/// <summary>
/// Encode sliding window.
/// </summary>
/// <param name="dataset">The dataset.</param>
/// <param name="inputWindow">The size of the input window.</param>
/// <param name="predictedWindow">The size of the prediction window.</param>
/// <param name="inputColumns">The number of input columns.</param>
/// <param name="predictedColumns">The number of predicted columns.</param>
/// <returns>The dataset.</returns>
public static IList<BasicData> SlidingWindow(
double[][] dataset,
int inputWindow,
int predictedWindow,
int[] inputColumns,
int[] predictedColumns)
{
IList<BasicData> result = new List<BasicData>();
var totalWindowSize = inputWindow + predictedWindow;
var datasetIndex = 0;
while (dataset.Length - datasetIndex >= totalWindowSize)
{
var item = new BasicData(
inputWindow*inputColumns.Length,
predictedWindow*predictedColumns.Length);
// input columns
var inputIdx = 0;
for (var i = 0; i < inputWindow; i++)
{
for (var j = 0; j < inputColumns.Length; j++)
{
item.Input[inputIdx++] = dataset[datasetIndex + i][inputColumns[j]];
}
}
// predicted columns
var predictIdx = 0;
for (var i = 0; i < predictedWindow; i++)
{
for (var j = 0; j < predictedColumns.Length; j++)
{
item.Ideal[predictIdx++] = dataset[datasetIndex + inputWindow + i][predictedColumns[j]];
}
}
datasetIndex++;
// add the data item
result.Add(item);
}
return result;
}
/// <summary>
/// Convert two 2D arrays into a List of BasicData elements. One array holds input and the other ideal
/// vectors.
/// </summary>
/// <param name="inputData">An array of input vectors.</param>
/// <param name="idealData">An array of ideal vectors.</param>
/// <returns>A list of BasicData elements.</returns>
public static IList<BasicData> ConvertArrays(double[][] inputData, double[][] idealData)
{
// create the list
var result = new List<BasicData>();
// get the lengths
var inputCount = inputData[0].Length;
var idealCount = idealData[0].Length;
// build the list
for (var row = 0; row < inputData.Length; row++)
{
var dataRow = new BasicData(inputCount, idealCount);
Array.Copy(inputData[row], dataRow.Input, inputCount);
Array.Copy(idealData[row], dataRow.Ideal, idealCount);
result.Add(dataRow);
}
return result;
}
/// <summary>
/// Extract a supervised training set. This has both input and expected (ideal) output.
/// </summary>
/// <param name="inputBegin">The first input column.</param>
/// <param name="inputCount">The number of columns for input.</param>
/// <param name="idealBegin">The first ideal column.</param>
/// <param name="idealCount">The number of columns for ideal.</param>
/// <returns>The training set.</returns>
public IList<BasicData> ExtractSupervised(int inputBegin, int inputCount, int idealBegin, int idealCount)
{
IList<BasicData> result = new List<BasicData>();
for (var rowIndex = 0; rowIndex < Count; rowIndex++)
{
var raw = _data[rowIndex];
var row = new BasicData(inputCount, idealCount);
for (var i = 0; i < inputCount; i++)
{
row.Input[i] = ConvertNumeric(raw, inputBegin + i);
}
for (var i = 0; i < idealCount; i++)
{
row.Ideal[i] = ConvertNumeric(raw, idealBegin + i);
}
result.Add(row);
}
return result;
}
/// <summary>
/// Extract and label an unsupervised training set.
/// </summary>
/// <param name="labelIndex">The column index to use for the label.</param>
/// <returns>The training set.</returns>
public IList<BasicData> ExtractUnsupervisedLabeled(int labelIndex)
{
IList<BasicData> result = new List<BasicData>();
var dimensions = HeaderCount - 1;
for (var rowIndex = 0; rowIndex < Count; rowIndex++)
{
var raw = _data[rowIndex];
var row = new BasicData(dimensions, 0, raw[labelIndex].ToString());
var colIndex = 0;
for (var rawColIndex = 0; rawColIndex < HeaderCount; rawColIndex++)
{
if (rawColIndex != labelIndex)
{
row.Input[colIndex++] = ConvertNumeric(raw, rawColIndex);
}
}
result.Add(row);
}
return result;
}
