public void OR_Test()
{
LearningApi api = new LearningApi();
api.UseActionModule <object, double[][]>((notUsed, ctx) =>
{
ctx.DataDescriptor = get2DDescriptor();
double[][] data = new double[4][];
data[0] = new double[] { 0, 0, 0, 0.0 };
data[1] = new double[] { 0, 1, 1, 0.0 };
data[2] = new double[] { 1, 0, 1, 0.0 };
data[3] = new double[] { 1, 1, 1, 0.0 };
return(data);
});
api.UseDeltaLearning(0.2, 1000);
IScore score = api.Run() as IScore;
double[][] testData = new double[3][];
testData[0] = new double[] { 0, 0, 0.0 };
testData[1] = new double[] { 1, 1, 0.0 };
testData[2] = new double[] { 0, 1, 0.0 };
var result = api.Algorithm.Predict(testData, api.Context) as DeltaLearningResult;
Assert.True(result.PredictedResults[0] == 0);
Assert.True(result.PredictedResults[1] == 1);
Assert.True(result.PredictedResults[2] == 1);
}
public void SimpleSequenceTest()
{
LearningApi api = new LearningApi();
api.UseActionModule <object, double[][]>((notUsed, ctx) =>
{
const int maxSamples = 10;
ctx.DataDescriptor = getDescriptor();
double[][] data = new double[maxSamples][];
//
// We generate following input vectors:
// IN Val - Expected OUT Val
// 1 - 0
// 2 - 0,
// ...
// maxSamples / 2 - 1,
// maxSamples / 2 + 1 - 1,
// maxSamples / 2 + 2 - 1,
for (int i = 0; i < maxSamples; i++)
{
data[i] = new double[2];
data[i][0] = i;
data[i][1] = (i > (maxSamples / 2)) ? 1 : 0;
}
return(data);
});
api.UseDeltaLearning(0.2, 1000);
IScore score = api.Run() as IScore;
double[][] testData = new double[4][];
testData[0] = new double[] { 2.0, 0.0 };
testData[1] = new double[] { 4.0, 0.0 };
testData[2] = new double[] { 6.0, 0.0 };
testData[3] = new double[] { 8.0, 0.0 };
var result = api.Algorithm.Predict(testData, api.Context) as DeltaLearningResult;
Assert.True(result.PredictedResults[0] == 0);
Assert.True(result.PredictedResults[1] == 0);
Assert.True(result.PredictedResults[2] == 1);
Assert.True(result.PredictedResults[3] == 1);
}
public void SimpleSequence2DTest()
{
LearningApi api = new LearningApi();
api.UseActionModule <object, double[][]>((notUsed, ctx) =>
{
const int maxSamples = 10000;
ctx.DataDescriptor = get2DDescriptor();
double[][] data = new double[maxSamples][];
for (int i = 0; i < maxSamples / 2; i++)
{
data[2 * i] = new double[3];
data[2 * i][0] = i;
data[2 * i][1] = 5.0;
data[2 * i][2] = 1.0;
data[2 * i + 1] = new double[3];
data[2 * i + 1][0] = i;
data[2 * i + 1][1] = -5.0;
data[2 * i + 1][2] = 0.0;
}
return(data);
});
api.UseDeltaLearning(0.2, 1000);
IScore score = api.Run() as IScore;
double[][] testData = new double[6][];
testData[0] = new double[] { 2.0, 5.0, 0.0 };
testData[1] = new double[] { 2, -5.0, 0.0 };
testData[2] = new double[] { 100, -5.0, 0.0 };
testData[3] = new double[] { 100, -5.0, 0.0 };
testData[4] = new double[] { 490, 5.0, 0.0 };
testData[5] = new double[] { 490, -5.0, 0.0 };
var result = api.Algorithm.Predict(testData, api.Context) as DeltaLearningResult;
Assert.True(result.PredictedResults[0] == 1);
Assert.True(result.PredictedResults[1] == 0);
Assert.True(result.PredictedResults[2] == 0);
Assert.True(result.PredictedResults[3] == 0);
Assert.True(result.PredictedResults[4] == 1);
Assert.True(result.PredictedResults[5] == 0);
}
