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Replace n_iter with max_iter in SGDClassifier

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Aurélien Geron 2018-05-08 12:36:43 +02:00
parent c4e72ddc49
commit fb3e68709e
1 changed files with 10 additions and 22 deletions
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32
05_support_vector_machines.ipynb
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@ -869,7 +869,7 @@
" ax.text(4.5, 2.5, 3.8, \"Decision function $h$\", fontsize=15)\n",
" ax.set_xlabel(r\"Petal length\", fontsize=15)\n",
" ax.set_ylabel(r\"Petal width\", fontsize=15)\n",
" ax.set_zlabel(r\"$h = \\mathbf{w}^t \\cdot \\mathbf{x} + b$\", fontsize=18)\n",
" ax.set_zlabel(r\"$h = \\mathbf{w}^T \\mathbf{x} + b$\", fontsize=18)\n",
" ax.legend(loc=\"upper left\", fontsize=16)\n",
"\n",
"fig = plt.figure(figsize=(11, 6))\n",
@ -1165,7 +1165,7 @@
"source": [
"from sklearn.linear_model import SGDClassifier\n",
"\n",
"sgd_clf = SGDClassifier(loss=\"hinge\", alpha = 0.017, n_iter = 50, random_state=42)\n",
"sgd_clf = SGDClassifier(loss=\"hinge\", alpha = 0.017, max_iter = 50, random_state=42)\n",
"sgd_clf.fit(X, y.ravel())\n",
"\n",
"m = len(X)\n",
@ -1235,9 +1235,7 @@
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets\n",
@ -1267,7 +1265,7 @@
"lin_clf = LinearSVC(loss=\"hinge\", C=C, random_state=42)\n",
"svm_clf = SVC(kernel=\"linear\", C=C)\n",
"sgd_clf = SGDClassifier(loss=\"hinge\", learning_rate=\"constant\", eta0=0.001, alpha=alpha,\n",
" n_iter=100000, random_state=42)\n",
" max_iter=100000, random_state=42)\n",
"\n",
"scaler = StandardScaler()\n",
"X_scaled = scaler.fit_transform(X)\n",
@ -1378,9 +1376,7 @@
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"np.random.seed(42)\n",
@ -1605,9 +1601,7 @@
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import fetch_california_housing\n",
@ -1627,9 +1621,7 @@
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
@ -1647,9 +1639,7 @@
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
@ -1784,9 +1774,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"metadata": {},
"outputs": [],
"source": []
}
@ -1807,7 +1795,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.5.2"
},
"nav_menu": {},
"toc": {