https://www.cnblogs.com/HolyShine/p/10819831.html
# from sklearn.datasets import load_iris import numpy as np #科学计算基础包 from scipy import sparse import matplotlib.pyplot as plt import pandas as pd from IPython.display import display import sys import matplotlib import sklearn from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier # x=np.array([[1,2,3],[4,5,6]]) # print("x:\n{}".format(x)) # eye=np.eye(4) # print("NumPy array:\n{}".format(eye)) # x=np.linspace(-10,10,100)#在 -10和 10 之间生成一个数列，共100个数 # #用正弦函数创建第二个数组 # y=np.sin(x) # plt.plot(x,y,marker="x")#no display,why? #pandas # data={\'Name\':["John","Anna","Peter","Linda"], # \'Location\':["New York","Paris","Berlin","London"], # \'Age\':[24,13,53,33] # } # data_pandas = pd.DataFrame(data) # display(data_pandas) # # display(data_pandas[data_pandas.Age>30]) # print(\'Python Version:{}\'.format(sys.version)) # print(\'Pandas Version:{}\'.format(pd.__version__)) # print(\'matplotlib Version:{}\'.format(matplotlib.__version__)) # print(\'matplotlib Version:{}\'.format(matplotlib.__version__)) # print(\'scikit-learn Version:{}\'.format(sklearn.__version__)) iris_dataset=load_iris() # print("Keys of iris_dataset:\n{}".format(iris_dataset.keys())) X_train,X_test, y_train, y_test=train_test_split( iris_dataset[\'data\'], iris_dataset[\'target\'], random_state=0 ) # print("X_train sharpe:{}".format(X_train.shape)) # print("y_train shape:{}".format(y_train.shape)) # # # iris_dtaframe=pd.DataFrame(X_train, columns=iris_dataset.feature_names) # grr=pd.scatter_matrix(iris_dtaframe, c=y_train, figsize=(15,15), marker=\'O\',hist_kwds={\'bins\':20}, s=60, alpha=.8, cmap=mglearn.cm3) #1.7.4 构建第一个模型：K邻近算法 knn=KNeighborsClassifier(n_neighbors=1) knn.fit(X_train, y_train) #out KNeighborsClassifier(algorithm=\'auto\',leaf_size=30,metric=\'minkowski\',metric_params=None, n_jobs=1, n_neighbors=1, p=2, weights=\'uniform\') X_new=np.array([[5,2.9,1,0.2]]) print("X_new.shape:{}".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))) print("Test set score:{:.2f}".format(knn.score(X_test, y_test)))