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@rmalouf
Last active August 29, 2015 14:09
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Baayen's measures of productivity (For Ling 523)
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{
"metadata": {
"name": "",
"signature": "sha256:bdb2ec02d6f1958897d4680ae120bb8cc00dbfd6b95dbb88d63f655d2ff578ad"
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"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import re\n",
"import nltk\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"from math import log, exp\n",
"\n",
"from collections import defaultdict, Iterable\n",
"from itertools import islice, repeat\n",
"\n",
"%precision 2\n",
"%matplotlib inline\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 86
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"Token frequency\n",
"==============="
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"brown = [ w.lower() for w in nltk.corpus.brown.words() if w.isalpha() ]\n",
"freq = nltk.FreqDist(brown)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ivity = [w for w in freq.keys() if w.endswith('ivity')]\n",
"iveness = [w for w in freq.keys() if w.endswith('iveness')]\n",
"print len(ivity), len(iveness)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"19 25\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print sorted(ivity)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[u'activity', u'captivity', u'compulsivity', u'conductivity', u'creativity', u'declivity', u'directivity', u'inactivity', u'nonreactivity', u'objectivity', u'passivity', u'procreativity', u'productivity', u'radioactivity', u'reactivity', u'relativity', u'selectivity', u'sensitivity', u'subjectivity']\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print sorted(iveness)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[u'acquisitiveness', u'aggressiveness', u'allusiveness', u'assertiveness', u'cohesiveness', u'creativeness', u'decisiveness', u'decorativeness', u'defensiveness', u'discursiveness', u'effectiveness', u'elusiveness', u'exclusiveness', u'expansiveness', u'expressiveness', u'forgiveness', u'incisiveness', u'inclusiveness', u'indecisiveness', u'ineffectiveness', u'objectiveness', u'obtrusiveness', u'passiveness', u'responsiveness', u'retentiveness']\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sum(freq[w] for w in ivity)/float(len(ivity)), sum(freq[w.replace('ivity', 'ive')] for w in ivity)/float(len(ivity))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"(12.11, 23.21)"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sum(freq[w] for w in iveness)/float(len(iveness)), sum(freq[w.replace('iveness', 'ive')] for w in iveness)/float(len(iveness))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"(3.32, 17.40)"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ax = plt.gca()\n",
"ax.plot([freq[w.replace('ivity', 'ive')] for w in ivity], [freq[w] for w in ivity], 'o', c='red', alpha=0.75, label='-ivity', mew=0.1)\n",
"ax.plot([freq[w.replace('iveness', 'ive')] for w in iveness], [freq[w] for w in iveness], 'o', c='blue', alpha=0.75, label='-iveness', mew=0.1)\n",
"ax.set_xscale('log')\n",
"ax.set_yscale('log')\n",
"ax.set_xlim(0,200)\n",
"ax.set_ylim(0,200)\n",
"ax.set_xlabel('Base frequency')\n",
"ax.set_ylabel('Derived frequency')\n",
"ax.legend(loc=2)\n",
"plt.savefig('tokenfreq.pdf', format='pdf')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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0YMEC5OTkNFvW0NAAvV6PnJwcFBQUYPPmzSgsLER0dDRKS0sBAFar1dOhedQd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\nZI6JgIhI5pgIiIhk7v8BcdyTIoBqBaYAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x111e06d90>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"Lexical growth\n",
"=============="
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'Type', len(freq)\n",
"print 'Tokens', sum(freq.itervalues())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Type 40234\n",
"Tokens 981716\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Stat(object):\n",
" \n",
" def compute(self, corpus):\n",
" w, r = [ ], [ ]\n",
" self.init()\n",
" for i, word in enumerate(corpus):\n",
" if i % 500 == 0:\n",
" w.append(i)\n",
" r.append(self.score())\n",
" self.add(word.lower())\n",
" w.append(i)\n",
" r.append(self.score())\n",
" return w, r\n",
" \n",
" def plot(self, w, r, filename):\n",
" ax = plt.gca()\n",
" if isinstance(r[0], Iterable): \n",
" for data, label in zip(zip(*r), self.labels): \n",
" ax.plot(w, data, label=label)\n",
" else:\n",
" ax.plot(w, r)\n",
" self.label(ax)\n",
" plt.savefig(filename, format='pdf')\n",
" plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Growth(Stat):\n",
" \n",
" labels = ['']\n",
" \n",
" def init(self):\n",
" self.v = nltk.FreqDist()\n",
" \n",
" def score(self):\n",
" return len(self.v)\n",
" \n",
" def add(self, word):\n",
" self.v[word] += 1\n",
" \n",
" def label(self, ax): \n",
" ax.set_xlabel('N')\n",
" ax.set_ylabel('V(N)')\n",
" plt.title('Lexical Growth')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"growth = Growth()\n",
"w, r = growth.compute(brown)\n",
"growth.plot(w, r, 'growth.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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cddddTJo0CQBvb2/b+zNnzmT8+PGA1vIpLS21vVdWVoZer8fPz4+ysrJm5WfO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\nnwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x117b216d0>"
]
}
],
"prompt_number": 92
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"Count occurrences of suffixes\n",
"=============================\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from cPickle import load\n",
"ness, ity, th = load(open('suffixes.dat','r'))\n",
"print len(ness), len(ity), len(th)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"314 290 19\n"
]
}
],
"prompt_number": 43
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"Realized productivity\n",
"=====================\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Realized(Stat):\n",
" \n",
" labels = ['ness','ity','th']\n",
" \n",
" def init(self):\n",
" self.v = nltk.ConditionalFreqDist()\n",
" \n",
" def score(self):\n",
" return tuple(len(self.v[suff]) for suff in self.labels)\n",
" \n",
" def add(self, word):\n",
" if word in ness:\n",
" self.v['ness'][word] += 1\n",
" elif word in ity:\n",
" self.v['ity'][word] += 1\n",
" elif word in th:\n",
" self.v['th'][word] += 1\n",
" \n",
" def label(self, ax):\n",
" ax.set_xlabel('N')\n",
" ax.set_ylabel('V(C,N)')\n",
" ax.legend(loc=2)\n",
" plt.title('Realized Productivity')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"realized = Realized()\n",
"w, r = realized.compute(brown)\n",
"realized.plot(w, r, 'realized.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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732aOR90C22ho8wUoKlS//UzI+/UYO9bY0YmSSJrFhChCFEUzHmXvXvjpJ8iy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\nRK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x117b5d490>"
]
}
],
"prompt_number": 96
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Relative(Realized):\n",
" \n",
" def label(self, ax):\n",
" ax.set_xlabel('N')\n",
" ax.set_ylabel('V(C,N)/N')\n",
" ax.legend(loc=2)\n",
" plt.title('Relative Realized Productivity')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"relative = Relative()\n",
"w, r = relative.compute(brown)\n",
"r2 = [ ]\n",
"for ((a,b,c),i) in zip(r,w):\n",
" if i > 0:\n",
" r2.append((a/float(i),b/float(i),c/float(i)))\n",
" else:\n",
" r2.append((0.0,0.0,0.0))\n",
"relative.plot(w, r2, 'relative.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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jNTWV/v37ExkZydq1a2nTpg1DhgyhTZs2ODg48MEHH1QYoqrYdE3HJoQQwrqu\n+CLc0aNH4+rqyuOPP067du2sVS6rMQyD5GcewXf23NouihBCXDds5iLcijz++OP07NmTRYsWWaM8\nNUPG1hNCCJtWYc0pLy+P7OxsmjRpUmr+mTNncHd3x8XFpUYKWN0MwyD5ib8xva8bo9qPIiogqraL\nJIQQNs9mak7jxo3jp59+KjN/69atPPPMM1YtlNUVF/P+L+8zb++82i6JEEKIclQYTrt372bQoEFl\n5t97771s3rzZqoWyOpPuEGGW27ULIYRNqjCccnNzK1zJfL2PTfdnV/JiuXWGEELYpArDqUmTJuUO\nGbRr164y56GuO3+Gk9SchBDCNlV4ndObb77JkCFD+Nvf/saNN96IUordu3ezcOFClixZUpNlrH5/\n9tYrljH2hBDCJlVYc+rSpQs7d+7EbDazYMECFi5ciFKqwpG+ryvSrCeEEDbtsqOS+/j48Morr9RU\nWWqONOsJIYRNq7Dm1L9/f5YtW1Zux4jz58+zdOlS+vXrZ9XCWYtRUnOSZj0hhLBJFdacFixYwHvv\nvceUKVOwt7enadOmKKVISUnBZDIxdOhQFi5cWJNlrT5/htOupF21XBAhhBDlqTCcpkyZwogRI3jl\nlVc4ffo0cXFxADRv3hxfX9+aKp91/FljSshKqGRBIYQQtaHCZr1WrVoxYcIEmjdvzuzZs3FyciIq\nKur6DyYoNbZeUXFRLRZECCFEeSoMp6effprt27ezefNmvLy8eOihh2jdujXTpk3j6NGjNVnGamdc\ndD+nBb8tqL2CCCGEKNcV3TJj7969PPjggxw4cIDi6/SGfYZhcLrXTfjctM0yT02pucEMhRDiemQz\nA7+WMJlMrFy5khEjRnDHHXcQFhbG119/XRNlsxrDJDcbFEIIW1Zhh4j169ezZMkSVq9eTZcuXRg+\nfDgff/wx9evXr8nyWYXcCVcIIWxbheE0c+ZMhg8fzptvvomXl1dNlsnqlNSchBDCplUYThs3bqzJ\nctQoVWzC3cmd7MLs2i6KEEKIclzxbdr/ClSxCQe7y47cJIQQohbVzXAyFeNg58CvD/9KB98OtV0c\nIYQQl6iT4cSfNSd3Z3d+S/mN84Xna7tEQgghLlInw0kV65qTu5M7AJN+mFTLJRJCCHGxOhlOhsmE\nvZ097s46nFLzUmu5REIIIS5WJ8PJrkg367k6ugJIs54QQtiYOhlOxp8dIuwMffhnc8/WcomEEEJc\nrE6GU0mdvGd4AAAgAElEQVTNqcTZ8xJOQghhS+rkxT72RcWWcIryj8Lb1buWSySEEOJidbPmVHwh\nnF7p8Qr5pvxaLpEQQoiL1clwcrio5uTl4kV6fnotl0gIIcTF6mQ42ZvM2Bv2AHjW8yQ9T8JJCCFs\nSd0Mp2Kzpebk6eIpNSchhLAxdTKcnEwKhz9rTh7OHmQVZFFsvj7v7CuEEH9FdTKcTHbgrHQ42dvZ\n08C5AZkFmbVcKiGEECXqZDgV2RvU+zOcQHeKSMtLq8USCSGEuFidDCeTvUE984VD93b15ui5o7VY\nIiGEEBerk+FUaA/O6sKh/5byG/0/61+LJbKuYnMxWQVZjP5mNM3fac4/Vv2DpKyk2i6WEEJUqE6O\nEFFkD04X9X+o71T/L9msF5MWQ8h7IWXmz909l7m75zK+63hm956NYRi1UDohhKhYna05XdysF9gg\nEIDcotzaKtI1mbdnHr0X96aouMgyL9+UbwmmTn6d+Kj/R/ww6gfUFIX5ZTM/jPqBH078wD83/pNP\n93+KMc0g+N1gsgqyauswhBDCok7WnPINM0X5Ooi+/BKKcvR9nZKzkwn2Cq7Nol0xk9nE37/9OwDj\nvxvPnH5zOHv+LF8e+hKA1AmpNHJtVGodwzDo2bInGx7YQM9FPZm9fTbdA7vT2LUxty28jS8Gf0FL\nz5Y1fixCCFGiztacjqUcBGDwYDi0bBgAp7JP1WaxqiSrIIt/bfsXcRlxJGUl4fiqIwBJzySxMXYj\nxjSDJm824bE1jwGUCaaLNXZrzMbRGxnWbhjr7l/H8qHL6RPch+B3g3n2u2cxK3ONHJMQQlzKUEqp\n2i5ETTIMgz2+8M8RvnTzTOall/T8wV8MYVD4IIa2G1q7BbyM1NxUGv+rseV5hE8E+07vY/E9ixnZ\nfiQxaTF0m9eN9Px0vhz8JTc3u/my4VSRP1L/YOyqsXi7erP4nsWWmzIKIeouwzCoybioszUn+2Kz\nJZgA/Nz9SMq2nR5s8/fOL9VJ463tb9H+w/YAzOg5gyc6P8G+0/v4sP+HjGw/EoBgr2DOTDhD0UtF\n3BV211UFE0CYdxjrR67H1dGV6AXRnM45fe0HJIQQV6BOnnMqtAf7otJNVn7uftfUrJdXlMfJzJOE\neYdda/EwmU08tPIhWAnToqfxW8pvLP9jOQDPdH2GSTdPwmQ28XiXx6tlf+VxdnBm0d2LmLZ5Gl3+\n04Wl9y2lk1+nUjdpFEIIa6mT3zRFduBoKl099Xf3Z0/ynivazumc0/jO9mXsjWOZu3suAKaXTNjb\n2VeyZsUOnD5A+490DenW5rcyZdMUy2v7/7Ef/wb+ADjYOVgtmEoYhsHU6Km0a9KObvO60cilEdvH\nbCe0UahV9yuEEHUynArt9W0zLhbUsAVxGXFXtJ3T53VzV0kwAXx/4nvuCLnjqst25NwRADaN3sSt\nQbdyPO04B04f4J7we656m9fqvjb3kfxsMssOLqP7f7vzYf8Pua/NfbVWHiHEX1+dDKd8BwhIN5Wa\n19ihBSfST1zRdlJyUiyPn+v2HI3dGvPerve4pdktuDm5AXDrglvZcnILoJvkfOr7MKH7hHIvfC0q\nLmLwssEMaDWAW4NuBSDEK4QQr7IX0tY03/q+PBn1JN0Cu3Hv0nsZvGwwK4atYGDrgRSYCnB2cK7t\nIgoh/kKs2iFi3bp1hIWFERoayqxZs8pdZty4cYSGhhIREcHevXsrXTctLY1evXrRqlUrevfuTUZG\nBgBxcXG4uLgQGRlJZGQkjz32WIXlslfwr+XZpeYZ533JKcwhpzCnysf32Gq9j+KXi/lX73/xaKdH\nWXNsDfVn1AdAKWUJJoC3drzFxB8mcv/X95OUlUS+KZ8/Uv8AdBdxp+lOAMwbOK/KZahpnfw6se8f\n+5jeYzpPrXuK+7++H89Znjy66lE2xW2qdP20vDQy82UEeCFEJZSVmEwmFRwcrGJjY1VhYaGKiIhQ\nhw4dKrXM6tWrVd++fZVSSu3YsUNFRUVVuu6ECRPUrFmzlFJKzZw5U02cOFEppVRsbKxq165dpeUC\n1LJwlALlRL5CP1RbtyoVPidc7U/ZX+VjfGbdM+of3/6j1Ly7Pr9LMRX1++nf1daTWxVTUWdyzqgj\nqUfU8sPL1Tvb31FMpdT05rY31eqjqxVTUZ/t/6zK+69tGXkZasyKMWrapmmq16JeiqmoB795UG2K\n3aRe3PCiysrPKrX89zHfW4759kW3q/d2vqfOnj9bS6UXQlwJK8ZFuazWrLdr1y5CQkIICgoCYNiw\nYaxYsYLw8HDLMitXrmT06NEAREVFkZGRQUpKCrGxsRWuu3LlSjZv3gzA6NGjiY6OZubMmVdUtsI/\n+ytEBmezM0Y3R6WkQEvPlpxIP8ENPjdUaTv5pnzaNmlbat43w75h+pbpTN081TJKQ2O3xjR2a0yr\nRq1QStE9sDvPrH+GrfFb6RbQjee+fw6A3sG9GX7D8Cs6ltrkUc+D/wz8j+V5dkE2L2x4gR4Le9At\nsBsL9y2kW0A3Ovh2IC0vjdnbZzOj5wz6hfZj+pbpTN4wmZd+fIlJN01iXNQ4XBxdavFohBC2xGrN\neklJSQQGBlqeBwQEkJSUVKVlTp06VeG6p0+fxsfHBwAfHx9On75wDU5sbCyRkZFER0ezdevWCstW\nEk6e9hfGkUtIuBBOVVFYXMh/9v6HBs4Nyrz2aKdHLcHk7+5f6jXDMOjs35lNozexfuR6tj60lVXD\nVwHwZJcnq7TvysydC5MmgWFAUBD8739w/rx+LSUFfvkF8vLgzBlYulTXHauDu7M7c/rN4fi44/z0\n4E8svmcxiVmJvLjxRWZvn82YyDFMunkS7X3a88XgL8h6IYudf9/JzqSdhL0fxuJ9i2VUCiEEYMUO\nEVUd6VpV4ZtRKVXu9gzDsMz38/MjISEBT09P9uzZw913383Bgwdxd3cvs96XZyAOSDj3BjAMV9do\n4uOhRVQLlhxcwgMRD1z2AtYvD33J4GWDAWhYr2GZ1xu5NuLGpjeyO3k3y4cuL3cb9nb29AruBUD/\nVv1RU6rvyut//OPC4/R0GDkSwsPhmWfg4YfLLj9sGMyZA489pgPtWpWMyxcdFM3PY36+7LKtGrXi\n66FfszV+KxO+n8DLm17m5mY309G3Iw/f+DD1nepfe4GEEFds06ZNbNq0qdb2b7Wak7+/PwkJCZbn\nCQkJBAQEXHaZxMREAgICyp3v769rID4+PqSk6F5yycnJNGnSBAAnJyc8PT0B6NixI8HBwRw7dqzc\nsvUJhKnATU3uB6Lx9ob4eGjpGcKupF14/8ub7ILsctcFiE2PBaCeQz16texV7jJr7l/DymEr6ezf\nucLtVKe0NDhxAoqLwctL1wSLiyEzE/LzYehQHUxt20JyMixcCJ99BomJ8Oij8O9/6xDLqXp/kGp1\nc7Ob+fmhn5ndezb5pnze3vE27jPcafVeKxbtW8TKIysZ/c1otsVv41zuOct6Siky8zOlk4UQ1Sw6\nOpqpU6daphpnrZNZRUVFqmXLlio2NlYVFBRU2iFi+/btlg4Rl1t3woQJaubMmUoppWbMmGHpEHH2\n7FllMpmUUkrFxMQof39/lZ6eXqZcgHqzm+4F8WzYKvXLL0qtW6dUly5KtbnlmOWE/Qe7Pqjw2Kb8\nOEUxFfXoqkev8V2qPk2b6o4drVop5eioVHFx2WViYipe//x5pf7+d6VatFBq0CClPvxQqbQ065W3\nKk6knVBfHvxStX6vtaWzRb3p9RRTUY+sfET9c8M/VfSCaMtn5j/bX0V+FKlGfT1K7Tm1R5nNZmUq\nNimz2Vy7ByLEX4AV46L8/Vlz42vWrFGtWrVSwcHB6vXXX1dKKfXRRx+pjz76yLLM448/roKDg1X7\n9u3V7t27L7uuUkqdO3dO9ezZU4WGhqpevXpZAuirr75Sbdu2VR06dFAdO3ZUq1atKrdMgHr9Zh1O\n08J1z7ikJKV8fZXy9TNZvujGrxtf4XE9tfYp9cTqJ9QfZ/+4+jenGhUU6GBavlypUaOUatbs6re1\nYIFSdnbK0otx/HilEhOVKu/7PSur7DxrKQmYYnOxOp1zWr288WUV9E6QGr18tDp27phKzExU/9v/\nP/XMumfUq5tfVQFvBVg+y5B3Q9R7O99TGXkZpbZZaCosM08IUb6aDqc6OSr51FthymZ4O2wu4w8/\nQnExuLrq8y2uL4SQTgz9QvuxesTqcrcx+pvR9Ajqwd86/K1mC1+BrVvhlltK4kQ32/n5Xds2TSY4\ncgRefVV3mujbF26/XTcBurjovx99BD16wF13wYMPQoOyfUNqjcls4kjqEWLSY3B3cufDXz9k2aFl\ngB7YNrcol/jMeABcHV15qMNDFBQXcEuzW7g3/F7LRdRCCK2mRyWvkyNElPTWc1dZcOQI9q1b4+cH\ncXFw16Hj9J+8iMkbJluWj8uIY1fSLoa0HQJARn5GuR0hasP330Pv3uD253epYVx7MAE4OOjzU0uW\nwL/+BR9/DM8+C++/D1On6mCaPx8OHIDXX4enn4aePeGtt6B9+6rvJzFR78vXVz/fskX3LnzkESgs\nhIICCAjQx5efr3sY+vpC8+bw3ntQVAQPPQQNL/k4HOwcaNukraWrf48WPTidc5r4zHjO5p5le8J2\nerbsSaRvJLFpibz69RJOF8ayavMSHnF/knY+bWnu1RSzMtM9sDunDrbELr0VnkVtMZkMBgyAyMjq\n6UAC+jidZZANISzqZM1p2v+F8/KWw2wMeIDbEhdBYSH/19ORn36CG26AffsUjd5oxMHHDjLg8wHs\nTt4NgPlls+4hOM3g+1Hfc3vL22v1WHJz9Rd1djb8/DN062b9fa5bB/fdBy1a6GACXVuLjYU1a3RN\nq18//feS/i8AmM1gd1E3nDvvhI0b4cYb4exZXVtr2FB/UZ+u4E4d9evrUCoo0DW3vXt1kDVsqGt3\nPXvq0OjeHYKrcGPjBx6AxYshMFCvm++QwopDa2jUIgGfxk5ku+3h6Lmj2DeKxwFnPIpaU3zwHs79\nNIg2AYG0bg0DB+rJtX4RTo72mIvtcHCA5cth0ybw9oYOHXTgt2wJx47B6tVw6pR+H9etg86dwdFR\nB2+XLro869dDVJQ+Li8vfexC1IaarjnVyXBa1OU9Ru266Jqi+HiGTQhk6VKoV09/SfZf0pu72t7B\ns+uftSzWpnEbdj+yG5fXXPjj8T9o7d26Fo7ggjffhAkTdI+8mmxSy8/X10n92TmylMxMmDlT17Ta\ntdNftA0a6NrNli0wfjzcf78OlcWLYfNm/eW8dOmFWsjUqbpZMT0dmjTRQZSXp/86OOjpiy/gttv0\ndVwpKbBoETRqpLf3xx+wf78OuA4ddJPjffeV/8X+7rvw1FNw9CiEXjTYel6eDo8DB/S2HnoI7rxT\nsSdZB9X6mPWsOLISX8dWNDF1JjUmgINJcdD5QxwKGmP6/W5czE0osEuje8sONMvvz6Gd/iQkgIeH\n7lnp7g533w1OTtC/v55/9KgO7x9/1D1I8/J0ub79VodxVJQO0J499Y8RqW2JmiLhZGWGYTDu4Vy6\nffIgw1iqZ/78M89+2Y233tI1ggcegEUnp+Ee9TX7T+8HoGPTjqVuqVGd1yVdLU9P/aX7ySe1XZKy\n4uPhtdfg9991eJz489rmRx7RgfLiizq8nn0W/hwkpNoVFuqAmT8ffvoJ7rlH7zszUwfhwYO6xvKf\n/8CYMVex/eJCfoz9kXXH15Gck4wy23Gz7x2Y4rpxhBWcyNtLS7+GZBWm813MdzT3aE7PFrfzwa6P\nCKwfTMP6ztR3qk+/0H442Tux//R+/N396R3cm87+nUvdO0spHU4//ww//AAbNsDhwzqgSmqLHTqU\nrpUKUZ0knKzMMAyefVYRM3s5y7lXz2zRgvcf2s0TL3ly3326Q8EvZzdTOCIagG+GfoNCcc/SC7et\nqO1wMpt1rWTLFh2otq6gQH/B1qtXO/tPSYFPP9Vh5eSkz8vFxOhzZffea/39m8wmfk74mW/++IaT\nmScZEzmGU9mn8KznyfqY9fxx7g86+HTAzrBjY9xGDp45iKujK808mnGj3434u/tza/NbubnZzZbO\nGhkZusmwJKzOntUdY0qmyEhdyxSiOkg4WZlhGNx1l+Lcip/4if+zzM98/1PeOXs/9erpoX9wyId/\nujAofBBfDtFDEe1J3kPnTzpjVuZaC6fBg+HXX3XnDdAX2sqv5b+e1NxUUnNTSclJYU/yHpKzk9mZ\ntJM9yXuI8I3gtqDb6NGiByFeIfi5++Fg50BSkv6x8tNPejp5Erp2vRBWUVG6p6UQV0N669WAFSsg\nnNLDE3lkJzJlij45D2Cv6lEMhHtfGKi2Y9OOmF4yXdPt3K/WiROlT+5HRurmPAmmvyZvV2+8Xb0J\n8w4jOijaMj+3KJefE35mY+xGXtz4Ir+e+hUXBxe6BnTlxqY34tPCh9G3dOPfTTuSleHItm06qCZN\n0ufP2rbV5/HattWdNDp3hk6dFG5uBmYz2F/BTZyLiotIyk6iuUfzKg9XJkRV1cmaEygac4Yz+Fx4\n4fHHYc4cMjN1r6+bbgIXnwRWLvG2idGyS/7vv/OOPoEvRIlzuefYlrCNrfFbOZt7lr3JezmedhxX\nR1e8XLxo7d2a7gHd6eDdjfNxbck47c5vR1PZf349+4qWkeH1AxTWx0gPpam5M818PAlv5o1L4xR+\njP+e1o1bcizzIO7O7ni5eHFj0xu5ockNvLPzHX4/8zv2hj03+t1IsbkYNyc3POt50qZxGzycPbjB\n5wZ86/vi7+5vE/+PxNWTZj0rKwkne0yYcNRn4xcu1FeSfvPNn8voThErVujrapycarfM2dm6x1tq\nqu6RJkRlMvMzic+MJy0vjdPnT/Nzws9sT9zOrqRdlmXCvcN55MZHuMW/J+fOOlLkfIZvftnJwfgk\nTmQcJ+2cHU7p7TGda0ZDx8YE+bsRl5KB4bsf++Y7qedsR8Gir0g/n0Ng1G4cfI7g6dSYRv6ZFDU4\nSqH9OdI4TmzmcfJMeQR6BGJv2FPfqT6BHoE42Tvh4+ZDuybtcHdyx9PFkxPpJ2havymBHoE0dm2M\nu7M7Hs4elppZcnYyC35bgIOdA+7O7uSb8nF1dMXD2QOFIsQrhHDvcLmI2goknKysJJwAFIbug/zA\nA7qr05934p05U4+I8PDD+vFtt9VOWb/+Wnd2yM2Fm2+uvltbiLpLKUVuUS71HOphb1e1NjyzWV9/\ntmePblXw9ITdu3VvRz8/fa1aUpLuoJGWBseP616a2dmwb58+LxrSupCgzocIDM0kJBgy7I+RV1hE\nw8Y5xObuJzUvleTsFJo1aIFJ5XPk3BGyCrJIzU3FQbliFNbH0xzGGZctBDt2p0lxJ4pdkmni6Yp7\nAzPn8s9gZ9iRnHOKo+eO4u3qjZuTG6FeoYR7h9OmcRvCG4fj5eKFb31fGe3+Kkg4WdnF4fRjo0FE\nH/xAX2zy+++QlVXqkv9XXtEjIkyeXLWmtMWLdfv+xx9XvTzr1+uT1SUnqnfs0CeuDQNat9Y1t44d\nda1p374rOFAhbIDZrMPpyBF9vdi+fXqKidHnvv74Q583veEG3Uv2jz/0D7KgIH2tl72Dol2Xs9w3\nPJdTRYdxLPAl849I0tL0dWuHDumpqEjvLzQUWoebaNrmBPkqiyy7k6SYDnPGfJgs58MUOCVRQDZu\nhjc+Ti3JyXDGlF8PP5eWeNu1xt0ciKujGx0CQ+gS7kdYazsaNJDryUDCyeouDqf27S/6wg8I0Mly\nUb/s3buhUyf9uLJ3yWyGJ56ADz+EVav0RZWVee01+Oc/ITpaj64wdqwOuPr1L9y6YvNmuPVWfSHp\nf/97RYcqhM0rGQvy9991V/ghQ/RoI4cP6/97fn6VDxFVXKwns1mH3uHDesrM1D/6DEMHodmsbyUT\nd7IY5ybxHDl3hEZNc2nWooCk7EQS846SaX+cInMBaeoEhUYWpAdjPhuC8/lQmjqH0LJhCI2MUHxd\n/WnsbUdQkA4uPz99mURwcPkXp/8VSDhZ2cXh1KLFhYtDufNOfSXmPReuZbq499KBA/qi0YosXapv\n2nfrrXqbv/9e/qgNRUUwfTq88IL+j9Otm87FZXpMUnr21OuePq174/3977qpxMPjynpSCSGuTXZB\nNjHpMRxNPc7+xGPsSzjOsXPHSSk8znmVRkNzS5xyQqmXG4LpTAh2GSGcPdoSx7xAgoMcCQ7W/9+d\nnPSQVZ6eOjCDgvTUooUeoqq2z2lXlYSTlV0cTvfeC1999ecLL72kf2K98kqp5T/9VPeTCAnR558q\nMns2PPccfPedDpqiIliwoOxy8fH64tkBA3Sb/bff6qaI0aNh+HA9Xwhh284XnicmPYbjacc5nnac\nY+eOcTz9OLHpsSRnJ+Pl1BRPowWNHVrQxLElKr0FKq0FLTxbkHqyMXGx9pyMMzh1Cnx8LoRVyd/8\nfH3huLOzHsKqeXNdO9u3T7esODtDs2Z6fmDghYGfrUnCycpKwik1Vf+SsVwn9NVXutfeypVl1vn9\nd+jTR1/46uhY0Xb1UDxvvgnnz+vrRyZOLDs0z/79+kLa4GBYu1af5irnTvJCiOtUUXER8ZnxxGbE\nEpseS2xGLCfST1iep+am4uzgTDOPZjT3CKKRfXPyzjthd94Xc3pz0k+74WT2olWTZtjlBODl4cTJ\nk7pJsk0bPSxXUZF+Hh+vp3r1dMtK/fq604q7u25tcXfXtwNycNAhVq+eHrPS0REaN9bntP38dOtN\nbq5uWr3/fr3upSScrKwknHJyLvm1EROjT/5cdHv4i916Kzz2mL7d+aVMJv1hp6ToX0GgA61HDz28\nTNu2um3dMPQV/C++qDtCfPutbmMXQtQdRcVFFBYXcjLzJCczThKXEUe+KZ8z589wMvMk2YXZlksB\nTmWfwtvVG08XTwpMBZiVmYb1GhLoEUhgAz0FNAikgQrE1zWQglwn7Au9yMuuR06O7kGZl6fPySUm\n6gAqLNSXpJR8X50+rb/2HBzA3x9mzNAj4F9KwsnKSsIpN/eSoVzMZv1T4sCBcm+I9PXXuulu27by\ntqn/XvpOLl4MU6boSlnHjvDLL/oWCZ98ooNJCCEux2Q2kZydzLm8czjbO2Nn2JFVkEVCVgIJmQkk\nZiXqx38+zzPlkVWQhbuTO/4N/AloEIC/uz/+7n8+bqAfuzu7U9+pPo1cGlV5dA8JJysrCaf8/HK6\nh959t64aDR9eZr3iYn3e6bPPSt836dtv9X18oPwefS+/rO9tBPpXSnq67iX4yy/VcjhCCFGKWZlJ\nzU0lMSuRpKwkkrKT9OPsJJKy9OOsgizyTHnkFObQtH5T/Nz9LNPLt76Mt6t3me3K2Ho1pNwx6W69\nVffdLiec7O11c9w//6lHgAY9GvS6dRAWpq+1KM+0afDbb/rcU2EhjBihe+AJIYQ12Bl2NHFrQhO3\nJnRs2vGyy+ab8knOTuZU9ilOZZ8iKTsJZ3vbuKirztacTKZyumbv2aPPBh4+XO66RUUQHq4vsr3t\nNn0vpa++0l3DX3yxavtPSNAnJoUQ4npS0zWnOjumdbk1p4gI3V3l5Mly13F01D3NJ0zQzXzp6frK\n9pILdatCgkkIISpXZ8Op3HOA9vb6YtxyupOXGD5cd9f88EPdE+a//9XdzIUQQlSfOhtOFbr7bsvo\n5OUxDB1M06bp7uING9Zg2YQQoo6QcLpU7956UL2UlAoXadMGnnxSd3D4q46jJYQQtUnC6VKurjBo\nkL6VxmVMngzvvivhJIQQ1lBne+td9qi3b9d9v48cqXxIZCGEqAOkt14NqLQDQ9euehCq9etrpDxC\nCCFKq5PhdN99lSxgGDBpkr7hkhBCiBpXJ8OpSi11Q4bou6Bt3Gj18gghhChNwqkiDg56eN6nnrpw\nD2ghhBA1ok6GU7mjQ5Rn0CBo2lR3yxNCCFFj6mQ4VbkDnmHABx/oW+Du32/VMgkhhLhAwqkyISHw\n1lv6HFRGhtXKJIQQ4gIJp6oYNUqPHHHPPVBQADk5kJlplbIJIYSQcKq6t98Gb299HmrmTD2G0a+/\nVnvZhBBCSDhVnb29vg1uw4b6+qf27aFvX5g3T98C9/vvITa22ssqhBB1UZ28E26Ve+tdytFRj7kX\nEqJrUIYBI0fqUcx/+gnMZhg/HiZO1GP0CSGEuCpSc7pSdnYwdaq+y2C7drBrF3TooM9B7dihx+ML\nCYHZs/W5KSGEEFdMwulaOTnBq6/qW+O2aQNLlsDatTq0WrbUt809erQadyiEEH99Ek7V5eK2wogI\nWLoUfv5Zz7/lFoiO1tdMJSdfWO70aRl9QgghyiHhZE0hITBrFiQk6GGQtm3TtatbboE33oC2bXUP\nwAED4N//1rfWNZtrqHBCCGG76uT9nJYtU5WPTG4tBQXwww+wZg0kJcEnn8CPP+p5GzdCaip06gRR\nUdCxo76roa+vPq8VEaH/tmmjb+khhBA1pKbv51Qnw+nLLxWDBtV2SSpw9iz88gvs3KnPW7m5wdix\nsG8f/Pab/nv8OAQGQqtWF6bQUP3X3//KuyOePg35+ToEnZ31ObLUVP28aVNwcdHLnTypz601bgz1\n68uNGIWoQyScrMwwDL7+WnHPPbVdkmtQUAAnTugQuXRKS9MB1azZhSkwUP8NCNBh4+V1IcC+/lp3\ni3d31wHVoAGcOwetW+vnKSk6sHx99fYDA/XrxcXQpIkOqsaNyz5u1EhfE+bpeeGvBJoQ1y0JJysz\nDIPlyxV3313bJbGS/HxITIT4eD0lJFx4nJiowyY7WweIry/s3q3Ph739tr6YODVVjyHYqpXenlL6\neUoKmEy6Cz1Abq6u5Z09C2fOlH187pxeLz39wt/8/LKBdfFfd3cdYO7uZR9f/NzFRUJOiBom4WRl\nhmHwzTeKu+6q7ZLUooICHSLJyTqobrutZr7sCwv19WDp6aVDKyNDT9nZesrJufzjwsKyIebqqicX\nl/DBY64AAAqxSURBVLKPK/pb0WvOznpycpIQFOJPNR1OVh0hYt26dTz99NMUFxfz97//nYkTJ5ZZ\nZty4caxduxZXV1cWLFhAZGTkZddNS0tj6NChnDx5kqCgIL744gsaNmwIwIwZM/jvf/+Lvb097777\nLr179y63XHX++8bZWTfPBQbW7H6dnC40/V2LoiI4f/5CaGVnQ16ers1V9Dc5ufJlSv4WFOipqEiX\nuSSsrnWqV0/XQFNS9HYrmhwd9d/cXN186+Cg51VlupJlq7LeVQ+nIsS1sVrNqbi4mNatW/PDDz/g\n7+9P586d+fzzzwkPD7css2bNGubMmcOaNWvYuXMnTz31FDt27Ljsus8//zze3t48//zzzJo1i/T0\ndGbOnMmhQ4cYMWIEv/zyC0lJSdx+++0cPXoUu0v+cxmGwcqVigEDrHHU15dNmzYRHR1d28WwCeW+\nF2azrqWVhFV1TErp3pagw6+wsOKpqEg3rzo768dVmUymqi9bwXqb8vOJNpv1c8PQ40o6OFz+79W+\ndq3rXzrZ2VX8/HKvmUy62btknp0d2Nmx6Y8/iG7XzvL84tdKTRXNt8ZrFc238q/uv0zNadeuXYSE\nhBAUFATAsGHDWLFiRalwWrlyJaNHjwYgKiqKjIwMUlJSiI2NrXDdlStXsnnzZgBGjx5NdHQ0M2fO\nZMWKFQwfPhxHR0eCgoIICQlh165ddO3atUzZ6nzN6U8STheU+17Y2enaTh3rtr9p6lSip07VQWo2\n684vJpP+e/HjS/9e7rXqXr+wsPS84uILZS3veWWvmc2605BhXHhuNrPpt9+IPnHiwvIXvWaZKpp/\nta9d7fbgysPOMMo+37Sp5ltVymG1cEpKSiLwogMMCAhg586dlS6TlJTEqVOnKlz39OnT+Pj4AODj\n48Pp06cBOHXqVKkgKtlWeaSlQogqKKk12dvrZsa6aOpUPV0PKgqt8kKtuPjCj4+SvyWTr29tHwlg\nxXAyqlg9qUo1USlV7vYMw7jsfip6zd6+SkUTQojrR0nt5y/CauHk7+9PQkKC5XlCQgIBAQGXXSYx\nMZGAgACKiorKzPf39wd0bSklJQVfX1+Sk5Np0qRJhdsqWedSd9wh7Xolpk2bVttFsBnyXlwg78UF\n8l7UEmUlRUVFqmXLlio2NlYVFBSoiIgIdejQoVLLrF69WvXt21cppdT27dtVVFRUpetOmDBBzZw5\nUyml1IwZM9TEiROVUkodPHhQRUREqIKCAnXixAnVsmVLZTabrXV4QgghrMhqNScHBwfmzJlDnz59\nKC4uZsyYMYSHhzN37lwAxo4dS79+/VizZg0hISG4ubkxf/78y64LMGnSJIYMGcK8efMsXckB2rRp\nw5AhQ2jTpg0ODg588MEHVW5aFEIIYVvq3EW4QgghbN9f5+xZJdatW0dYWBihoaHMmjWrtotzTRIS\nEujRowdt27alXbt2vPvuu4C+QLlXr160atWK3r17k5GRYVlnxowZhIaGEhYWxvr16y3zd+/ezQ03\n3EBoaChPPfWUZX5BQQFDhw4lNDSUrl27cvLkSctrCxcupFWrVrRq1YpFixbVwBFfXnFxMZGRkQz4\n8+K1uvo+ZGRkcN999xEeHk6bNm3YuXNnnX0vZsyYQdu2bbnhhhsYMWIEBQUFdea9eOihh/Dx8eGG\nkqHGqP3/E7Gxsfx/e/cX0lQbxwH8V2g3IZlaVBvBOnOLGmWRDoKuRCQwesu2LNigi9KEwDCvu8o/\nVPSHoquJF9XsKojIKBApUAOZXgVd1JEK66KznUpDpu37XpiH7TW1t963c+z5fq62Z398nh9s33me\n5zknGAxKaWmp1NXVydSPXMfO7uOKv8P09DQ0TYOu60in09+d/1pK3r17h+HhYQDA58+f4fP58Pz5\nc7S0tKCjowMA0N7ePmc+Lp1OQ9d1aJpmzceVl5fj2bNnAIC9e/eip6cHAHD9+nWcPHkSANDd3Y3D\nhw8DAAzDwKZNm5BKpZBKpazbdrp48SKOHj2Kffv2AYCydYhGo4jFYgBm5m1N01SyFrquw+PxYHJy\nEgAQDofR1dWlTC2ePHmCRCKBQCBgtdk1dtM0AQChUAh37twBADQ0NODGjRuLjkOJcOrv70d1dbV1\nv62tDW1tbTb26L+1f/9+PH78GH6/H+/fvwcwE2B+vx8A0Nraai0iAYDq6moMDAxgbGwMmzdvttrj\n8Tjq6+ut5wwODgKY+aIrKSkBANy+fRsNDQ3Wa+rr6xGPx//fAS7gzZs3qKysRG9vL2pqagBAyTqY\npgmPxzOnXcVaGIYBn8+HZDKJqakp1NTU4NGjR0rVQtf1nHCyc+yZTAYlJSX4+vUrgJnFb9nfx/NR\n4rDefJt9/wSjo6MyPDwswWBwwQ3K2cv4szc7Z7e7XC6rLtk1y8vLk1WrVolhGPO+l11Onz4t58+f\nzzlNlYp10HVd1qxZI8eOHZOdO3fK8ePHZWJiQslaFBUVSXNzs2zcuFE2bNgghYWFUlVVpWQtZtk5\n9mQyKYWFhdZnNPu9FqJEOP2pq/bGx8eltrZWrly5IgUFBTmPLbZB+U9w//59Wbt2rezYsWPezdwq\n1EFEZHp6WhKJhDQ2NkoikZCVK1dKe3t7znNUqcXLly/l8uXLMjo6KmNjYzI+Pi43b97MeY4qtfie\n3zn2X/k7SoTTj2wIXmqmpqaktrZWIpGI/PXt4lSzG5RFZNENym63W1wul7x9+3ZO++xrXr9+LSIz\nX3wfP36U4uJiR9Wyv79f7t27Jx6PR44cOSK9vb0SiUSUq4PIzK9Ut9st5eXlIiJy6NAhSSQSsm7d\nOuVqMTQ0JLt375bi4mLJy8uTgwcPysDAgJK1mGXXZ8LlcklRUZGYpimZb+f/W+gECTl+5njmUvMj\nG4KXkkwmg0gkgqamppz2n9mgXFFRgcHBQWQymTmTnrPHj+PxeM6kp8fjQSqVQjKZtG7bra+vz5pz\nUrUOe/bswYsXLwAAZ8+eRUtLi5K1GBkZwdatW/HlyxdkMhlEo1Fcu3ZNqVr8c87J7rGHQiF0d3cD\nmJmL4oKILA8ePIDP54OmaWhtbbW7O7/k6dOnWLZsGbZv346ysjKUlZWhp6cHhmGgsrISpaWlqKqq\nyvlQnDt3Dpqmwe/34+HDh1b70NAQAoEANE3DqVOnrPbJyUmEQiF4vV4Eg0Houm491tnZCa/XC6/X\ni66urt8y5sX09fVZq/VUrcPIyAh27dqFbdu24cCBAzBNU9ladHR0YMuWLQgEAohGo0in08rUoq6u\nDuvXr0d+fj7cbjc6OzttH/urV69QUVEBr9eLcDiMdDq96Di4CZeIiBxHiTknIiJaWhhORETkOAwn\nIiJyHIYTERE5DsOJiIgch+FERESOw3Aistny5cvlzJkz1v0LFy7w0uCkPIYTkc1WrFghd+/eFcMw\nROTPPRck0b/BcCKyWX5+vpw4cUIuXbpkd1eIHIPhROQAjY2NcuvWLfn06ZPdXSFyBIYTkQMUFBRI\nNBqVq1ev2t0VIkdgOBE5RFNTk8RiMZmYmLC7K0S2YzgROcTq1aslHA5LLBbjoghSHsOJyGbZQdTc\n3CwfPnywsTdEzsBLZhARkePwPyciInIchhMRETkOw4mIiByH4URERI7DcCIiIsdhOBERkeMwnIiI\nyHEYTkRE5Dh/AyAKjdS1VO1VAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x117b5c850>"
]
}
],
"prompt_number": 120
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"Expanding productivity\n",
"=====================\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Expanding(Realized):\n",
" \n",
" def score(self):\n",
" return tuple(len(self.v[suff].hapaxes()) for suff in self.labels)\n",
" \n",
" def label(self, ax):\n",
" ax.set_xlabel('N')\n",
" ax.set_ylabel('V(1,C,N)')\n",
" ax.legend(loc=2)\n",
" plt.title('Expanding Productivity') "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 100
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"expanding = Expanding()\n",
"w, r = expanding.compute(brown)\n",
"expanding.plot(w, r, 'expanding.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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fUbobf/rpp1hZWREQEFDoMYWNS5k2bZr2sY+PDz4+PsUcnSjNIiLAyUmt6vp3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\n4gp+912PcqkAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x117c03650>"
]
}
],
"prompt_number": 101
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"r[-1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 102,
"text": [
"(165, 111, 3)"
]
}
],
"prompt_number": 102
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"Category conditioned productivity\n",
"================================="
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'th', sum(freq[w] == 1 for w in th)/float(sum(freq[w] for w in th))\n",
"print 'ity', sum(freq[w] == 1 for w in ity)/float(sum(freq[w] for w in ity))\n",
"print 'ness', sum(freq[w] == 1 for w in ness)/float(sum(freq[w] for w in ness))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"th 0.00267618198037\n",
"ity 0.0510814542108\n",
"ness 0.165995975855\n"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'th:',[w for w in th if freq[w] == 1]\n",
"print 'ity:',[w for w in ity if freq[w] == 1]\n",
"print 'ness:',[w for w in ness if freq[w] == 1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"th: [u'tilth', u'drouth', u'girth']\n",
"ity: [u'practicability', u'sociality', u'carnality', u'reversibility', u'inviolability', u'immaturity', u'incorruptibility', u'spatiality', u'spirituality', u'rancidity', u'lethality', u'livability', u'passivity', u'conventionality', u'crudity', u'hypercellularity', u'rationality', u'lucidity', u'singularity', u'austerity', u'irrationality', u'intellectuality', u'convexity', u'domesticity', u'luminosity', u'superficiality', u'inequality', u'religiosity', u'artificiality', u'nullity', u'perfectibility', u'avidity', u'irritability', u'suavity', u'subhumanity', u'timidity', u'profitability', u'masculinity', u'incompatibility', u'lobularity', u'formability', u'proportionality', u'fatality', u'compressibility', u'profoundity', u'sentimentality', u'eventuality', u'mediocrity', u'imperfectability', u'scrupulosity', u'bellicosity', u'procreativity', u'congeniality', u'maneuverability', u'analyticity', u'obscenity', u'confidentiality', u'municipality', u'peculiarity', u'monumentality', u'permissibility', u'subjectivity', u'particularity', u'hypocellularity', u'survivability', u'periodicity', u'unreliability', u'autosuggestibility', u'virtuosity', u'inscrutability', u'piezoelectricity', u'tranquillity', u'musicality', u'credibility', u'suitability', u'rotundity', u'familarity', u'historicity', u'civility', u'directionality', u'perfectability', u'acidity', u'gullibility', u'informality', u'mutuality', u'reproducibility', u'selectivity', u'fecundity', u'joviality', u'punctuality', u'modality', u'predictability', u'spinnability', u'directivity', u'amorality', u'legibility', u'invulnerability', u'impossibility', u'compatability', u'marketability', u'indivisibility', u'legality', u'infirmity', u'determinability', u'inactivity', u'incredulity', u'solemnity', u'emotionality', u'liquidity', u'deductibility', u'corporeality']\n",
"ness: [u'abruptness', u'abstractedness', u'acquisitiveness', u'adroitness', u'agreeableness', u'allusiveness', u'aloofness', u'amateurishness', u'aptness', u'artfulness', u'assertiveness', u'astuteness', u'awkwardness', u'balkiness', u'baroness', u'blandness', u'brashness', u'brazenness', u'briskness', u'busyness', u'carefulness', u'cheerfulness', u'chumminess', u'clannishness', u'cloddishness', u'closeness', u'coarseness', u'cohesiveness', u'commonness', u'conciseness', u'courtliness', u'crassness', u'creativeness', u'credulousness', u'curtness', u'deadness', u'decorativeness', u'deftness', u'discursiveness', u'disorderliness', u'dizziness', u'dreariness', u'dullness', u'elusiveness', u'explicitness', u'expressiveness', u'expressivness', u'exquisiteness', u'extraneousness', u'familiarness', u'fierceness', u'fineness', u'forcefulness', u'fruitfulness', u'garishness', u'giddiness', u'givenness', u'gladness', u'godliness', u'greenness', u'grimness', u'guardedness', u'guiltiness', u'harshness', u'haughtiness', u'helpfulness', u'highness', u'hoarseness', u'hollowness', u'homesickness', u'huskiness', u'inappropriateness', u'incisiveness', u'inclusiveness', u'incompleteness', u'indecisiveness', u'indefiniteness', u'ineffectiveness', u'interconnectedness', u'joblessness', u'justness', u'kindliness', u'lightness', u'lousiness', u'ludicrousness', u'manliness', u'mustiness', u'narrowness', u'neatness', u'neighborliness', u'objectiveness', u'obtrusiveness', u'obviousness', u'orderliness', u'oversoftness', u'paleness', u'passiveness', u'pastness', u'pettiness', u'physicalness', u'pleasantness', u'pompousness', u'powerfulness', u'presentness', u'prettiness', u'proneness', u'queasiness', u'quickness', u'raggedness', u'realness', u'recklessness', u'relatedness', u'religiousness', u'retentiveness', u'robustness', u'roundness', u'ruddiness', u'rudeness', u'ruefulness', u'saintliness', u'scratchiness', u'selfishness', u'selflessness', u'shallowness', u'sharpness', u'shortness', u'shortsightedness', u'shrillness', u'singleness', u'sinuousness', u'skillfulness', u'slovenliness', u'slyness', u'solicitousness', u'soreness', u'soundness', u'squeamishness', u'staginess', u'steadiness', u'stiffness', u'suppleness', u'surfaceness', u'swiftness', u'tactlessness', u'tardiness', u'thinness', u'thoroughness', u'thoughtfulness', u'tidiness', u'tiredness', u'togetherness', u'tunefulness', u'underhandedness', u'unnaturalness', u'unpleasantness', u'unselfconsciousness', u'untidiness', u'untrustworthiness', u'vividness', u'vociferousness', u'wetness', u'wildness', u'wonderfulness', u'worthlessness', u'wretchedness']\n"
]
}
],
"prompt_number": 27
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class CategoryCond(Realized):\n",
" \n",
" def score(self):\n",
" result = [ ]\n",
" for suff in self.labels:\n",
" if self.v[suff].N() > 0:\n",
" result.append(len(self.v[suff].hapaxes()) / float(self.v[suff].N()))\n",
" else:\n",
" result.append(0.0)\n",
" return tuple(result)\n",
" \n",
" def label(self, ax):\n",
" ax.set_xlabel('N')\n",
" ax.set_ylabel('P(C,N)')\n",
" ax.legend(loc=1)\n",
" plt.title('Category Conditioned Productivity') "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 107
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"category = CategoryCond()\n",
"w, r = category.compute(brown)\n",
"category.plot(w, r, 'category.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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fQS6XAwAmTJiA/v37Y/v27QgJCYGjoyNWrFhh8FwCzP/hEEIIqT+TBhR1bp7axMfHm7II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\njLGU+AelKJJWzI+JOwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1166415d0>"
]
}
],
"prompt_number": 108
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print r[-1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(0.1659959758551308, 0.051081454210768526, 0.0026761819803746653)\n"
]
}
],
"prompt_number": 109
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"Hapax-conditioned productivity\n",
"=============================="
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'th', sum(freq[w] == 1 for w in th)/float(len(freq.hapaxes()))\n",
"print 'ity', sum(freq[w] == 1 for w in ity)/float(len(freq.hapaxes()))\n",
"print 'ness', sum(freq[w] == 1 for w in ness)/float(len(freq.hapaxes()))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"th 0.000195223530943\n",
"ity 0.00722327064489\n",
"ness 0.0107372942019\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class HapaxCond(Realized):\n",
" \n",
" def init(self):\n",
" self.v = nltk.ConditionalFreqDist()\n",
" self.all = nltk.FreqDist()\n",
" \n",
" def score(self):\n",
" hapax = float(len(self.all.hapaxes()))\n",
" if hapax > 0.0:\n",
" result = [ ]\n",
" for suff in self.labels:\n",
" result.append(len(self.v[suff].hapaxes()) / hapax)\n",
" return tuple(len(self.v[suff].hapaxes()) / hapax for suff in self.labels)\n",
" else:\n",
" return (0.0, 0.0, 0.0)\n",
"\n",
" def add(self, word):\n",
" self.all[word] += 1\n",
" if word in ness:\n",
" self.v['ness'][word] += 1\n",
" elif word in ity:\n",
" self.v['ity'][word] += 1\n",
" elif word in th:\n",
" self.v['th'][word] += 1\n",
" \n",
" def label(self, ax):\n",
" ax.set_xlabel('N')\n",
" ax.set_ylabel('P*(C,N)')\n",
" ax.legend(loc=2)\n",
" plt.title('Hapax Conditioned Productivity') "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 113
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"hapax = HapaxCond()\n",
"w, r = hapax.compute(brown)\n",
"hapax.plot(w, r, 'hapax.pdf')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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9RwnNnFmi4kgkzzVys2o5IT09HScnJ06ePIlnMUfkKq998qTMm6eEdq5XD+rU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\nmr7vabVASwu/ibejA5g0yf7e8uW8685kAm7cAIzGvg+Tyfl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"text": [
"<matplotlib.figure.Figure at 0x10049c310>"
]
}
],
"prompt_number": 114
}
],
"metadata": {}
}
]
}
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