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 TestData(dynamic node, IEnumerable <TestFrame> trainingData, IEnumerable <TestFrame> testData, int warmUps, string trainingLog, string testLog, Func <float[], Func <int, bool> > floatMapper)
{
var enode = node as IEnumerable <bool>;
var cl = new AlsingClassifier();
var warmupStart = DateTime.Now;
for (var w = 0; w < warmUps; w++)
{
foreach (var frame in trainingData)
{
node.Feed(floatMapper(frame.Columns));
}
}
var warmupEnd = DateTime.Now;
var trainingStart = DateTime.Now;
using (var log = trainingLog == null ? null : new StreamWriter(trainingLog, false, Encoding.ASCII))
foreach (var frame in trainingData)
{
node.Feed(floatMapper(frame.Columns));
cl.Train(frame.Category.ToString(), enode.ToArray());
if (log != null)
{
log.WriteLine(String.Join(" ", enode.Select(d => d ? '1' : '0')));
}
}
var trainingEnd = DateTime.Now;
node.Learning = false;
var acurate = 0;
var total = 0;
var accuracy = new Dictionary <string, Accuracy>();
var testStart = DateTime.Now;
using (var log = testLog == null ? null : new StreamWriter(testLog, false, Encoding.ASCII))
foreach (var frame in testData)
{
var activeCount = 0;
Func <int, bool> input = floatMapper(frame.Columns);
node.Feed(input);
foreach (var b in enode)
{
if (b)
{
activeCount++;
}
}
var matches = cl.FindMatches(enode.ToArray())
.OrderByDescending(c => c.Strength)
.Take(2)
.ToArray();
if (log != null)
{
log.WriteLine(String.Join(" ", enode.Select(d => d ? '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);
}
var testEnd = DateTime.Now;
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);
}
Console.WriteLine();
Console.WriteLine("Warm up:\t{0:g}", warmupEnd - warmupStart);
Console.WriteLine("Training:\t{0:g}", trainingEnd - trainingStart);
Console.WriteLine("Test:\t{0:g}", testEnd - testStart);
}
static void TestData(dynamic node, IEnumerable<TestFrame> trainingData, IEnumerable<TestFrame> testData, int warmUps, string trainingLog, string testLog, Func<float[], Func<int, bool>> floatMapper)
{
var enode = node as IEnumerable<bool>;
var cl = new AlsingClassifier();
var warmupStart = DateTime.Now;
for (var w = 0; w < warmUps; w++)
foreach (var frame in trainingData)
node.Feed(floatMapper(frame.Columns));
var warmupEnd = DateTime.Now;
var trainingStart = DateTime.Now;
using (var log = trainingLog == null ? null : new StreamWriter(trainingLog, false, Encoding.ASCII))
foreach (var frame in trainingData)
{
node.Feed(floatMapper(frame.Columns));
cl.Train(frame.Category.ToString(), enode.ToArray());
if (log != null) log.WriteLine(String.Join(" ", enode.Select(d => d ? '1' : '0')));
}
var trainingEnd = DateTime.Now;
node.Learning = false;
var acurate = 0;
var total = 0;
var accuracy = new Dictionary<string, Accuracy>();
var testStart = DateTime.Now;
using (var log = testLog == null ? null : new StreamWriter(testLog, false, Encoding.ASCII))
foreach (var frame in testData)
{
var activeCount = 0;
Func<int, bool> input = floatMapper(frame.Columns);
node.Feed(input);
foreach (var b in enode)
if (b)
activeCount++;
var matches = cl.FindMatches(enode.ToArray())
.OrderByDescending(c => c.Strength)
.Take(2)
.ToArray();
if (log != null) log.WriteLine(String.Join(" ", enode.Select(d => d ? '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);
}
var testEnd = DateTime.Now;
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);
Console.WriteLine();
Console.WriteLine("Warm up:\t{0:g}", warmupEnd - warmupStart);
Console.WriteLine("Training:\t{0:g}", trainingEnd - trainingStart);
Console.WriteLine("Test:\t{0:g}", testEnd - testStart);
}
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);
}
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);
}
}
public void CountActiveColumnsPerStepWithMovingInput()
{
var sp = new SpatialPooler(
inputCount: 100,
columnCount: 100,
desiredLocalActivity: 5,
minOverlap: 2,
maxSynapsesPerColumn: 20
);
var cl = new AlsingClassifier();
var values = new bool[100];
for (var i = 0; i < 100; i++)
{
values[i] = i < 5;
}
for (var t = 0; t < 10000; t++)
{
Func <int, bool> input = (i) => values[i];
sp.Feed(input);
values.Shift();
}
sp.Learning = false;
for (var t = 0; t < 100; t++)
{
Func <int, bool> input = (i) => values[i];
sp.Feed(input);
cl.Train((t / 10).ToString(), sp.ToArray());
values.Shift();
}
for (var i = 0; i < 100; i++)
{
values[i] = i < 6;
}
var rnd = new Random();
for (var t = 0; t < 1000; t++)
{
var activeCount = 0;
var noiseInput = (bool[])values.Clone();
for (var i = 0; i < 100; i++)
{
if (rnd.NextDouble() < 0.05)
{
noiseInput[i] = !noiseInput[i];
}
}
Func <int, bool> input = (i) => noiseInput[i];
sp.Feed(input);
foreach (var b in sp)
{
if (b)
{
activeCount++;
}
}
var matches = cl.FindMatches(sp.ToArray())
.OrderByDescending(c => c.Strength)
.Select(m => m.Identifier + ": " + Math.Round(m.Strength * 100, 2) + "%");
Console.Write(String.Join("", sp.Select(v => v ? "1" : "0")) + "\t");
Console.WriteLine(String.Join("\t", matches));
values.Shift();
}
}
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);
}
}
public void CountActiveColumnsPerStepWithMovingInput()
{
var sp = new SpatialPooler(
inputCount: 100,
columnCount: 100,
desiredLocalActivity: 5,
minOverlap: 2,
maxSynapsesPerColumn: 20
);
var cl = new AlsingClassifier();
var values = new bool[100];
for (var i = 0; i < 100; i++)
values[i] = i < 5;
for (var t = 0; t < 10000; t++)
{
Func<int, bool> input = (i) => values[i];
sp.Feed(input);
values.Shift();
}
sp.Learning = false;
for (var t = 0; t < 100; t++)
{
Func<int, bool> input = (i) => values[i];
sp.Feed(input);
cl.Train((t / 10).ToString(), sp.ToArray());
values.Shift();
}
for (var i = 0; i < 100; i++)
values[i] = i < 6;
var rnd = new Random();
for (var t = 0; t < 1000; t++)
{
var activeCount = 0;
var noiseInput = (bool[])values.Clone();
for (var i = 0; i < 100; i++)
if (rnd.NextDouble() < 0.05)
noiseInput[i] = !noiseInput[i];
Func<int, bool> input = (i) => noiseInput[i];
sp.Feed(input);
foreach (var b in sp)
if (b)
activeCount++;
var matches = cl.FindMatches(sp.ToArray())
.OrderByDescending(c => c.Strength)
.Select(m => m.Identifier + ": " + Math.Round(m.Strength * 100, 2) + "%");
Console.Write(String.Join("", sp.Select(v => v ? "1" : "0")) + "\t");
Console.WriteLine(String.Join("\t", matches));
values.Shift();
}
}
