ILFM experiment

ilfm_experiment.ipynb

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   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import numpy as np\n",
    "from ilfm import ILFM"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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    "collapsed": false
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   "source": [
    "# データ生成\n",
    "N = 300\n",
    "K = 3\n",
    "D = 10\n",
    "var_x=1.0\n",
    "var_y=0.1\n",
    "X = np.random.normal(loc=0, scale=np.sqrt(var_x), size=(K,D))\n",
    "Z = np.random.randint(low=0,high=2,size=(N,K))\n",
    "true_Y = Z.dot(X)\n",
    "Y = true_Y + np.random.normal(loc=0,scale=np.sqrt(var_y),size=(N,D))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ilfm.ILFM instance at 0x106308290>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# フィッティング\n",
    "alpha=0.01\n",
    "max_iter=100\n",
    "model = ILFM(var_x=var_x, var_y=var_y, alpha=alpha, max_iter=max_iter)\n",
    "model.fit(Y)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
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   "outputs": [
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     "name": "stdout",
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     "text": [
      "0.0642104688117\n"
     ]
    }
   ],
   "source": [
    "# 相対誤差\n",
    "print abs(model.Z.dot(model.X)-true_Y).sum()/abs(true_Y).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n"
     ]
    }
   ],
   "source": [
    "# 推定された潜在次元数（K）\n",
    "print model.sampled_features.sum()"
   ]
  }
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