Task 0 2 development

.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 mypy
+        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

.gitignore

@@ -129,4 +129,7 @@ dmypy.json
 .pyre/
 *.\#*
 data/
-pyodide
+pyodide
+
+# VS code configure
+.vscode/

README.md

@@ -1,6 +1,7 @@
+[![Open in Visual Studio Code](https://classroom.github.com/assets/open-in-vscode-2e0aaae1b6195c2367325f4f02e2d04e9abb55f0b24a779b69b11b9e10269abc.svg)](https://classroom.github.com/online_ide?assignment_repo_id=23504068&assignment_repo_type=AssignmentRepo)
 # MiniTorch Module 0
 
-<img src="https://minitorch.github.io/minitorch.svg" width="50%">
+![Result](images/result.png)
 
 * Docs: https://minitorch.github.io/
 

minitorch/datasets.py

@@ -4,7 +4,8 @@
 from typing import List, Tuple
 
 
-def make_pts(N):
+def make_pts(N: int) -> list:
+    """Generate N random points."""
     X = []
     for i in range(N):
         x_1 = random.random()
@@ -20,7 +21,8 @@ class Graph:
     y: List[int]
 
 
-def simple(N):
+def simple(N: int) -> Graph:
+    """Generate a simple dataset."""
     X = make_pts(N)
     y = []
     for x_1, x_2 in X:
@@ -29,7 +31,8 @@ def simple(N):
     return Graph(N, X, y)
 
 
-def diag(N):
+def diag(N: int) -> Graph:
+    """Generate a diagonal dataset."""
     X = make_pts(N)
     y = []
     for x_1, x_2 in X:
@@ -38,7 +41,8 @@ def diag(N):
     return Graph(N, X, y)
 
 
-def split(N):
+def split(N: int) -> Graph:
+    """Generate a split dataset."""
     X = make_pts(N)
     y = []
     for x_1, x_2 in X:
@@ -47,7 +51,8 @@ def split(N):
     return Graph(N, X, y)
 
 
-def xor(N):
+def xor(N: int) -> Graph:
+    """Generate an XOR dataset."""
     X = make_pts(N)
     y = []
     for x_1, x_2 in X:
@@ -56,7 +61,8 @@ def xor(N):
     return Graph(N, X, y)
 
 
-def circle(N):
+def circle(N: int) -> Graph:
+    """Generate a circular dataset."""
     X = make_pts(N)
     y = []
     for x_1, x_2 in X:
@@ -66,20 +72,32 @@ def circle(N):
     return Graph(N, X, y)
 
 
-def spiral(N):
+def spiral(N: int) -> Graph:
+    """Generate a spiral dataset."""
 
-    def x(t):
+    def x(t: float) -> float:
         return t * math.cos(t) / 20.0
 
-    def y(t):
+    def y(t: float) -> float:
         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.values():
+            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.values():
+            m.eval()
 
     def named_parameters(self) -> Sequence[Tuple[str, Parameter]]:
         """Collect all the parameters of this module and its descendents.
@@ -47,13 +49,19 @@ 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")
+        result = []
+        for k, v in self._parameters.items():
+            result.append((k, v))
+
+        for mod_name, mod in self._modules.items():
+            for sub_name, param in mod.named_parameters():
+                result.append((f"{mod_name}.{sub_name}", param))
+
+        return result
 
     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")
+        return [p for _, p in self.named_parameters()]
 
     def add_parameter(self, k: str, v: Any) -> Parameter:
         """Manually add a parameter. Useful helper for scalar parameters.
@@ -89,6 +97,7 @@ def __getattr__(self, key: str) -> Any:
         return None
 
     def __call__(self, *args: Any, **kwargs: Any) -> Any:
+        """Call the forward method."""
         return self.forward(*args, **kwargs)
 
     def __repr__(self) -> str: