From 967684304b9fb0d977289bacadedb3ab407214cd Mon Sep 17 00:00:00 2001
From: Simran Arora <emailsimran@gmail.com>
Date: Thu, 8 Feb 2024 20:58:30 +0000
Subject: [PATCH] modify general ar synthetic

---
 notebooks/test_data.ipynb          | 184 +++++++++++++++++++++++++++++
 zoology/data/associative_recall.py |   2 +-
 2 files changed, 185 insertions(+), 1 deletion(-)
 create mode 100644 notebooks/test_data.ipynb
 mode change 100644 => 100755 zoology/data/associative_recall.py

diff --git a/notebooks/test_data.ipynb b/notebooks/test_data.ipynb
new file mode 100644
index 0000000..efd10f9
--- /dev/null
+++ b/notebooks/test_data.ipynb
@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "# add autoreload\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.append(\"/code/zoology/zoology/data\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(583) tensor(-100)\n",
+      "tensor(5999) tensor(-100)\n",
+      "tensor(1962) tensor(-100)\n",
+      "tensor(5773) tensor(-100)\n",
+      "tensor(1958) tensor(-100)\n",
+      "tensor(6954) tensor(-100)\n",
+      "tensor(3194) tensor(-100)\n",
+      "tensor(6629) tensor(-100)\n",
+      "tensor(1958) tensor(6954)\n",
+      "tensor(780) tensor(-100)\n",
+      "tensor(6806) tensor(-100)\n",
+      "tensor(143) tensor(-100)\n",
+      "tensor(1729) tensor(-100)\n",
+      "tensor(6368) tensor(-100)\n",
+      "tensor(6014) tensor(-100)\n",
+      "tensor(4482) tensor(-100)\n",
+      "tensor(2079) tensor(-100)\n",
+      "tensor(305) tensor(-100)\n",
+      "tensor(3111) tensor(-100)\n",
+      "tensor(253) tensor(-100)\n",
+      "tensor(4161) tensor(-100)\n",
+      "tensor(516) tensor(-100)\n",
+      "tensor(1939) tensor(-100)\n",
+      "tensor(4073) tensor(-100)\n",
+      "tensor(1458) tensor(-100)\n",
+      "tensor(5804) tensor(-100)\n",
+      "tensor(3292) tensor(-100)\n",
+      "tensor(1369) tensor(-100)\n",
+      "tensor(3024) tensor(-100)\n",
+      "tensor(7556) tensor(-100)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from associative_recall import associative_recall\n",
+    "data  = associative_recall()\n",
+    "x= data.train_inputs[0]\n",
+    "y= data.train_labels[0]\n",
+    "\n",
+    "for i, (_x, _y) in enumerate(zip(x, y)):\n",
+    "    if i == 30:\n",
+    "        break\n",
+    "    print(_x, _y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor(583) tensor(-100)\n",
+      "tensor(4898) tensor(-100)\n",
+      "tensor(1962) tensor(-100)\n",
+      "tensor(4671) tensor(-100)\n",
+      "tensor(1958) tensor(-100)\n",
+      "tensor(7947) tensor(-100)\n",
+      "tensor(3194) tensor(-100)\n",
+      "tensor(6084) tensor(-100)\n",
+      "tensor(1962) tensor(4671)\n",
+      "tensor(5096) tensor(-100)\n",
+      "tensor(5725) tensor(-100)\n",
+      "tensor(6249) tensor(-100)\n",
+      "tensor(6204) tensor(-100)\n",
+      "tensor(2841) tensor(-100)\n",
+      "tensor(3194) tensor(6084)\n",
+      "tensor(401) tensor(-100)\n",
+      "tensor(6296) tensor(-100)\n",
+      "tensor(881) tensor(-100)\n",
+      "tensor(7629) tensor(-100)\n",
+      "tensor(2311) tensor(-100)\n",
+      "tensor(3502) tensor(-100)\n",
+      "tensor(7825) tensor(-100)\n",
+      "tensor(3207) tensor(-100)\n",
+      "tensor(910) tensor(-100)\n",
+      "tensor(8025) tensor(-100)\n",
+      "tensor(1454) tensor(-100)\n",
+      "tensor(1236) tensor(-100)\n",
+      "tensor(3014) tensor(-100)\n",
+      "tensor(5794) tensor(-100)\n",
+      "tensor(6338) tensor(-100)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from associative_recall import multiquery_ar\n",
+    "data  = multiquery_ar()\n",
+    "x= data.train_inputs[0]\n",
+    "y= data.train_labels[0]\n",
+    "\n",
+    "for i, (_x, _y) in enumerate(zip(x, y)):\n",
+    "    if i == 30:\n",
+    "        break\n",
+    "    print(_x, _y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/zoology/data/associative_recall.py b/zoology/data/associative_recall.py
old mode 100644
new mode 100755
index 32cdd3e..66361bb
--- a/zoology/data/associative_recall.py
+++ b/zoology/data/associative_recall.py
@@ -154,7 +154,7 @@ def _ar(
     inputs[rows, query_pos] = queries
     targets[rows, query_pos] = labels
 
-    inputs, targets = torch.tensor(inputs[:, :-1]), torch.tensor(targets[:, 1:])
+    inputs, targets = torch.tensor(inputs[:, :-1]), torch.tensor(targets[:, :-1])
     
     # replace all the 0 with random values
     if random_non_queries: