# 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)))