# -*- coding: utf-8 -*-
"""
Created on Sat Oct 13 19:26:26 2018

@author: fengjuan
"""
'''
K-近鄰演算法與其他模型最大不同在於該模型沒有引數訓練過程，即，沒有通過任何學習演算法訓練資料而且
只是根據測試樣本在訓練資料的分佈直接做出分類決策，因此k-近鄰屬於無 引數模型中非常簡答的一種
'''
#使用載入器讀取資料並存放在變數iris中
from sklearn.datasets import load_iris
iris=load_iris()
print(iris.data.shape)
print(iris.DESCR)
'''結果：(150, 4)
Iris Plants Database
====================

Notes
-----
Data Set Characteristics:
    :Number of Instances: 150 (50 in each of three classes)
    :Number of Attributes: 4 numeric, predictive attributes and the class
    :Attribute Information:
        - sepal length in cm
        - sepal width in cm
        - petal length in cm
        - petal width in cm
        - class:
                - Iris-Setosa
                - Iris-Versicolour
                - Iris-Virginica
    :Summary Statistics:

    ============== ==== ==== ======= ===== ====================
                    Min  Max   Mean    SD   Class Correlation
    ============== ==== ==== ======= ===== ====================
    sepal length:   4.3  7.9   5.84   0.83    0.7826
    sepal width:    2.0  4.4   3.05   0.43   -0.4194
    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)
    ============== ==== ==== ======= ===== ====================

    :Missing Attribute Values: None
    :Class Distribution: 33.3% for each of 3 classes.
    :Creator: R.A. Fisher
    :Donor: Michael Marshall (MARSHALL%[email protected])
    :Date: July, 1988

This is a copy of UCI ML iris datasets.
http://archive.ics.uci.edu/ml/datasets/Iris'''

#將資料分割，25%作為測試集，75%作為訓練集
from sklearn.cross_validation import train_test_split
X_train,X_test,y_train,y_test=train_test_split(iris.data,iris.target, 
                                               test_size=0.25,random_state=33)
print(y_train.shape)
print(y_test.shape)
'''：(112,)
 (38,)'''
#sklearn.preprocessing中匯入標準化模組
from sklearn.preprocessing import StandardScaler
ss=StandardScaler()
from sklearn.neighbors import KNeighborsClassifier
X_train=ss.fit_transform(X_train)
X_test=ss.transform(X_test)
knc=KNeighborsClassifier()
knc.fit(X_train,y_train)
y_predict=knc.predict(X_test)
from sklearn.metrics import classification_report
print('Accuracy of =K-NeighborsClassifier is:',knc.score(X_test,y_test))
print(classification_report(y_test,y_predict,target_names=iris.target_names.astype(str)))

'''結果：
Accuracy of =K-NeighborsClassifier is: 0.8947368421052632
             precision    recall  f1-score   support

     setosa       1.00      1.00      1.00         8
 versicolor       0.73      1.00      0.85        11
  virginica       1.00      0.79      0.88        19

avg / total       0.92      0.89      0.90        38
'''