def test():
"""
测试KNN算法
:return:
"""
group, labels = kNN.createDataSet()
print kNN.classify0([0, 0], group, labels, 3)
def main2():
'''
测试分类器
'''
group,labels = kNN.createDataSet()
result = kNN.classify0([0,0], group, labels, 3)
print result
def classify0Test():
"""
测试第一个分类器
:return:
"""
group, labels = kNN.createDataSet()
print('group:', group)
print('labels:', labels)
print('result:', kNN.classify0([0.1, 0.1], group, labels, 3))
def __test__():
dataSet, labels = kNN.createDataSet()
inX = array([1.2, 1.1])
k = 3
outputLabel = kNN.kNNClassify(inX, dataSet, labels, 3)
print("Your input is:", inX, "and classified to class: ", outputLabel)
inX = array([0.1, 0.3])
outputLabel = kNN.kNNClassify(inX, dataSet, labels, 3)
print("Your input is:", inX, "and classified to class: ", outputLabel)
def kNNtest():
# 生成数据集和类别标签
dataSet, labels = kNN.createDataSet()
# 定义一个未知类别的数据
with open('test_24.json', 'r') as file:
for line in file:
line = json.loads(line)
testid = line[0]
testtarget = line[1:]
testX = array([testtarget])
# 调用分类函数对未知数据分类
outputLabel = kNN.kNNClassify(testX, dataSet, labels, 1)
result = [outputLabel, testid]
with open('testPredict24.json', 'a') as file:
file.write(json.dumps(result) + '\n')
import kNN grps , lbls = kNN.createDataSet(); #rate = kNN.classify0([1,0.8],grps,lbls,3); rate = kNN.classify0([2,0.8],grps,lbls,3); print (rate)
# -*- coding: utf-8 -*- # writer : lgy # data : 2017-08-19 import kNN from numpy import * dataSet, labels = kNN.createDataSet() testX = array([1.2, 1.0]) k = 3 outputLabel = kNN.kNNClassify(testX, dataSet, labels, 3) print "Your input is:", testX, "and classified to class: ", outputLabel testX = array([0.1, 0.3]) outputLabel = kNN.kNNClassify(testX, dataSet, labels, 3) print "Your input is:", testX, "and classified to class: ", outputLabel
#! /usr/bin/env python
# -*- coding: utf-8 -*-
import kNN
if __name__ == '__main__':
data = kNN.createDataSet()
print data
# matrix = kNN.file2matrix('datingTestSet2.txt')
# print matrix
# kNN.datingClassTest()
kNN.handwritingClassTest()
''' 用于创建用于分析的数据集'''
from numpy import *
import operator
def createDataSet()
group = arrary([1.0,1.1],[1.0,1.0],[0,0],[0,0.1])
label = ["A","A","B","B"]
return group, labels
'''以下为引用该函数的举例'''
import kNN
group,lables=kNN.createDataSet()
'''
对未知类别属性的数据集中的每个点依次执行以下操作
1、计算已知类别数据集中点于当前点之间的距离；
2、按照距离依次递增次序排列；
3、选取与当前点距离最小的K个点；
4、确定前k个点所在类别的出现频率：
5、返回前K个点出现频率最高的类别作为当前点的预测分类。
'''
def classify0(inX, dataSet, labels, k)
dataSetSize = dataSet.shape[0]
diffMat = tile(intX, (dataSetSize,1))-dataSet
sqDiffMat = diffMat**2
sqDistances = sqDiffMat.sum(axis=1)
distances = sqDistances**0.5 '''两点之间距离计算'''
sortedDistIndicies = distances.argsort()
classCount = {}
for i in range(K): '''获得排名前k的距离点的类型'''
voteIlabel = labels[sortedDistIndicies[i]
classCount[voteIlabel] = classCount.get(voteIlabel,0)+1
def main1():
'''
生成数据
'''
group,labels = kNN.createDataSet()
def test_kNN(self):
group, labels = kNN.createDataSet()
result = kNN.classify0([0, 0], group, labels, 3)
expected = 'B'
self.assertEqual(result, expected)
def test1(self):
group,labels = kNN.createDataSet()
print(group)
result = kNN.classify0([0,0],group,labels,3)
print(result)
'''A wrapper around kNN.py. Include the kNN.py and datingTestSet2 in the current working directory'''
import kNN #importing the methods and variables from kNN.py. these methods/variables can be accessed as kNN.MethodName() or kNN.variableName
import matplotlib.pyplot as plt
import numpy as np
FigDating = plt.figure()
group, labels = kNN.createDataSet(
) #Create the data set with four items(2-D vectors). Each of them one of two labels associated with them
colormap1 = {'A': 'red', 'B': 'blue'} #Make a color map
ColoredGroupLabels = []
for things in labels: #Get a vector representing the colors
ColoredGroupLabels.append(colormap1[things]) #for each data item
ax1 = FigDating.add_subplot(311, xlim=(-0.1, 1.1), ylim=(
-.05,
1.15)) #Dividing the figure into 3 sub plots and selecting the top-most
ax1.scatter(
group[:, 0], group[:, 1], s=20, c=ColoredGroupLabels, marker='o'
) #Plotting the data as a scatter plot with color(c) property as per the labelling.
#Testing with new points
testvector = [0.75, 0.75] #first point
answer = kNN.classify0(testvector, group, labels, 3) #classify the first point
# type "print answer" to see result
ax1.scatter(testvector[0],
testvector[1],
s=20,
c=colormap1[answer],
# This is a sample Python script.
# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
from classify0 import classify0
from kNN import createDataSet
def print_hi(name):
# Use a breakpoint in the code line below to debug your script.
print(f'Hi, {name}') # Press Ctrl+F8 to toggle the breakpoint.
# Press the green button in the gutter to run the script.
if __name__ == '__main__':
print_hi('PyCharm')
group, labels = createDataSet()
print(classify0([0, 0], group, labels, 3))
# See PyCharm help at https://www.jetbrains.com/help/pycharm/
import sys
sys.path.append('F:\1masterpiece\python test\apriori')
import kNN
kNN.createDataSet()
from numpy import *
import sys
import matplotlib
import matplotlib.pyplot as plt
from imp import reload
sys.path.append(r"C:\Python34\code\machinelearninginaction\Ch02")
import kNN
reload(kNN)
df = kNN.createDataSet()
inputt = array([0.7, 0.8])
K = 3
output = kNN.classify(inputt, df, K)
print("测试数据为:", inputt, "分类结果为：", output)
fig = plt.figure(figsize=(6, 6)) #XY轴具有相同的刻度和比例
ax = fig.add_subplot(1, 1, 1)
plt.plot(df['x'], df['y'], 'ro') #画图
plt.plot(inputt[0], inputt[1], 'go')
count = 0
##添加标注
for label in df.index:
ax.annotate(label,
xy=df.values[count],
xytext=(df.values[count][0] + 0.1, df.values[count][1] + 0.05),
arrowprops=(dict(facecolor='b',
width=0.05,
shrink=0.05,
headwidth=1,
connectionstyle="arc3")))
import kNN
from numpy import *
dataSet, labels = kNN.createDataSet()
testX = array([1.9, 3.2])
k = 3
outputLabel = kNN.kNNClassify(testX, dataSet, labels, 3)
print("Your input is:", testX, "and classified to class: ", outputLabel)
testX = array([4.1, 3.3])
outputLabel = kNN.kNNClassify(testX, dataSet, labels, 3)
print("Your input is:", testX, "and classified to class: ", outputLabel)
from numpy import *
import matplotlib.pyplot as plt
import kNN
# create 5 my neighbors
neighbors,names = kNN.createDataSet(5)
# find two nearest neighbors to me
result = kNN.classify([0,0], neighbors, names, 2)
# x and y save positons of my neighbors
x = [0]* neighbors.shape[0]
y = [0]* neighbors.shape[0]
for i in range(0, neighbors.shape[0]):
x[i] = neighbors [i][0]
y[i] = neighbors [i][1]
# display my neighbors with blue color
plt.plot(x,y,'bo')
plt.axis([-0.2, 1.2, -0.2, 1.2])
# assign names to neighbors
for i, name in enumerate(names):
plt.annotate(name,(x[i],y[i]),(x[i]-0.08,y[i]+0.01))
# diplay me with red color
plt.plot([0],[0],'ro')
# display two nearest neighbors with messages and yellow color
for i, name in enumerate(names):
for r in result:
if name is r[0]:
plt.plot([x[i]],[y[i]],'yo')
plt.annotate('I am here',(x[i],y[i]),(x[i]+0.01,y[i]-0.05))
plt.show()
import kNN
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
# easy try
group, labels = kNN.createDataSet()
predict = kNN.classify0([0, 0], group, labels, 3)
print(predict)
# load data
datingDataMat, datingLabels = kNN.file2matrix('datingTestSet2.txt')
print(datingDataMat)
print(datingLabels[0:20])
# scatter plot
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datingDataMat[:, 0], datingDataMat[:, 1],
15.0 * np.array(datingLabels), 15.0 * np.array(datingLabels))
plt.show()
# normalization
normMat, ranges, minVals = kNN.autoNorm(datingDataMat)
print(normMat)
print(ranges)
print(minVals)
# test error rate
kNN.datingClassTest()
'''A wrapper around kNN.py. Include the kNN.py and datingTestSet2 in the current working directory'''
import kNN #importing the methods and variables from kNN.py. these methods/variables can be accessed as kNN.MethodName() or kNN.variableName
import matplotlib.pyplot as plt
import numpy as np
FigDating = plt.figure()
group,labels = kNN.createDataSet() #Create the data set with four items(2-D vectors). Each of them one of two labels associated with them
colormap1 = { 'A':'red', 'B':'blue'} #Make a color map
ColoredGroupLabels = []
for things in labels: #Get a vector representing the colors
ColoredGroupLabels.append(colormap1[things]) #for each data item
ax1 = FigDating.add_subplot(311, xlim=(-0.1,1.1), ylim=(-.05,1.15)) #Dividing the figure into 3 sub plots and selecting the top-most
ax1.scatter(group[:,0], group[:,1], s= 20, c= ColoredGroupLabels, marker = 'o' ) #Plotting the data as a scatter plot with color(c) property as per the labelling.
#Testing with new points
testvector = [.2, .2] #first point
answer = kNN.classify0(testvector,group, labels, 3) #classify the first point
# type "print answer" to see result
ax1.scatter(testvector[0], testvector[1], s= 20, c= colormap1[answer], marker = 'x' ) #plot first point
#second point - created, classified and plotted
testvector = [.5, .5]
answer = kNN.classify0(testvector,group, labels, 3)
ax1.scatter(testvector[0], testvector[1], s= 20, c= colormap1[answer], marker = 'x' )
#third point - created, classified and plotted
testvector = [.75, .75]
answer = kNN.classify0(testvector,group, labels, 3)
# coding=utf-8
__author__ = 'wuwen'
import kNN
import numpy as np
import operator
group, labels = kNN.createDataSet()
# k邻接算法 分类
# n维公式 d = ((x1-x2)^2+(y1-y2)^2+.......+(n1-n2)^2)^0.5
# 多维类推
# 思路：算出预测值在样本中的 各样本与输入值的距离，距离越近，类型就越相近。
def classify0(inX, dataSet, labels, k):
dataSetSize = dataSet.shape[0]
# 创建
diffMat = np.tile(inX, (dataSetSize, 1))
# 样本与输入做差
diffMat -= dataSet
# 差值的平方
diffMat **= 2
# 把样本中各维度的差的平方相加
sqDistances = diffMat.sum(axis=1)
# 开方，算出距离
sqDistances **= 0.5
# 距离排序，返回排序的索引数组
sortedDistances = sqDistances.argsort()
import kNN
import matplotlib
import matplotlib.pyplot as plt
from numpy import array
features, labels = kNN.createDataSet()
features
kNN.classify0([0, 0], features, labels, 3)
datamat, labels = kNN.file2matrix('datingTestSet.txt')
'''
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(datamat[:,1], datamat[:,2], 15.0*array(labels), 15.0*array(labels))
plt.show()
'''
normmat, ranges, minvals = kNN.autoNorm(datamat)
print(normmat)
print(ranges)
print(minvals)
# kNN.datingClassTest(0.2,7)
def classifyperson():
result = ['not at all', 'small doses', 'large dose']
ffmiles = float(input('frequent filter miles earned per year:'))
gametimepercent = float(input('% of time spent on game:'))