From e56111be811f9f0b34d12e279330dc51d288977f Mon Sep 17 00:00:00 2001
From: Lars Bogner <lars.bogner@outlook.de>
Date: Wed, 5 Nov 2025 09:58:30 +0100
Subject: [PATCH] Use ruff for formatting

---
 notebooks/01_simple-tests.ipynb | 9 +++++----
 src/easynn/feedforward.py       | 2 ++
 2 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/notebooks/01_simple-tests.ipynb b/notebooks/01_simple-tests.ipynb
index de74dcb..ac69c26 100644
--- a/notebooks/01_simple-tests.ipynb
+++ b/notebooks/01_simple-tests.ipynb
@@ -260,6 +260,7 @@
     "X_train_scaled, X_test_scaled = datamanip.scale_data(x_train, x_test)\n",
     "y_train_scaled, y_test_scaled = datamanip.scale_data(y_train, y_test)\n",
     "\n",
+    "\n",
     "def get_regression_model(\n",
     "    n_hidden_layers: int, n_neurons: int, activation: type = LeakyReLU\n",
     ") -> list[Layer]:\n",
@@ -383,7 +384,7 @@
     "beta = optimizers.Ridge_parameters(X_train_scaled, y_train_scaled, lam=1e-10)\n",
     "y_pred = X_test_scaled @ beta\n",
     "test_mse = mean_squared_error(y_test_scaled, y_pred)\n",
-    "print(f\"OLS Test MSE: {test_mse}\")\n"
+    "print(f\"OLS Test MSE: {test_mse}\")"
    ]
   },
   {
@@ -406,9 +407,9 @@
    "source": [
     "import plotting\n",
     "\n",
-    "plt.scatter(x_test[:,1], y_test_scaled, s=6, label=\"True Data\")\n",
-    "plt.scatter(x_test[:,1], y_pred, s=6, label=\"OLS Prediction\")\n",
-    "plt.scatter(x_test[:,1], y_pred_nn, s=6, label=\"NN Prediction\")\n",
+    "plt.scatter(x_test[:, 1], y_test_scaled, s=6, label=\"True Data\")\n",
+    "plt.scatter(x_test[:, 1], y_pred, s=6, label=\"OLS Prediction\")\n",
+    "plt.scatter(x_test[:, 1], y_pred_nn, s=6, label=\"NN Prediction\")\n",
     "plt.legend()\n",
     "plt.xlabel(\"$x$\")\n",
     "plt.ylabel(r\"$y / \\sigma_y$\")\n",
diff --git a/src/easynn/feedforward.py b/src/easynn/feedforward.py
index 4c8d4c5..835cfee 100644
--- a/src/easynn/feedforward.py
+++ b/src/easynn/feedforward.py
@@ -55,6 +55,7 @@ class Softmax(ActivationFunction):
         s = self.forward(values)
         return s * (1 - s)
 
+
 class Sigmoid(ActivationFunction):
     def forward(self, values: np.ndarray) -> np.ndarray:
         return 1 / (1 + np.exp(-values))
@@ -63,6 +64,7 @@ class Sigmoid(ActivationFunction):
         sig = self.forward(values)
         return sig * (1 - sig)
 
+
 # === Loss Functions ===