addGpFunction

Note: This function is not supported by Community Edition. You can get a trial of Shark from DolphinDB official website.

Syntax

addGpFunction(engine, func)

Arguments

engine is the engine object returned by createGPLearnEngine.

func is a user-defined function. Currently it does not support complex assignment, if or for statement. Only return statement can be used to return a combination of the training functions (see Appendix for supported functions). For example:
def f(x, y){
  return cos(x+y)
}

Details

Add a user-defined training function to the GPLearn engine.

Examples

def f(x, y){
  return cos(x+y)
}
addGpFunction(engine,f)

Appendix

The following table lists available functions for building and evolving programs. The parameter n indicates the sliding window size taken from windowRange. For all m-functions, if the current window is smaller than n, 0 is returned.

Function Number of Inputs Description
add(x,y) 2 Addition
sub(x,y) 2 Subtraction
mul(x,y) 2 Multiplication
div(x,y) 2 Division, returns 1 if the absolute value of the divisor is less than 0.001
max(x,y) 2 Maximum value
min(x,y) 2 Minimum value
sqrt(x) 1 Square root based on absolute value
log(x) 1 If x < 0.001, returns 0, otherwise returns log(abs(x))
neg(x) 1 Negation
reciprocal(x) 1 Reciprocal, returns 0 if the absolute value of x is less than 0.001
abs(x) 1 Absolute value
sin(x) 1 Sine function
cos(x) 1 Cosine function
tan(x) 1 Tangent function
sig(x) 1 Sigmoid function
mdiff(x, n) 1 n-th order difference of x
mcovar(x, y, n) 2 Covariance of x and y with a sliding window of size n
mcorr(x, y, n) 2 Correlation of x and y with a sliding window of size n
mstd(x, n) 1 Sample standard deviation of x with a sliding window of size n
mmax(x, n) 1 Maximum value of x with a sliding window of size n
mmin(x, n) 1 Minimum value of x with a sliding window of size n
msum(x, n) 1 Sum of x with a sliding window of size n
mavg(x, n) 1 Average of x with a sliding window of size n
mprod(x, n) 1 Product of x with a sliding window of size n
mvar(x, n) 1 Sample variance of x with a sliding window of size n
mvarp(x, n) 1 Population variance of x with a sliding window of size n
mstdp(x, n) 1 Population standard deviation of x with a sliding window of size n
mimin(x, n) 1 Index of the minimum value of x with a sliding window of size n
mimax(x, n) 1 Index of the maximum value of x with a sliding window of size n
mbeta(x, y, n) 2 Least squares estimate of the regression coefficient of x on y with a sliding window of size n
mwsum(x, y, n) 2 Inner product of x and y with a sliding window of size n
mwavg(x, y, n) 2 Weighted average of x using y as weights with a sliding window of size n