Add logistic save figures

4_logistic_regression/2-Logistic_regression.ipynb

@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -80,6 +80,7 @@
     "y=1/(1+np.e**(-X))\n",
     "plt.plot(X,y,'b-')\n",
     "plt.title(\"Logistic function\")\n",
+    "plt.savefig(\"logstic_fuction.pdf\")\n",
     "plt.show()"
    ]
   },
@@ -179,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -195,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -204,7 +205,7 @@
        "Text(0.5, 1.0, 'Original Data')"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -226,16 +227,17 @@
     "data, label = sklearn.datasets.make_moons(200, noise=0.30)\n",
     "\n",
     "plt.scatter(data[:,0], data[:,1], c=label)\n",
+    "plt.savefig(\"logistic_train_data.pdf\")\n",
     "plt.title(\"Original Data\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_decision_boundary(predict_func, data, label):\n",
+    "def plot_decision_boundary(predict_func, data, label, figName=None):\n",
     "    \"\"\"画出结果图\n",
     "    Args:\n",
     "        pred_func (callable): 预测函数\n",
@@ -253,13 +255,14 @@
     "\n",
     "    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)   #画出登高线并填充\n",
     "    plt.scatter(data[:, 0], data[:, 1], c=label, cmap=plt.cm.Spectral)\n",
+    "    if figName != None: plt.savefig(figName)\n",
     "    plt.show()\n",
     "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -305,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -324,7 +327,7 @@
    "source": [
     "logistic = Logistic(data, label)\n",
     "logistic.train(200)\n",
-    "plot_decision_boundary(lambda x: logistic.predict(x), data, label)"
+    "plot_decision_boundary(lambda x: logistic.predict(x), data, label, \"logistic_pred_res.pdf\")"
    ]
   },
   {