For the kNN algorithm, you need to choose the value for k, which is called n_neighbors in the scikit-learn implementation. Here’s how you can do this in Python: Python >>> from sklearn.neighbors import KNeighborsRegressor >>> knn_model = KNeighborsRegressor(n_neighbors=3) You ...
# 画出图表 plt.title("KNN algorithm implemented in Python") plt.ion() plt.scatter(x, y, c=colourlist, s=15) plt.scatter(data1[0], data1[1], c=colourlist1[0], marker='*', s=100) didntLike = mlines.Line2D([], [], color='black', marker='.', markersize=6, label='Actio...
{} for i in range(len(level)): level_dict[level[i]] = float(i) / (len(level) - 1) # level_dict[level[i]] = i for items in datalist[:]: items[attribute] = level_dict[items[attribute]] return datalist def KnnAlgorithm(dataTrain,sample,attribute,k): mergeData = dataTrain ...
knn = [k[-1] for k in knn_list]#提取knn_list中的最后一列即类别 count_pairs = Counter(knn)#求数组Knn中每个数字出现了几次,具体用法见下面 # max_count = sorted(count_pairs, key=lambda x: x)[-1] max_count = sorted(count_pairs.items(), key=lambda x: x[1])[-1][0]#计算类别...
KNN =KNeighborsClassifier(algorithm='auto', #在KNN中使用的算法,其他选项还有ball_tree,kd_tree,和brute leaf_size=30, #当使用和树有关的算法时的叶子数量 metric='minkowski',p=2, #使用的是明可夫斯基距离中的欧式距离 metric_params=None, n_jobs=1, #并行计算的线程数量 ...
clf = neighbors.KNeighborsClassifier(algorithm='kd_tree') clf.fit(x_train, y_train) '''测试结果的打印''' answer = clf.predict(x) print(x) print(answer) print(y) print(np.mean( answer == y)) '''准确率与召回率''' precision
KNeighborsClassifier(algorithm='auto',leaf_size=30,metric='minkowski',metric_params=None,n_jobs=1,n_neighbors=3,p=2,weights='uniform') knn_clf.score(X_test,y_test) 1.0 实现自己的StandardScaler importnumpyasnpclassStandardScaler:def__init__(self):self.mean_=None self.scale_=None deffit(se...
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform') #%%time#获得结果y_result=knn.predict(test_x.reshape(1000,1024))print("预测结果:\n{}".format(y_result[500:700]),"\n实际结果:\n{}".fo...
一、基于原生Python实现K近邻(KNN)K近邻算法（K-Nearest Neighbor，简称KNN算法）是一种基本的机器学习...
fit(X_train,y_train) KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski', metric_params=None, n_jobs=None, n_neighbors=5, p=2, weights='uniform') X_predict = x.reshape(1,-1) kNN_classifier.predict(X_predict) array([1]) 模仿sklearn重新封装knn import numpy as ...