/* Method: SetTrainData
*
* Set the training data to the input and output data provided.
*
* A copy of the data is made so there are no restrictions on the
* allocation of the input/output data..
*
* Parameters:
* dataLength - The number of training data
* input - The set of inputs (an array with the dimension dataLength*inputCount)
* output - The set of desired outputs (an array with the dimension dataLength*inputCount)
*
* See also:
* <Input>, <Output>
*/
public void SetTrainData(uint dataLength, double[] input, double[] output)
{
uint numInput = (uint)input.Length / dataLength;
uint numOutput = (uint)output.Length / dataLength;
InternalData.set_train_data(dataLength, numInput, input, numOutput, output);
}
/* Method: SetTrainData
*
* Set the training data to the input and output data provided.
*
* A copy of the data is made so there are no restrictions on the
* allocation of the input/output data.
*
* Parameters:
* input - The set of inputs (an array of arrays of double data)
* output - The set of desired outputs (an array of arrays of double data)
*
* See also:
* <Input>, <Output>
*/
public void SetTrainData(double[][] input, double[][] output)
{
int dataLength = input.Length;
int inputCount = input[0].Length;
int outputCount = output[0].Length;
double[] arrayInput = new double[dataLength * inputCount];
double[] arrayOutput = new double[dataLength * outputCount];
for (int i = 0; i < dataLength; i++)
{
for (int j = 0; j < inputCount; j++)
{
arrayInput[i * inputCount + j] = input[i][j];
}
for (int j = 0; j < outputCount; j++)
{
arrayOutput[i * outputCount + j] = output[i][j];
}
}
InternalData.set_train_data((uint)dataLength, (uint)inputCount, arrayInput, (uint)outputCount, arrayOutput);
}
