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jiangzhonglianjiangzhonglian
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添加了SVM无核函数的，注释参考有有核函数的
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Lines changed: 592 additions & 12 deletions

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‎input/6.SVM/testSetRBF.txt‎

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-0.214824 0.662756 -1.000000
2+
-0.061569 -0.091875 1.000000
3+
0.406933 0.648055 -1.000000
4+
0.223650 0.130142 1.000000
5+
0.231317 0.766906 -1.000000
6+
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7+
-0.557789 0.375797 -1.000000
8+
0.207123 -0.019463 1.000000
9+
0.286462 0.719470 -1.000000
10+
0.195300 -0.179039 1.000000
11+
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12+
0.384471 0.653336 -1.000000
13+
-0.117280 -0.153217 1.000000
14+
-0.238076 0.000583 1.000000
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16+
0.490767 -0.680029 -1.000000
17+
0.199894 -0.199381 1.000000
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20+
0.353588 -0.070617 1.000000
21+
0.020984 0.925720 -1.000000
22+
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23+
0.074952 0.042783 1.000000
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0.663418 0.436525 -1.000000
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0.402158 0.577744 -1.000000
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0.268066 -0.071621 1.000000
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-0.015165 0.359326 1.000000
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0.539368 -0.374972 -1.000000
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0.694424 0.641180 -1.000000
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0.079522 0.193198 1.000000
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0.253289 -0.285861 1.000000
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0.153738 0.491531 -1.000000
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0.388215 -0.656567 -1.000000
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0.049008 0.013499 1.000000
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0.068286 0.392741 1.000000
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0.004621 -0.042932 1.000000
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0.055413 -0.024380 1.000000
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0.035398 -0.333682 1.000000
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0.211795 0.024689 1.000000
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0.595222 0.209570 -1.000000
55+
0.229465 0.250409 1.000000
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0.384300 -0.176570 1.000000
58+
0.834912 -0.110321 -1.000000
59+
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60+
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61+
0.017390 0.152441 1.000000
62+
-0.293382 -0.139778 1.000000
63+
-0.203272 0.286855 1.000000
64+
0.957812 -0.152444 -1.000000
65+
0.004609 -0.070617 1.000000
66+
-0.755431 0.096711 -1.000000
67+
-0.526487 0.547282 -1.000000
68+
-0.246873 0.833713 -1.000000
69+
0.185639 -0.066162 1.000000
70+
0.851934 0.456603 -1.000000
71+
-0.827912 0.117122 -1.000000
72+
0.233512 -0.106274 1.000000
73+
0.583671 -0.709033 -1.000000
74+
-0.487023 0.625140 -1.000000
75+
-0.448939 0.176725 1.000000
76+
0.155907 -0.166371 1.000000
77+
0.334204 0.381237 -1.000000
78+
0.081536 -0.106212 1.000000
79+
0.227222 0.527437 -1.000000
80+
0.759290 0.330720 -1.000000
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0.204177 -0.023516 1.000000
82+
0.577939 0.403784 -1.000000
83+
-0.568534 0.442948 -1.000000
84+
-0.011520 0.021165 1.000000
85+
0.875720 0.422476 -1.000000
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0.297885 -0.632874 -1.000000
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-0.015821 0.031226 1.000000
88+
0.541359 -0.205969 -1.000000
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90+
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0.523902 -0.436156 -1.000000
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0.249281 -0.711840 -1.000000
93+
0.193449 0.574598 -1.000000
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95+
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96+
0.601559 -0.727041 -1.000000
97+
-0.791603 0.095651 -1.000000
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0.122020 0.850966 -1.000000
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-0.725568 -0.292022 -1.000000

‎input/6.SVM/testSetRBF2.txt‎

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0.676771 -0.486687 -1.000000
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0.008473 0.186070 1.000000
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-0.727789 0.594062 -1.000000
4+
0.112367 0.287852 1.000000
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0.383633 -0.038068 1.000000
6+
-0.927138 -0.032633 -1.000000
7+
-0.842803 -0.423115 -1.000000
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-0.003677 -0.367338 1.000000
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0.443211 -0.698469 -1.000000
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-0.473835 0.005233 1.000000
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0.616741 0.590841 -1.000000
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0.557463 -0.373461 -1.000000
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0.641594 -0.479861 -1.000000
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-0.188428 0.156443 1.000000
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0.267051 0.318101 1.000000
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0.222114 -0.528887 -1.000000
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0.030369 0.113317 1.000000
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0.392321 0.026089 1.000000
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0.037863 0.155137 1.000000
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‎src/python/6.SVM/svm-complete.py‎

Lines changed: 17 additions & 12 deletions
Original file line numberDiff line numberDiff line change
@@ -123,7 +123,7 @@ def selectJrand(i, m):
123123

124124

125125
def selectJ(i, oS, Ei): # this is the second choice -heurstic, and calcs Ej
126-
"""selectJ（）
126+
"""selectJ（返回最优的j和Ej）
127127
128128
内循环的启发式方法。
129129
选择第二个(内循环)alpha的alpha值
@@ -187,9 +187,6 @@ def updateEk(oS, k):
187187
Args:
188188
oS optStruct对象
189189
k 某一列的行号
190-
191-
Returns:
192-
193190
"""
194191

195192
# 求 误差：预测值-真实值的差
@@ -214,14 +211,15 @@ def clipAlpha(aj, H, L):
214211

215212

216213
def innerL(i, oS):
217-
"""
214+
"""innerL
218215
内循环代码
219216
Args:
220217
i 具体的某一行
221218
oS optStruct对象
222219
223220
Returns:
224-
221+
0 找不到最优的值
222+
1 找到了最优的值，并且oS.Cache到缓存中
225223
"""
226224

227225
# 求 Ek误差：预测值-真实值的差
@@ -311,20 +309,29 @@ def smoP(dataMatIn, classLabels, C, toler, maxIter, kTup=('lin', 0)):
311309
entireSet = True
312310
alphaPairsChanged = 0
313311

314-
# 循环遍历：
312+
# 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）
315313
while (iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):
316314
alphaPairsChanged = 0
317-
if entireSet: # 在数据集上遍历所有可能的alpha
315+
316+
# 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。
317+
if entireSet:
318+
# 在数据集上遍历所有可能的alpha
318319
for i in range(oS.m):
320+
# 是否存在alpha对，存在就+1
319321
alphaPairsChanged += innerL(i, oS)
320322
print("fullSet, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
321323
iter += 1
322-
else: # 遍历所有的非边界alpha值，也就是不在边界0或C上的值。
324+
325+
# 对已存在 alpha对，选出非边界的alpha值，进行优化。
326+
else:
327+
# 遍历所有的非边界alpha值，也就是不在边界0或C上的值。
323328
nonBoundIs = nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]
324329
for i in nonBoundIs:
325330
alphaPairsChanged += innerL(i, oS)
326331
print("non-bound, iter: %d i:%d, pairs changed %d" % (iter, i, alphaPairsChanged))
327332
iter += 1
333+
334+
# 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。
328335
if entireSet:
329336
entireSet = False # toggle entire set loop
330337
elif (alphaPairsChanged == 0):
@@ -438,6 +445,7 @@ def plotfig_SVM(xArr, yArr, ws, b, alphas):
438445

439446

440447

448+
441449

442450

443451
def testRbf(k1=1.3):
@@ -548,9 +556,6 @@ def calcEkK(oS, k):
548556
return Ek
549557

550558

551-
552-
553-
554559
def selectJK(i, oS, Ei): # this is the second choice -heurstic, and calcs Ej
555560
maxK = -1
556561
maxDeltaE = 0

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