Introduction to Neural Networks for C#, Session 8
| Course Name | Introduction to Neural Networks for C# |
| Instructor | jeffheaton |
| Session Title | Discuss MidTerm |
| Session Number | 8 |
Session Material
I present here my solution for the mid-term. You can use the link below to download a Visual Studio project for my solution.
Claudia Toll Free: 1-800-962-7798 Local: 702-384-2211 Address: 800 South 4th Street Las Vegas, NV. 89101 http://www.aspecialmemory.com/?s=vegascom April 10, 2009, 11:30 MGM Call with room number 314-960-7482 636-405-7482Analyze.cs: Used to calculate the min/max values for most of the inputs
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using HeatonResearchNeural.Util;
namespace Program1
{
public class Analyze
{
public static String[] COVER_TYPES = {
"Spruce/Fir", // 1
"Lodgepole Pine", // 2
"Ponderosa Pine", // 3
"Cottonwood/Willow", // 4
"Aspen", // 5
"Douglas-fir", // 6
"Krummholz" // 7
};
private double maxElevation;
private double minElevation;
private double maxAspect;
private double minAspect;
private double maxSlope;
private double minSlope;
private double maxHWater;
private double minHWater;
private double maxVWater;
private double minVWater;
private double maxRoad;
private double minRoad;
private double maxShade9;
private double minShade9;
private double maxShade12;
private double minShade12;
private double maxShade3;
private double minShade3;
private int[] coverCount;
private int count;
public void analyze(String filename)
{
this.count = 0;
this.maxElevation = Double.MinValue;
this.minElevation = Double.MaxValue;
this.maxAspect = Double.MinValue;
this.minAspect = Double.MaxValue;
this.maxSlope = Double.MinValue;
this.minSlope = Double.MaxValue;
this.maxHWater = Double.MinValue;
this.minHWater = Double.MaxValue;
; this.maxVWater = -1000;//Double.MIN_VALUE;
this.minVWater = Double.MaxValue;
this.maxShade9 = Double.MinValue;
this.minShade9 = Double.MaxValue;
this.maxShade12 = Double.MinValue;
this.minShade12 = Double.MaxValue;
this.maxShade3 = Double.MinValue;
this.minShade3 = Double.MaxValue;
this.maxRoad = Double.MinValue;
this.minRoad = Double.MaxValue;
this.coverCount = new int[Analyze.COVER_TYPES.Length];
Console.WriteLine("Please wait, performing initial analysis of data.");
ReadCSV csv = new ReadCSV(filename, false);
while (csv.Next())
{
double elevation = Double.Parse(csv.Get(0));
double aspect = Double.Parse(csv.Get(1));
double slope = Double.Parse(csv.Get(2));
double hWater = Double.Parse(csv.Get(3));
double vWater = Double.Parse(csv.Get(4));
double road = Double.Parse(csv.Get(5));
double shade9 = Double.Parse(csv.Get(6));
double shade12 = Double.Parse(csv.Get(7));
double shade3 = Double.Parse(csv.Get(8));
int cover = int.Parse(csv.Get(54));
maxElevation = Math.Max(maxElevation, elevation);
minElevation = Math.Min(minElevation, elevation);
maxAspect = Math.Max(maxAspect, aspect);
minAspect = Math.Min(minAspect, aspect);
maxSlope = Math.Max(maxSlope, slope);
minSlope = Math.Min(minSlope, slope);
maxHWater = Math.Max(maxHWater, hWater);
minHWater = Math.Min(minHWater, hWater);
maxVWater = Math.Max(maxVWater, vWater);
minVWater = Math.Min(minVWater, vWater);
maxRoad = Math.Max(maxRoad, road);
minRoad = Math.Min(minRoad, road);
maxShade9 = Math.Max(maxShade9, shade9);
minShade9 = Math.Min(minShade9, shade9);
maxShade12 = Math.Max(maxShade12, shade12);
minShade12 = Math.Min(minShade12, shade12);
maxShade3 = Math.Max(maxShade3, shade3);
minShade3 = Math.Min(minShade3, shade3);
this.coverCount[cover - 1]++;
count++;
}
Console.WriteLine("Record count: " + count);
Console.WriteLine("Elevation, Max: " + maxElevation);
Console.WriteLine("Elevation, Min: " + minElevation);
Console.WriteLine("Aspect, Max: " + maxAspect);
Console.WriteLine("Aspect, Min: " + minAspect);
Console.WriteLine("Slope, Max: " + maxSlope);
Console.WriteLine("Slope, Min: " + minSlope);
Console.WriteLine("H. Water, Max: " + maxHWater);
Console.WriteLine("H. Water, Min: " + minHWater);
Console.WriteLine("V. Water, Max: " + maxVWater);
Console.WriteLine("V. Water, Min: " + minVWater);
Console.WriteLine("Cover breakdown:");
for (int i = 0; i < Analyze.COVER_TYPES.Length; i++)
{
Console.WriteLine(Analyze.COVER_TYPES[i] + ": " + this.coverCount[i]);
}
}
public double getMaxElevation()
{
return maxElevation;
}
public double getMinElevation()
{
return minElevation;
}
public double getMaxAspect()
{
return maxAspect;
}
public double getMinAspect()
{
return minAspect;
}
public double getMaxSlope()
{
return maxSlope;
}
public double getMinSlope()
{
return minSlope;
}
public int[] getCoverCount()
{
return coverCount;
}
public int getCount()
{
return count;
}
public double getMaxHWater()
{
return maxHWater;
}
public double getMinHWater()
{
return minHWater;
}
public double getMaxVWater()
{
return maxVWater;
}
public double getMinVWater()
{
return minVWater;
}
public double getMaxRoad()
{
return maxRoad;
}
public double getMaxShade9()
{
return maxShade9;
}
public double getMaxShade12()
{
return maxShade12;
}
public double getMaxShade3()
{
return maxShade3;
}
public double getMinRoad()
{
return minRoad;
}
public double getMinShade9()
{
return minShade9;
}
public double getMinShade12()
{
return minShade12;
}
public double getMinShade3()
{
return minShade3;
}
}
}
Config.cs: Used to hold config information for the program.
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
namespace Program1
{
public class Config
{
public const string FILENAME = "c:\\data\\covtype.data";
public const int TRAINING_SIZE = 1000;
public const int HIDDEN_LAYER_1 = 13;
public const int HIDDEN_LAYER_2 = 0;
public const double LEARNING_RATE = 0.001;
public const double LEARNING_MOMENTUM = 0.3;
public const double TRUE = 1.0;
public const double FALSE = 0.0;
public const int EPOCHS = 5000;
public const int COVER_SAMPLES = 2000;
}
}
Convert.cs: Used to convert the file input data into a form useful for the neural network, using percents.
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using HeatonResearchNeural.Util;
namespace Program1
{
public class Convert
{
public static double percent(double value, double max, double min)
{
return (value - min) / (max - min);
}
public static void convert(Analyze analyze, ReadCSV line, double[] input, double[] ideal)
{
double elevation = Double.Parse(line.Get(0));
double aspect = Double.Parse(line.Get(1));
double slope = Double.Parse(line.Get(2));
double hWater = Double.Parse(line.Get(3));
double vWater = Double.Parse(line.Get(4));
double road = Double.Parse(line.Get(5));
double shade9 = Double.Parse(line.Get(6));
double shade12 = Double.Parse(line.Get(7));
double shade3 = Double.Parse(line.Get(8));
int cover = int.Parse(line.Get(54));
// now build a training input
input[0] = percent(elevation, analyze.getMaxElevation(), analyze.getMinElevation());
input[1] = percent(aspect, analyze.getMaxAspect(), analyze.getMinAspect());
input[2] = percent(slope, analyze.getMaxSlope(), analyze.getMinSlope());
input[3] = percent(hWater, analyze.getMaxHWater(), analyze.getMinHWater());
input[4] = percent(vWater, analyze.getMaxVWater(), analyze.getMinVWater());
input[5] = percent(road, analyze.getMaxRoad(), analyze.getMinRoad());
input[6] = percent(shade9, analyze.getMaxShade9(), analyze.getMinShade9());
input[7] = percent(shade12, analyze.getMaxShade12(), analyze.getMinShade12());
input[8] = percent(shade3, analyze.getMaxShade3(), analyze.getMinShade3());
if (ideal != null)
{
for (int i = 0; i < Analyze.COVER_TYPES.Length; i++)
ideal[i] = Config.FALSE;
ideal[cover - 1] = Config.TRUE;
}
}
}
}
Evaluate.cs: Used to evaluate the trained neural network with data that it was not trained with.
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using HeatonResearchNeural.Util;
using HeatonResearchNeural.Feedforward;
namespace Program1
{
public class Evaluate
{
private int correct;
private int incorrect;
public double percent(double value, double max, double min) {
return (value - min) / (max - min);
}
public int maxIndex(double[] d) {
int result = 0;
for (int i = 0; i < d.Length; i++) {
if (d[i] > d[result])
result = i;
}
return result;
}
public void evaluate(FeedforwardNetwork network, String filename,
Analyze analyze) {
double[] input = new double[9];
this.correct = 0;
this.incorrect = 0;
int count = 0;
ReadCSV csv = new ReadCSV(filename, false);
Console.WriteLine("Please wait, neural network is being evaluated.");
while (csv.Next()) {
count++;
Convert.convert(analyze, csv, input, null);
int cover = int.Parse(csv.Get(54));
cover--;
double[] output = network.ComputeOutputs(input);
int neuralCover = maxIndex(output);
// System.out.println("Actual=" + cover+",Neural="+neuralCover);
if (cover == neuralCover)
correct++;
else
incorrect++;
}
Console.WriteLine("Correct:" + correct);
Console.WriteLine("Incorrect:" + incorrect);
Console.WriteLine((double)correct / ((double)correct
+ (double) incorrect));
}
}
}
MidTerm.cs: Main entry point for the program.
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using HeatonResearchNeural.Feedforward;
namespace Program1
{
class MidTerm
{
static void Main(string[] args)
{
Analyze analyze = new Analyze();
analyze.analyze(Config.FILENAME);
Train train = new Train();
train.build(Config.FILENAME, analyze);
FeedforwardNetwork network = train.train();
Evaluate evaluate = new Evaluate();
evaluate.evaluate(network, Config.FILENAME, analyze);
}
}
}
Train.cs: Used to train the neural net.
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using HeatonResearchNeural.Util;
using HeatonResearchNeural.Feedforward;
using HeatonResearchNeural.Feedforward.Train.Backpropagation;
namespace Program1
{
public class Train
{
double[][] training;
double[][] ideal;
int [] coverCount;
double percent(double value, double max, double min) {
return (value - min) / (max - min);
}
public void build(String filename, Analyze analyze) {
int trainingSize = Config.COVER_SAMPLES * Analyze.COVER_TYPES.Length;
this.training = new double[Config.TRAINING_SIZE][];//9
this.ideal = new double[Config.TRAINING_SIZE][];// Analyze.COVER_TYPES.length
this.coverCount = new int[Analyze.COVER_TYPES.Length];
int skip = (analyze.getCount() / Config.TRAINING_SIZE) - 1;
int trainingIndex = 0;
ReadCSV csv = new ReadCSV(filename, false);
Console.WriteLine("Generating training set, skip = " + skip);
while (csv.Next() && (trainingIndex < Config.TRAINING_SIZE)) {
int cover = int.Parse(csv.Get(54))-1;
if( coverCount[cover]<Config.COVER_SAMPLES )
{
training[trainingIndex] = new double[9];
ideal[trainingIndex] = new double[Analyze.COVER_TYPES.Length];
Convert.convert(analyze, csv, training[trainingIndex],
ideal[trainingIndex]);
coverCount[cover]++;
trainingIndex++;
}
}
}
public FeedforwardNetwork train() {
FeedforwardNetwork network = new FeedforwardNetwork();
network.AddLayer(new FeedforwardLayer(this.training[0].Length));
network.AddLayer(new FeedforwardLayer(Config.HIDDEN_LAYER_1));
if (Config.HIDDEN_LAYER_2 > 0)
network.AddLayer(new FeedforwardLayer(Config.HIDDEN_LAYER_2));
network.AddLayer(new FeedforwardLayer(this.ideal[0].Length));
network.Reset();
// train the neural network
Backpropagation train = new Backpropagation(network,
this.training, this.ideal, Config.LEARNING_RATE,
Config.LEARNING_MOMENTUM);
int epoch = 1;
do {
train.Iteration();
Console.WriteLine("Epoch #" + epoch + " Error:" + train.Error);
epoch++;
} while (epoch < Config.EPOCHS);
return network;
}
}
}
Videos for this Session
Video | Title |
|---|---|
 | Introduction to Neural Networks for C#(Class 8) |
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