Fix runtime error in Task 0.5 due to numpy version

minitorch/module.py

@@ -32,12 +32,18 @@ def modules(self) -> Sequence[Module]:
     def train(self) -> None:
         """Set the mode of this module and all descendent modules to `train`."""
         # TODO: Implement for Task 0.4.
-        raise NotImplementedError("Need to implement for Task 0.4")
+        self.training = True
+        m: Dict[str, Module] = self.__dict__["_modules"]
+        for module in m.values():
+            module.train()
 
     def eval(self) -> None:
         """Set the mode of this module and all descendent modules to `eval`."""
         # TODO: Implement for Task 0.4.
-        raise NotImplementedError("Need to implement for Task 0.4")
+        self.training = False
+        m: Dict[str, Module] = self.__dict__["_modules"]
+        for module in m.values():
+            module.eval()
 
     def named_parameters(self) -> Sequence[Tuple[str, Parameter]]:
         """Collect all the parameters of this module and its descendents.
@@ -48,12 +54,25 @@ def named_parameters(self) -> Sequence[Tuple[str, Parameter]]:
 
         """
         # TODO: Implement for Task 0.4.
-        raise NotImplementedError("Need to implement for Task 0.4")
+        m: Dict[str, Module] = self.__dict__["_modules"]
+        p: Dict[str, Parameter] = self.__dict__["_parameters"]
+        nps = [(name, parameter) for name, parameter in p.items()]
+        for module_name, module in m.items():
+            nps += [
+                (".".join([module_name, name]), parameter)
+                for name, parameter in module.named_parameters()
+            ]
+        return nps
 
     def parameters(self) -> Sequence[Parameter]:
         """Enumerate over all the parameters of this module and its descendents."""
         # TODO: Implement for Task 0.4.
-        raise NotImplementedError("Need to implement for Task 0.4")
+        m: Dict[str, Module] = self.__dict__["_modules"]
+        p: Dict[str, Parameter] = self.__dict__["_parameters"]
+        ps = [parameter for parameter in p.values()]
+        for module in m.values():
+            ps += module.parameters()
+        return ps
 
     def add_parameter(self, k: str, v: Any) -> Parameter:
         """Manually add a parameter. Useful helper for scalar parameters.

minitorch/operators.py

@@ -1,9 +1,11 @@
-"""Collection of the core mathematical operators used throughout the code base."""
+"""
+Collection of the core mathematical operators used throughout the code base.
+"""
 
 import math
 
 # ## Task 0.1
-from typing import Callable, Iterable
+from typing import Callable, Iterable, Optional
 
 #
 # Implementation of a prelude of elementary functions.
@@ -33,6 +35,159 @@
 
 
 # TODO: Implement for Task 0.1.
+def mul(x: float, y: float) -> float:
+    """
+    Scalar multiplication.
+
+    Args:
+        x: A float value
+        y: A float value
+
+    Returns:
+        A float value x multiplied by y
+    """
+    return x * y
+
+
+def id(x: float) -> float:
+    """
+    Identity function.
+
+    Args:
+        x: A float value.
+
+    Returns:
+        The float input unchanged.
+    """
+    return x
+
+
+def add(x: float, y: float) -> float:
+    """
+    Addition function.
+
+    Args:
+        x: A float value.
+        y: A float value.
+
+    Returns:
+        A float value x added to y
+    """
+    return x + y
+
+
+def neg(x: float) -> float:
+    """
+    Negation function.
+
+    Args:
+        x: A float value.
+
+    Returns:
+        A float value x multiplied by -1.0
+    """
+    return -x
+
+
+def lt(x: float, y: float) -> bool:
+    """
+    Compares 2 float values.
+
+    Args:
+        x: A float value.
+        y: A float value.
+
+    Returns:
+        A boolean value. True if x is less than y.
+    """
+    return x < y
+
+
+def eq(x: float, y: float) -> bool:
+    """
+    Equality function.
+
+    Args:
+        x: A float value.
+        y: A float value.
+
+    Returns:
+        A boolean value. True if x is equal to y.
+    """
+    return x == y
+
+
+def max(x: float, y: float) -> float:
+    """
+    Max function.
+
+    Args:
+        x: A float value.
+        y: A float value.
+
+    Returns:
+        The larger value between x and y.
+    """
+    return x if x > y else y
+
+
+def is_close(
+    x: float, y: float, atol: Optional[float] = 1e-8, rtol: Optional[float] = 1e-5
+) -> bool:
+    """
+    Checks if x is close to y. Obtained equation from https://pytorch.org/docs/stable/generated/torch.isclose.html.
+
+    Args:
+        x: A float value.
+        y: A float value.
+        atol: Absolute tolerance. Default: 1e-8
+        rtol: Relative tolerance. Default: 1e-5
+
+    Returns:
+        A boolean value indicating if x is close to y
+    """
+    return math.fabs(x - y) <= (atol + rtol * math.fabs(y))
+
+
+def sigmoid(x: float) -> float:
+    """
+    Sigmoid function.
+
+    Args:
+        x: A float value
+
+    Returns:
+        A float value 1 / (1 + e^-x)
+    """
+    return 1.0 / (1.0 + math.exp(-x))
+
+
+def relu(x: float) -> float:
+    return 0.0 if x <= 0.0 else x
+
+
+def log(x: float) -> float:
+    return math.log(x)
+
+
+def exp(x: float) -> float:
+    return math.exp(x)
+
+
+def inv(x: float) -> float:
+    return 1.0 / x
+
+
+def log_back(x: float, y: float) -> float:
+    return y / x
+
+
+def inv_back(x: float, y: float) -> float:
+    return -y / x**2
+
+
+def relu_back(x: float, y: float) -> float:
+    return 0.0 if x <= 0 else y
 
 
 # ## Task 0.3
@@ -52,3 +207,37 @@
 
 
 # TODO: Implement for Task 0.3.
+def map(func: Callable[[float], float], xs: Iterable[float]) -> Iterable[float]:
+    return [func(x) for x in xs]
+
+
+def zipWith(
+    func: Callable[[float, float], float], xs: Iterable[float], ys: Iterable[float]
+) -> Iterable[float]:
+    return [func(x, y) for x, y in zip(xs, ys)]
+
+
+def reduce(func: Callable[[float, float], float], xs: Iterable[float]) -> float:
+    if len(xs) == 0:
+        return 0.0
+
+    acc = xs[0]
+    for x in xs[1:]:
+        acc = func(acc, x)
+    return acc
+
+
+def negList(xs: Iterable[float]) -> Iterable[float]:
+    return map(neg, xs)
+
+
+def addLists(xs: Iterable[float], ys: Iterable[float]) -> Iterable[float]:
+    return zipWith(add, xs, ys)
+
+
+def sum(xs: Iterable[float]) -> float:
+    return reduce(add, xs)
+
+
+def prod(xs: Iterable[float]) -> float:
+    return reduce(mul, xs)

requirements.txt

@@ -1,7 +1,7 @@
 colorama==0.4.3
 hypothesis == 6.54
 numba == 0.60
-numpy == 2.0.0
+numpy<2
 pre-commit == 2.20.0
 pytest == 8.3.2
 pytest-env

tests/test_operators.py

@@ -105,43 +105,49 @@ def test_sigmoid(a: float) -> None:
     * It is always between 0.0 and 1.0.
     * one minus sigmoid is the same as sigmoid of the negative
     * It crosses 0 at 0.5
-    * It is  strictly increasing.
+    * It is strictly increasing.
     """
     # TODO: Implement for Task 0.2.
-    raise NotImplementedError("Need to implement for Task 0.2")
+    assert 0.0 <= sigmoid(a) and sigmoid(a) <= 1.0
+    assert_close(1 - sigmoid(a), sigmoid(-a))
+    assert_close(sigmoid(0.0), 0.5)
 
 
 @pytest.mark.task0_2
 @given(small_floats, small_floats, small_floats)
 def test_transitive(a: float, b: float, c: float) -> None:
     """Test the transitive property of less-than (a < b and b < c implies a < c)"""
     # TODO: Implement for Task 0.2.
-    raise NotImplementedError("Need to implement for Task 0.2")
+    if lt(a, b) and lt(b, c):
+        assert lt(a, c)
 
 
 @pytest.mark.task0_2
-def test_symmetric() -> None:
+@given(small_floats, small_floats)
+def test_symmetric(a: float, b: float) -> None:
     """Write a test that ensures that :func:`minitorch.operators.mul` is symmetric, i.e.
     gives the same value regardless of the order of its input.
     """
     # TODO: Implement for Task 0.2.
-    raise NotImplementedError("Need to implement for Task 0.2")
+    assert_close(mul(a, b), mul(b, a))
 
 
 @pytest.mark.task0_2
-def test_distribute() -> None:
+@given(small_floats, small_floats, small_floats)
+def test_distribute(a: float, b: float, c: float) -> None:
     r"""Write a test that ensures that your operators distribute, i.e.
     :math:`z \times (x + y) = z \times x + z \times y`
     """
     # TODO: Implement for Task 0.2.
-    raise NotImplementedError("Need to implement for Task 0.2")
+    assert_close(mul(c, add(a, b)), add(mul(c, a), mul(c, b)))
 
 
 @pytest.mark.task0_2
-def test_other() -> None:
+@given(small_floats)
+def test_other(a: float) -> None:
     """Write a test that ensures some other property holds for your functions."""
     # TODO: Implement for Task 0.2.
-    raise NotImplementedError("Need to implement for Task 0.2")
+    assert id(a) == a
 
 
 # ## Task 0.3  - Higher-order functions
@@ -169,7 +175,10 @@ def test_sum_distribute(ls1: List[float], ls2: List[float]) -> None:
     is the same as the sum of each element of `ls1` plus each element of `ls2`.
     """
     # TODO: Implement for Task 0.3.
-    raise NotImplementedError("Need to implement for Task 0.3")
+    assert_close(
+        minitorch.operators.sum(addLists(ls1, ls2)),
+        add(minitorch.operators.sum(ls1), minitorch.operators.sum(ls2)),
+    )
 
 
 @pytest.mark.task0_3