Maker玩轉Machine Learning (室內定位運用KNN演算法 Part 1)

最近看了一些論文，發現室內定位愈來愈多人開始用Machine Learning來做

不看還好，一看驚為天人!  決定花點時間學習一下

家中財政部長問我為什麼每次寫文章都這麼鄉民口吻

為何不像週刊專欄或是科技新聞那樣專業?

決定這次換個方式寫，但寫的程度太差，部長大人應該開心不起來 XD

-----------------分隔線在此---------------------

Machine Learning眾人以ML簡之

Big Data、Machine Learning、Deep Learning集市以AI著稱

機器訓練得宜，則學術深博，常言道，得AI者，得天下

今 吾以knn首试，knn為何? wiki曰明於此

例參github而來，python語句

假令 今有四點如圖

a1、a2表A類，b1、b2表B類，此時入c如下

於眼焉能辨c為B類

若君興於圖，參碼之

from matplotlib import pyplot as plt

import numpy as np

a1 = np.array([1, 1])

a2 = np.array([5, 1])

b1 = np.array([1, 5])

b2 = np.array([5, 5])

b3 = np.array([3, 5])

c = np.array([3, 4])

X1, Y1 = a1

X2, Y2 = a2

X3, Y3 = b1

X4, Y4 = b2

X5, Y5 = b3

X6, Y6 = c

plt.title('show data')

plt.scatter(X1, Y1, color="blue", label="a1")

plt.scatter(X2, Y2, color="blue", label="a2")

plt.scatter(X3, Y3, color="red", label="b1")

plt.scatter(X4, Y4, color="red", label="b2")

plt.scatter(X5, Y5, color="red", label="b3")

plt.scatter(X6, Y6, color="red", label="c")

plt.legend(loc='upper left')

plt.annotate(r'a1(1,1)', xy=(X1, Y1), xycoords='data', xytext=(+10, +30), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.annotate(r'a2(5,1)', xy=(X2, Y2), xycoords='data', xytext=(+10, +30), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.annotate(r'b1(1,5)', xy=(X3, Y3), xycoords='data', xytext=(+10, +30), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.annotate(r'b2(5,5)', xy=(X4, Y4), xycoords='data', xytext=(+10, +30), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.annotate(r'b3(3,5)', xy=(X5, Y5), xycoords='data', xytext=(+10, +30), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.annotate(r'c(3,4)', xy=(X6, Y6), xycoords='data', xytext=(+10, +30), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.show()

Knn碼，例1于此

設k為3(見式3)，為取3點極近xy數組

from matplotlib import pyplot as plt

import numpy as np

import math

def Euclidean(vec1, vec2): #Euclidean間距算法

    npvec1, npvec2 = np.array(vec1), np.array(vec2)

    return math.sqrt(((npvec1-npvec2)**2).sum())

def Cosine(vec1, vec2): #Cosine間距算法

    npvec1, npvec2 = np.array(vec1), np.array(vec2)

    return npvec1.dot(npvec2)/(math.sqrt((npvec1**2).sum()) * math.sqrt((npvec2**2).sum()))

def create_trainset():

    trainset_tf = dict()

    trainset_tf[u'X1, Y1'] = np.array([1, 1])

    trainset_tf[u'X2, Y2'] = np.array([5, 1])

    trainset_tf[u'X3, Y3'] = np.array([1, 5])

    trainset_tf[u'X4, Y4'] = np.array([5, 5])

    trainset_tf[u'X5, Y5'] = np.array([3, 5])

    trainset_class = dict()

    trainset_class[u'X1, Y1'] = 'A'

    trainset_class[u'X2, Y2'] = 'A'

    trainset_class[u'X3, Y3'] = 'B'

    trainset_class[u'X4, Y4'] = 'B'

    trainset_class[u'X5, Y5'] = 'B'

    return trainset_tf, trainset_class

def knn_classify(input_tf, trainset_tf, trainset_class, k):

    xy = np.array([0, 0])

   

    tf_distance = dict()

    print '(1) 各點之遙' #python3用法 --> print ("(1) 各點之遙")

    for place in trainset_tf.keys():

        tf_distance[place] = Euclidean(trainset_tf.get(place), input_tf)

        print '\tTF(%s) = %f' % (place, tf_distance.get(place))

    class_count = dict()

    print '(2) 得K個鄰為類, k = %d' % k

    for i, place in enumerate(sorted(tf_distance, key=tf_distance.get, reverse=False)):

        current_class = trainset_class.get(place)

        print '\tTF(%s) = %f, class = %s' % (place, tf_distance.get(place), current_class)

        class_count[current_class] = class_count.get(current_class, 0) + 1

        xy = xy + trainset_tf[place]

        if (i + 1) >= k:

            break

           

    print '(3) K鄰均等之x與y -->' #式3

    finalx=float(xy[0])/k

    finaly=float(xy[1])/k

    print '\tx = %f' % finalx, ', y = %f'% finaly

   

    print '(4) K個鄰累計，最高者任其終類'

    input_class = ''

    for i, c in enumerate(sorted(class_count, key=class_count.get, reverse=True)):

        if i == 0:

            input_class = c

        print '\t%s類, 現%d次' % (c, class_count.get(c))

    print '(5) 結果 = %s' % input_class

input_tf = np.array([3, 4])

trainset_tf, trainset_class = create_trainset()

knn_classify(input_tf, trainset_tf, trainset_class, k=3)

結果

見結果，x於3，y於5

吾於碼中，設input_tf = np.array([3, 4])，表x於3，y於4

y於實測與預測相差1，頗為接近! 心喜! 信心大增!

故例2，吾設實測集合於下

def create_trainset():

    trainset_tf = dict()

    trainset_tf[u'T1, U1, V1, W1, X1, Y1, Z1'] = np.array([-55, -65, -65, -75, -85, 1, 1])

    trainset_tf[u'T2, U2, V2, W2, X2, Y2, Z2'] = np.array([-65, -55, -85, -75, -65, 5, 1])

    trainset_tf[u'T3, U3, V3, W3, X3, Y3, Z3'] = np.array([-65, -85, -55, -60, -65, 1, 5])

    trainset_tf[u'T4, U4, V4, W4, X4, Y4, Z4'] = np.array([-75, -75, -60, -55, -60, 5, 5])

    trainset_tf[u'T5, U5, V5, W5, X5, Y5, Z5'] = np.array([-85, -65, -65, -59, -55, 3, 5])

   #這太難寫了! 放棄! 直接寫白話文 XD
   #以[-55, -65, -65, -75, -85, 1, 1]為例
   #-55表示手環在第一個接收器的位置，-85離最遠

   #於空間內置放5個接收器，手環於5個地點發射，故5個接收器的強度都不同

   #5個負數代表5個接收器收到的RSSI，後面2個值(1,1)代表xy軸
#如果您有參加過我們的工作坊就知道Linkit 7697一秒可以送出多筆資料

   #但這只是模擬數據，實做是要用洗資料的方式

#而這個資料集是代表實測出來的建模，要給機器去學習比對，找出使用者目前位置

    trainset_class = dict()

    trainset_class[u'T1, U1, V1, W1, X1, Y1, Z1'] = 'A'

    trainset_class[u'T2, U2, V2, W2, X2, Y2, Z2'] = 'A'

    trainset_class[u'T3, U3, V3, W3, X3, Y3, Z3'] = 'B'

    trainset_class[u'T4, U4, V4, W4, X4, Y4, Z4'] = 'B'

    trainset_class[u'T5, U5, V5, W5, X5, Y5, Z5'] = 'B'

    return trainset_tf, trainset_class

此時，夫子戴手環入室，input_tf表夫子之室內數據

負數為各Linkit 7697所得RSSI，設xy數為零零，將由ML算之，設集如下

input_tf = np.array([-72, -72, -63, -53, -63, 0, 0])

ML得x為3, y為5，倒果為因，同上例1之xy得數

故窺Machine Learning之強大，何況Deep Learning乎?

如下為例2之碼

from matplotlib import pyplot as plt

import numpy as np

import math

def Euclidean(vec1, vec2): #Euclidean間距算法

    npvec1, npvec2 = np.array(vec1), np.array(vec2)

    return math.sqrt(((npvec1-npvec2)**2).sum())

def Cosine(vec1, vec2): #Cosine間距算法

    npvec1, npvec2 = np.array(vec1), np.array(vec2)

    return npvec1.dot(npvec2)/(math.sqrt((npvec1**2).sum()) * math.sqrt((npvec2**2).sum()))

def create_trainset():

    trainset_tf = dict()

    trainset_tf[u'T1, U1, V1, W1, X1, Y1, Z1'] = np.array([-55, -65, -65, -75, -85, 1, 1])

    trainset_tf[u'T2, U2, V2, W2, X2, Y2, Z2'] = np.array([-65, -55, -85, -75, -65, 5, 1])

    trainset_tf[u'T3, U3, V3, W3, X3, Y3, Z3'] = np.array([-65, -85, -55, -60, -65, 1, 5])

    trainset_tf[u'T4, U4, V4, W4, X4, Y4, Z4'] = np.array([-75, -75, -60, -55, -60, 5, 5])

    trainset_tf[u'T5, U5, V5, W5, X5, Y5, Z5'] = np.array([-85, -65, -65, -59, -55, 3, 5])

   

    trainset_class = dict()

    trainset_class[u'T1, U1, V1, W1, X1, Y1, Z1'] = 'A'

    trainset_class[u'T2, U2, V2, W2, X2, Y2, Z2'] = 'A'

    trainset_class[u'T3, U3, V3, W3, X3, Y3, Z3'] = 'B'

    trainset_class[u'T4, U4, V4, W4, X4, Y4, Z4'] = 'B'

    trainset_class[u'T5, U5, V5, W5, X5, Y5, Z5'] = 'B'

    return trainset_tf, trainset_class

def knn_classify(input_tf, trainset_tf, trainset_class, k):

    xy = np.array([0, 0])

   

    tf_distance = dict()

    print '(1) 各點之遙'

    for place in trainset_tf.keys():

        tf_distance[place] = Euclidean(trainset_tf.get(place), input_tf)

        print '\tTF(%s) = %f' % (place, tf_distance.get(place))

    class_count = dict()

    print '(2) 得K個鄰為類 k = %d' % k

    for i, place in enumerate(sorted(tf_distance, key=tf_distance.get, reverse=False)):

        current_class = trainset_class.get(place)

        print '\tTF(%s) = %f, class = %s' % (place, tf_distance.get(place), current_class)

        class_count[current_class] = class_count.get(current_class, 0) + 1

        xy = [(xy[0] + trainset_tf[place][5]),(xy[1] + trainset_tf[place][6])]

        if (i + 1) >= k:

            break

           

    print '(3) K鄰均等之x與y -->'

    finalx=float(xy[0])/k

    finaly=float(xy[1])/k

    print '\tx = %f' % finalx, ', y = %f'% finaly

   

    print '(4) K個鄰累計，最高者任其終類'

    input_class = ''

    for i, c in enumerate(sorted(class_count, key=class_count.get, reverse=True)): #擇小排序之

        if i == 0:

            input_class = c

        print '\t%s類, 現%d次' % (c, class_count.get(c))

    print '(5) 結果 = %s' % input_class

input_tf = np.array([-72, -72, -63, -53, -63, 0, 0])

trainset_tf, trainset_class = create_trainset()

knn_classify(input_tf, trainset_tf, trainset_class, k=3)

結果

若君難以窺得內文所然，正常!

吾於古文茫然，乃正港台北俗

此為現實定位模擬數值，將有實測之文

ML系列文將以不同風貌展現

2018/2/3於府城AI Robot解說此篇緣由

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