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https://github.com/apachecn/ailearning.git
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添加了SVM无核函数的，注释参考有有核函数的
This commit is contained in:
jiangzhonglian
100
input/6.SVM/testSetRBF.txt
Executable file
100
input/6.SVM/testSetRBF.txt
Executable file
@@ -0,0 +1,100 @@
|
||||
-0.214824 0.662756 -1.000000
|
||||
-0.061569 -0.091875 1.000000
|
||||
0.406933 0.648055 -1.000000
|
||||
0.223650 0.130142 1.000000
|
||||
0.231317 0.766906 -1.000000
|
||||
-0.748800 -0.531637 -1.000000
|
||||
-0.557789 0.375797 -1.000000
|
||||
0.207123 -0.019463 1.000000
|
||||
0.286462 0.719470 -1.000000
|
||||
0.195300 -0.179039 1.000000
|
||||
-0.152696 -0.153030 1.000000
|
||||
0.384471 0.653336 -1.000000
|
||||
-0.117280 -0.153217 1.000000
|
||||
-0.238076 0.000583 1.000000
|
||||
-0.413576 0.145681 1.000000
|
||||
0.490767 -0.680029 -1.000000
|
||||
0.199894 -0.199381 1.000000
|
||||
-0.356048 0.537960 -1.000000
|
||||
-0.392868 -0.125261 1.000000
|
||||
0.353588 -0.070617 1.000000
|
||||
0.020984 0.925720 -1.000000
|
||||
-0.475167 -0.346247 -1.000000
|
||||
0.074952 0.042783 1.000000
|
||||
0.394164 -0.058217 1.000000
|
||||
0.663418 0.436525 -1.000000
|
||||
0.402158 0.577744 -1.000000
|
||||
-0.449349 -0.038074 1.000000
|
||||
0.619080 -0.088188 -1.000000
|
||||
0.268066 -0.071621 1.000000
|
||||
-0.015165 0.359326 1.000000
|
||||
0.539368 -0.374972 -1.000000
|
||||
-0.319153 0.629673 -1.000000
|
||||
0.694424 0.641180 -1.000000
|
||||
0.079522 0.193198 1.000000
|
||||
0.253289 -0.285861 1.000000
|
||||
-0.035558 -0.010086 1.000000
|
||||
-0.403483 0.474466 -1.000000
|
||||
-0.034312 0.995685 -1.000000
|
||||
-0.590657 0.438051 -1.000000
|
||||
-0.098871 -0.023953 1.000000
|
||||
-0.250001 0.141621 1.000000
|
||||
-0.012998 0.525985 -1.000000
|
||||
0.153738 0.491531 -1.000000
|
||||
0.388215 -0.656567 -1.000000
|
||||
0.049008 0.013499 1.000000
|
||||
0.068286 0.392741 1.000000
|
||||
0.747800 -0.066630 -1.000000
|
||||
0.004621 -0.042932 1.000000
|
||||
-0.701600 0.190983 -1.000000
|
||||
0.055413 -0.024380 1.000000
|
||||
0.035398 -0.333682 1.000000
|
||||
0.211795 0.024689 1.000000
|
||||
-0.045677 0.172907 1.000000
|
||||
0.595222 0.209570 -1.000000
|
||||
0.229465 0.250409 1.000000
|
||||
-0.089293 0.068198 1.000000
|
||||
0.384300 -0.176570 1.000000
|
||||
0.834912 -0.110321 -1.000000
|
||||
-0.307768 0.503038 -1.000000
|
||||
-0.777063 -0.348066 -1.000000
|
||||
0.017390 0.152441 1.000000
|
||||
-0.293382 -0.139778 1.000000
|
||||
-0.203272 0.286855 1.000000
|
||||
0.957812 -0.152444 -1.000000
|
||||
0.004609 -0.070617 1.000000
|
||||
-0.755431 0.096711 -1.000000
|
||||
-0.526487 0.547282 -1.000000
|
||||
-0.246873 0.833713 -1.000000
|
||||
0.185639 -0.066162 1.000000
|
||||
0.851934 0.456603 -1.000000
|
||||
-0.827912 0.117122 -1.000000
|
||||
0.233512 -0.106274 1.000000
|
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0.583671 -0.709033 -1.000000
|
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-0.487023 0.625140 -1.000000
|
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-0.448939 0.176725 1.000000
|
||||
0.155907 -0.166371 1.000000
|
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0.334204 0.381237 -1.000000
|
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0.081536 -0.106212 1.000000
|
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0.227222 0.527437 -1.000000
|
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0.759290 0.330720 -1.000000
|
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0.204177 -0.023516 1.000000
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0.577939 0.403784 -1.000000
|
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-0.568534 0.442948 -1.000000
|
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-0.011520 0.021165 1.000000
|
||||
0.875720 0.422476 -1.000000
|
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0.297885 -0.632874 -1.000000
|
||||
-0.015821 0.031226 1.000000
|
||||
0.541359 -0.205969 -1.000000
|
||||
-0.689946 -0.508674 -1.000000
|
||||
-0.343049 0.841653 -1.000000
|
||||
0.523902 -0.436156 -1.000000
|
||||
0.249281 -0.711840 -1.000000
|
||||
0.193449 0.574598 -1.000000
|
||||
-0.257542 -0.753885 -1.000000
|
||||
-0.021605 0.158080 1.000000
|
||||
0.601559 -0.727041 -1.000000
|
||||
-0.791603 0.095651 -1.000000
|
||||
-0.908298 -0.053376 -1.000000
|
||||
0.122020 0.850966 -1.000000
|
||||
-0.725568 -0.292022 -1.000000
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100
input/6.SVM/testSetRBF2.txt
Executable file
100
input/6.SVM/testSetRBF2.txt
Executable file
@@ -0,0 +1,100 @@
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0.676771 -0.486687 -1.000000
|
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0.008473 0.186070 1.000000
|
||||
-0.727789 0.594062 -1.000000
|
||||
0.112367 0.287852 1.000000
|
||||
0.383633 -0.038068 1.000000
|
||||
-0.927138 -0.032633 -1.000000
|
||||
-0.842803 -0.423115 -1.000000
|
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-0.003677 -0.367338 1.000000
|
||||
0.443211 -0.698469 -1.000000
|
||||
-0.473835 0.005233 1.000000
|
||||
0.616741 0.590841 -1.000000
|
||||
0.557463 -0.373461 -1.000000
|
||||
-0.498535 -0.223231 -1.000000
|
||||
-0.246744 0.276413 1.000000
|
||||
-0.761980 -0.244188 -1.000000
|
||||
0.641594 -0.479861 -1.000000
|
||||
-0.659140 0.529830 -1.000000
|
||||
-0.054873 -0.238900 1.000000
|
||||
-0.089644 -0.244683 1.000000
|
||||
-0.431576 -0.481538 -1.000000
|
||||
-0.099535 0.728679 -1.000000
|
||||
-0.188428 0.156443 1.000000
|
||||
0.267051 0.318101 1.000000
|
||||
0.222114 -0.528887 -1.000000
|
||||
0.030369 0.113317 1.000000
|
||||
0.392321 0.026089 1.000000
|
||||
0.298871 -0.915427 -1.000000
|
||||
-0.034581 -0.133887 1.000000
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||||
0.405956 0.206980 1.000000
|
||||
0.144902 -0.605762 -1.000000
|
||||
0.274362 -0.401338 1.000000
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||||
0.397998 -0.780144 -1.000000
|
||||
0.037863 0.155137 1.000000
|
||||
-0.010363 -0.004170 1.000000
|
||||
0.506519 0.486619 -1.000000
|
||||
0.000082 -0.020625 1.000000
|
||||
0.057761 -0.155140 1.000000
|
||||
0.027748 -0.553763 -1.000000
|
||||
-0.413363 -0.746830 -1.000000
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||||
0.081500 -0.014264 1.000000
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||||
0.047137 -0.491271 1.000000
|
||||
-0.267459 0.024770 1.000000
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||||
-0.148288 -0.532471 -1.000000
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||||
-0.225559 -0.201622 1.000000
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||||
0.772360 -0.518986 -1.000000
|
||||
-0.440670 0.688739 -1.000000
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||||
0.329064 -0.095349 1.000000
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||||
0.970170 -0.010671 -1.000000
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||||
-0.689447 -0.318722 -1.000000
|
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-0.465493 -0.227468 -1.000000
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||||
-0.049370 0.405711 1.000000
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-0.166117 0.274807 1.000000
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0.054483 0.012643 1.000000
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||||
0.021389 0.076125 1.000000
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-0.104404 -0.914042 -1.000000
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0.294487 0.440886 -1.000000
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0.107915 -0.493703 -1.000000
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0.076311 0.438860 1.000000
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0.370593 -0.728737 -1.000000
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0.409890 0.306851 -1.000000
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0.285445 0.474399 -1.000000
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-0.870134 -0.161685 -1.000000
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-0.654144 -0.675129 -1.000000
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0.285278 -0.767310 -1.000000
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0.049548 -0.000907 1.000000
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0.030014 -0.093265 1.000000
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-0.128859 0.278865 1.000000
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0.307463 0.085667 1.000000
|
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0.023440 0.298638 1.000000
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0.053920 0.235344 1.000000
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||||
0.059675 0.533339 -1.000000
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0.817125 0.016536 -1.000000
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-0.108771 0.477254 1.000000
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-0.118106 0.017284 1.000000
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0.288339 0.195457 1.000000
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0.567309 -0.200203 -1.000000
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-0.202446 0.409387 1.000000
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-0.330769 -0.240797 1.000000
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-0.422377 0.480683 -1.000000
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-0.295269 0.326017 1.000000
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0.261132 0.046478 1.000000
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-0.492244 -0.319998 -1.000000
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-0.384419 0.099170 1.000000
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0.101882 -0.781145 -1.000000
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0.234592 -0.383446 1.000000
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-0.020478 -0.901833 -1.000000
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0.328449 0.186633 1.000000
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-0.150059 -0.409158 1.000000
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-0.155876 -0.843413 -1.000000
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-0.098134 -0.136786 1.000000
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0.110575 -0.197205 1.000000
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0.219021 0.054347 1.000000
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0.030152 0.251682 1.000000
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0.033447 -0.122824 1.000000
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-0.686225 -0.020779 -1.000000
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-0.911211 -0.262011 -1.000000
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0.572557 0.377526 -1.000000
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-0.073647 -0.519163 -1.000000
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-0.281830 -0.797236 -1.000000
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-0.555263 0.126232 -1.000000
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@@ -123,7 +123,7 @@ def selectJrand(i, m):
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def selectJ(i, oS, Ei): # this is the second choice -heurstic, and calcs Ej
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"""selectJ（）
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"""selectJ（返回最优的j和Ej）
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内循环的启发式方法。
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选择第二个(内循环)alpha的alpha值
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@@ -187,9 +187,6 @@ def updateEk(oS, k):
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Args:
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oS optStruct对象
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k 某一列的行号
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Returns:
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"""
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# 求 误差：预测值-真实值的差
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@@ -214,14 +211,15 @@ def clipAlpha(aj, H, L):
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def innerL(i, oS):
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"""
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"""innerL
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内循环代码
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Args:
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i 具体的某一行
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oS optStruct对象
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Returns:
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0 找不到最优的值
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1 找到了最优的值，并且oS.Cache到缓存中
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"""
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# 求 Ek误差：预测值-真实值的差
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@@ -311,20 +309,29 @@ def smoP(dataMatIn, classLabels, C, toler, maxIter, kTup=('lin', 0)):
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entireSet = True
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alphaPairsChanged = 0
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# 循环遍历：
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# 循环遍历：循环maxIter次 并且 （alphaPairsChanged存在可以改变 or 所有行遍历一遍）
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while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):
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alphaPairsChanged = 0
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if entireSet: # 在数据集上遍历所有可能的alpha
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# 当entireSet=true or 非边界alpha对没有了；就开始寻找 alpha对，然后决定是否要进行else。
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if entireSet:
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# 在数据集上遍历所有可能的alpha
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for i in range(oS.m):
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# 是否存在alpha对，存在就+1
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alphaPairsChanged += innerL(i, oS)
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print("fullSet, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
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iter += 1
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else: # 遍历所有的非边界alpha值，也就是不在边界0或C上的值。
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# 对已存在 alpha对，选出非边界的alpha值，进行优化。
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else:
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# 遍历所有的非边界alpha值，也就是不在边界0或C上的值。
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nonBoundIs = nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]
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for i in nonBoundIs:
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alphaPairsChanged += innerL(i, oS)
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print("non-bound, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
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iter += 1
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# 如果找到alpha对，就优化非边界alpha值，否则，就重新进行寻找，如果寻找一遍 遍历所有的行还是没找到，就退出循环。
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if entireSet:
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entireSet = False # toggle entire set loop
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elif (alphaPairsChanged == 0):
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@@ -438,6 +445,7 @@ if __name__ == "__main__":
|
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|
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|
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|
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|
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|
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|
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def testRbf(k1=1.3):
|
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@@ -548,9 +556,6 @@ def calcEkK(oS, k):
|
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return Ek
|
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|
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|
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|
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|
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|
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def selectJK(i, oS, Ei): # this is the second choice -heurstic, and calcs Ej
|
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maxK = -1
|
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maxDeltaE = 0
|
||||
|
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375
src/python/6.SVM/svm-complete_Non-Kernel.py
Normal file
375
src/python/6.SVM/svm-complete_Non-Kernel.py
Normal file
@@ -0,0 +1,375 @@
|
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#!/usr/bin/python
|
||||
# coding:utf8
|
||||
|
||||
"""
|
||||
Created on Nov 4, 2010
|
||||
Update on 2017-03-21
|
||||
Chapter 5 source file for Machine Learing in Action
|
||||
@author: Peter/geekidentity/片刻
|
||||
"""
|
||||
from numpy import *
|
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import matplotlib.pyplot as plt
|
||||
|
||||
|
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def loadDataSet(fileName):
|
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"""loadDataSet（对文件进行逐行解析，从而得到第行的类标签和整个数据矩阵）
|
||||
|
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Args:
|
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fileName 文件名
|
||||
Returns:
|
||||
dataMat 数据矩阵
|
||||
labelMat 类标签
|
||||
"""
|
||||
dataMat = []
|
||||
labelMat = []
|
||||
fr = open(fileName)
|
||||
for line in fr.readlines():
|
||||
lineArr = line.strip().split('\t')
|
||||
dataMat.append([float(lineArr[0]), float(lineArr[1])])
|
||||
labelMat.append(float(lineArr[2]))
|
||||
return dataMat, labelMat
|
||||
|
||||
|
||||
def selectJrand(i, m):
|
||||
"""
|
||||
随机选择一个整数
|
||||
Args:
|
||||
i 第一个alpha的下标
|
||||
m 所有alpha的数目
|
||||
Returns:
|
||||
j 返回一个不为i的随机数，在0~m之间的整数值
|
||||
"""
|
||||
j = i
|
||||
while j == i:
|
||||
j = int(random.uniform(0, m))
|
||||
return j
|
||||
|
||||
|
||||
def clipAlpha(aj, H, L):
|
||||
"""clipAlpha(调整aj的值，使aj处于 L<=aj<=H)
|
||||
Args:
|
||||
aj 目标值
|
||||
H 最大值
|
||||
L 最小值
|
||||
Returns:
|
||||
aj 目标值
|
||||
"""
|
||||
if aj > H:
|
||||
aj = H
|
||||
if L > aj:
|
||||
aj = L
|
||||
return aj
|
||||
|
||||
|
||||
def calcWs(alphas, dataArr, classLabels):
|
||||
"""
|
||||
基于alpha计算w值
|
||||
Args:
|
||||
alphas 拉格朗日乘子
|
||||
dataArr feature数据集
|
||||
classLabels 目标变量数据集
|
||||
|
||||
Returns:
|
||||
wc 回归系数
|
||||
"""
|
||||
X = mat(dataArr)
|
||||
labelMat = mat(classLabels).transpose()
|
||||
m, n = shape(X)
|
||||
w = zeros((n, 1))
|
||||
for i in range(m):
|
||||
w += multiply(alphas[i] * labelMat[i], X[i, :].T)
|
||||
return w
|
||||
|
||||
|
||||
'''
|
||||
#######********************************
|
||||
Non-Kernel VErsions below
|
||||
#######********************************
|
||||
'''
|
||||
|
||||
class optStruct:
|
||||
def __init__(self, dataMatIn, classLabels, C, toler): # Initialize the structure with the parameters
|
||||
self.X = dataMatIn
|
||||
self.labelMat = classLabels
|
||||
self.C = C
|
||||
self.tol = toler
|
||||
self.m = shape(dataMatIn)[0]
|
||||
self.alphas = mat(zeros((self.m, 1)))
|
||||
self.b = 0
|
||||
self.eCache = mat(zeros((self.m, 2))) # first column is valid flag
|
||||
|
||||
|
||||
def calcEk(oS, k):
|
||||
fXk = float(multiply(oS.alphas, oS.labelMat).T * (oS.X * oS.X[k, :].T)) + oS.b
|
||||
Ek = fXk - float(oS.labelMat[k])
|
||||
return Ek
|
||||
|
||||
|
||||
def selectJ(i, oS, Ei): # this is the second choice -heurstic, and calcs Ej
|
||||
maxK = -1
|
||||
maxDeltaE = 0
|
||||
Ej = 0
|
||||
oS.eCache[i] = [1, Ei] # set valid #choose the alpha that gives the maximum delta E
|
||||
validEcacheList = nonzero(oS.eCache[:, 0].A)[0]
|
||||
if (len(validEcacheList)) > 1:
|
||||
for k in validEcacheList: # loop through valid Ecache values and find the one that maximizes delta E
|
||||
if k == i: continue # don't calc for i, waste of time
|
||||
Ek = calcEk(oS, k)
|
||||
deltaE = abs(Ei - Ek)
|
||||
if (deltaE > maxDeltaE):
|
||||
maxK = k
|
||||
maxDeltaE = deltaE
|
||||
Ej = Ek
|
||||
return maxK, Ej
|
||||
else: # in this case (first time around) we don't have any valid eCache values
|
||||
j = selectJrand(i, oS.m)
|
||||
Ej = calcEk(oS, j)
|
||||
return j, Ej
|
||||
|
||||
|
||||
def updateEk(oS, k): # after any alpha has changed update the new value in the cache
|
||||
Ek = calcEk(oS, k)
|
||||
oS.eCache[k] = [1, Ek]
|
||||
|
||||
|
||||
def innerL(i, oS):
|
||||
Ei = calcEk(oS, i)
|
||||
if ((oS.labelMat[i] * Ei < -oS.tol) and (oS.alphas[i] < oS.C)) or (
|
||||
(oS.labelMat[i] * Ei > oS.tol) and (oS.alphas[i] > 0)):
|
||||
j, Ej = selectJ(i, oS, Ei) # this has been changed from selectJrand
|
||||
alphaIold = oS.alphas[i].copy()
|
||||
alphaJold = oS.alphas[j].copy()
|
||||
if (oS.labelMat[i] != oS.labelMat[j]):
|
||||
L = max(0, oS.alphas[j] - oS.alphas[i])
|
||||
H = min(oS.C, oS.C + oS.alphas[j] - oS.alphas[i])
|
||||
else:
|
||||
L = max(0, oS.alphas[j] + oS.alphas[i] - oS.C)
|
||||
H = min(oS.C, oS.alphas[j] + oS.alphas[i])
|
||||
if L == H:
|
||||
print("L==H")
|
||||
return 0
|
||||
eta = 2.0 * oS.X[i, :] * oS.X[j, :].T - oS.X[i, :] * oS.X[i, :].T - oS.X[j, :] * oS.X[j, :].T
|
||||
if eta >= 0:
|
||||
print("eta>=0")
|
||||
return 0
|
||||
oS.alphas[j] -= oS.labelMat[j] * (Ei - Ej) / eta
|
||||
oS.alphas[j] = clipAlpha(oS.alphas[j], H, L)
|
||||
updateEk(oS, j) # added this for the Ecache
|
||||
if (abs(oS.alphas[j] - alphaJold) < 0.00001):
|
||||
print("j not moving enough")
|
||||
return 0
|
||||
oS.alphas[i] += oS.labelMat[j] * oS.labelMat[i] * (alphaJold - oS.alphas[j]) # update i by the same amount as j
|
||||
updateEk(oS, i) # added this for the Ecache #the update is in the oppostie direction
|
||||
b1 = oS.b - Ei - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[i, :].T - oS.labelMat[j] * (
|
||||
oS.alphas[j] - alphaJold) * oS.X[i, :] * oS.X[j, :].T
|
||||
b2 = oS.b - Ej - oS.labelMat[i] * (oS.alphas[i] - alphaIold) * oS.X[i, :] * oS.X[j, :].T - oS.labelMat[j] * (
|
||||
oS.alphas[j] - alphaJold) * oS.X[j, :] * oS.X[j, :].T
|
||||
if (0 < oS.alphas[i]) and (oS.C > oS.alphas[i]):
|
||||
oS.b = b1
|
||||
elif (0 < oS.alphas[j]) and (oS.C > oS.alphas[j]):
|
||||
oS.b = b2
|
||||
else:
|
||||
oS.b = (b1 + b2) / 2.0
|
||||
return 1
|
||||
else:
|
||||
return 0
|
||||
|
||||
|
||||
def smoP(dataMatIn, classLabels, C, toler, maxIter): # full Platt SMO
|
||||
oS = optStruct(mat(dataMatIn), mat(classLabels).transpose(), C, toler)
|
||||
iter = 0
|
||||
entireSet = True
|
||||
alphaPairsChanged = 0
|
||||
while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):
|
||||
alphaPairsChanged = 0
|
||||
if entireSet: # go over all
|
||||
for i in range(oS.m):
|
||||
alphaPairsChanged += innerL(i, oS)
|
||||
print("fullSet, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
|
||||
iter += 1
|
||||
else: # go over non-bound (railed) alphas
|
||||
nonBoundIs = nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]
|
||||
for i in nonBoundIs:
|
||||
alphaPairsChanged += innerL(i, oS)
|
||||
print("non-bound, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
|
||||
iter += 1
|
||||
if entireSet:
|
||||
entireSet = False # toggle entire set loop
|
||||
elif (alphaPairsChanged == 0):
|
||||
entireSet = True
|
||||
print("iteration number: %d" % iter)
|
||||
return oS.b, oS.alphas
|
||||
|
||||
|
||||
def plotfig_SVM(xArr, yArr, ws, b, alphas):
|
||||
"""
|
||||
参考地址：
|
||||
http://blog.csdn.net/maoersong/article/details/24315633
|
||||
http://www.cnblogs.com/JustForCS/p/5283489.html
|
||||
http://blog.csdn.net/kkxgx/article/details/6951959
|
||||
"""
|
||||
|
||||
xMat = mat(xArr)
|
||||
yMat = mat(yArr)
|
||||
|
||||
# b原来是矩阵，先转为数组类型后其数组大小为（1,1），所以后面加[0]，变为(1,)
|
||||
b = array(b)[0]
|
||||
fig = plt.figure()
|
||||
ax = fig.add_subplot(111)
|
||||
|
||||
# 注意flatten的用法
|
||||
ax.scatter(xMat[:, 0].flatten().A[0], xMat[:, 1].flatten().A[0])
|
||||
|
||||
# x最大值，最小值根据原数据集dataArr[:, 0]的大小而定
|
||||
x = arange(-1.0, 10.0, 0.1)
|
||||
|
||||
# 根据x.w + b = 0 得到，其式子展开为w0.x1 + w1.x2 + b = 0, x2就是y值
|
||||
y = (-b-ws[0, 0]*x)/ws[1, 0]
|
||||
ax.plot(x, y)
|
||||
|
||||
for i in range(shape(yMat[0, :])[1]):
|
||||
if yMat[0, i] > 0:
|
||||
ax.plot(xMat[i, 0], xMat[i, 1], 'cx')
|
||||
else:
|
||||
ax.plot(xMat[i, 0], xMat[i, 1], 'kp')
|
||||
|
||||
# 找到支持向量，并在图中标红
|
||||
for i in range(100):
|
||||
if alphas[i] > 0.0:
|
||||
ax.plot(xMat[i, 0], xMat[i, 1], 'ro')
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 获取特征和目标变量
|
||||
dataArr, labelArr = loadDataSet('input/6.SVM/testSet.txt')
|
||||
# print labelArr
|
||||
|
||||
# b是常量值， alphas是拉格朗日乘子
|
||||
b, alphas = smoP(dataArr, labelArr, 0.6, 0.001, 40)
|
||||
print '/n/n/n'
|
||||
print 'b=', b
|
||||
print 'alphas[alphas>0]=', alphas[alphas > 0]
|
||||
print 'shape(alphas[alphas > 0])=', shape(alphas[alphas > 0])
|
||||
for i in range(100):
|
||||
if alphas[i] > 0:
|
||||
print dataArr[i], labelArr[i]
|
||||
# 画图
|
||||
ws = calcWs(alphas, dataArr, labelArr)
|
||||
plotfig_SVM(dataArr, labelArr, ws, b, alphas)
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
def testRbf(k1=1.3):
|
||||
dataArr, labelArr = loadDataSet('testSetRBF.txt')
|
||||
b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, ('rbf', k1)) # C=200 important
|
||||
datMat = mat(dataArr)
|
||||
labelMat = mat(labelArr).transpose()
|
||||
svInd = nonzero(alphas.A > 0)[0]
|
||||
sVs = datMat[svInd] # get matrix of only support vectors
|
||||
labelSV = labelMat[svInd]
|
||||
print("there are %d Support Vectors" % shape(sVs)[0])
|
||||
m, n = shape(datMat)
|
||||
errorCount = 0
|
||||
for i in range(m):
|
||||
kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))
|
||||
predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
|
||||
if sign(predict) != sign(labelArr[i]): errorCount += 1
|
||||
print("the training error rate is: %f" % (float(errorCount) / m))
|
||||
dataArr, labelArr = loadDataSet('testSetRBF2.txt')
|
||||
errorCount = 0
|
||||
datMat = mat(dataArr)
|
||||
labelMat = mat(labelArr).transpose()
|
||||
m, n = shape(datMat)
|
||||
for i in range(m):
|
||||
kernelEval = kernelTrans(sVs, datMat[i, :], ('rbf', k1))
|
||||
predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
|
||||
if sign(predict) != sign(labelArr[i]): errorCount += 1
|
||||
print("the test error rate is: %f" % (float(errorCount) / m))
|
||||
|
||||
|
||||
def img2vector(filename):
|
||||
returnVect = zeros((1, 1024))
|
||||
fr = open(filename)
|
||||
for i in range(32):
|
||||
lineStr = fr.readline()
|
||||
for j in range(32):
|
||||
returnVect[0, 32 * i + j] = int(lineStr[j])
|
||||
return returnVect
|
||||
|
||||
|
||||
def loadImages(dirName):
|
||||
from os import listdir
|
||||
hwLabels = []
|
||||
print(dirName)
|
||||
trainingFileList = listdir(dirName) # load the training set
|
||||
m = len(trainingFileList)
|
||||
trainingMat = zeros((m, 1024))
|
||||
for i in range(m):
|
||||
fileNameStr = trainingFileList[i]
|
||||
fileStr = fileNameStr.split('.')[0] # take off .txt
|
||||
classNumStr = int(fileStr.split('_')[0])
|
||||
if classNumStr == 9:
|
||||
hwLabels.append(-1)
|
||||
else:
|
||||
hwLabels.append(1)
|
||||
trainingMat[i, :] = img2vector('%s/%s' % (dirName, fileNameStr))
|
||||
return trainingMat, hwLabels
|
||||
|
||||
|
||||
def testDigits(kTup=('rbf', 10)):
|
||||
dataArr, labelArr = loadImages('trainingDigits')
|
||||
b, alphas = smoP(dataArr, labelArr, 200, 0.0001, 10000, kTup)
|
||||
datMat = mat(dataArr)
|
||||
labelMat = mat(labelArr).transpose()
|
||||
svInd = nonzero(alphas.A > 0)[0]
|
||||
sVs = datMat[svInd]
|
||||
labelSV = labelMat[svInd]
|
||||
print("there are %d Support Vectors" % shape(sVs)[0])
|
||||
m, n = shape(datMat)
|
||||
errorCount = 0
|
||||
for i in range(m):
|
||||
kernelEval = kernelTrans(sVs, datMat[i, :], kTup)
|
||||
predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
|
||||
if sign(predict) != sign(labelArr[i]): errorCount += 1
|
||||
print("the training error rate is: %f" % (float(errorCount) / m))
|
||||
dataArr, labelArr = loadImages('testDigits')
|
||||
errorCount = 0
|
||||
datMat = mat(dataArr)
|
||||
labelMat = mat(labelArr).transpose()
|
||||
m, n = shape(datMat)
|
||||
for i in range(m):
|
||||
kernelEval = kernelTrans(sVs, datMat[i, :], kTup)
|
||||
predict = kernelEval.T * multiply(labelSV, alphas[svInd]) + b
|
||||
if sign(predict) != sign(labelArr[i]): errorCount += 1
|
||||
print("the test error rate is: %f" % (float(errorCount) / m))
|
||||
|
||||
Reference in New Issue
Block a user