1

Author: WangGitHubWei

Chapter_1 Logistic Regression/lr_train.py

@@ -1,99 +1,99 @@
-# coding:UTF-8
-'''
-Date:20160901
-@author: zhaozhiyong
-'''
-import numpy as np
-
-def load_data(file_name):
-    '''导入训练数据
-    input:  file_name(string)训练数据的位置
-    output: feature_data(mat)特征
-            label_data(mat)标签
-    '''
-    f = open(file_name)  # 打开文件
-    feature_data = []
-    label_data = []
-    for line in f.readlines():
-        feature_tmp = []
-        lable_tmp = []
-        lines = line.strip().split("\t")
-        feature_tmp.append(1)  # 偏置项
-        for i in xrange(len(lines) - 1):
-            feature_tmp.append(float(lines[i]))
-        lable_tmp.append(float(lines[-1]))
-        
-        feature_data.append(feature_tmp)
-        label_data.append(lable_tmp)
-    f.close()  # 关闭文件
-    return np.mat(feature_data), np.mat(label_data)
-
-def sig(x):
-    '''Sigmoid函数
-    input:  x(mat):feature * w
-    output: sigmoid(x)(mat):Sigmoid值
-    '''
-    return 1.0 / (1 + np.exp(-x))
-
-def lr_train_bgd(feature, label, maxCycle, alpha):
-    '''利用梯度下降法训练LR模型
-    input:  feature(mat)特征
-            label(mat)标签
-            maxCycle(int)最大迭代次数
-            alpha(float)学习率
-    output: w(mat):权重
-    '''
-    n = np.shape(feature)[1]  # 特征个数
-    w = np.mat(np.ones((n, 1)))  # 初始化权重
-    i = 0
-    while i <= maxCycle:  # 在最大迭代次数的范围内
-        i += 1  # 当前的迭代次数
-        h = sig(feature * w)  # 计算Sigmoid值
-        err = label - h
-        if i % 100 == 0:
-            print "\t---------iter=" + str(i) + \
-            " , train error rate= " + str(error_rate(h, label))
-        w = w + alpha * feature.T * err  # 权重修正
-    return w
-
-def error_rate(h, label):
-    '''计算当前的损失函数值
-    input:  h(mat):预测值
-            label(mat):实际值
-    output: err/m(float):错误率
-    '''
-    m = np.shape(h)[0]
-    
-    sum_err = 0.0
-    for i in xrange(m):
-        if h[i, 0] > 0 and (1 - h[i, 0]) > 0:
-            sum_err -= (label[i,0] * np.log(h[i,0]) + \
-                        (1-label[i,0]) * np.log(1-h[i,0]))
-        else:
-            sum_err -= 0
-    return sum_err / m
-
-def save_model(file_name, w):
-    '''保存最终的模型
-    input:  file_name(string):模型保存的文件名
-            w(mat):LR模型的权重
-    '''
-    m = np.shape(w)[0]
-    f_w = open(file_name, "w")
-    w_array = []
-    for i in xrange(m):
-        w_array.append(str(w[i, 0]))
-    f_w.write("\t".join(w_array))
-    f_w.close()           
-
-if __name__ == "__main__":
-    # 1、导入训练数据
-    print "---------- 1.load data ------------"
-    feature, label = load_data("data.txt")
-    # 2、训练LR模型
-    print "---------- 2.training ------------"
-    w = lr_train_bgd(feature, label, 1000, 0.01)
-    # 3、保存最终的模型
-    print "---------- 3.save model ------------"
-    save_model("weights", w)
-    
+# coding:UTF-8
+'''
+Date:20160901
+@author: zhaozhiyong
+'''
+import numpy as np
+
+def load_data(file_name):
+    '''导入训练数据
+    input:  file_name(string)训练数据的位置
+    output: feature_data(mat)特征
+            label_data(mat)标签
+    '''
+    f = open(file_name)  # 打开文件
+    feature_data = []
+    label_data = []
+    for line in f.readlines():
+        feature_tmp = []
+        lable_tmp = []
+        lines = line.strip().split("\t")
+        feature_tmp.append(1)  # 偏置项
+        for i in range(len(lines) - 1):
+            feature_tmp.append(float(lines[i]))
+        lable_tmp.append(float(lines[-1]))
+        
+        feature_data.append(feature_tmp)
+        label_data.append(lable_tmp)
+    f.close()  # 关闭文件
+    return np.mat(feature_data), np.mat(label_data)
+
+def sig(x):
+    '''Sigmoid函数
+    input:  x(mat):feature * w
+    output: sigmoid(x)(mat):Sigmoid值
+    '''
+    return 1.0 / (1 + np.exp(-x))
+
+def lr_train_bgd(feature, label, maxCycle, alpha):
+    '''利用梯度下降法训练LR模型
+    input:  feature(mat)特征
+            label(mat)标签
+            maxCycle(int)最大迭代次数
+            alpha(float)学习率
+    output: w(mat):权重
+    '''
+    n = np.shape(feature)[1]  # 特征个数
+    w = np.mat(np.ones((n, 1)))  # 初始化权重
+    i = 0
+    while i <= maxCycle:  # 在最大迭代次数的范围内
+        i += 1  # 当前的迭代次数
+        h = sig(feature * w)  # 计算Sigmoid值
+        err = label - h
+        if i % 100 == 0:
+            print ("\t---------iter=" + str(i) + \
+            " , train error rate= " + str(error_rate(h, label)))
+        w = w + alpha * feature.T * err  # 权重修正
+    return w
+
+def error_rate(h, label):
+    '''计算当前的损失函数值
+    input:  h(mat):预测值
+            label(mat):实际值
+    output: err/m(float):错误率
+    '''
+    m = np.shape(h)[0]
+    
+    sum_err = 0.0
+    for i in range(m):
+        if h[i, 0] > 0 and (1 - h[i, 0]) > 0:
+            sum_err -= (label[i,0] * np.log(h[i,0]) + \
+                        (1-label[i,0]) * np.log(1-h[i,0]))
+        else:
+            sum_err -= 0
+    return sum_err / m
+
+def save_model(file_name, w):
+    '''保存最终的模型
+    input:  file_name(string):模型保存的文件名
+            w(mat):LR模型的权重
+    '''
+    m = np.shape(w)[0]
+    f_w = open(file_name, "w")
+    w_array = []
+    for i in range(m):
+        w_array.append(str(w[i, 0]))
+    f_w.write("\t".join(w_array))
+    f_w.close()           
+
+if __name__ == "__main__":
+    # 1、导入训练数据
+    print ("---------- 1.load data ------------")
+    feature, label = load_data("data.txt")
+    # 2、训练LR模型
+    print ("---------- 2.training ------------")
+    w = lr_train_bgd(feature, label, 1000, 0.01)
+    # 3、保存最终的模型
+    print ("---------- 3.save model ------------")
+    save_model("weights", w)
+    

Chapter_4 SVM/svm.py

@@ -235,7 +235,7 @@ def svm_predict(svm, test_sample_x):
     kernel_value = cal_kernel_value(svm.train_x, test_sample_x, svm.kernel_opt)
     # 2、计算预测值
     predict = kernel_value.T * np.multiply(svm.train_y, svm.alphas) + svm.b
-    return predict
+    return predict 
 
 def cal_accuracy(svm, test_x, test_y):
     '''计算预测的准确性