{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Phonetic similarity experiment\n", "\n", "This notebook contains the code necessary to compare the phonetic similarity figures in [Vitz and Winkler (1973)](https://www.researchgate.net/publication/232418589_Predicting_the_Judged_Similarity_of_Sound_of_English_words) to the cosine similarity obtained between items in the vector embedding described in my paper. CSV files with the experimental data are included with this repository. There is an additional experiment in Vitz and Winkler that I didn't include (haven't had time to transcribe the data yet!)." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `adjustText` module adds the nice arrows from the labels to the points in the scatterplots. Recommended!" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from adjustText import adjust_text" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The cosine similarity function..." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from numpy import dot\n", "from numpy.linalg import norm\n", "\n", "# cosine similarity\n", "def cosine(v1, v2):\n", " if norm(v1) > 0 and norm(v2) > 0:\n", " return dot(v1, v2) / (norm(v1) * norm(v2))\n", " else:\n", " return 0.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"space\" is defined as a dictionary, whose keys are the words and whose values are the vectors. These are parsed from a pre-computed set of vectors that were hard-coded to include the nonce words used in Vitz and Winkler." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "space = dict()\n", "for line in open(\"cmudict-0.7b-simvecs-vitz\", encoding=\"latin1\"):\n", " line = line.strip()\n", " word, vec_raw = line.split(\" \")\n", " word = word.lower()\n", " space[word] = np.array([float(x) for x in vec_raw.split()])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just to make sure everything loaded correctly:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.143984, -1.353644, -0.875339, 1.554793, 0.009237, 1.109775,\n", " 0.063728, 0.204943, -0.756014, 1.221403, -0.539748, -1.034402,\n", " 1.738177, -0.436004, -0.030675, 2.297866, -0.371826, 2.734982,\n", " 0.324265, 0.800061, 1.034471, -1.754671, 0.651967, -0.906308,\n", " 0.113644, -0.078052, -1.852303, -0.034216, 0.295794, -0.103845,\n", " -0.367562, -0.626249, -0.497218, 0.657135, -0.04856 , -1.20879 ,\n", " 0.384669, 0.239013, 0.56588 , -0.214006, -0.487608, 0.080424,\n", " -0.64105 , -1.014495, -1.184926, -0.8615 , 0.626092, 0.065884,\n", " -0.42884 , 0.467997])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "space[\"cheese\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `runexperiment()` function takes a CSV file with data from the Vitz and Winkler experiment, along with a vector space (as loaded above) and a \"standard word\" (i.e., the word against which phonetic similarity is being tested). It returns a Pandas dataframe." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def runexperiment(csv_filename, space, exp_word):\n", " df = pd.read_csv(csv_filename)\n", " df[\"embedding_cosine\"] = [cosine(space[exp_word], space[x]) for x in df[\"word\"]]\n", " df[\"vw_predicted\"] = [1-x for x in df[\"vw_predicted\"]]\n", " df.sort_values(by=\"embedding_cosine\")\n", " return df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `getplot()` function takes a dataframe as returned by `runexperiment` and plots it as a scatterplot. Note: The labels need to be changed manually in the function call (see the implementation below for details). To save the plot as a PDF, specify the `fname` parameter with the desired filename." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def getplot(df, x=\"embedding_cosine\", y=\"obtained\", labelx=\"Vector cosine similarity\",\n", " labely=\"Obtained (Vitz and Winkler)\",\n", " title=\"Standard word: sit\",\n", " fname=None):\n", " plt.figure(figsize=(6, 6), dpi=75) # change DPI here for print-ready\n", " plt.scatter(df[x], df[y], s=4.0)\n", " texts = []\n", " for i, text in enumerate(df[\"word\"]):\n", " row = df.iloc[i]\n", " texts.append(plt.text(row[x], row[y], row[\"word\"]))\n", " adjust_text(texts, arrowprops=dict(arrowstyle=\"->\", lw=0.5, alpha=0.5))\n", " plt.xlabel(labelx)\n", " plt.ylabel(labely)\n", " plt.title(title)\n", " if fname:\n", " plt.savefig(fname)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment 1: sit\n", "\n", "Results from the vector space:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = runexperiment(\"./vitz-1973-experiment-sit.csv\", space, \"sit\")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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MGEBubi4hISE8/fTTvg7Pr5U291ctIAx4DRhZ5FCGqh7zcmzn5cm5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\nwP+6NxtTmZSXRboigP1FtvcB56rLWCA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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "getplot(df, labelx=\"Cosine similarity\", labely=\"Obtained (Vitz and Winkler)\",\n", " title=\"Standard word: sit\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Correlation:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.64616745162692546" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.corrcoef(df[\"obtained\"], df[\"embedding_cosine\"])[0, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Results from Vitz and Winkler's \"PPD\" metric (\"predicted phonemic distance\"):" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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jeOKJJ9i2bRs1a9b8WTGJiE+gyeUfBex7IZiBiBRHREQEkZGRud9z5vnK2c4/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\nfPzxx3z//fe0bduW4cOHc+jQoQLHhMj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9veeSyPXNPOVS1W2qWh/TZF3rMruq/klVt7uP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\nYowxJuKscTHGGBNx/w/fN3GbRT4YGwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "getplot(df, \"vw_predicted\", \"obtained\", labelx=\"PPD (Vitz and Winkler)\",\n", " labely=\"Obtained (Vitz and Winkler)\",\n", " title=\"Standard word: sit\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Correlation:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0.94106502981668516" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.corrcoef(df[\"obtained\"], df[\"vw_predicted\"])[0, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment 2: plant" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vector space results:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = runexperiment(\"./vitz-1973-experiment-plant.csv\", space, \"plant\")" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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LKsYYY/zGkooxxhi/saRijDHGbyypGGOM8RtLKsYYY/zGkooxxhi/saRijDHGbyypGGOM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\nTCQ2NpbBgwdTXFyMzWajbdu2XLx4EZvNxi233ML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Aex6NynNc+WyDgclAD1Vth/33+TVP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\nGIbhNqZRMQzDMNzm/wP6wsT89ZR0YAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "getplot(df, labelx=\"Cosine similarity\", labely=\"Obtained (Vitz and Winkler)\",\n", " title=\"Standard word: plant\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Correlation:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.79536452040520444" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.corrcoef(df[\"obtained\"], df[\"vw_predicted\"])[0, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vitz and Winkler PPD results:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = runexperiment(\"./vitz-1973-experiment-plant.csv\", space, \"plant\")" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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MMTVc2cUgQ0JCSElJOa3MQw89xLhx45g9ezbXXXede6vfrKwsHnzwQUQEVWXu\n3LmAsxlt1KhRfPTRR0yfPp3Ro0cH8pHO21nnqYjIQeBb4BVgLZCtNWRii81TMcZUN38sBlkT+XJB\nycdw1lKmAEnAZyLyGfClqgZ+/r8xxpgazVOfyl9U9TeqGgfcC+wDxgOZIpIWgPiMMabGuhBqKVXl\n1RBhEWkKxAGdAQfQil/2VTHGGGMAz8u0/A1n89elwOc4O+d/D6Sr6in/h2eMMaY28dSnkg48q6qb\nAxGMMcaY2s3TMi3zAxWIMcaY2s+WXTHGGOMzllSMMcb4jCUVY4wxPuNp9Fchzm2DK6Wq9XwekTHG\nmFrLU0d9KICIPAYcxLnmlwBjgSZnu1ZELneVb4lzTsu7qvpQJeX6AfOA+kAq8FtVLa7KQxhjjKkZ\nvG3+utk1u/6Iqh5W1eeBkR6uKQKmqerVQDzQW0SGly3g2p9lAfBrVb0KaAQkV+0RjDHG1BTeJpUS\nEXFvHywiv8LDjHpV/VFV013vTwGbgDYViiUAu1W1dGXIvwGjvIzJGGNMDeNx6XuX24AUEYnE2fyV\nB3i9xbCINAFuBm6ocKo1kFvm8w8492wxxhhTC3mVVFQ1A3CISCPXZ693kBGResBi4DlV3VbxdBXu\nMwXnaskAREZGenupMcaYAPG2poKI9ASuBEJEnLlAVV/1cE0w8AaQoaqzKymSS/maSRucm4CdRlXn\n4ezQB5z7qXgbuzHGmMDwKqmISArOFYo3AaUjsxQ4a1IBXgKOAved4Xw60FpEol39KrcDS72JyRhj\nTM3jbU2lJxBdlaG+IpKIsy9mM7DJVbt5BedqxzNV9UZVLRaRicBiEakPrAFeq8oDGGOMqTm8TSrf\nAOHAz97eWFU/48x9JjeWKbca8GqbSmOMMTWbt0mlGMgSkdXAydKDqnqnX6IyxhhTK3mbVN5yvYwx\nxpgz8nZI8UJ/B2KMMab283b0VwRwPxALhJUeV9WKkxmNMcZcwLxdpmUhcADoBPwFOAZs9FdQ5sIz\nfvx4Fi1aVN1hlJOSksLEiRM9lps5c2YAojGmdvA2qVyuqs8CBaq6DBgNXO+/sMyFpKioqLpDOC+W\nVIz5hbdJ5ZTrz6MicgXOJrBm/gjI1D4hISE8/PDDxMbG0qVLF7KysgDYsGEDiYmJxMfH43A4WLly\nZblrHnnkEeLj41m6tPx81xdffJGBAweSn5/PW2+9RVxcHA6Hg7i4OL7//vuAxV/WqVOnmDx5MjEx\nMcTExLA3lTgcAAAgAElEQVRgwQIA7rnnHoqLi3E4HAwYMICSkhLuvPNOrrnmGuLi4hg/frzP4zWm\nJvN29Nfbrn6V/8M5C16AOX6LytQqxcXFtG7dmqysLN555x0mTJhAeno67du3JzU1ldDQUPLy8ujT\npw/bt293XxMVFcWmTZsA+OCDDwD4v//7P/7zn/+wbNky6tevz6OPPsqKFSto0aIFJ06coHSJoEDE\nX9b8+fPZv38/mZmZ/Pzzz3Tr1o2ePXvy7LPPMnfuXDIyMgDYuHEj33//PVu2bAHg0KFDPo/3QlVQ\nWExYaDAAqsqJEye4+OKLqzkqU5FXNRVVfUpVD6nqezg33bpSVZ/wb2imNrn11lsBGD58ODt27ODY\nsWPk5+czZswYYmJiGDJkCLm5uRw4cMB9zS233FLuHrNnz2b9+vUsWbKE+vXrA9CvXz+Sk5OZO3cu\n+/fvJywsDH+oLP6yUlNTmTBhAkFBQTRp0oRhw4axZs2a0+4TFRXF999/z6RJk1i6dCkXXXSRX+K9\n0Pz21S9pPXIaNz34V1atWsXHH3/M5s2bUbUlAGuaKu9Rr6qnqrJKsblwTZ8+3d2clJGRQXh4OAUF\nBQAEBwefliC6du3K1q1bycvLcx/7y1/+wjPPPMOJEyfo27cvn332WUCfoVTFGtKZakyNGzdm06ZN\nDB06lI8++ogePXpQUnLWrYeMBwWFxazI3kdweFM2/hRE777XM2DAALp37+6Xmqs5P1VOKsaUtWPH\nDgBee825ZNuyZcuIioqiQYMGHD58mFatWiEiLF68mIMHD571Xv369eMvf/kLN954I7m5zm12vvnm\nG+Lj43nggQcYOHCgu5nJ1yqLv2JsCxcuRFU5dOgQ7733Hn379gXg4osvdtds9u/fz8mTJxk6dChz\n5sxhx44dHD161C8xXyjCQoNJionkojax9Gh1ETu/+yYg3xsS4vUi7qYM+69mztnOnTvJy8sjODiY\n3Nxc4uLiCAkJISUlBYCHHnqIcePGMXv2bK677jratKm48efpBg4cyOzZs0lKSmLlypU88MADfPfd\nd4SEhNC2bVuSk32/2/SZ4i9r0qRJZGdnExsbi4jw4IMPcs011wAwdepUunbtSuvWrXn66aeZOHEi\nxcXFlJSU8OCDD7Ju3ToGDhxov1Wfh/nJXd19KuvWrWPbtm106tTJ43VFRUWWHAJMvGmTFJHZwF9V\ndXuZY/edYY+UgIiOjtbs7GzPBY3P/Xz8FIf3/0hOTg59+vQhNDS0Vg8LDgkJ8Wv8u3btYvv27fTt\n27dcZ7M5d4cOHaJZs2Y88MADvPfee4SGhrJw4UJiY2NJSUlh8eLFFBYW8v3337Nt2zZGjx5NTk4O\nBQUFDBw4kGeffRZwzo9q2LAhGRkZ5OXlcf/99zNlinMvwNK/F8eOHWPUqFEMHTqUqVOnVudjVxsR\n2aqqXi38620KHwsMFZHfqerHrmNjgGpLKqZ69HhyFT98k03Yzzv5csF0iou93g3hglNYWEh+fj4A\nR48eZdDvZ/N1WCeSYiKZn9y1mqOr3SIiIs46am/dunVs3ryZyy67DHAOU7/00kspKSlh5MiRrFix\ngkGDBgGQk5PDmjVr2Lt3L9HR0dx5552EhoYCzuQ1fPhw7rjjDsaOHVs9D1vLeNunkgcMAmaJSGmq\ntrr8Bebn46fYe+QkoREtOBUZw9Zvd7J27VpWrFjBN98Epp3bH/xRS9mzZw//+c9/3CPJro6JY0ux\ncwvs5Zv3UFBoydgXzjRqb8CAAe6EAvDSSy+550t98cUX5eYijRgxguDgYFq2bElERAR79+4FnMOW\n+/Xrx+9//3tLKFXgbU1FVTVHRPoAi0QkrgrXmjqi8cX1uKxRffYegRbNIriuZ0J1h1RjRUZGEhkZ\nWe7YTT06sXzzHpJiIq0JzM/KDrRYs2YNS5YsIS0tjfDwcO677z73KETAPXwdnP1rpb9kiAiJiYks\nW7aMESNGWJ+Yl7ytqfwIoKr5qnozznXArvFbVKbGWvfwAFLvTuBRxynPhU0585O7su1Pg63py4c8\njdoDOHz4MI0bNyY8PJyffvqJJUuWeH3/efPmERwczG9/+1ubE+Mlb5PKn8p+UNWHgUTfh2Nqg+IT\nR/nyyy9Pm3VuPLMaiu+UHbU3Y8YMXnnllUrLDR48mIYNG9KxY0dGjx5Nnz59vP4OEeGll16isLDw\ngu2krypvR39tVNUuno4Fko3+CpyKwzI3bNjAnj17uOyyy1BVEhKsGcwEnr9H7Zlf+Gz0l4jEAJ2B\nCBEpu6bGJUD9yq8ytc2iRYuYOXMm4eHh9O7dmy1btpCcnHzGYZk//fQTN954o3ty37Bhw2jVqhWZ\nmZnk5uby9NNP8+2337J06VJCQkJ47733iIyM5K233uLRRx8lKCiIkpIS3nvvPdq2bVvdj2+M8SFP\nzV/tgAFAI2BgmdfVwO3+Dc0Ewp49e5g2bRppaWmkp6eze/du97l169bx6quvsm3bNsA5LDM9PZ0X\nX3yRvLw8UlNTufrqq4mIiCAvL49PP/2UZcuWcfvtt3PVVVexadMmrr/+el588UUA9+KQGRkZrFu3\nrtzoHGOqymopNdNZk4qqvqOqE4BRqjqhzOtuVf0iQDEaP1q3bh2JiYlERkYiIuVmrJ9pWObvf//7\ncsMyRYThw4cTFBREbGwsBQUFjBw5EgCHw8HOnTuBwC0OaYypPmdNKiIy2PW2pYjcUvEVgPhMgJUd\nNnmmYZnZ2dmMGTOm0mGZIoKIuD8HBQW5f6OsKYtDGmP8x1PzV0/XnwMreQ3wY1wmQLp3787nn3/O\nvn37UFVef/31Ssudz7DMUoFaHNIYU308TWCcB+BqAjN1UIsWLXjyySfp3bs34eHhdO/evdK26sGD\nB/PKK6/QsWNHWrZsWaVhmaUCsTikMaZ6nXVIsYgcAL4EXgXeVtUTgQrMExtS7Dv5+fk0bNgQVWXS\npElERUUxbdq06g7LGFNDVGVIsafmr1bA34D/AnJFZKGIDBRbr6BOeeqpp4iPjyc6Opr8/Hyb5GWM\nOWdeTX4EEJEmwK9xrk58JfBPVf2DH2M7K6upGGNMYPiypuKmqgdxNoO9AOwHbju38IwxxtRVHpOK\niASJyGARWQTkAqOAx4EW/g7OmAtNamoqaWlpPr3n+PHjWbRokU/vacyZeFqm5XmcSeQ7YBFwl6oe\nCkRgxlyIUlNTCQkJOafRdcbUBJ6GFO8FeqnqzkAEY8yF5Pjx44wZM4bt27dTUlJCbGwsn3zyCSLC\n4sWLeeSRR7jyyiu5++67OX78OKrK008/zQ033AA4F1ScMWMGS5cupaSkhMWLF9OhQwcKCgq44447\n+PLLL2nbtq3t0W4CytPftvfPllBEJAxoq6pf+zYsY+q+Dz/8kObNm/PWW28Bzq1rn3vuOUJCQnjk\nkUcAOHLkCKmpqYSGhpKXl0efPn3Yvn07AMXFxXTo0IGMjAyeeeYZZs2axcsvv8z8+fMpLi5m69at\n7Nq1i5iYGP7nf/6n2p7TXFg89an8fyLykYhMFpEEEWkrIh1FZKiI/BlIA5oHIE5j6pzOnTuzatUq\n7r33XpYvX06jRo1OK5Ofn8+YMWOIiYlhyJAh5ObmcuDAAcC5JM6oUaMASEhIcK+xlpaWRnJyMiLC\n5ZdfTv/+/QP3UOaC52lByVuByUAk8P8DK4B/4py38jmQqKqf+jtIY+qidu3asXHjRnr16sXChQsZ\nNmzYaWWmT59Oly5dyMrKIiMjg/DwcPeaa0FBQYSGhgLlt8E1pjp5HP2lqt+p6qOq2l9VO6lqvKqO\nVdV/qWphIII0pi7KyckhNDSU0aNHM2fOHNavX094eDhHjhxxlzl8+DCtWrVy97McPHjQ43379u3L\nG2+8AUBeXh6ffPKJ357BmIq8nqdijPGdI0eOsHDhQnr27InD4SApKYm5c+cyfPhwVqxYQXx8PIsX\nL+ahhx7iySefxOFwsGbNGtq0aePx3pMmTUJE6NSpE7fddhuJibbztwkcr2fUn9PNRZ7DOSQ5UlUr\nHRQgIjnAMaC01jNWVbM83dtm1Jva6ujRo6xZs4ZBgwa5m6+Mqcl8tp2wD/wbeArY5aHcIFX1VMaY\nWm//wZ9Z9/labrjhBksopk7yNPmx5dnOq+puD+fXuu5T9ciMqWMmL9rAv//1Jt07tCIsLI3w8HAi\nIiJo27ate1MzY2o7TzWVzwAFBGgD/Ow63hj4AefCkr7wvogo8D7wmA0AMHVNQWExyzfvoWF0X7KB\nxD79KDp5gkOHDlFUVGRJxdQZnoYUX6mqUTiHEo9Q1UtV9VLgZmC5j2K4TlU7A4lAR+D+ygqJyBQR\nyS59HTpkq8WY2iMsNJikmEgAkmIiuaheCOHh4bRp06bcts3G1HZeddSLSJaqxlY4lqmqcV59iUjR\nmTrqK5QbDExR1Zs8lbWOelMbFRQWExYaXN1hGFMl/lj6/mcRmSgiYa7XRODwuYfoJCINRKSR630w\nzpFimed7X2NqKksopq7zNqmMAYYCu4E84EbXsbMSkRdFZBcQLCK7RGSeiHQTkQ9cRS4D0kQkE2cy\nEeCJqj6EMcaYmsGv81T8yZq/jDEmMHze/CUirUTknyKS5vocIyKTzydIY4wxdY+3zV9/AxYDpcuo\nbgN+55eIjDHG1FreJpVLVXUxzjkrqGoRYEuiGmOMKcfbpFIgIuG4koqIXAMU+C0qY4wxtZK3a389\nAnwItBGRpUB3wLaSM8YYU45XSUVV14jIUKAXzmG/d6jqT36NzBhjTK1TlVWKjwCbXNc0EJEGqvqD\nf8IyxhhTG3mVVETkdzgnJR4ESlyHFejgp7iMMcbUQt7WVO4DolX1R38GY4wxpnbzdvTXbksoxhhj\nPPG2ppIhIu8CbwEnSw+q6ht+icoYY0yt5G1SaQj8BPQpc0wBSyrGGGPcvB1SPMHfgRhjjKn9PO1R\nP1hVPxSRWyo7b81fxhhjyvJUU+mJcyb9wErOWfOXMcaYcs6aVFT1Udef1vxljDHGI69n1IvIQCAW\nCCs9pqpP+iMoY4wxtZO3M+pnA1FAD5xNXqOANX6MyxhjTC3k7eTHgcBIYL+q3g90Ay73W1TGGGNq\nJa/3U1HnZvbFInKxa4Xiln6MyxhjTC3kbZ/K9yISAfwbSBWRn3FuKWyMMca4eTv58deut0+JyGfA\nJcByv0VljDGmVvKq+UtEUkrfq2qaqr4HLPBXUMYYY2onb/tU4io51s2XgRhjjKn9PC3TchdwN9Ba\nRL4pcyocWOHPwIwxxtQ+nvpUXgXeA+YA95Q5flRVD/otKmOMMbXSWZu/VPWwquao6kjgGBDtekkg\ngjPGGFO7eNtR/xsgC5gI3AFkuo4ZY4wxbt7OU/kTkKCquwBEpBXwMfAvfwVmjDGm9vF29NfR0oQC\noKp5OJvDjDHGGDdPo796ud6micg/cHbcKzAWSPVvaMYYY2obTzWVJ1yveCASeACYhnPdr3j/hmbM\nhe3dd99l5syZAf/efv36sXbt2oB/r6kbPG3SdX2gAjHGlDds2DCGDRtWpWuKiooICfF6myRjfM5j\nn4qIhIrIrSIyy/UaJyL1AhGcMReCkJAQHn74YWJjY+nSpQtZWVkApKSkMHHiRABKSkqYPn063bt3\nJy4ujocffrjc9Y888gjx8fEsXbqUAwcO8F//9V/ExcURFxfHP/7xDwAyMzPp378/Xbt2pXfv3u7v\nOXjwIEOGDCE6OprRo0dTUFAQ4P8Cpi7x1KfSDlgJ5AHprsO3A4+KyGBV/dbP8RlT5xUXF9O6dWuy\nsrJ45513mDBhAunp6eXKpKSkALB+/XpKSkq4+eabWb58OUlJSRQXFxMVFcWmTZsAGDNmDDExMbz5\n5puAM2kUFhZy5513smTJElq1asWXX37JxIkTWbduHf/7v/9LXFwc77//Pps2baJbN1uByZw7T/Xk\nvwB/UtWUsgdFZJzrXJKf4jLmgnLrrbcCMHz4cCZMmMCxY+UHV37wwQd89dVXvP/++wAcO3aMb7/9\nlqQk5/+Ct9xyi7vsihUrmD9/vvtzkyZN2Lx5M1u2bGHIkCHu4wcPOhfFSEtL4/XXXwcgPj6euLjK\nlvozxjuekkr7igkFQFVfFZFH/BOSMaYiVWXWrFncfPPNp50LDg4mLCys3DGR8oteqCrt2rUjIyPD\nr3Ea46lPJbiygyISdKZzFco9JyK7RKToLGX6icgWEflORBaIiMf7mupVUFhc3SHUOa+99hoAy5Yt\nIyoqigYNGpQ7n5SUxPz58939Hbt372bPnj2V3mvQoEE899xz7s8HDx6kU6dOHD16lI8//hhwJpnS\n5rK+ffu6ayoZGRnuvhZjzoWnpPKRiLwsIu6/4a7383HOqPfk35xliXxXcloA/FpVrwIaAcle3NcE\nQElJCatXr2bVqlVkZGRw4MABJr2WTqc/fsjkRRuqO7xab//+/eTn5xMcHExubi5xcXHMmDGDV155\n5bSyt99+Oz179qRbt27ExsYycuRIfv7550rv+9xzz5GZmUlMTAydO3fmo48+IjQ0lLfffpvHH3+c\nzp07c80117BkyRIAZsyYQUZGBtHR0TzxxBPWp2LOizi3nj/DSWcCeRkYAnznOnwV8D5wh6p6Nate\nRIpU9bSmNhHpAcxS1T6uz4OAKarqcRxldHS0Zmdne/P15jypKgcOHGDp2+8yLWUVja9z5v1tfxpM\nWKhVLM/Fjh072LlzJ9dffz316tWjqOiMlXljqp2IbFXVaG/Kepqncgy4RUSuBK5xHd6iqjvPM8ZS\nrYHcMp9/AC6vrKCITAGmlH6OjIz0UQjGExEhKCiIVi0u49e338VH3xwiKSbSEso5KCgs5uvszRQU\nFNC/f//T+j6Mqe283aN+J+CrRFKW1/9Hqeo8YF7p5+jo6DNXsWqpkJCQSn9jTUlJYe3atSxYENgd\nnPv168fjjz9OmzZt2LZtG0lJSQwNDqagsNgSyjmYvGgDi//1Ju0aFJC28Bl3QrFaiqlLqnvqbS7l\nayZtgF1nKGuqgaqyb98+Bg4c6P5H0BJK1RUUFrN88x7CrupO7tGfSE37FNESRITu3bvTqFGj6g7R\nGJ/wdpVif0nHuVVxaVvd7cDSaoyn2lU2s7qsDRs2kJiYSHx8PA6Hg5UrV7rPhYSEMGPGDDp37kyX\nLl346quvGDZsGO3bt+e+++5zl6vKzGoRoVu3btZMc57CQoNJiokkuN5FDOvbjRsG/IqBAwfSv39/\nwsPDqzs8Y3xHVf32Al7EWfNQ15/zcI4G+6BMmf5ANrAdeAUI8ebeV199tdY1gM6bN09VVd9++23t\n2rWrqqr+/e9/19tvv11VVQ8fPqynTp1SVdVdu3ZpVFRUuev/9a9/qarqPffco+3atdOffvpJCwoK\n9PLLL9edO3fqqVOntEePHrpr1y5VVV2/fr12795dVVXvvvtuffDBB1VVdePGjRoUFKSffvppAJ78\nwnHiVFF1h2BMlQHZ6uW/+35t/lLV357h1I1lyqzGuUWxwfPM6vz8fCZOnEh2djYhISHk5uZy4MAB\nmjZtiogwYsQIABwOBz/++CNNmjQBoFOnTuTk5JCfn28zq6uRNR2auq66+1RMFU2fPp0uXbrw5ptv\nIiJceuml7glxQUFB7hVqg4KCqF+/vvu6oKAgioqKbGa1McavqrtPxVTgaWb14cOHadWqFSLC4sWL\n3bUMb9nM6uplqxGYus5qKjVI2ZnVISEh7pVpy3rooYcYN24cs2fP5rrrrqNNmzZV+o7SmdV33303\n9957L4WFhYwcOZL4+HhmzJjB2LFjiY6O5pprrrGZ1T42edEGlm/ew+BrLmP2yKs5dOgQhw4dok2b\nNlxyySXVHZ4xPnHWGfU1mc2oN7VJQWExnf74IfmbVyP1Lubf9wwisllTIiIiaNq0KcHB1tdiai6f\nzag3xvhG6ZDiD4quI7pkJwld4k9r2jSmLrA+FWMCZH5yV75+6ibeenIyq1ev5vjx42fc+rfsro++\nMH78eBYtWuSz+xlzJlZTMSaAwkKDITSYgQMHsnr16uoOxxifs5qKuSC9/fbbZGZmVtv3h4WFceON\nzulanlZROHXqFJMnTyYmJoaYmJhya8CFhIQwc+ZMHA4HcXFxfPPNNwAUFBQwduxYOnXqxKBBg9i/\nf39gHsxc8CypmAtOUVFRtSeVUmX3p3/00UeZMGHCaWXmz5/P/v37yczMJC0tjSeffJItW7a4r+/Q\noQMZGRmMGzeOWbNmua8pLi5m69atLFiwgLVr1wb0ucyFy5KKqVGOHz/OiBEjiIuLIyYmhunTp/PY\nY48xduxYrr/+etq2bcucOXN46aWXSEhIIDo6mtJRgJ7WRXvkkUeIj49nyZIlvPvuu0yfPh2Hw8Hn\nn39eXY8LlF9FYceOHaetopCamsqECRMICgqiSZMmDBs2jDVr1gDObQlGjRoFQEJCAjt3OhcTT0tL\nIzk5GRHh8ssvp3///gF8InMhsz4VU6N8+OGHNG/enLfeeguAQ4cO8dxzz7F161bWrl3L0aNHadeu\nHX/84x/58ssv+etf/8qsWbP4+9//Tvv27UlNTSU0NJS8vDz69OnD9u3bAedv9FFRUe6JnsuXL2fA\ngAEkJ9f8jUYrLuZZ9nNQUBChoaGAc57T+Syjb1saGF+wmoqpUTp37syqVau49957Wb58uXtJ+KSk\nJMLCwmjWrBlNmzZl2DDn5qAOh8P923l+fj5jxowhJiaGIUOGuNdFK3XLLbcE/oG84GkVhX79+rFw\n4UJUlUOHDvHee+/Rt2/fs96zb9++vPHGGwDk5eXxySefnLX85EUbbJto4xNWUzE1Srt27di4cSMf\nffQRCxcu5PnnnychIeG0dcxKP5euaQZnXxctODiYsLCwwD+QB96sojBp0iSys7OJjY1FRHjwwQe5\n5pprTr9ZhWvuuOMOOnXqRNu2bUlMTDxj2dK9Xk7kZLDku1MMa/YT9UKcNZYWLVoQHW3rvRrvWVIx\nPqGq7Nq1i8svr3Q36NOcOnWK0NDQ05p2du3aRZMmTRg9ejS9evWic+fOJCQkeHXPqqyLFh4ezpEj\nR7y6rz+VJsQnnnii3PHx48czfvx4AOrVq8cLL7xw1usBevfuTWpqKuAcXVZaA/LEPTGzJJboou30\n79e3XBI3piqs+cuct4KCAlasWMHJkyfLHVdVjh49ys6dO/nyyy9ZtWqV+/XFF19QWFh42r2ysrLo\n2bMnDoeDpKQk5s6d63UcDz30EE8++SQOh4M1a9acdV20W265heeff75GdNTXBPOTu/L1E0P498w7\nWLVqFVVdvumKK65g1y7npq033ngj+/btA+DPf/5zjUjeJnBs7S9TZWU7dH/44QcyMzOJi4vjxIkT\n7N+/393kBNCwYUOaNm1Ks2bNaNSoke0gWQv8+OOPfPvtt/Tp08fra6644grWrl1L69atvTpuahdb\n+8v4TelKu0kxkdx37SU8//zz9OjRg/3799O8eXO6du3KRRddVN1hmvPQokULjh49yogRI9i+fTsl\nJSUMHz6c0NBQvvvuO3bu3Mm+ffsYPnw4zzzzzGnXlyaSf/zjH+zevZtBgwYRGhrK+vXrqVevXjU8\nkQkka/4yXivt0AVYvnkPrdteyaxZs3A4HBw8eJCvv/6aw4cPV3OUxhc2b95M8+bNyczMZPPmzdx/\n//0ArFu3juXLl7N582Y+++wzli1bdsZ7/OEPf6Bly5asWLGCjIwMSygXCEsqxmulHboASTGRhIUG\nExoaytVXX83AgQO5/vrr3UOATe12pqHdN998M40aNaJ+/fr893//t3sSpjGlrPnLVMn85K5nnCQX\nHBzMxRdfXA1RGV8709Bu6xMznlhNxVSZzbqu+3bt2kVoaCijR49mzpw5rF+/HnAuxHnkyBFOnTrF\nm2++6XESZk0Zum0Cx2oqxpjTZGVl8eCDDyIiqCpz585l27ZtdO/e3T1keNiwYQwdOvSs95k8eTIj\nRozgoosuso76C4QNKTbGeOWxxx5zL8xpLixVGVJszV/GGGN8xmoqxhhjzspqKsYYY6qFJRVjjDE+\nY0nFGGOMz1hSMcYY4zOWVIwxxviMJRVjjDE+Y0nFGGOMz1hSMcYY4zN1IqmEhNS8JcxSUlKYOHFi\ndYdhjDEBVSeSyvkqKiqq7hCMMaZOqDNJZebMmTgcDuLi4vjmm28A2LBhA4mJicTHx+NwOFi5cqW7\nfOnCePHx8SxdupTx48czefJkEhMTadOmDf/85z/505/+RHx8PAkJCezZ49zx8MSJE0yaNInu3bsT\nGxvL888/777nrFmzaN++PT169OA///lPYP8DGGNMDeDXpCIi/URki4h8JyILROS0jThEJMdVJsP1\niq3q9xQXF9OhQwcyMjIYN24cs2bNAqB9+/akpqayadMm3n//fSZPnlzumqioKDZt2sRvfvMbAPLy\n8vj0009ZtmwZt99+O1dddRWbNm3i+uuv58UXXwTgqaeeokuXLqxfv5709HRSUlLIzs5m06ZNLFiw\ngI0bN5KWlkZmZua5/UczxphazG+dESISBCwAhqlqtoj8C0gGFlZSfJCq7jqP72LUqFEAJCQk8OGH\nHwKQn5/PxIkTyc7OJiQkhNzcXA4cOEDTpk35f+3deXhU5fnw8e89hD0JixDZTXnZAlkmIRDClogC\nolZWrQJqUWwVeevPFqsl1hWXX6m+poJYSwFBpAhVESSmrJFFghEJJBgMIGXRsIUQAmRh5nn/OCfj\nJCQwCTPJJPN8ritX5szZ7jMD585zznnuB2DixIlltjN69GgsFgthYWEUFhYybtw4gDKtnLVr13Lp\n0iXeeecdAPLz89m/fz9Hjhxh9OjRBAQEAHDvvfeyd+/e6h6SpmlaneTJO9z9gB+VUqWlhP8JPE7F\nSeW6WCwWGjZsCBhD2pbeI0lISCAqKorly5cjItxwww0UFhY6lmvSpEmZ7TRu3BgwkpSIOKYtFotj\nmzB76bUAAB16SURBVEopli5ditVqLbNuYmKiuw9L0zStzvHk5a9OwFGn6SNA50qW/dy89PWKiDR0\nVwDnzp2jY8eOiAgrV64kNzf3urc5atQoEhMTsdlsAGRnZ5Ofn8/QoUNZvXo1BQUFjqFWNU3TfI0n\nk4q4uNwQpVQEMAjoCcyocGMij4vIvtKfs2fPXnPDf/rTn3j11VexWq2kpKTQpUsXl4OvzLPPPou/\nvz8RERGEhobyyCOPUFxcTGRkJA899BCRkZEMGTKE8PDw696XpmlaXeOxQbpEJAaYrZQaak6PBKYr\npX55lXVuAx6/2jKlrneQLpvNxoULFwgMDKz2NjRN03yBtwzSlQZ0EpHSQB4GPnZeQESai0ig+boB\nMB6okcemzp49yw8//FATu9I0TfMZHksqSikbMBVYKSIHgQJgiYhEi8hac7EbgS9FZA9GMhHgFU/F\n5KywsLDMjfr8/Pya2K2maVq95tH6JkqpjUD5JlMacLs5/xBgLb9eTShNKjabje3bt9OoUSNiYmJq\nIxRN07R6o970qK+qwsJCfjxxkqSkJEJCQmo8oTjXK4uOjq7RfWuapnmK91VirCFPv7+Jb368yJiR\nw7jT7AxZW9LS0mp1/5qmae7iky2VwhIbmQ260qRzKF/sO0lhia1W43Futfj5+fHcc88RERFBVFQU\n6enp3HXXXXTv3p0//OEPjuWCg4N5+umnsVqt9O7dm02bNgGQlZVFbGwskZGR9OnTh08//bTGj0fT\nNN/lk0mlScMGjAptB8Co0HY0aXhFSbJaY7PZCAsLIz09nfj4eMaPH8+iRYvIyMhgxYoVHD582LGs\nUordu3ezdOlSHnjgAYqLi3nnnXf43e9+x7fffktGRgY333xz7R2Mpmk+x2cvf82b3JfCEptXJRQw\nSsSMHTsWMGqO/fTTT7Ru3RqAXr16cfjwYYKDgwF48MEHAYiMjKRNmzZkZ2czePBgXnrpJbKzs7nj\njjvo27dvrRyHpmm+ySdbKqW8LaGAUWes9HKYxWJx1B8rnXYe+0XkyqIF99xzD2vXrqV9+/ZMmzbN\nUbFZ0zStJvh0UqnrFi9eDEB6ejpnzpyhe/fuHDhwgM6dO/PII4/wxBNPsHPnzlqOUtM0X+Kzl7/q\nC6vVSnFxMYsWLaJRo0Z89NFHLF26lEaNGtGkSRPeffddx7I7duwgODiYdu3a1WLEmqbVZx6r/eVp\n11v7q64LDg5m69atdOrUyeV17HY7mzZtolu3btx0000ejE7TtPrEW2p/1Zi33nqLF1980fE6KCgI\nMDo4dunShaSkJAYMGEBkZCQxMTHs2rULgPvvv59ly5Y5tjN79mxmzpwJwObNmxk8eDB9+/ZlxIgR\nHD1qVPF/9913CQ0NdTzyWzo+S11gsVgYNGgQaWlpfPfdd473a/uRak3T6hGlVJ38CQkJUaV27dql\nbr75ZqWUUmPGjFH9+/dXmZmZatOmTWr8+PEqNzdX2Ww2pZRS33zzjRowYIBSSqmkpCR15513OrYT\nGRmpMjIy1JkzZ1RsbKzKy8tTSin10UcfqXvuuUcppVSnTp3UxYsXlVJK5eXlObbr7YqLi9Xx48fV\n119/rRYsWKASEhLUhQsX1KNL0tRNT69Rjy5Jq+0QNU3zUsA+5eK5uV7cU4mIiCArK4vCwkJ++OEH\npk2bRkpKCidOnCAuLo6cnBwmT57Mf//7X/z8/Dhw4AAAt956K1OnTuXMmTOcOnUKu91Onz59WLNm\nDfv37ycuLg4wLhuVDhMcGRnJxIkTueOOO7jrrrto0aJFrR13VezZs4emTZvStWtX+vbti4hQWGIj\nKSMHgKSMHK98xFrTtLqlXiQVi8VCdHQ0CxYsIDQ0lLi4OJ5//nlycnL429/+xrRp0/jNb37Dfffd\nx/nz52nVqhVg9F4fO3YsK1as4Pjx444x65VSDBkypMLe6J9++ik7duxg3bp1REdHs3nzZrp27Vqj\nx1sdFfVXKe0EmpSR43WdQOu6kpIS7HZ7mUfCAZ24tXqvXiQVgLi4OF5//XWee+45evbsSVZWFqdP\nnyYsLIxz5845bmj//e9/L7PepEmTmDFjBidPnmTDhg0AxMbG8tvf/paMjAxCQ0MpKSkhKyuLkJAQ\njhw5wsCBAxk4cCDbt2/nu+++qxNJpTLV6QSqT4zXdvHiRdLT0ykqKgIgICCAualn2fKTnVGh7Zg3\nWXdK1eqnepVUZsyYQXx8PGD0Pi8qKkJEmDVrFlOmTCEwMJDx48eXWW/AgAHk5OTQsWNHOnfuDECb\nNm1YtmwZU6dO5dKlS1y+fJlp06bRo0cPHnzwQfLy8gDo168fI0aMqNHj9ISqJIjHPvjG0bLRJ8bK\ntWjRgqFDhwLG5dNXX/9f/pPZgKad++hLjVq9ph8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RHBH5pCZicyU+c5nJIrLHjG+LiPR0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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "getplot(df, \"vw_predicted\", \"obtained\", labelx=\"PPD (Vitz and Winkler)\",\n", " labely=\"Obtained (Vitz and Winkler)\",\n", " title=\"Standard word: plant\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Correlation:" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.77618647906943061" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.corrcoef(df[\"obtained\"], df[\"embedding_cosine\"])[0, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiment 3: wonder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vector space results:" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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Yc5pUtcLHoBMTE13LMwP89NNPp0x22a9fPxYvXsyxY8fIy8vjrbfecu1z53hv\ncDepjAb+JCJXiMg/cTw1NtGLcRljTL31448/snbt2gofg37ooYc499xziYiIIDQ0lFtuuYW8vLyT\nzhMVFcXEiROJioqib9++hIeHu/a5c7w3VOWR4mbAl8DXqnqnV6Nygz1SbIypa3LzC/nxh82cOHGC\n2NjYmg7HbR6bpVhE8nGOonfyAUJF5E84Fn485/TDNMaYs8ft89fx3sefEx8WyNsPXFvT4XhNZU9/\nlTm63RhjjPty8wv5eN028nJ+Irkgj6XLltO+bRvatGlD06ZNazo8j3J7ZRnnOJJWJY9R1Z3eCMoY\nY+qTAH9fhvXoxJIGo0gMbU183zD27t3Lnj17zs6kIiJ34Bg/cgDHNPbgaBbr6qW4jDGmXnlpbA9y\n8wsJ8HdMidK+ffsajsg73K2pTAWCVXWPN4Mxxpj6rDih1GfuPlK82xKKMcaYyrhbU0kVkQ+BRcCJ\n4g9V9U2vRGWMMaZOcjepnAvsB/qV+ExxzEBsjDHGAG4mFVW90duBGGOMqfsqG/x4uap+WnLer5Ks\n+csYY0xJldVULsWxauOgMvZZ85cxxpiTVDaifrrzv9b8ZYwxplKVNX/9F/iqxLZeVYsqOsYYY8zZ\nq7Lmr2igj3MbDwSKyGqcSUZVk70bnjHGmLqkwsGPqpqlqv9W1UmqGgm0B94FbgCWVUeAxhhj6o5K\nHykWkVAcNZW+QCiQDSQBX3s1MmOMMXVOZX0qB4CfgHnA40C6uruqlzHGmLNOZTWVGThqKZOAROBr\nEfka+E5V870cmzHGmDqmsj6V2ar6R1UNB+4C9gETgI0isqIa4jPGGFOHuDVLsYi0AMKBCCASaMvv\n66oYY0yt8sgjj3jsXJmZmXTu3Nlj56vvKkwqIvJPEdkCZAATgb3AZKCFqiZ4PzxjjKk6TyaV01FQ\nUFCj169JldVU1gKjVLWFqo5Q1f9T1W9UNa86gjPGmKqaMmUKhYWFREZGMnDgQDZu3Ej//v3p0aMH\nffr0IS0tDYAZM2Ywfvx4BgwYQOfOnXnggQdc5/jiiy8IDg4mMjKSl19++aTzz5kzh7i4OCIiIrj5\n5pvJz3d0L3fs2JH77ruPmJgY5syZU303XNuoap3cunfvrsYYUxZfX19VVc3Ly9OePXtqdna2qqqu\nWbNG4+LiVFV1+vTpGhUVpb/++qseO3ZMW7durVlZWfrbb79p27ZtddOmTaqqOnnyZO3UqZOqqi5b\ntkzHjh3lhtR9AAAgAElEQVSrhYWFqqo6adIkffHFF1VV9eKLL9a//vWv1Xqf1QXHk79ufTe7u56K\nMcbUOVu2bGHz5s0MGzbM9dmBAwdcr4cNG0bDhg0BCAoKIjMzk/3799OmTRtCQkIAmDBhAosXLwbg\nk08+YcWKFURHRwOQm5vrOh5gzJgxXr+n2s6SijGm3lJVOnXqRGpqapn7GzRo4Hrt6+tbaV+IqjJl\nyhQmT55c5v7GjRuffrD1hLtr1BtjTK115MgRjh075nrfqFEjjh8/TlBQEEePHmXZMsesUqrKhg0b\nKjxXUFAQu3fvJj09HYA33njDtS8xMZHXXnuNQ4cOAXDw4EG2b9/u6dup0yp7+itfRPLK26orSGOM\nKU9OTg4rV648qdZx55130qNHDxITE3n//feZOXMmERERhISE8O6771Z4vgYNGpCUlMTo0aOJiorC\n39/ftW/gwIHcdttt9OvXj/DwcAYMGEB2drbX7q0uEnVj1hURmQEcwDHnlwDjgPNVtcae2wsODtbi\nvySMMWenrVu3kpWVRXx8PD4+1vDiLSLyg6oGu1PW3X+FK9Qxuv6Iqh5W1eeBq04/RGNMfZCbX1hj\n1/zss89YvHgxCQkJllBqEXc76otEZKCqfgEgIgOwEfXGnNVun7+OJZtySAxtzUtje1T7NWcMiqRl\ny5YsW7bM8Sirnx/R0dGcd9551RKLKZu7SWUikCQirXE0f+0CKlxiWETa42gua4MjAX2oqveXUS4B\neAFoACQDf1LV6v/zxxjjttz8QpZsygFgyaYccvMLCfD3rdZrPntNJK1atXLtLygoQES8GoOpnFt1\nRlVNVcciXV2BLqoararfV3JYAfC/qtodiAL6iMjIkgVExAeYC1ytqp2BpsDYqt6EMaZ6Bfj7khja\nGoDB3Vtw/713s3jxYlatWsXu3bspKvJ8Q0bJayaGtj4lifn5+eHr693EZirndkOkiFwKDAeuEJEb\nROSGisqr6h5VXet8nQdsADqUKhYL7FbV4h73fwKj3I3JGFNzXhrbg6ntdxCXn8bgwYMpKCjgwgsv\n5MCBA6xZs6bc40pO0JicnMzAgQOrdM2MRy+vtuY2U3VuNX+JSBKOGYo3AMVNUwr8y83jzweuAAaX\n2tUOyCrxfieOJYuNMXXA/0y6A4BffvmFbdu2sWPHDtq3b09oaKjXruntZjZzZtztU7kUCD6dvg4R\nOQdYCDynqhmld1fhPJNwLBYGQOvWrasaijGmimbNmsXhw4eZPn06s2bN4vHHH2ffvn3k5ubStWtX\nbrzxRvz9/XnooYeYPHkyvr6+/PDDD+zZs4exY8fyxBNPAI4JGv/85z9zzjnncPnll5d7vTlz5vDG\nG29w4sQJYmNjeemll04aJ2JqP3ebv34EmlT15CLiC7wJpKrq02UUyeLkmkkHoMyRRKr6gqoGF2/N\nmzevajjGmCqKj48nJSUFgJSUFAIDA0lPT2fVqlXExcWd1DHeokULNmzYQEpKCkuXLiUpKYns7Gxy\nc3OZMGEC77zzDqmpqZw4caLMay1fvpw1a9awatUqvv/+ewICApg7d2613KfxHHdrKoVAmogsB1y/\nEap6ayXHvQIcBaaWs38t0E5Egp39KjcB77kZkzHGyyIiIsjIyCA3N5ft27dzxx13kJKSwt69e4mP\nj2f//v2uss2aNWPkyJE0bNiQoKAgtyZoLKmyyRpN3eBuUlnk3NwmIr1xPIq8Cdjg/ItmHvAN8Iiq\nDlXVQhG5GVgoIg2AFOCN8s5pjKlePj4+xMTEMG/ePEJDQ4mPj2f69Onk5OQwe/Zs3nvv5L8BqzpB\nY0mVTdZo6ga3koqqvl7VE6vq15TfZzK0RLnlgFvD/40x1S8+Pp4nn3ySadOm0a1bNzIyMvjll18I\nCws7JamUpeQEjcHBwSdN0FhSYmIiU6dOZcKECTRr1oyDBw9y6NAhAgMDPX1LxovcXaO+uYg8JiIf\nisjnxZu3gzPG1Lz4+HiysrJISEgAHEkiNjbW7YGGFU3QWJJN1lg/uDuh5IfAl8DtwF04+j62qOp9\n3g2vfDahpDHGVA9vTCjZXlWfBXJV9SNgNPCH0w3QGGNM/eRuUileO+WoiHQEAoALvRGQMcaYusvd\np7/eF5HmwJM4HgMW4BmvRWWMMaZOcvfpryecLxeLSBsgQFWPeC8sY4wxdZG7NRUX5+SQtpSwMcaY\nU9hyacYYYzzGkooxxhiPcXfw49Mi0qnUZ+XN52WMMeYs5W5NZRzwiXNt+mJjvBCPMcaYOszdpLIL\nGAI8JSJ3Oj+zxaCNMcacxN2nv1RVM0WkHzBfRMKrcKwxxpizhLs1lT0AqnpMVa8AfgFCvBaVMcaY\nOsndpPJoyTeq+gDQ2/PhGGOMqcvcTSovlvHZC54MxBhjTN1XYb+IiIQCEUBzEbm+xK7zgAZlH2WM\nMeZsVVlneydgINAUGFTi86M41lQxxhhjXCpMKqr6AfCBiCSoanL1hGSMMaauqqz563JV/RRoU6r5\nCwBVfdNrkRljjKlzKmv+uhT4lJObvoopYEnFGGOMS2VJ5QUAVb2xGmIxxtQhSUlJrFy5krlz59Z0\nKKYWqeyR4nQRWSIi14lIw2qJyBhzVigoKKjpEIwXVJZU2gL/BK4BskTkdREZJCI275cxtZSfnx/T\npk0jIiKC6Ohovv/+e0aMGEGXLl2YOvX3ycU3btxI//796dGjB3369CEtLQ2AGTNmMG7cOP7whz9w\n8cUX88wzz/DKK68QGxtLcHAw6enprnPs3buXgQMH0q1bN8aPH09enmP9vh07djB8+HBiYmKIiYkh\nJSUFcNRuhg8fzpAhQwgNDa3Gn4qpNqrq1gacD/wJWAFkAU+5e6w3tu7du6sx5lSAvv3226qqOmXK\nFO3UqZPu379fc3NztX379rp9+3bNy8vTnj17anZ2tqqqrlmzRuPi4lRVdfr06dqjRw/97bffdN++\nfdqkSRP929/+pqqqL7zwgk6YMEFVVV977TVt1qyZ7tq1S4uKivSqq67SOXPmqKpq//79ddOmTaqq\numPHDg0MDNSioiJ97bXXtEWLFpqTk1OtPxNzZoB0dfO72e1JIVX1gIj8C8cYlbuBicA9nk5yxpgz\nIyJceeWVAERGRrJnzx7OP/98AIKCgsjMzOTYsWNs3ryZYcOGuY47cOCA63ViYiIBAQEEBATQokUL\nRowY4Trf22+/7So3cOBA2rRpA8ANN9zA/PnzmTBhAitXrmTMmN9Xx8jLy2Pfvn2uY1q1auWluzc1\nrdKkIiI+wGBgLHA5kALMBD7ybmjGmNPh4+ODn5+f63WDBg1O2ldQUICq0qlTJ1JTU8s8R+ljit8X\nH1+srJbwoqIiGjVqVO65GzduXPWbMnVGhX0qIvI8jrVUHgS+Arqo6ihVfU9V86ojQGOM5wUFBXH0\n6FGWLVsGOJrBN2zYUOXzfPHFF+zZswdVZf78+cTHx9O0aVNCQkKYN2+eq9z69es9Frup3SrrqN8L\nXKaqfVX1H6p6sDqCMsZUzeHDh/nqq6/cLu/v78/777/PzJkziYiIICQkhHfffbfK1+3duzfjxo0j\nKCiIhg0bcssttwCwYMECFi1aREREBMHBwTz//PNVPrepm8TRB1POTpFoVS33TwwRCQAuVtUt3giu\nIsHBwVryKRRjzja5+YUE+PuSlZXF5s2bGTBgAP7+/jUdlqmHROQHVQ12p2xlfSp/EZE2wHvAWmAf\nEAB0wTHR5GXAVKDak4oxZ7Pb569jyaYcYpscYcofLmbIkCFl9m8YU90qbP5S1fHA7UBr4P+Az4D/\n4Bi38g3QW1Xdr3MbY85Ybn4hSzblkPfLTr5ctY5DR46yefNmjh07VtOhGVP501+quhWYXg2xGGPc\nEODvS2Joa5ZsgpEJcVw+OIqcnBzS0tIICAggKiqqpkM0Z7EK+1RqM+tTMWe74j4VY7ytKn0q7i4n\nfLqBPCci2SJS7iQ/IpIpIptFJNW5hXkzJlP3FY/B8IakpCRuvvlmwDFdycyZM712rTNlCcXURt77\nv9PhHeAJILuSckNUtbIyxhhjarnKBj+2qWir7OSqulJVczwXrjEODzzwAGFhYURHR580EWLJmsXA\ngQNJTk4GICEhgXvvvZdevXpxySWXsGjRIle5p556ii5dutCzZ0++/fbbMq/322+/cdtttxEXF0dY\nWJiNuzCmHJU1f30NrHT+NwtIc25Zzs885WNn09djIlLmg/YiMklE0ou3gwdtHObZqrCwkHbt2pGW\nlsb06dO58Ub3lvs5cuQIq1at4u233+bee+8FYMOGDcydO5f169ezYsUKNm7cWOaxTzzxBNHR0axZ\ns4a1a9eSlJSE9ekZc6rKHikOVNVLcDxKfKWqXqCqFwBXAEs8FENfVY0AegPdcExWWVYsL6hqcPHW\nvHlzD13e1EXjx48HYOTIkfz8888cP3680mOuvvpqAHr06MGOHTsAWLFiBSNHjqRJkyY0aNCAa6+9\ntsxjP/nkE5577jkiIyPp2bMnBw4cYMsWG55lTGnu9qn0VtXbit+o6mIRecwTAahqlvO/x0VkLjDJ\nE+c1Zx8/Pz+Kiopc73Nzc0/aXzwpooicVM4dqsqCBQuIjIw880CNqcfcffrrkIjcLCIBzu1m4PCZ\nXlxEGotIU+drX2AUUHb7gzElvPHGGwB89NFHXHLJJTRu3JjAwEDXxIXbtm1za4LEfv36sXjxYo4d\nO0ZeXh5vvfVWmeUSExN57rnnKCwsBOCnn37iyJEjHrobY+oPd2sqY4DZwN8AxTH9/ZgKjwBE5B/A\nMMBXRLKBD4DXgEdUdSjQCnjPOb2+L/At4JEakKm/fH0d812Fh4fj5+dHUlISAKNGjWLBggUEBwcT\nHh7uqlVUNBYrKiqKiRMnEhUVxfnnn09UVFSZy9w+9NBD3HPPPURERADQokULFi5c6PmbM6aOs8GP\npt4r7j+5+OKLazgSY+omjw9+FJG2IvIfEVnhfB8qIrefSZDGVJcmTZqwf/9+1/tNmzaRn59fgxEZ\nU3+526fyT2Ah0NT5PgO4wysRGeNh5513HocPO7oAv/vuOwCbIt4YL3E3qVygqgtx9KegqgVAuVOv\nGFOb+Pr6UlRUxOrVq2ncuDGhoaE1HZIx9Za7SSVXRJrgTCoiEgLkVnyIMbXH9u3badasGcHBbjUL\nV4uXX36ZuXPnlrkvISGBlStXVnNExpw5d5/+egj4FOggIu8BccB1XovKGA/7w8DBdOrYoabDOMlt\nt91WeSFj6hi3aiqqmgIMB8YD84AIW5zL1BW3z1/HgJfTuH3+umq97qxZs3j44Yddr1u2bAk4BmV2\n6NCB6dOnu+YqO3DgAMOGDSM4OJjRo0efNHBz5cqVxMTEEB4ezrBhw8jJyaGoqIg2bdpw4sQJioqK\nOP/883nhhRcAxxxozz//PL/++itXXnkl4eHhhIaG8uCDD1br/ZuzU1Wmvj8CbMAxOLGxiNSuP/uM\nKUPxKokASzblkJtfWG3Xjo+PJyUlBYCUlBQCAwNJT09n1apVxMXFnbT878MPP0x4eDjp6ek8+OCD\nrgcKTpw4wXXXXccrr7zCxo0b6d+/P3/5y1/w8fEhNjaW1atXk5aWRpcuXVzXSk5OJiEhgU8//ZSW\nLVuyceNGNm3axN13lzkDkjEe5e4jxXcAvwBfAcuc2xdejMsYjyheJREgMbR1ta5BEhERQUZGBrm5\nuWzfvp2bbrqJlJQUkpOTiY+PP6nsihUrGDduHOAYkBkeHg5ARkYGrVu3Jjo6GoCbbrqJL7/8Evg9\naaWkpHDLLbfw008/kZuby9atWwkJCSEiIoIvvviCu+66iyVLltC0aVOM8TZ3aypTgWBV7aSqXZxb\nV28GZoynvDS2BxmPXs5LY3tU63V9fHyIiYlh3rx5hIaGupJAWUmlPCVrM6XfJyQkuJJKQkICQUFB\nJCUluWpBnTp1Yv369Vx22WW8/vrrjBgxwqP3Z0xZ3E0qu1V1j1cjMcaLTqeGUvwYclZWVqVly5uZ\nIj4+nieffJKEhAS6detGRkYGW7duJSws7JRyCxYsACA1NdW1Rky3bt3IyckhNTUVgHnz5tG/f38A\nIiMjSU9PZ9u2bXTu3JmEhASeeOIJEhISAMjOzsbf35/Ro0fzzDPPsGbNmir/DIypKnef/koVkQ+B\nRcCJ4g9V9U2vRGVMDSi55vuBAwf45ptviI6Opk2bNqgqx48f5+DBgxw8eJBDhw6Rl5fnqjn4+/vT\nt2/fU2oW8fHx3H333a4v+qCgIE6cOHFKuWnTpjFu3DiCg4MJCQkhJiYGcMys/Oabb3LzzTeTl5dH\nu3btmDdvHuCoCUVHR9OsWTPXtW677TZXLSgtLY377rsPEUFVmTNnjnd+cMaU4NbcXyLyWhkfq6pO\n9HxI7rG5v4wn3T5/HUs25ZAY2pqh5+1m586ddO7cGT8/P1QVEaFx48Y0b96c5s2b06xZM84555ya\nDtsjSiZTY8pSlbm/3KqpqKp7S+sZUweVfkLshqscHfu+vr506dKF9u3bn1KzqC9KJtPq7nMy9VOF\nSUVELlfVT0Xk+rL2W/OXqQ+KnxAr/nK9NM7x5Zqfn89PP/1ESkoK8fHx9S6xlE6mR3/NpeDEb/z2\n2+/beeedR/v27Ws4UlOXVFZTuRTHSPpBZexTwJKKqRdeGtvjlGYgf39/goODa9XULp5UOpmmp33P\nu+++S8+ePQkPD+fCCy/kvPPOq+kwTR1j66kYc5YrmUy/++47CgoKOH78OH5+fvWyhmaqzuN9Ks6T\nDgLCgIDiz1T18aqHZ4ypTUrWzmJjY1m+fDmhoaFceOGFllBMlbk7ov5pHOun3A2cD9wC2OBHY+qh\nhIQENm7ciI9PVWZxMsbB3d+aQcBVwH9V9W4gBrDeO1MrlZ7fq7Cw+ub7qg98fHwYPHiw1VLMaXG3\n+StXVVVECkWkkaruF5E2Xo3MmNPwp399x0dff0/Pi/y4vXdbAJo3b+4aTGjOXHJyMjNnzuSLL2z6\nP3Mqd5PKDhFpDrwDJIvIIRxLChtTa+TmF7Lk+50c2/g5357ozZsPjrNBffVMQUEBfn5udwWbGuDu\neipXq+pBVX0CR7/KHOBqr0ZmTBUF+PsyLKojzfpcT1QLOHJwv1vHlVyBccaMGa41Tkq+PtstWLCA\nrl270qNHDz744AMA1q1bR+/evYmKiiIyMpLPP//cVd7Pz49HHnmEyMhIwsPD+fHHHwHHWjJjx46l\ne/fuDBo0iKFDhzJ//nwAduzYwfDhw4mJiSEmJsY1lX9SUhLDhw9nyJAhthR0HeBWyheRJFWdAKCq\nK4o/AyZ4KzBjTsdLY3uQe00k5/gKy5cvp3PnznTs2LHCY2wFxorl5ORwzz33sG7dOlq3bs3o0aMB\n6NKlC8nJyfj7+7Nr1y769evHtm3bAEc/VteuXUlNTeXvf/87Tz31FK+++iovvfQSAD/88AO7du0i\nODiY6693jK2eOHEis2fPJiQkhJ07d5KQkOA63+rVq9m0aROtWrWqgZ+AqQp3O+rDy/jMGqlNrRTg\n74uPjw8DBgzAx8enSiswlufw4cNcc801hIWFERERweLFiwGYPHkyr7/+uut1bGwsAJmZma5+nJdf\nfpnQ0FAiIiKIjo4+aVXHumD16tX06dOHiy66CBHhhhtuAODYsWOMGTOG0NBQhg0bRlZWFr/88gvg\nmKJ/1KhRgOMx5e3btwOOdWOKk0jbtm1dMy4fO3aMlStXMmbMGCIjIxkxYgR5eXns27cPgIEDB1pC\nqSMqm6blf4A/A+1E5McSu5oAn3kzMGPOlIjQoUMH4uPjmTp1KnDyCoz79u07ZQXG8syYMYM2bdrw\n1ltvkZmZyaWXXsrGjRuJj49n8eLFjB8/nm+//RZV5ejRo67VFwEee+wxfvzxRxo2bMjhw4frzUSU\nDz74INHR0bz11luICBdccIErYfr4+ODv7w845lArKCgo8xzFP/uioiIaNWrkmuK/tMaNG3vhDow3\nVFZT+ReOx4mXOP9bvIUUN4cZU9tVZQXG8iQnJ3PzzTcD0LFjR3r27Ml3331Hv379+Oqrrzh06BAB\nAQEMHjyYlStXnnTuqKgorr/+eubOncuJEyfq3PiPnj178vXXX5OTk4OquvpADh8+TNu2bRERFi5c\nyIEDByo9V79+/fjPf/4DwO7du1m+fDkATZs2JSQkxDWtP8D69eu9cDfG2yr87VbVw6qaqapXAceB\nYOdmD7CbOsObKzBecMEFNGzYkH//+9/07dvXde6vvvqKvn37AvD+++9zzz33sGvXLmJiYvj55589\ne4Ne1rp1a/72t7/Rr18/YmJiXBNM3n///Tz++ONERkaSkpJChw4dKj3X7bffTkFBAd27d2fChAn0\n6NHDNb/YggULWLRoEREREQQHB/P888979b6Ml6hqpRvwR2AP8C7wHrAL+KM7x3pr6969uxrjrr//\n/e/avn17ffXVV1VVNSIiQtu2batFRUU6ffp0ffTRR1VVy309ZcoUnTp1qqqq7tixQ9u0aaP//e9/\nVVX1zjvv1Pbt2+vSpUv16NGjeskll2hUVJSqqubn5+u2bdtccQwePFg/+uij6rnpWqiwsFCPHz+u\nqqr79u3Tiy++WHfu3FnDUZnKAOnq5nezuw98PwrEqmo2gIi0BZYBb3s6yRnjDe6uwFie6dOnc+ut\ntxIWFoavry8vv/wyLVq0cJ37lVde4bLLLqNRo0ZceOGFXHrppYDjKajx48dz6NAhwNFpPXjwYM/f\nYB2Rl5dHv379yM/PJz8/n2nTptnU+vWMuys/rlXVmFKfrVPVGlvVx2YpNsaY6uGxWYpF5DLnyxUi\n8m8cHfcKjAOSzyRIY4wx9U9lzV+PlXp/b4nXF3k4FmOMMXVchUlFVf9QXYGYusvPz6/ccQjlSUpK\non///q4nhiZMmMDAgQMZO3asN0I0xlSTSh+YFxF/ERkvIk85txtExK3RWyLynIhki0i53zgikiAi\nm0Vkq4jMFRGbAfAskJSUxM6dO0/7eFWlqKjIgxEZYzyhwqQiIp1wzEZ8E+Dr3G4CfhCRLm6c/x0q\nmM5FRHyAucDVqtoZaArYn6p10AMPPEBYWBjR0dGkpaUBp07IOHDgQJKTk1mwYAFr167lxhtvJDIy\nkszMzJPOdeDAAa699lri4uKIjIxk4cKFgGMA4qWXXsro0aMJDQ1l165d1XZ/xhj3VNanMht4VFWT\nSn4oIjc49yVWdLCqrnSWL69ILLBbVYsf4/onMAl4vZK4TC1SWFhIu3btSEtL44MPPuDGG29k7dq1\n5ZYfM2YMr776KjNnzqRPnz6n7J88eTITJ05k8ODBHDp0iNjYWNccUevXr2fevHl0797da/djjDl9\nlTV/dSmdUABU9V9AJw9cvx2QVeL9TmxFyTpp/PjxAIwcOZKff/6Z48ePn/a5lixZwr333ktkZCQJ\nCQnk5eW5RqFHR0dbQjGmFqusplJm/4az2coTfR9uT/ciIpNw1GIAx9QRpnbz8/M7qd/D3dl5i4qK\nSE5OplmzZid9npyc7JpYUFX58ssv6dOnT72ZoNGY+qCymspSEXlVRFxThDpfv4RjRP2ZyuLkmkkH\nILusgqr6gqoGF2/Nmzf3wOWNp7zxxhsAfPTRR1xyySU0btyYwMBA16SA27ZtY8OGDa7yTZo04ciR\nI2WeKzExkWeffdb1fsOGDZQepCsixMTE8Nlnn7F/v2MxrtJr0xtjql9lSWUq0BjYLSLrRGQdsBvH\n1PdTPHD9tTim1S8eqXkTjrnFTB3i6+tLVlYW4eHhTJs2zTXT7KhRo8jLyyM4OJgHH3yQyMhI1zET\nJ05kypQpZXbUz549m23bthEWFkZISAj333//KUkFHDPbDh06lNTUVK576j2C/vopt89f59V7NcZU\nzN1pWgKBEOfbzaq63a2Ti/wDGAa0xTEJ5QfAa8AjqjrUWaY/8DzQAEgBblXVSgc92DQt5tixY2zf\nvp2d2bu4Yc4SGnaKxa9xczIevdzWpjfGgzw2TUsxZxJxK5GUOu5P5ewaWqLMchzT6RvjttWrV1NY\nWEhgYCChoaH8cf+FLNmUQ2Joa0soxtQgt2oqtZHVVExpufmFllCM8YKq1FTq1hJ0xlTAEooxNc+S\nijHGGI+xpGKMMcZjLKkYY4zxGEsqxhhjPMaSijHGGI+xpGKMMcZjLKkYY4zxGEsqplp17NiR7Owy\n5ww1xtQDllRMrVZQUOk0cMaYWsSSijljy5cvJy4ujoiICGJjYzlw4ADTpk0jNDSU0NBQHnnkkTKP\nW7lyJTExMYSHhzNs2DBycnIAxzLEY8aMoV+/fgwaNIi9e/fSv39/IiMjCQkJ4cUXX6zO2zPGVIFb\nE0oaU55ffvmFcePGsWzZMoKCgjh69CjLly/niy++cC0p3K9fP2JjY0lM/H316RMnTnDdddfxwQcf\nEB0dzdNPP81f/vIX3nrrLQBSU1NZvXo15557Ls888wyDBg3i/vvvB+DgwYPVf6PGGLdYTcWckVWr\nVtGrVy+CgoIAx+JbycnJjBkzhoCAAAICArj++uv58ssvTzouIyOD1q1bEx0dDcBNN910UpkRI0Zw\n7rnnAtCzZ09ee+01HnjgAVasWIEt0GZM7WVJxZyRsma5FpEK37tTpnjZYIDevXvz9ddf0717dx57\n7DEmTZqEMaZ2sqRizsill17KqlWryMjIAODo0aMkJCTw/9u797ioq/zx4683DBcVBBXzhrc0JUBu\nilla8DNNTTczqdYUNdTWLM2trbZy1Z+r/b7WtmVWVmtqedmvbWplppYWFpWZCYq3ShQTyxIvKCgX\n4fz+mGGWqw7IcH0/H495+PnMnPP5nDOD857zOedzzqpVq8jJySE7O5t///vf9O/fv1i+7t27c+LE\nCZKSkgBYsmRJqTSFUlNT8fX1JTY2llmzZrFjxw7nVkopVWnap6IqxRjD119/TUBAAMuXL2fMmDHk\n5drzBSgAACAASURBVOXh6enJxo0b+e677+jZsycA9957L4MHDy6W38PDg1WrVjFx4kRyc3Px9/e3\nL0Nc0ueff84LL7yAm5sbLi4uPP/8806vn1KqcnSRLlVhxhi+/PJL2rVrR5cuXWq6OEopJ6vy5YSV\nKnQx9xLffp1Ap06d6NSpU00XRylVy2hQUQ57cMX3rNu4la6u6Tw3+U5yc3Px8/PD19cXFxftnlNK\naVBRDsrOy2fj3hN4tg8iDQgI6kFmxhlSU1Np3ry5tlqUUoAGFeUgTzdXhgS3ZuPeEwwJbk1zH2+a\n+3jToUOHmi6aUqoW0aCiHLZoTE+y8/LxdHOt6aIopWopvRCuKkQDilLqcjSoKKWUqjIaVFS9ZbE4\n7+ru+PHjWbFiBQDR0dEkJCQ47VxK1SUaVJRSSlUZDSqqXpszZw5hYWGEhITw448/AvD999/Tt29f\nwsPDCQsL45NPPrGnt1gsZebJzs4mNjaWgIAABg0axMmTJ8s839GjRxk2bBi9evWiV69ebNu2zfmV\nVKoW0aCi6q38/Hy6detGUlISY8eOtc8Zdt111xEfH09iYiIbNmzgwQcfvGKeRYsWkZ+fz4EDB1i8\neHG5l7vi4uKYP38+O3fuZO3atdx///1lzuSsVH2lQ4pVvSUijBw5EoDIyEg2bdoEQGZmJhMnTmT/\n/v1YLBaOHTtGeno6fn5+5eb54osvmDRpEiJC+/bty5xROTMzk4SEBEaPHm1/Ljc3l99//51WrVo5\nu7pK1QoaVFS95eLigpubGwCurq729e6feeYZIiIiWL16NSJCixYtyM7OvmweRxQUFNC4cWP7dP5K\nNUR6+UvVO8YYjhw5Uu7rGRkZtGvXDhHhvffe4/Tp01c8ZlRUFKtWrQLg+PHjpVayBGjatClBQUHF\npvDftWtXJWqgVN2lQUXVK8YYtm3bRkFBQblpnnrqKZ599lnCwsLYtm2bQ1PNTJ48GREhICCAuLg4\n+vbtW2a6lStXsm7dOkJDQwkMDOSVV16pdF2Uqot0PRVVb2Rl55Kw7XOCg4Np165dTRdHqXqjIuup\nOLWlIiLRIrJPRA6JyGIRKTXHh4ik2tIk2R49nFkmVT/96Z3vaDcwjn99d5K2bdvWdHGUarCc1lEv\nIi7AYuAOY8x+EXkXGAO8XUbyQcaYNGeVRdVv2Xn5bNr3G00Co/nqp5Ns3PwJ7hZXPDw86NevHyJS\n00VUqsFw5uivSOAXY0zhNaq3gIcoO6goVWmebq7c3qMNG/cKQ4LDuX1wT8Dav6IBRanq5cyg4g8c\nK7L/M9C+nLQbRMQAG4DZxpg8J5ZL1UNlTcuvAUWp6ufMPhVH/0ffbIwJBfoC3YG/lHkwkYdEZH/h\n48yZM1VVTlVP6LT8StU8ZwaVYxRvmXQASvWbGGOO2f7NwtoHc1NZBzPGvGqMCSx8NGvWzAlFVkop\ndTWcGVR2Av4iUjgMbQKwtmgCEWkiIk1t267ASGCPE8uklFLKiZwWVIwx+cBE4D0RSQEygeUi0ktE\nPrYlawV8ISJ7sAYTAeY5q0xKKaWcy6lzfxljPgNK3jCzE7jd9vphIMyZZVBKKVV9dJoWpZRSVUaD\nilJKqSqjQaWWK7r+eadOnUhL04kHlFK1lwaVamaMuewMukopVZdpUHGCzz77jN69exMaGkpkZCRr\n167lxhtvJCYmhuDgYI4fP87UqVOJjIykR48ejBkzhpycHABOnz7N0KFDCQwMJCYmxr54VEl79uyh\nf//+9OzZk379+pGcnFydVVRKqbIZY+rk4/rrrze10cmTJ03btm3NgQMHjDHGnDt3zmzevNm4u7ub\n/fv329Olp6fbt6dOnWpef/11Y4wx06ZNM3/961+NMcbs2rXLuLi4mC+//NIYY0zHjh3NsWPHTG5u\nrrnhhhtMWlqaMcaYHTt2mN69e1dL/ZRSDQ+w3zj43azLCVex7du306dPHwICAgDw9vbG3d2diIgI\nrr/+enu6Dz74gEWLFpGdnU1GRob9ktgXX3zBypUrAQgPDyckJKTUOX744Qf27dvH0KFD7c85snqh\nUko5mwYVm5kzZ9KnTx9uv/32Uq916tSJhIQE/P39r3gcU86iZ02aNLFvp6amMnv2bL7//ntatmzJ\nwoUL2b17t8NlNcbQpUsXXQtdKVXr1Ls+lUuXLlUq35w5c8oMKBV14403sn37dg4ePAjA+fPnyc3N\nLZbm3LlzNGrUiGbNmnHhwgWWL19ufy0qKsreUklKSiqzryQgIIDz58+zdetWwBpkEhMTr7rsjtq5\ncyeTJ0++bJqJEydq0FOqAaoXQcVisTBjxgzCw8NZu3YtS5cupXfv3oSHhxMdHc2RI0cAiI+P56ab\nbmLEiBEEBgYybNgwzp49C8D48eNZsWIFAEePHqVfv34EBQXxpz/9qdzWR1n8/PxYvnw5Y8aMITQ0\nlAEDBpCZmVksTUhICNHR0QQEBHDrrbfSu3dv+2szZ84kKSmJwMBA5s2bR69evUqdw83Njffff5+5\nc+cSGhpKUFAQa9asqfD7djmXC869evXi9ddfv2z+xYsXExamkyUo1eA42vlS2x5FO+oB89Zbb9n3\ni3aCr1mzxvzxj380xhjz+eefG1dXV7N7925jjDHTp083jz32mDHGmHHjxpnly5cbY4y544477B3n\n77//vgHMsWPHrtybVce5urqaZ555xoSFhZnVq1ebJUuWmMjISBMWFmaioqLM4cOHjTHW9/HWW281\nxhhz4MAB06dPHxMWFmYCAwPNunXrjDHGREVF2QcYREVFmccff9zccMMNpnPnzmbt2rX2c86fP990\n7drV9O7d20yaNMlMmDChmmutlLoSGmJH/X333Wff/uGHH3jmmWdIT08nPz8fF5f/NsiKdn6PHz+e\niRMnljrWF198wb///W8Ahg8fjq+vr5NLXzvk5+dz7bXX2i+lnTp1ivvvvx+AtWvX8vTTT9vfl0Kv\nvfYa06ZNY9SoURhjOHfuXJnHPnfuHNu3b2fnzp2MGjWKESNGkJiYyNKlS0lMTMTd3Z3o6GgCA0tO\nFaeUqkvqRVBxdXXF09PTvj969GhWrVrFjTfeSHJyMiNGjLC/VpnVABvSCoKOBudC/fr1Y86cOfz0\n008MHTqUnj17lnncu+++G4CePXty9OhRwBq8//CHP+Dl5QXAvffeq/fbKFXH1Ys+lZLOnTtHu3bt\nAHjzzTeLvZaYmMjevXsBWL58OVFRUaXyF+0sX79+PQ1llcmygvOzzz5LcnIyq1evLvNGzHvuuYeP\nP/6YNm3aMGXKFJ5//vkyj+3h4QFYA3R5Mwo0pOCtVH1VL4PKc889R1RUFD179qTkCpGRkZHMnDmT\nwMBADh48yIwZM0rlX7BgAW+//TbBwcFs2LCBDh06VFfRa5XLBedChw4don379kyaNIlHHnmEHTt2\nOHz8m2++mY8++oisrCzy8vJ49913q6TcSqmaUy8uf5UcqTRhwgQmTJhg358zZ459u3HjxqxdW2wB\nSgCWLVtm3+7YsaN9EkdnysjI4MiRI9UySsoYQ1ZWFunp6Vy6dImuXbteMU9hcG7evHmxGy2Levfd\nd1m5ciXu7u54enpecVRYUREREYwdO5awsDCaN29OQEAAPj4+DudXStU+YiowXLY2CQwMNPv3769Q\nnvj4eObOncuWLVsqfL6jR4/SsWPHCucry+7duwkNDSUhIYGwsDB7n0JVyMvL49SpU/ZH0XtkvLy8\naNGiBa1ataJp06ZVds6rkZmZiZeXF3l5edx5552MHTuWe++9t6aLpZQqQkQOGGMcGkVTL1oqjoqO\njiY6OrpSeU+cOIHFYrFfDroa6enpFBQUkJWVhZeXF+np6fj5+Tmc3xhDRkYG6enpnDp1ivPnz9tf\nc3Nzo3nz5vj5+XHdddfh7u5+1eV1pqlTp5KUlMTFixcZOHCgvUNfKVU3NaiWytUwxrBhwwYGDRqE\nm5vbVR3rm2++oVmzZuTn55Oeno6/vz9dunQple7ixYv2Fsfp06fJz88HrB3aPj4+tGjRAj8/P7y8\nvLSTWynlNNpScQIRoU+fPmzdupXBgwdf1bG8vb1JSkqiWbNmdO/eHS8vLw4ePMipU6e4ePGiPZ2n\npyd+fn60bduWoKAgLBb9uCrKYrFUeuqeisz5BtZpdX7++WfuuOOOSp1PqfpAv6WuYMeOHfbLS7m5\nuXz55ZdERERwzTXXcDH3EvuTd9O5c2eaN29e7jGMMWRmZtpbHfv372f9+vXExMRw5MgRmjVrRosW\nLejQoQONGzeurqqpKpaUlERCQoIGFdWg6eWvSpq4+EvWb97KwL69WDF9GPDfTvL09HROnz5Nbm4u\ngwYNYvPmzXh5eeHn50eLFi3w8PDgzJkzFe6fqeiv7vj4eFxcXLjlllsqdJ76pOh7tm7dOmbPnk1B\nQQE9evTgjTfewNvbm/T0dB566CEOHDgAwFNPPcWoUaPsLZU2bdoQFxdHq1ateO655zh69CgPPfQQ\nJ06cAOCFF14gIiKCoKAgsrKyaN++PXFxcUybNq3G6q1UVdLLX06SnZePp5sr33z7HevWrcHNtw2f\nbt/Nx5vccLe44ubmRosWLWjRogXdunXD3d0dEWHAgAGljlMVHf5XEh8fj8ViadBBpdCJEyeYMmUK\n3377LR06dGDq1KnMnTuX+fPn88gjjxAcHMzq1auB4mvT5OTkEBMTQ2RkJE8//TQAcXFxvPzyywQF\nBfHzzz8THR1NSkoKc+bMISEhgcWLF9dIHZWqDRpsUCkMEEX98ssv7Nmzh7CwMFq3bl3stQdXfM/G\nvScYEtyam8xBQtr5ciALhg24hdsH33zZcz399NOsX78eNzc3ut79JDsyvPD78QPujOhgv/lywIAB\nzJgxg+joaFJTU3nwwQc5fvw4IsKCBQuKjVrLyspi5MiRDBs2jIcffpg9e/Ywffp0MjIyaNSoEYsW\nLcLd3Z3XX38dEeG9995jxowZxMTEVM2bVwd9++239OvXz34j64QJE3jggQcA2Lx5M4sWLbKnLXop\nMyYmhtjYWB599FHAOgQ6ISGB0aNH29Pk5uby+++/V0c1lKr1GmRQKRogFo3pybFjx9i7dy+tW7dm\n4MCBuLoWDzbZefls3Gu91LFx7wle/Pt9xMa6cuyXExw9fIiEhAT69etX5rny8/Px9/cnOTmZ/6xZ\nx5iHn6DNuJc49Hsml/LLnq5k9OjRTJ48mdjYWPLz84sNGT5z5gzDhw9n0qRJxMbGkpeXxwMPPMCa\nNWto164d3333HRMnTuTbb79l8uTJ9mUB6iNjDHl5eeTk5ODi4lJsIbSSSo6Ou9J+oZtvvplPPvmE\nhx56CA8PDwoKCmjcuLGuFaNUORpcUCkZINJ+/Y2XX36Z8PBw3N3diY+PL5XHGEMP+ZkdR07Tu3Nz\nvvqidJrU1FQ6depU5jnHjRsHwN0jRxA7bjwFudl0vcYLi2vpWXLOnz/PDz/8QGxsLGCdj6twlmRj\nDNHR0cyaNYu77roLcHxpYWNMrRp2nJ+fT05ODjk5OWRnZ9u3i+6XXNysLG5ubnh4eNCyZUuuvfba\nctP17t2byZMnk5aWhr+/P0uXLqV///4ADBo0iAULFtiD7+nTp+2tlSeeeIJly5YxcuRI1q5dS9Om\nTQkKCmLJkiXExcUBsGvXLiIiIvD29i53lmalGooGF1Q83VwZEtza3lLxb9OK+fPns3fvXo4fP05o\naCht27YtlW/AgAFlXjKrqMburiTOHMiiVw4Um1ix6GSN5Q2eEBH69u3LRx99xIgRIxCRKy4tfOHC\nBTZu3Eh4eHipS3qVYYwpFgDKCgo5OTnlThpZyNXVFQ8PD/vD09OTJk2a0Lx5c/u+m5tblQXC1q1b\n88orr9hX9wwODuaNN94ArHO9TZkyheDgYFxdXXn66aeL3dU/Y8YMZs2axd133817773HypUrefjh\nh1mwYAF5eXn06dOHJUuW0L9/f+bPn09YWJh21KsGq8GO/iorQOTn57Nnzx78/f1p2bLl1RYRsAaC\nRYsWMXnyZD766CNmz57Nzp07WblyJf/5z394//33SUlJISQkhA0bNhAdHU3fvn1LXf7y9fXFYrHY\nL3eJCG+88QaXLl0iICCAN998k763RONhcSEpKYnrr7+eRx99lIyMDJYtW2YfBXW5lkFOTs4VR5eJ\nCO7u7vYv/pKBoXC7rGnylVJ1U0VGfzXYoFJdLBYLTz75JOvXr8disbBs2TJCQkLIzs7mrrvuIjU1\nlZCQEI4dO8a8efPsyx9PnjyZX3/9FRcXFxYsWEBUVJQ9MBhjiIuLo3Hjxrz66qskJycz+N77OZl+\nCi834U9j7yUnJ4c2bdrYO+tHjx7NrbfeWm4QKNx3dXWtVZfJlFI1T4NKA5Odl0/A3zbZ9w/+fTCu\nFLBnzx5+//13OnfuTLdu3bT1oJSqlIoEFf2WscnOy6/pIlRaYT8RwJDg1ni6We+Z6dmzJ4MHD8bD\nw4OffvqphkuplGoItKVC6SHGdVVVDCRQSqmStKVSAdl5+az7+FMKci+yce8Jp7dYyjp+VlYWO3bs\n4Mcff7yqYzsjoMTHx5eaEaDQzJkz+fjjjyt97A8//LDYAmpKqbqvwQ0pLiU/jy4uJ0k56cqIQf2d\n+ku/aIto4R9D+f777zlz5gxNmjQhKCjospNS1kZXGxDuuOMOnXxRqXrGqS0VEYkWkX0ickhEFotI\nqW9sR9I40/bt29m0aDZv3hvIq/eFl5mmZOvi7NmzbNmypUIti5I3XZ7OOM/nn39un3gyMTGRrVu3\nEh8fz65du0hJSSE9PZ28vLzKV64SPvvsM3r37k1oaCiRkZGcPn2aixcvct999xEYGMiQIUPs99SM\nHz+eFStW2Lcffvhh+vXrR+fOnXn11Vftx7RYLMyZM4ewsDBCQkLs79uyZcuYOHGifXv48OEMGzaM\nbt26MXbsWHv+pKQkwsPDCQkJ4W9/+5suAaBULea0/50i4gIsBu4wxuwXkXeBMcDbFUnjTKdPn8Zi\nseDt7U2PoEB+/PFHAgICiqUp2rr4nz905bvvvqNRo0bcfPPNeHh4OHyukjddXtOiGU8++SS7d+/m\n1KlT9O3bFw8PD/Ly8jh//jxnzpzh6NGjZGRk2O8dKez/aty4Md7e3vj6+tK0aVMaN25svxGyaLqi\n/WUln2vSpEmpocPp6enExsaydetWAgICOH/+PN988w1JSUksX76ca6+9lmHDhrFmzZpic18VSk1N\nZdu2bfz2228EBgbywAMP4ObmRn5+Pt26dSMpKYl//OMfPP/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = runexperiment(\"./vitz-1973-experiment-wonder.csv\", space, \"wonder\")\n", "getplot(df, labelx=\"Cosine similarity\", labely=\"Obtained (Vitz and Winkler)\",\n", " title=\"Standard word: wonder\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Correlation:" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "0.95750943414944378" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.corrcoef(df[\"obtained\"], df[\"embedding_cosine\"])[0, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vitz and Winkler results:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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apbIxldU4xlHKKk6jOCoWV+RV4Bhwb1kbRaQx4KuqR50J6FpgYyVtGmNMpTW1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\nOzk5maVLl97w+bibn6839wb6sPSb/2XAc//HpU1gxeNKpk2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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "getplot(df, \"vw_predicted\", \"obtained\", labelx=\"Inverse PPD (Vitz and Winkler)\", labely=\"Obtained (Vitz and Winkler)\",\n", " title=\"Standard word: wonder\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Correlation:" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.91742184230511969" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.corrcoef(df[\"obtained\"], df[\"vw_predicted\"])[0, 1]" ] } ], "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": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }