Spaces:
Sleeping
Sleeping
File size: 2,311 Bytes
82bd48e d39c0a6 82bd48e d39c0a6 eb6195b d39c0a6 82bd48e d39c0a6 82bd48e 3b15555 82bd48e d05f2e2 82bd48e d39c0a6 82bd48e d05f2e2 3ae1f0b 82bd48e 3ae1f0b d39c0a6 3ae1f0b d39c0a6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
# Operators
## Pre-defined
First, note that pretty much any valid Julia function which
takes one or two scalars as input, and returns on scalar as output,
is likely to be a valid operator[^1].
A selection of these and other valid operators are stated below.
**Binary**
- `+`
- `-`
- `*`
- `/`
- `^`
- `max`
- `min`
- `mod`
- `cond`
- Equal to `(x, y) -> x > 0 ? y : 0`
- `greater`
- Equal to `(x, y) -> x > y ? 1 : 0`
- `logical_or`
- Equal to `(x, y) -> (x > 0 || y > 0) ? 1 : 0`
- `logical_and`
- Equal to `(x, y) -> (x > 0 && y > 0) ? 1 : 0`
**Unary**
- `neg`
- `square`
- `cube`
- `exp`
- `abs`
- `log`
- `log10`
- `log2`
- `log1p`
- `sqrt`
- `sin`
- `cos`
- `tan`
- `sinh`
- `cosh`
- `tanh`
- `atan`
- `asinh`
- `acosh`
- `atanh_clip`
- Equal to `atanh(mod(x + 1, 2) - 1)`
- `erf`
- `erfc`
- `gamma`
- `relu`
- `round`
- `floor`
- `ceil`
- `round`
- `sign`
## Custom
Instead of passing a predefined operator as a string,
you can just define a custom function as Julia code. For example:
```python
PySRRegressor(
...,
unary_operators=["myfunction(x) = x^2"],
binary_operators=["myotherfunction(x, y) = x^2*y"],
extra_sympy_mappings={
"myfunction": lambda x: x**2,
"myotherfunction": lambda x, y: x**2 * y,
},
)
```
Make sure that it works with
`Float32` as a datatype (for default precision, or `Float64` if you set `precision=64`). That means you need to write `1.5f3`
instead of `1.5e3`, if you write any constant numbers, or simply convert a result to `Float64(...)`.
PySR expects that operators not throw an error for any input value over the entire real line from `-3.4e38` to `+3.4e38`.
Thus, for invalid inputs, such as negative numbers to a `sqrt` function, you may simply return a `NaN` of the same type as the input. For example,
```julia
my_sqrt(x) = x >= 0 ? sqrt(x) : convert(typeof(x), NaN)
```
would be a valid operator. The genetic algorithm
will preferentially selection expressions which avoid
any invalid values over the training dataset.
<!-- Footnote for 1: -->
<!-- (Will say "However, you may need to define a `extra_sympy_mapping`":) -->
[^1]: However, you will need to define a sympy equivalent in `extra_sympy_mapping` if you want to use a function not in the above list.
|