prepare for experiment

problems/function_fitting/__init__.py

@@ -1,4 +1,5 @@
 from .function_fitting_problem import FunctionFittingProblem
 from .xor import *
 from .sin import *
-from .diy import *
+from .diy import *
+from .enhance_logic import *

problems/function_fitting/enhance_logic.py

@@ -0,0 +1,54 @@
+"""
+xor problem in multiple dimensions
+"""
+
+from itertools import product
+import numpy as np
+
+
+class EnhanceLogic:
+    def __init__(self, name="xor", n=2):
+        self.name = name
+        self.n = n
+        self.num_inputs = n
+        self.num_outputs = 1
+        self.batch = 2 ** n
+        self.forward_way = 'pop_batch'
+
+        self.inputs = np.array(generate_permutations(n), dtype=np.float32)
+
+        if self.name == "xor":
+            self.outputs = np.sum(self.inputs, axis=1) % 2
+        elif self.name == "and":
+            self.outputs = np.all(self.inputs==1, axis=1)
+        elif self.name == "or":
+            self.outputs = np.any(self.inputs==1, axis=1)
+        else:
+            raise NotImplementedError("Only support xor, and, or")
+        self.outputs = self.outputs[:, np.newaxis]
+
+
+    def refactor_config(self, config):
+        config.basic.forward_way = self.forward_way
+        config.basic.num_inputs = self.num_inputs
+        config.basic.num_outputs = self.num_outputs
+        config.basic.problem_batch = self.batch
+
+
+    def ask_for_inputs(self):
+        return self.inputs
+
+    def evaluate_predict(self, predict):
+        # print((predict - self.outputs) ** 2)
+        return -np.mean((predict - self.outputs) ** 2)
+
+
+
+def generate_permutations(n):
+    permutations = [list(i) for i in product([0, 1], repeat=n)]
+
+    return permutations
+
+
+if __name__ == '__main__':
+    _ = EnhanceLogic(4)