mglearn,一个机器学习的模块组件,本以为和其它的组件一样,pip安装一波就行了,实际上,行是行了,额,为啥用起来会有警告呢🐸,不明觉厉,但是确实在Jupyter里面不影响使用,暂且不管啥问题先用着
主要的是,昨天装了一个Anaconda,实际上用起来感觉还行,但为啥总感觉启动的时候有点不稳定🤣
conda install mglearn
!pip install mglearn
!pip install -i https://pypi.tuna.tsinghua.edu.cn/simple mglearn
#!/usr/bin/env python # coding: utf-8 # In[1]: import matplotlib.pyplot as plt # In[2]: import mglearn # In[3]: X,y = mglearn.datasets.make_wave(n_samples = 40) # In[4]: plt.plot(X,y,'o') plt.ylim(-3,3) plt.xlabel('Feature') plt.ylabel('Target') # In[5]: X,y = mglearn.datasets.make_forge() # In[6]: mglearn.discrete_scatter(X[:,0],X[:,1],y) # In[7]: mglearn.plots.plot_knn_classification(n_neighbors = 1) # In[8]: from sklearn.model_selection import train_test_split # In[9]: X,y = mglearn.datasets.make_forge() # In[10]: X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 0) # In[11]: print ('{}'.format(X_train.shape)) # In[12]: from sklearn.neighbors import KNeighborsClassifier # In[13]: clf = KNeighborsClassifier(n_neighbors = 3) # In[14]: clf.fit(X_train,y_train) # In[15]: print ('{}'.format(clf.score(X_test,y_test))) # In[16]: for num in range(1,3): clf = KNeighborsClassifier(n_neighbors = num) clf.fit(X_train,y_train) print ('{}'.format(clf.score(X_test,y_test))) print ('\n') # In[17]: X,y = mglearn.datasets.make_wave() # In[18]: mglearn.plots.plot_knn_regression(n_neighbors = 1) # In[19]: mglearn.plots.plot_knn_regression(n_neighbors = 1) # In[20]: mglearn.plots.plot_knn_regression(n_neighbors = 3) # In[ ]:
uC代码迁移有待了解一下,毕竟不能完全基于高阶操作系统甚至是高性能CPU、GPU以及其它的加成等,uC才是王道