From 11a3822879fa52a59b5fa34b411ab063c3aa0791 Mon Sep 17 00:00:00 2001
From: jiangzhonglian <jiang-s@163.com>
Date: Thu, 20 Apr 2017 00:30:48 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0SVM:alphas=E7=9A=84=E4=BC=98?=
 =?UTF-8?q?=E5=8C=96=E4=BF=AE=E6=94=B9=E9=87=8F=E7=9A=84=E6=B1=82=E8=A7=A3?=
 =?UTF-8?q?=E5=85=AC=E5=BC=8F?=
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---
 src/python/6.SVM/svm-complete.py            | 2 +-
 src/python/6.SVM/svm-complete_Non-Kernel.py | 2 +-
 src/python/6.SVM/svm-simple.py              | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/python/6.SVM/svm-complete.py b/src/python/6.SVM/svm-complete.py
index 743f096a7..a6f5aa41a 100644
--- a/src/python/6.SVM/svm-complete.py
+++ b/src/python/6.SVM/svm-complete.py
@@ -247,7 +247,7 @@ def innerL(i, oS):
             return 0
 
         # eta是alphas[j]的最优修改量，如果eta==0，需要退出for循环的当前迭代过程
-        # 类似：2ab <= a^2 + b^2
+        # 参考《统计学习方法》李航-P125~P128<序列最小最优化算法>
         eta = 2.0 * oS.K[i, j] - oS.K[i, i] - oS.K[j, j]  # changed for kernel
         if eta >= 0:
             print("eta>=0")
diff --git a/src/python/6.SVM/svm-complete_Non-Kernel.py b/src/python/6.SVM/svm-complete_Non-Kernel.py
index 6f2dd1dc7..51199e534 100644
--- a/src/python/6.SVM/svm-complete_Non-Kernel.py
+++ b/src/python/6.SVM/svm-complete_Non-Kernel.py
@@ -196,7 +196,7 @@ def innerL(i, oS):
             return 0
 
         # eta是alphas[j]的最优修改量，如果eta==0，需要退出for循环的当前迭代过程
-        # 类似：2ab <= a^2 + b^2
+        # 参考《统计学习方法》李航-P125~P128<序列最小最优化算法>
         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")
diff --git a/src/python/6.SVM/svm-simple.py b/src/python/6.SVM/svm-simple.py
index 990fba0d7..3d188659c 100644
--- a/src/python/6.SVM/svm-simple.py
+++ b/src/python/6.SVM/svm-simple.py
@@ -127,7 +127,7 @@ def smoSimple(dataMatIn, classLabels, C, toler, maxIter):
                     continue
 
                 # eta是alphas[j]的最优修改量，如果eta==0，需要退出for循环的当前迭代过程
-                # 类似：2ab <= a^2 + b^2
+                # 参考《统计学习方法》李航-P125~P128<序列最小最优化算法>
                 eta = 2.0 * dataMatrix[i, :]*dataMatrix[j, :].T - dataMatrix[i, :]*dataMatrix[i,:].T - dataMatrix[j, :]*dataMatrix[j, :].T
                 if eta >= 0:
                     print("eta>=0")