add softmax

WenDesi · WenDesi · commit 9b52d05e0e7f · 2016-12-16T21:51:27.000+08:00
diff --git a/softmax/softmax.py b/softmax/softmax.py
@@ -0,0 +1,131 @@
+# encoding=utf8
+
+import math
+import pandas as pd
+import numpy as np
+import random
+import time
+
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+
+
+class Softmax(object):
+
+    def __init__(self):
+        self.learning_step = 0.000001           # 学习速率
+        self.max_iteration = 100000             # 最大迭代次数
+        self.weight_lambda = 0.01               # 衰退权重
+
+    def cal_e(self,x,l):
+        '''
+        计算博客中的公式3
+        '''
+
+        theta_l = self.w[l]
+        product = np.dot(theta_l,x)
+
+        return math.exp(product)
+
+    def cal_probability(self,x,j):
+        '''
+        计算博客中的公式2
+        '''
+
+        molecule = self.cal_e(x,j)
+        denominator = sum([self.cal_e(x,i) for i in range(self.k)])
+
+        return molecule/denominator
+
+
+    def cal_partial_derivative(self,x,y,j):
+        '''
+        计算博客中的公式1
+        '''
+
+        first = int(y==j)                           # 计算示性函数
+        second = self.cal_probability(x,j)          # 计算后面那个概率
+
+        return -x*(first-second) + self.weight_lambda*self.w[j]
+
+    def predict_(self, x):
+        result = np.dot(self.w,x)
+        row, column = result.shape
+
+        # 找最大值所在的列
+        _positon = np.argmax(result)
+        m, n = divmod(_positon, column)
+
+        return m
+
+    def train(self, features, labels):
+        self.k = len(set(labels))
+
+        self.w = np.zeros((self.k,len(features[0])+1))
+        time = 0
+
+        while time < self.max_iteration:
+            print('loop %d' % time)
+            time += 1
+            index = random.randint(0, len(labels) - 1)
+
+            x = features[index]
+            y = labels[index]
+
+            x = list(x)
+            x.append(1.0)
+            x = np.array(x)
+
+            derivatives = [self.cal_partial_derivative(x,y,j) for j in range(self.k)]
+
+            for j in range(self.k):
+                self.w[j] -= self.learning_step * derivatives[j]
+
+    def predict(self,features):
+        labels = []
+        for feature in features:
+            x = list(feature)
+            x.append(1)
+
+            x = np.matrix(x)
+            x = np.transpose(x)
+
+            labels.append(self.predict_(x))
+        return labels
+
+
+if __name__ == '__main__':
+
+    print('Start read data')
+
+    time_1 = time.time()
+
+    raw_data = pd.read_csv('../data/train.csv', header=0)
+    data = raw_data.values
+
+    imgs = data[0::, 1::]
+    labels = data[::, 0]
+
+    # 选取 2/3 数据作为训练集， 1/3 数据作为测试集
+    train_features, test_features, train_labels, test_labels = train_test_split(
+        imgs, labels, test_size=0.33, random_state=23323)
+    # print train_features.shape
+    # print train_features.shape
+
+    time_2 = time.time()
+    print('read data cost '+ str(time_2 - time_1)+' second')
+
+    print('Start training')
+    p = Softmax()
+    p.train(train_features, train_labels)
+
+    time_3 = time.time()
+    print('training cost '+ str(time_3 - time_2)+' second')
+
+    print('Start predicting')
+    test_predict = p.predict(test_features)
+    time_4 = time.time()
+    print('predicting cost ' + str(time_4 - time_3) +' second')
+
+    score = accuracy_score(test_labels, test_predict)
+    print("The accruacy socre is " + str(score))
