Update the sync data v1

xautodl/datasets/math_base_funcs.py

@@ -5,15 +5,13 @@ import math
 import abc
 import copy
 import numpy as np
-from typing import Optional
 import torch
-import torch.utils.data as data
 
 
 class FitFunc(abc.ABC):
     """The fit function that outputs f(x) = a * x^2 + b * x + c."""
 
-    def __init__(self, freedom: int, list_of_points=None, params=None):
+    def __init__(self, freedom: int, list_of_points=None, params=None, xstr="x"):
         self._params = dict()
         for i in range(freedom):
             self._params[i] = None
@@ -24,6 +22,7 @@ class FitFunc(abc.ABC):
             self.fit(list_of_points=list_of_points)
         if params is not None:
             self.set(params)
+        self._xstr = str(xstr)
 
     def set(self, params):
         self._params = copy.deepcopy(params)
@@ -33,6 +32,13 @@ class FitFunc(abc.ABC):
             if value is None:
                 raise ValueError("The {:} is None".format(key))
 
+    @property
+    def xstr(self):
+        return self._xstr
+
+    def reset_xstr(self, xstr):
+        self._xstr = str(xstr)
+
     @abc.abstractmethod
     def __call__(self, x):
         raise NotImplementedError
@@ -106,8 +112,8 @@ class FitFunc(abc.ABC):
 class LinearFunc(FitFunc):
     """The linear function that outputs f(x) = a * x + b."""
 
-    def __init__(self, list_of_points=None, params=None):
-        super(LinearFunc, self).__init__(2, list_of_points, params)
+    def __init__(self, list_of_points=None, params=None, xstr="x"):
+        super(LinearFunc, self).__init__(2, list_of_points, params, xstr)
 
     def __call__(self, x):
         self.check_valid()
@@ -117,18 +123,19 @@ class LinearFunc(FitFunc):
         return weights[0] * x + weights[1]
 
     def __repr__(self):
-        return "{name}({a} * x + {b})".format(
+        return "{name}({a} * {x} + {b})".format(
             name=self.__class__.__name__,
             a=self._params[0],
             b=self._params[1],
+            x=self.xstr,
         )
 
 
 class QuadraticFunc(FitFunc):
     """The quadratic function that outputs f(x) = a * x^2 + b * x + c."""
 
-    def __init__(self, list_of_points=None, params=None):
-        super(QuadraticFunc, self).__init__(3, list_of_points, params)
+    def __init__(self, list_of_points=None, params=None, xstr="x"):
+        super(QuadraticFunc, self).__init__(3, list_of_points, params, xstr)
 
     def __call__(self, x):
         self.check_valid()
@@ -138,11 +145,12 @@ class QuadraticFunc(FitFunc):
         return weights[0] * x * x + weights[1] * x + weights[2]
 
     def __repr__(self):
-        return "{name}({a} * x^2 + {b} * x + {c})".format(
+        return "{name}({a} * {x}^2 + {b} * {x} + {c})".format(
             name=self.__class__.__name__,
             a=self._params[0],
             b=self._params[1],
             c=self._params[2],
+            x=self.xstr,
         )
 
 
@@ -165,12 +173,13 @@ class CubicFunc(FitFunc):
         return weights[0] * x ** 3 + weights[1] * x ** 2 + weights[2] * x + weights[3]
 
     def __repr__(self):
-        return "{name}({a} * x^3 + {b} * x^2 + {c} * x + {d})".format(
+        return "{name}({a} * {x}^3 + {b} * {x}^2 + {c} * {x} + {d})".format(
             name=self.__class__.__name__,
             a=self._params[0],
             b=self._params[1],
             c=self._params[2],
             d=self._params[3],
+            x=self.xstr,
         )