remove create_func....

problem/func_fit/__init__.py

@@ -0,0 +1,3 @@
+from .func_fit import FuncFit, FuncFitConfig
+from .xor import XOR
+from .xor3d import XOR3d

problem/func_fit/func_fit.py

@@ -0,0 +1,69 @@
+from typing import Callable
+from dataclasses import dataclass
+
+import jax
+import jax.numpy as jnp
+from config import ProblemConfig
+from core import Problem, State
+
+
+@dataclass(frozen=True)
+class FuncFitConfig(ProblemConfig):
+    error_method: str = 'mse'
+
+    def __post_init__(self):
+        assert self.error_method in {'mse', 'rmse', 'mae', 'mape'}
+
+
+class FuncFit(Problem):
+
+    def __init__(self, config: FuncFitConfig = FuncFitConfig()):
+        self.config = config
+        super().__init__(config)
+
+    def evaluate(self, randkey, state: State, act_func: Callable, params):
+
+        predict = act_func(state, self.inputs, params)
+
+        if self.config.error_method == 'mse':
+            loss = jnp.mean((predict - self.targets) ** 2)
+
+        elif self.config.error_method == 'rmse':
+            loss = jnp.sqrt(jnp.mean((predict - self.targets) ** 2))
+
+        elif self.config.error_method == 'mae':
+            loss = jnp.mean(jnp.abs(predict - self.targets))
+
+        elif self.config.error_method == 'mape':
+            loss = jnp.mean(jnp.abs((predict - self.targets) / self.targets))
+
+        else:
+            raise NotImplementedError
+
+        return -loss
+
+    def show(self, randkey, state: State, act_func: Callable, params):
+        predict = act_func(state, self.inputs, params)
+        inputs, target, predict = jax.device_get([self.inputs, self.targets, predict])
+        loss = -self.evaluate(randkey, state, act_func, params)
+        msg = ""
+        for i in range(inputs.shape[0]):
+            msg += f"input: {inputs[i]}, target: {target[i]}, predict: {predict[i]}\n"
+        msg += f"loss: {loss}\n"
+        print(msg)
+
+    @property
+    def inputs(self):
+        raise NotImplementedError
+
+    @property
+    def targets(self):
+        raise NotImplementedError
+
+    @property
+    def input_shape(self):
+        raise NotImplementedError
+
+    @property
+    def output_shape(self):
+        raise NotImplementedError

problem/func_fit/func_fitting.py

@@ -1,21 +0,0 @@
-from dataclasses import dataclass
-from typing import Callable
-
-from config import ProblemConfig
-from core import Problem, State
-
-
-@dataclass(frozen=True)
-class FuncFitConfig:
-    pass
-
-
-class FuncFit(Problem):
-    def __init__(self, config: ProblemConfig):
-        self.config = ProblemConfig
-
-    def setup(self, state=State()):
-        pass
-
-    def evaluate(self, state: State, act_func: Callable, params):
-        pass

problem/func_fit/xor.py

@@ -0,0 +1,36 @@
+import numpy as np
+
+from .func_fit import FuncFit, FuncFitConfig
+
+
+class XOR(FuncFit):
+
+    def __init__(self, config: FuncFitConfig = FuncFitConfig()):
+        self.config = config
+        super().__init__(config)
+
+    @property
+    def inputs(self):
+        return np.array([
+            [0, 0],
+            [0, 1],
+            [1, 0],
+            [1, 1]
+        ])
+
+    @property
+    def targets(self):
+        return np.array([
+            [0],
+            [1],
+            [1],
+            [0]
+        ])
+
+    @property
+    def input_shape(self):
+        return (4, 2)
+
+    @property
+    def output_shape(self):
+        return (4, 1)

problem/func_fit/xor3d.py

@@ -0,0 +1,44 @@
+import numpy as np
+
+from .func_fit import FuncFit, FuncFitConfig
+
+
+class XOR3d(FuncFit):
+
+    def __init__(self, config: FuncFitConfig = FuncFitConfig()):
+        self.config = config
+        super().__init__(config)
+
+    @property
+    def inputs(self):
+        return np.array([
+            [0, 0, 0],
+            [0, 0, 1],
+            [0, 1, 0],
+            [0, 1, 1],
+            [1, 0, 0],
+            [1, 0, 1],
+            [1, 1, 0],
+            [1, 1, 1],
+        ])
+
+    @property
+    def targets(self):
+        return np.array([
+            [0],
+            [1],
+            [1],
+            [0],
+            [1],
+            [0],
+            [0],
+            [1]
+        ])
+
+    @property
+    def input_shape(self):
+        return (8, 3)
+
+    @property
+    def output_shape(self):
+        return (8, 1)