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Finished q1
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@thomasbrus
thomasbrus committed May 20, 2016
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70 changes: 57 additions & 13 deletions advanced/practicum/week-3/lab-3.ipynb
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"/Users/thomasbrus/Code/machine-learning/lib/python2.7/site-packages/ipykernel/__main__.py:65: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
"/Users/thomasbrus/Code/machine-learning/lib/python2.7/site-packages/ipykernel/__main__.py:94: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n"
"/Users/thomasbrus/Code/machine-learning/lib/python2.7/site-packages/ipykernel/__main__.py:65: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n"
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"\n",
"Q1.4:\n",
"P(C|-A,-B) = (see Q1.3) = 0.152777777778\n",
"P(B|-A) = (see Q1.1) = 0.0694444444444\n",
"P(-B|-A) = (see Q1.1) = 0.025462962963\n",
"P(-A) = (see marginal probabilities of initial graph) = 0.22222222\n",
"P(-D|-A) = (given) = 0.25\n",
"P(-A,-B,C,-D) = P(C|-A,-B) * P(B|-A) * P(-A) * P(-D|-A) = 0.000589420433064\n"
"P(-A,-B,C,-D) = P(C|-A,-B) * P(B|-A) * P(-A) * P(-D|-A) = 0.000216120825457\n",
"\n",
"Q1.5:\n",
"p(B|A) = 0.0138888888889\n",
"p(C|A) = 0.152777777778\n",
"p(D|A) = 0.0\n",
"P(A) = 0.77777778\n",
"\n",
"P(B|-A) = 0.0694444444444\n",
"P(C|-A) = 0.180555555556\n",
"P(D|-A) = 0.166666666667\n",
"P(-A) = 0.22222222\n",
"\n",
"P(B,C,D) = P(B|A) * P(C|A) * P(D|A) * P(A) + P(B|-A) * P(C|-A) * P(D|-A) * P(-A) = 0.000464391856355\n",
"P(D) = (see marginial probabilities of initial graph) = 0.16666667\n",
"\n",
"P(B,C|D) = P(B,C,D) / P(D) = 0.0027863510824\n"
]
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"/Users/thomasbrus/Code/machine-learning/lib/python2.7/site-packages/ipykernel/__main__.py:65: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
"/Users/thomasbrus/Code/machine-learning/lib/python2.7/site-packages/ipykernel/__main__.py:94: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n"
"/Users/thomasbrus/Code/machine-learning/lib/python2.7/site-packages/ipykernel/__main__.py:65: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n"
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"# Answer Q1.1\n",
"print \"Q1.1:\"\n",
"print \"p(B|-A) = \", g.getMarginals(\"B\")[0]\n",
"# print \"p(-B|-A) = \", g.getMarginals(\"B\")[1]\n",
"\n",
"# Answer Q1.2\n",
"print \"\\nQ1,2:\"\n",
"print \"p(C|-A) = \", g.getMarginals(\"C\")[0]\n",
"\n",
"# print \"p(D|-A) = \", g.getMarginals(\"D\")[0]\n",
"\n",
"g.reset()\n",
"\n",
"g.addFactor(\"obs -B\", np.array([0.,1.]), \"B\")\n",
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"# Answer Q1.4\n",
"print \"\\nQ1.4:\"\n",
"print \"P(C|-A,-B) = (see Q1.3) = 0.152777777778\"\n",
"print \"P(B|-A) = (see Q1.1) = 0.0694444444444\"\n",
"print \"P(-B|-A) = (see Q1.1) = 0.025462962963\"\n",
"print \"P(-A) = (see marginal probabilities of initial graph) = 0.22222222\"\n",
"print \"P(-D|-A) = (given) = 0.25\"\n",
"\n",
"print \"P(-A,-B,C,-D) = P(C|-A,-B) * P(B|-A) * P(-A) * P(-D|-A) = \", 0.152777777778 * 0.0694444444444 * 0.22222222 * 0.25"
"print \"P(-A,-B,C,-D) = P(C|-A,-B) * P(B|-A) * P(-A) * P(-D|-A) = \", 0.152777777778 * 0.025462962963 * 0.22222222 * 0.25\n",
"\n",
"# Answer Q1.5\n",
"g2 = Graph()\n",
"g2.addVariables([\"True\",\"False\"], \"A\",\"B\",\"C\",\"D\")\n",
"g2.addFactor(\"p(B)\", pB, \"B\")\n",
"g2.addFactor(\"p(C)\", pC, \"C\")\n",
"g2.addFactor(\"p(A|B,C)\", pA_BC, \"A\",\"B\",\"C\")\n",
"g2.addFactor(\"P(D|A)\",pD_A,\"D\",\"A\")\n",
"\n",
"g2.addFactor(\"obs A\", np.array([1.,0.]), \"A\")\n",
"g2.propagateMessages()\n",
"\n",
"print \"\\nQ1.5:\"\n",
"print \"p(B|A) = \", g2.getMarginals(\"B\")[0]\n",
"print \"p(C|A) = \", g2.getMarginals(\"C\")[0]\n",
"print \"p(D|A) = \", g2.getMarginals(\"D\")[0]\n",
"print \"P(A) = 0.77777778\"\n",
"\n",
"print \"\\nP(B|-A) = 0.0694444444444\"\n",
"print \"P(C|-A) = 0.180555555556\"\n",
"print \"P(D|-A) = 0.166666666667\"\n",
"print \"P(-A) = 0.22222222\"\n",
"\n",
"print \"\\nP(B,C,D) = P(B|A) * P(C|A) * P(D|A) * P(A) + P(B|-A) * P(C|-A) * P(D|-A) * P(-A) = \", 0.0138888888889 * 0.152777777778 * 0.0 * 0.77777778 + 0.0694444444444 * 0.180555555556 * 0.166666666667 * 0.22222222\n",
"print \"P(D) = (see marginial probabilities of initial graph) = 0.16666667\"\n",
"\n",
"print \"\\nP(B,C|D) = P(B,C,D) / P(D) = \", 0.000464391856355 / 0.16666667"
]
},
{
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