Merge branch 'master' of github.com:fotobiolab-unb/pyduino-parallel

.github/workflows/docs.yml

@@ -24,12 +24,12 @@ jobs:
           python-version: '3.12'
 
       # ADJUST THIS: install all dependencies (including pdoc)
-      - run: pip install pdoc3
+      - run: pip install pdoc==14.4.0
       - run: pip install -r requirements.txt
       - run: sudo chmod 777 /var/log
       # ADJUST THIS: build your documentation into docs/.
       # We use a custom build script for pdoc itself, ideally you just run `pdoc -o docs/ ...` here.
-      - run: pdoc -o docs/ pyduino
+      - run: pdoc -d google --math --search -o docs pyduino/*
 
       - uses: actions/upload-pages-artifact@v3
         with:
@@ -47,5 +47,6 @@ jobs:
       name: github-pages
       url: ${{ steps.deployment.outputs.page_url }}
     steps:
-      - id: deployment
+      - name: Deploy to GitHub Pages
+        id: deployment
         uses: actions/deploy-pages@v4

pyduino/optimization/gradient_descent.py

@@ -3,22 +3,19 @@
 
 class GradientDescent(Optimizer):
     def __init__(self, population_size: int, ranges: list[float], damping: float = 0.01, rng_seed: int = 0):
-        """
+        r"""
         This gradient descent algorithm assumes that the optimization function 'f' is to be minimized, differentiable, and time independent.
 
         $$\frac{\mathrm{d} f}{\mathrm{d} t} = \frac{\partial f}{\partial \vec{x}}\frac{\mathrm{d} \vec{x}}{\mathrm{d} t}$$
 
         Where $\frac{\partial f}{\partial t}$ is assumed to be zero.
 
-        ranges list of pairs:
-            Maxima and minima that each parameter can cover.
-            Example: [(0, 10), (0, 10)] for two parameters ranging from 0 to 10.
-        population_size int:
-            Number of individuals in the population.
-        damping float:
-            Damping factor to avoid oscillations. Default is 0.01.
-        rng_seed int:
-            Seed for the random number generator. Default is 0.
+        Args:
+            ranges (list of pairs): Maxima and minima that each parameter can cover.
+                Example: [(0, 10), (0, 10)] for two parameters ranging from 0 to 10.
+            population_size (int): Number of individuals in the population.
+            damping (float, optional): Damping factor to avoid oscillations. Defaults to 0.01.
+            rng_seed (int, optional): Seed for the random number generator. Defaults to 0.
         """
         self.population_size = population_size
         self.ranges = np.array(ranges)