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 |