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- "cell_type" : " code" ,
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- "input" : [
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- " from random import random\n " ,
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- " \n " ,
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- " def random2d(n=1000):\n " ,
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- " return [[random() for j in range(n)] for i in range(n)]\n " ,
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- " \n " ,
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- " # now let's do 2d averaging\n " ,
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- " def average2d(a):\n " ,
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- " # there's no way to tell how many elements\n " ,
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- " # there are because each list in the list of lists\n " ,
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- " # could be a different length\n " ,
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- " n, s = 0, 0\n " ,
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- " for row in a:\n " ,
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- " for element in row:\n " ,
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- " s += element\n " ,
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- " n += 1\n " ,
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- " return s / n\n " ,
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- " \n " ,
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- " n = 3000\n " ,
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- " \n " ,
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- " R = random2d(n)\n " ,
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- " average2d(R)"
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- ],
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- "language" : " python" ,
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- "metadata" : {},
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- "outputs" : [
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- {
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- "metadata" : {},
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- "output_type" : " pyout" ,
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- "text" : [
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- " 0.5001795139728076"
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- ]
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- },
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- {
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- "cell_type" : " code" ,
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- "collapsed" : false ,
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- "input" : [
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- " %%timeit\n " ,
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- " \n " ,
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- " # how fast is this?\n " ,
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- " average2d(R)"
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- ],
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- "language" : " python" ,
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- "metadata" : {},
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- "outputs" : [
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- {
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- "output_type" : " stream" ,
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- "stream" : " stdout" ,
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- "text" : [
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- " 1 loops, best of 3: 604 ms per loop\n "
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- ]
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- }
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- ],
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- },
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- {
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- "cell_type" : " code" ,
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- "collapsed" : false ,
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- "input" : [
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- " import numpy as np\n " ,
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- " \n " ,
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- " R = np.random.uniform(size=(n,n))\n " ,
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- " R"
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- ],
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- "language" : " python" ,
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- "metadata" : {},
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- "outputs" : [
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- {
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- "metadata" : {},
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- "output_type" : " pyout" ,
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- "prompt_number" : 10 ,
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- "text" : [
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- " array([[ 0.16442591, 0.06019215, 0.13574315, ..., 0.95115628,\n " ,
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- " 0.32276626, 0.69702153],\n " ,
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- " [ 0.31768723, 0.93409853, 0.34722531, ..., 0.52851659,\n " ,
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- " 0.36773928, 0.9316535 ],\n " ,
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- " [ 0.91488032, 0.88127737, 0.87042717, ..., 0.85553466,\n " ,
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- " 0.30377961, 0.95292511],\n " ,
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- " ..., \n " ,
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- " [ 0.86619297, 0.64068635, 0.0671692 , ..., 0.52829387,\n " ,
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- " 0.52078797, 0.13971299],\n " ,
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- " [ 0.50046661, 0.2426449 , 0.59872748, ..., 0.76729318,\n " ,
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- " 0.5692301 , 0.74018849],\n " ,
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- " [ 0.87473508, 0.64598814, 0.91931996, ..., 0.71743977,\n " ,
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- " 0.27391853, 0.18721739]])"
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- ]
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- }
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- ],
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- "prompt_number" : 10
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- },
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- {
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- "cell_type" : " code" ,
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- "collapsed" : false ,
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- "input" : [
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- " np.mean(R)"
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- ],
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- "language" : " python" ,
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- "metadata" : {},
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- "outputs" : [
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- {
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- "metadata" : {},
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- "output_type" : " pyout" ,
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- "prompt_number" : 11 ,
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- "text" : [
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- " 0.50023059066229336"
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- ]
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- }
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- ],
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- "prompt_number" : 11
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- },
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- {
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- "cell_type" : " code" ,
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- "collapsed" : false ,
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- "input" : [
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- " %%timeit\n " ,
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- " \n " ,
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- " np.mean(R)"
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- ],
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- "language" : " python" ,
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- "metadata" : {},
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- "outputs" : [
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- {
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- "output_type" : " stream" ,
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- "stream" : " stdout" ,
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- "text" : [
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- " 100 loops, best of 3: 10.4 ms per loop\n "
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- ]
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- }
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- ],
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- "prompt_number" : 12
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- }
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- ],
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+ "cell_type" : " code" ,
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+ "collapsed" : false ,
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+ "input" : [
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+ " from random import random\n " ,
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+ " \n " ,
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+ " def random2d(n=1000):\n " ,
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+ " return [[random() for j in range(n)] for i in range(n)]\n " ,
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+ " \n " ,
20
+ " # now let's do 2d averaging\n " ,
21
+ " def average2d(a):\n " ,
22
+ " # there's no way to tell how many elements\n " ,
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+ " # there are because each list in the list of lists\n " ,
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+ " # could be a different length\n " ,
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+ " n, s = 0, 0\n " ,
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+ " for row in a:\n " ,
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+ " for element in row:\n " ,
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+ " s += element\n " ,
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+ " n += 1\n " ,
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+ " return s / n\n " ,
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+ " \n " ,
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+ " n = 3000\n " ,
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+ " \n " ,
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+ " R = random2d(n)\n " ,
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+ " average2d(R)"
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+ ],
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+ "language" : " python" ,
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+ "metadata" : {},
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+ "outputs" : [
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+ {
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+ "metadata" : {},
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+ "output_type" : " pyout" ,
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+ " 0.5001795139728076"
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "collapsed" : false ,
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+ "input" : [
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+ " %%timeit\n " ,
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+ " \n " ,
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+ " # how fast is this?\n " ,
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+ " average2d(R)"
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+ ],
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+ "language" : " python" ,
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+ "metadata" : {},
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+ "outputs" : [
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+ {
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+ "output_type" : " stream" ,
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+ "stream" : " stdout" ,
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+ "text" : [
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+ " 1 loops, best of 3: 604 ms per loop\n "
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+ ]
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+ }
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+ ],
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+ "prompt_number" : 3
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "collapsed" : false ,
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+ "input" : [
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+ " import numpy as np\n " ,
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+ " \n " ,
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+ " R = np.random.uniform(size=(n,n))\n " ,
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+ " R"
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+ ],
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+ "language" : " python" ,
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+ "metadata" : {},
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+ "outputs" : [
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+ {
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+ "metadata" : {},
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+ "output_type" : " pyout" ,
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+ "prompt_number" : 10 ,
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+ "text" : [
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+ " array([[ 0.16442591, 0.06019215, 0.13574315, ..., 0.95115628,\n " ,
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+ " 0.32276626, 0.69702153],\n " ,
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+ " [ 0.31768723, 0.93409853, 0.34722531, ..., 0.52851659,\n " ,
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+ " 0.36773928, 0.9316535 ],\n " ,
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+ " [ 0.91488032, 0.88127737, 0.87042717, ..., 0.85553466,\n " ,
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+ " 0.30377961, 0.95292511],\n " ,
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+ " ..., \n " ,
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+ " [ 0.86619297, 0.64068635, 0.0671692 , ..., 0.52829387,\n " ,
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+ " 0.52078797, 0.13971299],\n " ,
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+ " [ 0.50046661, 0.2426449 , 0.59872748, ..., 0.76729318,\n " ,
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+ " 0.5692301 , 0.74018849],\n " ,
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+ " [ 0.87473508, 0.64598814, 0.91931996, ..., 0.71743977,\n " ,
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+ " 0.27391853, 0.18721739]])"
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+ ]
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+ }
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+ ],
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+ "prompt_number" : 10
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "collapsed" : false ,
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+ "input" : [
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+ " np.mean(R)"
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+ ],
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+ "language" : " python" ,
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+ "metadata" : {},
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+ "outputs" : [
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+ {
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+ "metadata" : {},
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+ "output_type" : " pyout" ,
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+ "prompt_number" : 11 ,
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+ "text" : [
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+ " 0.50023059066229336"
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+ ]
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+ }
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+ ],
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+ "prompt_number" : 11
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+ },
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+ {
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+ "cell_type" : " code" ,
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+ "collapsed" : false ,
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+ "input" : [
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+ " %%timeit\n " ,
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+ " \n " ,
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+ " np.mean(R)"
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+ ],
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+ "language" : " python" ,
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+ "metadata" : {},
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+ "outputs" : [
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+ {
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+ "output_type" : " stream" ,
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+ "stream" : " stdout" ,
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+ "text" : [
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+ " 100 loops, best of 3: 10.4 ms per loop\n "
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+ ]
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+ }
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+ ],
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+ "prompt_number" : 12
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+ }
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}
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