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CCA.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code from google/CCA: https://github.com/google/svcca\n",
+    "\n",
+    "import os, sys\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "import pandas\n",
+    "import gzip\n",
+    "\n",
+    "sys.path.append(\"..\")\n",
+    "import cca_core"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _plot_helper(arr, xlabel, ylabel):\n",
+    "    plt.plot(arr, lw=2.0)\n",
+    "    plt.xlabel(xlabel)\n",
+    "    plt.ylabel(ylabel)\n",
+    "    plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "adding eps to diagonal and taking inverse\n",
+      "taking square root\n",
+      "dot products...\n",
+      "trying to take final svd\n",
+      "computed everything!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Toy Example of CCA in action\n",
+    "\n",
+    "# assume X_fake has 100 neurons and we have their activations on 1000 datapoints\n",
+    "A_fake = np.random.randn(100, 2000)\n",
+    "# Y_fake has 50 neurons with activations on the same 1000 datapoints\n",
+    "# Note X and Y do *not* have to have the same number of neurons\n",
+    "B_fake = np.random.randn(50, 2000)\n",
+    "\n",
+    "# computing CCA simliarty between X_fake, Y_fake\n",
+    "# We expect similarity should be very low, because the fake activations are not correlated\n",
+    "results = cca_core.get_cca_similarity(A_fake, B_fake, verbose=True)"
+   ]
+  },
+  {
+   "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": "3.5.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}