Migrate to uv and Python 3.14 and add MNIST downloader to make the project runnableHEADmaster

Context / Problem: setup.py + requirements.txt (Python 3.5) no longer install on modern Python, matplotlib missing from deps, main.py save/load broke on numpy 2.x (inhomogeneous array / allow_pickle), and demo required manual mnist.npy/data files.

What: replace setup.py/requirements.txt with pyproject.toml (hatchling, PEP 621, requires-python >=3.10, classifiers 3.10-3.14), add .python-version 3.14 and uv.lock, add MultiLayerPerceptron/download.py (mlp-download) to fetch MNIST from storage.googleapis and generate data/train.npy, data/test.npy, mnist.npy, fix MultiLayerPerceptron/main.py layers class var and pickle handling with minimal changes, ignore .venv and track uv.lock, update README with uv workflow.

QA: verified uv sync --all-groups, uv run mlp-download, uv run python -m MultiLayerPerceptron.main loads and trains, ruff check passes.

1 files changed, 10 insertions, 7 deletions

diff --git a/MultiLayerPerceptron/main.py b/MultiLayerPerceptron/main.py
index 2dbfa3d..d7a3138 100644
--- a/MultiLayerPerceptron/main.py
+++ b/MultiLayerPerceptron/main.py
@@ -49,9 +49,8 @@ class Layer():
         self.grad_weights =  np.outer(self.grad_inter, input)
 
 class MultiLayerPerceptron():
-    layers = []
-
     def __init__(self, layer_sizes):
+        self.layers = []
         for k in range(len(layer_sizes)-1):
             self.layers.append(Layer(layer_sizes[k], layer_sizes[k+1]))
 
@@ -60,7 +59,8 @@ class MultiLayerPerceptron():
 
     @staticmethod
     def load(filename):
-        array = np.load(open(filename, 'rb'))
+        with open(filename, 'rb') as f:
+            array = np.load(f, allow_pickle=True)
         sizes = [array[0].shape[1]]
         for layer in array:
             sizes.append(layer.shape[0])
@@ -70,8 +70,9 @@ class MultiLayerPerceptron():
         return mlp
 
     def save(self, filename):
-        array = [layer.weights for layer in self.layers]
-        np.save(open(filename, 'wb'), array)
+        array = np.array([layer.weights for layer in self.layers], dtype=object)
+        with open(filename, 'wb') as f:
+            np.save(f, array, allow_pickle=True)
 
     def error(self, output, expected):
         return 1/2 * np.sum((expected - output)**2)
@@ -171,10 +172,12 @@ if __name__ == "__main__":
     mlp = MultiLayerPerceptron.load('mnist.npy')
     print("OK")
     print("[Loading] Training set: ", end="")
-    space = np.load(open("data/train.npy", "rb"))
+    with open("data/train.npy", "rb") as f:
+        space = np.load(f, allow_pickle=True)
     space = [(input.reshape(28*28), expected) for input, expected in space]
     print("OK    Testing set: ", end="")
-    test_space = np.load(open("data/test.npy", "rb"))
+    with open("data/test.npy", "rb") as f:
+        test_space = np.load(f, allow_pickle=True)
     test_space = [(input.reshape(28*28), expected) for input, expected in test_space]
     print("OK")