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@infinite-Joy9l
Last active January 19, 2018 06:05
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{
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"For each member of the dataset, the result (Y) determines which variation of the cost function is used. The Y = 0 cost function punishes high probability estimations, and Y = 1 punishes low scores. The \"punishment\" makes the change in the gradient of ThetaCurrent - Average(CostFunction(Dataset)) greater"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def Cost_Function(X,Y,theta,m):\n",
" sumOfErrors = 0\n",
" for i in range(m):\n",
" xi = X[i]\n",
" hi = Hypothesis(theta,xi)\n",
" if Y[i] == 1:\n",
" error = Y[i] * math.log(hi)\n",
" elif Y[i] == 0:\n",
" error = (1-Y[i]) * math.log(1-hi)\n",
" sumOfErrors += error\n",
" const = -1/m\n",
" J = const * sumOfErrors\n",
" print('cost is ', J)\n",
" return J"
]
}
],
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