Task0

.github/workflows/classroom.yml

@@ -0,0 +1,67 @@
+name: Autograding Tests
+'on':
+- workflow_dispatch
+- repository_dispatch
+permissions:
+  checks: write
+  actions: read
+  contents: read
+jobs:
+  run-autograding-tests:
+    runs-on: ubuntu-latest
+    if: github.actor != 'github-classroom[bot]'
+    steps:
+    - name: Checkout code
+      uses: actions/checkout@v4
+    - name: Setup
+      id: setup
+      uses: classroom-resources/autograding-command-grader@v1
+      with:
+        test-name: Setup
+        setup-command: sudo -H pip3 install -qr requirements.txt; sudo -H pip3 install
+          flake8==5.0.4
+        command: flake8 --ignore "N801, E203, E266, E501, W503, F812, E741, N803,
+          N802, N806" minitorch/ tests/ project/; mypy minitorch/*
+        timeout: 10
+    - name: Task 0.1
+      id: task-0-1
+      uses: classroom-resources/autograding-command-grader@v1
+      with:
+        test-name: Task 0.1
+        setup-command: sudo -H pip3 install -qr requirements.txt
+        command: pytest -m task0_1
+        timeout: 10
+    - name: Task 0.2
+      id: task-0-2
+      uses: classroom-resources/autograding-command-grader@v1
+      with:
+        test-name: Task 0.2
+        setup-command: sudo -H pip3 install -qr requirements.txt
+        command: pytest -m task0_2
+        timeout: 10
+    - name: Task 0.3
+      id: task-0-3
+      uses: classroom-resources/autograding-command-grader@v1
+      with:
+        test-name: Task 0.3
+        setup-command: sudo -H pip3 install -qr requirements.txt
+        command: pytest -m task0_3
+        timeout: 10
+    - name: Task 0.4
+      id: task-0-4
+      uses: classroom-resources/autograding-command-grader@v1
+      with:
+        test-name: Task 0.4
+        setup-command: sudo -H pip3 install -qr requirements.txt
+        command: pytest -m task0_4
+        timeout: 10
+    - name: Autograding Reporter
+      uses: classroom-resources/autograding-grading-reporter@v1
+      env:
+        SETUP_RESULTS: "${{steps.setup.outputs.result}}"
+        TASK-0-1_RESULTS: "${{steps.task-0-1.outputs.result}}"
+        TASK-0-2_RESULTS: "${{steps.task-0-2.outputs.result}}"
+        TASK-0-3_RESULTS: "${{steps.task-0-3.outputs.result}}"
+        TASK-0-4_RESULTS: "${{steps.task-0-4.outputs.result}}"
+      with:
+        runners: setup,task-0-1,task-0-2,task-0-3,task-0-4

README.md

@@ -1,3 +1,4 @@
+[![Open in Visual Studio Code](https://classroom.github.com/assets/open-in-vscode-2e0aaae1b6195c2367325f4f02e2d04e9abb55f0b24a779b69b11b9e10269abc.svg)](https://classroom.github.com/online_ide?assignment_repo_id=20449663&assignment_repo_type=AssignmentRepo)
 # MiniTorch Module 0
 
 <img src="https://minitorch.github.io/minitorch.svg" width="50%">

minitorch/datasets.py

@@ -67,19 +67,29 @@ def circle(N):
 
 
 def spiral(N):
-
     def x(t):
         return t * math.cos(t) / 20.0
 
     def y(t):
         return t * math.sin(t) / 20.0
-    X = [(x(10.0 * (float(i) / (N // 2))) + 0.5, y(10.0 * (float(i) / (N //
-        2))) + 0.5) for i in range(5 + 0, 5 + N // 2)]
-    X = X + [(y(-10.0 * (float(i) / (N // 2))) + 0.5, x(-10.0 * (float(i) /
-        (N // 2))) + 0.5) for i in range(5 + 0, 5 + N // 2)]
+
+    X = [
+        (x(10.0 * (float(i) / (N // 2))) + 0.5, y(10.0 * (float(i) / (N // 2))) + 0.5)
+        for i in range(5 + 0, 5 + N // 2)
+    ]
+    X = X + [
+        (y(-10.0 * (float(i) / (N // 2))) + 0.5, x(-10.0 * (float(i) / (N // 2))) + 0.5)
+        for i in range(5 + 0, 5 + N // 2)
+    ]
     y2 = [0] * (N // 2) + [1] * (N // 2)
     return Graph(N, X, y2)
 
 
-datasets = {'Simple': simple, 'Diag': diag, 'Split': split, 'Xor': xor,
-    'Circle': circle, 'Spiral': spiral}
+datasets = {
+    "Simple": simple,
+    "Diag": diag,
+    "Split": split,
+    "Xor": xor,
+    "Circle": circle,
+    "Spiral": spiral,
+}

minitorch/module.py

@@ -31,13 +31,15 @@ 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
+        for m in self.modules():
+            m.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
+        for m in self.modules():
+            m.eval()
 
     def named_parameters(self) -> Sequence[Tuple[str, Parameter]]:
         """Collect all the parameters of this module and its descendents.
@@ -47,13 +49,21 @@ def named_parameters(self) -> Sequence[Tuple[str, Parameter]]:
             The name and `Parameter` of each ancestor parameter.
 
         """
-        # TODO: Implement for Task 0.4.
-        raise NotImplementedError("Need to implement for Task 0.4")
+        total = list(self._parameters.items())
+        for mod_name, mod in self._modules.items():
+            params = [
+                (f"{mod_name}.{pname}", pval) for pname, pval in mod.named_parameters()
+            ]
+            total.extend(params)
+        return total
 
     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")
+        total = list(self._parameters.values())
+        for m in self.modules():
+            params = m.parameters()
+            total.extend(params)
+        return total
 
     def add_parameter(self, k: str, v: Any) -> Parameter:
         """Manually add a parameter. Useful helper for scalar parameters.

minitorch/operators.py

@@ -1,54 +1,124 @@
 """Collection of the core mathematical operators used throughout the code base."""
 
 import math
+from typing import Callable, Iterable, Iterator
 
-# ## Task 0.1
-from typing import Callable, Iterable
-
-#
-# Implementation of a prelude of elementary functions.
-
-# Mathematical functions:
-# - mul
-# - id
-# - add
-# - neg
-# - lt
-# - eq
-# - max
-# - is_close
-# - sigmoid
-# - relu
-# - log
-# - exp
-# - log_back
-# - inv
-# - inv_back
-# - relu_back
-#
-# For sigmoid calculate as:
-# $f(x) =  \frac{1.0}{(1.0 + e^{-x})}$ if x >=0 else $\frac{e^x}{(1.0 + e^{x})}$
-# For is_close:
-# $f(x) = |x - y| < 1e-2$
-
-
-# TODO: Implement for Task 0.1.
-
-
-# ## Task 0.3
-
-# Small practice library of elementary higher-order functions.
-
-# Implement the following core functions
-# - map
-# - zipWith
-# - reduce
-#
-# Use these to implement
-# - negList : negate a list
-# - addLists : add two lists together
-# - sum: sum lists
-# - prod: take the product of lists
-
-
-# TODO: Implement for Task 0.3.
+
+def mul(x: float, y: float) -> float:
+    return x * y
+
+
+def id(x: float) -> float:
+    return x
+
+
+def add(x: float, y: float) -> float:
+    return x + y
+
+
+def neg(x: float) -> float:
+    return -x
+
+
+def lt(x: float, y: float) -> bool:
+    if x < y:
+        return True
+    else:
+        return False
+
+
+def max(x: float, y: float) -> float:
+    if x > y:
+        return x
+    else:
+        return y
+
+
+def eq(x: float, y: float) -> bool:
+    return x == y
+
+
+def is_close(x: float, y: float) -> bool:
+    epsilon = abs(x - y)
+    if epsilon > 1e-2:
+        return False
+    else:
+        return True
+
+
+def sigmoid(x: float) -> float:
+    return 1.0 / (1 + math.exp(-x))
+
+
+def relu(x: float) -> float:
+    return max(0, x)
+
+
+def log(x: float) -> float:
+    return math.log(x)
+
+
+def exp(x: float) -> float:
+    return math.exp(x)
+
+
+def log_back(x: float, b: float) -> float:
+    return b / x
+
+
+def inv(x: float) -> float:
+    return 1.0 / x
+
+
+def inv_back(x: float, b: float) -> float:
+    return -b / (x**2)
+
+
+def relu_back(x: float, b: float) -> float:
+    if x < 0:
+        return 0
+    else:
+        return b
+
+
+def map(xs: Iterable[float], fn: Callable[[float], float]) -> Iterator[float]:
+    for x in xs:
+        yield fn(x)
+
+
+def zipWith(
+    xs: Iterable[float], ys: Iterable[float], fn: Callable[[float, float], float]
+) -> Iterable[float]:
+    iter_ys = iter(ys)
+    for x in xs:
+        y = next(iter_ys)
+        yield fn(x, y)
+
+
+def reduce(xs: Iterable[float], fn: Callable[[float, float], float]) -> float:
+    iter_xs = iter(xs)
+
+    try:
+        result = next(iter_xs)
+    except StopIteration:
+        return 0
+
+    for x in iter_xs:
+        result = fn(result, x)
+    return result
+
+
+def negList(xs: list[float]) -> list[float]:
+    return list(map(xs, lambda x: -x))
+
+
+def addLists(xs: list[float], ys: list[float]) -> list[float]:
+    return list(zipWith(xs, ys, lambda x, y: x + y))
+
+
+def sum(xs: list[float]) -> float:
+    return reduce(xs, lambda x, y: x + y)
+
+
+def prod(xs: list[float]) -> float:
+    return reduce(xs, lambda x, y: x * y)