public void SimpleSequenceWithGapsTest()
{
LearningApi api = new LearningApi();
api.UseActionModule <object, double[][]>((notUsed, ctx) =>
{
const int maxSamples = 1000000;
ctx.DataDescriptor = getDescriptor();
double[][] data = new double[maxSamples / 3][];
//
// We generate following input vectors:
// IN Val - Expected OUT Val
// Every 3th number is given
for (int i = 0; i < maxSamples / 3; i++)
{
data[i] = new double[2];
data[i][0] = i * 3;
if ((i * 3) > (maxSamples / 2))
{
data[i][1] = 1;
}
else
{
data[i][1] = 0;
}
}
return(data);
});
api.UsePerceptron(0.05, 1000);
IScore score = api.Run() as IScore;
double[][] testData = new double[6][];
testData[0] = new double[] { 2.0, 0.0 };
testData[1] = new double[] { 1, 0.0 };
testData[2] = new double[] { 3, 0.0 };
testData[3] = new double[] { 3002, 0.0 };
testData[4] = new double[] { 6002.0, 0.0 };
testData[5] = new double[] { 9005, 0.0 };
var result = api.Algorithm.Predict(testData, api.Context) as PerceptronResult;
// TODO... THUS TEST iS NOT COMPLETED
//Assert.True(result.PredictedValues[0] == 0);
//Assert.True(result.PredictedValues[1] == 0);
//Assert.True(result.PredictedValues[2] == 0);
//Assert.True(result.PredictedValues[3] == 1);
//Assert.True(result.PredictedValues[4] == 1);
//Assert.True(result.PredictedValues[5] == 1);
}
public void SimpleSequenceTest()
{
LearningApi api = new LearningApi();
api.UseActionModule <object, double[][]>((notUsed, ctx) =>
{
const int maxSamples = 10000;
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.UsePerceptron(0.02, 10000, traceTotalError: true);
IScore score = api.Run() as IScore;
double[][] testData = new double[4][];
testData[0] = new double[] { 2.0, 0.0 };
testData[1] = new double[] { 2000.0, 0.0 };
testData[2] = new double[] { 6000.0, 0.0 };
testData[3] = new double[] { 5001, 0.0 };
var result = api.Algorithm.Predict(testData, api.Context) as PerceptronResult;
Assert.True(result.PredictedValues[0] == 0);
Assert.True(result.PredictedValues[1] == 0);
Assert.True(result.PredictedValues[2] == 1);
Assert.True(result.PredictedValues[3] == 1);
}
public void SimpleSequence2DTest()
{
LearningApi api = new LearningApi();
api.UseActionModule <object, double[][]>((notUsed, ctx) =>
{
const int maxSamples = 20000;
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.UsePerceptron(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 PerceptronResult;
Assert.True(result.PredictedValues[0] == 1);
Assert.True(result.PredictedValues[1] == 0);
Assert.True(result.PredictedValues[2] == 0);
Assert.True(result.PredictedValues[3] == 0);
Assert.True(result.PredictedValues[4] == 1);
Assert.True(result.PredictedValues[5] == 0);
}
public void BatchingTest()
{
LearningApi api = new LearningApi();
api.UseActionModule <object, double[][]>((notUsed, ctx) =>
{
const int batchSize = 500;
const int maxSamples = 10000;
ctx.DataDescriptor = getDescriptor();
if (data == null)
{
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;
}
}
if (currentBatch < maxSamples / batchSize)
{
List <double[]> batch = new List <double[]>();
batch.AddRange(data.Skip(currentBatch * batchSize).Take(batchSize));
ctx.IsMoreDataAvailable = true;
currentBatch++;
return(batch.ToArray());
}
else
{
ctx.IsMoreDataAvailable = false;
return(null);
}
});
api.UsePerceptron(0.02, 10000, traceTotalError: true);
IScore score = api.RunBatch() as IScore;
double[][] testData = new double[4][];
testData[0] = new double[] { 2.0, 0.0 };
testData[1] = new double[] { 2000.0, 0.0 };
testData[2] = new double[] { 6000.0, 0.0 };
testData[3] = new double[] { 5001, 0.0 };
var result = api.Algorithm.Predict(testData, api.Context) as PerceptronResult;
Assert.True(result.PredictedValues[0] == 0);
Assert.True(result.PredictedValues[1] == 0);
Assert.True(result.PredictedValues[2] == 1);
Assert.True(result.PredictedValues[3] == 1);
}