,0,2,1,1,1,0,2,2},{0,0,2,1,1,1,1,0,0},
{0,0,2,1,1,1,2,0,2},{0,0,2,1,1,1,2,2,0},{0,0,2,2,0,2,1,1,1},{0,0,2,2,2,0,1,1,1},
{0,1,0,0,0,2,1,1,1},{0,1,0,0,1,0,1,1,2},{0,1,0,0,1,0,2,1,1},{0,1,0,0,1,1,2,1,0},
{0,1,0,0,1,2,1,1,0},{0,1,0,0,1,2,2,1,2},{0,1,0,0,2,0,1,1,1},{0,1,0,1,1,0,0,1,2},
{0,1,0,1,1,1,0,0,2},{0,1,0,1,1,1,0,2,0},{0,1,0,1,1,1,2,0,0},{0,1,0,1,1,2,0,1,0},
{0,1,0,2,0,0,1,1,1},{0,1,0,2,1,0,0,1,1},{0,1,0,2,1,0,2,1,2},{0,1,0,2,1,1,0,1,0},
{0,1,0,2,1,2,0,1,2},{0,1,0,2,1,2,2,1,0},{0,1,1,0,0,1,2,0,1},{0,1,1,0,1,0,1,0,2},
{0,1,1,0,1,0,1,2,0},{0,1,1,0,1,0,2,1,0},{0,1,1,0,1,2,1,0,0},{0,1,1,2,0,1,0,0,1},
{0,1,1,2,1,0,0,1,0},{0,1,1,2,1,0,1,0,0},{0,1,2,0,1,0,1,1,0},{0,1,2,0,1,0,2,1,2},
{0,1,2,0,1,2,2,1,0},{0,1,2,1,1,0,0,1,0},{0,1,2,2,1,0,0,1,2},{0,1,2,2,1,0,2,1,0},
{0,1,2,2,1,2,0,1,0},{0,2,0,0,0,1,1,1,1},{0,2,0,0,1,0,1,1,1},{0,2,0,0,2,2,1,1,1},
{0,2,0,1,0,0,1,1,1},{0,2,0,1,1,1,0,0,1},{0,2,0,1,1,1,0,1,0},{0,2,0,1,1,1,0,2,2},
{0,2,0,1,1,1,1,0,0},{0,2,0,1,1,1,2,0,2},{0,2,0,1,1,1,2,2,0},{0,2,0,2,0,2,1,1,1},
{0,2,0,2,2,0,1,1,1},{0,2,1,0,0,1,1,0,1},{0,2,1,0,0,1,2,2,1},{0,2,1,0,1,0,1,0,1},
{0,2,1,0,1,0,1,1,0},{0,2,1,0,1,0,1,2,2},{0,2,1,0,1,1,1,0,0},{0,2,1,0,1,2,1,0,2},
{0,2,1,0,1,2,1,2,0},{0,2,1,0,2,1,2,0,1},{0,2,1,1,0,1,0,0,1},{0,2,1,1,1,0,1,0,0},
{0,2,1,2,0,1,0,2,1},{0,2,1,2,0,1,2,0,1},{0,2,1,2,1,0,1,0,2},{0,2,1,2,1,0,1,2,0},
{0,2,1,2,1,2,1,0,0},{0,2,1,2,2,1,0,0,1},{0,2,2,0,0,2,1,1,1},{0,2,2,0,2,0,1,1,1},
{0,2,2,1,1,1,0,0,2},{0,2,2,1,1,1,0,2,0},{0,2,2,1,1,1,2,0,0},{0,2,2,2,0,0,1,1,1},
{1,0,0,0,0,2,1,1,1},{1,0,0,0,1,0,1,2,1},{1,0,0,0,1,0,2,1,1},{1,0,0,0,1,1,0,2,1},
{1,0,0,0,1,1,2,0,1},{1,0,0,0,1,2,0,1,1},{1,0,0,0,1,2,1,0,1},{1,0,0,0,1,2,2,2,1},
{1,0,0,0,2,0,1,1,1},{1,0,0,1,0,0,1,1,2},{1,0,0,1,0,0,1,2,1},{1,0,0,1,0,1,1,2,0},
{1,0,0,1,0,2,1,1,0},{1,0,0,1,0,2,1,2,2},{1,0,0,1,1,0,0,2,1},{1,0,0,1,1,0,1,0,2},
{1,0,0,1,1,0,2,0,1},{1,0,0,1,1,1,0,0,2},{1,0,0,1,1,1,0,2,0},{1,0,0,1,1,1,2,0,0},
{1,0,0,1,1,2,0,0,1},{1,0,0,1,1,2,1,0,0},{1,0,0,1,2,0,1,0,1},{1,0,0,1,2,0,1,2,2},
{1,0,0,1,2,1,1,0,0},{1,0,0,1,2,2,1,0,2},{1,0,0,1,2,2,1,2,0},{1,0,0,2,0,0,1,1,1},
{1,0,0,2,1,0,0,1,1},{1,0,0,2,1,0,1,0,1},{1,0,0,2,1,0,2,2,1},{1,0,0,2,1,1,0,0,1},
{1,0,0,2,1,2,0,2,1},{1,0,0,2,1,2,2,0,1},{1,0,1,0,0,1,0,2,1},{1,0,1,0,1,0,0,2,1},
{1,0,1,0,1,0,1,0,2},{1,0,1,0,1,0,1,2,0},{1,0,1,0,1,0,2,0,1},{1,0,1,0,1,2,0,0,1},
{1,0,1,0,1,2,1,0,0},{1,0,1,0,2,1,0,0,1},{1,0,1,1,0,0,1,2,0},{1,0,1,1,2,0,1,0,0},
{1,0,1,2,1,0,0,0,1},{1,0,1,2,1,0,1,0,0},{1,0,2,0,1,0,0,1,1},{1,0,2,0,1,0,1,0,1},
{1,0,2,0,1,0,2,2,1},{1,0,2,0,1,1,0,0,1},{1,0,2,0,1,2,0,2,1},{1,0,2,0,1,2,2,0,1},
{1,0,2,1,0,0,1,1,0},{1,0,2,1,0,0,1,2,2},{1,0,2,1,0,2,1,2,0},{1,0,2,1,1,0,0,0,1},
{1,0,2,1,1,0,1,0,0},{1,0,2,1,2,0,1,0,2},{1,0,2,1,2,0,1,2,0},{1,0,2,1,2,2,1,0,0},
{1,0,2,2,1,0,0,2,1},{1,0,2,2,1,0,2,0,1},{1,0,2,2,1,2,0,0,1},{1,1,0,0,1,0,0,1,2},
{1,1,0,0,1,0,0,2,1},{1,1,0,0,1,0,2,0,1},{1,1,0,0,1,2,0,0,1},{1,1,0,0,1,2,0,1,0},
{1,1,0,1,0,0,1,0,2},{1,1,0,1,0,2,1,0,0},{1,1,0,2,1,0,0,0,1},{1,1,1,0,0,1,0,0,2},
{1,1,1,0,0,1,0,2,0},{1,1,1,0,0,1,2,0,0},{1,1,1,0,0,2,0,0,1},{1,1,1,0,0,2,0,1,0},
{1,1,1,0,0,2,0,2,2},{1,1,1,0,0,2,1,0,0},{1,1,1,0,0,2,2,0,2},{1,1,1,0,0,2,2,2,0},
{1,1,1,0,1,0,0,0,2},{1,1,1,0,1,0,0,2,0},{1,1,1,0,1,0,2,0,0},{1,1,1,0,2,0,0,0,1},
{1,1,1,0,2,0,0,1,0},{1,1,1,0,2,0,0,2,2},{1,1,1,0,2,0,1,0,0},{1,1,1,0,2,0,2,0,2},
{1,1,1,0,2,0,2,2,0},{1,1,1,0,2,2,0,0,2},{1,1,1,0,2,2,0,2,0},{1,1,1,0,2,2,2,0,0},
{1,1,1,1,0,0,0,0,2},{1,1,1,1,0,0,0,2,0},{1,1,1,1,0,0,2,0,0},{1,1,1,2,0,0,0,0,1},
{1,1,1,2,0,0,0,1,0},{1,1,1,2,0,0,0,2,2},{1,1,1,2,0,0,1,0,0},{1,1,1,2,0,0,2,0,2},
{1,1,1,2,0,0,2,2,0},{1,1,1,2,0,2,0,0,2},{1,1,1,2,0,2,0,2,0},{1,1,1,2,0,2,2,0,0},
{1,1,1,2,2,0,0,0,2},{1,1,1,2,2,0,0,2,0},{1,1,1,2,2,0,2,0,0},{1,1,2,0,1,0,0,0,1},
{1,1,2,0,1,0,0,1,0},{1,1,2,1,0,0,1,0,0},{1,2,0,0,1,0,0,1,1},{1,2,0,0,1,0,1,0,1},
{1,2,0,0,1,0,2,2,1},{1,2,0,0,1,1,0,0,1},{1,2,0,0,1,2,0,2,1},{1,2,0,0,1,2,2,0,1},
{1,2,0,1,0,0,1,0,1},{1,2,0,1,0,0,1,2,2},{1,2,0,1,0,1,1,0,0},{1,2,0,1,0,2,1,0,2},
{1,2,0,1,0,2,1,2,0},{1,2,0,1,1,0,0,0,1},{1,2,0,1,2,0,1,0,2},{1,2,0,1,2,2,1,0,0},
{1,2,0,2,1,0,0,2,1},{1,2,0,2,1,0,2,0,1},{1,2,0,2,1,2,0,0,1},{1,2,1,0,0,1,0,0,1},
{1,2,1,0,1,0,0,0,1},{1,2,1,0,1,0,1,0,0},{1,2,1,1,0,0,1,0,0},{1,2,2,0,1,0,0,2,1},
{1,2,2,0,1,0,2,0,1},{1,2,2,0,1,2,0,0,1},{1,2,2,1,0,0,1,0,2},{1,2,2,1,0,0,1,2,0},
{1,2,2,1,0,2,1,0,0},{1,2,2,1,2,0,1,0,0},{1,2,2,2,1,0,0,0,1},{2,0,0,0,0,1,1,1,1},
{2,0,0,0,1,0,1,1,1},{2,0,0,0,2,2,1,1,1},{2,0,0,1,0,0,1,1,1},{2,0,0,1,1,1,0,0,1},
{2,0,0,1,1,1,0,1,0},{2,0,0,1,1,1,0,2,2},{2,0,0,1,1,1,1,0,0},{2,0,0,1,1,1,2,0,2},
{2,0,0,1,1,1,2,2,0},{2,0,0,2,0,2,1,1,1},{2,0,0,2,2,0,1,1,1},{2,0,1,0,0,1,0,1,1},
{2,0,1,0,0,1,2,2,1},{2,0,1,0,1,0,1,0,1},{2,0,1,0,1,0,1,1,0},{2,0,1,0,1,0,1,2,2},
{2,0,1,0,1,1,0,0,1},{2,0,1,0,1,1,1,0,0},{2,0,1,0,1,2,1,0,2},{2,0,1,0,1,2,1,2,0},
{2,0,1,0,2,1,0,2,1},{2,0,1,0,2,1,2,0,1},{2,0,1,1,1,0,1,0,0},{2,0,1,2,0,1,0,2,1},
{2,0,1,2,1,0,1,0,2},{2,0,1,2,1,0,1,2,0},{2,0,1,2,1,2,1,0,0},{2,0,1,2,2,1,0,0,1},
{2,0,2,0,0,2,1,1,1},{2,0,2,0,2,0,1,1,1},{2,0,2,1,1,1,0,0,2},{2,0,2,1,1,1,0,2,0},
{2,0,2,1,1,1,2,0,0},{2,0,2,2,0,0,1,1,1},{2,1,0,0,1,0,0,1,1},{2,1,0,0,1,0,2,1,2},
{2,1,0,0,1,1,0,1,0},{2,1,0,0,1,2,0,1,2},{2,1,0,0,1,2,2,1,0},{2,1,0,2,1,0,0,1,2},
{2,1,0,2,1,2,0,1,0},{2,1,1,0,0,1,0,0,1},{2,1,1,0,1,0,0,1,0},{2,1,1,0,1,0,1,0,0},
{2,1,2,0,1,0,0,1,2},{2,1,2,0,1,0,2,1,0},{2,1,2,0,1,2,0,1,0},{2,1,2,2,1,0,0,1,0},
{2,2,0,0,0,2,1,1,1},{2,2,0,0,2,0,1,1,1},{2,2,0,1,1,1,0,0,2},{2,2,0,1,1,1,0,2,0},
{2,2,0,1,1,1,2,0,0},{2,2,0,2,0,0,1,1,1},{2,2,1,0,0,1,0,2,1},{2,2,1,0,0,1,2,0,1},
{2,2,1,0,1,0,1,0,2},{2,2,1,0,1,0,1,2,0},{2,2,1,0,1,2,1,0,0},{2,2,1,0,2,1,0,0,1},
{2,2,1,2,0,1,0,0,1},{2,2,1,2,1,0,1,0,0},{0,0,1,1,0,0,0,1,1},{0,0,1,1,1,0,0,0,1},
{0,0,1,1,1,0,0,1,0},{0,1,0,0,0,1,1,0,1},{0,1,0,0,1,0,1,0,1},{0,1,0,0,1,1,1,0,0},
{0,1,0,1,0,0,1,0,1},{0,1,0,1,1,0,0,0,1},{0,1,1,1,0,0,0,0,1},{1,0,0,0,0,1,1,1,0},
{1,0,0,0,1,1,0,1,0},{1,0,0,0,1,1,1,0,0},{1,0,1,0,0,1,0,1,0},{1,0,1,0,1,0,0,1,0},
{1,0,1,1,0,0,0,1,0},{1,1,0,0,0,1,1,0,0}
};
private double categoricalAtt[][];
private static double weights[] = {
-0.00000000000000063401, 0.00000000000000055700, 0.00000000000000012769,
-0.52573653474162341000, 0.43427498705107342000, 0.09146154769055023200,
0.00000000000000138130, -0.00000000000000118053, -0.00000000000000050631,
0.52573653474162607000, -0.43427498705107603000, -0.09146154769055094000,
-0.00000000000000057743, 0.00000000000000037314, -0.00000000000000023441,
0.52573653474162907000, -0.43427498705107787000, -0.09146154769055155100,
-0.00000000000000405476, 0.00000000000000339568, 0.00000000000000053496,
-0.52573653474162763000, 0.43427498705107587000, 0.09146154769055155100,
-0.00000000000000116499, 0.00000000000000111960, 0.00000000000000004464,
0.59181480684449950000, -0.48617039139374285000, -0.10564441545075645000,
0.33659693927260309000, -0.28023189914604213000, -0.05636504012656110000,
-0.00000000000000339401, 0.00000000000000312093, 0.00000000000000057542,
0.33659693927260292000, -0.28023189914604213000, -0.05636504012656087800,
0.00000000000000099480, -0.00000000000000067295, -0.00000000000000003901,
-0.33659693927260537000, 0.28023189914604435000, 0.05636504012656118300,
-0.00000000000000284785, 0.00000000000000269180, 0.00000000000000026089,
-0.33659693927260426000, 0.28023189914604330000, 0.05636504012656121800,
-0.59181480684449039000, 0.48617039139373414000, 0.10564441545075609000,
0.00000000000000098567, -0.00000000000000095474, -0.00000000000000021207,
-0.33659693927260698000, 0.28023189914604579000, 0.05636504012656142600,
-0.59181480684449372000, 0.48617039139373774000, 0.10564441545075645000,
0.33659693927260514000, -0.28023189914604435000, -0.05636504012656100300,
-0.00000000000000010012, 0.00000000000000001702, 0.00000000000000012437,
-0.33659693927260204000, 0.28023189914604152000, 0.05636504012656010100,
0.59181480684449428000, -0.48617039139373813000, -0.10564441545075638000,
0.33659693927260081000, -0.28023189914603991000, -0.05636504012656074600,
0.00000000000000216976, -0.00000000000000195478, -0.00000000000000023527,
0.39961448116107012000, -0.35734834346184241000, -0.04226613769922773400,
-0.33634249144114892000, 0.28239332896420155000, 0.05394916247694748300,
0.39961448116106396000, -0.35734834346183769000, -0.04226613769922723400,
-0.33634249144114703000, 0.28239332896420027000, 0.05394916247694724100,
-0.21667948075941171000, 0.12935693076722185000, 0.08732254999219028800,
-0.33634249144114398000, 0.28239332896419722000, 0.05394916247694688700,
0.39961448116106157000, -0.35734834346183453000, -0.04226613769922710200,
-0.33634249144114919000, 0.28239332896420105000, 0.05394916247694810100,
0.39961448116107307000, -0.35734834346184485000, -0.04226613769922824700,
-0.54188833749531484000, 0.49456532031183192000, 0.04732301718348254400,
0.00000000000000042643, -0.00000000000000052416, -0.00000000000000028161,
0.54188833749532672000, -0.49456532031184147000, -0.04732301718348516700,
0.00000000000000208148, -0.00000000000000170526, -0.00000000000000039120,
-0.00000000000001165642, 0.00000000000000998830, 0.00000000000000133016,
-0.00000000000000389738, 0.00000000000000286692, 0.00000000000000081238,
0.54188833749532805000, -0.49456532031184208000, -0.04732301718348581200,
-0.00000000000000308117, 0.00000000000000212213, 0.00000000000000117840,
-0.54188833749532439000, 0.49456532031183975000, 0.04732301718348420900,
0.20000000000000001000, 0.20000000000000001000, 0.20000000000000001000,
0.20000000000000001000, 0.20000000000000001000, 0.20000000000000001000,
0.20000000000000001000, 0.20000000000000001000, 0.20000000000000001000,
0.20000000000000001000, 0.20000000000000001000, 0.20000000000000001000,
0.20000000000000001000, 0.20000000000000001000, 0.20000000000000001000,
0.33333333333333331000, 0.33333333333333331000, 0.33333333333333331000,
0.00000000000000093850, -0.00000000000000054323, -0.00000000000000011761,
-0.03290466729806285100, 0.00000000000000063771, 0.00000000000000000000,
0.00000000000000000000, 0.00000000000000000000, 0.00000000000000000000};
// **********************************************************************
// MAIN
// **********************************************************************
public static void main(String[] args) throws Exception {
double xData[][]; // Input Attributes for Trainer
int yData[]; // Output Attributes for Trainer
int i, j; // array indicies
int nWeights = 0; // Number of weights obtained from network
String trainLogName = "BinaryClassificationNetworkEx2.log";
int[][] z;
// **********************************************************************
// PREPROCESS TRAINING PATTERNS
// **********************************************************************
long t0 = System.currentTimeMillis();
xData = new double[nObs][nInputs];
yData = new int[nObs];
/* Perform Binary Filtering. */
for (i=0;i<data.length;i++) {
for (j=0;j<data[0].length;j++) {
data[i][j]++;
}
}
int xx[] = new int[nObs];
UnsupervisedNominalFilter filter = new UnsupervisedNominalFilter(3);
for (i=0; i<9; i++) {
// Copy each variable to a temp var
for (j=0; j<nObs; j++) {
xx[j] = data[j][i];
}
// Perform binary filter on temp var
z = filter.encode(xx);
// Copy binary encoded var to xData
for (j=0; j<nObs; j++) {
for (int k=0; k<3; k++) {
xData[j][k+(i*3)] = (double) z[j][k];
}
}
}
for (i=0; i < nObs; i++) {
yData[i] = (i >= 626 ? 0 : 1);
}
// **********************************************************************
// CREATE FEEDFORWARD NETWORK
// **********************************************************************
FeedForwardNetwork network = new FeedForwardNetwork();
network.getInputLayer().createInputs(nInputs);
network.createHiddenLayer().createPerceptrons(nPerceptrons1);
network.createHiddenLayer().createPerceptrons(nPerceptrons2);
network.getOutputLayer().createPerceptrons(nOutputs);
network.linkAll();
network.setWeights(weights);
Perceptron perceptrons[] = network.getPerceptrons();
for (i=0; i < perceptrons.length-1; i++) {
perceptrons[i].setActivation(hiddenLayerActivation);
}
// **********************************************************************
// SET OUTPUT LAYER ACTIVATION FUNCTION TO LOGISTIC FOR BINARY CLASSIFICATION
// **********************************************************************
perceptrons[perceptrons.length-1].setActivation(outputLayerActivation);
BinaryClassification classification = new BinaryClassification(network);
QuasiNewtonTrainer stageITrainer = new QuasiNewtonTrainer();
QuasiNewtonTrainer stageIITrainer = new QuasiNewtonTrainer();
stageITrainer.setError(classification.getError());
stageIITrainer.setError(classification.getError());
stageITrainer.setMaximumTrainingIterations(8000);
stageITrainer.setMaximumStepsize(10.0);
stageIITrainer.setMaximumStepsize(10.0);
stageITrainer.setRelativeTolerance(10e-20);
stageIITrainer.setRelativeTolerance(10e-20);
stageIITrainer.setMaximumTrainingIterations(8000);
EpochTrainer trainer = new EpochTrainer(stageITrainer, stageIITrainer);
// Set Training Parameters
trainer.setNumberOfEpochs(20);
trainer.setEpochSize(nObs);
// Set random number seeds to produce repeatable output
trainer.setRandom(new Random(5555));
trainer.setRandomSamples(new Random(5555), new Random(5555));
// If tracing is requested setup training logger
if (trace) {
try {
Handler handler = new FileHandler(trainLogName);
Logger logger = Logger.getLogger("com.imsl.datamining.neural");
logger.setLevel(Level.FINEST);
logger.addHandler(handler);
handler.setFormatter(QuasiNewtonTrainer.getFormatter());
System.out.println("--> Training Log Created in "+
trainLogName);
} catch (Exception e) {
System.out.println("--> Cannot Create Training Log.");
}
}
classification.train(trainer, xData, yData);
System.out.println("trainer.getErrorValue = "+trainer.getErrorValue());
System.out.println("StageITrainer.getErrorValue = "+stageITrainer.getErrorValue());
System.out.println("StageIITrainer.getErrorValue = "+stageIITrainer.getErrorValue());
// **********************************************************************
// DISPLAY TRAINING STATISTICS
// **********************************************************************
double stats[] = classification.computeStatistics(xData, yData);
System.out.println("***********************************************");
System.out.println("--> Cross-entropy error: "+(float)stats[0]);
System.out.println("--> Classification error rate: "+(float)stats[1]);
System.out.println("***********************************************");
System.out.println("");
// **********************************************************************
// OBTAIN AND DISPLAY NETWORK WEIGHTS AND GRADIENTS
// **********************************************************************
double weight[] = network.getWeights();
double gradient[] = trainer.getErrorGradient();
double wg[][] = new double[weight.length][2];
for(i = 0; i < weight.length; i++)
{
wg[i][0] = weight[i];
wg[i][1] = gradient[i];
}
PrintMatrixFormat pmf = new PrintMatrixFormat();
pmf.setNumberFormat(new java.text.DecimalFormat("0.000000"));
pmf.setColumnLabels(new String[]{"Weights", "Gradients"});
new PrintMatrix().print(pmf,wg);
// **********************************************************************
// forecast the network
// **********************************************************************
double report[][] = new double[nObs][2];
for ( i = 0; i < 50; i++) {
report[i][0] = yData[i];
report[i][1] = classification.predictedClass(xData[i]);
}
pmf = new PrintMatrixFormat();
pmf.setColumnLabels(new String[]{"Expected","Predicted"});
new PrintMatrix("Forecast").print(pmf, report);
long t1 = System.currentTimeMillis();
double small = 1.e-7;
double time = t1-t0; //Math.max(small, (double)(t1-t0)/(double)iters);
time = time/1000;
System.out.println("****************Time: "+time);
System.out.println("trainer.getErrorValue = "+trainer.getErrorValue());
System.out.println("StageITrainer.getErrorValue = "+stageITrainer.getErrorValue());
System.out.println("StageIITrainer.getErrorValue = "+stageIITrainer.getErrorValue());
}
}
--> Training Log Created in BinaryClassificationNetworkEx2.log
trainer.getErrorValue = 1.4572899893203097
StageITrainer.getErrorValue = 482.27809835973795
StageIITrainer.getErrorValue = 1.4572899893203097
***********************************************
--> Cross-entropy error: 1.4572899
--> Classification error rate: 0.0020876827
***********************************************
Weights Gradients
0 2.944218 -0.000103
1 11.572133 -0.002104
2 -10.464978 0.031618
3 18.197968 -0.099090
4 26.552980 -0.007697
5 -12.948492 -0.004065
6 -6.920502 0.026458
7 12.449166 0.002808
8 -22.044311 -0.101337
9 -28.603049 -0.000001
10 11.236107 0.000069
11 2.983088 -0.000000
12 -10.165526 0.000501
13 1.947292 -0.000411
14 -19.153976 -0.000000
15 -8.400962 -0.004047
16 -12.026586 -0.000877
17 -6.175538 0.034892
18 16.752667 -0.302597
19 27.202764 -0.007699
20 7.846400 -0.000000
21 9.415102 0.000000
22 -0.717963 -0.000000
23 -22.044410 0.000000
24 -36.633994 0.000001
25 2.960843 -0.000053
26 9.591344 0.025231
27 -0.050062 0.000035
28 4.260128 0.101759
29 -11.478470 0.000000
30 -10.507361 0.000000
31 17.400312 0.024341
32 -4.365829 0.030967
33 20.318348 -0.002424
34 -40.598205 -0.007753
35 11.547888 -0.004180
36 -13.955145 0.000013
37 -12.967388 0.003914
38 -24.426023 -0.198309
39 28.236830 0.000055
40 1.006560 0.000081
41 4.508082 -0.000000
42 7.094010 0.000046
43 2.986456 -0.000105
44 -9.215039 -0.000001
45 -18.006465 -0.000063
46 6.899860 0.026472
47 -9.835696 0.002747
48 17.886021 -0.214726
49 1.824013 0.000000
50 12.255996 -0.003981
51 -2.444224 -0.002118
52 4.933440 0.032021
53 -22.233104 0.013710
54 -11.061314 -0.007698
55 6.440296 -0.000056
56 3.000394 0.000000
57 -4.155388 0.000159
58 2.669732 0.000177
59 -11.757717 0.000000
60 27.524414 -0.003981
61 55.921417 -0.000001
62 -1.707040 0.004514
63 20.846132 -0.135316
64 -7.685032 -0.000001
65 -27.369955 -0.000119
66 -40.908826 0.026472
67 -4.823267 0.030411
68 -23.581984 -0.065522
69 3.181360 -0.007697
70 1.828543 -0.000000
71 -6.090504 -0.002116
72 -0.988539 0.000002
73 0.790178 -0.000000
74 -17.522973 -0.000000
75 -25.682496 -0.003981
76 -11.943730 0.025216
77 -6.535236 0.031614
78 19.237103 0.115771
79 -23.303947 -0.007697
80 20.785871 -0.000061
81 -0.398901 -0.000875
82 4.220983 0.002898
83 -21.805541 -0.316308
84 5.385592 0.000000
85 7.992170 -0.000058
86 20.561026 0.000014
87 -5.836442 0.000415
88 -0.392213 -0.000302
89 -3.883298 -0.000001
90 -6.908613 -0.004065
91 10.546036 0.001241
92 -39.677236 -0.000000
93 18.095781 0.056472
94 9.597760 0.000000
95 3.294999 -0.000034
96 -21.179800 -0.002104
97 31.118283 0.034927
98 -22.059079 -0.358786
99 8.161085 -0.007698
100 5.313321 -0.000000
101 19.102103 0.025217
102 0.740958 -0.000000
103 2.578075 0.101476
104 -38.607568 0.000000
105 -8.339988 -0.000035
106 1.639758 0.000013
107 -3.726085 0.034889
108 15.554956 -0.422305
109 42.980892 -0.007752
110 2.181836 -0.004065
111 4.443285 0.001241
112 2.587092 0.000036
113 -22.489842 0.119719
114 -36.432494 -0.000000
115 8.075659 -0.000000
116 0.979978 0.023101
117 -7.488272 0.000002
118 5.319442 0.101748
119 -26.819508 0.000054
120 3.541003 -0.004128
121 4.189919 -0.000875
122 -9.620799 0.002783
123 19.398187 -0.202984
124 -3.194663 0.000055
125 -5.228130 0.000083
126 -3.757340 0.025216
127 9.792260 0.032081
128 -21.755460 0.002231
129 -7.715387 -0.007753
130 2.769206 -0.000055
131 7.901853 0.000014
132 -10.435488 0.000062
133 1.168304 -0.000086
134 -9.954674 -0.000001
135 -51.555283 -0.071057
136 4.958954 -0.000000
137 -7.408263 0.000000
138 39.517621 -0.070072
139 13.036423 -0.000000
140 24.059711 0.000000
141 -20.165068 -0.061182
142 2.978425 0.000000
143 -3.346216 -0.000001
144 91.294581 -0.053340
145 4.700837 0.000000
146 33.962649 -0.000005
147 58.702284 -0.401485
148 3.416411 0.000000
149 4.415371 -0.002499
150 171.784942 -0.005808
151 -45.805688 -0.010427
152 12.976783 -0.010230
153 1.348388 -0.004100
154 7.967453 0.024354
155 -8.634125 0.034927
156 -1.937680 -0.200838
157 -21.314065 -0.007698
158 -58.810144 -0.445640
159 13.151796 0.000000
160 -0.728858 -0.002499
161 -56.918496 -0.010427
Forecast
Expected Predicted
0 1 1
1 1 1
2 1 1
3 1 1
4 1 1
5 1 1
6 1 1
7 1 1
8 1 1
9 1 1
10 1 1
11 1 1
12 1 1
13 1 1
14 1 1
15 1 1
16 1 1
17 1 1
18 1 1
19 1 1
20 1 1
21 1 1
22 1 1
23 1 1
24 1 1
25 1 1
26 1 1
27 1 1
28 1 1
29 1 1
30 1 1
31 1 1
32 1 1
33 1 1
34 1 1
35 1 1
36 1 1
37 1 1
38 1 1
39 1 1
40 1 1
41 1 1
42 1 1
43 1 1
44 1 1
45 1 1
46 1 1
47 1 1
48 1 1
49 1 1
50 0 0
51 0 0
52 0 0
53 0 0
54 0 0
55 0 0
56 0 0
57 0 0
58 0 0
59 0 0
60 0 0
61 0 0
62 0 0
63 0 0
64 0 0
65 0 0
66 0 0
67 0 0
68 0 0
69 0 0
70 0 0
71 0 0
72 0 0
73 0 0
74 0 0
75 0 0
76 0 0
77 0 0
78 0 0
79 0 0
80 0 0
81 0 0
82 0 0
83 0 0
84 0 0
85 0 0
86 0 0
87 0 0
88 0 0
89 0 0
90 0 0
91 0 0
92 0 0
93 0 0
94 0 0
95 0 0
96 0 0
97 0 0
98 0 0
99 0 0
100 0 0
101 0 0
102 0 0
103 0 0
104 0 0
105 0 0
106 0 0
107 0 0
108 0 0
109 0 0
110 0 0
111 0 0
112 0 0
113 0 0
114 0 0
115 0 0
116 0 0
117 0 0
118 0 0
119 0 0
120 0 0
121 0 0
122 0 0
123 0 0
124 0 0
125 0 0
126 0 0
127 0 0
128 0 0
129 0 0
130 0 0
131 0 0
132 0 0
133 0 0
134 0 0
135 0 0
136 0 0
137 0 0
138 0 0
139 0 0
140 0 0
141 0 0
142 0 0
143 0 0
144 0 0
145 0 0
146 0 0
147 0 0
148 0 0
149 0 0
150 0 0
151 0 0
152 0 0
153 0 0
154 0 0
155 0 0
156 0 0
157 0 0
158 0 0
159 0 0
160 0 0
161 0 0
162 0 0
163 0 0
164 0 0
165 0 0
166 0 0
167 0 0
168 0 0
169 0 0
170 0 0
171 0 0
172 0 0
173 0 0
174 0 0
175 0 0
176 0 0
177 0 0
178 0 0
179 0 0
180 0 0
181 0 0
182 0 0
183 0 0
184 0 0
185 0 0
186 0 0
187 0 0
188 0 0
189 0 0
190 0 0
191 0 0
192 0 0
193 0 0
194 0 0
195 0 0
196 0 0
197 0 0
198 0 0
199 0 0
200 0 0
201 0 0
202 0 0
203 0 0
204 0 0
205 0 0
206 0 0
207 0 0
208 0 0
209 0 0
210 0 0
211 0 0
212 0 0
213 0 0
214 0 0
215 0 0
216 0 0
217 0 0
218 0 0
219 0 0
220 0 0
221 0 0
222 0 0
223 0 0
224 0 0
225 0 0
226 0 0
227 0 0
228 0 0
229 0 0
230 0 0
231 0 0
232 0 0
233 0 0
234 0 0
235 0 0
236 0 0
237 0 0
238 0 0
239 0 0
240 0 0
241 0 0
242 0 0
243 0 0
244 0 0
245 0 0
246 0 0
247 0 0
248 0 0
249 0 0
250 0 0
251 0 0
252 0 0
253 0 0
254 0 0
255 0 0
256 0 0
257 0 0
258 0 0
259 0 0
260 0 0
261 0 0
262 0 0
263 0 0
264 0 0
265 0 0
266 0 0
267 0 0
268 0 0
269 0 0
270 0 0
271 0 0
272 0 0
273 0 0
274 0 0
275 0 0
276 0 0
277 0 0
278 0 0
279 0 0
280 0 0
281 0 0
282 0 0
283 0 0
284 0 0
285 0 0
286 0 0
287 0 0
288 0 0
289 0 0
290 0 0
291 0 0
292 0 0
293 0 0
294 0 0
295 0 0
296 0 0
297 0 0
298 0 0
299 0 0
300 0 0
301 0 0
302 0 0
303 0 0
304 0 0
305 0 0
306 0 0
307 0 0
308 0 0
309 0 0
310 0 0
311 0 0
312 0 0
313 0 0
314 0 0
315 0 0
316 0 0
317 0 0
318 0 0
319 0 0
320 0 0
321 0 0
322 0 0
323 0 0
324 0 0
325 0 0
326 0 0
327 0 0
328 0 0
329 0 0
330 0 0
331 0 0
332 0 0
333 0 0
334 0 0
335 0 0
336 0 0
337 0 0
338 0 0
339 0 0
340 0 0
341 0 0
342 0 0
343 0 0
344 0 0
345 0 0
346 0 0
347 0 0
348 0 0
349 0 0
350 0 0
351 0 0
352 0 0
353 0 0
354 0 0
355 0 0
356 0 0
357 0 0
358 0 0
359 0 0
360 0 0
361 0 0
362 0 0
363 0 0
364 0 0
365 0 0
366 0 0
367 0 0
368 0 0
369 0 0
370 0 0
371 0 0
372 0 0
373 0 0
374 0 0
375 0 0
376 0 0
377 0 0
378 0 0
379 0 0
380 0 0
381 0 0
382 0 0
383 0 0
384 0 0
385 0 0
386 0 0
387 0 0
388 0 0
389 0 0
390 0 0
391 0 0
392 0 0
393 0 0
394 0 0
395 0 0
396 0 0
397 0 0
398 0 0
399 0 0
400 0 0
401 0 0
402 0 0
403 0 0
404 0 0
405 0 0
406 0 0
407 0 0
408 0 0
409 0 0
410 0 0
411 0 0
412 0 0
413 0 0
414 0 0
415 0 0
416 0 0
417 0 0
418 0 0
419 0 0
420 0 0
421 0 0
422 0 0
423 0 0
424 0 0
425 0 0
426 0 0
427 0 0
428 0 0
429 0 0
430 0 0
431 0 0
432 0 0
433 0 0
434 0 0
435 0 0
436 0 0
437 0 0
438 0 0
439 0 0
440 0 0
441 0 0
442 0 0
443 0 0
444 0 0
445 0 0
446 0 0
447 0 0
448 0 0
449 0 0
450 0 0
451 0 0
452 0 0
453 0 0
454 0 0
455 0 0
456 0 0
457 0 0
458 0 0
459 0 0
460 0 0
461 0 0
462 0 0
463 0 0
464 0 0
465 0 0
466 0 0
467 0 0
468 0 0
469 0 0
470 0 0
471 0 0
472 0 0
473 0 0
474 0 0
475 0 0
476 0 0
477 0 0
478 0 0
479 0 0
480 0 0
481 0 0
482 0 0
483 0 0
484 0 0
485 0 0
486 0 0
487 0 0
488 0 0
489 0 0
490 0 0
491 0 0
492 0 0
493 0 0
494 0 0
495 0 0
496 0 0
497 0 0
498 0 0
499 0 0
500 0 0
501 0 0
502 0 0
503 0 0
504 0 0
505 0 0
506 0 0
507 0 0
508 0 0
509 0 0
510 0 0
511 0 0
512 0 0
513 0 0
514 0 0
515 0 0
516 0 0
517 0 0
518 0 0
519 0 0
520 0 0
521 0 0
522 0 0
523 0 0
524 0 0
525 0 0
526 0 0
527 0 0
528 0 0
529 0 0
530 0 0
531 0 0
532 0 0
533 0 0
534 0 0
535 0 0
536 0 0
537 0 0
538 0 0
539 0 0
540 0 0
541 0 0
542 0 0
543 0 0
544 0 0
545 0 0
546 0 0
547 0 0
548 0 0
549 0 0
550 0 0
551 0 0
552 0 0
553 0 0
554 0 0
555 0 0
556 0 0
557 0 0
558 0 0
559 0 0
560 0 0
561 0 0
562 0 0
563 0 0
564 0 0
565 0 0
566 0 0
567 0 0
568 0 0
569 0 0
570 0 0
571 0 0
572 0 0
573 0 0
574 0 0
575 0 0
576 0 0
577 0 0
578 0 0
579 0 0
580 0 0
581 0 0
582 0 0
583 0 0
584 0 0
585 0 0
586 0 0
587 0 0
588 0 0
589 0 0
590 0 0
591 0 0
592 0 0
593 0 0
594 0 0
595 0 0
596 0 0
597 0 0
598 0 0
599 0 0
600 0 0
601 0 0
602 0 0
603 0 0
604 0 0
605 0 0
606 0 0
607 0 0
608 0 0
609 0 0
610 0 0
611 0 0
612 0 0
613 0 0
614 0 0
615 0 0
616 0 0
617 0 0
618 0 0
619 0 0
620 0 0
621 0 0
622 0 0
623 0 0
624 0 0
625 0 0
626 0 0
627 0 0
628 0 0
629 0 0
630 0 0
631 0 0
632 0 0
633 0 0
634 0 0
635 0 0
636 0 0
637 0 0
638 0 0
639 0 0
640 0 0
641 0 0
642 0 0
643 0 0
644 0 0
645 0 0
646 0 0
647 0 0
648 0 0
649 0 0
650 0 0
651 0 0
652 0 0
653 0 0
654 0 0
655 0 0
656 0 0
657 0 0
658 0 0
659 0 0
660 0 0
661 0 0
662 0 0
663 0 0
664 0 0
665 0 0
666 0 0
667 0 0
668 0 0
669 0 0
670 0 0
671 0 0
672 0 0
673 0 0
674 0 0
675 0 0
676 0 0
677 0 0
678 0 0
679 0 0
680 0 0
681 0 0
682 0 0
683 0 0
684 0 0
685 0 0
686 0 0
687 0 0
688 0 0
689 0 0
690 0 0
691 0 0
692 0 0
693 0 0
694 0 0
695 0 0
696 0 0
697 0 0
698 0 0
699 0 0
700 0 0
701 0 0
702 0 0
703 0 0
704 0 0
705 0 0
706 0 0
707 0 0
708 0 0
709 0 0
710 0 0
711 0 0
712 0 0
713 0 0
714 0 0
715 0 0
716 0 0
717 0 0
718 0 0
719 0 0
720 0 0
721 0 0
722 0 0
723 0 0
724 0 0
725 0 0
726 0 0
727 0 0
728 0 0
729 0 0
730 0 0
731 0 0
732 0 0
733 0 0
734 0 0
735 0 0
736 0 0
737 0 0
738 0 0
739 0 0
740 0 0
741 0 0
742 0 0
743 0 0
744 0 0
745 0 0
746 0 0
747 0 0
748 0 0
749 0 0
750 0 0
751 0 0
752 0 0
753 0 0
754 0 0
755 0 0
756 0 0
757 0 0
758 0 0
759 0 0
760 0 0
761 0 0
762 0 0
763 0 0
764 0 0
765 0 0
766 0 0
767 0 0
768 0 0
769 0 0
770 0 0
771 0 0
772 0 0
773 0 0
774 0 0
775 0 0
776 0 0
777 0 0
778 0 0
779 0 0
780 0 0
781 0 0
782 0 0
783 0 0
784 0 0
785 0 0
786 0 0
787 0 0
788 0 0
789 0 0
790 0 0
791 0 0
792 0 0
793 0 0
794 0 0
795 0 0
796 0 0
797 0 0
798 0 0
799 0 0
800 0 0
801 0 0
802 0 0
803 0 0
804 0 0
805 0 0
806 0 0
807 0 0
808 0 0
809 0 0
810 0 0
811 0 0
812 0 0
813 0 0
814 0 0
815 0 0
816 0 0
817 0 0
818 0 0
819 0 0
820 0 0
821 0 0
822 0 0
823 0 0
824 0 0
825 0 0
826 0 0
827 0 0
828 0 0
829 0 0
830 0 0
831 0 0
832 0 0
833 0 0
834 0 0
835 0 0
836 0 0
837 0 0
838 0 0
839 0 0
840 0 0
841 0 0
842 0 0
843 0 0
844 0 0
845 0 0
846 0 0
847 0 0
848 0 0
849 0 0
850 0 0
851 0 0
852 0 0
853 0 0
854 0 0
855 0 0
856 0 0
857 0 0
858 0 0
859 0 0
860 0 0
861 0 0
862 0 0
863 0 0
864 0 0
865 0 0
866 0 0
867 0 0
868 0 0
869 0 0
870 0 0
871 0 0
872 0 0
873 0 0
874 0 0
875 0 0
876 0 0
877 0 0
878 0 0
879 0 0
880 0 0
881 0 0
882 0 0
883 0 0
884 0 0
885 0 0
886 0 0
887 0 0
888 0 0
889 0 0
890 0 0
891 0 0
892 0 0
893 0 0
894 0 0
895 0 0
896 0 0
897 0 0
898 0 0
899 0 0
900 0 0
901 0 0
902 0 0
903 0 0
904 0 0
905 0 0
906 0 0
907 0 0
908 0 0
909 0 0
910 0 0
911 0 0
912 0 0
913 0 0
914 0 0
915 0 0
916 0 0
917 0 0
918 0 0
919 0 0
920 0 0
921 0 0
922 0 0
923 0 0
924 0 0
925 0 0
926 0 0
927 0 0
928 0 0
929 0 0
930 0 0
931 0 0
932 0 0
933 0 0
934 0 0
935 0 0
936 0 0
937 0 0
938 0 0
939 0 0
940 0 0
941 0 0
942 0 0
943 0 0
944 0 0
945 0 0
946 0 0
947 0 0
948 0 0
949 0 0
950 0 0
951 0 0
952 0 0
953 0 0
954 0 0
955 0 0
956 0 0
957 0 0
****************Time: 16.784
trainer.getErrorValue = 1.4572899893203097
StageITrainer.getErrorValue = 482.27809835973795
StageIITrainer.getErrorValue = 1.4572899893203097
Link to Java source.