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Author: WangGitHubWei

Chapter_2 Softmax Regression/softmax_regression_train.py

@@ -1,95 +1,98 @@
-# coding:UTF-8
-'''
-Date:20160805
-@author: zhaozhiyong
-'''
-import numpy as np
-
-def load_data(inputfile):
-    '''导入训练数据
-    input:  inputfile(string)训练样本的位置
-    output: feature_data(mat)特征
-            label_data(mat)标签
-            k(int)类别的个数
-    '''
-    f = open(inputfile)  # 打开文件
-    feature_data = []
-    label_data = []
-    for line in f.readlines():
-        feature_tmp = []
-        feature_tmp.append(1)  # 偏置项
-        lines = line.strip().split("\t")
-        for i in xrange(len(lines) - 1):
-            feature_tmp.append(float(lines[i]))
-        label_data.append(int(lines[-1]))
-        
-        feature_data.append(feature_tmp)
-    f.close()  # 关闭文件
-    return np.mat(feature_data), np.mat(label_data).T, len(set(label_data))
-
-def cost(err, label_data):
-    '''计算损失函数值
-    input:  err(mat):exp的值
-            label_data(mat):标签的值
-    output: sum_cost / m(float):损失函数的值
-    '''
-    m = np.shape(err)[0]
-    sum_cost = 0.0
-    for i in xrange(m):
-        if err[i, label_data[i, 0]] / np.sum(err[i, :]) > 0:
-            sum_cost -= np.log(err[i, label_data[i, 0]] / np.sum(err[i, :]))
-        else:
-            sum_cost -= 0
-    return sum_cost / m
-    
-
-def gradientAscent(feature_data, label_data, k, maxCycle, alpha):
-    '''利用梯度下降法训练Softmax模型
-    input:  feature_data(mat):特征
-            label_data(mat):标签
-            k(int):类别的个数
-            maxCycle(int):最大的迭代次数
-            alpha(float):学习率
-    output: weights(mat)：权重
-    '''
-    m, n = np.shape(feature_data)
-    weights = np.mat(np.ones((n, k)))  # 权重的初始化
-    i = 0
-    while i <= maxCycle:
-        err = np.exp(feature_data * weights)
-        if i % 500 == 0:
-            print "\t-----iter: ", i , ", cost: ", cost(err, label_data)
-        rowsum = -err.sum(axis=1)
-        rowsum = rowsum.repeat(k, axis=1)
-        err = err / rowsum
-        for x in range(m):
-            err[x, label_data[x, 0]] += 1
-        weights = weights + (alpha / m) * feature_data.T * err      
-        i += 1           
-    return weights
-
-def save_model(file_name, weights):
-    '''保存最终的模型
-    input:  file_name(string):保存的文件名
-            weights(mat):softmax模型
-    '''
-    f_w = open(file_name, "w")
-    m, n = np.shape(weights)
-    for i in xrange(m):
-        w_tmp = []
-        for j in xrange(n):
-            w_tmp.append(str(weights[i, j]))
-        f_w.write("\t".join(w_tmp) + "\n")
-    f_w.close()
-            
-if __name__ == "__main__":
-    inputfile = "SoftInput.txt"
-    # 1、导入训练数据
-    print "---------- 1.load data ------------"
-    feature, label, k = load_data(inputfile)
-    # 2、训练Softmax模型
-    print "---------- 2.training ------------"
-    weights = gradientAscent(feature, label, k, 10000, 0.4)
-    # 3、保存最终的模型
-    print "---------- 3.save model ------------"
-    save_model("weights", weights)
+# coding:UTF-8
+'''
+Date:20160805
+@author: zhaozhiyong
+'''
+import numpy as np
+
+def load_data(inputfile):
+    '''导入训练数据
+    input:  inputfile(string)训练样本的位置
+    output: feature_data(mat)特征
+            label_data(mat)标签
+            k(int)类别的个数
+    '''
+    f = open(inputfile)  # 打开文件
+    feature_data = []
+    label_data = []
+    for line in f.readlines():
+        feature_tmp = []
+        feature_tmp.append(1)  # 偏置项
+        lines = line.strip().split("\t")
+        for i in range(len(lines) - 1):
+            feature_tmp.append(float(lines[i]))
+        label_data.append(int(lines[-1]))
+        
+        feature_data.append(feature_tmp)
+    f.close()  # 关闭文件
+    return np.mat(feature_data), np.mat(label_data).T, len(set(label_data))
+
+def cost(err, label_data):
+    '''计算损失函数值
+    input:  err(mat):exp的值
+            label_data(mat):标签的值
+    output: sum_cost / m(float):损失函数的值
+    '''
+    m = np.shape(err)[0]
+    sum_cost = 0.0
+    for i in range(m):
+        if err[i, label_data[i, 0]] / np.sum(err[i, :]) > 0:
+            sum_cost -= np.log(err[i, label_data[i, 0]] / np.sum(err[i, :]))
+        else:
+            sum_cost -= 0
+    return sum_cost / m
+    
+
+def gradientAscent(feature_data, label_data, k, maxCycle, alpha):
+    '''利用梯度下降法训练Softmax模型
+    input:  feature_data(mat):特征
+            label_data(mat):标签
+            k(int):类别的个数
+            maxCycle(int):最大的迭代次数
+            alpha(float):学习率
+    output: weights(mat)：权重
+    '''
+    m, n = np.shape(feature_data)
+    weights = np.mat(np.ones((n, k)))  # 权重的初始化
+    i = 0
+    while i <= maxCycle:
+        err = np.exp(feature_data * weights)  #得到一个shape(m,k)的mat
+        
+        if i % 500 == 0:
+            print ("\t-----iter: ", i , ", cost: ", cost(err, label_data))
+        rowsum = -err.sum(axis=1) #而当加入axis=1以后就是将一个矩阵的每一行向量相加
+        rowsum = rowsum.repeat(k, axis=1)
+        #   axis=0,沿着y轴复制，实际上增加了行数,axis=1,沿着x轴复制，实际上增加列数
+
+        err = err / rowsum
+        for x in range(m):
+            err[x, label_data[x, 0]] += 1   #得到的是标签的类型
+        weights = weights + (alpha / m) * feature_data.T * err      
+        i += 1           
+    return weights
+
+def save_model(file_name, weights):
+    '''保存最终的模型
+    input:  file_name(string):保存的文件名
+            weights(mat):softmax模型
+    '''
+    f_w = open(file_name, "w")
+    m, n = np.shape(weights)
+    for i in range(m):
+        w_tmp = []
+        for j in range(n):
+            w_tmp.append(str(weights[i, j]))
+        f_w.write("\t".join(w_tmp) + "\n")
+    f_w.close()
+            
+if __name__ == "__main__":
+    inputfile = "SoftInput.txt"
+    # 1、导入训练数据
+    print ("---------- 1.load data ------------")
+    feature, label, k = load_data(inputfile)
+    # 2、训练Softmax模型
+    print ("---------- 2.training ------------")
+    weights = gradientAscent(feature, label, k, 10000, 0.4)
+    # 3、保存最终的模型
+    print ("---------- 3.save model ------------")
+    save_model("weights", weights)