Feat/module0 tensor

.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

.github/workflows/minitorch.yml

@@ -0,0 +1,40 @@
+name: CI (Module 0)
+
+on:
+  push:
+  pull_request:
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+          cache: "pip"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          # если есть requirements.txt — подтянем
+          if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+          # установить сам пакет (editable) + тестовые зависимости
+          pip install -e .
+          pip install pytest hypothesis
+
+      - name: Lint (optional, можно удалить)
+        run: |
+          pip install flake8 pep8-naming
+          flake8 --ignore "N801,E203,E266,E501,W503,F812,F401,F841,E741,N803,N802,N806" minitorch/ tests/ project/
+
+      - name: Run tests (Module 0 only)
+        run: |
+          echo "Module 0"
+          pytest -q -m task0_1
+          pytest -q -m task0_2
+          pytest -q -m task0_3
+          pytest -q -m task0_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=20760858&assignment_repo_type=AssignmentRepo)
 # MiniTorch Module 0
 
 <img src="https://minitorch.github.io/minitorch.svg" width="50%">

minitorch/datasets.py

@@ -73,13 +73,26 @@ def x(t):
 
     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)]
-    y2 = [0] * (N // 2) + [1] * (N // 2)
+
+    half = N // 2
+    idx_range = range(5, 5 + half)
+
+    t_pos = [10.0 * (float(i) / half) for i in idx_range]
+    t_neg = [-10.0 * (float(i) / half) for i in idx_range]
+
+    X1 = [(x(t) + 0.5, y(t) + 0.5) for t in t_pos]
+    X2 = [(y(t) + 0.5, x(t) + 0.5) for t in t_neg]
+
+    X = X1 + X2
+    y2 = [0] * half + [1] * half
     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

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any, Dict, Optional, Sequence, Tuple
+from typing import Any, Dict, Optional, Sequence, Tuple, List
 
 
 class Module:
@@ -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 child in self._modules.values():
+            child.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 child in self._modules.values():
+            child.eval()
 
     def named_parameters(self) -> Sequence[Tuple[str, Parameter]]:
         """Collect all the parameters of this module and its descendents.
@@ -47,13 +49,20 @@ 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")
+        out: List[Tuple[str, Parameter]] = []
+
+        for name, p in self._parameters.items():
+            out.append((name, p))
+
+        for child_name, child_mod in self._modules.items():
+            for sub_name, p in child_mod.named_parameters():
+                out.append((f"{child_name}.{sub_name}", p))
+
+        return out
 
     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.

minitorch/operators.py

@@ -3,7 +3,7 @@
 import math
 
 # ## Task 0.1
-from typing import Callable, Iterable
+from typing import Callable, Iterable, Iterator, List, TypeVar, Sequence
 
 #
 # Implementation of a prelude of elementary functions.
@@ -32,7 +32,73 @@
 # $f(x) = |x - y| < 1e-2$
 
 
-# TODO: Implement for Task 0.1.
+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) -> float:
+    return 1.0 if x < y else 0.0
+
+
+def eq(x: float, y: float) -> float:
+    return 1.0 if x == y else 0.0
+
+
+def max(x: float, y: float) -> float:
+    return x if x > y else y
+
+
+def is_close(x: float, y: float) -> bool:
+    return abs(x - y) < 1e-2
+
+
+def sigmoid(x: float) -> float:
+    if x >= 0.0:
+        z = math.exp(-x)
+        return 1.0 / (1.0 + z)
+    else:
+        z = math.exp(x)
+        return z / (1.0 + z)
+
+
+def relu(x: float) -> float:
+    return x if x > 0.0 else 0.0
+
+
+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(a: float, b: float) -> float:
+    return b / a
+
+
+def inv_back(a: float, b: float) -> float:
+    return -b / (a * a)
+
+
+def relu_back(a: float, b: float) -> float:
+    return b if a > 0.0 else 0.0
 
 
 # ## Task 0.3
@@ -51,4 +117,49 @@
 # - prod: take the product of lists
 
 
-# TODO: Implement for Task 0.3.
+T = TypeVar("T")
+U = TypeVar("U")
+
+
+def map(fn: Callable[[T], U], it: Iterable[T]) -> List[U]:
+    out: List[U] = []
+    for v in it:
+        out.append(fn(v))
+    return out
+
+
+def zipWith(fn: Callable[[T, U], T], a: Iterable[T], b: Iterable[U]) -> List[T]:
+    out: List[T] = []
+    ia = iter(a)
+    ib = iter(b)
+    while True:
+        try:
+            va = next(ia)
+            vb = next(ib)
+        except StopIteration:
+            break
+        out.append(fn(va, vb))
+    return out
+
+
+def reduce(fn: Callable[[T, T], T], it: Iterable[T], start: T) -> T:
+    acc: T = start
+    for v in it:
+        acc = fn(acc, v)
+    return acc
+
+
+def negList(ls: Iterable[float]) -> List[float]:
+    return map(neg, ls)
+
+
+def addLists(a: Iterable[float], b: Iterable[float]) -> List[float]:
+    return zipWith(add, a, b)
+
+
+def sum(ls: Iterable[float]) -> float:
+    return reduce(add, ls, 0.0)
+
+
+def prod(ls: Iterable[float]) -> float:
+    return reduce(mul, ls, 1.0)