public void OutputShouldNotBeZero()
{
var values = new [] {
0.5f, 0.1f, 0.0f, 1.0f, 0.7f, 0.66f, 0.99f
};
var map = BitMapper.Map(values, 0f, 1f, 50);
var atleastOneIsTrue = false;
for (int i = 0, l = values.Length * 50; i < l; i++)
{
var b = map(i);
Console.Write(b ? "1" : "0");
if (i % 50 == 49)
{
Console.WriteLine();
}
if (b)
{
atleastOneIsTrue = true;
}
}
Assert.IsTrue(atleastOneIsTrue, "At least one output bit should be true.");
}
public void TestData()
{
var trainingData = ReadTestData("../../../../Data/train_sensor.txt", "../../../../Data/train_category.txt");
var floatPrecision = 50;
var cl = Create();
foreach (var frame in trainingData)
{
var bits = AsBitArray(BitMapper.Map(frame.Columns, -0.6f, 2.21f, floatPrecision), frame.Columns.Length * floatPrecision);
cl.Train(frame.Category.ToString(), bits);
}
var testData = ReadTestData("../../../../Data/test_sensor.txt", "../../../../Data/test_category.txt");
var acurate = 0;
var total = 0;
var accuracy = new Dictionary <string, Accuracy>();
foreach (var frame in testData)
{
var bits = AsBitArray(BitMapper.Map(frame.Columns, -0.6f, 2.21f, floatPrecision), frame.Columns.Length * floatPrecision);
var matches = cl.FindMatches(bits)
.OrderByDescending(c => c.Strength)
.Take(2)
.ToArray();
var cat = frame.Category.ToString();
if (matches[0].Identifier == cat)
{
acurate++;
}
total++;
Accuracy v;
if (!accuracy.TryGetValue(cat, out v))
{
accuracy.Add(cat, v = new Accuracy());
}
if (matches[0].Identifier == frame.Category.ToString())
{
v.acurate++;
}
v.total++;
Console.WriteLine("Expected: {0} Matched: {1} ({3:p}) Runner Up: {2} ({4:p})", frame.Category, matches[0].Identifier, matches[1].Identifier, matches[0].Strength, matches[1].Strength);
}
Console.WriteLine("Accuracy: {0:p}", ((float)acurate) / ((float)total));
foreach (var pair in accuracy)
{
Console.WriteLine("Accuracy {1}: {0:p}", ((float)pair.Value.acurate) / ((float)pair.Value.total), pair.Key);
}
}
public void TestData()
{
var trainingData = ReadTestData("../../../../Data/train_sensor.txt", "../../../../Data/train_category.txt");
var columnCount = 300;
var cellsPerColumn = 1;
var floatPrecision = 50;
var node = new Node(
inputCount: 32 * floatPrecision,
columnCount: columnCount,
cellsPerColumn: cellsPerColumn,
desiredLocalActivity: 10,
minOverlap: 2,
maxSynapsesPerColumn: 20,
newSynapseCount: 2
);
var cl = new AlsingClassifier();
foreach (var frame in trainingData)
{
node.Feed(BitMapper.Map(frame.Columns, -0.6f, 2.21f, floatPrecision));
}
using (var log = new StreamWriter("../../../../Data/train_result.txt", false, Encoding.ASCII))
foreach (var frame in trainingData)
{
node.Feed(BitMapper.Map(frame.Columns, -0.6f, 2.21f, floatPrecision));
cl.Train(frame.Category.ToString(), node.ToArray());
log.WriteLine(node.Select(v => v ? '1' : '0').ToArray());
}
node.Learning = false;
var testData = ReadTestData("../../../../Data/test_sensor.txt", "../../../../Data/test_category.txt");
var acurate = 0;
var total = 0;
var accuracy = new Dictionary <string, Accuracy>();
using (var log = new StreamWriter("../../../../Data/test_result.txt", false, Encoding.ASCII))
foreach (var frame in testData)
{
var activeCount = 0;
Func <int, bool> input = BitMapper.Map(frame.Columns, -0.6f, 2.21f, floatPrecision);
node.Feed(input);
foreach (var b in node)
{
if (b)
{
activeCount++;
}
}
var matches = cl.FindMatches(node.ToArray())
.OrderByDescending(c => c.Strength)
.Take(2)
.ToArray();
log.WriteLine(node.Select(d => d ? '1' : '0').ToArray());
/*for (var i = 0; i < 32 * 50; i++)
* Console.Write(input(i) ? "1" : "0");
* Console.WriteLine();
*
* Console.WriteLine(String.Join("", outputData.Select(v => v ? "1" : "0")));*/
var cat = frame.Category.ToString();
if (matches[0].Identifier == cat)
{
acurate++;
}
total++;
Accuracy v;
if (!accuracy.TryGetValue(cat, out v))
{
accuracy.Add(cat, v = new Accuracy());
}
if (matches[0].Identifier == frame.Category.ToString())
{
v.acurate++;
}
v.total++;
Console.WriteLine("Expected: {0} Matched: {1} ({3:p}) Runner Up: {2} ({4:p}) Activity Count: {5}", frame.Category, matches[0].Identifier, matches[1].Identifier, matches[0].Strength, matches[1].Strength, activeCount);
}
Console.WriteLine("Accuracy: {0:p}", ((float)acurate) / ((float)total));
foreach (var pair in accuracy)
{
Console.WriteLine("Accuracy {1}: {0:p}", ((float)pair.Value.acurate) / ((float)pair.Value.total), pair.Key);
}
}
static void Main(string[] args)
{
if (args.Length == 0)
{
Console.WriteLine("Usage: htm TRAINING-SENSOR-FILE TRAINING-CATEGORY-FILE TEST-SENSOR-FILE TEST-CATEGORY-FILE [spatialonly] [PARAMETER VALUE [PARAMETER VALUE ...]]");
Console.WriteLine("Parameters (Default Value):");
Console.WriteLine("nodesPerLevel (1)");
Console.WriteLine("columnCount (100)");
Console.WriteLine("cellsPerColumn (4)");
Console.WriteLine("desiredLocalActivity (10)");
Console.WriteLine("minOverlap (2)");
Console.WriteLine("maxSynapsesPerColumn (2)");
Console.WriteLine("connectedPerm (0.2)");
Console.WriteLine("permanenceInc (0.01)");
Console.WriteLine("permanenceDec (0.01)");
Console.WriteLine("slidingWindowLength (1000)");
Console.WriteLine("activationThreshold (1)");
Console.WriteLine("minThreshold (1)");
Console.WriteLine("learningRadius (10)");
Console.WriteLine("newSynapseCount (10)");
Console.WriteLine("floatPrecision (50)");
Console.WriteLine("warmUps (1)");
Console.WriteLine();
Console.WriteLine("trainingLog (NONE) - File name to log training output");
Console.WriteLine("testLog (NONE) - File name to log test output");
return;
}
string trainingLog = null;
string testLog = null;
int floatPrecision = 50;
int columnCount = 100, cellsPerColumn = 4, desiredLocalActivity = 10, minOverlap = 2, maxSynapsesPerColumn = 2;
float connectedPerm = 0.2f, permanenceInc = 0.01f, permanenceDec = 0.01f;
int slidingWindowLength = 1000, activationThreshold = 1;
int minThreshold = 1;
int learningRadius = 10, newSynapseCount = 10;
int warmUps = 1;
bool spatialOnly = false;
int[] nodesPerLevel = new[] { 1 };
var ci = System.Globalization.CultureInfo.InvariantCulture;
for (var i = 4; i < args.Length; i++)
{
var cmd = args[i].ToLowerInvariant();
switch (cmd)
{
case "nodesperlevel":
var levels = new List <int>();
int v;
while (++i < args.Length)
{
if (!int.TryParse(args[i], out v))
{
i--;
break;
}
levels.Add(v);
}
nodesPerLevel = levels.ToArray();
break;
case "warmups":
warmUps = int.Parse(args[++i], ci);
break;
case "columncount":
columnCount = int.Parse(args[++i], ci);
break;
case "cellspercolumn":
cellsPerColumn = int.Parse(args[++i], ci);
break;
case "desiredlocalactivity":
desiredLocalActivity = int.Parse(args[++i], ci);
break;
case "minoverlap":
minOverlap = int.Parse(args[++i], ci);
break;
case "maxsynapsespercolumn":
maxSynapsesPerColumn = int.Parse(args[++i], ci);
break;
case "connectedperm":
connectedPerm = float.Parse(args[++i], ci);
break;
case "permanenceinc":
permanenceInc = float.Parse(args[++i], ci);
break;
case "permanencedec":
permanenceDec = float.Parse(args[++i], ci);
break;
case "slidingwindowlength":
slidingWindowLength = int.Parse(args[++i], ci);
break;
case "activationthreshold":
activationThreshold = int.Parse(args[++i], ci);
break;
case "minthreshold":
minThreshold = int.Parse(args[++i], ci);
break;
case "learningradius":
learningRadius = int.Parse(args[++i], ci);
break;
case "newsynapsecount":
newSynapseCount = int.Parse(args[++i], ci);
break;
case "floatprecision":
floatPrecision = int.Parse(args[++i], ci);
break;
case "traininglog":
trainingLog = args[++i];
break;
case "testlog":
testLog = args[++i];
break;
case "spatialonly":
spatialOnly = true;
break;
default:
Console.WriteLine("Unknown argument: " + args[i]);
return;
}
}
var trainingData = ReadTestData(args[0], args[1]);
var testData = ReadTestData(args[2], args[3]);
var allValues = trainingData.SelectMany(r => r.Columns)
.Union(trainingData.SelectMany(r => r.Columns));
float min = allValues.Min(), max = allValues.Max();
object node;
if (spatialOnly)
{
node = new SpatialPooler(
trainingData.First().Columns.Length *floatPrecision,
columnCount, desiredLocalActivity, minOverlap, maxSynapsesPerColumn,
connectedPerm, permanenceInc, permanenceDec,
slidingWindowLength
);
}
else
{
node = new Tree(
nodesPerLevel,
trainingData.First().Columns.Length *floatPrecision,
columnCount, cellsPerColumn, desiredLocalActivity, minOverlap, maxSynapsesPerColumn,
connectedPerm, permanenceInc, permanenceDec,
slidingWindowLength, activationThreshold,
minThreshold,
learningRadius, newSynapseCount
);
}
TestData(node, trainingData, testData, warmUps, trainingLog, testLog, c => BitMapper.Map(c, min, max, floatPrecision));
}
public void TestData()
{
var trainingData = ReadTestData("../../../../Data/train_sensor.txt", "../../../../Data/train_category.txt");
var columnCount = 300;
var sp = new SpatialPooler(
inputCount: 32 * 50,
columnCount: columnCount,
desiredLocalActivity: 10,
minOverlap: 2,
maxSynapsesPerColumn: 20
);
var cl = new AlsingClassifier();
foreach (var frame in trainingData)
{
sp.Feed(BitMapper.Map(frame.Columns, -0.6f, 2.1f, 50));
}
using (var log = new StreamWriter("../../../../Data/train_result_spatial.txt", false, Encoding.ASCII))
foreach (var frame in trainingData)
{
sp.Feed(BitMapper.Map(frame.Columns, -0.6f, 2.1f, 50));
cl.Train(frame.Category.ToString(), sp.ToArray());
log.WriteLine(String.Join(" ", sp.Select(v => v ? '1' : '0')));
}
sp.Learning = false;
var testData = ReadTestData("../../../../Data/test_sensor.txt", "../../../../Data/test_category.txt");
using (var log = new StreamWriter("../../../../Data/test_result_spatial.txt", false, Encoding.ASCII))
foreach (var frame in testData)
{
var activeCount = 0;
Func <int, bool> input = BitMapper.Map(frame.Columns, -0.6f, 2.1f, 50);
sp.Feed(input);
foreach (var b in sp)
{
if (b)
{
activeCount++;
}
}
log.WriteLine(String.Join(" ", sp.Select(v => v ? '1' : '0')));
var matches = cl.FindMatches(sp.ToArray())
.OrderByDescending(c => c.Strength)
.Take(2)
.ToArray();
/*for (var i = 0; i < 32 * 50; i++)
* Console.Write(input(i) ? "1" : "0");
* Console.WriteLine();
*
* Console.WriteLine(String.Join("", outputData.Select(v => v ? "1" : "0")));*/
Console.WriteLine("Expected: {0} Matched: {1} ({3:p}) Runner Up: {2} ({4:p}) Activity Count: {5}", frame.Category, matches[0].Identifier, matches[1].Identifier, matches[0].Strength, matches[1].Strength, activeCount);
}
}
