update on today's lectures

doc/LectureNotes/_build/jupyter_execute/Clustering.ipynb

@@ -475,7 +475,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration 5\n",
-      "Runtime: 0.4884450435638428 seconds\n"
+      "Runtime: 0.4787557125091553 seconds\n"
      ]
     }
    ],
@@ -604,7 +604,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 5\n",
-      "Runtime: 0.44310808181762695 seconds\n"
+      "Runtime: 0.4250478744506836 seconds\n"
      ]
     }
    ],
@@ -745,7 +745,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 11\n",
-      "Runtime: 0.8929829597473145 seconds\n",
+      "Runtime: 0.8499350547790527 seconds\n",
       " "
      ]
     }
@@ -877,7 +877,7 @@
      "output_type": "stream",
      "text": [
       "Converged at iteration: 5\n",
-      "Runtime: 0.0037839412689208984 seconds\n"
+      "Runtime: 0.003952980041503906 seconds\n"
      ]
     }
    ],

doc/LectureNotes/_build/jupyter_execute/chapter1.py

@@ -2120,16 +2120,13 @@ TestError = np.zeros(maxdegree)
 TrainError = np.zeros(maxdegree)
 polydegree = np.zeros(maxdegree)
 x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)
-scaler = StandardScaler()
-scaler.fit(x_train)
-x_train_scaled = scaler.transform(x_train)
-x_test_scaled = scaler.transform(x_test)
+
 
 for degree in range(maxdegree):
     model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))
-    clf = model.fit(x_train_scaled,y_train)
-    y_fit = clf.predict(x_train_scaled)
-    y_pred = clf.predict(x_test_scaled) 
+    clf = model.fit(x_train,y_train)
+    y_fit = clf.predict(x_train)
+    y_pred = clf.predict(x_test) 
     polydegree[degree] = degree
     TestError[degree] = np.mean( np.mean((y_test - y_pred)**2) )
     TrainError[degree] = np.mean( np.mean((y_train - y_fit)**2) )

doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb

@@ -2400,7 +2400,13 @@
       "Learning rate  =  10.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.08888888888888889\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  10.0\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.10555555555555556\n",

doc/LectureNotes/chapter1.ipynb

@@ -3411,16 +3411,13 @@
     "TrainError = np.zeros(maxdegree)\n",
     "polydegree = np.zeros(maxdegree)\n",
     "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)\n",
-    "scaler = StandardScaler()\n",
-    "scaler.fit(x_train)\n",
-    "x_train_scaled = scaler.transform(x_train)\n",
-    "x_test_scaled = scaler.transform(x_test)\n",
+    "\n",
     "\n",
     "for degree in range(maxdegree):\n",
     "    model = make_pipeline(PolynomialFeatures(degree=degree), LinearRegression(fit_intercept=False))\n",
-    "    clf = model.fit(x_train_scaled,y_train)\n",
-    "    y_fit = clf.predict(x_train_scaled)\n",
-    "    y_pred = clf.predict(x_test_scaled) \n",
+    "    clf = model.fit(x_train,y_train)\n",
+    "    y_fit = clf.predict(x_train)\n",
+    "    y_pred = clf.predict(x_test) \n",
     "    polydegree[degree] = degree\n",
     "    TestError[degree] = np.mean( np.mean((y_test - y_pred)**2) )\n",
     "    TrainError[degree] = np.mean( np.mean((y_train - y_fit)**2) )\n",