Create svm_train.py

Author: zhaozhiyong19890102

Chapter_4 SVM/svm_train.py

@@ -0,0 +1,55 @@
+# coding:UTF-8
+
+import numpy as np
+import svm
+
+def load_data_libsvm(data_file):
+    '''导入训练数据
+    input:  data_file(string):训练数据所在文件
+    output: data(mat):训练样本的特征
+            label(mat):训练样本的标签
+    '''
+    data = []
+    label = []
+    f = open(data_file)
+    for line in f.readlines():
+        lines = line.strip().split(' ')
+        
+        # 提取得出label
+        label.append(float(lines[0]))
+        # 提取出特征，并将其放入到矩阵中
+        index = 0
+        tmp = []
+        for i in xrange(1, len(lines)):
+            li = lines[i].strip().split(":")
+            if int(li[0]) - 1 == index:
+                tmp.append(float(li[1]))
+            else:
+                while(int(li[0]) - 1 > index):
+                    tmp.append(0)
+                    index += 1
+                tmp.append(float(li[1]))
+            index += 1
+        while len(tmp) < 13:
+            tmp.append(0)
+        data.append(tmp)
+    f.close()
+    return np.mat(data), np.mat(label).T
+
+if __name__ == "__main__":
+    # 1、导入训练数据
+    print "------------ 1、load data --------------"
+    dataSet, labels = load_data_libsvm("heart_scale")
+    # 2、训练SVM模型
+    print "------------ 2、training ---------------"
+    C = 0.6
+    toler = 0.001
+    maxIter = 500
+    svm_model = svm.SVM_training(dataSet, labels, C, toler, maxIter)
+    # 3、计算训练的准确性
+    print "------------ 3、cal accuracy --------------"
+    accuracy = svm.cal_accuracy(svm_model, dataSet, labels)  
+    print "The training accuracy is: %.3f%%" % (accuracy * 100)
+    # 4、保存最终的SVM模型
+    print "------------ 4、save model ----------------"
+    svm.save_svm_model(svm_model, "model_file")