{
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   "cell_type": "markdown",
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   "source": [
    "# 多层感知机分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "王**"
   ]
  },
  {
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   "source": [
    "```\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "h = 0.02 # step size in the mesh\n",
    "\n",
    "alphas = np.arange(1,5)\n",
    "\n",
    "names = []\n",
    "for i in alphas:\n",
    "    names.append('n of neurons ' + str(i))\n",
    "\n",
    "classifiers = []\n",
    "for i in alphas: \n",
    "    p=np.ones(i,dtype = np.int32)*int(3)\n",
    "    classifiers.append(MLPClassifier(solver='lbfgs',activation='relu',learning_rate_init=0.1,alpha=1e-5, hidden_layer_sizes=p,random_state=1))\n",
    "\n",
    "X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,\n",
    "                           random_state=0, n_clusters_per_class=1)\n",
    "rng = np.random.RandomState(2)\n",
    "X += 2 * rng.uniform(size=X.shape)\n",
    "linearly_separable = (X, y)\n",
    "\n",
    "datasets = [\n",
    "            make_moons(n_samples=500,noise=0.2, random_state=0),\n",
    "            make_circles(n_samples=500,noise=0.2, factor=0.5, random_state=1),\n",
    "            linearly_separable]\n",
    "            \n",
    "\n",
    "\n",
    "figure = plt.figure(figsize=(17, 9))\n",
    "i = 1\n",
    "# iterate over datasets\n",
    "for X, y in datasets:\n",
    "    # preprocess dataset, split into training and test part\n",
    "    X0=(X[:,0]-min(X[:,0]))/(max(X[:,0])-min(X[:,0]))*255\n",
    "    X1=(X[:,1]-min(X[:,1]))/(max(X[:,1])-min(X[:,1]))*255\n",
    "    X=np.vstack((X0,X1))\n",
    "    X=X.T\n",
    "    \n",
    "    \n",
    "    X = StandardScaler().fit_transform(X)\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)\n",
    "\n",
    "    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "\n",
    "    # just plot the dataset first\n",
    "    cm = plt.cm.RdBu\n",
    "    cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n",
    "    ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
    "    # Plot the training points\n",
    "    ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n",
    "    # and testing points\n",
    "    ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.5)\n",
    "    ax.set_xlim(xx.min(), xx.max())\n",
    "    ax.set_ylim(yy.min(), yy.max())\n",
    "    ax.set_xticks(())\n",
    "    ax.set_yticks(())\n",
    "    i += 1\n",
    "\n",
    "    # iterate over classifiers\n",
    "    for name, clf in zip(names, classifiers):\n",
    "        ax = plt.subplot(len(datasets), len(classifiers) + 1, i)\n",
    "        clf.fit(X_train, y_train)\n",
    "        score = clf.score(X_test, y_test)\n",
    "\n",
    "        # Plot the decision boundary. For that, we will assign a color to each\n",
    "        # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "        if hasattr(clf, \"decision_function\"):\n",
    "            Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])\n",
    "        else:\n",
    "            Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n",
    "\n",
    "        # Put the result into a color plot\n",
    "        Z = Z.reshape(xx.shape)\n",
    "        ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)\n",
    "\n",
    "        # Plot also the training points\n",
    "        ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)\n",
    "        # and testing points\n",
    "        ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,\n",
    "                   alpha=0.5)\n",
    "\n",
    "        ax.set_xlim(xx.min(), xx.max())\n",
    "        ax.set_ylim(yy.min(), yy.max())\n",
    "        ax.set_xticks(())\n",
    "        ax.set_yticks(())\n",
    "        ax.set_title(name)\n",
    "        ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),\n",
    "                size=15, horizontalalignment='right')\n",
    "        i += 1\n",
    "\n",
    "figure.subplots_adjust(left=.02, right=.98)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "'''\n",
    "plt.plot(alphas, score) \n",
    "plt.axis([0, 11, 0, 1])\n",
    "plt.ylabel('score')\n",
    "plt.xlabel('the number of neurons')\n",
    "'''\n",
    "```"
   ]
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