from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
def knn_selector():
iris = load_iris()
x_train,x_test,y_train,y_test = train_test_split(iris.data, iris.target, test_size=0.3)
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
estimator = KNeighborsClassifier(n_neighbors = 3)
estimator.fit(x_train, y_train)
# estimator.predict(x_test)
score = estimator.score(x_test, y_test)
print("score: ", score)
if __name__ == "__main__":
knn_selector()
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
# 获取数据集
# 划分数据集
# 标准化
# 创建模型
# 模型训练
# 模型预测与评估
def knn_selector():
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size= 0.3)
# print(X_train)
# plt.plot(X_train[:,0])
# plt.show()
transfer = StandardScaler()
X_train = transfer.fit_transform(X_train)
X_test = transfer.transform(X_test)
# print(X_train[:,0])
# plt.plot(X_train[:,0])
# plt.show()
estimator = KNeighborsClassifier(n_neighbors = 3)
estimator.fit(X_train, y_train)
score = estimator.score(X_test, y_test)
print("准确率: ", score)
if __name__ == "__main__":
knn_selector()
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
#------------------------------引入数据------------------------------
iris = datasets.load_iris() # 引入 iris 鸢尾花数据集
# 鸢尾花数据集 包含 4个 特征变量
iris_X = iris.data # 特征变量
iris_y = iris.target # 目标值
# iris['data']
# iris['target']
X_train, X_test, y_train, y_test = train_test_split(iris_X, iris_y, test_size=0.3)
#---------------------------训练数据
knn = KNeighborsClassifier() # 引入训练方法
knn.fit(X_train,y_train) # 进行填充测试数据进行训练
knn.predict(X_test) # 预测 特征值
'''
array([2, 1, 2, 0, 0, 1, 1, 2, 0, 1, 0, 2, 0, 1, 0, 2, 1, 2, 2, 2, 2, 2, 1,
1, 1, 1, 0, 2, 1, 2, 0, 1, 1, 0, 0, 2, 0, 0, 1, 0, 2, 1, 1, 2, 2])
'''
y_test # 真实的 特征值
'''
array([2, 1, 2, 0, 0, 1, 2, 2, 0, 1, 0, 2, 0, 1, 0, 2, 1, 2, 2, 2, 2, 2, 1,
1, 1, 2, 0, 2, 2, 2, 0, 1, 1, 0, 0, 2, 0, 0, 1, 0, 1, 1, 1, 2, 2])
'''
print(test_y)
print(pre)
print( sum(abs(pre - test_y)) / len(pre) )
knn.score(test_X, test_y)