代码如下
from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier import pandas as pd import numpy as np import mglearn import matplotlib.pyplot as plt iris_dataset = load_iris() print('keys of iris_dataset:n{}'.format(iris_dataset.keys())) print('target names:n{}'.format(iris_dataset['target_names'])) print('feature names:n{}'.format(iris_dataset['feature_names'])) print('type of data:n{}'.format(type(iris_dataset['data']))) print('shape of data:n{}'.format(iris_dataset['data'].shape)) print('first five rows of data:n{}'.format(iris_dataset['data'][:5])) print('type of target:n{}'.format(type(iris_dataset['target']))) print('shape of target:n{}'.format(type(iris_dataset['target'].shape))) print('target:]n{}'.format(iris_dataset['target'])) x_train,x_test,y_train,y_test = train_test_split(iris_dataset['data'],iris_dataset['target'],random_state=0) print('x_train shape:{}'.format(x_train.shape)) print('y_train shape:{}'.format(y_train.shape)) print('x_test shape:{}'.format(x_test.shape)) print('y_test shape:{}'.format(y_test.shape)) iris_dataframe = pd.DataFrame(x_train,columns = iris_dataset.feature_names) grr = pd.plotting.scatter_matrix(iris_dataframe,c=y_train,figsize=(15,15),marker='o',hist_kwds={'bins':20},s=60,alpha=.8,cmap=mglearn.cm3) knn = KNeighborsClassifier(n_neighbors=1) knn.fit(x_train,y_train) x_new = np.array([[5,2.9,1,0.2]]) print('x_new.shape:n{}'.format(x_new.shape)) prediction = knn.predict(x_new) print('prediction:{}'.format(prediction)) print('predicted target name:{}'.format(iris_dataset['target_names'][prediction])) y_pred = knn.predict(x_test) print('test set predictions:n{}'.format(y_pred)) print('test set score:{:.2f}'.format(np.mean(y_pred == y_test))) plt.show() 12345678910111213141516171819202122232425262728293031323334353637383940414243444546
