RealKernelProcessor<T>.Correlate Method

public override void Correlate(
	int signalLength,
	ReadOnlySpan<T> signal,
	int signalStride,
	int resultLength,
	Span<T> result,
	int resultStride
)

Public Overrides Sub Correlate ( 
	signalLength As Integer,
	signal As ReadOnlySpan(Of T),
	signalStride As Integer,
	resultLength As Integer,
	result As Span(Of T),
	resultStride As Integer
)

public:
virtual void Correlate(
	int signalLength, 
	ReadOnlySpan<T> signal, 
	int signalStride, 
	int resultLength, 
	Span<T> result, 
	int resultStride
) override

abstract Correlate : 
        signalLength : int * 
        signal : ReadOnlySpan<'T> * 
        signalStride : int * 
        resultLength : int * 
        result : Span<'T> * 
        resultStride : int -> unit 
override Correlate : 
        signalLength : int * 
        signal : ReadOnlySpan<'T> * 
        signalStride : int * 
        resultLength : int * 
        result : Span<'T> * 
        resultStride : int -> unit 

Parameters

signalLength  Int32

The length of the input signal.

signal  ReadOnlySpan<T>

A read-only span containing the real input signal.

signalStride  Int32

The stride between successive elements in the signal. Use 1 for contiguous data.

resultLength  Int32

The expected length of the result (should match GetOutputLength(Int32)).

result  Span<T>

A span to store the correlation result. Must have capacity for at least resultLength elements.

resultStride  Int32

The stride between successive elements in the result. Use 1 for contiguous output.

Real correlation computes: y[n] = Σ_m x[n - (m - a)] · h[m] with a time-reversed kernel. For real-valued signals, this is equivalent to convolving with the time-reversed kernel. This operation is commonly used for template matching and signal similarity measurement.

The kernel reversal is handled internally and efficiently - the kernel array is not actually reversed in memory. The implementation uses optimized algorithms that account for the reversal during computation.

Comparison with other libraries:

• NumPy np.correlate(signal, kernel, mode): Use the corresponding ConvolutionMode value.
• SciPy scipy.signal.correlate: Similar behavior with support for different boundary modes.

Reference