#coding:utf-8

%matplotlib inline

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.neighbors import KNeighborsClassifier

import numpy as np

import matplotlib.pyplot as plt

iris_dataset = load_iris() # 获取数据

# print("keys of iris_dataset:\n{}".format(iris_dataset.keys()))

# print(iris_dataset["DESCR"][:193]+"\n...")

# print("target names:{}".format(iris_dataset["target_names"]))

# print("feature names:{}".format(iris_dataset["feature_names"]))

# print(iris_dataset["data"][:5])

# print(iris_dataset["data"], iris_dataset["target"])

# 对数据进行拆分，分为训练数据和测试数据

x_train, x_test, y_train, y_test = train_test_split(iris_dataset["data"], iris_dataset["target"], random_state=0)

# print(x_train, x_test, y_train, y_test)

knn = KNeighborsClassifier(n_neighbors=1) # 获取KNN对象

knn.fit(x_train, y_train) # 训练模型

# 评估模型

y_pre = knn.predict(x_test)

score = knn.score(x_test, y_test) # 调用打分函数

print("test set predictions:\n{}".format(y_test))

print("test set score:{:.2f}".format(score))

if score > 0.9:

x_new = np.array([[5, 2.9, 1, 0.3]])

print("x_new.shape:{}".format(x_new.shape))

prediction = knn.predict(x_new) # 预测

print("prediction:{}".format(prediction))

print("predicted target name:{}".format(iris_dataset["target_names"][prediction]))

# 可视化展示

plt.title("KNN Classification")

plt.plot(x_train, y_train, "b.") # 训练数据打点

plt.plot(x_test, y_test, "y.") # 测试数据打点

plt.plot(x_new, prediction, "ro") # 预测数据打点

plt.show()

else:

print("used train or test data is not available !")