{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn.decomposition import PCA\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"\n",
"np.random.seed(1001)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# 随机生成训练数据\n",
"n = 200\n",
"x = np.linspace(-4, 4, n)\n",
"error = np.random.randn(n)\n",
"y = 1 * x + error\n",
"data = np.c_[x, y]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"PCA(n_components=2) In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. "
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# 将模型结果可视化\n",
"fig = plt.figure(figsize=(12, 6), dpi=80)\n",
"# 原始数据\n",
"ax = fig.add_subplot(1, 2, 1)\n",
"ax.scatter(data[:, 0], data[:, 1], alpha=0.8)\n",
"ax.set_xlim([-6, 6])\n",
"ax.set_ylim([-6, 6])\n",
"ax.get_xaxis().set_visible(False)\n",
"ax.get_yaxis().set_visible(False)\n",
"# 模型结果\n",
"ax = fig.add_subplot(1, 2, 2)\n",
"ax.scatter(data[:, 0], data[:, 1], alpha=0.8)\n",
"m = model.mean_\n",
"for v, l in zip(model.components_, model.explained_variance_):\n",
" start, end = m, m + 1.5 * np.sqrt(l) * v\n",
" ax.annotate(\"\", xy=end, xytext=start, arrowprops=dict(facecolor=\"k\", width=2.0))\n",
"ax.set_xlim([-6, 6])\n",
"ax.set_ylim([-6, 6])\n",
"ax.get_xaxis().set_visible(False)\n",
"ax.get_yaxis().set_visible(False)\n",
"plt.savefig('pca_example.png', dpi=200)\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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