Remove todos

Author: dantp-ai

minitorch/operators.py

@@ -12,49 +12,48 @@
 
 def mul(x: float, y: float) -> float:
     "$f(x, y) = x * y$"
-    # TODO: Implement for Task 0.1.
     return x * y
 
 
 def id(x: float) -> float:
     "$f(x) = x$"
-    # TODO: Implement for Task 0.1.
+
     return x
 
 
 def add(x: float, y: float) -> float:
     "$f(x, y) = x + y$"
-    # TODO: Implement for Task 0.1.
+
     return x + y
 
 
 def neg(x: float) -> float:
     "$f(x) = -x$"
-    # TODO: Implement for Task 0.1.
+
     return -x
 
 
 def lt(x: float, y: float) -> float:
     "$f(x) =$ 1.0 if x is less than y else 0.0"
-    # TODO: Implement for Task 0.1.
+
     return float(x < y)
 
 
 def eq(x: float, y: float) -> float:
     "$f(x) =$ 1.0 if x is equal to y else 0.0"
-    # TODO: Implement for Task 0.1.
+
     return float(x == y)
 
 
 def max(x: float, y: float) -> float:
     "$f(x) =$ x if x is greater than y else y"
-    # TODO: Implement for Task 0.1.
+
     return x if x >= y else y
 
 
 def is_close(x: float, y: float) -> float:
     "$f(x) = |x - y| < 1e-2$"
-    # TODO: Implement for Task 0.1.
+
     if x >= y:
         return x - y <= 1e-2
     else:
@@ -73,7 +72,7 @@ def sigmoid(x: float) -> float:
 
     for stability.
     """
-    # TODO: Implement for Task 0.1.
+
     return (1.0 / (1.0 + math.exp(-x))) if x >= 0 else math.exp(x) / (1 + math.exp(x))
 
 
@@ -83,7 +82,7 @@ def relu(x: float) -> float:
 
     (See https://en.wikipedia.org/wiki/Rectifier_(neural_networks) .)
     """
-    # TODO: Implement for Task 0.1.
+
     return x if x > 0.0 else 0
 
 
@@ -102,26 +101,26 @@ def exp(x: float) -> float:
 
 def log_back(x: float, d: float) -> float:
     r"If $f = log$ as above, compute $d \times f'(x)$"
-    # TODO: Implement for Task 0.1.
+
     return d / x
 
 
 def inv(x: float) -> float:
     "$f(x) = 1/x$"
-    # TODO: Implement for Task 0.1.
+
     if x != 0.0:
         return 1 / x
 
 
 def inv_back(x: float, d: float) -> float:
     r"If $f(x) = 1/x$ compute $d \times f'(x)$"
-    # TODO: Implement for Task 0.1.
+
     return -(x ** (-2)) * d
 
 
 def relu_back(x: float, d: float) -> float:
     r"If $f = relu$ compute $d \times f'(x)$"
-    # TODO: Implement for Task 0.1.
+
     return d * (x > 0.0)