public void TestOverlapPattern()
{
SetUp();
Classification <double> result = Compute <double>(_classifier, 0, new[] { 1, 5 }, 9, 9);
result = Compute <double>(_classifier, 1, new[] { 1, 5 }, 9, 9);
result = Compute <double>(_classifier, 1, new[] { 1, 5 }, 9, 9);
result = Compute <double>(_classifier, 2, new[] { 3, 5 }, 2, 2);
// Since overlap - should be previous with 100%
CheckValue(result, 9, 9.0, 1.0);
result = Compute <double>(_classifier, 3, new[] { 3, 5 }, 2, 2);
// Second example: now new value should be more probable than old
Assert.IsTrue(result.GetStat(1, 2) > result.GetStat(1, 9));
}
public void TestMultistepSimple()
{
_classifier = new CLAClassifier(new[] { 1, 2 }, 0.001, 0.3, 0);
Classification <double> result = null;
int recordNum = 0;
for (int i = 0; i < 100; i++, recordNum++)
{
result = Compute <double>(_classifier, recordNum, new[] { i % 10 }, i % 10, (i % 10) * 10);
}
// Only should return one actual value bucket.
Assert.IsTrue(Arrays.AreEqual(new double[] { 0.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0 }, result.GetActualValues()));
Assert.AreEqual(1.0, result.GetStat(1, 0), 0.1);
for (int i = 1; i < 10; i++)
{
Assert.AreEqual(0.0, result.GetStat(1, i), 0.1);
}
Assert.AreEqual(1.0, result.GetStat(2, 1), 0.1);
}
public void TestSingleValue0Steps()
{
var classifier = new SDRClassifier(new[] { 0 }, 1.0);
// Enough times to perform Inference and learn associations
Classification <double> retVal = null;
for (int i = 0; i < 10; i++)
{
retVal = _compute(classifier, i, new[] { 1, 5 }, 0, 10);
}
Assert.AreEqual(retVal.GetActualValue(0), 10);
Assert.IsTrue(retVal.GetStat(0, 0) > 0.9, "value of 1 must be greater then 0.9 and is " + retVal.GetStat(0, 0));
}
public void TestSingleValue0Steps()
{
_classifier = new CLAClassifier(new[] { 0 }, 0.001, 0.3, 0);
// Enough times to perform Inference and learn associations
Classification <double> retVal = null;
for (int recordNum = 0; recordNum < 10; recordNum++)
{
retVal = Compute <double>(_classifier, recordNum, new[] { 1, 5 }, 0, 10);
}
Assert.AreEqual(10.0, retVal.GetActualValue(0), .00001);
Assert.AreEqual(1.0, retVal.GetStat(0, 0), .00001);
}
public void TestSerializeCLAClassifier()
{
CLAClassifier classifier = new CLAClassifier(new int[] { 1 }, 0.1, 0.1, 0);
int recordNum = 0;
Map <String, Object> classification = new Map <string, object>();
classification.Add("bucketIdx", 4);
classification.Add("actValue", 34.7);
Classification <double> result = classifier.Compute <double>(recordNum, classification, new int[] { 1, 5, 9 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 5);
classification.Add("actValue", 41.7);
result = classifier.Compute <double>(recordNum, classification, new int[] { 0, 6, 9, 11 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 5);
classification.Add("actValue", 44.9);
result = classifier.Compute <double>(recordNum, classification, new int[] { 6, 9 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 4);
classification.Add("actValue", 42.9);
result = classifier.Compute <double>(recordNum, classification, new int[] { 1, 5, 9 }, true, true);
recordNum += 1;
// Serialize the Metrics too just to be sure everything can be serialized
SerialConfig config = new SerialConfig("testSerializeCLAClassifier", SerialConfig.SERIAL_TEST_DIR);
IPersistenceAPI api = Persistence.Get(config);
api.Write(classifier);
// Deserialize the Metrics
CLAClassifier serializedClassifier = api.Read <CLAClassifier>();
Assert.IsNotNull(serializedClassifier);
//Using the deserialized classifier, continue test
classification.Add("bucketIdx", 4);
classification.Add("actValue", 34.7);
result = serializedClassifier.Compute <double>(recordNum, classification, new int[] { 1, 5, 9 }, true, true);
recordNum += 1;
Assert.IsTrue(Arrays.AreEqual(new int[] { 1 }, result.StepSet()));
Assert.AreEqual(35.520000457763672, result.GetActualValue(4), 0.00001);
Assert.AreEqual(42.020000457763672, result.GetActualValue(5), 0.00001);
Assert.AreEqual(6, result.GetStatCount(1));
Assert.AreEqual(0.0, result.GetStat(1, 0), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 1), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 2), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 3), 0.00001);
Assert.AreEqual(0.12300123, result.GetStat(1, 4), 0.00001);
Assert.AreEqual(0.87699877, result.GetStat(1, 5), 0.00001);
}
public void TestSerialization()
{
_classifier = new CLAClassifier(new[] { 1 }, 0.1, 0.1, 0);
int recordNum = 0;
Map <string, object> classification = new Map <string, object>();
classification.Add("bucketIdx", 4);
classification.Add("actValue", 34.7);
Classification <double> result = _classifier.Compute <double>(recordNum, classification, new[] { 1, 5, 9 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 5);
classification.Add("actValue", 41.7);
result = _classifier.Compute <double>(recordNum, classification, new[] { 0, 6, 9, 11 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 5);
classification.Add("actValue", 44.9);
result = _classifier.Compute <double>(recordNum, classification, new[] { 6, 9 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 4);
classification.Add("actValue", 42.9);
result = _classifier.Compute <double>(recordNum, classification, new[] { 1, 5, 9 }, true, true);
recordNum += 1;
// Configure serializer
SerialConfig config = new SerialConfig("testSerializerClassifier", SerialConfig.SERIAL_TEST_DIR);
IPersistenceAPI api = Persistence.Get(config);
// 1. Serialize
byte[] data = api.Write(_classifier, "testSerializeClassifier");
// 2. Deserialize
CLAClassifier serialized = api.Read <CLAClassifier>(data);
// Using the deserialized classifier, continue test
classification.Add("bucketIdx", 4);
classification.Add("actValue", 34.7);
result = serialized.Compute <double>(recordNum, classification, new[] { 1, 5, 9 }, true, true);
recordNum += 1;
Assert.IsTrue(Arrays.AreEqual(new[] { 1 }, result.StepSet()));
Assert.AreEqual(35.520000457763672, result.GetActualValue(4), 0.00001);
Assert.AreEqual(42.020000457763672, result.GetActualValue(5), 0.00001);
Assert.AreEqual(6, result.GetStatCount(1));
Assert.AreEqual(0.0, result.GetStat(1, 0), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 1), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 2), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 3), 0.00001);
Assert.AreEqual(0.12300123, result.GetStat(1, 4), 0.00001);
Assert.AreEqual(0.87699877, result.GetStat(1, 5), 0.00001);
}
public void TestComputeComplex()
{
_classifier = new CLAClassifier(new[] { 1 }, 0.1, 0.1, 0);
int recordNum = 0;
Map <string, object> classification = new Map <string, object>();
classification.Add("bucketIdx", 4);
classification.Add("actValue", 34.7);
Classification <double> result = _classifier.Compute <double>(recordNum, classification, new[] { 1, 5, 9 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 5);
classification.Add("actValue", 41.7);
result = _classifier.Compute <double>(recordNum, classification, new[] { 0, 6, 9, 11 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 5);
classification.Add("actValue", 44.9);
result = _classifier.Compute <double>(recordNum, classification, new[] { 6, 9 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 4);
classification.Add("actValue", 42.9);
result = _classifier.Compute <double>(recordNum, classification, new[] { 1, 5, 9 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 4);
classification.Add("actValue", 34.7);
result = _classifier.Compute <double>(recordNum, classification, new[] { 1, 5, 9 }, true, true);
recordNum += 1;
Assert.IsTrue(Arrays.AreEqual(new[] { 1 }, result.StepSet()));
Assert.AreEqual(35.520000457763672, result.GetActualValue(4), 0.00001);
Assert.AreEqual(42.020000457763672, result.GetActualValue(5), 0.00001);
Assert.AreEqual(6, result.GetStatCount(1));
Assert.AreEqual(0.0, result.GetStat(1, 0), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 1), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 2), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 3), 0.00001);
Assert.AreEqual(0.12300123, result.GetStat(1, 4), 0.00001);
Assert.AreEqual(0.87699877, result.GetStat(1, 5), 0.00001);
}
public void CheckValue <T>(Classification <T> retVal, int index, object value, double probability)
{
Assert.AreEqual(retVal.GetActualValue(index), value);
Assert.AreEqual(probability, retVal.GetStat(1, index), 0.01);
}
public void TestMissingRecords()
{
_classifier = new CLAClassifier(new[] { 1 }, 0.1, 0.1, 0);
int recordNum = 0;
Map <string, object> classification = new Map <string, object>();
classification.Add("bucketIdx", 0);
classification.Add("actValue", 0);
_classifier.Compute <double>(recordNum, classification, new[] { 1, 3, 5 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 1);
classification.Add("actValue", 1);
_classifier.Compute <double>(recordNum, classification, new[] { 2, 4, 6 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 2);
classification.Add("actValue", 2);
_classifier.Compute <double>(recordNum, classification, new[] { 1, 3, 5 }, true, true);
recordNum += 1;
classification.Add("bucketIdx", 1);
classification.Add("actValue", 1);
_classifier.Compute <double>(recordNum, classification, new[] { 2, 4, 6 }, true, true);
recordNum += 1;
// ----------------------------------------------------------------------------------
// At this point, we should have learned [1, 3, 5] => bucket 1
// [2, 4, 6] => bucket 2
classification.Add("bucketIdx", 2);
classification.Add("actValue", 2);
Classification <double> result = _classifier.Compute <double>(recordNum, classification, new[] { 1, 3, 5 }, true, true);
recordNum += 1;
Assert.AreEqual(0.0, result.GetStat(1, 0), 0.00001);
Assert.AreEqual(1.0, result.GetStat(1, 1), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 2), 0.00001);
classification.Add("bucketIdx", 1);
classification.Add("actValue", 1);
result = _classifier.Compute <double>(recordNum, classification, new[] { 2, 4, 6 }, true, true);
recordNum += 1;
Assert.AreEqual(0.0, result.GetStat(1, 0), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 1), 0.00001);
Assert.AreEqual(1.0, result.GetStat(1, 2), 0.00001);
// ----------------------------------------------------------------------------------
// Feed in records that skip and make sure they don't mess up what we learned
//
// If we skip a record, the CLA should NOT learn that [2,4,6] from
// the previous learning associates with bucket 0
recordNum += 1; // <----- Does the skip
classification.Add("bucketIdx", 0);
classification.Add("actValue", 0);
result = _classifier.Compute <double>(recordNum, classification, new[] { 1, 3, 5 }, true, true);
recordNum += 1;
Assert.AreEqual(0.0, result.GetStat(1, 0), 0.00001);
Assert.AreEqual(1.0, result.GetStat(1, 1), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 2), 0.00001);
// If we skip a record, the CLA should NOT learn that [1,3,5] from
// the previous learning associates with bucket 0
recordNum += 1; // <----- Does the skip
classification.Add("bucketIdx", 0);
classification.Add("actValue", 0);
result = _classifier.Compute <double>(recordNum, classification, new[] { 2, 4, 6 }, true, true);
recordNum += 1;
Assert.AreEqual(0.0, result.GetStat(1, 0), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 1), 0.00001);
Assert.AreEqual(1.0, result.GetStat(1, 2), 0.00001);
// If we skip a record, the CLA should NOT learn that [2,4,6] from
// the previous learning associates with bucket 0
recordNum += 1; // <----- Does the skip
classification.Add("bucketIdx", 0);
classification.Add("actValue", 0);
result = _classifier.Compute <double>(recordNum, classification, new[] { 1, 3, 5 }, true, true);
recordNum += 1;
Assert.AreEqual(0.0, result.GetStat(1, 0), 0.00001);
Assert.AreEqual(1.0, result.GetStat(1, 1), 0.00001);
Assert.AreEqual(0.0, result.GetStat(1, 2), 0.00001);
}
