added code for downloading the murdock 1962 dataset

Author: jeremymanning

content/assignments/Assignment_2:Search_of_Associative_Memory_Model/sam_assignment_template.ipynb

@@ -41,39 +41,97 @@
         "import pandas as pd\n",
         "import numpy as np\n",
         "import matplotlib.pyplot as plt\n",
-        "import seaborn as sns"
+        "import seaborn as sns\n",
+        "\n",
+        "import requests\n",
+        "import os\n",
+        "from tempfile import NamedTemporaryFile"
       ]
     },
     {
       "cell_type": "markdown",
-      "metadata": {
-        "id": "atpXuzOFKDSb"
-      },
+      "metadata": {},
       "source": [
-        "Basic skeleton for the SAM model"
+        "Download dataset and store as `sequence` objects containing the `presented` and `recalled` items for each trial.  The sequences are stored in nested dictionaries in the form\n",
+        "```\n",
+        "dict[list length][presentation rate]\n",
+        "```"
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {
-        "id": "PUXuEy_3-q3E"
-      },
+      "metadata": {},
       "outputs": [],
       "source": [
         "class item(object):\n",
-        "  count = 0\n",
+        "    idx = 1\n",
         "\n",
-        "  def __init__(self):\n",
-        "    self.id = count\n",
-        "    count += 1\n",
+        "    def __init__(self, val=None):\n",
+        "        if val is None:\n",
+        "            self.id = item.idx\n",
+        "            item.idx += 1\n",
+        "        else:\n",
+        "            self.id = val\n",
         "\n",
         "\n",
         "class sequence(object):\n",
-        "  def __init__(self, items):\n",
-        "    self.items = items\n",
+        "    def __init__(self, items):\n",
+        "        self.items = items\n",
+        "\n",
+        "\n",
+        "def load_recall_data():\n",
+        "    base_url = \"https://raw.githubusercontent.com/ContextLab/memory-models-course/refs/heads/main/content/assignments/Assignment_2%3ASearch_of_Associative_Memory_Model/Murd62%20data/\"\n",
+        "    filenames = [\"fr10-2.txt\", \"fr15-2.txt\", \"fr20-1.txt\", \"fr20-2.txt\", \"fr30-1.txt\", \"fr40-1.txt\"]\n",
+        "\n",
+        "    presented = {}\n",
+        "    recalled = {}\n",
+        "\n",
+        "    for filename in filenames:\n",
+        "        list_len, pres_rate = map(int, filename.replace(\".txt\", \"\").replace(\"fr\", \"\").split(\"-\"))\n",
+        "        if list_len not in presented:\n",
+        "            presented[list_len] = {}\n",
+        "            recalled[list_len] = {}\n",
+        "        if pres_rate not in presented[list_len]:\n",
+        "            presented[list_len][pres_rate] = []\n",
+        "            recalled[list_len][pres_rate] = []\n",
         "\n",
+        "        # Download the file\n",
+        "        url = base_url + filename\n",
+        "        response = requests.get(url)\n",
+        "        response.raise_for_status()\n",
+        "        lines = response.text.strip().split(\"\\n\")\n",
         "\n",
+        "        for line in lines:\n",
+        "            recall_ids = [int(x) for x in line.strip().split() if int(x) != 88]\n",
+        "            recall_seq = sequence([item(val) for val in recall_ids])\n",
+        "            presented_seq = sequence([item(val) for val in range(1, list_len + 1)])\n",
+        "\n",
+        "            presented[list_len][pres_rate].append(presented_seq)\n",
+        "            recalled[list_len][pres_rate].append(recall_seq)\n",
+        "\n",
+        "    return presented, recalled\n",
+        "\n",
+        "presented, recalled = load_recall_data()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "atpXuzOFKDSb"
+      },
+      "source": [
+        "Basic skeleton for the SAM model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PUXuEy_3-q3E"
+      },
+      "outputs": [],
+      "source": [
         "class STS(object):\n",
         "  def __init__(self, r, q, s_f, s_b, max_items=None, lts=None):\n",
         "    self.r = r\n",
@@ -142,8 +200,8 @@
       },
       "source": [
         "Other tasks:\n",
-        "  - Fit params to [Murdock (1962) dataset](https://github.com/ContextLab/memory-models-course/tree/main/content/assignments/Assignment_2%3ASearch_of_Associative_Memory_Model/Murd62%20data)\n",
-        "     - Need to define a \"loss\" function.  I suggest computing MSE for one or more behavioral curves, computed for a subset of the Murdock (1962) participants/lists\n",
+        "  - Fit params to [Murdock (1962) dataset](https://github.com/ContextLab/memory-models-course/tree/main/content/assignments/Assignment_2%3ASearch_of_Associative_Memory_Model/Murd62%20data) that you downloaded with the `load_data` function.\n",
+        "     - You'll need to define a \"loss\" function.  I suggest computing MSE for one or more behavioral curves, computed for a subset of the Murdock (1962) participants/lists\n",
         "     - I suggest using [scipy.optimize.minimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html) to estimate the model parameters.\n",
         "  - Create observed/predicted plots for held-out data:\n",
         "     - p(first recall)\n",