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liujianquan [刘健全]
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‎.gitignore‎

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.idea
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.idea
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.python-version

‎.python-version‎

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3.6.3

‎notebook/.ipynb_checkpoints/Untitled-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'matplotlib'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-1-98673fdda090>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
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]
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}
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],
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"source": [
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"from sklearn import datasets\n",
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"from sklearn.linear_model import LinearRegression\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"loaded_data = datasets.load_boston()\n",
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"data_X = loaded_data.data\n",
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"data_y = loaded_data.target"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = LinearRegression()\n",
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"model.fit(data_X, data_y)\n",
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"\n",
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"print(model.predict(data_X[:4, :]))\n",
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"print(data_y[:4])\n",
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"\n",
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"X, y = datasets.make_regression(n_samples=100, n_features=1, n_targets=1, noise=10)\n",
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"plt.scatter(X, y)\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎notebook/.ipynb_checkpoints/sk4_learning_pattern-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn import datasets\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.neighbors import KNeighborsClassifier"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[[5.1 3.5 1.4 0.2]\n",
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" [4.9 3. 1.4 0.2]]\n",
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"[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
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" 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
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" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
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" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
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" 2 2]\n",
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"[1 2 1 0 0 0 2 0 1 0 0 0 0 1 1 0 0 2 0 2 0 2 1 0 2 2 2 1 1 2 2 0 1 2 2 0 1\n",
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" 2 1 1 2 2 1 2 2 2 1 0 0 1 1 0 0 1 2 0 1 1 0 0 1 2 0 2 2 0 2 1 2 1 1 2 0 0\n",
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" 0 1 2 1 1 2 1 0 0 0 1 2 2 0 0 2 2 0 1 2 2 2 2 0 1 0 0 2 1 2 1]\n",
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"[0 0 2 1 0 0 1 0 2 1 1 2 0 2 2 2 1 1 0 2 1 1 2 1 0 0 0 1 2 1 1 2 0 1 0 1 1\n",
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" 2 2 1 0 2 0 1 2]\n",
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"[0 0 2 1 0 0 1 0 2 1 1 1 0 2 2 1 2 1 0 2 1 1 2 1 0 0 0 1 2 1 1 2 0 1 0 1 1\n",
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" 2 2 1 0 2 0 1 2]\n"
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]
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}
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],
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"source": [
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"iris = datasets.load_iris()\n",
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"iris_X = iris.data\n",
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"iris_y = iris.target\n",
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"\n",
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"print(iris_X[:2, :])\n",
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"print(iris_y)\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(\n",
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" iris_X, iris_y, test_size=0.3)\n",
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"\n",
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"print(y_train)\n",
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"\n",
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"knn = KNeighborsClassifier()\n",
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"knn.fit(X_train, y_train)\n",
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"print(knn.predict(X_test))\n",
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"print(y_test)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎notebook/.ipynb_checkpoints/sk5_datesets-checkpoint.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn import datasets\n",
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"from sklearn.linear_model import LinearRegression\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"loaded_data = datasets.load_boston()\n",
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"data_X = loaded_data.data\n",
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"data_y = loaded_data.target"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/Liu/.pyenv/versions/3.6.3/lib/python3.6/site-packages/sklearn/linear_model/base.py:509: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
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" linalg.lstsq(X, y)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[30.00821269 25.0298606 30.5702317 28.60814055]\n",
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"[24. 21.6 34.7 33.4]\n"
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]
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},
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{
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"data": {
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"image/png": 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LTb8fEQ8Xj+0EvgBMAf8+Iva2qx9mUHtYJ22F0h1b11VNAlcb4vH0UOsmbZtaWgwGP42I/1zRvh64E7gYWAk8BnwwIqou8uKppVavtJqBtKmgy5fl+Ok/TSZOI92990hDS1mbdZJGppZmMUx0FXBXRJyMiJeAo0wHBrOmJW1XWbqTLw3rwMwdy946MQGaXim0csinVWsEpc1kMusU7Q4GN0p6RtLtkpYX2/qAV8vOOVZsm0XSdkkjkkaOHz+edIrZDNUSxYOb+nhy6FL6evOzir0mpoKzz1rKS7t+lSeHLj0zlNOKJHBagHJAsE7SVDCQ9JikZxP+XAV8E/h54ELgdeC/NPr7I2JPRAxExMCKFSua6aotINXusmvdyQ+PFqouA1GpFUngWvUGZp2gqQRyRFxWz3mS/jvw18WXBeCCssOrim1mNf3B8KEZVbuVCd1qieLSHXqapLv9ViSBvRy1dYN2ziY6PyJeL778BPBs8ecHgb+U9EdMJ5DXAt9vVz9s4RgeLcxavgFmDgNd8qEViee8c3KSr37ncOpmNLklSr3bb7YwrJ4CNbOstTNn8J8kHZL0DHAJ8CWAiDgM3AP8EPhb4IZaM4nMYPruPG3u22tj4wyPFrj3QCHxnLHxiapVxu9779K2Tfl0vYF1g7Y9GUTEv6ly7Fbg1nZ9ti1M1YZVVvbmm9qGcqzO5SjmwvUG1g28NpF1jWp7ALxzcrKpjenbPWTjNYis03k5Cusa1XYWGxufYPZGkrPlc0tmnechGzM/GVgXKR9uSXpCqFVLX6oqLv2OpA1nPJRji5V3OrOutGboodSLf/kyExHw9vhEzYt70s5l5TubmXUj73RmC161/EHljmP1qFW5bLbQORhY5uodnik/r3dZjtwSzVhcrlyjBV0uDLPFzsHAMlVrn+C082rtTNbo7CAXhtli59lElql61+2pVkOQWzJzflBpdlAjK4W6MMwWOwcDy1TaMExhbLzuTevf996ls3YcAxpaKbSencvMFjIPE1mmqiWCy4eMqp03dmKC0S9fPqNty659DSeEXRhmi5mfDCxT1QrJ4N0L+I6t61KLynqX5WYNBzkhbNYYBwPLVGl4pkfp9cOvjY0zuKmPz2zunxUQcj3i7RMTM4aDdvzV05yTzyX+LieEzZI5GFhbNJK8HdzUx+kqxY+lC/gtgxv52jUXzhjXX7pEnK44f+J0cGpyyglhswY4Z2AtV+900XJpOQHBjAt45bj+6qGHEn/fiYnTfP2aC728hFmdHAys5eZSzbtj67pZy0EI+Mzm/jlfwJ0QNqufg4G13FySt3Nd83/5slxiAdryZck5AzNL5mBgLZc25FOa9ZN2sZ/LnfzNH9/Ajm8/zcTUuzmHXI+4+eMb5v4FzBYhJ5Ct5ZKmi+Z6xE//abLuIrB6DW7qY/enPjIjqbz7Ux/x8JBZg/xkYC2XNOSTtBNZq1YFdW7ArHkOBtYWlQGh2kb2Zpa9poaJJH1a0mFJpyUNVBzbKemopCOStpa1byu2HZU01MznW+cqTS8tVAkE4CIws07RbM7gWeBq4InyRknrgWuBDcA24E8l9UjqAb4BXAGsB64rnmsLTLVVRktcBGbWOZoaJoqI5wA0eymBq4C7IuIk8JKko8DFxWNHI+LF4vvuKp77w2b6YZ2n2vCPwEVgZh2mXTmDPmB/2etjxTaAVyvaP9qmPliG0qaXNrodpZnNj5rBQNJjwAcSDt0UEQ+0vkszPns7sB2gv7+/nR9lLTQ8WuCdk5Oz2j0sZNa5agaDiLhsDr+3AFxQ9npVsY0q7UmfvQfYAzAwMFAtD2kdonJdopLly3Lc/PENHhYy61DtKjp7ELhW0lmS1gBrge8DTwFrJa2R9B6mk8wPtqkPloG0xPGy9yx1IDDrYE3lDCR9AvgTYAXwkKSDEbE1Ig5LuofpxPAkcENETBXfcyOwF+gBbo+Iw019A+so3lTGrDs1O5vofuD+lGO3ArcmtD8MPNzM51q2hkcLqQvKpSWOXU9g1tm8NpE1pLKYrHKNoaR1iZw4Nut8DgbWkGp7FcC721iWLxx329UbnS8w63Bem8gaUk9OwAvHmXUfPxlYQ9LG/p0TMOtuDgbWEOcEzBYmDxNZQ+a6PaWZdTYHA2uYcwJmC4+HiczMzMHAzMwcDMzMDAcDMzPDwcDMzHAwMDMzHAzMzAwHAzMzw8HAzMxwMDAzMxwMzMwMBwMzM8PBwMzMaDIYSPq0pMOSTksaKGtfLWlc0sHin/9WduwiSYckHZX0x5LUTB/MzKx5zT4ZPAtcDTyRcOyFiLiw+Of6svZvAr8NrC3+2dZkH8zMrElNBYOIeC4ijtR7vqTzgX8WEfsjIoC/AAab6YOZmTWvnTmDNZJGJf0vSf+q2NYHHCs751ixzczMMlRzpzNJjwEfSDh0U0Q8kPK214H+iPiJpIuAYUkbGu2cpO3AdoD+/v5G325mZnWqGQwi4rJGf2lEnAROFn8+IOkF4INAAVhVduqqYlva79kD7AEYGBiIRvthZmb1acswkaQVknqKP/8c04niFyPideAfJW0uziL6HJD2dGFmZvOk2amln5B0DPiXwEOS9hYP/WvgGUkHgW8D10fEm8Vj/w74H8BR4AXgb5rpg5mZNU/Tk3o638DAQIyMjGTdDTOzriHpQEQM1D7TFchmZoaDgZmZ4WBgZmY4GJiZGQ4GZmaGg4GZmeFgYGZmOBiYmRkOBmZmhoOBmZnhYGBmZjgYmJkZDgZmZoaDgZmZ4WBgZmY4GJiZGQ4GZmaGg4GZmeFgYGZmOBiYmRlNBgNJuyU9L+kZSfdL6i07tlPSUUlHJG0ta99WbDsqaaiZzzczs9Zo9sngUeAXIuLDwN8BOwEkrQeuBTYA24A/ldQjqQf4BnAFsB64rniumZllqKlgEBGPRMRk8eV+YFXx56uAuyLiZES8BBwFLi7+ORoRL0bEKeCu4rlmZpahVuYMfgv4m+LPfcCrZceOFdvS2s3MLENLa50g6THgAwmHboqIB4rn3ARMAne0snOStgPbAfr7+1v5q83MrEzNYBARl1U7Luk3gV8DPhYRUWwuABeUnbaq2EaV9qTP3gPsARgYGIi088zMrDnNzibaBvwucGVEnCg79CBwraSzJK0B1gLfB54C1kpaI+k9TCeZH2ymD2Zm1ryaTwY1/FfgLOBRSQD7I+L6iDgs6R7gh0wPH90QEVMAkm4E9gI9wO0RcbjJPpiZWZP07shOZxsYGIiRkZGG3jM8WmD33iO8NjbOyt48O7auY3CT89VmtjhIOhARA/Wc2+yTQccaHi2w875DjE9MAVAYG2fnfYcAHBDMzCos2OUodu89ciYQlIxPTLF775GMemRm1rkWbDB4bWy8oXYzs8VswQaDlb35htrNzBazBRsMdmxdRz7XM6Mtn+thx9Z1GfXIzKxzLdgEcilJ7NlEZma1LdhgANMBwRd/M7PaFuwwkZmZ1c/BwMzMHAzMzMzBwMzMcDAwMzO6aKE6SceBl7PuR4pzgR9n3YkW83fqDv5O3SGr7/SzEbGinhO7Jhh0Mkkj9a4M2C38nbqDv1N36Ibv5GEiMzNzMDAzMweDVtmTdQfawN+pO/g7dYeO/07OGZiZmZ8MzMzMwaBlJP1HSc9IOijpEUkrs+5TsyTtlvR88XvdL6k36z41S9KnJR2WdFpSR8/uqEXSNklHJB2VNJR1f5ol6XZJb0h6Nuu+tIqkCyQ9LumHxf/v/kPWfUrjYNA6uyPiwxFxIfDXwJez7lALPAr8QkR8GPg7YGfG/WmFZ4GrgSey7kgzJPUA3wCuANYD10lan22vmvbnwLasO9Fik8DvRMR6YDNwQ6f+d3IwaJGI+Meyl2cDXZ+MiYhHImKy+HI/sCrL/rRCRDwXEQthI+yLgaMR8WJEnALuAq7KuE9NiYgngDez7kcrRcTrEfGD4s//D3gO6Mh19Rf0fgbzTdKtwOeAt4FLMu5Oq/0WcHfWnbAz+oBXy14fAz6aUV+sDpJWA5uA72Xbk2QOBg2Q9BjwgYRDN0XEAxFxE3CTpJ3AjcDN89rBOaj1nYrn3MT04+4d89m3uarnO5nNJ0nvA+4FvlgxitAxHAwaEBGX1XnqHcDDdEEwqPWdJP0m8GvAx6JL5iE38N+pmxWAC8peryq2WYeRlGM6ENwREfdl3Z80zhm0iKS1ZS+vAp7Pqi+tImkb8LvAlRFxIuv+2AxPAWslrZH0HuBa4MGM+2QVJAn4M+C5iPijrPtTjYvOWkTSvcA64DTTq6teHxFdfacm6ShwFvCTYtP+iLg+wy41TdIngD8BVgBjwMGI2Jptr+ZG0q8AXwd6gNsj4taMu9QUSXcCv8z0Cp8/Am6OiD/LtFNNkvRLwP8GDjF9bQD4/Yh4OLteJXMwMDMzDxOZmZmDgZmZ4WBgZmY4GJiZGQ4GZmaGg4GZmeFgYGZmOBiYmRnw/wGlENlU2zpZEwAAAABJRU5ErkJggg==\n",
49+
"text/plain": [
50+
"<Figure size 432x288 with 1 Axes>"
51+
]
52+
},
53+
"metadata": {
54+
"needs_background": "light"
55+
},
56+
"output_type": "display_data"
57+
}
58+
],
59+
"source": [
60+
"model = LinearRegression()\n",
61+
"model.fit(data_X, data_y)\n",
62+
"\n",
63+
"print(model.predict(data_X[:4, :]))\n",
64+
"print(data_y[:4])\n",
65+
"\n",
66+
"X, y = datasets.make_regression(n_samples=100, n_features=1, n_targets=1, noise=10)\n",
67+
"plt.scatter(X, y)\n",
68+
"plt.show()"
69+
]
70+
},
71+
{
72+
"cell_type": "code",
73+
"execution_count": null,
74+
"metadata": {},
75+
"outputs": [],
76+
"source": []
77+
}
78+
],
79+
"metadata": {
80+
"kernelspec": {
81+
"display_name": "Python 3",
82+
"language": "python",
83+
"name": "python3"
84+
},
85+
"language_info": {
86+
"codemirror_mode": {
87+
"name": "ipython",
88+
"version": 3
89+
},
90+
"file_extension": ".py",
91+
"mimetype": "text/x-python",
92+
"name": "python",
93+
"nbconvert_exporter": "python",
94+
"pygments_lexer": "ipython3",
95+
"version": "3.6.3"
96+
}
97+
},
98+
"nbformat": 4,
99+
"nbformat_minor": 2
100+
}

‎notebook/Untitled.ipynb‎

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,76 @@
1+
{
2+
"cells": [
3+
{
4+
"cell_type": "code",
5+
"execution_count": 1,
6+
"metadata": {},
7+
"outputs": [
8+
{
9+
"ename": "ModuleNotFoundError",
10+
"evalue": "No module named 'matplotlib'",
11+
"output_type": "error",
12+
"traceback": [
13+
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
14+
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
15+
"\u001b[0;32m<ipython-input-1-98673fdda090>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
16+
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'matplotlib'"
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]
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}
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],
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"source": [
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"from sklearn import datasets\n",
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"from sklearn.linear_model import LinearRegression\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"loaded_data = datasets.load_boston()\n",
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"data_X = loaded_data.data\n",
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"data_y = loaded_data.target"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = LinearRegression()\n",
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"model.fit(data_X, data_y)\n",
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"\n",
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"print(model.predict(data_X[:4, :]))\n",
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"print(data_y[:4])\n",
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"\n",
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"X, y = datasets.make_regression(n_samples=100, n_features=1, n_targets=1, noise=10)\n",
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"plt.scatter(X, y)\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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