Curve Fitting (Linear) (G Dataflow)

A Boolean that specifies whether to reset the internal state of the node.

This input is available only if you wire a double-precision, floating-point number to y or signal.

Default: False

Input signal.

This input accepts a waveform or a 1D array of waveforms.

This input changes to y when the data type is a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.

Dependent values representing the y-values of the data set.

This input accepts a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.

This input changes to signal when the data type is a waveform or a 1D array of waveforms.

Independent values representing the x-values of the data set.

This input accepts a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.

This input is available only if you wire a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers to y or signal.

An array of weights for the data set.

This input is available only if you wire one of the following data types to signal or y:

• Waveform
• 1D array of waveforms
• 1D array of double-precision, floating-point numbers

Value that determines when to stop the iterative adjustment of the slope and intercept when you use the Least Absolute Residual or Bisquare methods.

If tolerance is less than or equal to 0, this node sets tolerance to 0.0001.

This input is available only if you wire one of the following data types to signal or y.

• Waveform
• 1D array of waveforms
• 1D array of double-precision, floating-point numbers

Default: 0.0001

Length of each set of data. The node performs computation for each set of data.

When you set block size to zero, the node calculates a cumulative solution for the input data from the time that you called or initialized the node. When block size is greater than zero, the node calculates the solution for only the newest set of input data.

This input is available only if you wire a double-precision, floating-point number to signal or y.

Default: 100

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error	error in contains an error
If no error occurred before the node runs, the node begins execution normally. If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.	If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Default: No error

Upper and lower constraints for the slope and intercept of the calculated best linear fit.

This input is available only if you wire one of the following data types to signal or y:

• Waveform
• 1D array of waveforms
• 1D array of double-precision, floating-point numbers

slope min

Lower bound for the slope.

Default: -Infinity

slope max

Upper bound for the slope.

Default: Infinity

intercept min

Lower bound for the intercept.

Default: -Infinity

intercept max

Upper bound for the intercept.

Default: Infinity

Method of fitting data to a line.

This input is available only if you wire one of the following data types to signal or y:

• Waveform
• 1D array of waveforms
• 1D array of double-precision, floating-point numbers

Algorithm for the Bisquare Method

The bisquare method of fitting finds the slope and intercept using an iterative process, as shown in the following illustration.

The node calculates residue according to the following equation:

$\frac{1}{N}\underset{i=0}{\overset{N-1}{\sum }}{w}_i{(f_i-y_i)}^2$

where

• N is the length of y or the number of data values in a waveform
• w_i is the i^th element of weight
• f_i is the i^th element of best linear fit
• y_i is the i^th element of y or the i^th data value in a waveform.

Default: Least Square

Linear curve that best fits the input signal.

This output can return the following data types:

• Waveform
• 1D array of waveforms
• 1D array of double-precision, floating-point numbers

Slope of the calculated best linear fit.

This output can return a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.

Intercept of the calculated best linear fit.

This output can return a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error	error in contains an error
If no error occurred before the node runs, the node begins execution normally. If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.	If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Weighted mean error of the fitted model.

This output can return a double-precision, floating-point number or a 1D array of double-precision, floating-point numbers.