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   "source": [
    "Documents for the Linear, Ridge and Lasso regressions in the sklearn package:\n",
    "\n",
    "Linear: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\n",
    "\n",
    "Ridge: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html\n",
    "\n",
    "Lasso: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.15631421 2.24887843]\n"
     ]
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "import numpy as np\n",
    "\n",
    "# Form the coefficient matrix of the linear system\n",
    "X_transpose = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])\n",
    "\n",
    "\n",
    "# The underlying linear function is y = 1 * x_1 + 2 * x_2 + epsilon\n",
    "epsilon = np.random.rand(4,)\n",
    "y = np.dot(X_transpose, np.array([1, 2])) + epsilon\n",
    "\n",
    "\n",
    "# Various regression models\n",
    "#clf = linear_model.LinearRegression()\n",
    "#clf = linear_model.Ridge(alpha=0.1)\n",
    "clf = linear_model.Lasso(alpha=0.1)\n",
    "\n",
    "# Fit the model to the data\n",
    "clf.fit(X_transpose, y)\n",
    "\n",
    "\n",
    "# Print the regression coefficients\n",
    "print(clf.coef_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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