From 3ad4f112a1ab86bd15f9ca0bb1a68415ca7a41ee Mon Sep 17 00:00:00 2001
From: geekidentity <geekidentity@163.com>
Date: Tue, 14 Mar 2017 22:07:14 +0800
Subject: [PATCH] add classify0() in kNN.py

---
 src/python/02.kNN/kNN.py | 33 ++++++++++++++++++++++++++++++---
 1 file changed, 30 insertions(+), 3 deletions(-)

diff --git a/src/python/02.kNN/kNN.py b/src/python/02.kNN/kNN.py
index acebbd2c..cd75ebca 100644
--- a/src/python/02.kNN/kNN.py
+++ b/src/python/02.kNN/kNN.py
@@ -1,6 +1,5 @@
 '''
 导入科学计算包numpy和运算符模块operator
-@author: geekidentity
 '''
 from numpy import *
 import operator
@@ -10,9 +9,37 @@ import operator
 
  调用方式
  import kNN
- group, labels = createDateSet()11
+ group, labels = kNN.createDataSet()
 '''
 def createDataSet():
     group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])
     labels = ['A','A','B','B']
-    return group, labels
\ No newline at end of file
+    return group, labels
+
+
+def classify0(inX, dataSet, labels, k):
+    '''
+    inX: 用于分类的输入向量
+    dataSet: 输入的训练样本集
+    labels: 标签向量
+    k: 选择最近邻居的数目
+    注意：labels元素数目和dataSet行数相同；程序使用欧式距离公式.
+
+    预测数据所在分类可在输入下列命令
+    kNN.classify0([0,0], group, labels, 3)
+    '''
+    # 1. 距离计算
+    dataSetSize = dataSet.shape[0]
+    diffMat = tile(inX, (dataSetSize,1)) - dataSet
+    sqDiffMat = diffMat**2
+    sqDistances = sqDiffMat.sum(axis=1)
+    distances = sqDistances**0.5
+    sortedDistIndicies = distances.argsort()
+    # 2. 选择距离最小的k个点
+    classCount = {}
+    for i in range(k):
+        voteIlabel = labels[sortedDistIndicies[i]]
+        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
+    # 3. 排序
+    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
+    return sortedClassCount[0][0]
\ No newline at end of file