machinelearning

Machine learning code
git clone git://git.laack.co/machinelearning.git
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broadcasting.ipynb (8157B)

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      3   {
      4    "cell_type": "code",
      5    "execution_count": 3,
      6    "metadata": {},
      7    "outputs": [],
      8    "source": [
      9     "import numpy as np\n",
     10     "\n",
     11     "x = np.random.random((32,10))\n",
     12     "y = np.random.random((10,))"
     13    ]
     14   },
     15   {
     16    "cell_type": "code",
     17    "execution_count": 4,
     18    "metadata": {},
     19    "outputs": [
     20     {
     21      "data": {
     22       "text/plain": [
     23        "1"
     24       ]
     25      },
     26      "execution_count": 4,
     27      "metadata": {},
     28      "output_type": "execute_result"
     29     }
     30    ],
     31    "source": [
     32     "y.ndim"
     33    ]
     34   },
     35   {
     36    "cell_type": "code",
     37    "execution_count": 5,
     38    "metadata": {},
     39    "outputs": [
     40     {
     41      "data": {
     42       "text/plain": [
     43        "2"
     44       ]
     45      },
     46      "execution_count": 5,
     47      "metadata": {},
     48      "output_type": "execute_result"
     49     }
     50    ],
     51    "source": [
     52     "x.ndim"
     53    ]
     54   },
     55   {
     56    "cell_type": "code",
     57    "execution_count": 6,
     58    "metadata": {},
     59    "outputs": [],
     60    "source": [
     61     "y = np.expand_dims(y,axis=0)"
     62    ]
     63   },
     64   {
     65    "cell_type": "code",
     66    "execution_count": 8,
     67    "metadata": {},
     68    "outputs": [
     69     {
     70      "data": {
     71       "text/plain": [
     72        "(1, 10)"
     73       ]
     74      },
     75      "execution_count": 8,
     76      "metadata": {},
     77      "output_type": "execute_result"
     78     }
     79    ],
     80    "source": [
     81     "y.shape"
     82    ]
     83   },
     84   {
     85    "cell_type": "code",
     86    "execution_count": 12,
     87    "metadata": {},
     88    "outputs": [
     89     {
     90      "data": {
     91       "text/plain": [
     92        "array([[0.478351  , 0.26084096, 0.54553878, 0.16474249, 0.50081789,\n",
     93        "        0.27506882, 0.02410212, 0.07660294, 0.24260611, 0.8664726 ]])"
     94       ]
     95      },
     96      "execution_count": 12,
     97      "metadata": {},
     98      "output_type": "execute_result"
     99     }
    100    ],
    101    "source": [
    102     "y"
    103    ]
    104   },
    105   {
    106    "cell_type": "code",
    107    "execution_count": 13,
    108    "metadata": {},
    109    "outputs": [],
    110    "source": [
    111     "Y = np.concatenate([y] * 32)"
    112    ]
    113   },
    114   {
    115    "cell_type": "code",
    116    "execution_count": 15,
    117    "metadata": {},
    118    "outputs": [
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    128      "output_type": "execute_result"
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    131    "source": [
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    135   {
    136    "cell_type": "code",
    137    "execution_count": 16,
    138    "metadata": {},
    139    "outputs": [
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    207       ]
    208      },
    209      "execution_count": 16,
    210      "metadata": {},
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    214    "source": [
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    223    "name": "python3"
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