Source code for eulerpi.examples.cpp.python_reference_plants

import jax.numpy as jnp
import numpy as np
from jax import jacrev, jit

from eulerpi.core.model import ArtificialModelInterface, JaxModel, Model


[docs] class JaxPlant(JaxModel, ArtificialModelInterface): """A plant model which inherits from the JaxModel to define the jacobian Param0: Water [0,1] Param1: Sun [0,1] Data0: Size [0,2] # the more water and sun the better Data1: Health [0,1], to much water is not good, too much sun is not good Data2: Sciarid :P """ param_dim = 2 data_dim = 3 PARAM_LIMITS = np.array([[0, 1], [0, 1]]) CENTRAL_PARAM = np.array([0.5, 0.5]) def __init__( self, central_param: np.ndarray = CENTRAL_PARAM, param_limits: np.ndarray = PARAM_LIMITS, name: str = None, **kwargs, ) -> None: super().__init__( central_param, param_limits, name, **kwargs, )
[docs] @classmethod def forward(cls, param): return jnp.array( [ param[0] * param[1], jnp.prod(jnp.sin(jnp.pi * param)), jnp.exp(param[0]) - 0.999, ] )
[docs] def generate_artificial_params(self, num_samples: int): return np.random.rand(num_samples, 2)
@jit def fw(param): return jnp.array( [ param[0] * param[1], jnp.prod(jnp.sin(jnp.pi * param)), jnp.exp(param[0]) - 0.999, ] ) fwJac = jit(jacrev(fw)) @jit def bw(param): return fwJac(param)
[docs] class ExternalPlant(Model, ArtificialModelInterface): """A plant model which uses functions defined outside the class to evaluate the forward pass and the jacobian Param0: Water [0,1] Param1: Sun [0,1] Data0: Size [0,2] # the more water and sun the better Data1: Health [0,1], to much water is not good, too much sun is not good Data2: Trauerfliegen :P """ param_dim = 2 data_dim = 3 PARAM_LIMITS = np.array([[0, 1], [0, 1]]) CENTRAL_PARAM = np.array([0.5, 0.5]) def __init__( self, central_param: np.ndarray = CENTRAL_PARAM, param_limits: np.ndarray = PARAM_LIMITS, name: str = None, ) -> None: super().__init__( central_param, param_limits, name, )
[docs] def forward(self, param): return fw(param)
[docs] def jacobian(self, param): return bw(param)
[docs] def generate_artificial_params(self, num_samples: int): return np.random.rand(num_samples, 2)