From d7ae7227262c184833946aeab3d0055d5c74d319 Mon Sep 17 00:00:00 2001
From: "copilot-swe-agent[bot]" <198982749+Copilot@users.noreply.github.com>
Date: Fri, 3 Oct 2025 16:21:23 +0000
Subject: [PATCH] Add learning rate parameter to Perceptron training functions

Co-authored-by: leestott <2511341+leestott@users.noreply.github.com>
---
 .../03-Perceptron/Perceptron.ipynb            | 27 ++++++++++++++-----
 1 file changed, 20 insertions(+), 7 deletions(-)

diff --git a/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb b/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb
index 3d3f57ac..7a82a008 100644
--- a/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb
+++ b/lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb
@@ -232,7 +232,7 @@
    },
    "outputs": [],
    "source": [
-    "def train(positive_examples, negative_examples, num_iterations = 100):\n",
+    "def train(positive_examples, negative_examples, num_iterations = 100, learning_rate = 0.01):\n",
     "    num_dims = positive_examples.shape[1]\n",
     "    \n",
     "    # Initialize weights. \n",
@@ -251,11 +251,11 @@
     "\n",
     "        z = np.dot(pos, weights)   \n",
     "        if z < 0: # positive example was classified as negative\n",
-    "            weights = weights + pos.reshape(weights.shape)\n",
+    "            weights = weights + learning_rate * pos.reshape(weights.shape)\n",
     "\n",
     "        z  = np.dot(neg, weights)\n",
     "        if z >= 0: # negative example was classified as positive\n",
-    "            weights = weights - neg.reshape(weights.shape)\n",
+    "            weights = weights - learning_rate * neg.reshape(weights.shape)\n",
     "            \n",
     "        # Periodically, print out the current accuracy on all examples \n",
     "        if i % report_frequency == 0:             \n",
@@ -268,6 +268,19 @@
     "    return weights"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note on Learning Rate**: The `learning_rate` parameter (default `0.01`) controls how much we adjust the weights during each training step. This implements the gradient descent update formula:\n",
+    "\n",
+    "$$\\mathbf{w}^{\\tau + 1}=\\mathbf{w}^{\\tau} + \\eta \\mathbf{x}_{n} t_{n}$$\n",
+    "\n",
+    "- A larger learning rate (e.g., `1.0`) makes the perceptron learn faster but may overshoot the optimal solution\n",
+    "- A smaller learning rate (e.g., `0.001`) learns more slowly but may converge more precisely\n",
+    "- You can experiment by calling: `train(pos_examples, neg_examples, learning_rate=0.1)`\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -435,7 +448,7 @@
    },
    "outputs": [],
    "source": [
-    "def train_graph(positive_examples, negative_examples, num_iterations = 100):\n",
+    "def train_graph(positive_examples, negative_examples, num_iterations = 100, learning_rate = 0.01):\n",
     "    num_dims = positive_examples.shape[1]\n",
     "    weights = np.zeros((num_dims,1)) # initialize weights\n",
     "    \n",
@@ -451,11 +464,11 @@
     "\n",
     "        z = np.dot(pos, weights)   \n",
     "        if z < 0:\n",
-    "            weights = weights + pos.reshape(weights.shape)\n",
+    "            weights = weights + learning_rate * pos.reshape(weights.shape)\n",
     "\n",
     "        z  = np.dot(neg, weights)\n",
     "        if z >= 0:\n",
-    "            weights = weights - neg.reshape(weights.shape)\n",
+    "            weights = weights - learning_rate * neg.reshape(weights.shape)\n",
     "            \n",
     "        if i % report_frequency == 0:             \n",
     "            pos_out = np.dot(positive_examples, weights)\n",
@@ -1077,4 +1090,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}
\ No newline at end of file