cost_function.ipynb

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    "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"
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    "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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