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@xaedes
Created July 13, 2016 15:06
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{
"metadata": {
"name": "",
"signature": "sha256:a439077a2d48e7c3332010d99ad47e3933c29ea101d7d19850a7802aa45da880"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = np.array([[4,5,20],[0,0,18],[0,1,1],[6,6,20],[0,0,20]]).astype(\"float32\")\n",
"PLACED,MIN,MAX = range(3)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 79
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"eps=1e-3"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 80
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#a=(data[:,MIN]-data[:,PLACED])/(data[:,MIN]+eps)\n",
"#b=(data[:,MAX]-data[:,PLACED])/(data[:,MAX]+eps)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a=norm2(data[:,PLACED]-data[:,MIN])\n",
"b=norm2(data[:,PLACED]-data[:,MAX])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 88
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 89,
"text": [
"array([ 0., 1., 0., 1., 1.], dtype=float32)"
]
}
],
"prompt_number": 89
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 90,
"text": [
"array([ 0.21052632, 0.10526316, 1. , 0.31578946, 0. ], dtype=float32)"
]
}
],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(a)\n",
"plot(b)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 91,
"text": [
"[<matplotlib.lines.Line2D at 0x7f99f5154d10>]"
]
},
{
"metadata": {},
"output_type": "display_data",
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9qVGmBjHhMapL8RveunK3PMm2e+bznZ/Tvlp7rzZ2kMy72bLtzoyLGMeHmz7k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\nQgghhBB/+f8wGKI8bqjElgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f99f5154b10>"
]
}
],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(norm2(-np.log(a+eps)))\n",
"plot(norm2(-np.log(b+eps)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 94,
"text": [
"[<matplotlib.lines.Line2D at 0x7f99f4efb490>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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eetJSJ5g7a2ZuvgMu5/F1O5zLYDWC9ecR4CzqL3GAX4F9gP96fHY4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\n",
"text": [
"<matplotlib.figure.Figure at 0x7f99f4efb990>"
]
}
],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"wa,wb=1,1\n",
"c=wa*norm2(-np.log(a+eps))+wb*norm2(-np.log(b+eps))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 110
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(norm2(c))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 111,
"text": [
"[<matplotlib.lines.Line2D at 0x7f99f47cb690>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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YzJlxQH+ga/ljyV/fO4BrgTLgMaC6NBna5F5aaltyrlwJTZq4jkYAnn7aXmxnz47esv5k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\ndq1O0oSBlQ0AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f99f48b0a10>"
]
}
],
"prompt_number": 111
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"wa,wb=1,1\n",
"d=wa*a+wb*b"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 117
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(np.log(1/(eps+norm2(d))))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 118,
"text": [
"[<matplotlib.lines.Line2D at 0x7f99f440b790>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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k+Q2wHXgZ6FToOecBzxV8PROoDtTx6f7JSiZO8GdVkBcfAj8X83gQ2rKkGMF9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\nRERERERERERERERERERERERERKLg/wG9rNSVSEecgAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f99f47a6890>"
]
}
],
"prompt_number": 118
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a_ = 1/(a+eps)\n",
"#b_ = 1/(b+eps)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 85
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print a_\n",
"#print b_"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 9.99999939e+02 9.99000967e-01 9.99999939e+02 9.99000967e-01\n",
" 9.99000967e-01]\n"
]
}
],
"prompt_number": 86
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(norm2(a_))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 87,
"text": [
"[<matplotlib.lines.Line2D at 0x7f99f5189f90>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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i8y6H+wy6cp9379ueHu/zLof7DLrSvNu2p8nmvdsc7jPqQvPuhdQ02bx3m8N9Rm1v3m3b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\nBhoAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f99f5211510>"
]
}
],
"prompt_number": 87
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(-np.log(a))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"-c:1: RuntimeWarning: divide by zero encountered in log\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 68,
"text": [
"[<matplotlib.lines.Line2D at 0x7f99f53deb90>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7f99f5448990>"
]
}
],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"c=-np.log(a_+b_)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(c)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 31,
"text": [
"[<matplotlib.lines.Line2D at 0x7f99f5cbba90>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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TqdcAXgTOBPYC92Nz6yUpUy/HpEnw4otWxdEr+/ZB06bW4eiMM7x7XskuP/4I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\nREREREREwu7/A2LRmelv5R4hAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f99f5dfe990>"
]
}
],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def norm1(arr):\n",
" arr2=(arr+arr.max())\n",
" return arr2 / arr2.min()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def norm2(arr):\n",
" return (arr-arr.min())/(arr.max()-arr.min())"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(norm1(c))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 37,
"text": [
"[<matplotlib.lines.Line2D at 0x7f99f5a900d0>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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layWmAqAU2AqUAdOxM/XKDlZ6fBLwaaYhElVU6EKqZO7uu63nvKQku+8zebIt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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f99f5b685d0>"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(norm2(c))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 38,
"text": [
"[<matplotlib.lines.Line2D at 0x7f99f59d4a10>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7f99f5a61910>"
]
}
],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x=np.linspace(-1,1,100)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"y=x**2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(x,y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 46,
"text": [
"[<matplotlib.lines.Line2D at 0x7f99f5721f10>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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g9mwldik5JXeJmyOPtKT++ecwZ441tRo82EoQiWzHDnj6aUvklSvbm+Ddd6tv\nj0RGyV3irk4dGDfOOhcOHmwj+ffeS7xJ1+3boUsXe5P77TdYtgxefNHeBEUipeQuzlx0EXz6Kbz5\npk0e1qkDAwbArl2uI4utH36ADh2gdm07AGXuXOjTx443FIkWJXdxqlQpO9vz00/tgImxY+Hkk6Fz\n5+Cd/DR/vs09nH66ddNctMje1GrUcB2ZBJGSu3jG5ZfD5MmW6H/6yco1t99uh074tWTz88/Qt68t\nBW3e3Orq335rv61Ureo6OgmycKZsmgBvAEnAAKBbPtf0ApoCfwD3AYvyuUZLIaVYdu6E1FSry2/Y\nYMfF3X47XHCBtycb//zTDhfPOQ3p6qttFUyTJrb+X6Q4YrUUMgnojSX404EWwGl5rrkWqAnUAh4C\n+hQ3CCm+tLQ01yHE3FFHQatWVpOeOdPWfd9zj5VtnnjCEmg0llNG47ncssXW8jdvbrXznj3h0kth\n7Vprh3zddYmT2BPhtekHRSX3+sAaYD2QCaQCN+W55kZgSPbn84CjAW1RibFE+wd02mnw0ku2THDK\nFFsv37WrnTjUqJH9bMYMG+0XV3Gfy1DIfpMYPdp6qZ99tk0Gjxljo/TVq2HWLHjkETj22OLH43eJ\n9tr0qtJF/LwSsDHX15uABmFcUxnYEnF0Ivk4/XT76NQJfvnFavSffgrPP2+TlFWqWL3+9NPtTeGU\nU2z9eMWKxRs979plk7obN1rCXrECVq6EJUtsbX6DBvbRt6+VikoX9a9JJI6KejmGWyTPWw9ScV3i\n4sgj4YYb7AMgM9OScM7HmDF2sMWmTbZZqEIFK/cccYR9lC5txwTOmQO7d9ubxS+/2ETor79aiaVS\nJahVy94sGjaEs86y0pCX6/4iRb08LwRSsJo7QEcgi/0nVfsCaVjJBiADuIIDR+5rAC36EhEpnrXY\nvGZUlc6+cTWgDLCY/CdUJ2V/fiGg3n8iIj7QFFiFjbw7Zn+vVfZHjt7ZP18CnBvX6EREREREJDK3\nA8uBvyh8NN8Eq9OvBjrEIS4/Kg9MB74BpmHLTfOzHliKbSKbH5fI/CWc11qv7J8vAdRwt3BFPZ/J\nwE7s9bgI6BS3yPxnEDZPWdjx8p55bdYBagOfUHByT8LKOdWAg8m/pi/wH6B99ucdgFcLuG4d9kYg\nBwrntZZ7/qgBmj8qTDjPZzIwIa5R+ddlWMIuKLkX+7UZy94yGdhIszDhbJKS/TeKDQFuLuRaLdDL\nnzbkRVdC/EB0AAABe0lEQVS4/3b1egzP58BPhfy82K9N143D8tsAVclRLF5WkX1LS7dQ8P/UEDAD\nWAi0jENcfhLOa62gDXlyoHCezxBwMVZGmIS1MJGSKfZrM9I9ddOBE/L5/rPAxDD+vjY77VPQc/lc\nnq9DFPy8XQL8AByXfb8MbEQg2pAXbeE8L18BVbCGgk2BD7BSrZRMsV6bkSb3qyP8+5ux//k5qmDv\nSImosOdyC5b4/wecCPxYwHU/ZP+5FRiP/eqs5G7Cea3lvaZy9vfkQOE8n7/m+nwy8DY2J7QjtqEF\nkidfm58A5xXws3A2SYlNqOasRniG/CdUywFHZH9+GJAONI59aL6hDXnRFc7zWZF9o836WH1eClaN\n8CZUnb82b8FqRH9iI87J2d8/Cfg413X5bZKS/ZXHaul5l0Lmfi5Pwf6BLQaWoecyP9qQF11FPZ+P\nYq/FxcAXWFKS/I0Avgf2YHnzfvTaFBEREREREREREREREREREREREREREREREREv+3/WT/Qgka5C\ntAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f99f594ee50>"
]
}
],
"prompt_number": 46
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot(x,norm2(-np.log(y)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 49,
"text": [
"[<matplotlib.lines.Line2D at 0x7f99f565e1d0>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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VQMekViciIiIiIpUzCMgBCim/Nd8P66dfA4xMQl1e1ACYD6wG3sOmm5ZmPfA1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\nH9uS9cGyEGjUAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f99f5765dd0>"
]
}
],
"prompt_number": 49
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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