| from sklearn import datasets | |
| import matplotlib.pyplot as plt | |
| iris = datasets.load_iris() | |
| digits = datasets.load_digits() | |
| fig = plt.figure(figsize=(8,8)) | |
| fig.subplots_adjust(left=0,right=1,bottom=0,top=1,hspace=0.05,wspace=0.05) | |
| for i in range(100): | |
| ax = fig.add_subplot(10,10,i+1,xticks=[],yticks=[]) | |
| ax.imshow(digits.images[i],cmap=plt.cm.binary,interpolation='nearest') | |
| ax.text(0,7,str(digits.target[i]),color='green') | |
| plt.show() | |
| x= digits.data | |
| y= digits.target | |
| from sklearn.model_selection import train_test_split | |
| xtrain,xtest,ytrain,ytest = train_test_split(x,y,test_size=0.2,random_state=0) | |
| from sklearn.linear_model import Perceptron | |
| perceptron_model = Perceptron(tol=1e-3,random_state=0) | |
| perceptron_model.fit(xtrain,ytrain) | |
| perceptron_prediction=perceptron_model.predict(xtest) | |
| from sklearn import metrics |