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   "source": [
    "# ML logistic regression - assignment 2\n",
    "\n",
    "## Logistic Regression from scratch"
   ]
  },
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   "source": [
    "class MyOwnLogisticRegression:\n",
    "    def __init__(self, learning_rate=0.001, n_iters=1000):\n",
    "        self.lr = learning_rate\n",
    "        self.n_iters = n_iters\n",
    "        self.weights = None\n",
    "        self.bias = None\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        n_samples, n_features = X.shape\n",
    "\n",
    "        # init parameters\n",
    "        self.weights = np.zeros(n_features)\n",
    "        self.bias = 0\n",
    "\n",
    "        # gradient descent\n",
    "        for _ in range(self.n_iters):\n",
    "            # approximate y with linear combination of weights and x, plus bias\n",
    "            linear_model = np.dot(X, self.weights) + self.bias\n",
    "            # apply sigmoid function\n",
    "            y_predicted = self._sigmoid(linear_model)\n",
    "\n",
    "            # compute gradients\n",
    "            dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y))\n",
    "            db = (1 / n_samples) * np.sum(y_predicted - y)\n",
    "            # update parameters\n",
    "            self.weights -= self.lr * dw\n",
    "            self.bias -= self.lr * db\n",
    "\n",
    "    def predict(self, X):\n",
    "        linear_model = np.dot(X, self.weights) + self.bias\n",
    "        y_predicted = self._sigmoid(linear_model)\n",
    "        y_predicted_cls = [1 if i > 0.5 else 0 for i in y_predicted]\n",
    "        return np.array(y_predicted_cls)\n",
    "\n",
    "    def _sigmoid(self, x):\n",
    "        return 1 / (1 + np.exp(-x))"
   ]
  }
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