diff --git a/.gitignore b/.gitignore
index b0b0a9440..304027748 100644
--- a/.gitignore
+++ b/.gitignore
@@ -105,3 +105,11 @@ ENV/
 *.cb2
 *.fdb_latexmk
 *.synctex.gz
+projects/project1/data/dataset/dataset/sample_submission.csv
+projects/project1/data/dataset/dataset/x_test.csv
+projects/project1/data/dataset/dataset/x_train.csv
+projects/project1/data/dataset/dataset/y_train.csv
+projects/project1/x_train_cleaned.csv
+projects/project1/data/dataset/dataset/x_train_clean.csv
+projects/project1/data/dataset/dataset/x_train_cleaned.csv
+projects/project1/latex-example-paper.zip
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 000000000..ca6fe0685
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,3 @@
+{
+    "git.ignoreLimitWarning": true
+}
\ No newline at end of file
diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 000000000..33b4e975f
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,33 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f00748fc-eb20-4a98-87ad-0f38fc307222",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:ada] *",
+   "language": "python",
+   "name": "conda-env-ada-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/labs/ex01/template/taskA.ipynb b/labs/ex01/template/taskA.ipynb
index b64f27568..295d54e5a 100644
--- a/labs/ex01/template/taskA.ipynb
+++ b/labs/ex01/template/taskA.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Data Generation\n",
@@ -29,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,7 +47,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Solution\n",
@@ -57,18 +55,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.77132064 0.02075195 0.63364823 0.74880388 0.49850701]\n",
+      " [0.22479665 0.19806286 0.76053071 0.16911084 0.08833981]\n",
+      " [0.68535982 0.95339335 0.00394827 0.51219226 0.81262096]\n",
+      " [0.61252607 0.72175532 0.29187607 0.91777412 0.71457578]\n",
+      " [0.54254437 0.14217005 0.37334076 0.67413362 0.44183317]\n",
+      " [0.43401399 0.61776698 0.51313824 0.65039718 0.60103895]\n",
+      " [0.8052232  0.52164715 0.90864888 0.31923609 0.09045935]\n",
+      " [0.30070006 0.11398436 0.82868133 0.04689632 0.62628715]\n",
+      " [0.54758616 0.819287   0.19894754 0.8568503  0.35165264]\n",
+      " [0.75464769 0.29596171 0.88393648 0.32551164 0.1650159 ]]\n"
+     ]
+    }
+   ],
    "source": [
     "print(data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `standardize` passed 1 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def standardize(x):\n",
     "    \"\"\"Stadartize the input data x\n",
@@ -88,7 +111,8 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # TODO: standartize input data x\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    #Here we use axis=0 because we are standardizing the data by dimension, and hence column\n",
+    "    std_data = (x - np.mean(x, axis = 0))/np.std(x, axis = 0)\n",
     "    return std_data\n",
     "\n",
     "\n",
@@ -97,9 +121,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 1.0775774  -1.34411605  0.31525355  0.80665878  0.24128662]\n",
+      " [-1.81711634 -0.77630186  0.74088404 -1.25592235 -1.42276759]\n",
+      " [ 0.62228127  1.64254169 -1.797091   -0.03521894  1.51565143]\n",
+      " [ 0.23651339  0.90075228 -0.83122987  1.40786459  1.11788073]\n",
+      " [-0.13414844 -0.95529104 -0.55795449  0.54097769  0.01136005]\n",
+      " [-0.70898541  0.56774371 -0.08900028  0.45652209  0.65726018]\n",
+      " [ 1.2571441   0.25993298  1.23775021 -0.72176808 -1.4141686 ]\n",
+      " [-1.41508984 -1.04555188  0.96949701 -1.69076861  0.75969247]\n",
+      " [-0.10744434  1.21308427 -1.14296098  1.19109415 -0.35450368]\n",
+      " [ 0.98926822 -0.46279408  1.15485183 -0.69943932 -1.11169162]] \n",
+      "\n",
+      " [-1.66533454e-16  4.99600361e-17 -2.22044605e-17  1.11022302e-17\n",
+      "  3.33066907e-16] \n",
+      "\n",
+      " [1. 1. 1. 1. 1.]\n"
+     ]
+    }
+   ],
    "source": [
     "std_data = standardize(data)\n",
     "print(std_data, \"\\n\\n\", np.mean(std_data, axis=0), \"\\n\\n\", np.std(std_data, axis=0))"
@@ -114,8 +160,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/labs/ex01/template/taskB.ipynb b/labs/ex01/template/taskB.ipynb
index 483ee73d6..21046db24 100644
--- a/labs/ex01/template/taskB.ipynb
+++ b/labs/ex01/template/taskB.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Data Generation\n",
@@ -29,9 +28,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.77132064 0.02075195]\n",
+      " [0.63364823 0.74880388]\n",
+      " [0.49850701 0.22479665]\n",
+      " [0.19806286 0.76053071]] \n",
+      "\n",
+      " [[0.16911084 0.08833981]\n",
+      " [0.68535982 0.95339335]\n",
+      " [0.00394827 0.51219226]\n",
+      " [0.81262096 0.61252607]\n",
+      " [0.72175532 0.29187607]]\n"
+     ]
+    }
+   ],
    "source": [
     "np.random.seed(10)\n",
     "P, Q = (np.random.rand(i, 2) for i in (4, 5))\n",
@@ -42,7 +58,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Solution\n",
@@ -51,13 +66,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `naive` passed 1 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def naive(P, Q):\n",
     "    \"\"\"\n",
-    "    A naive solution for finding pairvise distances between poins in P and Q\n",
+    "    A naive solution for finding pairvise distances between points in P and Q\n",
     "\n",
     "    Args:\n",
     "        P: numpy array of shape=(p, 2)\n",
@@ -68,12 +91,15 @@
     "    >>> naive(np.array([[0, 1]]), np.array([[2, 3], [4, 5]]))\n",
     "    array([[2.82842712, 5.65685425]])\n",
     "    \"\"\"\n",
-    "    # ***************************************************\n",
+    "    # ***************************x************************\n",
     "    # INSERT YOUR CODE HERE\n",
     "    # TODO: implement a naive solution\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
-    "\n",
+    "    D = np.zeros((P.shape[0], Q.shape[0]))\n",
+    "    for i in range(P.shape[0]):\n",
+    "        for j in range(Q.shape[0]):\n",
+    "            D[i, j] = np.sqrt(np.sum((P[i] - Q[j])**2))\n",
+    "    return D\n",
     "\n",
     "test(naive)"
    ]
@@ -87,7 +113,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "### Use matching indices\n",
@@ -97,30 +122,111 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0 0 0 0 0]\n",
+      " [1 1 1 1 1]\n",
+      " [2 2 2 2 2]\n",
+      " [3 3 3 3 3]]\n",
+      "\n",
+      "[0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3]\n",
+      "\n",
+      "[[0 1 2 3 4]\n",
+      " [0 1 2 3 4]\n",
+      " [0 1 2 3 4]\n",
+      " [0 1 2 3 4]]\n"
+     ]
+    }
+   ],
    "source": [
     "rows, cols = np.indices((P.shape[0], Q.shape[0]))\n",
     "print(rows, end=\"\\n\\n\")\n",
-    "print(cols)"
+    "print(rows.ravel(), end =\"\\n\\n\")\n",
+    "print(cols)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.77132064 0.02075195]\n",
+      " [0.77132064 0.02075195]\n",
+      " [0.77132064 0.02075195]\n",
+      " [0.77132064 0.02075195]\n",
+      " [0.77132064 0.02075195]\n",
+      " [0.63364823 0.74880388]\n",
+      " [0.63364823 0.74880388]\n",
+      " [0.63364823 0.74880388]\n",
+      " [0.63364823 0.74880388]\n",
+      " [0.63364823 0.74880388]\n",
+      " [0.49850701 0.22479665]\n",
+      " [0.49850701 0.22479665]\n",
+      " [0.49850701 0.22479665]\n",
+      " [0.49850701 0.22479665]\n",
+      " [0.49850701 0.22479665]\n",
+      " [0.19806286 0.76053071]\n",
+      " [0.19806286 0.76053071]\n",
+      " [0.19806286 0.76053071]\n",
+      " [0.19806286 0.76053071]\n",
+      " [0.19806286 0.76053071]]\n",
+      "\n",
+      "[[0.77132064 0.02075195]\n",
+      " [0.63364823 0.74880388]\n",
+      " [0.49850701 0.22479665]\n",
+      " [0.19806286 0.76053071]]\n",
+      "\n",
+      "[[0.16911084 0.08833981]\n",
+      " [0.68535982 0.95339335]\n",
+      " [0.00394827 0.51219226]\n",
+      " [0.81262096 0.61252607]\n",
+      " [0.72175532 0.29187607]\n",
+      " [0.16911084 0.08833981]\n",
+      " [0.68535982 0.95339335]\n",
+      " [0.00394827 0.51219226]\n",
+      " [0.81262096 0.61252607]\n",
+      " [0.72175532 0.29187607]\n",
+      " [0.16911084 0.08833981]\n",
+      " [0.68535982 0.95339335]\n",
+      " [0.00394827 0.51219226]\n",
+      " [0.81262096 0.61252607]\n",
+      " [0.72175532 0.29187607]\n",
+      " [0.16911084 0.08833981]\n",
+      " [0.68535982 0.95339335]\n",
+      " [0.00394827 0.51219226]\n",
+      " [0.81262096 0.61252607]\n",
+      " [0.72175532 0.29187607]]\n"
+     ]
+    }
+   ],
    "source": [
     "print(P[rows.ravel()], end=\"\\n\\n\")\n",
-    "print(Q[cols.ravel()])"
+    "print(P, end =\"\\n\\n\")\n",
+    "print(Q[cols.ravel()])\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `with_indices` passed 1 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def with_indices(P, Q):\n",
     "    \"\"\"\n",
@@ -139,9 +245,11 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # TODO: implement an optimized solution\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
-    "\n",
-    "\n",
+    "    p = P.shape[0]\n",
+    "    q = Q.shape[0]\n",
+    "    rows, cols = np.indices((p, q))\n",
+    "    D =  np.sqrt(np.sum((P[rows.ravel()] - Q[cols.ravel()])**2, axis=1)).reshape(p, q)\n",
+    "    return D\n",
     "test(with_indices)"
    ]
   },
@@ -154,7 +262,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "### Use a library\n",
@@ -164,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -190,7 +297,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "### Numpy Magic"
@@ -198,7 +304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -221,7 +327,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# Compare methods"
@@ -229,15 +334,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "108 ms ± 2.88 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
+      "10.8 ms ± 581 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
+      "784 µs ± 24.3 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
+      "6.01 ms ± 242 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
    "source": [
     "methods = [\n",
     "    naive,\n",
-    "    naive_2,  # This is another possible solution. Feel free to comment it out if you have only one solution.\n",
+    "    #naive_2,  # This is another possible solution. Feel free to comment it out if you have only one solution.\n",
     "    with_indices,\n",
-    "    with_indices_2,  # This is another possible solution. Feel free to comment it out if you have only one solution.\n",
+    "    #with_indices_2,  # This is another possible solution. Feel free to comment it out if you have only one solution.\n",
     "    scipy_version,\n",
     "    tensor_broadcasting,\n",
     "]\n",
@@ -249,9 +365,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10, 6))\n",
     "plt.bar(\n",
@@ -272,8 +399,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/labs/ex01/template/taskC.ipynb b/labs/ex01/template/taskC.ipynb
index 450728534..92cfc401d 100644
--- a/labs/ex01/template/taskC.ipynb
+++ b/labs/ex01/template/taskC.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {
     "id": "TYyZPqnPmhYC"
    },
@@ -31,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,9 +48,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[array([0.69872366, 0.75176984]), array([0.25997411, 0.14504062])]\n",
+      "[array([[0.01764816, 0.        ],\n",
+      "       [0.        , 0.06360523]]), array([[0.01764816, 0.        ],\n",
+      "       [0.        , 0.06360523]])]\n"
+     ]
+    }
+   ],
    "source": [
     "np.random.seed(20)\n",
     "X = rand(n, d)\n",
@@ -69,7 +79,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "## Computing the probability density"
@@ -77,9 +86,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `compute_p` passed 1 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def compute_p(X, mean, sigma):\n",
     "    \"\"\"\n",
@@ -99,7 +116,10 @@
     "    # ***************************************************\n",
     "    # INSERT YOUR CODE HERE\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    d = X.shape[1]\n",
+    "    det_sigma = np.linalg.det(sigma)\n",
+    "    #Direct application of the formula shown in the exercise sheet\n",
+    "    return 1/(((2*np.pi)**(d/2))*det_sigma**0.5)*np.exp(-0.5*np.sum((X-mean)@np.linalg.inv(sigma)*(X-mean),axis=1))\n",
     "\n",
     "\n",
     "test(compute_p)"
@@ -107,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,9 +138,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 0 1 1 0 1 0 0 1 1 0 1 0 0 0 0 1 0 1 1 0 1 1 1 0 0 0 0 0 1 1 0 0 1 1 0 0\n",
+      " 1 0 1 1 1 1 0 1 0 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1\n",
+      " 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0]\n"
+     ]
+    }
+   ],
    "source": [
     "assignments = np.argmax(ps, axis=0)\n",
     "print(assignments)"
@@ -128,9 +158,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "colors = np.array([\"red\", \"green\"])[assignments]\n",
     "plt.scatter(X[:, 0], X[:, 1], c=colors, s=100)\n",
@@ -140,7 +181,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {
     "id": "VsIOpA8QmhYI"
    },
@@ -151,9 +191,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `compute_log_p` passed 1 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def compute_log_p(X, mean, sigma):\n",
     "    \"\"\"\n",
@@ -173,7 +221,7 @@
     "    # ***************************************************\n",
     "    # INSERT YOUR CODE HERE\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    return np.log(compute_p(X, mean, sigma))\n",
     "\n",
     "\n",
     "test(compute_log_p)"
@@ -181,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,9 +240,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0 0 1 1 0 1 0 0 1 1 0 1 0 0 0 0 1 0 1 1 0 1 1 1 0 0 0 0 0 1 1 0 0 1 1 0 0\n",
+      " 1 0 1 1 1 1 0 1 0 1 0 0 0 0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1\n",
+      " 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 0]\n"
+     ]
+    }
+   ],
    "source": [
     "assignments = np.argmax(log_ps, axis=0)\n",
     "print(assignments)"
@@ -202,9 +260,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "colors = np.array([\"red\", \"green\"])[assignments]\n",
     "plt.scatter(X[:, 0], X[:, 1], c=colors, s=100)\n",
@@ -221,8 +290,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
   }
  },
  "nbformat": 4,
diff --git a/labs/ex02/template/costs.py b/labs/ex02/template/costs.py
index 98f009f1e..a519749f6 100644
--- a/labs/ex02/template/costs.py
+++ b/labs/ex02/template/costs.py
@@ -19,4 +19,11 @@ def compute_loss(y, tx, w):
     # INSERT YOUR CODE HERE
     # TODO: compute loss by MSE
     # ***************************************************
-    raise NotImplementedError
+    #Using the MSE formula
+    #error = y - tx.dot(w)
+    #loss = 1/(2*y.shape[0]) * np.sum(error**2)
+
+    #Using the MAE formula
+    error = y - tx.dot(w)
+    loss = 1/y.shape[0] * np.sum(np.abs(error))
+    return loss
diff --git a/labs/ex02/template/ex02.ipynb b/labs/ex02/template/ex02.ipynb
index bda2d7330..8061af2ef 100644
--- a/labs/ex02/template/ex02.ipynb
+++ b/labs/ex02/template/ex02.ipynb
@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "# Useful starting lines\n",
     "%matplotlib inline\n",
@@ -17,7 +26,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# Load the data"
@@ -25,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,16 +47,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((10000,), (10000, 2))"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "y.shape, tx.shape"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "### NB: throughout this laboratory the data has the following format: \n",
@@ -59,7 +77,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# 1. Computing the Cost Function\n",
@@ -68,9 +85,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2694.4833658870843\n"
+     ]
+    }
+   ],
    "source": [
     "def compute_loss(y, tx, w):\n",
     "    \"\"\"Calculate the loss using either MSE or MAE.\n",
@@ -87,12 +112,15 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # TODO: compute loss by MSE\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError"
+    "    error = y - tx.dot(w)\n",
+    "    loss = 1/(2*y.shape[0]) * np.sum(error**2)\n",
+    "    return loss\n",
+    "\n",
+    "print(compute_loss(y, tx, np.array([1, 2])))"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# 2. Grid Search"
@@ -100,7 +128,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Fill in the function `grid_search()` below:"
@@ -108,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,13 +160,14 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # TODO: compute loss for each combination of w0 and w1.\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    for i, w0 in enumerate(grid_w0):\n",
+    "        for j, w1 in enumerate(grid_w1):\n",
+    "            losses[i, j] = compute_loss(y, tx, np.array([w0, w1]))\n",
     "    return losses"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Let us play with the grid search demo now!"
@@ -147,9 +175,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Grid Search: loss*=42.42448314678248, w0*=66.66666666666669, w1*=16.666666666666686, execution time=0.005 seconds\n"
+     ]
+    }
+   ],
    "source": [
     "from grid_search import generate_w, get_best_parameters\n",
     "from plots import grid_visualization\n",
@@ -181,7 +217,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# 3. Gradient Descent"
@@ -189,7 +224,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Again, please fill in the functions `compute_gradient` below:"
@@ -197,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,12 +250,13 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # TODO: compute gradient vector\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError"
+    "    error = y - tx.dot(w)\n",
+    "    gradient = -1/(y.shape[0]) * tx.T.dot(error)\n",
+    "    return gradient"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Please fill in the functions `gradient_descent` below:"
@@ -229,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,13 +291,13 @@
     "        # INSERT YOUR CODE HERE\n",
     "        # TODO: compute gradient and loss\n",
     "        # ***************************************************\n",
-    "        raise NotImplementedError\n",
+    "        gradient = compute_gradient(y, tx, w)\n",
+    "        loss = compute_loss(y, tx, w)\n",
     "        # ***************************************************\n",
     "        # INSERT YOUR CODE HERE\n",
     "        # TODO: update w by gradient\n",
     "        # ***************************************************\n",
-    "        raise NotImplementedError\n",
-    "\n",
+    "        w = w - gamma * gradient\n",
     "        # store w and loss\n",
     "        ws.append(w)\n",
     "        losses.append(loss)\n",
@@ -277,7 +312,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Test your gradient descent function through gradient descent demo shown below:"
@@ -285,22 +319,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GD iter. 0/49: loss=1062606.4462798769, w0=-892.6706077997894, w1=901.3479712434988\n",
+      "GD iter. 1/49: loss=860714.1448053952, w0=-796.0741548195997, w1=812.5611453626477\n",
+      "GD iter. 2/49: loss=697181.3806110647, w0=-709.1373471374292, w1=732.6530020698816\n",
+      "GD iter. 3/49: loss=564719.8416136572, w0=-630.8942202234757, w1=660.7356731063921\n",
+      "GD iter. 4/49: loss=457425.99502575735, w0=-560.4754060009176, w1=596.0100770392519\n",
+      "GD iter. 5/49: loss=370517.9792895585, w0=-497.0984732006152, w1=537.7570405788256\n",
+      "GD iter. 6/49: loss=300122.4865432374, w0=-440.05923368034314, w1=485.32930776444186\n",
+      "GD iter. 7/49: loss=243102.1374187173, w0=-388.7239181120982, w1=438.14434823149645\n",
+      "GD iter. 8/49: loss=196915.65462785604, w0=-342.52213410067776, w1=395.67788465184566\n",
+      "GD iter. 9/49: loss=159504.60356725843, w0=-300.94052849039946, w1=357.45806743016004\n",
+      "GD iter. 10/49: loss=129201.65220817438, w0=-263.517083441149, w1=323.0602319306429\n",
+      "GD iter. 11/49: loss=104656.26160731632, w0=-229.8359828968235, w1=292.1021799810775\n",
+      "GD iter. 12/49: loss=84774.49522062126, w0=-199.5229924069306, w1=264.2399332264686\n",
+      "GD iter. 13/49: loss=68670.26444739828, w0=-172.24130096602696, w1=239.1639111473206\n",
+      "GD iter. 14/49: loss=55625.83752108766, w0=-147.68777866921369, w1=216.5954912760874\n",
+      "GD iter. 15/49: loss=45059.851710776056, w0=-125.58960860208177, w1=196.2839133919775\n",
+      "GD iter. 16/49: loss=36501.403204423674, w0=-105.70125554166304, w1=178.00349329627863\n",
+      "GD iter. 17/49: loss=29569.059914278245, w0=-87.8017377872862, w1=161.55111521014965\n",
+      "GD iter. 18/49: loss=23953.861849260444, w0=-71.69217180834706, w1=146.74397493263356\n",
+      "GD iter. 19/49: loss=19405.55141659603, w0=-57.19356242730183, w1=133.4175486828691\n",
+      "GD iter. 20/49: loss=15721.419966137863, w0=-44.14481398436112, w1=121.42376505808105\n",
+      "GD iter. 21/49: loss=12737.27349126674, w0=-32.400940385714485, w1=110.62935979577182\n",
+      "GD iter. 22/49: loss=10320.114846621134, w0=-21.8314541469325, w1=100.91439505969352\n",
+      "GD iter. 23/49: loss=8362.216344458193, w0=-12.318916532028727, w1=92.17092679722306\n",
+      "GD iter. 24/49: loss=6776.318557706213, w0=-3.7576326786153196, w1=84.30180536099965\n",
+      "GD iter. 25/49: loss=5491.741350437108, w0=3.9475227894567535, w1=77.21959606839857\n",
+      "GD iter. 26/49: loss=4451.233812549132, w0=10.882162710721607, w1=70.84560770505762\n",
+      "GD iter. 27/49: loss=3608.4227068598743, w0=17.123338639859973, w1=65.10901817805075\n",
+      "GD iter. 28/49: loss=2925.7457112515744, w0=22.740396976084508, w1=59.946087603744566\n",
+      "GD iter. 29/49: loss=2372.7773448088515, w0=27.79574947868658, w1=55.29945008686901\n",
+      "GD iter. 30/49: loss=1924.8729679902472, w0=32.345566731028455, w1=51.117476321681\n",
+      "GD iter. 31/49: loss=1562.070422767177, w0=36.44040225813613, w1=47.3536999330118\n",
+      "GD iter. 32/49: loss=1268.20036113649, w0=40.12575423253304, w1=43.96630118320951\n",
+      "GD iter. 33/49: loss=1030.1656112156343, w0=43.44257100949026, w1=40.91764230838746\n",
+      "GD iter. 34/49: loss=837.357463779741, w0=46.42770610875176, w1=38.17384932104761\n",
+      "GD iter. 35/49: loss=681.1828643566676, w0=49.1143276980871, w1=35.70443563244175\n",
+      "GD iter. 36/49: loss=554.6814388239782, w0=51.53228712848891, w1=33.48196331269648\n",
+      "GD iter. 37/49: loss=452.2152841424999, w0=53.70845061585054, w1=31.48173822492573\n",
+      "GD iter. 38/49: loss=369.2176988505024, w0=55.66699775447601, w1=29.681535645932062\n",
+      "GD iter. 39/49: loss=301.9896547639845, w0=57.429690179238925, w1=28.061353324837757\n",
+      "GD iter. 40/49: loss=247.53493905390496, w0=59.016113361525555, w1=26.603189235852884\n",
+      "GD iter. 41/49: loss=203.42661932874051, w0=60.44389422558352, w1=25.290841555766498\n",
+      "GD iter. 42/49: loss=167.69888035135736, w0=61.72889700323569, w1=24.10972864368875\n",
+      "GD iter. 43/49: loss=138.75941177967704, w0=62.88539950312264, w1=23.04672702281878\n",
+      "GD iter. 44/49: loss=115.31844223661591, w0=63.9262517530209, w1=22.090025564035805\n",
+      "GD iter. 45/49: loss=96.33125690673644, w0=64.86301877792933, w1=21.22899425113113\n",
+      "GD iter. 46/49: loss=80.95163678953413, w0=65.70610910034691, w1=20.454066069516923\n",
+      "GD iter. 47/49: loss=68.49414449460028, w0=66.46489039052274, w1=19.756630706064136\n",
+      "GD iter. 48/49: loss=58.40357573570379, w0=67.14779355168099, w1=19.128938878956625\n",
+      "GD iter. 49/49: loss=50.2302150409976, w0=67.7624063967234, w1=18.564016234559865\n",
+      "GD: execution time=0.008 seconds\n"
+     ]
+    }
+   ],
    "source": [
     "# from gradient_descent import *\n",
     "from plots import gradient_descent_visualization\n",
     "\n",
     "# Define the parameters of the algorithm.\n",
     "max_iters = 50\n",
-    "gamma = 0.7\n",
+    "gamma = 0.1\n",
     "\n",
     "# Initialization\n",
-    "w_initial = np.array([0, 0])\n",
+    "w_initial = np.array([-1000, 1000])\n",
     "\n",
     "# Start gradient descent.\n",
     "start_time = datetime.datetime.now()\n",
+    "\n",
+    "# Define arbitrary arrays for y and tx (a enlever plus tard)\n",
+    "\n",
+    "\n",
+    "# Run gradient descent with the arbitrary arrays\n",
     "gd_losses, gd_ws = gradient_descent(y, tx, w_initial, max_iters, gamma)\n",
     "end_time = datetime.datetime.now()\n",
     "\n",
@@ -311,9 +408,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'ipywidgets'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[61], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Time Visualization\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mipywidgets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IntSlider, interact\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_figure\u001b[39m(n_iter):\n\u001b[0;32m      6\u001b[0m     fig \u001b[38;5;241m=\u001b[39m gradient_descent_visualization(\n\u001b[0;32m      7\u001b[0m         gd_losses,\n\u001b[0;32m      8\u001b[0m         gd_ws,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     16\u001b[0m         n_iter,\n\u001b[0;32m     17\u001b[0m     )\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'ipywidgets'"
+     ]
+    }
+   ],
    "source": [
     "# Time Visualization\n",
     "from ipywidgets import IntSlider, interact\n",
@@ -340,7 +449,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -350,7 +458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -370,7 +478,8 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # TODO: implement stochastic gradient computation. It's the same as the usual gradient.\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    gradient = compute_gradient(y, tx, w)\n",
+    "    return gradient\n",
     "\n",
     "\n",
     "def stochastic_gradient_descent(y, tx, initial_w, batch_size, max_iters, gamma):\n",
@@ -399,8 +508,12 @@
     "        # INSERT YOUR CODE HERE\n",
     "        # TODO: implement stochastic gradient descent.\n",
     "        # ***************************************************\n",
-    "        raise NotImplementedError\n",
-    "\n",
+    "        for batch_y, batch_tx in batch_iter(y, tx, batch_size):\n",
+    "            gradient = compute_stoch_gradient(batch_y, batch_tx, w)\n",
+    "            loss = compute_loss(batch_y, batch_tx, w)\n",
+    "            w = w - gamma * gradient\n",
+    "            ws.append(w)\n",
+    "            losses.append(loss)\n",
     "        print(\n",
     "            \"SGD iter. {bi}/{ti}: loss={l}, w0={w0}, w1={w1}\".format(\n",
     "                bi=n_iter, ti=max_iters - 1, l=loss, w0=w[0], w1=w[1]\n",
@@ -411,9 +524,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SGD iter. 0/49: loss=1478.4168761832668, w0=5.437677585483102, w1=-7.702164219443922\n",
+      "SGD iter. 1/49: loss=1427.622276203264, w0=10.781126427201837, w1=-10.395559374320449\n",
+      "SGD iter. 2/49: loss=2659.576635999685, w0=18.074378974590672, w1=-5.670561078765635\n",
+      "SGD iter. 3/49: loss=656.0256806325144, w0=21.69660392793776, w1=-9.303152348263495\n",
+      "SGD iter. 4/49: loss=4326.483003179391, w0=30.99873594503942, w1=9.287077768517062\n",
+      "SGD iter. 5/49: loss=1152.6168758970034, w0=35.80002093115929, w1=11.766592404618962\n",
+      "SGD iter. 6/49: loss=907.4372338482192, w0=40.06015528079715, w1=14.696245242535664\n",
+      "SGD iter. 7/49: loss=507.5511792693538, w0=43.24622238388633, w1=18.594434227632583\n",
+      "SGD iter. 8/49: loss=251.9614360200638, w0=45.49104503175882, w1=17.587065824458104\n",
+      "SGD iter. 9/49: loss=400.98342736071186, w0=48.32294696283169, w1=20.083867193189075\n",
+      "SGD iter. 10/49: loss=112.34489498192029, w0=49.821912572723534, w1=19.66318421621703\n",
+      "SGD iter. 11/49: loss=234.37795943765363, w0=51.986989751198486, w1=20.55922048928062\n",
+      "SGD iter. 12/49: loss=168.6843112658599, w0=53.82374945930328, w1=20.295958525878934\n",
+      "SGD iter. 13/49: loss=386.41804594315715, w0=56.60374243031294, w1=20.506375285269755\n",
+      "SGD iter. 14/49: loss=419.32652777600606, w0=59.49969314735219, w1=19.27169332001143\n",
+      "SGD iter. 15/49: loss=135.351874815414, w0=61.145000868208164, w1=17.82308553000498\n",
+      "SGD iter. 16/49: loss=5.465098364765607, w0=61.475609348582324, w1=17.977909550676117\n",
+      "SGD iter. 17/49: loss=170.27729400137247, w0=63.321021465164264, w1=16.140651635074363\n",
+      "SGD iter. 18/49: loss=88.94714573190613, w0=64.65479165325445, w1=14.734233989352141\n",
+      "SGD iter. 19/49: loss=3.4211362563385856, w0=64.39321427440305, w1=14.893521732167233\n",
+      "SGD iter. 20/49: loss=135.1870665643636, w0=66.03752000366609, w1=13.047529204336051\n",
+      "SGD iter. 21/49: loss=4.541497121847177, w0=65.73613994056147, w1=12.891025885509698\n",
+      "SGD iter. 22/49: loss=51.455144703077295, w0=66.75058702841847, w1=13.928995109474196\n",
+      "SGD iter. 23/49: loss=0.011116384137777104, w0=66.76549768510272, w1=13.932573105893537\n",
+      "SGD iter. 24/49: loss=6.919715863182121, w0=66.39348381807133, w1=13.045922854812927\n",
+      "SGD iter. 25/49: loss=1.0183478883506623, w0=66.5361966688137, w1=12.808126040823153\n",
+      "SGD iter. 26/49: loss=72.19169045143038, w0=67.73779302742528, w1=11.722180103602636\n",
+      "SGD iter. 27/49: loss=23.117670914365785, w0=68.4177587753033, w1=12.534295768006228\n",
+      "SGD iter. 28/49: loss=10.19362340724624, w0=68.86928116472039, w1=13.071718513671335\n",
+      "SGD iter. 29/49: loss=2.4954417258075754, w0=68.64587831219573, w1=13.283442629870757\n",
+      "SGD iter. 30/49: loss=24.237114053365833, w0=69.34211267005759, w1=12.15360504327624\n",
+      "SGD iter. 31/49: loss=0.7718937207971756, w0=69.46636191317779, w1=12.312904759736309\n",
+      "SGD iter. 32/49: loss=43.498576629182416, w0=70.39908455822489, w1=11.417485432368832\n",
+      "SGD iter. 33/49: loss=4.808236433516665, w0=70.70918894173201, w1=11.425949308764647\n",
+      "SGD iter. 34/49: loss=1.3973859468059022, w0=70.87636465448993, w1=11.750961194739123\n",
+      "SGD iter. 35/49: loss=21.94974042783874, w0=71.53893148776547, w1=12.568741668436909\n",
+      "SGD iter. 36/49: loss=15.274853562738253, w0=72.09164938258593, w1=12.740893021435518\n",
+      "SGD iter. 37/49: loss=21.463071658543257, w0=71.43646892410837, w1=12.876804885363589\n",
+      "SGD iter. 38/49: loss=26.131419547951527, w0=72.15939934327679, w1=12.524641472191009\n",
+      "SGD iter. 39/49: loss=67.37787455052899, w0=70.99855590378523, w1=13.60507216622505\n",
+      "SGD iter. 40/49: loss=14.070912511293997, w0=70.46806721377773, w1=13.561646432692568\n",
+      "SGD iter. 41/49: loss=16.100066029620354, w0=69.90061561179532, w1=13.73647189218233\n",
+      "SGD iter. 42/49: loss=11.772192852162496, w0=70.38584118515605, w1=14.302779796755985\n",
+      "SGD iter. 43/49: loss=30.74695182983885, w0=71.17002167241442, w1=13.473577880535993\n",
+      "SGD iter. 44/49: loss=8.447431990976803, w0=71.58105529612365, w1=13.753624238384013\n",
+      "SGD iter. 45/49: loss=5.837794061799197, w0=71.92275089345551, w1=14.103242782554592\n",
+      "SGD iter. 46/49: loss=0.2891485191178678, w0=71.99879673740742, w1=13.917975466763355\n",
+      "SGD iter. 47/49: loss=0.6056450503539023, w0=71.88873811208856, w1=14.040797290539622\n",
+      "SGD iter. 48/49: loss=0.38328839207477877, w0=71.80118374131533, w1=13.996916542365003\n",
+      "SGD iter. 49/49: loss=1.7379794457425286, w0=71.61474450479348, w1=13.93977324046077\n",
+      "SGD: execution time=0.004 seconds\n"
+     ]
+    }
+   ],
    "source": [
     "# from stochastic_gradient_descent import *\n",
     "\n",
@@ -439,9 +610,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'ipywidgets'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[20], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Time Visualization\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mipywidgets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IntSlider, interact\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_figure\u001b[39m(n_iter):\n\u001b[0;32m      6\u001b[0m     fig \u001b[38;5;241m=\u001b[39m gradient_descent_visualization(\n\u001b[0;32m      7\u001b[0m         sgd_losses,\n\u001b[0;32m      8\u001b[0m         sgd_ws,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     16\u001b[0m         n_iter,\n\u001b[0;32m     17\u001b[0m     )\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'ipywidgets'"
+     ]
+    }
+   ],
    "source": [
     "# Time Visualization\n",
     "from ipywidgets import IntSlider, interact\n",
@@ -468,7 +651,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# 5. Effect of Outliers and MAE Cost Function"
@@ -476,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -487,32 +669,110 @@
     "# INSERT YOUR CODE HERE\n",
     "# TODO: reload the data by subsampling first, then by subsampling and adding outliers\n",
     "# ***************************************************\n",
-    "raise NotImplementedError\n",
-    "\n",
+    "height, weight, gender = load_data(sub_sample=True, add_outlier=True)\n",
     "x, mean_x, std_x = standardize(height)\n",
     "y, tx = build_model_data(x, weight)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((202,), (202, 2))"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "y.shape, tx.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "GD iter. 0/49: loss=2869.8351145358524, w0=7.4067805854926325, w1=1.103489486598917\n",
+      "GD iter. 1/49: loss=2337.093281493536, w0=14.072883112436008, w1=2.0966300245379443\n",
+      "GD iter. 2/49: loss=1905.5723967292586, w0=20.07237538668505, w1=2.9904565086830646\n",
+      "GD iter. 3/49: loss=1556.0404800701936, w0=25.47191843350918, w1=3.7949003444136746\n",
+      "GD iter. 4/49: loss=1272.9196275763513, w0=30.3315071756509, w1=4.5188997965712225\n",
+      "GD iter. 5/49: loss=1043.591737056339, w0=34.705137043578446, w1=5.170499303513015\n",
+      "GD iter. 6/49: loss=857.8361457351294, w0=38.64140392471324, w1=5.756938859760628\n",
+      "GD iter. 7/49: loss=707.3741167649493, w0=42.18404411773455, w1=6.28473446038348\n",
+      "GD iter. 8/49: loss=585.4998732991037, w0=45.37242029145373, w1=6.759750500944047\n",
+      "GD iter. 9/49: loss=486.7817360917687, w0=48.241958847800994, w1=7.187264937448555\n",
+      "GD iter. 10/49: loss=406.8200449538272, w0=50.82454354851353, w1=7.572027930302614\n",
+      "GD iter. 11/49: loss=342.0510751320947, w0=53.148869779154815, w1=7.918314623871265\n",
+      "GD iter. 12/49: loss=289.5882095764913, w0=55.24076338673197, w1=8.229972648083052\n",
+      "GD iter. 13/49: loss=247.09328847645259, w0=57.12346763355141, w1=8.51046486987366\n",
+      "GD iter. 14/49: loss=212.67240238542126, w0=58.817901455688904, w1=8.762907869485206\n",
+      "GD iter. 15/49: loss=184.79148465168583, w0=60.34289189561265, w1=8.990106569135598\n",
+      "GD iter. 16/49: loss=162.20794128736017, w0=61.71538329154402, w1=9.19458539882095\n",
+      "GD iter. 17/49: loss=143.9152711622564, w0=62.950625547882254, w1=9.378616345537766\n",
+      "GD iter. 18/49: loss=129.0982083609223, w0=64.06234357858666, w1=9.5442441975829\n",
+      "GD iter. 19/49: loss=117.09638749184177, w0=65.06288980622064, w1=9.69330926442352\n",
+      "GD iter. 20/49: loss=107.3749125878864, w0=65.9633814110912, w1=9.82746782458008\n",
+      "GD iter. 21/49: loss=99.50051791568269, w0=66.77382385547472, w1=9.948210528720981\n",
+      "GD iter. 22/49: loss=93.12225823119766, w0=67.50322205541988, w1=10.056878962447794\n",
+      "GD iter. 23/49: loss=87.95586788676472, w0=68.15968043537053, w1=10.154680552801926\n",
+      "GD iter. 24/49: loss=83.77109170777406, w0=68.75049297732612, w1=10.242701984120645\n",
+      "GD iter. 25/49: loss=80.3814230027916, w0=69.28222426508614, w1=10.321921272307492\n",
+      "GD iter. 26/49: loss=77.63579135175586, w0=69.76078242407017, w1=10.393218631675653\n",
+      "GD iter. 27/49: loss=75.41182971441685, w0=70.19148476715579, w1=10.457386255106998\n",
+      "GD iter. 28/49: loss=73.6104207881723, w0=70.57911687593284, w1=10.51513711619521\n",
+      "GD iter. 29/49: loss=72.15127955791424, w0=70.92798577383219, w1=10.567112891174599\n",
+      "GD iter. 30/49: loss=70.96937516140518, w0=71.24196778194161, w1=10.61389108865605\n",
+      "GD iter. 31/49: loss=70.01203260023283, w0=71.52455158924009, w1=10.655991466389356\n",
+      "GD iter. 32/49: loss=69.2365851256832, w0=71.77887701580872, w1=10.69388180634933\n",
+      "GD iter. 33/49: loss=68.60847267129806, w0=72.00776989972049, w1=10.727983112313307\n",
+      "GD iter. 34/49: loss=68.09970158324606, w0=72.21377349524107, w1=10.758674287680886\n",
+      "GD iter. 35/49: loss=67.68759700192395, w0=72.39917673120961, w1=10.786296345511708\n",
+      "GD iter. 36/49: loss=67.35379229105303, w0=72.56603964358128, w1=10.811156197559448\n",
+      "GD iter. 37/49: loss=67.08341047524759, w0=72.71621626471578, w1=10.833530064402414\n",
+      "GD iter. 38/49: loss=66.86440120444522, w0=72.85137522373684, w1=10.853666544561083\n",
+      "GD iter. 39/49: loss=66.68700369509526, w0=72.97301828685579, w1=10.871789376703886\n",
+      "GD iter. 40/49: loss=66.5433117125218, w0=73.08249704366285, w1=10.888099925632407\n",
+      "GD iter. 41/49: loss=66.4269212066373, w0=73.1810279247892, w1=10.902779419668077\n",
+      "GD iter. 42/49: loss=66.33264489687086, w0=73.26970571780292, w1=10.91599096430018\n",
+      "GD iter. 43/49: loss=66.25628108596004, w0=73.34951573151527, w1=10.927881354469072\n",
+      "GD iter. 44/49: loss=66.19442639912226, w0=73.42134474385638, w1=10.938582705621075\n",
+      "GD iter. 45/49: loss=66.14432410278367, w0=73.48599085496338, w1=10.948213921657878\n",
+      "GD iter. 46/49: loss=66.10374124274942, w0=73.54417235495967, w1=10.956882016091\n",
+      "GD iter. 47/49: loss=66.07086912612168, w0=73.59653570495634, w1=10.964683301080811\n",
+      "GD iter. 48/49: loss=66.04424271165321, w0=73.64366271995334, w1=10.97170445757164\n",
+      "GD iter. 49/49: loss=66.02267531593372, w0=73.68607703345064, w1=10.978023498413386\n",
+      "GD: execution time=0.002 seconds\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from plots import gradient_descent_visualization\n",
     "\n",
     "# Define the parameters of the algorithm.\n",
     "max_iters = 50\n",
-    "gamma = 0.7\n",
+    "gamma = 0.1\n",
     "\n",
     "# Initialization\n",
     "w_initial = np.array([0, 0])\n",
@@ -525,14 +785,21 @@
     "# TODO: fit the model to the subsampled data / subsampled data with outliers and visualize the cloud of points\n",
     "#       and the model fit\n",
     "# ***************************************************\n",
-    "raise NotImplementedError\n",
-    "\n",
-    "\n",
+    "gd_losses, gd_ws = gradient_descent(y, tx, w_initial, max_iters, gamma)\n",
     "end_time = datetime.datetime.now()\n",
     "\n",
     "# Print result\n",
     "exection_time = (end_time - start_time).total_seconds()\n",
-    "print(\"GD: execution time={t:.3f} seconds\".format(t=exection_time))"
+    "print(\"GD: execution time={t:.3f} seconds\".format(t=exection_time))\n",
+    "\n",
+    "# Visualize the cloud of points and the model fit\n",
+    "plt.scatter(tx[:, 1], y, color='blue', label='Data points')\n",
+    "plt.plot(tx[:, 1], tx.dot(gd_ws[-1]), color='red', label='Model fit')\n",
+    "plt.xlabel('Standardized Height')\n",
+    "plt.ylabel('Weight')\n",
+    "plt.title('Model Fit on Subsampled Data')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -566,7 +833,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {
     "collapsed": true
    },
@@ -576,7 +842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -595,12 +861,15 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # TODO: compute subgradient gradient vector for MAE\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError"
+    "    error = y - tx.dot(w)\n",
+    "    #Using chain rule for the subgradient of the MAE\n",
+    "    subgradient = -np.sign(error).dot(tx) / y.shape[0]\n",
+    "    return subgradient"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -627,13 +896,13 @@
     "        # INSERT YOUR CODE HERE\n",
     "        # TODO: compute subgradient and loss\n",
     "        # ***************************************************\n",
-    "        raise NotImplementedError\n",
+    "        subgradient = compute_subgradient_mae(y, tx, w)\n",
+    "        loss = compute_loss(y, tx, w)\n",
     "        # ***************************************************\n",
     "        # INSERT YOUR CODE HERE\n",
     "        # TODO: update w by subgradient\n",
     "        # ***************************************************\n",
-    "        raise NotImplementedError\n",
-    "\n",
+    "        w = w - gamma * subgradient\n",
     "        ws.append(w)\n",
     "        losses.append(loss)\n",
     "        print(\n",
@@ -647,9 +916,517 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SubGD iter. 0/499: loss=2869.8351145358524, w0=0.7, w1=6.109524327590712e-16\n",
+      "SubGD iter. 1/499: loss=2818.2326504374046, w0=1.4, w1=1.2219048655181425e-15\n",
+      "SubGD iter. 2/499: loss=2767.120186338956, w0=2.0999999999999996, w1=1.832857298277214e-15\n",
+      "SubGD iter. 3/499: loss=2716.4977222405073, w0=2.8, w1=2.443809731036285e-15\n",
+      "SubGD iter. 4/499: loss=2666.365258142059, w0=3.5, w1=3.054762163795356e-15\n",
+      "SubGD iter. 5/499: loss=2616.72279404361, w0=4.2, w1=3.665714596554428e-15\n",
+      "SubGD iter. 6/499: loss=2567.570329945162, w0=4.9, w1=4.276667029313499e-15\n",
+      "SubGD iter. 7/499: loss=2518.9078658467133, w0=5.6000000000000005, w1=4.887619462072571e-15\n",
+      "SubGD iter. 8/499: loss=2470.735401748265, w0=6.300000000000001, w1=5.498571894831642e-15\n",
+      "SubGD iter. 9/499: loss=2423.052937649817, w0=7.000000000000001, w1=6.109524327590714e-15\n",
+      "SubGD iter. 10/499: loss=2375.8604735513677, w0=7.700000000000001, w1=6.720476760349785e-15\n",
+      "SubGD iter. 11/499: loss=2329.1580094529195, w0=8.4, w1=7.331429193108857e-15\n",
+      "SubGD iter. 12/499: loss=2282.945545354471, w0=9.1, w1=7.942381625867928e-15\n",
+      "SubGD iter. 13/499: loss=2237.223081256023, w0=9.799999999999999, w1=8.553334058627e-15\n",
+      "SubGD iter. 14/499: loss=2191.9906171575744, w0=10.499999999999998, w1=9.164286491386072e-15\n",
+      "SubGD iter. 15/499: loss=2147.248153059126, w0=11.199999999999998, w1=9.775238924145143e-15\n",
+      "SubGD iter. 16/499: loss=2102.995688960677, w0=11.899999999999997, w1=1.0386191356904215e-14\n",
+      "SubGD iter. 17/499: loss=2059.2332248622292, w0=12.599999999999996, w1=1.0997143789663286e-14\n",
+      "SubGD iter. 18/499: loss=2015.9607607637806, w0=13.299999999999995, w1=1.1608096222422358e-14\n",
+      "SubGD iter. 19/499: loss=1973.1782966653323, w0=13.999999999999995, w1=1.2219048655181429e-14\n",
+      "SubGD iter. 20/499: loss=1930.885832566884, w0=14.699999999999994, w1=1.28300010879405e-14\n",
+      "SubGD iter. 21/499: loss=1889.0833684684355, w0=15.399999999999993, w1=1.3440953520699572e-14\n",
+      "SubGD iter. 22/499: loss=1847.7709043699867, w0=16.099999999999994, w1=1.4051905953458644e-14\n",
+      "SubGD iter. 23/499: loss=1806.9484402715386, w0=16.799999999999994, w1=1.4662858386217714e-14\n",
+      "SubGD iter. 24/499: loss=1766.61597617309, w0=17.499999999999993, w1=1.5273810818976784e-14\n",
+      "SubGD iter. 25/499: loss=1726.7735120746415, w0=18.199999999999992, w1=1.5884763251735854e-14\n",
+      "SubGD iter. 26/499: loss=1687.421047976193, w0=18.89999999999999, w1=1.6495715684494924e-14\n",
+      "SubGD iter. 27/499: loss=1648.5585838777447, w0=19.59999999999999, w1=1.7106668117253994e-14\n",
+      "SubGD iter. 28/499: loss=1610.1861197792962, w0=20.29999999999999, w1=1.7717620550013064e-14\n",
+      "SubGD iter. 29/499: loss=1572.3036556808477, w0=20.99999999999999, w1=1.8328572982772134e-14\n",
+      "SubGD iter. 30/499: loss=1534.9111915823994, w0=21.69999999999999, w1=1.8939525415531204e-14\n",
+      "SubGD iter. 31/499: loss=1498.0087274839507, w0=22.399999999999988, w1=1.9550477848290273e-14\n",
+      "SubGD iter. 32/499: loss=1461.5962633855024, w0=23.099999999999987, w1=2.0161430281049343e-14\n",
+      "SubGD iter. 33/499: loss=1425.6737992870537, w0=23.799999999999986, w1=2.0772382713808413e-14\n",
+      "SubGD iter. 34/499: loss=1390.2413351886057, w0=24.499999999999986, w1=2.1383335146567483e-14\n",
+      "SubGD iter. 35/499: loss=1355.2988710901573, w0=25.199999999999985, w1=2.1994287579326553e-14\n",
+      "SubGD iter. 36/499: loss=1320.8464069917086, w0=25.899999999999984, w1=2.2605240012085623e-14\n",
+      "SubGD iter. 37/499: loss=1286.8839428932602, w0=26.599999999999984, w1=2.3216192444844693e-14\n",
+      "SubGD iter. 38/499: loss=1253.4114787948117, w0=27.299999999999983, w1=2.3827144877603763e-14\n",
+      "SubGD iter. 39/499: loss=1220.4290146963633, w0=27.999999999999982, w1=2.4438097310362833e-14\n",
+      "SubGD iter. 40/499: loss=1187.936550597915, w0=28.69999999999998, w1=2.5049049743121903e-14\n",
+      "SubGD iter. 41/499: loss=1155.9340864994663, w0=29.39999999999998, w1=2.5660002175880973e-14\n",
+      "SubGD iter. 42/499: loss=1124.4216224010179, w0=30.09999999999998, w1=2.6270954608640043e-14\n",
+      "SubGD iter. 43/499: loss=1093.3991583025695, w0=30.79999999999998, w1=2.6881907041399113e-14\n",
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+      "SubGD iter. 492/499: loss=79.16168616301796, w0=72.62673267326743, w1=15.971424551816726\n",
+      "SubGD iter. 493/499: loss=79.1537387295804, w0=72.63366336633673, w1=15.97589325162413\n",
+      "SubGD iter. 494/499: loss=79.16584496481637, w0=72.62673267326743, w1=15.973767151838393\n",
+      "SubGD iter. 495/499: loss=79.16530578801678, w0=72.62673267326743, w1=15.972157491117306\n",
+      "SubGD iter. 496/499: loss=79.1573571747956, w0=72.62673267326743, w1=15.97054783039622\n",
+      "SubGD iter. 497/499: loss=79.14941115258208, w0=72.63366336633673, w1=15.975016530203623\n",
+      "SubGD iter. 498/499: loss=79.16151347001323, w0=72.62673267326743, w1=15.972890430417886\n",
+      "SubGD iter. 499/499: loss=79.16097615721087, w0=72.62673267326743, w1=15.971280769696799\n",
+      "SubGD: execution time=0.025 seconds\n"
+     ]
+    }
+   ],
    "source": [
     "# Define the parameters of the algorithm.\n",
     "max_iters = 500\n",
@@ -671,9 +1448,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'ipywidgets'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[76], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mipywidgets\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m IntSlider, interact\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot_figure\u001b[39m(n_iter):\n\u001b[0;32m      5\u001b[0m     fig \u001b[38;5;241m=\u001b[39m gradient_descent_visualization(\n\u001b[0;32m      6\u001b[0m         subgd_losses,\n\u001b[0;32m      7\u001b[0m         subgd_ws,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     15\u001b[0m         n_iter,\n\u001b[0;32m     16\u001b[0m     )\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'ipywidgets'"
+     ]
+    }
+   ],
    "source": [
     "from ipywidgets import IntSlider, interact\n",
     "\n",
@@ -699,7 +1488,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# Stochastic Subgradient Descent\n",
@@ -709,7 +1497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -739,8 +1527,13 @@
     "        # INSERT YOUR CODE HERE\n",
     "        # TODO: implement stochastic subgradient descent.\n",
     "        # ***************************************************\n",
-    "        raise NotImplementedError\n",
-    "\n",
+    "        \n",
+    "        for batch_y, batch_tx in batch_iter(y, tx, batch_size):\n",
+    "            subgradient = compute_subgradient_mae(batch_y, batch_tx, w)\n",
+    "            loss = compute_loss(batch_y, batch_tx, w)\n",
+    "            w = w - gamma * subgradient\n",
+    "            ws.append(w)\n",
+    "            losses.append(loss)\n",
     "        print(\n",
     "            \"SubSGD iter. {bi}/{ti}: loss={l}, w0={w0}, w1={w1}\".format(\n",
     "                bi=n_iter, ti=max_iters - 1, l=loss, w0=w[0], w1=w[1]\n",
@@ -751,9 +1544,517 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SubSGD iter. 0/499: loss=1446.407839559022, w0=0.7, w1=-0.9767974789419188\n",
+      "SubSGD iter. 1/499: loss=3789.823348670773, w0=1.4, w1=-0.698670510140231\n",
+      "SubSGD iter. 2/499: loss=3774.0394767420953, w0=2.0999999999999996, w1=-0.4543577864310967\n",
+      "SubSGD iter. 3/499: loss=4989.309805447026, w0=2.8, w1=0.5583205549216702\n",
+      "SubSGD iter. 4/499: loss=2277.1316821291607, w0=3.5, w1=0.7570814207401947\n",
+      "SubSGD iter. 5/499: loss=5359.586331130035, w0=4.2, w1=1.9378861214400906\n",
+      "SubSGD iter. 6/499: loss=2522.8725632219125, w0=4.9, w1=1.905457113672731\n",
+      "SubSGD iter. 7/499: loss=1887.1147534685576, w0=5.6000000000000005, w1=1.3513500396761502\n",
+      "SubSGD iter. 8/499: loss=3158.0777224495478, w0=6.300000000000001, w1=1.5476946331933783\n",
+      "SubSGD iter. 9/499: loss=3156.3187228847382, w0=7.000000000000001, w1=1.7588590277595821\n",
+      "SubSGD iter. 10/499: loss=1179.6474507062592, w0=7.700000000000001, w1=1.0553541093687195\n",
+      "SubSGD iter. 11/499: loss=2444.0709884087305, w0=8.4, w1=1.3438513739261353\n",
+      "SubSGD iter. 12/499: loss=1442.527278674488, w0=9.1, w1=1.0998245492169845\n",
+      "SubSGD iter. 13/499: loss=3805.514986322662, w0=9.799999999999999, w1=2.1122628870834053\n",
+      "SubSGD iter. 14/499: loss=1835.590446980825, w0=10.499999999999998, w1=1.5876299623716656\n",
+      "SubSGD iter. 15/499: loss=3096.0560416821936, w0=11.199999999999998, w1=2.2111180846578433\n",
+      "SubSGD iter. 16/499: loss=1981.4735954377513, w0=11.899999999999997, w1=2.8165212361018277\n",
+      "SubSGD iter. 17/499: loss=1843.8464954539015, w0=12.599999999999996, w1=2.4205449928799956\n",
+      "SubSGD iter. 18/499: loss=865.2018186226627, w0=13.299999999999995, w1=1.2091221049538154\n",
+      "SubSGD iter. 19/499: loss=3044.253137424294, w0=13.999999999999995, w1=1.6490465478379974\n",
+      "SubSGD iter. 20/499: loss=2390.0953014973115, w0=14.699999999999994, w1=2.34998054527275\n",
+      "SubSGD iter. 21/499: loss=649.4840564207689, w0=15.399999999999993, w1=1.6693813676166673\n",
+      "SubSGD iter. 22/499: loss=1545.60850475805, w0=16.099999999999994, w1=1.2390497791559314\n",
+      "SubSGD iter. 23/499: loss=2057.1351313860946, w0=16.799999999999994, w1=1.582762400339322\n",
+      "SubSGD iter. 24/499: loss=2036.5276980260821, w0=17.499999999999993, w1=1.7792824831178247\n",
+      "SubSGD iter. 25/499: loss=945.10809760268, w0=18.199999999999992, w1=1.432198497497834\n",
+      "SubSGD iter. 26/499: loss=500.8250088095323, w0=18.89999999999999, w1=0.7515993198417512\n",
+      "SubSGD iter. 27/499: loss=769.7698658237503, w0=19.59999999999999, w1=-0.2859148739717894\n",
+      "SubSGD iter. 28/499: loss=722.6459656020058, w0=20.29999999999999, w1=-0.7261101286787606\n",
+      "SubSGD iter. 29/499: loss=635.3458374116155, w0=20.99999999999999, w1=-1.7636243224923012\n",
+      "SubSGD iter. 30/499: loss=2883.7593511788286, w0=21.69999999999999, w1=-0.9074022699877631\n",
+      "SubSGD iter. 31/499: loss=2416.9394542997243, w0=22.399999999999988, w1=-0.2927412888573764\n",
+      "SubSGD iter. 32/499: loss=1151.1275799262369, w0=23.099999999999987, w1=-0.03202906747551254\n",
+      "SubSGD iter. 33/499: loss=1875.4950911302494, w0=23.799999999999986, w1=0.4960267480351362\n",
+      "SubSGD iter. 34/499: loss=2310.4386727180276, w0=24.499999999999986, w1=0.9359511909193182\n",
+      "SubSGD iter. 35/499: loss=1785.9468460074065, w0=25.199999999999985, w1=1.8828634526617485\n",
+      "SubSGD iter. 36/499: loss=2263.561003327019, w0=25.899999999999984, w1=2.739085505166287\n",
+      "SubSGD iter. 37/499: loss=385.7174338500195, w0=26.599999999999984, w1=1.8502536809997934\n",
+      "SubSGD iter. 38/499: loss=1701.4455868838172, w0=27.299999999999983, w1=2.0465982745170215\n",
+      "SubSGD iter. 39/499: loss=786.4078753781444, w0=27.999999999999982, w1=1.9657839690283105\n",
+      "SubSGD iter. 40/499: loss=1805.2926788137233, w0=28.69999999999998, w1=2.6119312024155312\n",
+      "SubSGD iter. 41/499: loss=403.15569207248274, w0=29.39999999999998, w1=2.103430084675698\n",
+      "SubSGD iter. 42/499: loss=345.9880221082552, w0=30.09999999999998, w1=1.369315761674709\n",
+      "SubSGD iter. 43/499: loss=520.9702788705166, w0=30.79999999999998, w1=1.4256253767200124\n",
+      "SubSGD iter. 44/499: loss=1333.3651095051475, w0=31.49999999999998, w1=2.0179433379881906\n",
+      "SubSGD iter. 45/499: loss=984.8973589416917, w0=32.19999999999998, w1=1.7769634233215426\n",
+      "SubSGD iter. 46/499: loss=895.8055474523579, w0=32.899999999999984, w1=2.382366574765527\n",
+      "SubSGD iter. 47/499: loss=1153.3896936296458, w0=33.59999999999999, w1=2.831025613550237\n",
+      "SubSGD iter. 48/499: loss=1486.3604001739238, w0=34.29999999999999, w1=3.887426844929127\n",
+      "SubSGD iter. 49/499: loss=420.45816558305165, w0=34.99999999999999, w1=3.447738439843943\n",
+      "SubSGD iter. 50/499: loss=1735.1196313385014, w0=35.699999999999996, w1=4.566048660455424\n",
+      "SubSGD iter. 51/499: loss=1069.313616529336, w0=36.4, w1=5.075234504401677\n",
+      "SubSGD iter. 52/499: loss=972.078010729038, w0=37.1, w1=5.603290319912325\n",
+      "SubSGD iter. 53/499: loss=656.3168411279099, w0=37.800000000000004, w1=5.347858438810754\n",
+      "SubSGD iter. 54/499: loss=1314.1908462103586, w0=38.50000000000001, w1=5.960850935994314\n",
+      "SubSGD iter. 55/499: loss=194.03229371562793, w0=39.20000000000001, w1=5.120823364534503\n",
+      "SubSGD iter. 56/499: loss=231.23882637327, w0=39.90000000000001, w1=4.84089437209804\n",
+      "SubSGD iter. 57/499: loss=573.6401417841317, w0=40.600000000000016, w1=4.444918128876208\n",
+      "SubSGD iter. 58/499: loss=134.28690015025037, w0=41.30000000000002, w1=3.0909612348429802\n",
+      "SubSGD iter. 59/499: loss=922.1626682912996, w0=42.00000000000002, w1=3.3564449370672107\n",
+      "SubSGD iter. 60/499: loss=362.3657247812496, w0=42.700000000000024, w1=2.669750301561074\n",
+      "SubSGD iter. 61/499: loss=390.2114656296359, w0=43.40000000000003, w1=2.5595761846803335\n",
+      "SubSGD iter. 62/499: loss=887.4838524374816, w0=44.10000000000003, w1=3.3055618382677308\n",
+      "SubSGD iter. 63/499: loss=1234.4736554178191, w0=44.80000000000003, w1=4.668313233659477\n",
+      "SubSGD iter. 64/499: loss=209.3169725032069, w0=45.500000000000036, w1=3.989966237684934\n",
+      "SubSGD iter. 65/499: loss=98.73743315064134, w0=46.20000000000004, w1=3.2057335096456097\n",
+      "SubSGD iter. 66/499: loss=612.3220411054152, w0=46.90000000000004, w1=4.15264577138804\n",
+      "SubSGD iter. 67/499: loss=585.2460798824205, w0=47.600000000000044, w1=4.305539528502622\n",
+      "SubSGD iter. 68/499: loss=671.4506070499489, w0=48.30000000000005, w1=4.50188412201985\n",
+      "SubSGD iter. 69/499: loss=305.0482906804949, w0=49.00000000000005, w1=4.483304663120973\n",
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+      "SubSGD iter. 480/499: loss=8.011378505934944, w0=73.50000000000014, w1=13.587664874885462\n",
+      "SubSGD iter. 481/499: loss=0.2846586569824997, w0=72.80000000000014, w1=14.56446235382738\n",
+      "SubSGD iter. 482/499: loss=11.403593375730173, w0=72.10000000000014, w1=14.987701778599895\n",
+      "SubSGD iter. 483/499: loss=13.284777676658836, w0=72.80000000000014, w1=15.611189900886073\n",
+      "SubSGD iter. 484/499: loss=0.19388672914515773, w0=72.10000000000014, w1=14.849832950267004\n",
+      "SubSGD iter. 485/499: loss=9.194295660645468, w0=71.40000000000013, w1=15.339516483663592\n",
+      "SubSGD iter. 486/499: loss=0.5043770072669796, w0=72.10000000000014, w1=15.13468296378837\n",
+      "SubSGD iter. 487/499: loss=0.6293035791215708, w0=71.40000000000013, w1=14.229507860601306\n",
+      "SubSGD iter. 488/499: loss=10.607919663147289, w0=72.10000000000014, w1=14.6796876068076\n",
+      "SubSGD iter. 489/499: loss=66.61246011659337, w0=72.80000000000014, w1=15.027654653198013\n",
+      "SubSGD iter. 490/499: loss=84.88943193386883, w0=72.10000000000014, w1=15.147546466686345\n",
+      "SubSGD iter. 491/499: loss=0.31552018982376473, w0=72.80000000000014, w1=15.675602282196994\n",
+      "SubSGD iter. 492/499: loss=8.556387932504698, w0=72.10000000000014, w1=16.30041838200794\n",
+      "SubSGD iter. 493/499: loss=7.633623940518852, w0=72.80000000000014, w1=15.529595315939753\n",
+      "SubSGD iter. 494/499: loss=35.05239717065858, w0=72.10000000000014, w1=15.961387894223174\n",
+      "SubSGD iter. 495/499: loss=1.8395789541375203, w0=71.40000000000013, w1=16.35682468616618\n",
+      "SubSGD iter. 496/499: loss=4.001265794880103, w0=72.10000000000014, w1=17.15199441128353\n",
+      "SubSGD iter. 497/499: loss=6913.281085111043, w0=72.80000000000014, w1=14.38169471439454\n",
+      "SubSGD iter. 498/499: loss=1.9083506783977329, w0=73.50000000000014, w1=14.357728874591448\n",
+      "SubSGD iter. 499/499: loss=6.972310937952388, w0=74.20000000000014, w1=15.897791212978634\n",
+      "SubSGD: execution time=0.020 seconds\n"
+     ]
+    }
+   ],
    "source": [
     "# Define the parameters of the algorithm.\n",
     "max_iters = 500\n",
@@ -820,7 +2121,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/labs/ex02/template/gradient_descent.py b/labs/ex02/template/gradient_descent.py
index 597a6c5c5..5f1523ef6 100644
--- a/labs/ex02/template/gradient_descent.py
+++ b/labs/ex02/template/gradient_descent.py
@@ -3,7 +3,7 @@
 
 Gradient Descent
 """
-
+from costs import compute_loss
 
 def compute_gradient(y, tx, w):
     """Computes the gradient at w.
@@ -20,7 +20,9 @@ def compute_gradient(y, tx, w):
     # INSERT YOUR CODE HERE
     # TODO: compute gradient vector
     # ***************************************************
-    raise NotImplementedError
+    error = y - tx.dot(w)
+    gradient = -1/(y.shape[0]) * tx.T.dot(error)
+    return gradient
 
 
 def gradient_descent(y, tx, initial_w, max_iters, gamma):
@@ -46,12 +48,13 @@ def gradient_descent(y, tx, initial_w, max_iters, gamma):
         # INSERT YOUR CODE HERE
         # TODO: compute gradient and loss
         # ***************************************************
-        raise NotImplementedError
+        gradient = compute_gradient(y, tx, w)
+        loss = compute_loss(y, tx, w)
         # ***************************************************
         # INSERT YOUR CODE HERE
         # TODO: update w by gradient
         # ***************************************************
-        raise NotImplementedError
+        w = w - gamma * gradient
 
         # store w and loss
         ws.append(w)
diff --git a/labs/ex02/template/grid_plot.png b/labs/ex02/template/grid_plot.png
index bfa8d8dbd..a524db938 100644
Binary files a/labs/ex02/template/grid_plot.png and b/labs/ex02/template/grid_plot.png differ
diff --git a/labs/ex02/template/grid_search.py b/labs/ex02/template/grid_search.py
index 397251e98..726d5fd66 100644
--- a/labs/ex02/template/grid_search.py
+++ b/labs/ex02/template/grid_search.py
@@ -26,3 +26,25 @@ def get_best_parameters(w0, w1, losses):
 # TODO: Paste your implementation of grid_search
 #       here when it is done.
 # ***************************************************
+def grid_search(y, tx, grid_w0, grid_w1):
+    """Algorithm for grid search.
+
+    Args:
+        y: numpy array of shape=(N, )
+        tx: numpy array of shape=(N,2)
+        grid_w0: numpy array of shape=(num_grid_pts_w0, ). A 1D array containing num_grid_pts_w0 values of parameter w0 to be tested in the grid search.
+        grid_w1: numpy array of shape=(num_grid_pts_w1, ). A 1D array containing num_grid_pts_w1 values of parameter w1 to be tested in the grid search.
+
+    Returns:
+        losses: numpy array of shape=(num_grid_pts_w0, num_grid_pts_w1). A 2D array containing the loss value for each combination of w0 and w1
+    """
+
+    losses = np.zeros((len(grid_w0), len(grid_w1)))
+    # ***************************************************
+    # INSERT YOUR CODE HERE
+    # TODO: compute loss for each combination of w0 and w1.
+    # ***************************************************
+    for i, w0 in enumerate(grid_w0):
+        for j, w1 in enumerate(grid_w1):
+            losses[i, j] = compute_loss(y, tx, np.array([w0, w1]))
+    return losses
\ No newline at end of file
diff --git a/labs/ex02/template/stochastic_gradient_descent.py b/labs/ex02/template/stochastic_gradient_descent.py
index df53fd817..553f9bd51 100644
--- a/labs/ex02/template/stochastic_gradient_descent.py
+++ b/labs/ex02/template/stochastic_gradient_descent.py
@@ -5,7 +5,7 @@
 """
 from helpers import batch_iter
 from costs import compute_loss
-
+from gradient_descent import compute_gradient
 
 def compute_stoch_gradient(y, tx, w):
     """Compute a stochastic gradient at w from just few examples n and their corresponding y_n labels.
@@ -23,7 +23,8 @@ def compute_stoch_gradient(y, tx, w):
     # INSERT YOUR CODE HERE
     # TODO: implement stochastic gradient computation. It's the same as the usual gradient.
     # ***************************************************
-    raise NotImplementedError
+    gradient = compute_gradient(y, tx, w)
+    return gradient
 
 
 def stochastic_gradient_descent(y, tx, initial_w, batch_size, max_iters, gamma):
@@ -52,7 +53,12 @@ def stochastic_gradient_descent(y, tx, initial_w, batch_size, max_iters, gamma):
         # INSERT YOUR CODE HERE
         # TODO: implement stochastic gradient descent.
         # ***************************************************
-        raise NotImplementedError
+        for batch_y, batch_tx in batch_iter(y, tx, batch_size):
+            gradient = compute_stoch_gradient(batch_y, batch_tx, w)
+            loss = compute_loss(batch_y, batch_tx, w)
+            w = w - gamma * gradient
+            ws.append(w)
+            losses.append(loss)
 
         print(
             "SGD iter. {bi}/{ti}: loss={l}, w0={w0}, w1={w1}".format(
diff --git a/labs/ex02/template/subgradient_mae.py b/labs/ex02/template/subgradient_mae.py
index 8b8fb7dbc..0e7f0528d 100644
--- a/labs/ex02/template/subgradient_mae.py
+++ b/labs/ex02/template/subgradient_mae.py
@@ -16,4 +16,7 @@ def compute_subgradient_mae(y, tx, w):
     # INSERT YOUR CODE HERE
     # TODO: compute subgradient gradient vector for MAE
     # ***************************************************
-    raise NotImplementedError
+    error = y - tx.dot(w)
+    #Using chain rule for the subgradient of the MAE
+    subgradient = -np.sign(error).dot(tx) / y.shape[0]
+    return subgradient
diff --git a/labs/ex03/template/build_polynomial.py b/labs/ex03/template/build_polynomial.py
index f2f92321f..b4bd5bb86 100644
--- a/labs/ex03/template/build_polynomial.py
+++ b/labs/ex03/template/build_polynomial.py
@@ -12,4 +12,10 @@ def build_poly(x, degree):
     # this function should return the matrix formed
     # by applying the polynomial basis to the input data
     # ***************************************************
-    raise NotImplementedError
+    
+    #We can also use np.vander(x, degree+1) to get the same result
+    
+    poly = np.zeros((len(x), degree+1))
+    for i in range(degree+1):
+        poly[:, i] = x**i
+    return poly
diff --git a/labs/ex03/template/ex03.ipynb b/labs/ex03/template/ex03.ipynb
index 1dc10c2e8..58ad5fe37 100644
--- a/labs/ex03/template/ex03.ipynb
+++ b/labs/ex03/template/ex03.ipynb
@@ -2,7 +2,6 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "#### Import python files from another directory\n",
@@ -12,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +25,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "you can now import your desired files, for example, we can import grid_search.py with:"
@@ -34,9 +32,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[-100.  -25.   50.  125.  200.] [-150.  -75.    0.   75.  150.]\n"
+     ]
+    }
+   ],
    "source": [
     "import grid_search  # You then need to call your functions using grid_search.function_name()\n",
     "import grid_search as gs  # You then need to call your functions using gs.function_name()\n",
@@ -49,7 +55,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "As you can see we are now able to call functions from the grid_search.py file."
@@ -57,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +80,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# 1 Least squares and linear basis functions models\n",
@@ -84,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -108,12 +112,14 @@
     "    # least squares: TODO\n",
     "    # returns mse, and optimal weights\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError"
+    "    w = np.linalg.solve(tx.T.dot(tx), tx.T.dot(y))\n",
+    "    error = y - tx.dot(w)\n",
+    "    mse = 1/(2*len(y)) * np.sum(error**2)\n",
+    "    return w, mse"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "### You can test your implementation here"
@@ -121,9 +127,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "❌ The are some issues with your implementation of `least_squares`:\n",
+      "**********************************************************************\n",
+      "File \"__main__\", line 13, in least_squares\n",
+      "Failed example:\n",
+      "    least_squares(np.array([0.1,0.2]), np.array([[2.3, 3.2], [1., 0.1]]))\n",
+      "Expected:\n",
+      "    (array([ 0.21212121, -0.12121212]), 8.666684749742561e-33)\n",
+      "Got:\n",
+      "    (array([ 0.21212121, -0.12121212]), 2.946672814912471e-32)\n",
+      "**********************************************************************\n"
+     ]
+    }
+   ],
    "source": [
     "test(least_squares)\n",
     "# NB:\n",
@@ -139,12 +162,14 @@
     "#\n",
     "# In this case,\n",
     "# Failing the test doesn't necessarily mean\n",
-    "# your implementation is wrong.:)"
+    "# your implementation is wrong.:)\n",
+    "\n",
+    "#Here I fail the test with a number close to zero, but not exactly the same as the expected output, \n",
+    "#so  I consider my implementation correct."
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "### Load the data\n",
@@ -153,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -171,12 +196,20 @@
     "    # this code should compare the optimal weights obtained\n",
     "    # by least squares vs. grid search\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError"
+    "    w_ls, mse_ls = least_squares(y, tx)\n",
+    "    grid_losses = grid_search(y, tx, w0, w1)\n",
+    "    \n",
+    "    min_loss_idx = np.argmin(grid_losses)\n",
+    "    min_loss_idx = (min_loss_idx // grid_losses.shape[1], min_loss_idx % grid_losses.shape[1])\n",
+    "    w_gs = np.array([w0[min_loss_idx[0]], w1[min_loss_idx[1]]])\n",
+    "    mse_gs = grid_losses[min_loss_idx]\n",
+    "    \n",
+    "    print(\"Least squares: \", w_ls, mse_ls)\n",
+    "    print(\"Grid search: \", w_gs, mse_gs)"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Test it here"
@@ -184,16 +217,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Least squares:  [73.293922   13.47971243] 15.385887868829402\n",
+      "Grid search:  [50.  0.] 23.64677337261977\n"
+     ]
+    }
+   ],
    "source": [
     "test_your_least_squares()"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "## 1.2 Least squares with a linear basis function model\n",
@@ -204,9 +245,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shape of x (50,)\n",
+      "shape of y (50,)\n"
+     ]
+    }
+   ],
    "source": [
     "# load dataset\n",
     "x, y = load_data()\n",
@@ -216,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,21 +290,31 @@
     "    # this function should return the matrix formed\n",
     "    # by applying the polynomial basis to the input data\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError"
+    "    poly = np.zeros((len(x), degree+1))\n",
+    "    for i in range(degree+1):\n",
+    "        poly[:, i] = x**i\n",
+    "    return poly"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `build_poly` passed 1 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "test(build_poly)"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Once your implementation of `build_poly` passes the test, copy it to `build_polynomial.py`\n",
@@ -263,11 +323,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
     "from plots import *\n",
+    "from math import sqrt\n",
     "\n",
     "\n",
     "def polynomial_regression():\n",
@@ -280,19 +341,20 @@
     "    num_row = 2\n",
     "    num_col = 2\n",
     "    f, axs = plt.subplots(num_row, num_col)\n",
-    "\n",
+    "    weights= []\n",
     "    for ind, degree in enumerate(degrees):\n",
     "        # ***************************************************\n",
     "        # INSERT YOUR CODE HERE\n",
     "        # form the data to do polynomial regression.: TODO\n",
     "        # ***************************************************\n",
-    "        raise NotImplementedError\n",
+    "        data_poly = build_poly(x, degree)\n",
     "        # ***************************************************\n",
     "        # INSERT YOUR CODE HERE\n",
     "        # least square and calculate RMSE: TODO\n",
     "        # ***************************************************\n",
-    "        raise NotImplementedError\n",
-    "\n",
+    "        least_squares_poly = least_squares(y, data_poly)\n",
+    "        weights = least_squares_poly[0]\n",
+    "        rmse = sqrt(2*least_squares_poly[1])\n",
     "        print(\n",
     "            \"Processing {i}th experiment, degree={d}, rmse={loss}\".format(\n",
     "                i=ind + 1, d=degree, loss=rmse\n",
@@ -307,7 +369,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Run polynomial regression"
@@ -315,16 +376,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing 1th experiment, degree=1, rmse=0.47187607963421874\n",
+      "Processing 2th experiment, degree=3, rmse=0.25858277667737484\n",
+      "Processing 3th experiment, degree=7, rmse=0.24965870360907552\n",
+      "Processing 4th experiment, degree=12, rmse=0.24328247481248674\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "polynomial_regression()"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Your results should look like this:"
@@ -332,7 +413,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "![alt text](visualize_polynomial_regression.png)"
@@ -340,7 +420,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# 2 Evaluating model predication performance\n",
@@ -350,10 +429,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
+    "\n",
     "def split_data(x, y, ratio, seed=1):\n",
     "    \"\"\"\n",
     "    split the dataset based on the split ratio. If ratio is 0.8\n",
@@ -383,21 +463,37 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # split the data based on the given ratio: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError"
+    "    num_samples = len(y)\n",
+    "    indices = np.random.permutation(num_samples)\n",
+    "    split_point = int(np.floor(ratio * num_samples))\n",
+    "\n",
+    "    x_tr = x[indices[:split_point]]\n",
+    "    x_te = x[indices[split_point:]]\n",
+    "    y_tr = y[indices[:split_point]]\n",
+    "    y_te = y[indices[split_point:]]\n",
+    "\n",
+    "    return x_tr, x_te, y_tr, y_te"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `split_data` passed 1 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "test(split_data)"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Then, test your `split_data` function below."
@@ -405,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -422,33 +518,36 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # split the data, and return train and test data: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    x_tr, x_te, y_tr, y_te = split_data(x, y, ratio, seed)\n",
     "    # ***************************************************\n",
     "    # INSERT YOUR CODE HERE\n",
     "    # form train and test data with polynomial basis function: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    data_tr = build_poly(x_tr, degree)\n",
+    "    data_te = build_poly(x_te, degree)\n",
     "    # ***************************************************\n",
     "    # INSERT YOUR CODE HERE\n",
     "    # calculate weight through least square: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    weights, loss_mse = least_squares(y_tr, data_tr)\n",
     "    # ***************************************************\n",
     "    # INSERT YOUR CODE HERE\n",
     "    # calculate RMSE for train and test data,\n",
     "    # and store them in rmse_tr and rmse_te respectively: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    rmse_tr = sqrt(2*loss_mse) \n",
+    "    loss_mse_te = 1/(2*len(y_te)) * np.sum((y_te - data_te.dot(weights))**2)\n",
+    "    rmse_te = sqrt(2*loss_mse_te)\n",
     "    print(\n",
     "        \"proportion={p}, degree={d}, Training RMSE={tr:.3f}, Testing RMSE={te:.3f}\".format(\n",
     "            p=ratio, d=degree, tr=rmse_tr, te=rmse_te\n",
     "        )\n",
-    "    )"
+    "    )\n",
+    "    return x_tr, x_te, y_tr, y_te, weights"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Demo time"
@@ -456,9 +555,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "proportion=0.9, degree=1, Training RMSE=0.494, Testing RMSE=0.181\n",
+      "proportion=0.9, degree=3, Training RMSE=0.264, Testing RMSE=0.206\n",
+      "proportion=0.9, degree=7, Training RMSE=0.254, Testing RMSE=0.220\n",
+      "proportion=0.9, degree=12, Training RMSE=0.242, Testing RMSE=0.250\n",
+      "proportion=0.7, degree=1, Training RMSE=0.516, Testing RMSE=0.352\n",
+      "proportion=0.7, degree=3, Training RMSE=0.249, Testing RMSE=0.308\n",
+      "proportion=0.7, degree=7, Training RMSE=0.227, Testing RMSE=0.333\n",
+      "proportion=0.7, degree=12, Training RMSE=0.223, Testing RMSE=0.328\n",
+      "proportion=0.5, degree=1, Training RMSE=0.455, Testing RMSE=0.531\n",
+      "proportion=0.5, degree=3, Training RMSE=0.239, Testing RMSE=0.296\n",
+      "proportion=0.5, degree=7, Training RMSE=0.232, Testing RMSE=0.284\n",
+      "proportion=0.5, degree=12, Training RMSE=0.205, Testing RMSE=1.548\n",
+      "proportion=0.1, degree=1, Training RMSE=0.428, Testing RMSE=0.534\n",
+      "proportion=0.1, degree=3, Training RMSE=0.085, Testing RMSE=0.460\n",
+      "proportion=0.1, degree=7, Training RMSE=0.000, Testing RMSE=2.254\n",
+      "proportion=0.1, degree=12, Training RMSE=0.000, Testing RMSE=4.651\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x800 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "seed = 6\n",
     "degrees = [1, 3, 7, 12]\n",
@@ -476,12 +608,13 @@
     "        )\n",
     "        plot_fitted_curve(y_tr, x_tr, w, degree, axs[ind_d][ind % num_col])\n",
     "        axs[ind_d][ind].set_title(f\"Degree: {degree}, Split {split_ratio}\")\n",
-    "plt.tight_layout()"
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Your graph should look like this:"
@@ -489,7 +622,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "![alt text](split_demo.png)"
@@ -497,7 +629,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# Ridge Regression\n",
@@ -506,7 +637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,21 +657,31 @@
     "    >>> ridge_regression(np.array([0.1,0.2]), np.array([[2.3, 3.2], [1., 0.1]]), 1)\n",
     "    array([0.03947092, 0.00319628])\n",
     "    \"\"\"\n",
-    "    raise NotImplementedError"
+    "    #Used the formula from the slides\n",
+    "    w = np.linalg.solve(tx.T.dot(tx) + 2 * tx.shape[0] * lambda_ * np.eye(tx.shape[1]), tx.T.dot(y))\n",
+    "    return w "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `ridge_regression` passed 2 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "test(ridge_regression)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -553,12 +694,13 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # split the data, and return train and test data: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    x_tr, x_te, y_tr, y_te = split_data(x, y, ratio, seed)\n",
     "    # ***************************************************\n",
     "    # INSERT YOUR CODE HERE\n",
     "    # form train and test data with polynomial basis function: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    x_tr_poly = build_poly(x_tr, degree)\n",
+    "    x_te_poly = build_poly(x_te, degree)\n",
     "\n",
     "    rmse_tr = []\n",
     "    rmse_te = []\n",
@@ -567,7 +709,11 @@
     "        # INSERT YOUR CODE HERE\n",
     "        # ridge regression with a given lambda\n",
     "        # ***************************************************\n",
-    "        raise NotImplementedError\n",
+    "        w = ridge_regression(y_tr, x_tr_poly, lambda_)\n",
+    "        mse_tr = 1/(2*len(y_tr)) * np.sum((y_tr - x_tr_poly.dot(w))**2)\n",
+    "        mse_te = 1/(2*len(y_te)) * np.sum((y_te - x_te_poly.dot(w))**2)\n",
+    "        rmse_tr.append(sqrt(2*mse_tr))\n",
+    "        rmse_te.append(sqrt(2*mse_te))\n",
     "        print(\n",
     "            \"proportion={p}, degree={d}, lambda={l:.3f}, Training RMSE={tr:.3f}, Testing RMSE={te:.3f}\".format(\n",
     "                p=ratio, d=degree, l=lambda_, tr=rmse_tr[ind], te=rmse_te[ind]\n",
@@ -578,7 +724,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {
     "collapsed": true,
     "jupyter": {
@@ -591,19 +736,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "proportion=0.5, degree=7, lambda=0.000, Training RMSE=0.227, Testing RMSE=0.338\n",
+      "proportion=0.5, degree=7, lambda=0.000, Training RMSE=0.227, Testing RMSE=0.337\n",
+      "proportion=0.5, degree=7, lambda=0.000, Training RMSE=0.227, Testing RMSE=0.336\n",
+      "proportion=0.5, degree=7, lambda=0.000, Training RMSE=0.227, Testing RMSE=0.335\n",
+      "proportion=0.5, degree=7, lambda=0.000, Training RMSE=0.228, Testing RMSE=0.334\n",
+      "proportion=0.5, degree=7, lambda=0.001, Training RMSE=0.228, Testing RMSE=0.333\n",
+      "proportion=0.5, degree=7, lambda=0.001, Training RMSE=0.229, Testing RMSE=0.329\n",
+      "proportion=0.5, degree=7, lambda=0.003, Training RMSE=0.230, Testing RMSE=0.319\n",
+      "proportion=0.5, degree=7, lambda=0.007, Training RMSE=0.232, Testing RMSE=0.302\n",
+      "proportion=0.5, degree=7, lambda=0.016, Training RMSE=0.237, Testing RMSE=0.283\n",
+      "proportion=0.5, degree=7, lambda=0.037, Training RMSE=0.246, Testing RMSE=0.276\n",
+      "proportion=0.5, degree=7, lambda=0.085, Training RMSE=0.264, Testing RMSE=0.298\n",
+      "proportion=0.5, degree=7, lambda=0.193, Training RMSE=0.291, Testing RMSE=0.348\n",
+      "proportion=0.5, degree=7, lambda=0.439, Training RMSE=0.317, Testing RMSE=0.401\n",
+      "proportion=0.5, degree=7, lambda=1.000, Training RMSE=0.336, Testing RMSE=0.441\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "seed = 56\n",
     "degree = 7\n",
     "split_ratio = 0.5\n",
-    "ridge_regression_demo(x, y, degree, split_ratio, seed)"
+    "ridge_regression_demo(x, y, degree, split_ratio, seed)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Your plot should look like:"
@@ -611,11 +788,55 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "![alt text](ridge_regression.png)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "def f(x, w):\n",
+    "    return np.dot(x, w)\n",
+    "\n",
+    "def L_n(x_n, y_n, w, epsilon=1e-5):\n",
+    "    return ((f(x_n, w) - y_n)**2) / (2 * (y_n + epsilon))\n",
+    "\n",
+    "# Generate some sample data\n",
+    "x_n = np.linspace(-10, 10, 3)\n",
+    "y_n = np.sin(x_n)  # Example target function\n",
+    "w = np.array([1.0, 2.0, 2.0])  # Example weight\n",
+    "\n",
+    "# Compute the loss for each x_n\n",
+    "losses = L_n(x_n, y_n, w)\n",
+    "\n",
+    "# Plot the function\n",
+    "plt.plot(x_n, losses, label=r'$L_n(f(x_n, w)) = \\frac{(f(x_n, w) - y_n)^2}{2(y_n + \\epsilon)}$')\n",
+    "plt.xlabel(r'$x_n$')\n",
+    "plt.ylabel(r'$L_n$')\n",
+    "plt.title('Plot of the Loss Function')\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {
diff --git a/labs/ex04/template/build_polynomial.py b/labs/ex04/template/build_polynomial.py
index 6953940b0..7bc0f6863 100644
--- a/labs/ex04/template/build_polynomial.py
+++ b/labs/ex04/template/build_polynomial.py
@@ -19,9 +19,12 @@ def build_poly(x, degree):
            [1.  , 1.5 , 2.25]])
     """
     # ***************************************************
-    # COPY YOUR CODE FROM EX03 HERE
+    # INSERT YOUR CODE HERE
     # polynomial basis function: TODO
     # this function should return the matrix formed
     # by applying the polynomial basis to the input data
     # ***************************************************
-    raise NotImplementedError
+    poly = np.zeros((len(x), degree+1))
+    for i in range(degree+1):
+        poly[:, i] = x**i
+    return poly
diff --git a/labs/ex04/template/cross_validation.png b/labs/ex04/template/cross_validation.png
index c68cc4d2e..fc0e030ce 100644
Binary files a/labs/ex04/template/cross_validation.png and b/labs/ex04/template/cross_validation.png differ
diff --git a/labs/ex04/template/ex04.ipynb b/labs/ex04/template/ex04.ipynb
index a2ec74a82..33e6f5363 100644
--- a/labs/ex04/template/ex04.ipynb
+++ b/labs/ex04/template/ex04.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -18,7 +18,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# Cross-Validation and Bias-Variance decomposition\n",
@@ -28,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,16 +68,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `build_k_indices` passes some basic tests.\n"
+     ]
+    }
+   ],
    "source": [
     "test(build_k_indices)"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "For the following cross_validation( ) function you need to implement, you can help yourselves of the build_poly( ) and ridge_regression( ) functions that you implemented in lab 3. Copy paste the code in the build_polynomial.py and ridge_regression.py files, they should pass the two following tests."
@@ -86,9 +92,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `build_poly` passes some basic tests.\n",
+      "✅ Your `ridge_regression` passes some basic tests.\n"
+     ]
+    }
+   ],
    "source": [
     "from costs import compute_mse\n",
     "from ridge_regression import ridge_regression\n",
@@ -101,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,30 +142,46 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # get k'th subgroup in test, others in train: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    test_indices = k_indices[k]\n",
+    "    train_indices = np.delete(k_indices, k, axis=0).flatten()\n",
+    "    \n",
+    "    x_train = x[train_indices]\n",
+    "    y_train = y[train_indices]\n",
+    "    x_test = x[test_indices]\n",
+    "    y_test = y[test_indices]\n",
     "    # ***************************************************\n",
     "    # INSERT YOUR CODE HERE\n",
     "    # form data with polynomial degree: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    tx_train = build_poly(x_train, degree)\n",
+    "    tx_test = build_poly(x_test, degree)\n",
     "    # ***************************************************\n",
     "    # INSERT YOUR CODE HERE\n",
     "    # ridge regression: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    weights = ridge_regression(y_train, tx_train, lambda_)\n",
     "    # ***************************************************\n",
     "    # INSERT YOUR CODE HERE\n",
     "    # calculate the loss for train and test data: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    loss_tr = np.sqrt(2 * compute_mse(y_train, tx_train, weights))\n",
+    "    loss_te = np.sqrt(2 * compute_mse(y_test, tx_test, weights))\n",
     "    return loss_tr, loss_te"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `cross_validation` passes some basic tests.\n"
+     ]
+    }
+   ],
    "source": [
     "# can lead to a numerical error if you use an older version than Python 3.9\n",
     "test(cross_validation)"
@@ -158,9 +189,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For polynomial expansion up to degree 7, the choice of lambda which leads to the best test rmse is 0.00853 with a test rmse of 0.298\n"
+     ]
+    }
+   ],
    "source": [
     "from plots import cross_validation_visualization\n",
     "\n",
@@ -190,7 +229,17 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # cross validation over lambdas: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    for lambda_ in lambdas:\n",
+    "        rmse_tr_temp = []\n",
+    "        rmse_te_temp = []\n",
+    "        for k in range(k_fold):\n",
+    "            loss_tr, loss_te = cross_validation(y, x, k_indices, k, lambda_, degree)\n",
+    "            rmse_tr_temp.append(loss_tr)\n",
+    "            rmse_te_temp.append(loss_te)\n",
+    "        rmse_tr.append(np.mean(rmse_tr_temp))\n",
+    "        rmse_te.append(np.mean(rmse_te_temp))\n",
+    "    best_lambda = lambdas[np.argmin(rmse_te)]\n",
+    "    best_rmse = np.min(rmse_te)\n",
     "\n",
     "    cross_validation_visualization(lambdas, rmse_tr, rmse_te)\n",
     "    print(\n",
@@ -205,7 +254,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Your output should look like this for seed = 12, degree = 7 and k_fold = 4:\n",
@@ -215,7 +263,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "You can play around with the number of folds and the degree of your polynomial expansion."
@@ -223,16 +270,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "For polynomial expansion up to degree 10, the choice of lambda which leads to the best test rmse is 0.00002 with a test rmse of 0.312\n"
+     ]
+    }
+   ],
    "source": [
     "best_lambda, best_rmse = cross_validation_demo(10, 4, np.logspace(-10, -2, 30))"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "In the previous task we did a grid search over several values of $\\lambda$ for a fixed degree. We can also perform a grid search amongst $\\lambda$ and degrees simultaneously:"
@@ -240,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,16 +321,41 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # cross validation over degrees and lambdas: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    rmse_tr = []\n",
+    "    rmse_te = []\n",
+    "    \n",
+    "    best_rmse = float('inf')\n",
+    "    for degree in degrees:\n",
+    "        for lambda_ in lambdas:\n",
+    "            rmse_tr_temp = []\n",
+    "            rmse_te_temp = []\n",
+    "            for k in range(k_fold):\n",
+    "                loss_tr, loss_te = cross_validation(y, x, k_indices, k, lambda_, degree)\n",
+    "                rmse_tr_temp.append(loss_tr)\n",
+    "                rmse_te_temp.append(loss_te)\n",
+    "            mean_rmse_te = np.mean(rmse_te_temp)\n",
+    "            if mean_rmse_te < best_rmse:\n",
+    "                best_rmse = mean_rmse_te\n",
+    "                best_lambda = lambda_\n",
+    "                best_degree = degree\n",
     "\n",
     "    return best_degree, best_lambda, best_rmse"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `best_degree_selection` passes some basic tests.\n",
+      "The best rmse of 0.290 is obtained for a degree of 7 and a lambda of 0.00452.\n"
+     ]
+    }
+   ],
    "source": [
     "# can lead to a numerical error if you use an older version than Python 3.9\n",
     "test(best_degree_selection)\n",
@@ -292,7 +371,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "## Bias-Variance Decomposition"
@@ -300,7 +378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,7 +396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -334,19 +412,17 @@
     "    ax.set_title(\"Polynomial degree \" + str(degree))\n",
     "    ax.set_ylim(-1, 2)\n",
     "\n",
-    "\n",
     "# helper plot function\n",
     "def plot_f(weights, degree, ax, label=None):\n",
     "    xvals = np.arange(-1, 1, 0.01)\n",
     "    tx = build_poly(xvals, degree)\n",
     "    f = tx.dot(weights)\n",
     "    ax.plot(xvals, f, color=\"black\", alpha=1, label=label)\n",
-    "    ax.set_ylim(-1, 2)"
+    "    ax.set_ylim(-1, 2)\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Implement the following function: for 15 random datapoints, it finds the optimal fit (using the least square formula, with no regularisation λ) for a polynomial expansion of degree 1, 3 and 6."
@@ -354,9 +430,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from least_squares import least_squares\n",
     "\n",
@@ -383,7 +470,9 @@
     "        # ***************************************************\n",
     "        # INSERT YOUR CODE HERE\n",
     "        # ***************************************************\n",
-    "\n",
+    "        tx = build_poly(xs, degree)\n",
+    "        weights, _ = least_squares(ys, tx)\n",
+    "        plot_poly(xs, ys, weights, degree, axs[index_degree])\n",
     "        plot_fstar(axs[index_degree])\n",
     "        axs[index_degree].legend()\n",
     "    plt.show()\n",
@@ -394,7 +483,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Your output should ressemble (for seed = 2) to this: \n",
@@ -403,7 +491,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Now to illustrate the bias variance tradeoff we will repeat many times the previous experiment but using a different random seed each time. We also plot (in plain black) the mean of all the orange functions obtained."
@@ -411,9 +498,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def bias_variance_demo(sigma, degrees):\n",
     "    \"\"\"Illustration of the bias-variance tradeoff.\n",
@@ -432,7 +530,17 @@
     "        # ***************************************************\n",
     "        # INSERT YOUR CODE HERE\n",
     "        # ***************************************************\n",
-    "\n",
+    "        all_weights = []\n",
+    "        for seed in seeds:\n",
+    "            np.random.seed(seed)\n",
+    "            xs = np.random.uniform(-1, 1, num_data)\n",
+    "            ys = f_star(xs) + sigma * np.random.randn(num_data)\n",
+    "            tx = build_poly(xs, degree)\n",
+    "            weights, _ = least_squares(ys, tx)\n",
+    "            all_weights.append(weights)\n",
+    "            plot_poly(xs, ys, weights, degree, axs[index_degree], alpha=0.1)\n",
+    "        mean_weights = np.mean(all_weights, axis=0)\n",
+    "        plot_f(mean_weights, degree, axs[index_degree], label=\"mean fit\")\n",
     "        plot_fstar(axs[index_degree])\n",
     "        axs[index_degree].legend()\n",
     "    plt.show()\n",
@@ -443,7 +551,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Your output should ressemble to this: \n",
@@ -453,7 +560,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "ada",
    "language": "python",
    "name": "python3"
   },
@@ -467,7 +574,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/labs/ex04/template/least_squares.py b/labs/ex04/template/least_squares.py
index f7ba7d821..a584e6f68 100644
--- a/labs/ex04/template/least_squares.py
+++ b/labs/ex04/template/least_squares.py
@@ -27,4 +27,7 @@ def least_squares(y, tx):
     # least squares: TODO
     # returns optimal weights, MSE
     # ***************************************************
-    raise NotImplementedError
+    w = np.linalg.solve(tx.T.dot(tx), tx.T.dot(y))
+    error = y - tx.dot(w)
+    mse = 1/(2*len(y)) * np.sum(error**2)
+    return w, mse
diff --git a/labs/ex04/template/ridge_regression.py b/labs/ex04/template/ridge_regression.py
index 7176ad9a5..1f5c8d0f8 100644
--- a/labs/ex04/template/ridge_regression.py
+++ b/labs/ex04/template/ridge_regression.py
@@ -27,4 +27,5 @@ def ridge_regression(y, tx, lambda_):
     # COPY YOUR CODE FROM EX03 HERE
     # ridge regression: TODO
     # ***************************************************
-    raise NotImplementedError
+    w = np.linalg.solve(tx.T.dot(tx) + 2 * tx.shape[0] * lambda_ * np.eye(tx.shape[1]), tx.T.dot(y))
+    return w 
diff --git a/labs/ex05/template/classification_by_least_square.png b/labs/ex05/template/classification_by_least_square.png
index 755a49c8a..02e66113a 100644
Binary files a/labs/ex05/template/classification_by_least_square.png and b/labs/ex05/template/classification_by_least_square.png differ
diff --git a/labs/ex05/template/classification_by_logistic_regression_gradient_descent.png b/labs/ex05/template/classification_by_logistic_regression_gradient_descent.png
index 500e9537e..44a992bb9 100644
Binary files a/labs/ex05/template/classification_by_logistic_regression_gradient_descent.png and b/labs/ex05/template/classification_by_logistic_regression_gradient_descent.png differ
diff --git a/labs/ex05/template/classification_by_logistic_regression_newton_method.png b/labs/ex05/template/classification_by_logistic_regression_newton_method.png
index 09ffe2de2..fd20983f1 100644
Binary files a/labs/ex05/template/classification_by_logistic_regression_newton_method.png and b/labs/ex05/template/classification_by_logistic_regression_newton_method.png differ
diff --git a/labs/ex05/template/classification_by_logistic_regression_penalized_gradient_descent.png b/labs/ex05/template/classification_by_logistic_regression_penalized_gradient_descent.png
index 0c34bd202..2c902e666 100644
Binary files a/labs/ex05/template/classification_by_logistic_regression_penalized_gradient_descent.png and b/labs/ex05/template/classification_by_logistic_regression_penalized_gradient_descent.png differ
diff --git a/labs/ex05/template/ex05.ipynb b/labs/ex05/template/ex05.ipynb
index f82d07501..09e3b08c6 100644
--- a/labs/ex05/template/ex05.ipynb
+++ b/labs/ex05/template/ex05.ipynb
@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "# Useful starting lines\n",
     "%matplotlib inline\n",
@@ -20,7 +29,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# Logistic Regression\n",
@@ -30,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -49,7 +57,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Use `least_squares` to compute w, and visualize the results."
@@ -57,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,17 +86,16 @@
     "    # classify the data by linear regression: TODO\n",
     "    # ***************************************************\n",
     "    # w = least squares with respect to tx and y\n",
+    "    w, mse = least_squares(y, tx)\n",
     "\n",
     "    # visualize your classification.\n",
     "    visualization(y, x, mean_x, std_x, w, \"classification_by_least_square\")\n",
     "\n",
-    "\n",
     "least_square_classification_demo(y, x)"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "#### The `least_square_classification_demo` is expected to show\n",
@@ -99,7 +105,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "## Logistic Regression"
@@ -107,7 +112,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Compute your cost by negative log likelihood."
@@ -115,9 +119,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `sigmoid` passed 2 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def sigmoid(t):\n",
     "    \"\"\"apply sigmoid function on t.\n",
@@ -133,7 +145,7 @@
     "    >>> sigmoid(np.array([0.1, 0.1]))\n",
     "    array([0.52497919, 0.52497919])\n",
     "    \"\"\"\n",
-    "    raise NotImplementedError\n",
+    "    return (1+np.exp(-t))**-1\n",
     "\n",
     "\n",
     "test(sigmoid)"
@@ -141,9 +153,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `calculate_loss` passed 4 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def calculate_loss(y, tx, w):\n",
     "    \"\"\"compute the cost by negative log likelihood.\n",
@@ -167,19 +187,29 @@
     "\n",
     "    # ***************************************************\n",
     "    # INSERT YOUR CODE HERE\n",
-    "    # TODO\n",
+    "    \n",
+    "    # calculate the loss using the negative log likelihood\n",
+    "    \n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
-    "\n",
+    "    loss = -1/(y.shape[0])*np.sum(y*np.log(sigmoid(tx.dot(w))) + (1-y)*np.log(1-sigmoid(tx.dot(w))))\n",
+    "    return loss\n",
     "\n",
     "test(calculate_loss)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `calculate_gradient` passed 5 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def calculate_gradient(y, tx, w):\n",
     "    \"\"\"compute the gradient of loss.\n",
@@ -205,7 +235,8 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError(\"Calculate gradient\")\n",
+    "    gradient = tx.T.dot(sigmoid(tx.dot(w)) - y) / y.shape[0]\n",
+    "    return gradient\n",
     "\n",
     "\n",
     "test(calculate_gradient)"
@@ -213,7 +244,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "### Using Gradient Descent\n",
@@ -222,9 +252,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `learning_by_gradient_descent` passed 7 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def learning_by_gradient_descent(y, tx, w, gamma):\n",
     "    \"\"\"\n",
@@ -256,7 +294,10 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    loss = calculate_loss(y, tx, w)\n",
+    "    gradient = calculate_gradient(y, tx, w)\n",
+    "    w = w - gamma*gradient\n",
+    "    return loss, w\n",
     "\n",
     "\n",
     "test(learning_by_gradient_descent)"
@@ -264,7 +305,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Demo!"
@@ -272,9 +312,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current iteration=0, loss=0.6931471805599452\n",
+      "Current iteration=100, loss=0.2572780775822991\n",
+      "Current iteration=200, loss=0.2354612766230435\n",
+      "Current iteration=300, loss=0.22402749546644216\n",
+      "Current iteration=400, loss=0.217439348921451\n",
+      "Current iteration=500, loss=0.21337866206030554\n",
+      "Current iteration=600, loss=0.21074981111571242\n",
+      "Current iteration=700, loss=0.2089845098624057\n",
+      "Current iteration=800, loss=0.20776551243295824\n",
+      "Current iteration=900, loss=0.2069051929895466\n",
+      "Current iteration=1000, loss=0.20628738195411772\n",
+      "Current iteration=1100, loss=0.20583744941158436\n",
+      "Current iteration=1200, loss=0.20550598727687253\n",
+      "Current iteration=1300, loss=0.20525946348042884\n",
+      "Current iteration=1400, loss=0.2050746451887704\n",
+      "Current iteration=1500, loss=0.20493515330970438\n",
+      "Current iteration=1600, loss=0.20482926953052655\n",
+      "Current iteration=1700, loss=0.2047485043640546\n",
+      "Current iteration=1800, loss=0.20468664114263546\n",
+      "Current iteration=1900, loss=0.2046390853160284\n",
+      "Current iteration=2000, loss=0.20460241400596174\n",
+      "Current iteration=2100, loss=0.20457405952827123\n",
+      "Current iteration=2200, loss=0.2045520841171981\n",
+      "Current iteration=2300, loss=0.20453501771155272\n",
+      "Current iteration=2400, loss=0.20452173995435652\n",
+      "Current iteration=2500, loss=0.20451139357783976\n",
+      "Current iteration=2600, loss=0.20450332031614316\n",
+      "Current iteration=2700, loss=0.20449701314932475\n",
+      "Current iteration=2800, loss=0.2044920804924756\n",
+      "Current iteration=2900, loss=0.20448821919174934\n",
+      "Current iteration=3000, loss=0.20448519406015744\n",
+      "Current iteration=3100, loss=0.20448282230082498\n",
+      "Current iteration=3200, loss=0.20448096160389256\n",
+      "Current iteration=3300, loss=0.2044795010189353\n",
+      "Current iteration=3400, loss=0.20447835393401814\n",
+      "loss=0.20447827357183826\n"
+     ]
+    }
+   ],
    "source": [
     "from helpers import de_standardize\n",
     "\n",
@@ -319,7 +402,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "#### The `logistic_regression_gradient_descent_demo` is expected to show\n",
@@ -329,7 +411,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Calculate your hessian below"
@@ -337,9 +418,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `calculate_hessian` passed 4 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def calculate_hessian(y, tx, w):\n",
     "    \"\"\"return the Hessian of the loss function.\n",
@@ -364,7 +453,10 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # calculate Hessian: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    sig = sigmoid(tx.dot(w))\n",
+    "    S = np.diag((sig * (1 - sig)).flatten())\n",
+    "    hessian = tx.T.dot(S).dot(tx) / y.shape[0]\n",
+    "    return hessian\n",
     "\n",
     "\n",
     "test(calculate_hessian)"
@@ -372,7 +464,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Write a function below to return loss, gradient, and hessian."
@@ -380,9 +471,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `logistic_regression` passed 6 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def logistic_regression(y, tx, w):\n",
     "    \"\"\"return the loss, gradient of the loss, and hessian of the loss.\n",
@@ -414,7 +513,10 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # return loss, gradient, and Hessian: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    loss = calculate_loss(y, tx, w)\n",
+    "    gradient = calculate_gradient(y, tx, w)\n",
+    "    hessian = calculate_hessian(y, tx, w)\n",
+    "    return loss, gradient, hessian\n",
     "\n",
     "\n",
     "test(logistic_regression)"
@@ -422,7 +524,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "### Using Newton's method\n",
@@ -431,9 +532,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `learning_by_newton_method` passed 8 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def learning_by_newton_method(y, tx, w, gamma):\n",
     "    \"\"\"\n",
@@ -467,12 +576,12 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # return loss, gradient and Hessian: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    loss, gradient, hessian = logistic_regression(y, tx, w)\n",
     "    # ***************************************************\n",
     "    # INSERT YOUR CODE HERE\n",
     "    # update w: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    w = w - gamma * np.linalg.solve(hessian, gradient)\n",
     "    return loss, w\n",
     "\n",
     "\n",
@@ -481,7 +590,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "demo"
@@ -489,9 +597,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current iteration=0, the loss=0.6931471805599452\n",
+      "Current iteration=1, the loss=0.317057768695479\n",
+      "Current iteration=2, the loss=0.23652293099675287\n",
+      "Current iteration=3, the loss=0.20998733711242237\n",
+      "Current iteration=4, the loss=0.20478199318618967\n",
+      "Current iteration=5, the loss=0.20447559968244786\n",
+      "Current iteration=6, the loss=0.2044741280881354\n",
+      "Current iteration=7, the loss=0.20447412804945292\n",
+      "loss=0.20447412804945295\n"
+     ]
+    }
+   ],
    "source": [
     "def logistic_regression_newton_method_demo(y, x):\n",
     "    # init parameters\n",
@@ -535,7 +659,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "#### The `logistic_regression_newton_method_demo` is expected to show\n",
@@ -545,7 +668,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "### Using penalized logistic regression\n",
@@ -554,9 +676,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `penalized_logistic_regression` passed 7 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def penalized_logistic_regression(y, tx, w, lambda_):\n",
     "    \"\"\"return the loss and gradient.\n",
@@ -587,7 +717,9 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # return loss, gradient, and Hessian: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    loss = calculate_loss(y, tx, w)\n",
+    "    gradient = calculate_gradient(y, tx, w) + 2 * lambda_ * w\n",
+    "    return loss, gradient\n",
     "\n",
     "\n",
     "test(penalized_logistic_regression)"
@@ -595,9 +727,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `learning_by_penalized_gradient` passed 9 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):\n",
     "    \"\"\"\n",
@@ -633,12 +773,12 @@
     "    # INSERT YOUR CODE HERE\n",
     "    # return loss, gradient: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    loss, gradient = penalized_logistic_regression(y, tx, w, lambda_)\n",
     "    # ***************************************************\n",
     "    # INSERT YOUR CODE HERE\n",
     "    # update w: TODO\n",
     "    # ***************************************************\n",
-    "    raise NotImplementedError\n",
+    "    w = w - gamma * gradient\n",
     "    return loss, w\n",
     "\n",
     "\n",
@@ -647,9 +787,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Current iteration=0, loss=0.6931471805599452\n",
+      "Current iteration=100, loss=0.25835336206313136\n",
+      "Current iteration=200, loss=0.23768541265662857\n",
+      "Current iteration=300, loss=0.22712668322710844\n",
+      "Current iteration=400, loss=0.2211184138878508\n",
+      "Current iteration=500, loss=0.2174333178698857\n",
+      "Current iteration=600, loss=0.21504465517934657\n",
+      "Current iteration=700, loss=0.21343008764925422\n",
+      "Current iteration=800, loss=0.212302813494644\n",
+      "Current iteration=900, loss=0.21149551766400648\n",
+      "Current iteration=1000, loss=0.21090565347598225\n",
+      "Current iteration=1100, loss=0.21046773835439975\n",
+      "Current iteration=1200, loss=0.21013848456834341\n",
+      "Current iteration=1300, loss=0.20988842069910996\n",
+      "Current iteration=1400, loss=0.2096969707485196\n",
+      "Current iteration=1500, loss=0.2095494580108115\n",
+      "Current iteration=1600, loss=0.20943522174621151\n",
+      "Current iteration=1700, loss=0.20934639893305243\n",
+      "Current iteration=1800, loss=0.2092771154797812\n",
+      "Current iteration=1900, loss=0.2092229364207028\n",
+      "Current iteration=2000, loss=0.20918048405174877\n",
+      "Current iteration=2100, loss=0.20914716752475251\n",
+      "Current iteration=2200, loss=0.20912098803478935\n",
+      "Current iteration=2300, loss=0.20910039632009852\n",
+      "Current iteration=2400, loss=0.20908418704334317\n",
+      "Current iteration=2500, loss=0.20907141961915415\n",
+      "Current iteration=2600, loss=0.20906135829594882\n",
+      "Current iteration=2700, loss=0.20905342644552197\n",
+      "Current iteration=2800, loss=0.20904717145987406\n",
+      "Current iteration=2900, loss=0.20904223764696395\n",
+      "Current iteration=3000, loss=0.20903834520980788\n",
+      "Current iteration=3100, loss=0.20903527388499335\n",
+      "Current iteration=3200, loss=0.20903285017095866\n",
+      "Current iteration=3300, loss=0.20903093733527944\n",
+      "Current iteration=3400, loss=0.20902942758175372\n",
+      "Current iteration=3500, loss=0.20902823590137642\n",
+      "loss=0.20902797948572713\n"
+     ]
+    }
+   ],
    "source": [
     "def logistic_regression_penalized_gradient_descent_demo(y, x):\n",
     "    # init parameters\n",
@@ -692,7 +876,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "#### The `logistic_regression_penalized_gradient_descent_demo` is expected to show\n",
@@ -702,8 +885,22 @@
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "ada",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/labs/ex05/template/least_squares.py b/labs/ex05/template/least_squares.py
index 4e32acee0..16bae1fa9 100644
--- a/labs/ex05/template/least_squares.py
+++ b/labs/ex05/template/least_squares.py
@@ -14,4 +14,7 @@ def least_squares(y, tx):
     # least squares: TODO
     # returns mse, and optimal weights
     # ***************************************************
-    raise NotImplementedError
+    w = np.linalg.solve(tx.T.dot(tx), tx.T.dot(y))
+    error = y - tx.dot(w)
+    mse = 1/(2*len(y)) * np.sum(error**2)
+    return w, mse
diff --git a/labs/ex06/template/ex06.ipynb b/labs/ex06/template/ex06.ipynb
index b291f21bd..2bf543acf 100644
--- a/labs/ex06/template/ex06.ipynb
+++ b/labs/ex06/template/ex06.ipynb
@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "# Useful starting lines\n",
     "%matplotlib inline\n",
@@ -24,7 +33,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "# Support Vector Machines\n",
@@ -34,9 +42,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(N, D) = (569, 31)\n"
+     ]
+    }
+   ],
    "source": [
     "from sklearn import datasets\n",
     "\n",
@@ -51,7 +67,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "## Prepare cost and prediction functions"
@@ -59,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,21 +98,31 @@
     "    \"\"\"\n",
     "    ####################################\n",
     "    ### ___ Enter your code here ___ ###\n",
+    "    primal_obj = (1/X.shape[0]) * np.sum(np.maximum(0, 1 - y * X.dot(w))) + (lambda_/2) * np.linalg.norm(w)**2\n",
+    "    return primal_obj\n",
     "    ####################################"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `calculate_primal_objective` passed 4 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "test(calculate_primal_objective)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -120,21 +145,30 @@
     "    \"\"\"\n",
     "    ####################################\n",
     "    ### ___ Enter your code here ___ ###\n",
+    "    accuracy = np.mean(y == np.sign(X.dot(w)))\n",
+    "    return accuracy\n",
     "    ####################################"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `calculate_accuracy` passed 4 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "test(calculate_accuracy)"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "## Stochastic Gradient Descent for SVM"
@@ -142,7 +176,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Compute the (stochastic) subgradient for the n-th summand of the SVM optimization objective"
@@ -150,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -177,21 +210,34 @@
     "\n",
     "    ####################################\n",
     "    ### ___ Enter your code here ___ ###\n",
+    "    if 1 - y[n] * X[n].dot(w) > 0:\n",
+    "        subgrad = -y[n] * X[n]\n",
+    "    else:\n",
+    "        subgrad = np.zeros_like(w)\n",
+    "    subgrad += lambda_ * w\n",
+    "    return subgrad\n",
     "    ####################################"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Your `calculate_stochastic_gradient` passed 4 tests.\n"
+     ]
+    }
+   ],
    "source": [
     "test(calculate_stochastic_gradient)"
    ]
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Implement stochastic gradient descent: Pick a data point uniformly at random and update w based on the gradient for the n-th summand of the objective"
@@ -199,9 +245,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final training accuracy = 90.33 %\n",
+      "Training time: 7.1 seconds \n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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ifn6+qNPpxF69eonz5s0TW1pa5HueeeYZsU+fPqJarfb4OoP99x03bpwIQLzuuuv8Pm61WsUnnnhCHDFihGgwGMTMzExx0KBB4q9//WvxwIEDId9HolgTRDGMbRpERFEQBAELFizACy+8kOilJJ1nn30Wt99+OxobGwP27RBRaCxdERG1I/X19di6dStWrVqFoUOHMsghaiU2IxMRtSPffPMNLr/8cuh0Orz66quJXg5R0mPpioiIiFIWMzpERESUshjoEBERUcpioENEREQpq8PvunI4HCgvL0dWVlabjZ8nIiKi1hFFEY2NjSgqKoJKFThv0+EDnfLycvTs2TPRyyAiIqIoHDt2LOhU8w4f6EiHzB07dgzZ2dkJXg0RERGFo6GhAT179gx5WGyHD3SkclV2djYDHSIioiQTqu2EzchERESUsjpsoLNixQqUlJRgzJgxiV4KERERxUmHn4zc0NCAnJwc1NfXs3RFRESUJML9/t1hMzpERESU+hjoEBERUcpioENEREQpi4EOERERpSwGOkRERJSyGOgQERFRymKgQ0RERCmLgQ4RERGlLAY6RERElLI6/KGe8bLwX7tQ32yFIAhQCYAAyH8GBAgCXNelPztvEuD8s3TNdbv8Z/m66wGV4LyWZdDilvP7Iiddm7CvmYiIqL1hoBMnX/1Ui5omS5u+pkYl4M6pA9v0NYmIiNozBjpx8uCsoWix2iECEEURogiIcP7uUPzZ43FRdD0W/HMAwOEQXfcBP9U04YPd5Vi3r4qBDhERkQIDnTiZMbxbm71WbZMZa/aU44fyBpxsaEFhtqHNXpuIiKg9YzNyCsjP1GNEj1wAwPp9VYldDBERUTvCQCdFTBrYBQCwjoEOERGRjIFOipg8yBnobD5YA7PNnuDVEBERtQ8MdFLEkKJsdM7Sw2ixY3tZXaKXQ0RE1C4w0EkRKpWASQM7A2D5ioiISMJAJ4VIfTrr9zPQISIiAhjopJTz+hdAqxZQVmNEWY0x0cshIiJKOAY6KSTLoMWY3nkAuM2ciIgIYKCTcli+IiIicmOgk2ImubaZb/vpFIxmW4JXQ0RElFgMdFJM384Z6JWXDovdgc0HaxK9HCIiooRioJNiBEGQhweyfEVERB0dA50UNNE1T2f9vmqI0nHnREREHRADnRR0zhn5SNOqUdnQgtKKhkQvh4iIKGEY6KQgg1aN8f3yAQBf7K9O8GqIiIgSh4FOipJ2X/E4CCIi6sgY6KSoia55Ot8crUOd0ZLg1RARESUGA50U1T03DYO6ZsEhAht+ZPmKiIg6JgY6KWwSt5kTEVEHx0AnhUnzdDb8WA27g9vMiYio42Ggk8LO7JmLnDQtTpus+OZoXaKXQ0RE1OYY6KQwjVqF8wc4hwdy9xUREXVEDHRS3ORBrinJnKdDREQdEAOdFHd+/84QBGBvRQMq6psTvRwiIqI2xUAnxeVn6jGyZy4A59lXREREHQkDnQ5g8kBOSSYioo6JgU4HIM3T2XywBmabPcGrISIiajsMdDqAIUXZ6JKlR7PVjm0/nUr0coiIiNoMA50OQBAETGL5ioiIOiAGOh2E8jgIUeSUZCIi6hgY6HQQ5/UvgFYt4EitCWU1xkQvh4iIqE0w0OkgMvUajO2TB4DlKyIi6jgY6HQgUp8OTzMnIqKOgoFOByKdZv512Sk0mW0JXg0REVH8MdDpQPoUZKA4Px1Wu4hNB2oSvRwiIqK4Y6DTgSi3ma9nnw4REXUADHQ6mMncZk5ERB0IA50O5uwz8pCmVaOq0YwfyhsSvRwiIqK4YqDTweg1aozvVwCA5SsiIkp9DHQ6IKl8tY7bzImIKMWlRKBz+eWXo1OnTrjqqqsSvZSkMGlQZwDA7mOnUdtkTvBqiIiI4iclAp1FixbhtddeS/Qykka3nDQM7pYNUQS+PFCd6OUQERHFTUoEOpMmTUJWVlail5FUJg10ZnXW7WOgQ0REqSvhgc6XX36JmTNnoqioCIIg4P333/e5Z+XKlejTpw8MBgNGjRqFjRs3tv1CU4zUp7NhfxVsdkeCV0NERBQfCQ90jEYjRowYgRdeeMHv46tXr8btt9+O+++/H9988w0mTJiA6dOn4+jRo1G9ntlsRkNDg8evjujMXp2Qm65FQ4sNu46eTvRyiIiI4iLhgc706dPxxz/+EVdccYXfx5966inceOONuOmmmzB48GA888wz6NmzJ1588cWoXu+RRx5BTk6O/Ktnz56tWX7SUqsEXDDAWb7iIZ9ERJSqEh7oBGOxWLBz505MnTrV4/rUqVOxZcuWqJ7zvvvuQ319vfzr2LFjsVhqUuJxEERElOo0iV5AMDU1NbDb7SgsLPS4XlhYiMrKSvnjadOmYdeuXTAajejRowfee+89jBkzxu9z6vV66PX6uK47WVwwoDNUArCvshEnTjeje25aopdEREQUU+060JEIguDxsSiKHtc++eSTtl5SSuiUocOZvTph55E6rN9XhevPKU70koiIiGKqXZeuCgoKoFarPbI3AFBVVeWT5aHoSNvMv2CfDhERpaB2HejodDqMGjUKn332mcf1zz77DOPGjUvQqlLLJNc2880Ha9FitSd4NURERLGV8NJVU1MTDh48KH9cVlaG3bt3Iy8vD7169cLixYsxe/ZsjB49Gueeey5eeuklHD16FLfccksCV506Srplo2u2AZUNLRi3fB20agEqQYAAZ8lQpQJUgvIaoFWr8NvJ/TFjeLdEL5+IiCiohAc6O3bswKRJk+SPFy9eDACYO3cuVq1ahWuuuQa1tbVYtmwZKioqMHToUHz88ccoLmY/SSwIgoBLzyzCXzb8hFNGS9if9+qWwwx0iIio3RNEURQTvYhEamhoQE5ODurr65GdnZ3o5SSEKIo4VG2ExeaACBGiCDhEEQ7X76Lrz6II7Dl2Gn/6eC8Gd8vGf2+bkOilExFRBxXu9++EZ3Qo8QRBQL8umWHdq1E7d7s1ma3xXBIREVFMdNhAZ8WKFVixYgXsdjbgRiJT7/wr09Rii+rzmy12/HWjs0wmCIBaEKBSCRAEqRfIeU1w9QWpBGBMnzycc0Z+LL8MIiLqIDpsoLNgwQIsWLBATn1ReKRAx2iOLkD8tLQST332Y0Sfk65TY/cfpkKnadebBImIqB3qsIEORSfT4PwrY7E7YLbZodeoI/r8OlfDc/8umbiopNDV+yPCIYqwOzx7guyiiH9tOwqTxY4msw15Gl3Mvx4iIkptDHQoIhk691+ZphYb9JmRBTpGizMTdGavXNx98aCQ97+76zharA4YzTbkZTDQISKiyLAWQBFRqwSk65zBTZM58j6dZlegk64LL8aWe4KieC0iIiIGOhSx1gQfRovzc6RgKZQMuSeIgQ4REUWOgQ5FTOrTiWbnlTujE2ago2NGh4iIosdAhyKW1aqMTnSlq2h3eRERUcfGQIciltGKQMdkjrR05byPpSsiIooGAx2KmJRlaYyidGWSMjr68DI6rQmqiIiIOmygs2LFCpSUlGDMmDGJXkrSkXp0osmymKRmZG14GZ1MNiMTEVErdNhAZ8GCBSgtLcX27dsTvZSk05oeHXdGJ7JdV00WBjpERBS5DhvoUPQyYlC6ygizGZnby4mIqDUY6FDE5O3lrSldhdmMnCk3I3PXFRERRY6BDkUsqxVZFiObkYmIqA0x0KGIRZvRsdkdsNgcANiMTEREbYOBDkUsU68FEHmPjsnqLj+F3YysY6BDRETRY6BDEZOG+EWa0ZGOf1CrBOjU4f3VY+mKiIhag4EORSzLldGJNMtiVExFFgQhrM/hERBERNQaDHQoYtEe6mmK8EBPgEdAEBFR6zDQoYhlKob4ORxi2J8X6Qwd5WsZLTaIYvivRUREBHTgQIdHQERPCj5E0bPBOBSja4ZOWkQZHedrOUSgOYLXIiIiAjpwoMMjIKJn0KqgVjl7bCIpKTVHkdFx9vM4/8yGZCIiilSHDXQoeoIgRHWCudyMHObWcum13FvMmdEhIqLIMNChqGRGse1bKj1F0owMsCGZiIiix0CHoiIHOhFldKRAJ/zSFcBZOkREFD0GOhSVaI6BaI7wQE/5tXgMBBERRYmBDkUlmtKVfKBnpBkdHTM6REQUHQY6FBX30EBr2J9jijKjk8HpyEREFCUGOhSVzCiyLNFMRgaATDYjExFRlBjoUFSkjE5jJKUrNiMTEVEbY6BDUYmmQbjZ6rw3I4I5OtG+FhEREcBAh6KUFcXBnq3N6EhHSBAREYWLgQ5FJZpyUnOUPTru12IzMhERRYaBDkUlqiMgot11pWs/zciiKPIUdSKiJNJhAx2eXt46UjNyJOWk5mjn6LSjZuQbX92BWS9shs3uSPRSiIgoDB020OHp5a2TFc0REEk+GVkURazbV4XvTtTjZKM5oWshIqLwdNhAh1on0iMg7A4RLVZnFiT6gYGJDXQcioqVqR1kl4iIKDQGOhQV6ViGcHt0pJPLAXfgEvZrubajJ7oZ2a6IdKTjLIiIqH1joENRkbaXm20OWMPoV5EyIIIA6DWR/bVrL6Urh6IJ2cSt7kRESYGBDkVFmZUJJwCRjn/I0GkgCEJUr9VstXtkVdqaTfHaJm51JyJKCgx0KCpatQoGrfOvTzjlK6kROS3C/hzAndFRPk8ieJaumNEhIkoGDHQoapkRbPtuljM6kQc6eo0KapUzC5TI8pVDmdFhjw4RUVJgoENRi6R3xhjlDB0AEAShXQwNVJauEt0vRERE4WGgQ1GL5ATz5ihn6Miv1Q6OgfBsRmZGh4goGTDQoahlRjA0UD7QM8Kt5ZL2MEuHPTpERMmHgQ5FLZIeHZNrjk66NrqMTns4BsLOXVdEREmHgQ5FLZIeHWmOTrq+daWr9pLRYemKiCg5MNChqMk9OmFtL5eakaPN6CS+GdnOgYFEREmHgQ5FLVOvBRDu9nLnPRlR7LoClKWrxGVSeAQEEVHy6bCBzooVK1BSUoIxY8YkeilJK1M6gyqCjE40AwOdr9XOSlfcXk5ElBQ6bKCzYMEClJaWYvv27YleStKKbmBgazM67SPQYUaHiCg5dNhAh1ov0xB+6UrKxCRzRoeHehIRJR8GOhS1iDI6ru3lGVHuupInIycwwPCcjMyMDhFRMmCgQ1HLMkQyMFCajJy8zcjKs66amdEhIkoKDHQoapH0zZhaub28PZSuPJqRrXaPwIeIiNqnqAMdi8WC/fv3w2bjT7YdVUSTkVtxqCfQ/o6AEEWgxcbyFRFRexdxoGMymXDjjTciPT0dQ4YMwdGjRwEAixYtwvLly2O+QGq/5NKV2QZRDJ7dMLXyUM92sevK62tknw4RUfsXcaBz3333Yc+ePfjiiy9gMBjk61OmTMHq1atjujhq36SMjt0hosXqCHqvqZXby9tb6QrgzisiomQQ8Xed999/H6tXr8Y555wDQRDk6yUlJTh06FBMF0ftW7pODUFwlnEazdaAW8cdDlEOdKLdXu4+AqJ9TEYGmNEhIkoGEWd0qqur0aVLF5/rRqPRI/Ch1CcIAjJ1UqYl8Dd9ZS9LtNvLpYyOxe6AxRY8exQvzOgQESWfiAOdMWPG4KOPPpI/loKbv/71rzj33HNjtzJKCplhbDFXBkEGTet6dJzPl5gAwyF6BzrM6BARtXcRl64eeeQRXHzxxSgtLYXNZsOzzz6LH374AVu3bsWGDRvisUZqx6RMS6PZGvCeZsXWcpUquqyfVq2CTqOCxeaA0WJDpwxdVM/TGjZmdIiIkk7EGZ1x48Zh8+bNMJlM6Nu3Lz799FMUFhZi69atGDVqVDzWSO2YvBsqWEanlTuuJO6G5MRkUtijQ0SUfKLaAjNs2DC8+uqrsV4LJSFpi3mwoxlaO0NHkqFX45QxcVvMfUtXzOgQEbV3EWd0Jk2ahL/97W+or6+Px3ooyWSGkdFp7QwdSYYusVvM7V490DzBnIio/Ys40Bk2bBh+//vfo2vXrrjyyivx/vvvw2KxxGNtlATcPTqhm5FjV7pKVKDjGemYEjjTh4iIwhNxoPPcc8/hxIkT+OCDD5CVlYW5c+eia9euuPnmm9mM3AGF06PTbLV53Nvq12JGh4iIwhTVWVcqlQpTp07FqlWrcPLkSfzlL3/B119/jcmTJ8d6fdTOKY+BCETK6KRpkzyjwx4dIqKk06ofsSsrK/Hmm2/i9ddfx7fffosxY8bEal2UJMI52FPaXt76jI5rOnKCMil2r5QO5+gQEbV/EWd0Ghoa8Morr+Ciiy5Cz5498eKLL2LmzJn48ccfsW3btnisMS5WrFiBkpISBmetFNbAQFfmI9rjHyQJL115nVvK7eVERO1fxD9iFxYWolOnTrj66qvx8MMPJ22gsGDBAixYsAANDQ3IyclJ9HKSVkQZnSRvRna45uioVQLsDpGlKyKiJBBxoPPBBx9gypQpUKmiau+hFBNOoOPO6CR3M7I0GTnLoMFpk5XNyERESSDiaGXq1KkMckgWTqBjilFGJyPRGR1XM3K2QQuA28uJiJJBWD9in3XWWfj888/RqVMnnHnmmUFPKd+1a1fMFkftXzg9OiZpjk4rm5EzpWbkBB8BIe00YzMyEVH7F9Z3nksvvRR6vV7+c7BAhzqWLL0zuxE0o2N1BTqt3F4uTUZuD6UrIPixF0RE1D6EFegsXbpU/vMDDzwQr7VQEpK2fJssdtgdItR+TieXSjzSvdFqL83IWVLpihkdIqJ2L+JmmzPOOAO1tbU+10+fPo0zzjgjJoui5CGVroDAGQ6paTdWzciJHhgoZXQsNges3uOSiYioXYk40Dl8+DDsdt+fZM1mM44fPx6TRVHy0GvU0Kmdf40C9ek0uwKgWDUjx7p0tb+yMayt4nJGR9Fr1GxlVoeIqD0L+0fsNWvWyH/+5JNPPGbP2O12fP755+jTp09sV0dJIdOgwSmjJWAA4s7oxKh0ZbFDFMWY9Ip9d7weM1/YhBnDu2HFtWcFvVfq0THo1FAJgEMEWix2eRcWERG1P2EHOpdddhkAQBAEzJ071+MxrVaL3r1748knn4zp4ig5ZOjVOGUEGgNmdKTt5bE5AsLuEGG2OWBoZXMzABw9ZXL+XmsKea+060qjEpCmVcNosbNPh4ionQv7O4/D4exF6NOnD7Zv346CgoK4LYqSS6ZeC6DZb++MKIpy7056a0tXikCpyWyLSaBjcZVhzbbQAYs0R0ctCEjTaWC02Fm6IiJq5yL+EbusrCwe66AklhWkd8Zsc0A69Lu1c3RUKgHpOjVMFjuMZhsKMvWtej7A2VAMAC3W0E3FUulKpRKQpnP2JTHQISJq3yJuRl60aBGee+45n+svvPACbr/99lisiZJMsKGByixPWgwyMLFuSJYCnbAyOorSVbrWuY5mlq6IiNq1iAOdd955B+PHj/e5Pm7cOLz99tsxWRQlFyn4aPQTfEg9LAatyu+MnUi5Z+nEJsCwuI4kN9tCZ3TsioyOwVWGY6BDRNS+RRzo1NbW+j3tOzs7GzU1NTFZFCWXYIP8TDFqRJZkyMdAhJfROXCyMWijsZzRCaN0JQU6akFAmtb5v46JpSsionYt4kCnX79+WLt2rc/1//73vxwY2EFJA/T8lZPcJ5e3vmwFRHYMRGOLFbNe2Iyf/2VLwHvkHh2bc8t6MNLAQLVKQLprHS3M6BARtWsR/5i9ePFiLFy4ENXV1Zg8eTIA4PPPP8eTTz6JZ555JtbroyQgZXT8bS+P1dZy79cKJ6NzpNaEZqtzZ5TN7oBG7RvXS7uuRBGw2kXoNIHLa3JGx7W9HEBYgwaJiChxIv7uM3/+fJjNZvzpT3/CQw89BADo3bs3XnzxRcyZMyfmC6T2L1iDsBSQxCyjE0EzcmV9i/xnsy1AoKPozTHb7NBpAic5PQIdqUcnjJIXERElTlQ/Zt9666249dZbUV1djbS0NGRmZsZ6XZRE5O3lLVafx6Tt16090FOSEUEzckV9s/znFqtd/lwlZaDTYnUgyxD4+fxldLi9nIiofYu4RwcAbDYb/ve//+Hdd9+V+xrKy8vR1NQU08VRcpC2l/sLPqRradpYla5czchhlIwqvDI6/ki7rpz3BA9aPAcGSruuWLoiImrPIv7uc+TIEVx88cU4evQozGYzLrroImRlZeGxxx5DS0sL/vznP8djndSOZQbdXu460DPGGZ1IS1ctATIvnqWr4GUo5fZyZnSIiJJDxBmd2267DaNHj0ZdXR3S0tLk65dffjk+//zzmC6OkoM7+PAtXUnby1t7/IMkkmbkckXpKnBGRxHohOi3sSnPutJJzcgMdIiI2rOIMzqbNm3C5s2bodPpPK4XFxfjxIkTMVsYJY+sIJOR3YFObEpX0vOE06MTXkbHfb0l3NKV6yiKYM9LRETtQ8QZHYfDAbvd9x/348ePIysrKyaLouQSbFqxKUYHekrS5d1OwTM6oiiG16NjCz+jI5euBEE+UJSTkYmI2reIA52LLrrIY16OIAhoamrC0qVLcckll8RybXG1YsUKlJSUYMyYMYleStKTmpEtdodPQ68U/MQuoyNNRg4eYNSZrB7BTaDMizWCZmTlrqt0lq6IiJJCxIHO008/jQ0bNqCkpAQtLS249tpr0bt3b5w4cQKPPvpoPNYYFwsWLEBpaSm2b9+e6KUkPeUwQO/ylZR5iVUzshQwhcqkKLeWA2FmdMJsRlZuL2fpioiofYv4x+yioiLs3r0bb7zxBnbt2gWHw4Ebb7wR1113nUdzMnUcUobDZLGjyWxDfqZefsy9vTw2gY7cBByidFVxusXj40ABidkeOusjkZI/zrOumNEhIkoGUdUT0tLSMH/+fMyfPz/W66EklanXyIGOknwEhJ9hfdFID/PU8IoGz0AnNhkd5+Oek5EZ6BARtWdhffdZs2YNpk+fDq1WizVr1gS9NzMzE4MGDUJRUVFMFkjJIdOgQVWj2ad0Fa9DPUNlUiq9S1dh7LoKHeg4f/cIdJjRISJq18IKdC677DJUVlaiS5cuuOyyy0Ler1ar8dhjj+GOO+5o7fooSWQFGOQX60M9lfNrHA4RKpX/Qzi9S1fhzdEJsb1c2YzsmvTMjA4RUfsWVjOyw+FAly5d5D8H+9XS0oK//vWveOyxx+K6cGpfAk0sNsZpezkQfO6NtLVc6qUJFOhYbcpdV6EGBjofVwkCDDrn/zrNVrt8DAoREbU/sfkxW0Gn0+HKK6/Et99+G+unpnYsM0CgE+vJyMqmZpPFHnDburTrqjg/HfsqGwMPDIwko+OKZzRqQX5dUXQGSIYYNVsTEVFsRRXo7N+/H88//zz27t0LQRAwaNAgLFy4EIMGDQIAZGVl4amnnorpQql9y/QzHVkUxZhPRlapBBi0KrRYHQH7Y5TDAvsUZGBfZWNMt5erBAEGjTsZ2myxM9AhImqnIp6j8/bbb2Po0KHYuXMnRowYgeHDh2PXrl0YNmwY3nrrrXiskZKAvx4di90hBwfpMZqjA4RuSD6tGBZYnJ8BILxDPUNuL1f06GjUKujUzv99TOzTISJqtyL+Mfvuu+/Gfffdh2XLlnlcX7p0Ke655x78/Oc/j9niKHlIPTqNioyOSTG9OD2GGY80nRowuvt/vEmHeRZk6uRzuPwd7yCKomfpKtyBgYIgr8PSHDizREREiRdxRqeyshJz5szxuX799dejsrIyJoui5COVrpSnikuBiE6jgkYd8V+1gELN0pEO8+yaY4DeVWLy17isPP4BCCPQURzqCYDTkYmIkkDE330mTpyIjRs3+lzftGkTJkyYEJNFUfLxV7pyby2Pbf9KWojSVbkr0OmWkyb3zvjL6CizOUDos66U28sB8LwrIqIkEPbAQMmsWbNwzz33YOfOnTjnnHMAAF999RXeeustPPjgg/FZJbV7cjOyR0Ynto3Ikgw5wPBfupKGBXYLkdGxeGVwWkKcXm6TAx3nx/IJ5szoEBG1W2EPDPS2cuVKrFy50uPaggULcMstt8RkYZRcpAZhjx6dGM/QkYTKpFQoSldBMzq2aDM6zkjHPR05+LlbRESUOGEFOg5H8J90ifxldKRm5FgHOqFKV9JU5KKctIgyOv6CISW5R0fwLF0xo0NE1H7FfGAgdUxZei0Az2Zkadt1rEtX0g6uQJmUygZ3RkdqMA6vRye80pXKq3TFHh0iovYrqq0wGzZswMyZM9GvXz/0798fs2bN8tugTB2Hv4GBJnN8SldpQUpXoiii/LSzR6coJ00e7Bdej054pSuNK9IJ9yR1IiJKnIgDnddffx1TpkxBeno6Fi1ahIULFyItLQ0XXngh/vWvf8VjjZQEMlwDAZssNjkgkKci62PcjKwPHOgohwV2ydZDH9Guq/BKV1JGh9vLiYjav4i/A/3pT3/yOZn8tttuw1NPPYWHHnoI1157bUwXSMlBKl2JorNklanXuJuRY3w8Qrrco+NbupIakfMzdDBo1TBonVGJv0bjSJqRHQ4R0tmdyoGBznUw0CEiaq8izuj89NNPmDlzps/1WbNmoaysLCaLouRj0Krk+TJSn468vTyGxz8A7kyKvwBDOsyzW64BAKDXhN51pXGtO9j2crvihHKpdJXG7eVERO1exIFOz5498fnnn/tc//zzz9GzZ8+YLIqSjyAI8gnm0hZz98DAGDcjB+mNkbeWZ6cBgJzR8dujY3dek4+JCJLRkY5/AHxLV+zRISJqvyL+DnTnnXdi0aJF2L17N8aNGwdBELBp0yasWrUKzz77bDzWSEkiU69BfbNV3mIuZXbSYj1HRx94e/lJeceVHoA7o2O1i7A7RDnrBAAWmzN4yTJoUefq7RFFEYIgwJtDkdGRj4Dg9nIionYv4kDn1ltvRdeuXfHkk0/i3//+NwBg8ODBWL16NS699NKYL5CSh5TRkXZeSdvLY30ERLo28GRkKZuUm6YD4M7oAM6MjXKru9SMLGV0RNEZEOk0voGOTZnR8erRYUaHiKj9iqqmcPnll+Pyyy+P9VooyXkPDXRvL49P6cpfRkcKdKTT1KWMDuDs00nXue+VenSyDVr5WovNDp3Gt6LrcCh7dDgwkIgoWcTuSGnq8DK9DvY0xasZOUig02S2OtfiCrrUKgFatavZ2KsHRwp0MhTb3wNNR1b26HifXs6MDhFR+8VAh2LGPTTQGWzIgU7Mz7pyvo6/TIoUZGUpgpdAO68srsBHr1HJR0UEakiWAh1BgNzDE+ooCiIiSjwGOhQzmTrvjE68S1e+PTpNrvO1MhWBjnuWjmegY7U7gxedR6ATIKMjSlOR3f07HBhIRNT+MdChmHH36Di/8ccvoyMFGA6PkhLgziZl+MnoeAckUjOyTq2SJygHClqk11EpdmTJW9cZ6BARtVsMdChm3D063qWrWGd03M/nXb6SS1cGRaATIKMjfazTqAJmfSRSoKPcni4HUCGOjiAiosQJ6zvQ4sWLw37Cp556KurFUHLL8jrY0126im1Gx6BVQRBcx01YbB5lKqOf0lXAjI4i0Ak2QRnwH+jIwREzOkRE7VZYgc4333wT1pP5G7RGHUeGYteVxeaQe2BiPRlZEASka9UwWuweO54cDlHO6GQaQvfoeAY6wZuRpYGBnoEOMzpERO1dWN+B1q9fH+91UApQHgGhDEBiPRnZ+ZwaGC12jx1PRkVzsmdGx38vjXQEhFatcgctATI60sBAtaAsXTmf1+4QYbU7oFWzEkxE1N7wX2aKGSmLYrTY5KBDqxb8DuBrLX87r6RsjkYlyEEI4M68+Oy6ch0BEcn2cpWfjI6/5yYiovYhqprC9u3b8dZbb+Ho0aOwWCwej7377rsxWRglnyzFERDxakSW+JuObFSUrQQ/mZegu65CbC93uC5rVL7PKz23MotERETtQ8Q/ar/55psYP348SktL8d5778FqtaK0tBTr1q1DTk5OPNYYFytWrEBJSQnGjBmT6KWkDGWPTrwakSX+piPLxz94BVeBMjr+m5H9Z3RsrkhHub1cEISAQRQREbUPEQc6Dz/8MJ5++ml8+OGH0Ol0ePbZZ7F3715cffXV6NWrVzzWGBcLFixAaWkptm/fnuilpAxlj068ZuhIpGBG2Qvkb2s5EDijE8n2cn/NyJ7PzdIVEVF7FHGgc+jQIcyYMQMAoNfrYTQaIQgC7rjjDrz00ksxXyAlDynAMNscaGh2ztKJV+lKyugoG5Clbe3eJaSAGR1X6UqrVmR0As7Rcf6u8Qp03M/NjA4RUXsUcaCTl5eHxsZGAED37t3x/fffAwBOnz4Nk8kU29VRUlFOI65qNAOIX0ZHPjncT0YnM0BGx7ssZVWWrkLMxPHXjAwg5G4tAPipugn/2HoYVjuzPkREbS3iH7cnTJiAzz77DMOGDcPVV1+N2267DevWrcNnn32GCy+8MB5rpCShdTX1mm0OVDW0AIh/oGPyE+hk6L0DneAZHWUzcqCZOHY/28udzx16aOAfP9qLdfuq0L1TGiYPKgzyVRERUaxFHOi88MILaGlxfhO77777oNVqsWnTJlxxxRVYsmRJzBdIySXLoIG5yeLO6MRpJ1K6n5PDpdJVlk/pKsCuK1dQo9e45+gEzOgE6NEJVBZTOukK+uqM1oD3EBFRfET8XSgvL0/+s0qlwt1334277747poui5JWp16BGGeho41268p2j492jEzCj43cycqDt5YECndC7rqTdYBaWroiI2lzUP25XVVWhqqoKDofnP97Dhw9v9aIoeUn9MVIWw7uMFCvuZuTQPToBMzoRNCPbAvTouA/2DBboODM5Fg4VJCJqcxF/F9q5cyfmzp2LvXv3QnSl8yWCIMBu5+6TjkzKpsS9GVkbpBk5ioxOqMyM1KPju+sq+PZyURTdGR0GOkREbS7iQOeGG27AgAED8Le//Q2FhYU8yJM8SEFGTVO8d11JPTqht5frQ2R0dGFkdOQ5Ot7NyCF6e1qsDjkbxNIVEVHbizjQKSsrw7vvvot+/frFYz2U5KQgQ0r2xe0ICH3gXVe+28vD6NHRBj/ryl268rxukEtX/oMYqWzl7/WJiCj+Ip6jc+GFF2LPnj3xWAulAO8gIxHby30HBvoPYpS7rtzbxCNrRg6ULZI0tLgzTixdERG1vYh/3H755Zcxd+5cfP/99xg6dCi0Wq3H47NmzYrZ4ij5ZOo9/z7Ea3t5mtZP6SpEj453H41cutKo5BJUoKZieY6OV0rHEKLkpczoMNAhImp7EX8X2rJlCzZt2oT//ve/Po+xGZky9Z4ZnPhvL/edoxNo15Uyo2N3iHLwolWHzui4BwZ6Xg/VxCwFXwBg4f8bRERtLuLS1aJFizB79mxUVFTA4XB4/GKQQ97ZlHR9fAKdDKlHxxr+ritlRkd5HIPH6eWBJiMHPNQz+BEQjSxdERElVMSBTm1tLe644w4UFnKUPfnKNHiVruJ2qKfnZGSLzSEHKQF7dBRBkTKg8TgCIsT28kADAwPtumLpiogosSIOdK644gqsX78+HmuhFOAdZGTEeY6OxeaAze6AUVEi8jnrSuu7M0oZdGjVQsijHBxRHgHhkdHh9nIiojYX8Y/bAwYMwH333YdNmzZh2LBhPs3IixYtitniKPl4BzppcQp0lM9rstrlspVBq4JW7d0w7PzYYnNAFEUIguDRiCwIguIIiADby+2u7eVCZEdAcNcVEVFiRbXrKjMzExs2bMCGDRs8HhMEgYFOB+fdCJwRp9KVXqOCSgAcorMh2d2fo/W9V9EQbbY5YNCq3TN0XEGRe46OOxhSkjI63pORQx0BwTk6RESJFdXAQKJA2qoZWRAEZOg0aDTbYPIIdHxfT8roAM5dVQatWm5G1rkekwIWUXSWmKSPJfYAZ125e3TYjExE1B5F3KNDFEyWIqOjVglyxiQe0uShgbaAW8sBQKNWyb01UubFO6MjBSyA/8yLzRH8CIhwMjrs0SEianthZXQWL16Mhx56CBkZGVi8eHHQe5966qmYLIySk7IROF2njutZaMrpyI0BtpZLDBoVjBa7nHkx2zwzOsqAzGx1AAbPzw84GVnercWMDhFRexRWoPPNN9/AanX+ZLpr166A37x4wCela9UQBGcJKF7HP0iUW8yNQXp0AGfmxWix+2Z0XIGK1JBstjn8NiQHmqPj3nUVxsBABjpERG0urEBHuZ38iy++iNdaKAWoVAIyXb0z8WpElmTI05EVpasAPUEGr8nHypPL5Xu0aphtDr/ZmYBzdCIZGMjSFRFRm4uogcJms0Gj0eD777+P13ooBUh9MvHaWi5J81e68tOjA/j20kjZFa2iUTnYFnO5GTnC7eUcGEhElFgRBToajQbFxcU86oGCkvp04p3RkUpjRotdkdEJULryyuhIu670ioyOcou5N3ug7eVS6SpARodzdIiIEiviLTG///3vcd999+HUqVPxWA+lAKkhON4ZHel4iWaLTdGj4/815YyO1X+PDuDeYu4vaLHbA5WuXMMI7Q65YVlittk9ghvO0SEiansR/8j93HPP4eDBgygqKkJxcTEyMjI8Ht+1a1fMFkfJSdpinhGnGToSZekq0IGeEndZytWj4yfQkcpQz687gLd2HIMgCFAJwJSSQjmj4ztHx3MYoTK4U/bnKF+TiIjaTsSBzqWXXsrdVRSUFGzE60BPibsZWdmj4790ZfDK6Jj9NCN3ztQDALYcqvX43E9LT+KykUUA/E1Gdn++9NyXr9yMshqjzxosdv9Tl4mIKH4i/k70wAMPxGEZlErcgU4itpdHltFRNiP/8fJh+PSHStjsIkSIaLY48PT/fkR9s1X+PO9mZI1aBY1KgM0hwmxzoKzWiH2VjR73jOyZi93HTjtf18/UZSIiip+wAx2TyYS77roL77//PqxWK6ZMmYLnnnsOBQUF8VwfJaG8DB0AIDfNf3YlVtzNyMrt5QEGBnpldKx+Mjrdc9Nww/g+8sc2uzPQAYA6kwWAb4+O9NxNZhtarHZYXQFRr7x0/POmsyGKQEGWDiV/+ASAM8BioENE1HbCDnSWLl2KVatW4brrroPBYMAbb7yBW2+9FW+99VY810dJaPa5xQCAX57dK66vk64oXTWF2l4eRo+ON41ahUy9Bk1mG+pMzm3i/gIdvUaFJrNz67rV1bScrlOjZ146AHg0KUfSp1NvsuK7E/UY1zffpzeIiIjCE3ag8+677+Jvf/sbfvGLXwAArr/+eowfPx52ux1qNX9CJbcendJx3yWD4/466YrSlTSvJnBGx3NGjhRw6IMEOgCQbXAGOqdDZHQA59BAi2v0gjKAUqkEubwVydDAhz4qxds7j+Pv80Zj8qDCsD+PiIjcwt5efuzYMUyYMEH+eOzYsdBoNCgvL4/LwohCSVcc6mm0OAOMrIAZHc8JxhZ76IwOAGS7ym+njK6Mjp9GYnn+jtUOi82ZvdF6HWYqvU4kGZ2K+mbX7y1hfw4REXkKO9Cx2+3Q6XQe1zQaDWw2W4DPIIovaSt3rdEiTy7OiDCjE+p0dSnQkTI6/kpI8jEQNoccQGnVnvdFE+jIB5AGGEZIREShhV26EkUR8+bNg16vl6+1tLTglltu8Zil8+6778Z2hUQBpLtKRtUNZgCAILiveQuU0fHOvHjLdm1Xtzn8T0YG3BkdZTOyzqvhWAqoIhkaKN3LQYNERNELO9CZO3euz7Xrr78+poshioSUvZFn6Og0AZt2A2Z0QpauPP8XCZbRMdscit1cATI6EfToSGsNdDI6ERGFFnag88orr8RzHUQR8z5iIlDZCvA93iHsQMdrAKG/Hh3lwZ6Ben+8S1e7jtZh8erd+P2MEkwp8d9o3B4yOqIo4jf/3IUMvQZP/HxEwtZBRBStiM+6ImovvAcSBtpaDrQmo+MZ6PgtXclBlPtsK59mZLVnoPOb13fhcK0JN722I+Brt4cenYr6Fvz3+0q8vfN4wBPaiYjaMwY6lLTStZ6BTaCt5UDgHh19yB6dMEpXckbHEbD3R++V0TFaQjfxt0Xp6u+byvDQh6UQRdHv49WNZvnP3md3ERElAwY6lLS8S1eBtpYDgTM6Wk3wQXzeGR1/cZE0R8dss8Pq2l4esHQlBVghMknO54t/6erxT/bjb5vKcLyu2e/jNU3uQEcaykhElEzie+oiURzpNCpo1YI8jTgjyCGiAefohBh26dOjo/INUJQDA0W4Ap0Qc3RCbWsH4h/oOBwiml3lqAbXwEVvyoxOEzM6RJSEmNGhpJam2E4erEdHH6NdV34HBmoU28vtATI6Xj06+gDb4CU2u0OeDWSOU2+McgdYoCDGo3Rl9h8MERG1Zwx0KKmlK7I4EfXoRLvrys/teq17e7m7Gdn/9nJzmKUrZRYnki3pp00WPPXZjzhSawx5r7K5OFDPUHUTMzpElNwY6FBSU+68iqhHx8/p5f7kePXoqMLdXu5VEpMGCPo7Y8vfbiZloBPJrqs3vj6G5z4/gBfWHQx5r/I1AjUasxmZiJIdAx1Kaul6d0ARzhwd34xOZM3IGnXg7eUtNoc8Gdm7ydm7dKXcvVXf7FsSUu60imTX1YGTjQCAI6dMIe9VBliBGo3ZjExEyY6BDiU15RbzYKUr74yONcxm5Cy9BsokTrCMjtkjoxO8GblFkaXxF+goH4+kGfmnGmfJqvy0/11UgV4jnB4dBjpElIwY6FBSSwuzdKXM6IiiGHaPjkoleARQ6hCHeloDTEaW5+jY7a51uLMpp02hMjrhBTqiKOKn6iYAQGV9i9zMHIjyNYwBghiWrogo2THQoaSm7NEJJ6MDOPtzwg10AM+GZL+Bjry93A6La46Oz2Rkr4xOs8UdZPgtXXlkdMIrXZ0yWtDgCkZsDtEjSPFHmdFp9BPoGM02GC3K8hZ3XRFR8mGgQ0lNmdEJp0cHcH6DNwfIvPij7NMJtr08aOnKq0en2SOjY/F5zmiakaWyleREiPKVMoDyV7pS9ucEuoeIqL1joENJLSPM7eVatSD32vxQXq/o0QknoxOidKXYXu5uRg4+GVkZ6IRuRg4z0HGVrSSh+nSUGR1/28u9M0Ls0SGiZMRAh5JauNvLBUGQhwte+9dtkI52CivQSQtVuvK3vTzAHB2bcxCgxRa8GTma0pV3RidUoKN8Xn/9N94ZnQZmdIgoCTHQoaQWbukKAK4/pxhdsvQoyNSjIFOHmSOKfCYf+xOqR0fZ6ByoGVlZuvKem+M/o+O56yrQoZtKP1UbXet1fk2RZHT8ZWukjI4UILJ0RUTJiGddUVILtxkZAP7vksH4v0sGR/wayqGBQbeX2+yKyciBm5Gbwwp03PeIImC1iyFn/kilq3F9C7D2h0qcON0S9H6PychBAp0+BRkorWhg6YqIklJKZHQ+/PBDDBw4EP3798fLL7+c6OVQG5KOgNCqhbBOBI+GMuvjb2Cg8lDPkHN07A6PHVcAUFHfgp+qmzyue/flhDoGwmZ34KhrSOB5/QsAhFO6Cj5HRzr+oU/nDOc9DHSIKAklfUbHZrNh8eLFWL9+PbKzs3HWWWfhiiuuQF5eXqKXRm1Ayuhk6jUQ/GRbYsGjdBXsUE9lRifQHB2bw6fn5uuyU5j85AYUZOqx8e5JSNOpfQ7yNFvtQTNWx+uaYbWLMGhVGFXcCQBQXh+qdKXo0QmS0TmjwBXosHRFREko6TM6X3/9NYYMGYLu3bsjKysLl1xyCT755JNEL4vaiBTohOrPaQ1lM7LKX4+OK6MjioDJlZUJtr282eIMhtJ1apR0y0ZOmhaC4Gz+PXLK2WfjndEJtfPqpxpn2ap3fgZ6dEoD4BxEGGgQoPdzGs02nz6g6iaL/JyAM6sUyXEURETtQcIDnS+//BIzZ85EUVERBEHA+++/73PPypUr0adPHxgMBowaNQobN26UHysvL0f37t3lj3v06IETJ060xdKpHeiUrgMA5Gfq4/YaHtvLg/ToAECDq9/GpxlZWbpyZVK65hjw8W0TsGfpVPTtnAnAOfQPiCLQcTUi9+2ciSyDVt6BVhEkq6PMGjlE+PQO1bgyOr0L0uVrnI5MRMkm4YGO0WjEiBEj8MILL/h9fPXq1bj99ttx//3345tvvsGECRMwffp0HD16FAD87kaJVwmD2p8xvfNw/yWD8eCsIXF7jVDby3VqlTyjx+YIPRlZCiik3UwAkOcK2OqMzkDJO3MSKpNS5tpa3sdVZuqe68zqBGtI9t79pSxNiaJ7snJhtgEZOu68IqLklPAenenTp2P69OkBH3/qqadw44034qabbgIAPPPMM/jkk0/w4osv4pFHHkH37t09MjjHjx/H2WefHfD5zGYzzGb3fJCGhoYYfBWUKCqVgF+df0ZcXyPU9nJBcDZCK7drB9teLjUdewQ6Gc5A55TR+XfTexpyqOnIp12ZpIJM5/N0z03DvsrGoA3J3lmiJrMNXVx/bmi2yQ3QBZl6ZBm0MFrsbEgmoqST8IxOMBaLBTt37sTUqVM9rk+dOhVbtmwBAIwdOxbff/89Tpw4gcbGRnz88ceYNm1awOd85JFHkJOTI//q2bNnXL8GSn7KXVf+Ah3AvfNKog0yMFDKpCg/p5Mc6DgDlhafjE7wQEcKnqRdaEWujE6wQMcno6MIYqqbnJmgbIMGBq0ama5SGEtXRJRs2nWgU1NTA7vdjsLCQo/rhYWFqKysBABoNBo8+eSTmDRpEs4880zcddddyM/PD/ic9913H+rr6+Vfx44di+vXQMkvVOkKcJ9gLtGrPT/216Nj8MjoOF+jznXulU9GJ0TpSmo6lgYoFsmlqwgyOoogpspVtirIcvY+STu+mNEhomST8NJVOLx7bkRR9Lg2a9YszJo1K6zn0uv10Ovj17hKqSdTp0HXbAOMFlvAYyb0Ws+fGbRew/2U28vl0pVOGeg4/07WBmpGDlG6koKnDL0U6BgARJbRUW4xr2pwBjpds53PI33dPMGciJJNuw50CgoKoFar5eyNpKqqyifLQxQvKpWANQvHw+YQPU5BV/LO6Pg0I7syPJ7NyO575IyOHOhEVroyyX0/zv+lu4eR0WnxCp6UW9FPNjhLV4WuQEfO6LB0RURJpl2XrnQ6HUaNGoXPPvvM4/pnn32GcePGJWhV1BF1yTbI5SB/lFvMBQHQqPz36Fjs7h4dZTOytE0+8Pby4KUrkytISfcqXVXWt8Du8H9OlvScUj+RsixV6Qp0umR7lq54sCcRJZuEBzpNTU3YvXs3du/eDQAoKyvD7t275e3jixcvxssvv4y///3v2Lt3L+644w4cPXoUt9xySwJXTeRJrwhatGqVT7lVCnTsDlEOKAyK0lW+q3QlBzpe2RZp4vI3R+vw3fF6n9c3WaVmZOdzdsnSQ60SYLWL8inkZTVGXPLsRqzZUw7AndGRXlvZaCyVrgqzpNKVM+PEHh0iSjYJL13t2LEDkyZNkj9evHgxAGDu3LlYtWoVrrnmGtTW1mLZsmWoqKjA0KFD8fHHH6O4uDhRSybyoTxnS6/2/flBud28odnVOOyx68oZSJwyWSCKopxt0alVronEDhjNNvzyr19Bq1Zh9x+mejRGS6WrdFfmRaNWoWu2ASdON+PE6WYUZhuwYX8VSisa8O6u45g1okh+jfxMHSobWvyWrrrmuEpXBpauiCg5JTzQmThxot+hf0q/+c1v8Jvf/KaNVkQUOeUOKu9zrgDPIyGk08r9zdGx2BwwWexy6So7TYOaJgvMNjsq6pvRYnWgxeqAyWKTsyw2u0PO+KQrnrNbjjPQqax3Bi1S2UnK3MgZHddUaWW25mSj1KPjfCyLu66IKEklvHRFlAqUgY73OVeA51wd6ZgI5a6rdJ1G7vM5ZbS4Ax1XMGO2OuRyEgCPk85Nit1TyufMcW2Lb2yx+v1dyugUuIIsKVsjiiJOul6rS5ZnRodzdIgo2XTYQGfFihUoKSnBmDFjEr0USgHK0pX31nLAOSJBKl9JGR3vIYN5ioZkKQjJcgUrZpsD1U3uQMekCHSkoEcleK5DKmMZzc7HpZKZFKyY5YyOK9BxZWvqm61yhsi7GZnby4ko2XTYQGfBggUoLS3F9u3bE70USgHKXVfeW8slUu+Ov9IVoJiObLLIQUiOHOjYPTI6Ros7syIFPRk6jUcTdKZrpo7Ue9NoljI6rtKVlNHxKl1J2ZxO6Vp5Oz0zOkSUrDpsoEMUS8o5Ov5KVwB8MjregY583lWTsnTlDDDMNgeqGt0HdHqUriyeU5ElGa7jIJosnpmcJrMNVrsDVruzN867R8d7hg7AHh0iSl4Jb0YmSgUePTp+mpGV1+WBgTr/gU6dSVG6UvToSL09gGfpSt5x5fV8UunKJJeu3J9f22SR/+xdujopz9BRBDrS9nJmdIgoyTCjQxQDyt6YUBkdicHr2AhpaGCtshk5Tcro2OXzp4BAgY7nzy0+pStFkFKteK4C1xwdKYiRXqdrtvuoFLl0xYwOESUZBjpEMWDwGhjoj3cA5NOM7MronGxogTRxQd51ZXN4BCfNVnfA0WzxnIoskQIfqZ9HOdVYOp1cqxbkYErK6Ejb0ZWlK6kZ2WJzhJzSTETUnjDQIYoBZXYmVOlKEqhHp+K0uxcnW7HrKlBGR9pV5V0Ky/TeddXiLl3VNDpLVwaNWr7PZLHD7hD9lq6ke5TPR0SUDBjoEMWA8rDPgBkd70AnQI+OdM4U4G4Cbmyxyk3MgP85Ot4ZnQxFA3GL1S5vGQcgb1XXa1VyWQpwZn9ONkrHP7hLV2qVID8/+3SIKJkw0CGKAb1HRsd3jg7gW7ry2V7u6tGpqHeeOK7XqORM0Yk6z1PIPefoOAOPDK8enQxXYGKy2Hy2hUtlML1GDb1G7T7Ys8WGKj+7rgDlwZ6pM0unutEccjI7ESU3BjpEMRBqMjLgrxnZM9CRdj9JRzPoNSo5U1ShyPIA/ufo+GwvV5SuGr2CEynQkQKpnDTna+8/2SiXyLwDnSxD224xP1xj9MhCxdoHu09gzJ/+h1e3HI7baxBR4nXYQIeTkSmWwmlGVu7MErymGAPujI58v1Yt3+OddGgOY3u5snQVLKMDADOGdQUAPPrffbA7RKgEoCDTcz2ZbbjFfPvhU5j4xBdY8v73cXuNH8obAAClFQ1xew0iSrwOG+hwMjLFksf28jCakdO0ao8pxgCQm671eU691v9zmfwODPQqXendpSvvclNNk2dG5+YL+kKjErCvshGAc1qyxitga8uhgT+cqAcA7DvZGLfXkLbdcwgiUWrrsIEOUSxFur3cuz9H+jzpyAfpOZVNzoDzPCvAf0YnI0BGx2oXccpo8XjMO6PTPTcNV5zVXX7cu2wFuHt02mKWTo1roGGd17pjyd98ISJKPQx0iGIg0u3l3v05EmnnFSD16Hg+V7ecNADuLA7gDnp8SleKDE/5ac8eHylYUa771on95ECqUDEsUNLZtQvrcI3R79qVWqx2/HPbEZSfbg55L+A8yPTYKZP8sZRximeg0+TaJs+MDlFqY6BDFAP6CM66AnwbhyWdFOUrvUblEzT1LkgH4DVHR25G9ixdqVWCHMhUunZyeVXLPAKuPgUZ+NnwIgBAj07pPmsbVdwJALDj8Cm/a1das6cc97/3PZ74ZH/IewHgmr9sxZSnNsiBjRToNLrO5YoHKVjkdnmi1MZAhygGwjm9XKd2BxX+SlcAkJfhzqRIW7+VivMzALjPywICT0YG3OWmCte04y5Znpka74zRg7OG4PYp/XHz+Wf4PNeYPnkAgO/LG+SyTyCHqpsAACfCyOg0W+w4UNUEs82Bw7XObFG14iyuOlN8sjrs0SHqGBjoEMWAx+nlYTYj+5OXocjoaH1LV73zfTM6gXZdOa85Ax1pCGH33DTPdXvP8snQ4fYpA1DkdZ/0uUU5BtgdInYfO+3x2Ae7T+DX/9ghBw3S3J9wgpTyencwJPUO1SimQNcZ4zO3R1orMzpEqY2BDlEM6D0yOgEGBip7dAKVrrx7dLx2XUkZHZPZd46O96GegLshWcroeAcw3oFUKFJW5+syz/LVi18cwic/nMS6fVUA3JmcOlPoIEXZx1Pd5BzgJ5WunM8Rn4yO9L41WWxwODg0kChVMdAhigFlRidQ8KC8bghwT75HoKP26ffp6eqdMVl9t5f7L105r0mBQ/dOwTM6oYzp7Qx0tnv16UjByk+ukpX0cZ3REnLysHLqc3WjGU1mm3x6u/Qc8SBldETR8/0kotTCQIcoBlQqQQ5KwtpeHrAZ2TOjo1GroHFthcrL0MknjUdaupJijR7eGZ0oA51vjp6Wm4SNZpt8MnqZa5qxNF3Z5hBDbkf3yOg0mj1OaQeAU3HI6Iii6NFnxPIVUepioEMUI1KZKZxDPQP36CgCHdfzSZmgLll6OXCx2BywO0SIoqjYXu5bulKeOg60vnTVv0smctK0aLba5cnCUlkMAH6qNqKivtljkvPpED02JxRb36sbzfIMHfnzwyh/Rcpsc0BZrWoyp875XUTkiYEOUYxIO6RiN0fHeY+UdemcpffI2pgsNljsDthc37H9ZYmk6ciS3HSdR5AVaelKpRIw2mubeaUi0CmrMfocQBoqI+Pdo6PszwHgM+wwFrx3WnFoIFHq6rCBDs+6olgzhMrohFG6UgY6Bq+MTucsPfQalTwLp9li95iQHKx0JclJ08iHcyqfOxJSQ7LUp1Oh2DXVZLZh9/HTHveHaiY+4VW68g504tGj4709nlvMiVJXhw10eNYVxZqUHQkUPIRTuurkL6Mjl64MEAQB6VrpDCu73J+jU6v8Bljepassg9Yj0Ik0owMAQ4qyATjLVIBnRgcANh2o8fg4WKDicIgegVJ1o1neWi4FevHYdWU0ezYfs0eHKHV12ECHKNb6dnZu/e6V7ztVGAgv0MnSa+Tt6VKAo1NkdAD3BGRloBMoQ5ThFehkG7TI9jhPK/J/AorznF/n0VMmOBwiyr0CnR2H6zw+DlZ6qmkyw2oX5aMnzDYHymqdR0H065Lp/Pw49OgYLV6lK2Z0iFKWb/ciEUXl2V+ciZoms9/jE4Dw5ugIgoBO6TpUNZrlQEfKukhTjaUSlclig82h8rjmTdmjo3EdCZFlUB4zEXlGpyjXALVKgNm1u0o6XiJLr0Gj2dk3BADZBg0aWmxBm4mPu8pW3XLS0NBsRaPZhr0VzibnAV2y8P2JhriUrrxLVczoEKUuZnSIYsSgVQcMcgBAH+L0conUpyM1IV89uifO6pWL8/oVAFAGOmFkdBQ9OlkGDQRB8CpdRf5PgEatkicsHz1lknddnX1Gvsd9Q7vnAAjejCw1IhflGuSMlTSLp39hFoD4lK5M3qUrZnSIUhYDHaI2Ek7pCnCXqKSy0/XnFOPd34yX+3eUgY7UjJzhZ2u58jkAyCWrbI9m5MgzOgBQ7CrPHak1ysdLjOvrP9AJlpFxBzppKHB93dK27/6u0lVjS+wP9mQzMlHHwUCHqI14nl4e+H+926f0x7xxvTFpYGe/j0s7qZqtNrnXJHCPjvu6lMlRlq6iyegAQM88Z6Czv7JRLk2N6+cOdFQCMKhr6IyMtBW9e26aHOBJ+nbJlHt3/D1HRX0zZv9tG9bvr4p4/dxeTtRxsEeHqI2EM0cHAEYV52FUcV7Ax9MUGR2t2pn+CNyjoyhd6X0zOtHsugKAYlegs8115lWGTo0BXbJg0KrQYnWga7a7FBXsUE5pWGBRbprHtGfA2ZOUk6ZFncmKOqMVXbIMHo+/se0oNh6owU/VRmy4ayI0Abb1+2OyMKND1FEwo0PURnRh9uiEIgU1zeGUrnTK0pVvRieaOTqAu3T1Q3k9AKBbbhpUKgG9XYeOdu+UJh9nESyjI5WuvDM6aVo1MvQauVzn7zl2HHHu7jpxuhn/23syovU3uXp0pCxXUwsnIxOlKgY6RG0k3IxOKBE1I3uUrrSu31uf0ZFKV1I/TbccZ7alb2dnX01RbppHkBLoYM/yenePjjLQKchyfm6eFCx59fnY7A7sPnZa/viVzYcjWr/Uo9M127luZnSIUhcDHaI2Em4zcihpWuUcncAnlwOeAwOz5UAnFhmdDI+PpYDhzF65AIBh3XPkIMVqF/0GEkaze+u5ctcVABRkOv+c63oO751b+yobYbLYka5TQ6MSsK3slJxdCocU6BS61s0eHaLUxUCHqI3o1e5gJFAGJhzu0pUtZEYn3Wt7ufJ3IPqMTqZeg3zFFGcpozNvXG98+NvzMG9cb6Tp1HKzs79ZOlLZKtugQZZBi86ZvoFOXobW7+fvdJWtRvfOw/Rh3QAAqyLI6khN3IXM6BClPAY6RG1Er3WfUxUoAxMOKagxKkpXgXp0dBqV3BskBTjZMcjoAO7yFQB0zXHO1dGoVRjaPUduDJb6dPxNRz7kmpcjZYe6+MnoSOUv78+X+nNGF3fC3HOLAQAffVcBh8N/icybdARE1xzn6zDQIUpdHTbQ4aGe1NYMWjXuvXgQ7po20KN8FCnPZuTgpSvA3acjzdHJz3QGD1l65wDBaBUrjrrolmvwe0+whuS9FY0AgIGubeh5GTo5EOycGbxHZ5cr0BlV3AkjeuZCrRJgsthR7XUgaCBNXqWrphZbwD4iIkpuHXZ7+YIFC7BgwQI0NDQgJycn0cuhDuLXF/Rt9XNI2ZtGs03OyAQrhaXrNKgzWeVt5YXZBjwwswSds/wHJ+EqVmR0pNKVt7wgu6b2VzoDHWnejkatQn6GDjVNFnl4oL9AqaK+GSdON0MlACN75kKrVqEo14Bjp5pxpNYkBy/BmLxKVzaHCLPN0aomcSVRFGG1ix59WUSUGPy/kCjJ9Ct07mz66lAtymqcJ4gHy+h0yda7fncHAPPG98GM4d1atQ5l6apbdprfe3LTnVmkU35m6eyrdJ5pNahrtnxNKll19i5dKXp0pP6cwd2y5TlBvVxrOXrKFNbapdJV5yy9nEWKZUPyb/65C+c88jlOx+H4CiKKDAMdoiRzVq9OOOeMPFjsDhyscva5pAfo0QGAR64YhseuHI4ze+bGdB1Sb02aVi3P6PEmZXS8v+GbLDYccQUlg7plydd/fcEZmDK4C87r7zzXS9qJVeFqXAbcp6OPLu4kX+slnahea/R4HYvNgcfW7sO3x097XJdKV1l6DTJd710s+3S+/LEap4wWlJY3xOw5iSg6DHSIktAdUwZ4fBwsozOoazauHtOzVf04/gzvkYPRxZ1w3dm9Aj53boBm5B9PNkEUgYJMnZzFAYDLz+yBl+eOkXuYzujsDGCqGs1ocA31K3Wdbj5CEbjJZ295ZXQ+/q4CK784hCUf/OBxXSpdZeg1yJSHBsYm0DGabTC6msSrGsPrGSKi+GGgQ5SEzj4j3+MQzdbs4oqWQavG27eOw+9/VhLwnjxX6cq7R2e/n7KVP9kGLQpdpTcpe3XI9Xv/Lu5MUKDS1U+u0t4PJ+rlKdJmmx1Wu7PxOEOvkWcNNZpjMx1ZGdycdB14SkSJw0CHKEndcZE7qxOsdJVI8nRkrx4d7x1XwfRznWJ+sKoJp4wW1LqyQ327uIcWyoFOrWegc9wV+NgcojxJWerPAZxndMU6o1OlCG6Y0SFKPAY6RElqTO883HReH0wZ3AX9XcFAexNoe7n3jqtg+rmOlThU1SRndbrnpnkEd71cpatao8Wj1+ZYnTvw2XXU2dsjTUXWa1TQqFVyRidWPTrK4IaBDlHitc8fA4koLMHKRu2Bv+3loij63XEViDKjIzVA9/MK7LINWnRKd550frTWhJIi5/MqS1k7DjtPWpemIksBjnywZ4wCHWW5qoqlK6KEY0aHiOJGajSuabLIQ/+qG82oM1mhEoD+haEzUX2lQKfandHxDnQAoJcrCDp6ytmX02K142SDO6Oy6+hpOByinNFJdw1SlHt0YlS6qlZkcaqZ0SFKOAY6RBQ3XXMMKOmWDbtDxAe7TwBwHsgJAL0LMsIa0CcFNcdOmeSDO/0FOsVeDcknXFvS03VqpGnVqG+24lB1k9yjIw1ezAhSutpysAYlf1iLN78+GuZXzNIVUXvDQIeI4urq0T0AAP/ecRyAe1Dg4DDKVoBzeGC2QQOH6D7jym9GxxXoHHE1JEsBT6+8dIzo6Zx+vvNInZzRkUtXenczck2TGccU5a6/by6DyWLH8+sOwh7mOVpVje5yVZPZJm9lJ6LEYKBDRHF12ZndoVOrUFrRgO+O1+Oz0pMAwmtEBgBBEOTARgo2pAZlJakhWQpwpB1XPfPSMco1XHDHkTo5cyNlcqRdVwermjDt6S8x9ekvcbzOhMYWK778sQaAMzu06WBNWOtVlssAoKqBWR2iRGKgQ0RxlZuuw9QhhQCA376xC9sP1yFNq8blZ3UP+zmUGZz8DJ28bV3Ju3R1rM5ZuurZKR2ji/MAODM68onvco+Oc9bP1p9qUWu0oNlqxyubD2PdvipY7A75+f+9/VhYa5UakFWuGYosXxElFgMdIoq7a8b0BAAcdpWVFl80AD06pQf7FA/KQMdf2QpwZ3RO1DXDZnfIM3V65aXhrF6doBKAshqjfCyD1KMjZXQAQKt2Ridvfn0U/97hDGwuHNQFAPBpaSVqQ5yO3mK1o8HV1CytU1nKioW3dhzD12WnYvqcRKmMgQ4Rxd34vgXonus8+HNIUTZuGN87os8PJ9ApzDJAp1HB5hBRUd8iz9DpmZeOnHQtzuvfGQDwwR5nU3SG1/ZyAHjuF2diYGEWjBY7Nh+sBQAsnjoAw7rnwGoXsWrLYRytNaHFaoc/0i4rnUYlT26OZenqu+P1uOvtb/HbN3bF7DmJUl2HDXRWrFiBkpISjBkzJtFLIUp5KpWAe6YPwogeOXjy6hHQqCP7p6dfZ3c/T6BAR6US0Me1xXzLoRq5qVg6Zf1KV6msxeosR0mlq7P75OH8AZ3x+xmDMX1YN9w0oY/8nMX56Sjp5jwrDACeX3cQ5z++HuOWr0NFvfugUYk0Q6dLll4+kDSWpatvjtW5XsccMrtERE4dNtBZsGABSktLsX379kQvhahDmDWiCB8sPC+sIYHeundKg17j/OcqUKADAFeNcu7wevqzA3IJqUcnZyZp2pCu8g4rwJ3RSddp8Nr8sbhpwhnOdY4sQhdXkDJ9aDcIgoArzuyO8/oVoEuWHjq1CqeMFjy2dr/P60tBTWG2AV2ypUAndqWr70/Uy3+WZgoRUXAdNtAhouShVgmYN643zuqVKzcW+3PdOb2Qn6FDpSuzUpCpl4+KMGjV+NmIbvK9mXr/g+H1GjX+dPkwTBzYWS6xZeg1eP2ms/H1/VPw9q3nAgDe++aEfH6WpEqR0emSZQAQ26GB351okP98gIEOUVgY6BBRUrjvksF49zfjkRbkpPZ0nQa/vuAM+eOeeWkej0sZH+neQC4qKcSqG8aiMNvg89jwHrm48izn8yz7zw8QRfd8HSmj4wx0XBmdGPXotFjtOHCyUf6YGR2i8DDQIaKUcv05xch3bT/v6bWz66xenXBGgbOPpyDTd4t6uO6+eCDStGrsOnoa//m2Qr4uBzpxKF3tr2yETTG0kIEOUXgY6BBRSknXafC7aQMBAOf1K/B4TBAEvHj9KCz5WQnOd+3CikZhtgG3TuwLAFj+8V55F9ZJP6WrOpMVFpvD/xNF4HvX8Red0p1zfw5UNQa7nYhcGOgQUcr55dhe+O6BqfJuKaWBXbNw43l9oJIm+kXpVxPOQFGOAeX1LXh5408A3P04XbIN6JSulefyVMdgh5TUiDxjuLPP6GSDGQ0tVhjNNmw8UO1RQiMiNwY6RJSSsgzauD5/mk6Ne6YPAgCs/OIQTja0KHZd6SEIAjpnSn06rS9ffe9qRB7XtwBdXb1DB6uacO+732H2377GvyI4eJSoI2GgQ0QUpVkjinBmr1yYLHZc+sJmnDJaAEAuW3V2BSTvfXMCc/7+Nf62qcxv5sUR4sBQi82B/a5T34cW5chb7DcfqMHH3zl7hKRDU4M5XmfC5Ss3440Ig6LTJgu++qmWWSNKSgx0iIiiJAgCHrp0KPIUW9r1GpXcRyPtvHpt6xF8+WM1HvqwFIv/vUfu6alpMuPut/egZOlaLHn/e/nAUW8/nmyExe5AtkGDnnlpcqDzly9/kg863XPsNA7XGIOu96Uvf8I3R0/j0bX7Ak539naougmXPLsRv3jpK9z7zndhn+JO1F4E3l9JREQhDe2egy33TsbGAzVYt68Ko4o7QRCcvTl9XDu8MvUaXFRSiDV7yvHeNyew4cdqFOen41BVkzzY8B9fHcG6fVVYMKkfJvQvkCc6myw2rNpyWH4tQRDQv9AZ6EiBUZZeg0azDR/sLsdtU/r7XafJYsN7u5zHX5w2WfHJD5W4dGTwg1W/P1GPuX//GrWuTNXqHcfQZLHh6atHQqfhz8mUHBjoEBG1kkGrxkUlhbiopNDj+sLJ/TC0ew4m9CtApwwdfj6qBxb8axdOGS1ymWtIUTauP6cYK9YfxPG6Zvzfe98BcPb59O2ciSO1Jpw47TxuYtaIIgBAv87u6dCd0rW4++JBuO/d7/DB7hNYdGE/OdBS+nBPBRoVGaM3vj4aNNA5bbJgzt+/ximjBUO7Z+P6s4ux5IPv8dG3FSjKMeD+GSVRvltEbYuBDhFRnGQbtHJwAgDj+hVg872TcbCqCSfqmqFVqzBpUBeoVQJmjSjCK5vLsH5/NfYcO42TDWacdA0b7J6bhmWXDsGFg52BVP9C99lfPx/dEzNHFOGBNT/gpxojvj/RgGE9cnzW8k9XX86cc4vxj6+O4KufTqGsxihnnby9sO4gThkt6N8lE//61TnINmiRZdBiwb924fWvjuI3E/uhU0b0s4iI2goDHSKiNpSu02B4j1wM75HrcT1Dr8HCyf2xcHJ/NJlt+PFkIw5VNcHuEDFrZJHHJOe8DB36ds7A8bpmXDu2FzL1GkwpKcRH31bg0hWboFGpkJehQ++CdPQpyER+hg57jp2GVi1g0YX9ceyUCev3V+OlL3/CzBHdUGe0os5kgdXuwCXDusFsdeDVrYcBAL//WQmyXTvYLhnWFYO7ZWNvRQNe/+oIfnuh/zKZt8YWKyrrW9CnICPiA12JWksQO3gbfUNDA3JyclBfX4/s7MgPGyQiSoSqhhYYLXY5I7Ptp1pc+/K2oM3CM4Z3w4prz8InP1Ti1//Y6feedJ0avfLSsa+yERP6F+AfN57t8fgHu0/gtjd3Iz9Dh833ToZBG/hIDgCob7bishWbUVZjhF6jwpm9cvHHy4bJDdXStyB/5TaiYML9/s1Ah4EOEaWIxhYrjGY77KKIqoYWlNUY5V8NLTYsnVmCvp0zYbU7cMMr27G3ogGdMnTolK5Fp3TnzrFvjzsHEwoC8PGiCRjczfPfRavdgYmPf4ETp5vx28n9cPHQrmix2rG3ohFVjWb0KUjHoK7ZGOgqr938jx34394qj+fo2zkDH/52Ak6ZLLjp1R3I0mvw8rzRcuaIKBwMdMLEQIeIyEkURazZU46/bSrD1JJCLJzsvzT1yuYyPPif0qDP1T03DYO7ZeN/e09Cp1bhrVvORZpOjetf3oaqRjNmjSjCt8dP43CtCQAwoX8B/j5vDLSu0tYX+6vw4H9KMaF/AR6YOaTVk6wp9TDQCRMDHSKiyLRY7bj3nW+xr7IRtUYL1IKAQd2yUJhlwE81TSgtb4DR4p7T88gVw/DLsb0AAJsP1uD6v22D9J2nKMeAOpMVzVY7rjizO2aNLMKuo6fx/LoD8j3zxvXG0pklsNpF7DxSh00Hq3Gwqgk/G16Enw3vBrPNgb9++ROazDbccdGAkOU0Sg0MdMLEQIeIKLZarHb8b+9JfLinAoO7ZftseV/+333484ZDKMzW49+/Phc/nmzCzf/YAe/vRhcM6IwNP1YDAEb0zMWBk40wWTwHHV5UUoiDVU0ocw1LPLNXLl6eMxr5ruM3JKIo4seTTRAEYIBi1xolLwY6IaxYsQIrVqyA3W7Hjz/+yECHiKiN2B0iPt97EiN75qKL65iMj76twBtfH0Wt0QKHQ8Svzj8DV43qgVe3HMbSNT/In1uQqcOE/p3RKV2H17Yehs3VfN0lSw+zzYH6ZiuKcgyY0L8zinLTYLHbUdtkwZZDtTh6ylkmu+LM7lg6cwhyXBOs7Q4RZTVNyE7Tysd3UPvHQCdMzOgQEbVv/9lTjsr6FozvV4BBXbPkfp3vT9Rj+X/34YzOGbhz6kBUN5oxf9V2OaDxpteoYLU74BCdW/R7uaZPHzjZCKPFDpUATC3pip+NcJ4Q7xCBwV2z0LdzpkePUIvVjharHbnpnCOUSAx0wsRAh4godTS0WPG/0pM4UmtCZX0L0nRq5KRpMahrFi4Y2Bl7Kxpx11t78JPXuWAGrQotVoff58wyaNA9Nw05aVqcNllxsNo532hsnzxcfmZ39CnIQIZOg/0nG/F1WS0OVRtR3WiGIAC3XtAXV4/uyWbqOGCgEyYGOkREHUuL1Y6dR+pgsthhszvQp3MG+nfJwqHqJry29TC+P9EAnUYFu0NEaXkDmsM8ADWQsb3zMHlwF2ToNWhssaL8dDM0KhUuGNAZ5/bNZ/N0lBjohImBDhERBWK1O3DgZBOqm8yob7YiQ6dGSVE2RBF4f/cJrNtbhVMmC5pabOjeKQ1n98nH8B45KMzW45ujp/Hkpz8GDZR0ahUKMnXIy9QhL0OP/AwdMvRqaFQqZBk06NEpDd1y0uAQRZhtDucvqx3ZaVqUdMtGj05pHXbYIgOdMDHQISKieDl2yoTXtx1BTaMFTWYrMvTOMtgpowWf761CZUNLq54/xxXwDO6WjfxMHbIMGucvvRZZBg0yDRp0StehMNsAdYqVzxjohImBDhERJYIoijhe14xaowV1RgtqjRacMpphsthhtTtw2mTFsbpmnKxvgUYtQK9RQa9RQ6dRoarRjAMnG+VdZ6Fo1QK656YhL0OH7DQtsg1a5KRpkZuuRecsPfIydEjTqmHQqlGQqUe3XAOy9Jp2nS0K9/s3D/UkIiJKAEEQ0DMvHT1du78iZbbZceBkE0orGnDgZCPqm61obLG5flnRaHb++bTJAqtdxOFakzyJOhxatYAsgyszpHdlilwfZ+mdf85N16IgU4/8TJ38e166rl0d3spAh4iIKAnpNWoM7Z6Dod1zgt5nd4ioqG/G8bpmnDZZ0dBiRUOz89cpkwU1jRacMlpgttlhsthR1ejsR7LaRZwyOh+LhCAAndLdZ6jlpuvwyBXD0DlLH/qT44CBDhERUQpTqwT06JSOHp3CzxyZLDbUmaxokrJDLTZXhsj5Z+l6ncmKmiYzapssqGky45TJAlGEIkBybuN/XDU8Tl9daAx0iIiIyEO6ToN0XeQhgt3hzALVNJlRZ7LgtMmK0yYrstMSdzI9Ax0iIiKKCbVKQOcsfcLKVP60n24hIiIiohhjoENEREQpi4EOERERpSwGOkRERJSyGOgQERFRymKgQ0RERCmLgQ4RERGlLAY6RERElLIY6BAREVHKYqBDREREKYuBDhEREaUsBjpERESUshjoEBERUcrqsKeXr1ixAitWrIDNZgMANDQ0JHhFREREFC7p+7YoikHvE8RQd6S448ePo2fPnoleBhEREUXh2LFj6NGjR8DHO3yg43A4UF5ejqysLIwdOxbbt2/3uWfMmDE+10Nda2hoQM+ePXHs2DFkZ2fH7wsIsp54fn6o+6N9nO915PcGuyeS99nf9US+z8HWGY/Pb8v3uiP/nQ7nfv77EZvPT/V/P0RRRGNjI4qKiqBSBe7E6bClK4lKpZIjQbVa7fc/gL/r4V7Lzs5uk/95Aq09Xp8f6v5oH+d7Hfm9we6J5H32dz2R73Og14/X57fle92R/06Hcz///YjN53eEfz9ycnJC3sNmZIUFCxaEfT3ca22lta8d6eeHuj/ax/leR35vsHsieZ/9XU/k+xyL12+v73VH/jsdzv389yM2n9/R//2QdPjSVbw0NDQgJycH9fX1bfbTb0fF97pt8H1uO3yv2w7f67aRyPeZGZ040ev1WLp0KfR6faKXkvL4XrcNvs9th+912+F73TYS+T4zo0NEREQpixkdIiIiSlkMdIiIiChlMdAhIiKilMVAh4iIiFIWAx0iIiJKWQx0EuDDDz/EwIED0b9/f7z88suJXk5Ku/zyy9GpUydcddVViV5KSjt27BgmTpyIkpISDB8+HG+99Vail5SSGhsbMWbMGIwcORLDhg3DX//610QvKeWZTCYUFxfjd7/7XaKXktI0Gg1GjhyJkSNH4qabborpc3N7eRuz2WwoKSnB+vXrkZ2djbPOOgvbtm1DXl5eopeWktavX4+mpia8+uqrePvttxO9nJRVUVGBkydPYuTIkaiqqsJZZ52F/fv3IyMjI9FLSyl2ux1msxnp6ekwmUwYOnQotm/fjvz8/EQvLWXdf//9OHDgAHr16oUnnngi0ctJWQUFBaipqYnLczOj08a+/vprDBkyBN27d0dWVhYuueQSfPLJJ4leVsqaNGkSsrKyEr2MlNetWzeMHDkSANClSxfk5eXh1KlTiV1UClKr1UhPTwcAtLS0wG63gz+rxs+BAwewb98+XHLJJYleCrUCA50Iffnll5g5cyaKioogCALef/99n3tWrlyJPn36wGAwYNSoUdi4caP8WHl5Obp37y5/3KNHD5w4caItlp50WvteU/hi+V7v2LEDDocDPXv2jPOqk08s3ufTp09jxIgR6NGjB+6++24UFBS00eqTSyze69/97nd45JFH2mjFySsW73VDQwNGjRqF8847Dxs2bIjp+hjoRMhoNGLEiBF44YUX/D6+evVq3H777bj//vvxzTffYMKECZg+fTqOHj0KAH5/+hIEIa5rTlatfa8pfLF6r2trazFnzhy89NJLbbHspBOL9zk3Nxd79uxBWVkZ/vWvf+HkyZNttfyk0tr3+oMPPsCAAQMwYMCAtlx2UorF3+vDhw9j586d+POf/4w5c+agoaEhdgsUKWoAxPfee8/j2tixY8VbbrnF49qgQYPEe++9VxRFUdy8ebN42WWXyY8tWrRI/Oc//xn3tSa7aN5ryfr168Urr7wy3ktMGdG+1y0tLeKECRPE1157rS2WmfRa83dacsstt4j//ve/47XElBHNe33vvfeKPXr0EIuLi8X8/HwxOztbfPDBB9tqyUkrFn+vL774YnH79u0xWxMzOjFksViwc+dOTJ061eP61KlTsWXLFgDA2LFj8f333+PEiRNobGzExx9/jGnTpiViuUktnPeaYiOc91oURcybNw+TJ0/G7NmzE7HMpBfO+3zy5En5J92GhgZ8+eWXGDhwYJuvNdmF814/8sgjOHbsGA4fPownnngCv/rVr/CHP/whEctNauG813V1dTCbzQCA48ePo7S0FGeccUbM1qCJ2TMRampqYLfbUVhY6HG9sLAQlZWVAJxb6J588klMmjQJDocDd999N3dMRCGc9xoApk2bhl27dsFoNKJHjx547733MGbMmLZeblIL573evHkzVq9ejeHDh8v1+X/84x8YNmxYWy83aYXzPh8/fhw33ngjRFGEKIpYuHAhhg8fnojlJrVw//2g1gvnvd67dy9+/etfQ6VSQRAEPPvsszHdicxAJw68e25EUfS4NmvWLMyaNautl5WSQr3X3NEWO8He6/POOw8OhyMRy0o5wd7nUaNGYffu3QlYVWoK9e+HZN68eW20otQV7L0eN24cvvvuu7i9NktXMVRQUAC1Wu3zE0FVVZVPNEutw/e67fC9bht8n9sO3+u20x7eawY6MaTT6TBq1Ch89tlnHtc/++wzjBs3LkGrSk18r9sO3+u2wfe57fC9bjvt4b1m6SpCTU1NOHjwoPxxWVkZdu/ejby8PPTq1QuLFy/G7NmzMXr0aJx77rl46aWXcPToUdxyyy0JXHVy4nvddvhetw2+z22H73Xbaffvdcz2b3UQ69evFwH4/Jo7d658z4oVK8Ti4mJRp9OJZ511lrhhw4bELTiJ8b1uO3yv2wbf57bD97rttPf3mmddERERUcpijw4RERGlLAY6RERElLIY6BAREVHKYqBDREREKYuBDhEREaUsBjpERESUshjoEBERUcpioENEREQpi4EOERERpSwGOkTUaocPH4YgCNi9e3eilyLbt28fzjnnHBgMBowcOdLvPRMnTsTtt9/epusKhyAIeP/99xO9DKKUwECHKAXMmzcPgiBg+fLlHtfff/99CIKQoFUl1tKlS5GRkYH9+/fj888/93vPu+++i4ceekj+uHfv3njmmWfaaIXAAw884DcIq6iowPTp09tsHUSpjIEOUYowGAx49NFHUVdXl+ilxIzFYon6cw8dOoTzzjsPxcXFyM/P93tPXl4esrKyon6NQFqzbgDo2rUr9Hp9jFZD1LEx0CFKEVOmTEHXrl3xyCOPBLzHXwbhmWeeQe/eveWP582bh8suuwwPP/wwCgsLkZubiwcffBA2mw133XUX8vLy0KNHD/z973/3ef59+/Zh3LhxMBgMGDJkCL744guPx0tLS3HJJZcgMzMThYWFmD17NmpqauTHJ06ciIULF2Lx4sUoKCjARRdd5PfrcDgcWLZsGXr06AG9Xo+RI0di7dq18uOCIGDnzp1YtmwZBEHAAw884Pd5lKWriRMn4siRI7jjjjsgCIJHJmzLli04//zzkZaWhp49e2LRokUwGo3y471798Yf//hHzJs3Dzk5OfjVr34FALjnnnswYMAApKen44wzzsCSJUtgtVoBAKtWrcKDDz6IPXv2yK+3atUqef3K0tV3332HyZMnIy0tDfn5+bj55pvR1NTk89/siSeeQLdu3ZCfn48FCxbIrwUAK1euRP/+/WEwGFBYWIirrrrK73tClGoY6BClCLVajYcffhjPP/88jh8/3qrnWrduHcrLy/Hll1/iqaeewgMPPICf/exn6NSpE7Zt24ZbbrkFt9xyC44dO+bxeXfddRfuvPNOfPPNNxg3bhxmzZqF2tpaAM5yzAUXXICRI0dix44dWLt2LU6ePImrr77a4zleffVVaDQabN68GX/5y1/8ru/ZZ5/Fk08+iSeeeALffvstpk2bhlmzZuHAgQPyaw0ZMgR33nknKioq8Lvf/S7k1/zuu++iR48eWLZsGSoqKlBRUQHAGWRMmzYNV1xxBb799lusXr0amzZtwsKFCz0+//HHH8fQoUOxc+dOLFmyBACQlZWFVatWobS0FM8++yz++te/4umnnwYAXHPNNbjzzjsxZMgQ+fWuueYan3WZTCZcfPHF6NSpE7Zv34633noL//vf/3xef/369Th06BDWr1+PV199FatWrZIDpx07dmDRokVYtmwZ9u/fj7Vr1+L8888P+Z4QpQSRiJLe3LlzxUsvvVQURVE855xzxPnz54uiKIrvvfeeqPzffOnSpeKIESM8Pvfpp58Wi4uLPZ6ruLhYtNvt8rWBAweKEyZMkD+22WxiRkaG+MYbb4iiKIplZWUiAHH58uXyPVarVezRo4f46KOPiqIoikuWLBGnTp3q8drHjh0TAYj79+8XRVEUL7jgAnHkyJEhv96ioiLxT3/6k8e1MWPGiL/5zW/kj0eMGCEuXbo06PNccMEF4m233SZ/XFxcLD799NMe98yePVu8+eabPa5t3LhRVKlUYnNzs/x5l112Wch1P/bYY+KoUaPkj/399xBFUQQgvvfee6IoiuJLL70kdurUSWxqapIf/+ijj0SVSiVWVlaKouj+b2az2eR7fv7zn4vXXHONKIqi+M4774jZ2dliQ0NDyDUSpRpmdIhSzKOPPopXX30VpaWlUT/HkCFDoFK5/3koLCzEsGHD5I/VajXy8/NRVVXl8Xnnnnuu/GeNRoPRo0dj7969AICdO3di/fr1yMzMlH8NGjQIgLOfRjJ69Oiga2toaEB5eTnGjx/vcX38+PHya8XSzp07sWrVKo91T5s2DQ6HA2VlZUHX/fbbb+O8885D165dkZmZiSVLluDo0aMRvf7evXsxYsQIZGRkyNfGjx8Ph8OB/fv3y9eGDBkCtVotf9ytWzf5v89FF12E4uJinHHGGZg9ezb++c9/wmQyRbQOomTFQIcoxZx//vmYNm0a/u///s/nMZVKBVEUPa4p+zgkWq3W42NBEPxeczgcIdcj9bo4HA7MnDkTu3fv9vh14MABjzKK8ht6OM8rEUUxLjvMHA4Hfv3rX3usec+ePThw4AD69u0r3+e97q+++gq/+MUvMH36dHz44Yf45ptvcP/990fcqBzs61JeD/bfJysrC7t27cIbb7yBbt264Q9/+ANGjBiB06dPR7QWomSkSfQCiCj2li9fjpEjR2LAgAEe1zt37ozKykqPb56xnH3z1VdfyUGLzWbDzp075V6Ss846C++88w569+4NjSb6f3qys7NRVFSETZs2eQRIW7ZswdixY1u1fp1OB7vd7nHtrLPOwg8//IB+/fpF9FybN29GcXEx7r//fvnakSNHQr6et5KSErz66qswGo1yMLV582aoVCqf/77BaDQaTJkyBVOmTMHSpUuRm5uLdevW4YorrojgqyJKPszoEKWgYcOG4brrrsPzzz/vcX3ixImorq7GY489hkOHDmHFihX473//G7PXXbFiBd577z3s27cPCxYsQF1dHebPnw8AWLBgAU6dOoVf/vKX+Prrr/HTTz/h008/xfz580N+s/d211134dFHH8Xq1auxf/9+3Hvvvdi9ezduu+22Vq2/d+/e+PLLL3HixAl5N9g999yDrVu3YsGCBXIGas2aNfjtb38b9Ln69euHo0eP4s0338ShQ4fw3HPP4b333vN5vbKyMuzevRs1NTUwm80+z3PdddfBYDBg7ty5+P7777F+/Xr89re/xezZs1FYWBjW1/Xhhx/iueeew+7du3HkyBG89tprcDgcGDhwYJjvDFHyYqBDlKIeeughnzLV4MGDsXLlSqxYsQIjRozA119/HdaOpHAtX74cjz76KEaMGIGNGzfigw8+QEFBAQCgqKgImzdvht1ux7Rp0zB06FDcdtttyMnJ8egHCseiRYtw55134s4778SwYcOwdu1arFmzBv3792/V+pctW4bDhw+jb9++6Ny5MwBg+PDh2LBhAw4cOIAJEybgzDPPxJIlS9CtW7egz3XppZfijjvuwMKFCzFy5Ehs2bJF3o0lufLKK3HxxRdj0qRJ6Ny5M9544w2f50lPT8cnn3yCU6dOYcyYMbjqqqtw4YUX4oUXXgj768rNzcW7776LyZMnY/Dgwfjzn/+MN954A0OGDAn7OYiSlSB6/0tIRERElCKY0SEiIqKUxUCHiIiIUhYDHSIiIkpZDHSIiIgoZTHQISIiopTFQIeIiIhSFgMdIiIiSlkMdIiIiChlMdAhIiKilMVAh4iIiFIWAx0iIiJKWf8PzUma+0aLTwMAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def sgd_for_svm_demo(y, X):\n",
     "    xs = np.unique(np.round(np.logspace(0, 5, 201)))[:-1]\n",
@@ -248,7 +313,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "## Coordinate Descent (Ascent) for SVM"
@@ -256,7 +320,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "Compute the closed-form update for the n-th variable alpha, in the dual optimization problem, given alpha and the current corresponding w"
@@ -264,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -297,21 +360,44 @@
     "\n",
     "    ####################################\n",
     "    ### ___ Enter your code here ___ ###\n",
+    "    g = 1 - y_n * x_n.dot(w)\n",
+    "    alpha[n] = min(max(old_alpha_n + lambda_ * g / np.dot(x_n.T, x_n), 0), 1)\n",
+    "    # compute the corresponding update on the primal vector w\n",
+    "    w += 1/lambda_ * (alpha[n] - old_alpha_n) * y_n * x_n\n",
+    "\n",
+    "    return w, alpha\n",
     "    ####################################"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "❌ The are some issues with your implementation of `calculate_coordinate_update`:\n",
+      "**********************************************************************\n",
+      "File \"__main__\", line 20, in calculate_coordinate_update\n",
+      "Failed example:\n",
+      "    calculate_coordinate_update(y_test, x_test, 1, alpha_test, w_test, 0)\n",
+      "Expected:\n",
+      "    (array([-0.1,  0.1,  0.3]), array([0.5, 0.1]))\n",
+      "Got:\n",
+      "    (array([-0.1,  0.1,  0.3]), array([0.3, 0.1]))\n",
+      "**********************************************************************\n"
+     ]
+    }
+   ],
    "source": [
     "test(calculate_coordinate_update)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -344,18 +430,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "❌ The are some issues with your implementation of `calculate_dual_objective`:\n",
+      "**********************************************************************\n",
+      "File \"__main__\", line 18, in calculate_dual_objective\n",
+      "Failed example:\n",
+      "    calculate_dual_objective(y_test, x_test, w_test, alpha_test, 1)\n",
+      "Expected:\n",
+      "    -0.035\n",
+      "Got nothing\n",
+      "**********************************************************************\n"
+     ]
+    }
+   ],
    "source": [
     "test(calculate_dual_objective)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final training accuracy = 92.09 %\n",
+      "Training time: 2.7 seconds \n"
+     ]
+    }
+   ],
    "source": [
     "# Notice that the gap is going to 0\n",
     "def coordinate_descent_for_svm_demo(y, X):\n",
@@ -406,7 +527,6 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
    "source": [
     "#### The gap between the primal cost and the dual cost should go to 0 !"
@@ -415,7 +535,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "torch",
+   "display_name": "ada",
    "language": "python",
    "name": "python3"
   },
@@ -429,7 +549,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,
diff --git a/projects/project1/clean_data_testing.py b/projects/project1/clean_data_testing.py
new file mode 100644
index 000000000..212f5d675
--- /dev/null
+++ b/projects/project1/clean_data_testing.py
@@ -0,0 +1,20 @@
+import numpy as np
+from poly import *
+from utils import apply_pca_given_components, build_poly
+from normalization import normalize_data
+
+
+def clean_test_data(x_te, labels, features, median_and_most_probable_class, mean, W, degree) :
+    
+    mask = [feature in features.keys() for feature in labels]
+    x_te = x_te[:, mask]
+    
+    for feature in features :
+        nan = median_and_most_probable_class[feature]
+        x_te[:, features[feature]] = np.nan_to_num(x_te[:,features[feature]], nan = median_and_most_probable_class[feature])
+
+    x_te = normalize_data(x_te)
+    x_te = apply_pca_given_components(x_te, mean, W)
+    poly_x_te = build_poly(x_te, degree)
+    return poly_x_te
+
diff --git a/projects/project1/cleaning_data.py b/projects/project1/cleaning_data.py
new file mode 100644
index 000000000..08bbbd107
--- /dev/null
+++ b/projects/project1/cleaning_data.py
@@ -0,0 +1,86 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from helpers import *
+from utils import remove_features, find_key_by_value
+from poly import *
+#Defining some constants
+import numpy as np
+import matplotlib.pyplot as plt
+from helpers import *
+from utils import remove_features, find_key_by_value, create_pca, upsample_class_1_to_percentage, build_poly
+from normalization import z_score_normalization, min_max_normalization, normalize_data
+
+from config import dictionary_features, category_features
+
+
+#Defining some constants
+
+median_and_most_probable_class = {}
+ACCEPTABLE_NAN_PERCENTAGE = 0.1
+ACCEPTABLE_NAN_ROW_PERCENTAGE = 0.4
+
+def clean_train_data(x_train,y_train, labels, up_sampling_percentage, degree):
+    """
+    Cleaning data
+    :param x_train: training data
+    :return: cleaned data
+    """
+    y_train[y_train == -1] = 0
+    x_train, y_train = upsample_class_1_to_percentage(x_train, y_train, up_sampling_percentage)
+    
+    #Removing the first label which is the id
+    features_number = x_train.shape[1]
+    features = list(dict.fromkeys(labels))  
+    features = [features[i]  for i in range(features_number)]
+    features = {word: index for index, word in enumerate(features)}
+    
+    #We handle the date and rescale some of the features
+    #x_train = handling_data(x_train, features)
+
+    #Removing columns with more than ACCEPTABLE_NAN_PERCENTAGE of NaN values
+    mask_nan_columns = [(np.count_nonzero(np.isnan(x_train[:, i]))/x_train.shape[0]) <= ACCEPTABLE_NAN_PERCENTAGE for i in range (features_number)]
+    x_train = x_train[:, mask_nan_columns]
+
+    #Creating features list
+    features = list(dict.fromkeys(labels))  
+    features = [features[i]  for i in range (features_number) if mask_nan_columns[i]]
+    features = {word: index for index, word in enumerate(features)}
+   
+    
+    
+    #We remove the features that are not useful
+    
+    x_train = handle_nan(x_train, features)
+    
+    #normalize the data
+    x_train = normalize_data(x_train)
+    x_train, W, mean = create_pca(x_train)
+    poly_x = build_poly(x_train, degree)
+
+    return poly_x, y_train,  features, median_and_most_probable_class, W, mean
+
+
+def handle_nan(x_train, features) :
+    
+    # Replace NaN in categorical features with the median value
+    for feature in features:
+        
+        median_value = np.nanmedian(x_train[:, features[feature]])
+        median_and_most_probable_class[feature] = median_value
+        x_train[: ,features[feature]] = np.nan_to_num(x_train[:,features[feature]], nan = median_value)
+        
+    return x_train
+
+def drop_na_row(x, y) :
+    x = x.copy()
+    y = y.copy()
+    mask_nan_rows = [(np.count_nonzero(np.isnan(x[i, :]))/x.shape[1]) <= ACCEPTABLE_NAN_ROW_PERCENTAGE for i in range(x.shape[0])]
+
+    x = x[mask_nan_rows,:]
+    y = y[mask_nan_rows]
+    return x,y
+
+        
+
+
+
diff --git a/projects/project1/config.py b/projects/project1/config.py
new file mode 100644
index 000000000..dbede883e
--- /dev/null
+++ b/projects/project1/config.py
@@ -0,0 +1,80 @@
+dictionary_features = {
+    **dict.fromkeys(['_STATE', 'FMONTH', 'IDATE', 'IMONTH', 'IDAY', 'IYEAR',
+                     'DISPCODE', 'SEQNO', '_PSU', 'SEX', 'QSTVER', '_STSTR',
+                     '_STRWT', '_RAWRAKE', '_WT2RAKE', '_DUALUSE', '_LLCPWT',
+                     '_DRDXAR1', '_RACE_G1', '_AGE80', '_AGE_G', 'HTIN4', 'HTM4', '_BMI5', '_BMI5CAT',
+                     'FTJUDA1_', 'BEANDAY_', 'GRENDAY_', 'ORNGDAY_', 'VEGEDA1_', '_MISFRTN', '_MISVEGN',
+                     '_FRTRESP', '_VEGRESP', '_FRUTSUM', '_FRUTSUM', '_FRT16', '_VEG23', '_FRUITEX',
+                     '_VEGETEX', 'QSTLANG', 'FRUTDA1_', '_VEGESUM'
+                   
+                    ], {}),
+    
+    **dict.fromkeys(['ALCDAY5', 'STRENGTH'], {'dont_know_not_sure' : 777, 'none' : 888, 'refused' : 999}),
+    
+    **dict.fromkeys(['PHYSHLTH', 'MENTHLTH'], {'dont_know_not_sure' : 77, 'none' : 88, 'refused' : 99}),
+    
+    **dict.fromkeys(['CHILDREN'], {'none' : 88, 'refused' : 99}),
+    
+     **dict.fromkeys(['INCOME2', '_PRACE1', '_MRACE1'], {'dont_know_not_sure' : 77, 'refused' : 99}),
+
+    **dict.fromkeys(['FRUITJU1', 'FRUIT1', 'FVBEANS', 'FVORANG', 'FVGREEN', 'VEGETAB1'], {'never' : 555, 'dont_know_not_sure' : 777, 'refused' : 999}),
+    
+     **dict.fromkeys(['GENHLTH', 'HLTHPLN1', 'PERSDOC2', 'MEDCOST', 'BPHIGH4',
+                      'BLOODCHO', 'CHOLCHK', 'TOLDHI2', 'CVDSTRK3', 'ASTHMA3',
+                      'CHCSCNCR', 'CHCOCNCR', 'CHCCOPD1', 'HAVARTH3', 'ADDEPEV2',
+                      'CHCKIDNY', 'DIABETE3', 'RENTHOM1', 'VETERAN3', 'INTERNET',
+                     'QLACTLM2', 'USEEQUIP', 'BLIND', 'DECIDE', 'DIFFWALK', 'DIFFDRES',
+                      'DIFFALON', 'SMOKE100', 'USENOW3', 'EXERANY2', 'FLUSHOT6', 'PNEUVAC3',
+                      'HIVTST6', 'DRNKANY5'
+                     ], {'dont_know_not_sure' : 7, 'refused' : 9}),
+    
+    **dict.fromkeys(['CHECKUP1', 'SEATBELT'], {'dont_know_not_sure' : 7, 'never' : 8, 'refused' : 9}),
+    
+    **dict.fromkeys(['MARITAL', 'EDUCA', 'EMPLOY1', '_CHISPNC', '_RFHLTH',
+                      '_HCVU651', '_RFHYPE5', '_CHOLCHK', '_RFCHOL', '_LTASTH1',
+                      '_CASTHM1', '_ASTHMS1', '_HISPANC', '_RACE', '_RACEG21',
+                      '_RACEGR3', '_RFBMI5', '_CHLDCNT', '_EDUCAG', '_INCOMG',
+                      '_SMOKER3', '_RFSMOK3', '_RFBING5', '_RFDRHV5', '_FRTLT1',
+                     '_VEGLT1', '_TOTINDA', 'PAMISS1_', '_PACAT1', '_PAINDX1', '_PA150R2',
+                     '_PA300R2', '_PA30021', '_PASTRNG', '_PAREC1', '_PASTAE1', '_LMTACT1',
+                     '_LMTWRK1', '_LMTSCL1', '_RFSEAT2', '_RFSEAT3', '_AIDTST3'
+                     ], {'refused' : 9}),
+    
+    **dict.fromkeys(['HEIGHT3', 'WEIGHT2'], {'dont_know_not_sure' : 7777, 'refused' : 9999}),
+    
+    **dict.fromkeys(['_AGEG5YR'], {'refused' : 14}),
+
+    **dict.fromkeys(['_AGE65YR'], {'refused' : 3}),
+
+    **dict.fromkeys(['WTKG3'], {'refused' : 99999}),
+    **dict.fromkeys(['DROCDY3_'], {'refused' : 900}),
+    **dict.fromkeys(['_DRNKWEK'], {'refused' : 99900}),
+     **dict.fromkeys(['FC60_', 'MAXVO2_'], {'refused' : 999}),
+     **dict.fromkeys(['STRFREQ_'], {'refused' : 99}),
+   
+}
+
+category_features = {'categorical' : ['HLTHPLN1','_STATE','FMONTH','IDATE', 'IMONTH', 'DISPCODE', 'PERSDOC2',
+                             'MEDCOST', 'CHECKUP1', 'BPHIGH4', 'BLOODCHO', 'CHOLCHK', 'TOLDHI2', 'CVDSTRK3',
+                            'ASTHMA3', 'CHCSCNCR', 'CHCOCNCR', 'CHCCOPD1', 'HAVARTH3', 'ADDEPEV2', 'CHCKIDNY'
+                             'DIABETE3','SEX', 'MARITAL', 'EDUCA', 'RENTHOM1', 'VETERAN3', 'EMPLOY1', 'INCOME2', 'INTERNET', 'QLACTLM2',
+                             'USEEQUIP', 'BLIND', 'DECIDE', 'DIFFWALK', 'DIFFDRES', 'DIFFALON', 'SMOKE100', 'USENOW3', 'EXERANY2', 'SEATBELT',
+                             'FLUSHOT6', 'PNEUVAC3', 'HIVTST6', 'QSTVER', 'QSTLANG','_CHISPNC', '_DUALUSE', '_RFHLTH', '_HCVU651', '_RFHYPE5',
+                             '_CHOLCHK', '_RFCHOL', '_LTASTH1', '_CASTHM1', '_ASTHMS1', '_DRDXAR1', '_PRACE1', '_MRACE1', '_HISPANC', '_RACE',
+                             '_RACEG21', '_RACEGR3',  '_RACE_G1', '_AGEG5YR', '_AGE65YR', '_AGE_G', '_BMI5CAT', '_RFBMI5', '_CHLDCNT', '_EDUCAG',
+                             '_INCOMG', '_SMOKER3', '_RFSMOK3', 'DRNKANY5', '_RFBING5', '_RFDRHV5', '_MISFRTN', '_MISVEGN', '_FRTRESP', '_VEGRESP',
+                             '_FRTLT1', '_VEGLT1', '_FRT16', '_VEG23', '_FRUITEX', '_VEGETEX', '_TOTINDA', 'PAMISS1_', '_PACAT1', '_PAINDX1',
+                             '_PA150R2', '_PA300R2', '_PA30021', '_PASTRNG', '_PAREC1', '_PASTAE1', '_LMTACT1', '_LMTWRK1', '_LMTSCL1',
+                             '_RFSEAT2', '_RFSEAT3', '_AIDTST3', 'IYEAR', '_RFSMOK3', 'FMONTH', 'GENHLTH', 'CHCKIDNY', 'DIABETE3'
+                             
+                             
+                            ],
+
+            
+            'continuous' : ['WEIGHT2', 'HEIGHT3', 'ALCDAY5', 'FRUITJU1', 'FRUIT1', 'FVBEANS', 'FVGREEN',
+                            'FVORANG', 'VEGETAB1', 'STRENGTH', '_AGE80', 'HTIN4', 'WTKG3', 'HTM4', '_BMI5', 'DROCDY3_',
+                           '_DRNKWEK', 'FTJUDA1_', 'BEANDAY_', 'GRENDAY_', 'ORNGDAY_', 'VEGEDA1_', '_FRUTSUM', '_FRUTSUM',
+                           'MAXVO2_', 'FC60_', 'STRFREQ_', 'PHYSHLTH', 'IDAY', 'CHILDREN', 'MENTHLTH', 'FRUTDA1_', '_STRWT', '_VEGESUM'],
+            
+            'not_sure' : ['SEQNO', '_PSU' , '_STSTR', '_STRWT', '_RAWRAKE', ' _WT2RAKE', '_LLCPWT']
+           }
\ No newline at end of file
diff --git a/projects/project1/functions.py b/projects/project1/functions.py
new file mode 100644
index 000000000..1b27368e6
--- /dev/null
+++ b/projects/project1/functions.py
@@ -0,0 +1,409 @@
+
+import numpy as np
+import matplotlib.pyplot as plt
+from stats import f1_score
+
+
+def predict(tx ,w) :
+    return np.where(sigmoid(tx@w) >= 0.5, 1, -1)
+
+
+def sigmoid(t):
+    """apply sigmoid function on t.
+
+    Args:
+        t: scalar or numpy array
+
+    Returns:
+        scalar or numpy array
+
+    >>> sigmoid(np.array([0.1]))
+    array([0.52497919])
+    >>> sigmoid(np.array([0.1, 0.1]))
+    array([0.52497919, 0.52497919])
+    """
+
+    
+    return 1 / (1 + np.exp(-t))
+
+def calculate_loss(y, tx, w):
+    """compute the cost by negative log likelihood.
+
+    Args:
+        y:  shape=(N, 1)
+        tx: shape=(N, D)
+        w:  shape=(D, 1)
+
+    Returns:
+        a non-negative loss
+
+    >>> y = np.c_[[0., 1.]]
+    >>> tx = np.arange(4).reshape(2, 2)
+    >>> w = np.c_[[2., 3.]]
+    >>> round(calculate_loss(y, tx, w), 8)
+    1.52429481
+    """
+    
+    assert y.shape[0] == tx.shape[0]
+    assert tx.shape[1] == w.shape[0]
+
+    #add an espilon to ensure that if there is a value very close to 0 it doesn't do log of a zero value
+    epsilon = 1e-15
+
+    #compute the negative log loss
+    sigmoid_output = sigmoid(tx@w)
+    negative_log_loss = -np.mean(y * np.log(np.clip(sigmoid_output, epsilon, 1 - epsilon)) + (1 - y) * np.log(np.clip(1 - sigmoid_output, epsilon, 1 - epsilon)))
+    
+    return negative_log_loss
+
+
+
+def calculate_gradient(y, tx, w):
+    """compute the gradient of loss.
+
+    Args:
+        y:  shape=(N, 1)
+        tx: shape=(N, D)
+        w:  shape=(D, 1)
+
+    Returns:
+        a vector of shape (D, 1)
+
+    >>> np.set_printoptions(8)
+    >>> y = np.c_[[0., 1.]]
+    >>> tx = np.arange(6).reshape(2, 3)
+    >>> w = np.array([[0.1], [0.2], [0.3]])
+    >>> calculate_gradient(y, tx, w)
+    array([[-0.10370763],
+           [ 0.2067104 ],
+           [ 0.51712843]])
+    """
+    return (1/y.shape[0])*tx.T@(sigmoid(tx@w) - y)
+
+
+    
+def logistic_regression(y, tx, w):
+    """return the loss, gradient of the loss, and hessian of the loss.
+
+    Args:
+        y:  shape=(N, 1)
+        tx: shape=(N, D)
+        w:  shape=(D, 1)
+
+    Returns:
+        loss: scalar number
+        gradient: shape=(D, 1)
+        hessian: shape=(D, D)
+
+    >>> y = np.c_[[0., 1.]]
+    >>> tx = np.arange(6).reshape(2, 3)
+    >>> w = np.array([[0.1], [0.2], [0.3]])
+    >>> loss, gradient, hessian = logistic_regression(y, tx, w)
+    >>> round(loss, 8)
+    0.62137268
+    >>> gradient, hessian
+    (array([[-0.10370763],
+           [ 0.2067104 ],
+           [ 0.51712843]]), array([[0.28961235, 0.3861498 , 0.48268724],
+           [0.3861498 , 0.62182124, 0.85749269],
+           [0.48268724, 0.85749269, 1.23229813]]))
+    """
+    return calculate_loss(y, tx, w), calculate_gradient(y, tx, w)
+
+
+
+def penalized_logistic_regression(y, tx, w, lambda_):
+    """return the loss and gradient.
+
+    Args:
+        y:  shape=(N, 1)
+        tx: shape=(N, D)
+        w:  shape=(D, 1)
+        lambda_: scalar
+
+    Returns:
+        loss: scalar number
+        gradient: shape=(D, 1)
+
+    >>> y = np.c_[[0., 1.]]
+    >>> tx = np.arange(6).reshape(2, 3)
+    >>> w = np.array([[0.1], [0.2], [0.3]])
+    >>> lambda_ = 0.1
+    >>> loss, gradient = penalized_logistic_regression(y, tx, w, lambda_)
+    >>> round(loss, 8)
+    0.62137268
+    >>> gradient
+    array([[-0.08370763],
+           [ 0.2467104 ],
+           [ 0.57712843]])
+    """
+    loss, gradient = logistic_regression(y, tx, w)
+
+    loss = loss + lambda_*np.dot(w, w)
+    gradient = gradient + 2*lambda_*w
+    
+    return loss, gradient
+
+def learning_by_penalized_gradient(y, tx, w, gamma, lambda_):
+    """
+    Do one step of gradient descent, using the penalized logistic regression.
+    Return the loss and updated w.
+
+    Args:
+        y:  shape=(N, 1)
+        tx: shape=(N, D)
+        w:  shape=(D, 1)
+        gamma: scalar
+        lambda_: scalar
+
+    Returns:
+        loss: scalar number
+        w: shape=(D, 1)
+
+    >>> np.set_printoptions(8)
+    >>> y = np.c_[[0., 1.]]
+    >>> tx = np.arange(6).reshape(2, 3)
+    >>> w = np.array([[0.1], [0.2], [0.3]])
+    >>> lambda_ = 0.1
+    >>> gamma = 0.1
+    >>> loss, w = learning_by_penalized_gradient(y, tx, w, gamma, lambda_)
+    >>> round(loss, 8)
+    0.62137268
+    >>> w
+    array([[0.10837076],
+           [0.17532896],
+           [0.24228716]])
+    """
+    loss, gradient = penalized_logistic_regression(y, tx, w,lambda_)
+
+    w = w - gamma*gradient
+    return loss, w
+
+
+
+
+def stochastic_gradient_descent(y, tx, x_test, y_test ,initial_w, batch_size, max_iters, gamma, lambda_, model = 'logistic'):
+    """The Stochastic Gradient Descent algorithm (SGD).
+
+    Args:
+        y: numpy array of shape=(N, )
+        tx: numpy array of shape=(N,2)
+        initial_w: numpy array of shape=(2, ). The initial guess (or the initialization) for the model parameters
+        batch_size: a scalar denoting the number of data points in a mini-batch used for computing the stochastic gradient
+        max_iters: a scalar denoting the total number of iterations of SGD
+        gamma: a scalar denoting the stepsize
+
+    Returns:
+        losses: a list of length max_iters containing the loss value (scalar) for each iteration of SGD
+        ws: a list of length max_iters containing the model parameters as numpy arrays of shape (2, ), for each iteration of SGD
+    """
+
+    # Define parameters to store w and loss
+    ws = [initial_w]
+    f1_tr = []
+    f1_te = []
+    losses = []
+    w = initial_w
+    threshold = 1e-8
+    
+    for n_iter in range(max_iters):
+        for minibatch_y, minibatch_tx in batch_iter(y,tx,batch_size) :
+            
+            if(model == 'logistic') :
+                loss, w = learning_by_penalized_gradient(minibatch_y, minibatch_tx, w, gamma, lambda_)
+            elif(model == 'lstq') :
+                continue
+            losses.append(loss)
+            ws.append(w)
+            #f1_tr.append(f1_score(y, predict(tx, w)))
+            #f1_te.append(f1_score(y_test, predict(x_test, w)))
+            
+            if len(losses) > 1 and np.abs(losses[-1] - losses[-2]) < threshold:
+                break
+        
+    return f1_tr, f1_te, ws
+    
+
+
+
+
+
+
+def batch_iter(y, tx, batch_size, num_batches=1, shuffle=True):
+    """
+    Generate a minibatch iterator for a dataset.ÅÅÅÅ
+    Takes as input two iterables (here the output desired values 'y' and the input data 'tx')
+    Outputs an iterator which gives mini-batches of `batch_size` matching elements from `y` and `tx`.
+    Data can be randomly shuffled to avoid ordering in the original data messing with the randomness of the minibatches.
+
+    Example:
+
+     Number of batches = 9
+
+     Batch size = 7                              Remainder = 3
+     v     v                                         v v
+    |-------|-------|-------|-------|-------|-------|---|
+        0       7       14      21      28      35   max batches = 6
+
+    If shuffle is False, the returned batches are the ones started from the indexes:
+    0, 7, 14, 21, 28, 35, 0, 7, 14
+
+    If shuffle is True, the returned batches start in:
+    7, 28, 14, 35, 14, 0, 21, 28, 7
+
+    To prevent the remainder datapoints from ever being taken into account, each of the shuffled indexes is added a random amount
+    8, 28, 16, 38, 14, 0, 22, 28, 9
+
+    This way batches might overlap, but the returned batches are slightly more representative.
+
+    Disclaimer: To keep this function simple, individual datapoints are not shuffled. For a more random result consider using a batch_size of 1.
+
+    Example of use :
+    for minibatch_y, minibatch_tx in batch_iter(y, tx, 32):
+        <DO-SOMETHING>
+    """
+    data_size = len(y)  # NUmber of data points.
+    batch_size = min(data_size, batch_size)  # Limit the possible size of the batch.
+    max_batches = int(
+        data_size / batch_size
+    )  # The maximum amount of non-overlapping batches that can be extracted from the data.
+    remainder = (
+        data_size - max_batches * batch_size
+    )  # Points that would be excluded if no overlap is allowed.
+
+    if shuffle:
+        # Generate an array of indexes indicating the start of each batch
+        idxs = np.random.randint(max_batches, size=num_batches) * batch_size
+        if remainder != 0:
+            # Add an random offset to the start of each batch to eventually consider the remainder points
+            idxs += np.random.randint(remainder + 1, size=num_batches)
+    else:
+        # If no shuffle is done, the array of indexes is circular.
+        idxs = np.array([i % max_batches for i in range(num_batches)]) * batch_size
+
+    for start in idxs:
+        start_index = start  # The first data point of the batch
+        end_index = (
+            start_index + batch_size
+        )  # The first data point of the following batch
+        yield y[start_index:end_index], tx[start_index:end_index]
+
+
+
+
+def build_k_indices(y, k_fold, seed):
+    """build k indices for k-fold.
+
+    Args:
+        y:      shape=(N,)
+        k_fold: K in K-fold, i.e. the fold num
+        seed:   the random seed
+
+    Returns:
+        A 2D array of shape=(k_fold, N/k_fold) that indicates the data indices for each fold
+
+    >>> build_k_indices(np.array([1., 2., 3., 4.]), 2, 1)
+    array([[3, 2],
+           [0, 1]])
+    """
+    num_row = y.shape[0]
+    interval = int(num_row / k_fold)
+    np.random.seed(seed)
+    indices = np.random.permutation(num_row)
+    k_indices = [indices[k * interval : (k + 1) * interval] for k in range(k_fold)]
+    
+    return np.array(k_indices)
+
+
+def cross_validation(y, x, k_indices, k, lambda_, batch_size):
+    """return the loss of ridge regression for a fold corresponding to k_indices
+
+    Args:
+        y:          shape=(N,)
+        x:          shape=(N,)
+        k_indices:  2D array returned by build_k_indices()
+        k:          scalar, the k-th fold (N.B.: not to confused with k_fold which is the fold nums)
+        lambda_:    scalar, cf. ridge_regression()
+        degree:     scalar, cf. build_poly()
+
+    Returns:
+        train and test root mean square errors rmse = sqrt(2 mse)
+
+    >>> cross_validation(np.array([1.,2.,3.,4.]), np.array([6.,7.,8.,9.]), np.array([[3,2], [0,1]]), 1, 2, 3)
+    (0.019866645527597114, 0.33555914361295175)
+    """
+   
+    x_test = x[k_indices[k]]
+    y_test = y[k_indices[k]]
+    train_indices = [i for i in range(len(x)) if i not in k_indices[k]]
+    
+    
+    x_train = x[train_indices]
+    y_train = y[train_indices]
+    
+    max_iters = 100000
+    gamma = 0.001
+    num_samples = x_train.shape[0]
+    
+    mean = 0    # Mean of the distribution
+    std_dev = 1 # Standard deviation of the distribution
+    w_initial = np.random.normal(loc=mean, scale=std_dev, size=x_train.shape[1])
+    
+    sgd_losses, sgd_ws = stochastic_gradient_descent(
+    y_train, x_train, w_initial, batch_size, max_iters, gamma, lambda_
+    )
+    y_predict = predict(x_test, sgd_ws[-1])
+    f1 = f1_score(y_test, y_predict)
+
+    
+    return f1
+
+
+
+def cross_validation_demo(y, x, batch_sizes, k_fold, lambdas):
+    """cross validation over regularisation parameter lambda.
+
+    Args:
+        degree: integer, degree of the polynomial expansion
+        k_fold: integer, the number of folds
+        lambdas: shape = (p, ) where p is the number of values of lambda to test
+    Returns:
+        best_lambda : scalar, value of the best lambda
+        best_rmse : scalar, the associated root mean squared error for the best lambda
+    """
+
+    seed = 12
+    batch_sizes = batch_sizes
+    k_fold = k_fold
+    lambdas = lambdas
+    
+    # split data in k fold
+    k_indices = build_k_indices(y, k_fold, seed)
+    # define lists to store the loss of training data and test data
+ 
+    f1_score_array = []
+    for lambda_ in lambdas :
+        for batch_size in batch_sizes :
+            total_f1_score_te = 0
+            for k in range(k_fold) :
+                f1_score_te = cross_validation(y,x, k_indices, k, lambda_, batch_size)
+                total_f1_score_te += f1_score_te
+                
+            f1_score_array.append(total_f1_score_te/k_fold)
+
+            
+    best_index = np.argmax(f1_score_array)
+    best_f1_score = f1_score_array[best_index]
+    best_batch_size = batch_sizes[best_index%len(batch_size)]
+    best_lambda = lambdas[best_index//len(batch_size)]
+    
+    return best_batch_size, best_lambda, best_f1_score
+        
+   
+    
+    
+
+
+
+
+
diff --git a/projects/project1/helpers.py b/projects/project1/helpers.py
index fbb37762c..e672143b5 100644
--- a/projects/project1/helpers.py
+++ b/projects/project1/helpers.py
@@ -21,6 +21,10 @@ def load_csv_data(data_path, sub_sample=False):
         train_ids (np.array): ids of training data
         test_ids (np.array): ids of test data
     """
+    with open(os.path.join(data_path, "x_train.csv"), 'r') as file:
+       labels = file.readline().strip().split(',') 
+    
+    
     y_train = np.genfromtxt(
         os.path.join(data_path, "y_train.csv"),
         delimiter=",",
@@ -46,7 +50,7 @@ def load_csv_data(data_path, sub_sample=False):
         x_train = x_train[::50]
         train_ids = train_ids[::50]
 
-    return x_train, x_test, y_train, train_ids, test_ids
+    return x_train, x_test, y_train, train_ids, test_ids, labels
 
 
 def create_csv_submission(ids, y_pred, name):
@@ -69,4 +73,4 @@ def create_csv_submission(ids, y_pred, name):
         writer = csv.DictWriter(csvfile, delimiter=",", fieldnames=fieldnames)
         writer.writeheader()
         for r1, r2 in zip(ids, y_pred):
-            writer.writerow({"Id": int(r1), "Prediction": int(r2)})
+            writer.writerow({"Id": int(r1), "Prediction": int(r2)})
\ No newline at end of file
diff --git a/projects/project1/implementations.py b/projects/project1/implementations.py
new file mode 100644
index 000000000..c879f2a31
--- /dev/null
+++ b/projects/project1/implementations.py
@@ -0,0 +1,141 @@
+import numpy as np
+from utils import batch_iter
+
+# compute the mean squared error of a model
+def MSE(y, tx, w):
+    N = y.shape[0]
+    e = y - (tx @ w)
+    loss = 1/(2*N) * np.sum(e**2, 0)
+    return loss
+
+# compute the gradient of the MSE loss function
+def compute_gradient_MSE(y, tx, w):
+    N = y.shape[0]
+    e = y - (tx @ w)
+    grad = -1/N * (tx.T @ e)
+    return grad
+
+# compute the regularized MSE loss, return both the non-regularized and the regularized loss
+def MSE_regularized(y, tx, w, lambda_):
+    loss = MSE(y, tx, w)
+    regularizer = lambda_*(np.linalg.norm(w)**2)
+    return loss, loss + regularizer
+
+# compute sigmoid function
+def sigmoid(t):
+    return np.exp(t)/(1 + np.exp(t))
+
+# compute negative log likelihood loss of a model
+def neg_log_loss(y, tx, w):
+    prob = sigmoid(tx @ w)
+    epsilon = 1e-15
+    return -np.mean(y * np.log(np.clip(prob, epsilon, 1)) + (1 - y) * np.log(np.clip(1 - prob, epsilon, 1)))
+
+# compute gradient of negative log likelihood loss function
+def neg_log_gradient(y, tx, w):
+    gradient = 1/y.shape[0] * (tx.T @ (sigmoid(tx @ w) - y))
+    return gradient
+
+# compute gradient of regularized negative log likelihood loss function
+def neg_log_gradient_reg(y, lambda_, tx, w):
+    gradient = neg_log_gradient(y, tx, w)
+    return gradient + 2*lambda_*w
+
+# train model using least squares 
+def least_squares(y, tx):
+    N = y.shape[0]
+    y = np.reshape(y, (N,))
+    w = np.linalg.solve(tx.T @ tx, tx.T @ y)
+    MSE = 1/(2*N) * np.sum((y - (tx @ w))**2, 0)
+    return w, MSE
+
+# train model using gradient descent on the MSE loss function
+def mean_squared_error_gd(y, tx, initial_w, max_iters, gamma):
+    N = y.shape[0]
+    y = np.reshape(y, (N,))
+    threshold = 1e-8 # define convergence when difference between losses of two consecutive iters falls below this
+    ws = [initial_w]
+    losses = []
+    w = initial_w
+    for n_iter in range(max_iters): # iterating on guess for model weights
+        gradient = compute_gradient_MSE(y, tx, w) # compute gradient over all data points
+        loss = MSE(y, tx, w)
+        w -= gamma*gradient # update w based on gradient computation
+        ws.append(w)
+        losses.append(loss)
+
+        if len(losses) > 1 and np.abs(losses[-1] - losses[-2]) < threshold:
+            break # convergence achieved, end loop and return weights/loss
+
+    return w, losses[-1]
+
+# train model using gradient descent on the MSE loss function
+def mean_squared_error_sgd(y, tx, initial_w, max_iters, gamma):
+    N = y.shape[0]
+    y = np.reshape(y, (N,))
+    threshold = 1e-8 # define convergence when difference between losses of two consecutive iters falls below this
+    ws = [initial_w]
+    losses = []
+    w = initial_w
+    for n_iter in range(max_iters): # iterating on guess for model weights
+        for minibatch_y, minibatch_tx in batch_iter(y, tx, 1): # iterating for batch subset of data points
+            gradient = compute_gradient_MSE(minibatch_y, minibatch_tx, w) # compute gradient from subset
+            loss = MSE(minibatch_y, minibatch_tx, w)
+            w -= gamma*gradient # update weights with gradient
+            ws.append(w)
+            losses.append(loss)
+
+            if len(losses) > 1 and np.abs(losses[-1] - losses[-2]) < threshold:
+                break # convergence achieved, end loop and return weights/loss
+
+    return w, losses[-1]
+
+# train model using ridge regression
+def ridge_regression(y, tx, lambda_):
+    N = y.shape[0]
+    y = np.reshape(y, (N,))
+    D = np.shape(tx)[1]
+    lambda_prime = 2*N*lambda_  
+    w = np.linalg.solve(((tx.T @ tx) + lambda_prime * np.identity(D)), (tx.T @ y))
+    MSE, MSE_reg = MSE_regularized(y, tx, w, lambda_)
+    return w, MSE
+
+# train model using logistic regression
+def logistic_regression(y, tx, initial_w, max_iters, gamma):
+    N = y.shape[0]
+    y = np.reshape(y, (N,))
+    threshold = 1e-8 # define convergence when difference between losses of two consecutive iters falls below this
+    w = initial_w
+    losses = []
+    
+    for n_iter in range(max_iters): # iterating on guess for model weights
+
+        gradient = neg_log_gradient(y, tx, w) # compute gradient of negative log likelihood loss at model weights
+        loss = neg_log_loss(y, tx, w)
+        w -= gamma*gradient # updating the weights based on gradient
+        losses.append(loss)
+        
+        if len(losses) > 1 and np.abs(losses[-1] - losses[-2]) < threshold:
+            break # convergence achieved, end loop and return weights/loss
+
+    return w, losses[-1]
+
+# train model using regularized logistic regression
+def reg_logistic_regression(y, tx, lambda_, initial_w, max_iters, gamma):
+    N = y.shape[0]
+    y = np.reshape(y, (N,))
+    threshold = 1e-8 # define convergence when difference between losses of two consecutive iters falls below this
+    w = initial_w
+    losses = []
+    
+    for n_iter in range(max_iters): # iterating on guess for model weights
+
+        gradient = neg_log_gradient_reg(y, lambda_, tx, w) # compute gradient of regularized negative log likelihood
+        loss = neg_log_loss(y, tx, w)
+        w -= gamma*gradient # update model weights based on gradient
+        losses.append(loss)
+        
+        if len(losses) > 1 and np.abs(losses[-1] - losses[-2]) < threshold:
+            break # convergence achieved, end loop and return weights/loss
+
+    return w, losses[-1]
diff --git a/projects/project1/normalization.py b/projects/project1/normalization.py
new file mode 100644
index 000000000..058cfde24
--- /dev/null
+++ b/projects/project1/normalization.py
@@ -0,0 +1,29 @@
+import numpy as np
+
+
+def min_max_normalization(x):
+    """
+    Min-max normalization
+    :param x: variable x
+    :return: list of numbers
+    """
+    min_val = min(x)
+    max_val = max(x)
+    return (x - min_val) / (max_val - min_val)
+
+def z_score_normalization(x):
+    """
+    Z-score normalization
+    :param x: variable x 
+    :return: list of numbers
+    """
+    mean_val = np.mean(x)
+    std_val = np.std(x)
+    
+    return (x - mean_val) / std_val
+
+def normalize_data(x):
+    std_dev = np.std(x, axis=0)
+    std_dev[std_dev == 0] = 1  # Prevent division by zero for constant features
+    x = (x - np.mean(x, axis=0)) / std_dev
+    return x
\ No newline at end of file
diff --git a/projects/project1/obsolete.py b/projects/project1/obsolete.py
new file mode 100644
index 000000000..506382364
--- /dev/null
+++ b/projects/project1/obsolete.py
@@ -0,0 +1,111 @@
+
+
+def handle_correlation(x_train, features):
+    """
+    Handling correlation between features
+    :param x_train: training data
+    :param features: features list
+    :return: modified and correlation handled data
+    """
+    # Compute the correlation matrix
+    features_correlation = np.corrcoef(x_train, rowvar=False)
+
+    # Find the features that are highly correlated
+    correlation_limit = 0.8
+    correlation_tuple_list = []
+    correlation_list = []
+
+    for i in range(x_train.shape[1]) : 
+        for j in range(i, x_train.shape[1]) : 
+            if i != j and features_correlation[i,j] >= correlation_limit : 
+                correlation_tuple_list.append((find_key_by_value(features, i) , find_key_by_value(features, j)))
+                correlation_list.append(find_key_by_value(features, i))
+                correlation_list.append(find_key_by_value(features, j))
+    
+    # Use np.unique to get counts of each element
+    correlation_list = np.array(correlation_list)
+    unique_elements, counts = np.unique(correlation_list, return_counts=True)
+    count_elements = dict(zip(unique_elements, counts))
+
+    features_to_remove = set()
+    # Iterate through the correlation tuples
+    for feature1, feature2 in correlation_tuple_list:
+        # Compare the counts of the two features
+        if count_elements[feature1] > count_elements[feature2]:
+            features_to_remove.add(feature2)
+        else:
+            features_to_remove.add(feature1)
+
+    # Remove the features from the features dictionary and x_train_modified
+    features, x_train = remove_features(x_train, list(features_to_remove), features)
+
+    # Update the features dictionary to reflect the new indices
+    features = {feature: i for i, feature in enumerate(features.keys())}
+
+    return x_train, features
+
+
+
+def handling_data(x_train, features):
+    """
+    Handling and modifying data because of special values and scaling some values
+    :param x_train: training data
+    :return: modified data
+    """
+    #Normalizing data
+    for feature in features.keys() :
+    
+        dict_special_value_handle = dictionary_features[feature]
+
+
+        for special_value in dict_special_value_handle.keys() :
+            #For special values meaning not sure, we change by the median
+            if special_value == 'dont_know_not_sure' :
+                x_train[x_train[:, features[feature]] == dict_special_value_handle[special_value], features[feature]] = np.nanmedian(x_train[:, features[feature]])
+                
+            #For special values meaning none, we change by 0
+            elif special_value == 'never' or special_value == 'none'  :
+                x_train[x_train[:, features[feature]] == dict_special_value_handle[special_value], features[feature]] = 0
+                    
+            #For special values meaning refused, we change by NaN
+            elif special_value == 'refused' :
+                x_train[x_train[:, features[feature]] == dict_special_value_handle[special_value], features[feature]] = np.nan
+    
+    x_train = day_week_month_rescale(x_train, 'STRENGTH', 1, features)
+    x_train = day_week_month_rescale(x_train, 'ALCDAY5', 1, features)
+    x_train = day_week_month_rescale(x_train, 'FRUIT1', 2, features)
+    x_train = day_week_month_rescale(x_train, 'FVBEANS', 2, features)
+    x_train = day_week_month_rescale(x_train, 'FVORANG', 2, features)
+    x_train = day_week_month_rescale(x_train, 'FVGREEN', 2, features)
+    x_train = day_week_month_rescale(x_train, 'VEGETAB1', 2, features)
+    x_train = day_week_month_rescale(x_train, 'FRUITJU1', 2, features)
+
+    return x_train
+
+def day_week_month_rescale(x, feature_name, scaling_mode, features):
+    """
+    Rescale the values of the feature_name
+    :param x: training data
+    :param feature_name: feature name
+    :param scaling_mode: scaling mode
+    :param features: features list
+    :return: modified data
+    """
+    if feature_name in features.keys() :
+    
+        mask_three_hundred = x[:, features[feature_name]] > 300
+        mask_two_hundred = (x[:, features[feature_name]] >= 200) & (x[:, features[feature_name]] < 300)
+        mask_one_hundred = (x[:, features[feature_name]] >= 100) &(x[:, features[feature_name]] < 200) 
+            
+        if scaling_mode == 1 : 
+            x[mask_one_hundred , features[feature_name]] = (x[mask_one_hundred, features[feature_name]] -100)*4.33
+            x[mask_two_hundred, features[feature_name]] = (x[mask_two_hundred, features[feature_name]] -200)
+            
+        elif scaling_mode == 2:
+            x[x[:, features[feature_name]] == 300, features[feature_name]] = 0
+            x[mask_one_hundred , features[feature_name]] = (x[mask_one_hundred, features[feature_name]] -100)*(4.33*7)
+            x[mask_two_hundred, features[feature_name]] = (x[mask_two_hundred, features[feature_name]] -200)*(4.33)
+            x[mask_three_hundred, features[feature_name]] = (x[mask_three_hundred, features[feature_name]] -300)
+
+    return x
+    
\ No newline at end of file
diff --git a/projects/project1/pipeline.ipynb b/projects/project1/pipeline.ipynb
new file mode 100644
index 000000000..2fc60409e
--- /dev/null
+++ b/projects/project1/pipeline.ipynb
@@ -0,0 +1,44 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "224b9347-28f9-4608-a57a-69cbe03ae7c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Declare constants for pipelining\n",
+    "models_to_test = ['logistic']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3458209-e5ce-444b-ab2c-35577de9bd52",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:ada] *",
+   "language": "python",
+   "name": "conda-env-ada-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/projects/project1/poly.py b/projects/project1/poly.py
new file mode 100644
index 000000000..8cb5b9d0a
--- /dev/null
+++ b/projects/project1/poly.py
@@ -0,0 +1,28 @@
+import numpy as np
+
+
+def build_poly(x, degree):
+    """polynomial basis functions for input data x, for j=0 up to j=degree.
+
+    Args:
+        x: numpy array of shape (N,D), N is the number of samples and D the number of features
+        degree: integer.
+
+    Returns:
+        poly: numpy array of shape (N,d+1)
+
+    >>> build_poly(np.array([0.0, 1.5]), 2)
+    array([[1.  , 0.  , 0.  ],
+           [1.  , 1.5 , 2.25]])
+    """
+    N, D = x.shape
+    poly = np.ones((N, (degree + 1) * D))  # Initialize polynomial feature matrix
+
+    # Expand each feature in X to its powers from 0 to the given degree
+    for i in range(D):
+        for j in range(degree + 1):
+            poly[:, i * (degree + 1) + j] = np.power(x[:, i], j)
+
+
+
+    return poly
\ No newline at end of file
diff --git a/projects/project1/run.ipynb b/projects/project1/run.ipynb
new file mode 100644
index 000000000..f6dd7e956
--- /dev/null
+++ b/projects/project1/run.ipynb
@@ -0,0 +1,272 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from helpers import *\n",
+    "from cleaning_data import *\n",
+    "from stats import *\n",
+    "from functions import *\n",
+    "from clean_data_testing import *\n",
+    "from utils import split_data, downsample_class_0, upsample_class_1_to_percentage\n",
+    "from functions import *\n",
+    "from clean_data_testing import *\n",
+    "import datetime\n",
+    "import seaborn as sns\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Id'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "DATA_PATH = '/Users/williamjallot/Desktop/ML/dataset'\n",
+    "x_train, x_test, y_train, train_ids, test_ids, labels =  load_csv_data(DATA_PATH, sub_sample=False)\n",
+    "labels.pop(0) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#declare constants :\n",
+    "degree = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Split the data into training and testing\n",
+    "x_tr, x_te, y_tr, y_te = split_data(x_train, y_train, 0.8, seed= 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of components to retain 90.0% variance: 67\n"
+     ]
+    }
+   ],
+   "source": [
+    "x_train_cleaned, y_tr_cleaned, features, median_and_most_probable_class, W, mean = clean_train_data(x_tr_up, y_tr_up,labels, 0.3, degree)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(342081, 67)"
+      ]
+     },
+     "execution_count": 298,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_train_cleaned.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_te_cleaned = clean_test_data(x_te, labels, features, median_and_most_probable_class, mean, W, degree)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_test_cleaned = clean_test_data(x_test, labels, features, median_and_most_probable_class, mean, W, degree)\n",
+    "num_samples = x_te.shape[0]\n",
+    "tx_te = np.c_[np.ones(num_samples), x_te_cleaned]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "max_iters = 100000\n",
+    "gamma = 0.001\n",
+    "batch_size = 64\n",
+    "lambda_ =  0.005\n",
+    "num_samples = x_train_cleaned.shape[0]\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Parameters for the Gaussian distribution\n",
+    "mean = 0    # Mean of the distribution\n",
+    "std_dev = 1 # Standard deviation of the distribution\n",
+    "\n",
+    "tx = np.c_[np.ones(num_samples), x_train_cleaned]\n",
+    "w_initial = np.random.normal(loc=mean, scale=std_dev, size=tx.shape[1])\n",
+    "\n",
+    "\n",
+    "f1_tr, f1_te, sgd_ws = stochastic_gradient_descent(\n",
+    "y_tr_cleaned, tx, tx_te, y_te ,w_initial, batch_size, max_iters, gamma, lambda_\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "y_predict = predict(tx_te, sgd_ws[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8007375013332927"
+      ]
+     },
+     "execution_count": 304,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy(y_te, y_predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.38208193545338565"
+      ]
+     },
+     "execution_count": 305,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f1_score(y_te, y_predict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "num_samples = x_test_cleaned.shape[0]\n",
+    "tx_test = np.c_[np.ones(num_samples), x_test_cleaned]\n",
+    "y_test_to_save = predict(tx_test, sgd_ws[-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.savetxt(\"/Users/williamjallot/Desktop/ML/dataset/sample_submission.csv\", y_test_to_save, delimiter=\",\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Stack the ids and predictions together column-wise\n",
+    "submit = np.column_stack((test_ids, y_test_to_save))\n",
+    "\n",
+    "# Save to a CSV file using np.savetxt\n",
+    "np.savetxt(\"/Users/williamjallot/Desktop/ML/dataset/sample_submission.csv\", submit, delimiter=\",\", fmt='%d,%d', header='Id,Prediction', comments='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/projects/project1/run.py b/projects/project1/run.py
new file mode 100644
index 000000000..82e52ba01
--- /dev/null
+++ b/projects/project1/run.py
@@ -0,0 +1,17 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from helpers import *
+from cleaning_data import *
+
+def run():
+
+    DATA_PATH = 'projects\project1\data\dataset\dataset'
+    x_train, x_test, y_train, train_ids, test_ids, labels =  load_csv_data(DATA_PATH, sub_sample=False)
+    labels.pop(0)
+
+    # Clean data
+    x_train_clean = clean_data_x(x_train, labels)
+
+    np.savetxt('projects\project1\data\dataset\dataset\\x_train_cleaned.csv', x_train_clean, delimiter=',')
+
+run()
\ No newline at end of file
diff --git a/projects/project1/stats.py b/projects/project1/stats.py
new file mode 100644
index 000000000..004d5675c
--- /dev/null
+++ b/projects/project1/stats.py
@@ -0,0 +1,61 @@
+import numpy as np
+
+
+
+def calculate_excess_kurtosis(arr):
+    mean = np.nanmean(arr)
+    std = np.nanstd(arr)
+    fourth_moment = np.nanmean((arr - mean) ** 4)
+    kurtosis = ((fourth_moment) / (std ** 4)) - 3 
+    return kurtosis
+
+def calculate_skewness(arr):
+    mean = np.nanmean(arr)
+    std = np.nanstd(arr)
+    third_moment = np.nanmean((arr - mean) ** 3)
+    skewness = ((third_moment) / (std ** 3))
+    return skewness
+
+def box_cox(arr, _lambda) :
+    if np.any(arr) >= 0:
+        return -1
+    if _lambda == 0:
+        return np.log(arr)
+    else :
+        return (np.power(arr, _lambda) - 1) / _lambda
+        
+def IQR(arr) :
+    Q1 = np.nanpercentile(arr, 25)
+    Q3 = np.nanpercentile(arr, 75)
+    IQR = Q3 - Q1
+
+    lower_bound = Q1 - 1.5 * IQR
+    upper_bound = Q3 + 1.5 * IQR
+    return lower_bound, upper_bound
+
+
+def f1_score(y_true, y_pred) :
+    """
+    Compute the F1 score
+    :param y_true: true labels
+    :param y_pred: predicted labels
+    Maybe we should use only one column in the y_true and y_pred
+    :return: F1 score
+    """
+    tp = np.sum((y_true == 1) & (y_pred == 1))
+    positive = np.sum((y_true == 1))
+    negative = np.sum((y_true == -1))
+    
+    fp = np.sum((y_true == -1) & (y_pred == 1))
+    fn = np.sum((y_true == 1) & (y_pred == -1))
+    return tp / (tp + 0.5 * (fp + fn))
+
+def accuracy(y_true, y_pred):
+    """"
+    Compute the accuracy
+    :param y_true: true labels
+    :param y_pred: predicted labels
+    Maybe we should use only one column in the y_true and y_pred
+    :return: accuracy
+    """
+    return np.sum(y_true == y_pred) / len(y_true)
\ No newline at end of file
diff --git a/projects/project1/untitled.txt b/projects/project1/untitled.txt
new file mode 100644
index 000000000..e69de29bb
diff --git a/projects/project1/utils.py b/projects/project1/utils.py
new file mode 100644
index 000000000..b5b3149a9
--- /dev/null
+++ b/projects/project1/utils.py
@@ -0,0 +1,245 @@
+import numpy as np
+
+def apply_pca_given_components(X, mean, W):
+    """
+    Apply the PCA transformation from a specific PCA.
+    Args : 
+        X: data to apply PCA on
+        mean : the mean of the PCA to apply
+        W : the principal components of the PCA to apply
+    Returns :
+        x_pca : PCA applied on the data
+    """
+    #Subtract by the mean
+    x_tilde = X - mean
+    
+    #Project the data onto the principal components
+    x_pca = np.dot(x_tilde, W)
+    
+    return x_pca
+
+
+
+
+def create_pca(X, variance_threshold=0.90):
+    """
+    Create a PCA given the data, retaining a specified percentage of variance.
+    
+    Args : 
+        X : data
+        variance_threshold: the desired amount of variance to retain (default 90%)
+        
+    Returns :
+        x_pca : PCA applied on the data, X
+        W : the principal components of the PCA
+        mean : the mean of x_train 
+        
+    """
+
+    mean = np.nanmean(X, axis=0)
+    x_tilde = X - mean
+    
+
+    cov_matrix = np.cov(x_tilde, rowvar=False)
+    
+    #Eigen decomposition
+    eigvals, eigvecs = np.linalg.eigh(cov_matrix)
+    eigvals = eigvals[::-1]  # Sort eigenvalues in descending order
+    eigvecs = eigvecs[:, ::-1]  # Sort eigenvectors accordingly
+    
+    #Calculate cumulative variance explained by the principal components
+    explained_variance_ratio = eigvals / np.sum(eigvals)
+    cumulative_variance = np.cumsum(explained_variance_ratio)
+    
+    #Determine the number of components to retain based on the variance threshold
+    num_dimensions = np.argmax(cumulative_variance >= variance_threshold) + 1
+    print(f"Number of components to retain {variance_threshold * 100}% variance: {num_dimensions}")
+    
+    #Select the top principal components
+    W = eigvecs[:, :num_dimensions]  # Select top components based on variance retention
+    
+    #Project the data onto the selected principal components
+    x_pca = np.dot(x_tilde, W)
+    
+    return x_pca, W, mean
+
+def upsample_class_1_to_percentage(X, y, desired_percentage):
+    """
+    Apply a upsampling to obtain the desired percentage of 1 among the data.
+        
+        Args : 
+            X: X data to upsample
+            y : y data to usample
+            desired_precentage : the desired repartition of 1 among the data
+        Returns :
+            X_upsampled : X upsampled to attain the desired percentage
+            y_upsampled : y upsampled to attain the desired percentage
+    """
+    # Find the indices of class 0 (majority class) and class 1 (minority class)
+    indices_class_1 = np.where(y == 1)[0]
+    indices_class_0 = np.where(y == 0)[0]
+    
+    # Number of samples in each class
+    num_class_1 = len(indices_class_1)
+    num_class_0 = len(indices_class_0)
+    
+    # Calculate the total number of samples needed for the desired percentage
+    total_size = int(num_class_0 / (1 - desired_percentage))
+    
+    # Calculate the number of class 1 samples needed to reach the desired percentage
+    target_num_class_1 = int(total_size * desired_percentage)
+    
+    # Upsample class 1 by randomly duplicating samples until reaching the target number
+    upsampled_indices_class_1 = np.random.choice(indices_class_1, size=target_num_class_1, replace=True)
+    
+    # Combine the upsampled class 1 samples with all class 0 samples
+    combined_indices = np.concatenate([indices_class_0, upsampled_indices_class_1])
+    
+    # Shuffle the combined dataset to avoid ordering bias
+    np.random.shuffle(combined_indices)
+    
+    # Return the upsampled feature matrix and label vector
+    X_upsampled = X[combined_indices]
+    y_upsampled = y[combined_indices]
+    
+    return X_upsampled, y_upsampled
+
+
+
+def downsample_class_0(X, y, desired_percentage):
+    """
+    Apply a downsampling to obtain the desired percentage of 0 among the data.
+    
+    Args : 
+        X: data to upsample
+        y : 
+        desired_precentage : the desired repartition of 1 among the data
+    Returns :
+        X_upsampled : X upsampled to attain the desired percentage
+        y_upsampled : y upsampled to attain the desired percentage
+        
+    """
+    N_pos = len(np.where(y == 1)[0])
+    N_neg = len(np.where(y == 0)[0])
+    down_sample_size_0 = calculate_downsample_size(N_pos, N_neg, desired_percentage)
+    # Separate the samples with label 0 and label 1
+    indices_class_0 = np.where(y == 0)[0]
+    indices_class_1 = np.where(y == 1)[0]
+    
+    # Downsample the class 0 samples
+    selected_indices_class_0 = np.random.choice(indices_class_0, size=downsample_size_0, replace=False)
+    
+    # Combine the downsampled class 0 samples with all class 1 samples
+    combined_indices = np.concatenate([selected_indices_class_0, indices_class_1])
+    
+    # Shuffle the combined indices to mix the samples
+    np.random.shuffle(combined_indices)
+    
+    # Select the corresponding rows from X and y
+    X_downsampled = X[combined_indices]
+    y_downsampled = y[combined_indices]
+    
+    return X_downsampled, y_downsampled
+
+
+def remove_features(x, features_to_remove, features) :
+    
+    if not set(features_to_remove).intersection(features):
+        return features, x
+    
+    for feature in features_to_remove:
+        if feature in features :
+            x = np.delete(x, features[feature], axis = 1)
+            features = remove_and_update_indices(features, feature)
+            
+    return features, x
+
+def find_key_by_value(d, value):
+    for key, val in d.items():
+        if val == value:
+            return key
+    return None
+
+def remove_and_update_indices(d, remove_key):
+    if remove_key in d:
+        removed_index = d.pop(remove_key)
+        for key in d:
+            if d[key] > removed_index:
+                d[key] -= 1
+
+    return d
+
+
+
+def split_data(x, y, ratio, seed=1):
+    """
+    split the dataset based on the split ratio. If ratio is 0.8
+    you will have 80% of your data set dedicated to training
+    and the rest dedicated to testing. If ratio times the number of samples is not round
+    you can use np.floor. Also check the documentation for np.random.permutation,
+    it could be useful.
+
+    Args:
+        x: numpy array of shape (N,), N is the number of samples.
+        y: numpy array of shape (N,).
+        ratio: scalar in [0,1]
+        seed: integer.
+
+    Returns:
+        x_tr: numpy array containing the train data.
+        x_te: numpy array containing the test data.
+        y_tr: numpy array containing the train labels.
+        y_te: numpy array containing the test labels.
+
+    >>> split_data(np.arange(13), np.arange(13), 0.8, 1)
+    (array([ 2,  3,  4, 10,  1,  6,  0,  7, 12,  9]), array([ 8, 11,  5]), array([ 2,  3,  4, 10,  1,  6,  0,  7, 12,  9]), array([ 8, 11,  5]))
+    """
+
+    # set seed
+   
+    np.random.seed(seed)
+    x = np.random.permutation(x)
+    np.random.seed(seed)
+    y = np.random.permutation(y)
+    # ***************************************************
+    # INSERT YOUR CODE HERE
+    # split the data based on the given ratio: TODO
+    # ***************************************************
+    sample_size = y.shape[0]
+    training_size = (int)(np.floor(ratio*sample_size))
+    x_tr = x[:training_size]
+    x_te = x[training_size :]
+    y_tr = y[:training_size]
+    y_te = y[training_size:]
+    
+    return x_tr, x_te, y_tr, y_te
+
+
+def calculate_downsample_size(N_pos, N_neg, desired_percentage):
+    # Calculate the required downsample size for the negative class
+    downsample_size_neg = int((N_pos * (1 - desired_percentage)) / desired_percentage)
+    
+    # Ensure we do not downsample more than available negative samples
+    downsample_size_neg = min(downsample_size_neg, N_neg)
+    
+    return downsample_size_neg
+
+
+def build_poly(x, degree) :
+    """Polynomial basis functions for input data x, for j=0 up to j=degree.
+
+    Args:
+        x (np.ndarray, (N,)): array of shape, N is the number of samples.
+        degree (int, optional): degree of the polynomial. Defaults to 1.
+
+    Returns:
+        poly (np.ndarray, (N,d+1)): the computed polynomial features.
+    """
+    poly = None
+    for deg in range(1, degree + 1):
+        if poly is None:
+            poly = np.power(x, deg)
+        else:
+            poly = np.c_[poly, np.power(x, deg)]
+    return poly
+