/// <summary>
/// Trains a classifier for each channel so long as (int)minSpikes worth of spikes have been collected
/// for that channel in the training buffer
/// </summary>
private void Train()
{
// Clear old channel models
channelsToSort = new List <int>();
channelModels.Clear();
trained = false;
totalNumberOfUnits = 0;
// Clear old unit dictionary
unitDictionary = new Hashtable();
// Add the zero unit to the dictionary
unitDictionary.Add(0, 0);
// Clear the old unit2channel map
unit2Channel = new Dictionary <int, int>();
// Make sure we have something in the training matrix
if (trainingSpikes.Buffer.Count == 0)
{
throw new InvalidOperationException("The training data set was empty");
}
for (int i = 0; i < numberChannels; ++i)
{
// Current channel
int currentChannel = i;
// Get the spikes that belong to this channel
List <SpikeEvent> spikesOnChan = trainingSpikes.Buffer.Where(x => x.Channel == currentChannel).ToList();
// Project channel data
if (spikesOnChan.Count >= minSpikes)
{
// Train a channel model for this channel
ChannelModel thisChannelModel = new ChannelModel(currentChannel, maxK, totalNumberOfUnits, pValue, projectionDimension);
// Project Data
if (projectionType == "PCA")
{
thisChannelModel.PCCompute(spikesOnChan.ToList());
}
else
{
thisChannelModel.HaarCompute(spikesOnChan.ToList());
}
// Train Classifier
thisChannelModel.Train();
// If there was a training failure (e.g. convergence)
if (!thisChannelModel.trained)
{
continue;
}
// Note that we have to sort spikes on this channel
channelsToSort.Add(currentChannel);
// Update the unit dicationary and increment the total number of units
for (int k = 1; k <= thisChannelModel.K; ++k)
{
unitDictionary.Add(totalNumberOfUnits + k, k);
unit2Channel.Add(totalNumberOfUnits + k, i);
}
totalNumberOfUnits += thisChannelModel.K;
// Add the channel model to the list
channelModels.Add(thisChannelModel);
}
}
}
/// <summary>
/// Trains a classifier for each channel so long as (int)minSpikes worth of spikes have been collected
/// for that channel in the training buffer
/// </summary>
private void Train()
{
// Clear old channel models
channelsToSort = new List<int>();
channelModels.Clear();
trained = false;
totalNumberOfUnits = 0;
// Clear old unit dictionary
unitDictionary = new Hashtable();
// Add the zero unit to the dictionary
unitDictionary.Add(0, 0);
// Clear the old unit2channel map
unit2Channel = new Dictionary<int, int>();
// Make sure we have something in the training matrix
if (trainingSpikes.Buffer.Count == 0)
{
throw new InvalidOperationException("The training data set was empty");
}
for (int i = 0; i < numberChannels; ++i)
{
// Current channel
int currentChannel = i;
// Get the spikes that belong to this channel
List<SpikeEvent> spikesOnChan = trainingSpikes.Buffer.Where(x => x.Channel == currentChannel).ToList();
// Project channel data
if (spikesOnChan.Count >= minSpikes)
{
// Train a channel model for this channel
ChannelModel thisChannelModel = new ChannelModel(currentChannel, maxK, totalNumberOfUnits, pValue, projectionDimension);
// Project Data
if (projectionType == "PCA")
thisChannelModel.PCCompute(spikesOnChan.ToList());
else
thisChannelModel.HaarCompute(spikesOnChan.ToList());
// Train Classifier
thisChannelModel.Train();
// If there was a training failure (e.g. convergence)
if (!thisChannelModel.trained)
continue;
// Note that we have to sort spikes on this channel
channelsToSort.Add(currentChannel);
// Update the unit dicationary and increment the total number of units
for (int k = 1; k <= thisChannelModel.K; ++k)
{
unitDictionary.Add(totalNumberOfUnits + k, k);
unit2Channel.Add(totalNumberOfUnits + k, i);
}
totalNumberOfUnits += thisChannelModel.K;
// Add the channel model to the list
channelModels.Add(thisChannelModel);
}
}
}
/// <summary>
/// Trains a classifier for each channel so long as (int)minSpikes worth of spikes have been collected
/// for that channel in the training buffer. This uses to time points as the projection: The peak sample
/// of the waveform and sample at some fixed (mSecToSecondSample) delay from the peak sample. Preferably,
/// this should be in the middle of the AHP.
/// </summary>
/// <param name="peakSample"> Sample that the peak of the waveform occured at </param>
/// <param name="mSecToSecondSample">Delay, in msec, to get the second data point</param>
private void Train(int peakSample, double mSecToSecondSample, int sampleFreqHz)
{
// Clear old channel models
channelsToSort = new List <int>();
channelModels.Clear();
trained = false;
totalNumberOfUnits = 0;
// Clear old unit dictionary
unitDictionary = new Hashtable();
// Add the zero unit to the dictionary
unitDictionary.Add(0, 0);
// Clear the old unit2channel map
unit2Channel = new Dictionary <int, int>();
// Set the inflection sample
inflectionSample = peakSample;
secondInflectionIndex = peakSample + (int)(sampleFreqHz * (mSecToSecondSample / 1000));
// Make sure we have something in the training matrix
if (trainingSpikes.Buffer.Count == 0)
{
throw new InvalidOperationException("The training data set was empty");
}
for (int i = 0; i < numberChannels; ++i)
{
// Current channel
int currentChannel = i;
// Get the spikes that belong to this channel
List <SpikeEvent> spikesOnChan = trainingSpikes.Buffer.Where(x => x.Channel == currentChannel).ToList();
// Project channel data
if (spikesOnChan.Count >= minSpikes)
{
// Train a channel model for this channel
ChannelModel thisChannelModel = new ChannelModel(currentChannel, maxK, totalNumberOfUnits, pValue);
// Project Data
thisChannelModel.DoubleInflectProject(spikesOnChan.ToList(), inflectionSample, secondInflectionIndex);
// Train Classifier
thisChannelModel.Train();
// If there was a training failure (e.g. convergence)
if (!thisChannelModel.trained)
{
continue;
}
// Note that we have to sort spikes on this channel
channelsToSort.Add(currentChannel);
// Update the unit dicationary and increment the total number of units
for (int k = 1; k <= thisChannelModel.K; ++k)
{
unitDictionary.Add(totalNumberOfUnits + k, k);
unit2Channel.Add(totalNumberOfUnits + k, i);
}
totalNumberOfUnits += thisChannelModel.K;
// Add the channel model to the list
channelModels.Add(thisChannelModel);
}
}
// All finished
trained = true;
}
/// <summary>
/// Trains a classifier for each channel so long as (int)minSpikes worth of spikes have been collected
/// for that channel in the training buffer. This uses to time points as the projection: The peak sample
/// of the waveform and sample at some fixed (mSecToSecondSample) delay from the peak sample. Preferably,
/// this should be in the middle of the AHP.
/// </summary>
/// <param name="peakSample"> Sample that the peak of the waveform occured at </param>
/// <param name="mSecToSecondSample">Delay, in msec, to get the second data point</param>
private void Train(int peakSample, double mSecToSecondSample, int sampleFreqHz)
{
// Clear old channel models
channelsToSort = new List<int>();
channelModels.Clear();
trained = false;
totalNumberOfUnits = 0;
// Clear old unit dictionary
unitDictionary = new Hashtable();
// Add the zero unit to the dictionary
unitDictionary.Add(0, 0);
// Clear the old unit2channel map
unit2Channel = new Dictionary<int, int>();
// Set the inflection sample
inflectionSample = peakSample;
secondInflectionIndex = peakSample + (int)(sampleFreqHz * (mSecToSecondSample / 1000));
// Make sure we have something in the training matrix
if (trainingSpikes.Buffer.Count == 0)
{
throw new InvalidOperationException("The training data set was empty");
}
for (int i = 0; i < numberChannels; ++i)
{
// Current channel
int currentChannel = i;
// Get the spikes that belong to this channel
List<SpikeEvent> spikesOnChan = trainingSpikes.Buffer.Where(x => x.Channel == currentChannel).ToList();
// Project channel data
if (spikesOnChan.Count >= minSpikes)
{
// Train a channel model for this channel
ChannelModel thisChannelModel = new ChannelModel(currentChannel, maxK, totalNumberOfUnits, pValue);
// Project Data
thisChannelModel.DoubleInflectProject(spikesOnChan.ToList(), inflectionSample, secondInflectionIndex);
// Train Classifier
thisChannelModel.Train();
// If there was a training failure (e.g. convergence)
if (!thisChannelModel.trained)
continue;
// Note that we have to sort spikes on this channel
channelsToSort.Add(currentChannel);
// Update the unit dicationary and increment the total number of units
for (int k = 1; k <= thisChannelModel.K; ++k)
{
unitDictionary.Add(totalNumberOfUnits + k, k);
unit2Channel.Add(totalNumberOfUnits + k, i);
}
totalNumberOfUnits += thisChannelModel.K;
// Add the channel model to the list
channelModels.Add(thisChannelModel);
}
}
// All finished
trained = true;
}
