# coding=utf8
import random

import numpy as np
import tensorflow as tf
from sklearn import svm
from sklearn import preprocessing  

right0 = 0.0  # 记录预测为1且实际为1的结果数
error0 = 0  # 记录预测为1但实际为0的结果数
right1 = 0.0  # 记录预测为0且实际为0的结果数
error1 = 0  # 记录预测为0但实际为1的结果数

for file_num in range(10):
    # 在十个随机生成的不相干数据集上进行测试，将结果综合
    print('testing NO.%d dataset.......' % file_num)
    ff = open('digit_train_' + file_num.__str__() + '.data')
    rr = ff.readlines()
    x_test2 = []
    y_test2 = []
    
    for i in range(len(rr)):
        x_test2.append(list(map(int, map(float, rr[i].split(' ')[:256]))))
        y_test2.append(list(map(int, rr[i].split(' ')[256:266])))
    ff.close()
    # 以上是读出训练数据
    ff2 = open('digit_test_' + file_num.__str__() + '.data')
    rr2 = ff2.readlines()
    x_test3 = []
    y_test3 = []
    for i in range(len(rr2)):
        x_test3.append(list(map(int, map(float, rr2[i].split(' ')[:256]))))
        y_test3.append(list(map(int, rr2[i].split(' ')[256:266])))
    ff2.close()
    # 以上是读出测试数据

    sess = tf.InteractiveSession()
    # 建立一个tensorflow的会话

    # 初始化权值向量
    def weight_variable(shape):
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)


    # 初始化偏置向量
    def bias_variable(shape):
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)


    # 二维卷积运算，步长为1，输出大小不变
    def conv2d(x, W):
        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


    # 池化运算，将卷积特征缩小为1/2
    def max_pool_2x2(x):
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

    # 给x，y留出占位符，以便未来填充数据
    x = tf.placeholder("float", [None, 256])
    y_ = tf.placeholder("float", [None, 10])
    # 设置输入层的W和b
    W = tf.Variable(tf.zeros([256, 10]))
    b = tf.Variable(tf.zeros([10]))
    # 计算输出，采用的函数是softmax（输入的时候是one hot编码）
    y = tf.nn.softmax(tf.matmul(x, W) + b)
    # 第一个卷积层，5x5的卷积核，输出向量是32维
    w_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    x_image = tf.reshape(x, [-1, 16, 16, 1])
    # 图片大小是16*16，,-1代表其他维数自适应
    h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)
    # 采用的最大池化，因为都是1和0，平均池化没有什么意义

    # 第二层卷积层，输入向量是32维，输出64维，还是5x5的卷积核
    w_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])

    h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    # 全连接层的w和b
    w_fc1 = weight_variable([4 * 4 * 64, 256])
    b_fc1 = bias_variable([256])
    # 此时输出的维数是256维
    h_pool2_flat = tf.reshape(h_pool2, [-1, 4 * 4 * 64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
    # h_fc1是提取出的256维特征，很关键。后面就是用这个输入到SVM中

    # 设置dropout，否则很容易过拟合
    keep_prob = tf.placeholder("float")
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    # 输出层，在本实验中只利用它的输出反向训练CNN，至于其具体数值我不关心
    w_fc2 = weight_variable([256, 10])
    b_fc2 = bias_variable([10])

    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, w_fc2) + b_fc2)
    cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
    # 设置误差代价以交叉熵的形式
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    # 用adma的优化算法优化目标函数
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
    sess.run(tf.global_variables_initializer())
    for i in range(3000):
        # 跑3000轮迭代，每次随机从训练样本中抽出50个进行训练
        batch = ([], [])
        p = random.sample(range(795), 50)
        for k in p:
            batch[0].append(x_test2[k])
            batch[1].append(y_test2[k])
        if i % 100 == 0:
            train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
            # print "step %d, train accuracy %g" % (i, train_accuracy)
        train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.6})
        # 设置dropout的参数为0.6，测试得到，大点收敛的慢，小点立刻出现过拟合

    print("test accuracy %g" % accuracy.eval(feed_dict={x: x_test3, y_: y_test3, keep_prob: 1.0}))
    # def my_test(input_x):
    #     y = tf.nn.softmax(tf.matmul(sess.run(x), W) + b)
    
    for h in range(len(y_test2)):
        if np.argmax(y_test2[h]) == 7:
            y_test2[h] = 1
        else:
            y_test2[h] = 0
    for h in range(len(y_test3)):
        if np.argmax(y_test3[h]) == 7:
            y_test3[h] = 1
        else:
            y_test3[h] = 0
    # 以上两步都是为了将源数据的one hot编码改为1和0，我的学号尾数为7
    x_temp = []
    for g in x_test2:
        x_temp.append(sess.run(h_fc1, feed_dict={x: np.array(g).reshape((1, 256))})[0])
    # 将原来的x带入训练好的CNN中计算出来全连接层的特征向量，将结果作为SVM中的特征向量
    x_temp2 = []
    for g in x_test3:
        x_temp2.append(sess.run(h_fc1, feed_dict={x: np.array(g).reshape((1, 256))})[0])

    clf = svm.SVC(C=0.9, kernel='linear')  #linear kernel  
#    clf = svm.SVC(C=0.9, kernel='rbf')   #RBF kernel  
    # SVM选择了RBF核，C选择了0.9  
#    x_temp = preprocessing.scale(x_temp)  #normalization
    
    clf.fit(x_temp, y_test2)
    # SVM选择了RBF核，C选择了0.9
    print('svm testing accuracy:')  
    print(clf.score(x_temp2,y_test3)) 
    
    for j in range(len(x_temp2)):
        # 验证时出现四种情况分别对应四个变量存储
        #这里报错了,需要对其进行reshape（1，-1）
        if clf.predict(x_temp2[j].reshape(1,-1))[0] == y_test3[j] == 1:
            right0 += 1
        elif clf.predict(x_temp2[j].reshape(1,-1))[0] == y_test3[j] == 0:
            right1 += 1
        elif clf.predict(x_temp2[j].reshape(1,-1))[0] == 1 and y_test3[j] == 0:
            error0 += 1
        else:
            error1 += 1

accuracy = right0 / (right0 + error0)  # 准确率
recall = right0 / (right0 + error1)  # 召回率
print('svm right ratio ', (right0 + right1) / (right0 + right1 + error0 + error1))
print ('accuracy ', accuracy)
print ('recall ', recall)
print ('F1 score ', 2 * accuracy * recall / (accuracy + recall))  # 计算F1值
