深度学习之逻辑回归的实现 -- sigmoid
1 什么是逻辑回归
1.1逻辑回归与线性回归的区别：
线性回归预测的是一个连续的值，不论是单变量还是多变量（比如多层感知器），他都返回的是一个连续的值，放在图中就是条连续的曲线，他常用来表示的数学方法是Y=aX+b；
与之相对的，逻辑回归给出的值并不是连续的，而是 类似于“是” 和 “否” 的回答，这就类似于二元分类的问题。
1.2逻辑回归实现（sigmoid）：
在逻辑回归算法中，我们常使用的激活函数是Sigmoid函数，他能够将数据映射到 0 到 1 之间，并且通过映射判断，如果映射到的值在 1 ，就返回出一个正面的结果，与之相反，当映射的值为0时就返回一个负面的结果，这就是我们上面所提到的回答： “是”或“否”。那么，什么是Sigmoid函数呢？
Sigmoid函数是一种在生物学中常见的S型函数，也称为S型生长曲线，他的值我们可以看做是恒在 0 到 1 之间的（因为这段区间使我们真正所关心的）。sigmoid的形式如下图所示： 
深度学习网络本质上来说也是一种多层映射网络，当我们输入特征后，在通过如多层感知器的映射后，会一层层的映射到一个最终的形式。使用Sigmoid函数的意义就在于，他会在最后的映射中将结果映射成为0 到 1 之间的值，这时候我们就可以将映射后的值看做是神经网络给出的概率的结果。
1.3逻辑回归的常用损失函数（交叉熵）：
在线性回归中，我们常用 “mse” （平方差） 来进行损失的刻画，但是“mse”一般来进行惩罚的是损失与原有的数据集在同一个数量级的情况，假如说数量级特别的庞大，但是损失值比较小，那么所得到的损失就会很小，不利于我们的训练。针对这种情形，我们在逻辑回归中（同时在大多数的二分类问题中）使用更有效的方法————交叉熵，他会给我们展现出一种更大的损失。下面这个图就直观的显示出了L2（均方差）与logistic（交叉熵）之间关于在处理损失的差别。
在keras中，我们使用的函数是binary_crossentropy,下面会以一个例子的形式来使用交叉熵实现逻辑回归。
2逻辑回归的简单实现
这是一个关于信用卡是否存在欺诈行为的预测。
我们给出部分数据集，并查看是否为一个二分类问题
data = pd.read_csv('tensorflow_study\dataset\credit-a.csv')
# 查看数据
print(data.head())
# 查看数据是否为二分类问题
print(data.iloc[:,-1].value_counts())
然后，我们取出数据，并建立一个神经网络模型，这里采用两个隐藏层，使得训练时拟合程度更高一些。
# 取出除了最后一列的所有数据
x = data.iloc[:, :-1]
# 取出数据并进行替换
y = data.iloc[:, -1].replace(-1,0) # 模拟神经网络创建顺序模型,添加两个隐藏层
# 第一层是获取到的4个单元的隐藏层，数据集是15个数据的元组,使用relu激活
# 第二层是一个简单的数据处理层
# 第三层是输出层，使用Sigmoid进行激活，完成映射
model = tf.keras.Sequential(
[tf.keras.layers.Dense(4,input_shape=(15,),activation='relu'),
tf.keras.layers.Dense(4,activation='relu'),
tf.keras.layers.Dense(1,activation='sigmoid')]
) model.summary()
查看一下我们创建的模型是否符合我们的需求
再配置一个优化器，采用TensorFlow的梯度下降算法进行优化，使用交叉熵作为损失函数，并计算其正确率，开始训练我们的模型，再调用原始数据集中的前三个数据进行预测测试。
model.summary() # 配置优化器
# 使用梯度下降算法进行优化，使用交叉熵作为损失函数，并计算其正确率
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['acc']
) # 训练模型
history = model.fit(x,y,epochs=100)
t_data = data.iloc[:3,:-1]
print(model.predict(t_data))
结果显而易见
这时候，我们也可以通过pandas进行对我们模型的训练过程进行可视化查看，方便我们能够更加准确的针对我们的模型训练做一些改进。
# 查看我们在训练过程中的loss和acc的变化情况
# 散点图展示数据
plt.figure(1) ax1 = plt.subplot(2,1,1)
ax2 = plt.subplot(2,1,2)
plt.sca(ax1)
plt.title('loss ')
plt.plot(history.epoch,history.history.get('loss'))
plt.sca(ax2)
plt.title('acc ')
plt.plot(history.epoch,history.history.get('acc'))
plt.show()
在这里，我们就会明显的发现，当我们训练到18次的时候，loss的变化就趋于稳定状态了，二acc也是跟随着loss的稳定趋于更小的波动。
：：下面附上源码和数据
'''
@Author: mountain
@Date: 2020-03-30 16:11:00
@Description: 逻辑回归 --预测信用卡是否存在欺诈行为
'''
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf data = pd.read_csv('tensorflow_study\dataset\credit-a.csv') # 查看数据
print(data.head())
# 查看数据是否为二分类问题
print(data.iloc[:,-1].value_counts()) # 取出除了最后一列的所有数据
x = data.iloc[:, :-1]
# 取出数据并进行替换
y = data.iloc[:, -1].replace(-1,0) # 模拟神经网络创建顺序模型,添加两个隐藏层
# 第一层是获取到的4个单元的隐藏层，数据集是15个数据的元组,使用relu激活
# 第二层是一个简单的数据处理层
# 第三层是输出层，使用Sigmoid进行激活，完成映射
model = tf.keras.Sequential(
[tf.keras.layers.Dense(4,input_shape=(15,),activation='relu'),
tf.keras.layers.Dense(4,activation='relu'),
tf.keras.layers.Dense(1,activation='sigmoid')]
) model.summary() # 配置优化器
# 使用梯度下降算法进行优化，使用交叉熵作为损失函数，并计算其正确率
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['acc']
) # 训练模型
history = model.fit(x,y,epochs=100) t_data = data.iloc[:3,:-1]
print(model.predict(t_data)) # 查看我们在训练过程中的loss和acc的变化情况
# 散点图展示数据
plt.figure(1) ax1 = plt.subplot(2,1,1)
ax2 = plt.subplot(2,1,2)
plt.sca(ax1)
plt.title('loss ')
plt.plot(history.epoch,history.history.get('loss'))
plt.sca(ax2)
plt.title('acc ')
plt.plot(history.epoch,history.history.get('acc'))
plt.show()
ljhg
0,30.83,0,0,0,9,0,1.25,0,0,1,1,0,202,0,-1
1,58.67,4.46,0,0,8,1,3.04,0,0,6,1,0,43,560,-1
1,24.5,0.5,0,0,8,1,1.5,0,1,0,1,0,280,824,-1
0,27.83,1.54,0,0,9,0,3.75,0,0,5,0,0,100,3,-1
0,20.17,5.625,0,0,9,0,1.71,0,1,0,1,2,120,0,-1
0,32.08,4,0,0,6,0,2.5,0,1,0,0,0,360,0,-1
0,33.17,1.04,0,0,7,1,6.5,0,1,0,0,0,164,31285,-1
1,22.92,11.585,0,0,2,0,0.04,0,1,0,1,0,80,1349,-1
0,54.42,0.5,1,1,5,1,3.96,0,1,0,1,0,180,314,-1
0,42.5,4.915,1,1,9,0,3.165,0,1,0,0,0,52,1442,-1
0,22.08,0.83,0,0,0,1,2.165,1,1,0,0,0,128,0,-1
0,29.92,1.835,0,0,0,1,4.335,0,1,0,1,0,260,200,-1
1,38.25,6,0,0,5,0,1,0,1,0,0,0,0,0,-1
0,48.08,6.04,0,0,5,0,0.04,1,1,0,1,0,0,2690,-1
1,45.83,10.5,0,0,8,0,5,0,0,7,0,0,0,0,-1
0,36.67,4.415,1,1,5,0,0.25,0,0,10,0,0,320,0,-1
0,28.25,0.875,0,0,6,0,0.96,0,0,3,0,0,396,0,-1
1,23.25,5.875,0,0,8,0,3.17,0,0,10,1,0,120,245,-1
0,21.83,0.25,0,0,1,1,0.665,0,1,0,0,0,0,0,-1
1,19.17,8.585,0,0,2,1,0.75,0,0,7,1,0,96,0,-1
0,25,11.25,0,0,0,0,2.5,0,0,17,1,0,200,1208,-1
0,23.25,1,0,0,0,0,0.835,0,1,0,1,2,300,0,-1
1,47.75,8,0,0,0,0,7.875,0,0,6,0,0,0,1260,-1
1,27.42,14.5,0,0,10,1,3.085,0,0,1,1,0,120,11,-1
1,41.17,6.5,0,0,8,0,0.5,0,0,3,0,0,145,0,-1
1,15.83,0.585,0,0,0,1,1.5,0,0,2,1,0,100,0,-1
1,47,13,0,0,3,2,5.165,0,0,9,0,0,0,0,-1
0,56.58,18.5,0,0,1,2,15,0,0,17,0,0,0,0,-1
0,57.42,8.5,0,0,11,1,7,0,0,3,1,0,0,0,-1
0,42.08,1.04,0,0,9,0,5,0,0,6,0,0,500,10000,-1
0,29.25,14.79,0,0,12,0,5.04,0,0,5,0,0,168,0,-1
0,42,9.79,0,0,10,1,7.96,0,0,8,1,0,0,0,-1
0,49.5,7.585,0,0,3,2,7.585,0,0,15,0,0,0,5000,-1
1,36.75,5.125,0,0,11,0,5,0,1,0,0,0,0,4000,-1
1,22.58,10.75,0,0,8,0,0.415,0,0,5,0,0,0,560,-1
0,27.83,1.5,0,0,9,0,2,0,0,11,0,0,434,35,-1
0,27.25,1.585,0,0,2,1,1.835,0,0,12,0,0,583,713,-1
1,23,11.75,0,0,10,1,0.5,0,0,2,0,0,300,551,-1
0,27.75,0.585,1,1,2,0,0.25,0,0,2,1,0,260,500,-1
0,54.58,9.415,0,0,13,7,14.415,0,0,11,0,0,30,300,-1
0,34.17,9.17,0,0,0,0,4.5,0,0,12,0,0,0,221,-1
0,28.92,15,0,0,0,1,5.335,0,0,11,1,0,0,2283,-1
0,29.67,1.415,0,0,9,1,0.75,0,0,1,1,0,240,100,-1
0,39.58,13.915,0,0,9,0,8.625,0,0,6,0,0,70,0,-1
0,56.42,28,1,1,0,0,28.5,0,0,40,1,0,0,15,-1
0,54.33,6.75,0,0,0,1,2.625,0,0,11,0,0,0,284,-1
1,41,2.04,1,1,8,1,0.125,0,0,23,0,0,455,1236,-1
0,31.92,4.46,0,0,2,1,6.04,0,0,3,1,0,311,300,-1
0,41.5,1.54,0,0,3,2,3.5,1,1,0,1,0,216,0,-1
0,23.92,0.665,0,0,0,0,0.165,1,1,0,1,0,100,0,-1
1,25.75,0.5,0,0,0,1,0.875,0,1,0,0,0,491,0,-1
0,26,1,0,0,8,0,1.75,0,1,0,0,0,280,0,-1
0,37.42,2.04,0,0,9,0,0.04,0,1,0,0,0,400,5800,-1
0,34.92,2.5,0,0,9,0,0,0,1,0,0,0,239,200,-1
0,34.25,3,0,0,2,1,7.415,0,1,0,0,0,0,0,-1
0,23.33,11.625,1,1,9,0,0.835,0,1,0,0,0,160,300,-1
0,23.17,0,0,0,2,0,0.085,0,1,0,1,0,0,0,-1
0,44.33,0.5,0,0,3,1,5,0,1,0,0,0,320,0,-1
0,35.17,4.5,0,0,10,1,5.75,1,1,0,0,2,711,0,-1
0,43.25,3,0,0,8,1,6,0,0,11,1,0,80,0,-1
0,56.75,12.25,0,0,6,0,1.25,0,0,4,0,0,200,0,-1
0,31.67,16.165,0,0,1,0,3,0,0,9,1,0,250,730,-1
1,23.42,0.79,1,1,8,0,1.5,0,0,2,0,0,80,400,-1
1,20.42,0.835,0,0,8,0,1.585,0,0,1,1,0,0,0,-1
0,26.67,4.25,0,0,2,0,4.29,0,0,1,0,0,120,0,-1
0,34.17,1.54,0,0,2,0,1.54,0,0,1,0,0,520,50000,-1
1,36,1,0,0,0,0,2,0,0,11,1,0,0,456,-1
0,25.5,0.375,0,0,6,0,0.25,0,0,3,1,0,260,15108,-1
0,19.42,6.5,0,0,9,1,1.46,0,0,7,1,0,80,2954,-1
0,35.17,25.125,0,0,10,1,1.625,0,0,1,0,0,515,500,-1
0,32.33,7.5,0,0,11,2,1.585,0,1,0,0,2,420,0,1
1,38.58,5,0,0,2,0,13.5,0,1,0,0,0,980,0,1
0,44.25,0.5,0,0,6,0,10.75,0,1,0,1,2,400,0,1
0,44.83,7,1,1,0,0,1.625,1,1,0,1,0,160,2,1
0,20.67,5.29,0,0,8,0,0.375,0,0,1,1,0,160,0,1
0,34.08,6.5,0,0,12,0,0.125,0,1,0,0,0,443,0,1
1,19.17,0.585,1,1,12,0,0.585,0,1,0,0,0,160,0,1
0,21.67,1.165,1,1,5,0,2.5,0,0,1,1,0,180,20,1
0,21.5,9.75,0,0,0,0,0.25,0,1,0,1,0,140,0,1
0,49.58,19,0,0,13,7,0,0,0,1,1,0,94,0,1
1,27.67,1.5,0,0,6,0,2,0,1,0,1,2,368,0,1
0,39.83,0.5,0,0,6,0,0.25,0,1,0,1,2,288,0,1
0,27.25,0.625,0,0,12,0,0.455,0,1,0,0,0,200,0,1
0,37.17,4,0,0,0,2,5,0,1,0,0,2,280,0,1
0,25.67,2.21,1,1,12,0,4,0,1,0,1,0,188,0,1
0,34,4.5,0,0,12,0,1,0,1,0,0,0,240,0,1
1,49,1.5,0,0,4,3,0,0,1,0,0,0,100,27,1
0,62.5,12.75,1,1,0,1,5,0,1,0,1,0,112,0,1
0,31.42,15.5,0,0,0,0,0.5,0,1,0,1,0,120,0,1
0,52.33,1.375,1,1,0,1,9.46,0,1,0,0,0,200,100,1
0,28.75,1.5,1,1,0,0,1.5,0,1,0,0,0,0,225,1
1,28.58,3.54,0,0,3,2,0.5,0,1,0,0,0,171,0,1
0,23,0.625,1,1,12,0,0.125,0,1,0,1,0,180,1,1
1,22.5,11,1,1,8,0,3,0,1,0,0,0,268,0,1
1,28.5,1,0,0,8,0,1,0,0,2,0,0,167,500,1
0,37.5,1.75,1,1,0,2,0.25,0,1,0,0,0,164,400,1
0,35.25,16.5,1,1,0,0,4,0,1,0,1,0,80,0,1
0,18.67,5,0,0,8,0,0.375,0,0,2,1,0,0,38,1
0,25,12,0,0,5,0,2.25,0,0,2,0,0,120,5,1
0,27.83,4,1,1,3,1,5.75,0,0,2,0,0,75,0,1
0,54.83,15.5,0,0,11,5,0,0,0,20,1,0,152,130,1
0,28.75,1.165,0,0,5,0,0.5,0,1,0,1,2,280,0,1
1,25,11,1,1,12,0,4.5,0,1,0,1,0,120,0,1
0,40.92,2.25,1,1,10,1,10,0,1,0,0,0,176,0,1
1,19.75,0.75,0,0,0,0,0.795,0,0,5,0,0,140,5,1
0,29.17,3.5,0,0,9,0,3.5,0,0,3,0,0,329,0,1
1,24.5,1.04,1,1,13,7,0.5,0,0,3,1,0,180,147,1
0,24.58,12.5,0,0,9,0,0.875,0,1,0,0,0,260,0,1
1,33.75,0.75,0,0,5,2,1,0,0,3,0,0,212,0,1
0,20.67,1.25,1,1,0,1,1.375,0,0,3,0,0,140,210,1
1,25.42,1.125,0,0,8,0,1.29,0,0,2,1,0,200,0,1
0,37.75,7,0,0,8,1,11.5,0,0,7,0,0,300,5,1
0,52.5,6.5,0,0,5,0,6.29,0,0,15,1,0,0,11202,-1
0,57.83,7.04,0,0,6,0,14,0,0,6,0,0,360,1332,-1
1,20.75,10.335,0,0,2,1,0.335,0,0,1,0,0,80,50,-1
0,39.92,6.21,0,0,8,0,0.04,0,0,1,1,0,200,300,-1
0,25.67,12.5,0,0,2,0,1.21,0,0,67,0,0,140,258,-1
1,24.75,12.5,0,0,12,0,1.5,0,0,12,0,0,120,567,-1
1,44.17,6.665,0,0,8,0,7.375,0,0,3,0,0,0,0,-1
1,23.5,9,0,0,8,0,8.5,0,0,5,0,0,120,0,-1
0,34.92,5,0,0,10,1,7.5,0,0,6,0,0,0,1000,-1
0,47.67,2.5,0,0,6,2,2.5,0,0,12,0,0,410,2510,-1
0,22.75,11,0,0,8,0,2.5,0,0,7,0,0,100,809,-1
0,34.42,4.25,0,0,3,2,3.25,0,0,2,1,0,274,610,-1
1,28.42,3.5,0,0,9,0,0.835,0,1,0,1,2,280,0,-1
0,67.75,5.5,0,0,11,5,13,0,0,1,0,0,0,0,-1
0,20.42,1.835,0,0,0,0,2.25,0,0,1,1,0,100,150,-1
1,47.42,8,0,0,11,2,6.5,0,0,6,1,0,375,51100,-1
0,36.25,5,0,0,0,2,2.5,0,0,6,1,0,0,367,-1
0,32.67,5.5,0,0,8,1,5.5,0,0,12,0,0,408,1000,-1
0,48.58,6.5,0,0,8,1,6,0,1,0,0,0,350,0,-1
0,39.92,0.54,1,1,12,0,0.5,0,0,3,1,0,200,1000,-1
0,33.58,2.75,0,0,6,0,4.25,0,0,6,1,0,204,0,-1
1,18.83,9.5,0,0,9,0,1.625,0,0,6,0,0,40,600,-1
1,26.92,13.5,0,0,8,1,5,0,0,2,1,0,0,5000,-1
1,31.25,3.75,0,0,2,1,0.625,0,0,9,0,0,181,0,-1
1,56.5,16,0,0,4,7,0,0,0,15,1,0,0,247,-1
0,43,0.29,1,1,2,1,1.75,0,0,8,1,0,100,375,-1
0,22.33,11,0,0,9,0,2,0,0,1,1,0,80,278,-1
0,27.25,1.665,0,0,2,1,5.085,0,0,9,1,0,399,827,-1
0,32.83,2.5,0,0,2,1,2.75,0,0,6,1,0,160,2072,-1
0,23.25,1.5,0,0,8,0,2.375,0,0,3,0,0,0,582,-1
1,40.33,7.54,1,1,8,1,8,0,0,14,1,0,0,2300,-1
1,30.5,6.5,0,0,0,2,4,0,0,7,0,0,0,3065,-1
1,52.83,15,0,0,0,0,5.5,0,0,14,1,0,0,2200,-1
1,46.67,0.46,0,0,2,1,0.415,0,0,11,0,0,440,6,-1
1,58.33,10,0,0,8,0,4,0,0,14,1,0,0,1602,-1
0,37.33,6.5,0,0,6,1,4.25,0,0,12,0,0,93,0,-1
0,23.08,2.5,0,0,0,0,1.085,0,0,11,0,0,60,2184,-1
0,32.75,1.5,0,0,2,1,5.5,0,0,3,0,0,0,0,-1
1,21.67,11.5,1,1,4,3,0,0,0,11,0,0,0,0,-1
1,28.5,3.04,1,1,10,1,2.54,0,0,1,1,0,70,0,-1
1,68.67,15,0,0,11,5,0,0,0,14,1,0,0,3376,-1
0,28,2,0,0,5,1,4.165,0,0,2,0,0,181,0,-1
0,34.08,0.08,1,1,6,2,0.04,0,0,1,0,0,280,2000,-1
0,27.67,2,0,0,10,1,1,0,0,4,1,0,140,7544,-1
0,44,2,0,0,6,0,1.75,0,0,2,0,0,0,15,-1
0,25.08,1.71,0,0,10,0,1.665,0,0,1,0,0,395,20,-1
0,32,1.75,1,1,11,1,0.04,0,1,0,0,0,393,0,-1
1,60.58,16.5,0,0,8,0,11,0,1,0,0,0,21,10561,-1
1,40.83,10,0,0,8,1,1.75,0,1,0,1,0,29,837,-1
0,19.33,9.5,0,0,8,0,1,0,1,0,0,0,60,400,-1
1,32.33,0.54,0,0,2,0,0.04,0,1,0,1,0,440,11177,-1
0,36.67,3.25,0,0,8,1,9,0,1,0,0,0,102,639,-1
0,37.5,1.125,1,1,1,0,1.5,1,1,0,0,0,431,0,-1
1,25.08,2.54,1,1,12,0,0.25,0,1,0,0,0,370,0,-1
0,41.33,0,0,0,0,2,15,0,1,0,1,0,0,0,-1
0,56,12.5,0,0,5,1,8,0,1,0,0,0,24,2028,-1
1,49.83,13.585,0,0,5,1,8.5,0,1,0,0,0,0,0,-1
0,22.67,10.5,0,0,8,1,1.335,0,1,0,1,0,100,0,-1
0,27,1.5,1,1,9,0,0.375,0,1,0,0,0,260,1065,-1
0,25,12.5,0,0,12,0,3,0,1,0,0,2,20,0,-1
1,26.08,8.665,0,0,12,0,1.415,0,1,0,1,0,160,150,-1
1,18.42,9.25,0,0,8,0,1.21,0,0,4,1,0,60,540,-1
0,20.17,8.17,0,0,12,0,1.96,0,0,14,1,0,60,158,-1
0,47.67,0.29,0,0,0,2,15,0,0,20,1,0,0,15000,-1
1,21.25,2.335,0,0,3,2,0.5,0,0,4,1,2,80,0,-1
1,20.67,3,0,0,8,0,0.165,0,0,3,1,0,100,6,-1
1,57.08,19.5,0,0,0,0,5.5,0,0,7,1,0,0,3000,-1
1,22.42,5.665,0,0,8,0,2.585,0,0,7,1,0,129,3257,-1
0,48.75,8.5,0,0,0,1,12.5,0,0,9,1,0,181,1655,-1
0,40,6.5,0,0,12,2,3.5,0,0,1,1,0,0,500,-1
0,40.58,5,0,0,0,0,5,0,0,7,1,0,0,3065,-1
1,28.67,1.04,0,0,0,0,2.5,0,0,5,0,0,300,1430,-1
1,33.08,4.625,0,0,8,1,1.625,0,0,2,1,0,0,0,-1
0,21.33,10.5,0,0,0,0,3,0,1,0,0,0,0,0,-1
0,42,0.205,0,0,3,1,5.125,0,1,0,1,0,400,0,-1
0,41.75,0.96,0,0,10,0,2.5,0,1,0,1,0,510,600,-1
0,22.67,1.585,1,1,9,0,3.085,0,0,6,1,0,80,0,-1
0,34.5,4.04,1,1,3,2,8.5,0,0,7,0,0,195,0,-1
0,28.25,5.04,1,1,0,2,1.5,0,0,8,0,0,144,7,-1
0,33.17,3.165,1,1,10,0,3.165,0,0,3,0,0,380,0,-1
0,48.17,7.625,0,0,9,1,15.5,0,0,12,1,0,0,790,-1
0,27.58,2.04,1,1,12,0,2,0,0,3,0,0,370,560,-1
0,22.58,10.04,0,0,10,0,0.04,0,0,9,1,0,60,396,-1
1,24.08,0.5,0,0,8,1,1.25,0,0,1,1,0,0,678,-1
1,41.33,1,0,0,3,2,2.25,0,1,0,0,0,0,300,-1
1,20.75,10.25,0,0,8,0,0.71,0,0,2,0,0,49,0,-1
0,36.33,2.125,1,1,9,0,0.085,0,0,1,1,0,50,1187,-1
1,35.42,12,0,0,8,1,14,0,0,8,1,0,0,6590,-1
0,28.67,9.335,0,0,8,1,5.665,0,0,6,1,0,381,168,-1
0,35.17,2.5,0,0,5,0,4.5,0,0,7,1,0,150,1270,-1
0,39.5,4.25,0,0,0,2,6.5,0,0,16,1,0,117,1210,-1
0,39.33,5.875,0,0,2,1,10,0,0,14,0,0,399,0,-1
0,24.33,6.625,1,1,1,0,5.5,0,1,0,0,2,100,0,-1
0,60.08,14.5,0,0,13,7,18,0,0,15,0,0,0,1000,-1
0,23.08,11.5,0,0,3,0,3.5,0,0,9,1,0,56,742,-1
0,26.67,2.71,1,1,2,0,5.25,0,0,1,1,0,211,0,-1
0,48.17,3.5,0,0,12,0,3.5,0,1,0,1,2,230,0,-1
0,41.17,4.04,0,0,2,1,7,0,0,8,1,0,320,0,-1
0,55.92,11.5,0,0,13,7,5,0,0,5,1,0,0,8851,-1
0,53.92,9.625,0,0,11,0,8.665,0,0,5,1,0,0,0,-1
1,18.92,9.25,1,1,0,0,1,0,0,4,0,0,80,500,-1
1,50.08,12.54,0,0,12,0,2.29,0,0,3,0,0,156,0,-1
0,65.42,11,0,0,11,5,20,0,0,7,0,0,22,0,-1
1,17.58,9,0,0,12,0,1.375,0,1,0,0,0,0,0,-1
1,18.83,9.54,0,0,12,0,0.085,0,1,0,1,0,100,0,-1
1,37.75,5.5,0,0,8,0,0.125,0,1,0,0,0,228,0,-1
0,23.25,4,0,0,0,2,0.25,0,1,0,0,0,160,0,-1
0,18.08,5.5,0,0,5,0,0.5,0,1,0,1,0,80,0,-1
1,22.5,8.46,1,1,10,0,2.46,1,1,0,1,0,164,0,-1
0,19.67,0.375,0,0,8,0,2,0,0,2,0,0,80,0,-1
0,22.08,11,0,0,2,0,0.665,0,1,0,1,0,100,0,-1
0,25.17,3.5,0,0,2,0,0.625,0,0,7,1,0,0,7059,-1
1,47.42,3,0,0,10,0,13.875,0,0,2,0,0,519,1704,-1
0,33.5,1.75,0,0,10,1,4.5,0,0,4,0,0,253,857,-1
0,27.67,13.75,0,0,9,0,5.75,0,1,0,0,0,487,500,-1
1,58.42,21,0,0,3,2,10,0,0,13,1,0,0,6700,-1
1,20.67,1.835,0,0,8,0,2.085,0,0,5,1,0,220,2503,-1
0,26.17,0.25,0,0,3,2,0,0,1,0,0,0,0,0,-1
0,21.33,7.5,0,0,12,0,1.415,0,0,1,1,0,80,9800,-1
0,42.83,4.625,0,0,8,0,4.58,0,1,0,1,2,0,0,-1
0,38.17,10.125,0,0,10,0,2.5,0,0,6,1,0,520,196,-1
0,20.5,10,1,1,0,0,2.5,0,1,0,1,2,40,0,-1
0,48.25,25.085,0,0,9,0,1.75,0,0,3,1,0,120,14,-1
0,28.33,5,0,0,9,0,11,0,1,0,0,0,70,0,-1
0,18.5,2,0,0,3,0,1.5,0,0,2,1,0,120,300,-1
0,33.17,3.04,1,1,0,1,2.04,0,0,1,0,0,180,18027,-1
0,45,8.5,0,0,2,1,14,0,0,1,0,0,88,2000,-1
1,19.67,0.21,0,0,8,1,0.29,0,0,11,1,0,80,99,-1
0,21.83,11,0,0,10,0,0.29,0,0,6,1,0,121,0,-1
0,40.25,21.5,0,0,11,5,20,0,0,11,1,0,0,1200,-1
0,41.42,5,0,0,8,1,5,0,0,6,0,0,470,0,-1
1,17.83,11,0,0,10,1,1,0,0,11,1,0,0,3000,-1
0,23.17,11.125,0,0,10,1,0.46,0,0,1,1,0,100,0,-1
0,18.17,10.25,0,0,0,1,1.085,1,1,0,1,0,320,13,1
0,20,11.045,0,0,0,0,2,1,1,0,0,0,136,0,1
0,20,0,0,0,1,0,0.5,1,1,0,1,0,144,0,1
1,20.75,9.54,0,0,3,0,0.04,1,1,0,1,0,200,1000,1
1,24.5,1.75,1,1,0,0,0.165,1,1,0,1,0,132,0,1
0,32.75,2.335,0,0,1,1,5.75,1,1,0,0,0,292,0,1
1,52.17,0,1,1,13,7,0,1,1,0,1,0,0,0,1
1,48.17,1.335,0,0,3,8,0.335,1,1,0,1,0,0,120,1
1,20.42,10.5,1,1,10,1,0,1,1,0,0,0,154,32,1
0,50.75,0.585,0,0,13,7,0,1,1,0,1,0,145,0,1
0,17.08,0.085,1,1,0,0,0.04,1,1,0,1,0,140,722,1
0,18.33,1.21,1,1,11,6,0,1,1,0,1,0,100,0,1
1,32,6,0,0,1,0,1.25,1,1,0,1,0,272,0,1
0,59.67,1.54,0,0,8,0,0.125,0,1,0,0,0,260,0,-1
0,18,0.165,0,0,8,4,0.21,1,1,0,1,0,200,40,-1
0,32.33,2.5,0,0,0,0,1.25,1,1,0,0,0,280,0,1
0,18.08,6.75,1,1,6,0,0.04,1,1,0,1,0,140,0,1
0,38.25,10.125,1,1,5,0,0.125,1,1,0,1,0,160,0,1
0,30.67,2.5,0,0,2,1,2.25,1,1,0,0,2,340,0,1
0,18.58,5.71,0,0,1,0,0.54,1,1,0,1,0,120,0,1
1,19.17,5.415,0,0,3,1,0.29,1,1,0,1,0,80,484,1
1,18.17,10,1,1,8,1,0.165,1,1,0,1,0,340,0,1
0,16.25,0.835,0,0,6,0,0.085,0,1,0,1,2,200,0,1
0,21.17,0.875,1,1,0,1,0.25,1,1,0,1,0,280,204,1
0,23.92,0.585,1,1,2,1,0.125,1,1,0,1,0,240,1,1
0,17.67,4.46,0,0,0,0,0.25,1,1,0,1,2,80,0,1
1,16.5,1.25,0,0,8,0,0.25,1,0,1,1,0,108,98,1
0,23.25,12.625,0,0,0,0,0.125,1,0,2,1,0,0,5552,1
0,17.58,10,0,0,9,1,0.165,1,0,1,1,0,120,1,1
0,29.5,0.58,0,0,9,0,0.29,1,0,1,1,0,340,2803,1
0,18.83,0.415,1,1,0,0,0.165,1,0,1,1,0,200,1,1
1,21.75,1.75,1,1,4,3,0,1,1,0,1,0,160,0,1
0,23,0.75,0,0,6,0,0.5,1,1,0,0,2,320,0,1
1,18.25,10,0,0,9,0,1,1,0,1,1,0,120,1,1
0,25.42,0.54,0,0,9,0,0.165,1,0,1,1,0,272,444,1
0,35.75,2.415,0,0,9,0,0.125,1,0,2,1,0,220,1,1
1,16.08,0.335,0,0,13,7,0,1,0,1,1,0,160,126,1
1,31.92,3.125,0,0,13,7,3.04,1,0,2,0,0,200,4,1
0,69.17,9,0,0,13,7,4,1,0,1,1,0,70,6,1
0,32.92,2.5,0,0,12,0,1.75,1,0,2,0,0,720,0,1
0,16.33,2.75,0,0,12,0,0.665,1,0,1,1,0,80,21,1
0,22.17,12.125,0,0,0,0,3.335,1,0,2,0,0,180,173,1
1,57.58,2,0,0,13,7,6.5,1,0,1,1,0,0,10,1
0,18.25,0.165,0,0,1,0,0.25,1,1,0,0,2,280,0,1
0,23.42,1,0,0,0,0,0.5,1,1,0,0,2,280,0,1
1,15.92,2.875,0,0,8,0,0.085,1,1,0,1,0,120,0,1
1,24.75,13.665,0,0,8,1,1.5,1,1,0,1,0,280,1,1
0,48.75,26.335,1,1,13,7,0,0,1,0,0,0,0,0,1
0,23.5,2.75,0,0,13,7,4.5,1,1,0,1,0,160,25,1
0,18.58,10.29,0,0,13,7,0.415,1,1,0,1,0,80,0,1
0,27.75,1.29,0,0,5,1,0.25,1,1,0,0,2,140,0,1
1,31.75,3,1,1,4,3,0,1,1,0,1,0,160,20,1
1,24.83,4.5,0,0,9,0,1,1,1,0,0,0,360,6,1
0,19,1.75,1,1,0,0,2.335,1,1,0,0,0,112,6,1
1,16.33,0.21,0,0,12,0,0.125,1,1,0,1,0,200,1,1
1,18.58,10,0,0,1,0,0.415,1,1,0,1,0,80,42,1
0,16.25,0,1,1,12,0,0.25,1,1,0,1,0,60,0,1
0,23,0.75,0,0,6,0,0.5,0,1,0,0,2,320,0,1
0,21.17,0.25,1,1,0,1,0.25,1,1,0,1,0,280,204,1
0,17.5,22,2,2,13,8,0,1,1,0,0,1,450,1e+005,-1
0,19.17,0,1,1,6,2,0,1,1,0,0,2,500,1,-1
0,36.75,0.125,1,1,0,0,1.5,1,1,0,0,0,232,113,-1
0,21.25,1.5,0,0,9,0,1.5,1,1,0,1,0,150,8,-1
1,18.08,0.375,2,2,2,7,10,1,1,0,0,2,300,0,-1
1,33.67,0.375,0,0,2,0,0.375,1,1,0,1,0,300,44,-1
0,48.58,0.205,1,1,5,0,0.25,0,0,11,1,0,380,2732,-1
0,33.67,1.25,0,0,9,0,1.165,1,1,0,1,0,120,0,1
1,29.5,1.085,1,1,10,0,1,1,1,0,1,0,280,13,1
0,30.17,1.085,1,1,0,0,0.04,1,1,0,1,0,170,179,1
0,34.83,2.5,1,1,9,0,3,1,1,0,1,2,200,0,1
1,33.25,2.5,1,1,0,0,2.5,1,1,0,0,0,0,2,1
0,34.08,2.5,0,0,0,0,1,1,1,0,1,0,460,16,1
1,25.25,12.5,0,0,1,0,1,1,1,0,0,0,180,1062,1
0,34.75,2.5,0,0,2,2,0.5,1,1,0,1,0,348,0,1
0,27.67,0.75,0,0,8,1,0.165,1,1,0,0,0,220,251,1
0,47.33,6.5,0,0,0,0,1,1,1,0,0,0,0,228,1
1,34.83,1.25,1,1,3,1,0.5,1,1,0,0,0,160,0,1
1,33.25,3,1,1,12,0,2,1,1,0,1,0,180,0,1
0,28,3,0,0,9,0,0.75,1,1,0,0,0,300,67,1
1,39.08,4,0,0,0,0,3,1,1,0,1,0,480,0,1
0,42.75,4.085,0,0,12,0,0.04,1,1,0,1,0,108,100,1
0,26.92,2.25,0,0,3,2,0.5,1,1,0,0,0,640,4000,1
0,33.75,2.75,0,0,3,2,0,1,1,0,1,0,180,0,1
0,38.92,1.75,0,0,5,0,0.5,1,1,0,0,0,300,2,1
0,62.75,7,0,0,11,5,0,1,1,0,1,0,0,12,1
0,26.75,4.5,1,1,0,2,2.5,1,1,0,1,0,200,1210,1
0,63.33,0.54,0,0,0,0,0.585,0,0,3,0,0,180,0,1
0,27.83,1.5,0,0,9,0,2.25,1,0,1,0,0,100,3,1
1,26.17,2,0,0,4,3,0,1,1,0,0,0,276,1,1
0,22.17,0.585,1,1,13,7,0,1,1,0,1,0,100,0,1
0,22.5,11.5,1,1,6,0,1.5,1,1,0,0,0,0,4000,1
0,30.75,1.585,0,0,1,0,0.585,1,1,0,0,2,0,0,1
0,36.67,2,0,0,3,0,0.25,1,1,0,0,0,221,0,1
1,16,0.165,0,0,12,0,1,1,0,2,0,0,320,1,1
0,41.17,1.335,0,0,1,0,0.165,1,1,0,1,0,168,0,1
1,19.5,0.165,0,0,8,0,0.04,1,1,0,0,0,380,0,1
0,32.42,3,0,0,1,0,0.165,1,1,0,0,0,120,0,1
1,36.75,4.71,0,0,13,7,0,1,1,0,1,0,160,0,1
1,30.25,5.5,0,0,5,0,5.5,1,1,0,0,2,100,0,1
0,23.08,2.5,0,0,13,7,0.085,1,1,0,0,0,100,4208,1
0,26.83,0.54,0,0,5,7,0,1,1,0,1,0,100,0,1
0,16.92,0.335,1,1,5,0,0.29,1,1,0,1,2,200,0,1
0,24.42,2,0,0,11,6,0.165,1,0,2,1,0,320,1300,1
0,42.83,1.25,0,0,6,0,13.875,1,0,1,0,0,352,112,1
1,22.75,6.165,0,0,12,0,0.165,1,1,0,1,0,220,1000,1
0,39.42,1.71,1,1,6,0,0.165,1,1,0,1,2,400,0,1
1,23.58,11.5,1,1,5,1,3,1,1,0,0,0,20,16,1
0,21.42,0.75,1,1,7,4,0.75,1,1,0,0,0,132,2,1
0,33,2.5,1,1,9,0,7,1,1,0,0,0,280,0,1
0,26.33,13,0,0,11,6,0,1,1,0,0,0,140,1110,1
1,45,4.585,0,0,5,1,1,1,1,0,0,2,240,0,1
0,26.25,1.54,0,0,9,0,0.125,1,1,0,1,0,100,0,1
1,20.83,0.5,1,1,11,6,1,1,1,0,1,0,260,0,1
0,28.67,14.5,0,0,1,0,0.125,1,1,0,1,0,0,286,1
0,20.67,0.835,1,1,0,0,2,1,1,0,0,2,240,0,1
0,34.42,1.335,0,0,3,2,0.125,1,1,0,0,0,440,4500,1
0,33.58,0.25,0,0,3,2,4,1,1,0,0,2,420,0,1
0,43.17,5,0,0,3,2,2.25,1,1,0,0,0,141,0,1
1,22.67,7,0,0,0,0,0.165,1,1,0,1,0,160,0,1
1,24.33,2.5,1,1,3,2,4.5,1,1,0,1,0,200,456,1
1,56.83,4.25,1,1,13,7,5,1,1,0,0,0,0,4,1
0,22.08,11.46,0,0,5,0,1.585,1,1,0,0,0,100,1212,1
0,34,5.5,1,1,0,0,1.5,1,1,0,0,0,60,0,1
0,22.58,1.5,1,1,12,0,0.54,1,1,0,0,0,120,67,1
0,21.17,0,0,0,0,0,0.5,1,1,0,0,2,0,0,1
0,26.67,14.585,0,0,3,2,0,1,1,0,0,0,178,0,1
0,22.92,0.17,0,0,6,0,0.085,1,1,0,1,2,0,0,1
0,15.17,7,0,0,11,0,1,1,1,0,1,0,600,0,1
0,39.92,5,0,0,3,2,0.21,1,1,0,1,0,550,0,1
0,27.42,12.5,0,0,12,2,0.25,1,1,0,0,0,720,0,1
0,24.75,0.54,0,0,6,0,1,1,1,0,0,0,120,1,1
0,41.17,1.25,1,1,9,0,0.25,1,1,0,1,0,0,195,1
1,33.08,1.625,0,0,1,0,0.54,1,1,0,0,0,0,0,1
0,29.83,2.04,1,1,10,1,0.04,1,1,0,1,0,128,1,1
1,23.58,0.585,1,1,13,7,0.125,1,1,0,1,0,120,87,1
0,26.17,12.5,1,1,5,1,1.25,1,1,0,0,0,0,17,1
0,31,2.085,0,0,0,0,0.085,1,1,0,1,0,300,0,1
0,20.75,5.085,1,1,4,0,0.29,1,1,0,1,0,140,184,1
0,28.92,0.375,0,0,0,0,0.29,1,1,0,1,0,220,140,1
1,51.92,6.5,0,0,3,2,3.085,1,1,0,0,0,73,0,1
1,22.67,0.335,0,0,8,0,0.75,1,1,0,1,2,160,0,1
0,34,5.085,1,1,3,2,1.085,1,1,0,0,0,480,0,1
1,69.5,6,0,0,13,7,0,1,1,0,1,2,0,0,1
1,19.58,0.665,1,1,0,0,1,1,0,1,1,0,2000,2,1
0,16,3.125,0,0,9,0,0.085,1,0,1,1,0,0,6,1
0,17.08,0.25,0,0,8,0,0.335,1,0,4,1,0,160,8,1
0,31.25,2.835,0,0,13,7,0,1,0,5,1,0,176,146,1
0,25.17,3,0,0,0,0,1.25,1,0,1,1,0,0,22,1
1,22.67,0.79,0,0,3,0,0.085,1,1,0,1,0,144,0,1
0,40.58,1.5,0,0,3,2,0,1,1,0,1,2,300,0,1
0,22.25,0.46,0,0,5,0,0.125,1,1,0,0,0,280,55,1
1,22.25,1.25,1,1,13,7,3.25,1,1,0,1,0,280,0,1
0,22.5,0.125,1,1,5,0,0.125,1,1,0,1,0,200,70,1
0,23.58,1.79,0,0,0,0,0.54,1,1,0,0,0,136,1,1
0,38.42,0.705,0,0,0,0,0.375,1,0,2,1,0,225,500,1
1,26.58,2.54,1,1,13,7,0,1,1,0,0,0,180,60,1
0,35,2.5,0,0,3,0,1,1,1,0,0,0,210,0,1
0,20.42,1.085,0,0,8,0,1.5,1,1,0,1,0,108,7,1
0,29.42,1.25,0,0,9,0,1.75,1,1,0,1,0,200,0,1
0,26.17,0.835,0,0,2,0,1.165,1,1,0,1,0,100,0,1
0,33.67,2.165,0,0,0,0,1.5,1,1,0,1,1,120,0,1
0,24.58,1.25,0,0,0,0,0.25,1,1,0,1,0,110,0,1
1,27.67,2.04,0,0,9,0,0.25,1,1,0,0,0,180,50,1
0,37.5,0.835,0,0,11,0,0.04,1,1,0,1,0,120,5,1
0,49.17,2.29,0,0,13,7,0.29,1,1,0,1,0,200,3,1
0,33.58,0.335,1,1,2,0,0.085,1,1,0,1,0,180,0,1
0,51.83,3,1,1,13,7,1.5,1,1,0,1,0,180,4,1
0,22.92,3.165,1,1,0,0,0.165,1,1,0,1,0,160,1058,1
0,21.83,1.54,0,0,5,0,0.085,1,1,0,0,0,356,0,1
0,25.25,1,0,0,12,0,0.5,1,1,0,1,0,200,0,1
0,58.58,2.71,0,0,0,0,2.415,1,1,0,0,0,320,0,1
0,19,0,1,1,13,7,0,1,0,4,1,0,45,1,1
0,19.58,0.585,0,0,13,7,0,1,0,3,1,0,350,769,1
1,53.33,0.165,0,0,13,7,0,1,1,0,0,2,62,27,1
1,27.17,1.25,0,0,13,7,0,1,0,1,1,0,92,300,1
0,25.92,0.875,0,0,5,0,0.375,1,0,2,0,0,174,3,1
0,23.08,0,0,0,5,0,1,1,0,11,1,2,0,0,1
0,39.58,5,0,0,13,7,0,1,0,2,1,0,17,1,1
0,30.58,2.71,1,1,6,0,0.125,1,1,0,0,2,80,0,1
0,17.25,3,0,0,5,0,0.04,1,1,0,0,0,160,40,1
1,17.67,0,1,1,4,7,0,1,1,0,1,0,86,0,1
0,16.5,0.125,0,0,0,0,0.165,1,1,0,1,0,132,0,1
1,27.33,1.665,0,0,13,7,0,1,1,0,1,0,340,1,1
0,31.25,1.125,0,0,13,7,0,1,0,1,1,0,96,19,1
0,20,7,0,0,0,0,0.5,1,1,0,1,0,0,0,1
0,39.5,1.625,0,0,0,0,1.5,1,1,0,1,0,0,316,1
0,36.5,4.25,0,0,8,0,3.5,1,1,0,1,0,454,50,1
0,52.42,1.5,0,0,1,0,3.75,1,1,0,0,0,0,350,1
0,36.17,18.125,0,0,9,0,0.085,1,1,0,1,0,320,3552,1
0,29.67,0.75,1,1,0,0,0.04,1,1,0,1,0,240,0,1
0,36.17,5.5,0,0,3,2,5,1,1,0,1,0,210,687,1
0,25.67,0.29,1,1,0,0,1.5,1,1,0,0,0,160,0,1
1,24.5,2.415,1,1,0,0,0,1,1,0,1,0,120,0,1
0,24.08,0.875,0,0,6,0,0.085,1,0,4,1,0,254,1950,1
0,21.92,0.5,0,0,0,0,0.125,1,1,0,1,0,360,0,1
1,36.58,0.29,0,0,13,7,0,1,0,10,1,0,200,18,1
1,23,1.835,0,0,4,3,0,1,0,1,1,0,200,53,1
1,27.58,3,0,0,6,0,2.79,1,0,1,0,0,280,10,1
0,31.08,3.085,0,0,0,0,2.5,1,0,2,0,0,160,41,1
1,30.42,1.375,0,0,9,1,0.04,1,0,3,1,0,0,33,1
0,22.08,2.335,0,0,5,0,0.75,1,1,0,1,0,180,0,1
0,16.33,4.085,0,0,3,1,0.415,1,1,0,0,0,120,0,1
1,21.92,11.665,0,0,5,1,0.085,1,1,0,1,0,320,5,1
0,21.08,4.125,1,1,3,1,0.04,1,1,0,1,0,140,100,1
0,17.42,6.5,0,0,3,0,0.125,1,1,0,1,0,60,100,1
0,19.17,4,1,1,3,0,1,1,1,0,0,0,360,1000,1
0,20.67,0.415,0,0,0,0,0.125,1,1,0,1,0,0,44,1
0,26.75,2,0,0,1,0,0.75,1,1,0,0,0,80,0,1
0,23.58,0.835,0,0,3,1,0.085,1,1,0,0,0,220,5,1
0,39.17,2.5,1,1,3,1,10,1,1,0,0,2,200,0,1
0,22.75,11.5,0,0,3,0,0.415,1,1,0,1,0,0,0,1
1,16.92,0.5,0,0,3,0,0.165,1,0,6,0,0,240,35,1
0,23.5,3.165,1,1,5,0,0.415,1,0,1,0,0,280,80,1
1,17.33,9.5,0,0,12,0,1.75,1,0,10,0,0,0,10,1
0,23.75,0.415,1,1,0,0,0.04,1,0,2,1,0,128,6,1
0,34.67,1.08,0,0,6,0,1.165,1,1,0,1,2,28,0,1
0,74.83,19,1,1,13,7,0.04,1,0,2,1,0,0,351,1
0,28.17,0.125,1,1,5,0,0.085,1,1,0,1,0,216,2100,1
0,24.5,13.335,1,1,12,0,0.04,1,1,0,0,0,120,475,1
0,18.83,3.54,1,1,13,7,0,1,1,0,0,0,180,1,1
1,47.25,0.75,0,0,8,1,2.75,0,0,1,1,0,333,892,-1
0,24.17,0.875,0,0,8,0,4.625,0,0,2,0,0,520,2000,-1
0,39.25,9.5,0,0,6,0,6.5,0,0,14,1,0,240,4607,-1
1,20.5,11.835,0,0,0,1,6,0,1,0,1,0,340,0,-1
1,18.83,4.415,1,1,0,1,3,0,1,0,1,0,240,0,-1
0,19.17,9.5,0,0,9,0,1.5,0,1,0,1,0,120,2206,-1
1,25,0.875,0,0,10,1,1.04,0,1,0,0,0,160,5860,-1
0,20.17,9.25,0,0,0,0,1.665,0,0,3,0,0,40,28,-1
0,25.75,0.5,0,0,0,0,1.46,0,0,5,0,0,312,0,-1
0,20.42,7,0,0,0,0,1.625,0,0,3,1,0,200,1391,-1
0,39,5,0,0,2,0,3.5,0,0,10,0,0,0,0,-1
1,64.08,0.165,0,0,13,7,0,0,0,1,1,0,232,100,-1
0,28.25,5.125,0,0,10,0,4.75,0,0,2,1,0,420,7,-1
1,28.75,3.75,0,0,0,0,1.085,0,0,1,0,0,371,0,-1
0,31.33,19.5,0,0,0,0,7,0,0,16,1,0,0,5000,-1
1,18.92,9,0,0,12,0,0.75,0,0,2,1,0,88,591,-1
1,24.75,3,0,0,8,1,1.835,0,0,19,1,0,0,500,-1
1,30.67,12,0,0,0,0,2,0,0,1,1,0,220,19,-1
0,21,4.79,1,1,9,0,2.25,0,0,1,0,0,80,300,-1
0,13.75,4,1,1,9,0,1.75,0,0,2,0,0,120,1000,-1
1,46,4,0,0,4,3,0,0,1,0,1,0,100,960,-1
1,44.33,0,0,0,0,0,2.5,0,1,0,1,0,0,0,-1
0,20.25,9.96,0,0,11,6,0,0,1,0,1,0,0,0,-1
0,22.67,2.54,1,1,0,1,2.585,0,1,0,1,0,0,0,-1
1,60.92,5,0,0,12,0,4,0,0,4,1,0,0,99,-1
0,16.08,0.75,0,0,0,0,1.75,0,0,5,0,0,352,690,-1
1,28.17,0.375,0,0,8,0,0.585,0,0,4,1,0,80,0,-1
0,39.17,1.71,0,0,10,0,0.125,0,0,5,0,0,480,0,-1
1,30,5.29,0,0,11,6,2.25,0,0,5,0,0,99,500,-1
0,22.83,3,0,0,6,0,1.29,0,0,1,1,0,260,800,-1
1,22.5,8.5,0,0,8,0,1.75,0,0,10,1,0,80,990,1
1,28.58,1.665,0,0,8,0,2.415,0,1,0,0,0,440,0,1
0,45.17,1.5,0,0,0,0,2.5,0,1,0,0,0,140,0,1
0,41.58,1.75,0,0,5,0,0.21,0,1,0,1,0,160,0,1
1,57.08,0.335,0,0,3,2,1,0,1,0,0,0,252,2197,1
1,55.75,7.08,0,0,5,1,6.75,0,0,3,0,0,100,50,1
0,43.25,25.21,0,0,8,1,0.21,0,0,1,1,0,760,90,1
1,25.33,2.085,0,0,0,1,2.75,0,1,0,0,0,360,1,1
1,24.58,0.67,0,0,12,1,1.75,0,1,0,1,0,400,0,1
0,43.17,2.25,0,0,3,2,0.75,0,1,0,1,0,560,0,1
0,40.92,0.835,0,0,13,7,0,0,1,0,1,0,130,1,1
0,31.83,2.5,0,0,12,0,7.5,0,1,0,0,0,523,0,1
1,33.92,1.585,1,1,13,7,0,0,1,0,1,0,320,0,1
1,24.92,1.25,0,0,13,7,0,0,1,0,1,0,80,0,1
0,35.25,3.165,0,0,10,1,3.75,0,1,0,0,0,680,0,1
0,34.25,1.75,0,0,9,2,0.25,0,1,0,0,0,163,0,1
0,19.42,1.5,1,1,2,0,2,0,1,0,0,0,100,20,1
0,42.75,3,0,0,3,2,1,0,1,0,1,0,0,200,1
0,19.67,10,1,1,5,1,0.835,0,1,0,0,0,140,0,1
0,36.33,3.79,0,0,9,0,1.165,0,1,0,0,0,200,0,1
0,30.08,1.04,1,1,3,2,0.5,0,0,10,0,0,132,28,1
0,44.25,11,1,1,1,0,1.5,0,1,0,1,2,0,0,1
0,23.58,0.46,1,1,9,0,2.625,0,0,6,0,0,208,347,1
0,23.92,1.5,0,0,1,1,1.875,0,0,6,1,0,200,327,-1
0,33.17,1,0,0,10,0,0.75,0,0,7,0,0,340,4071,-1
0,48.33,12,0,0,6,0,16,0,1,0,1,2,110,0,-1
0,76.75,22.29,0,0,11,5,12.75,0,0,1,0,0,0,109,-1
0,51.33,10,0,0,3,2,0,0,0,11,1,0,0,1249,-1
0,34.75,15,0,0,7,4,5.375,0,0,9,0,0,0,134,-1
0,38.58,3.335,0,0,9,0,4,0,0,14,1,0,383,1344,-1
1,22.42,11.25,1,1,10,1,0.75,0,0,4,1,0,0,321,-1
0,41.92,0.42,0,0,0,1,0.21,0,0,6,1,0,220,948,-1
0,29.58,4.5,0,0,9,0,7.5,0,0,2,0,0,330,0,-1
1,32.17,1.46,0,0,9,0,1.085,0,0,16,1,0,120,2079,-1
0,51.42,0.04,0,0,10,1,0.04,0,1,0,1,0,0,3000,-1
1,22.83,2.29,0,0,8,1,2.29,0,0,7,0,0,140,2384,-1
1,25,12.33,0,0,2,1,3.5,0,0,6,1,0,400,458,-1
0,26.75,1.125,0,0,10,1,1.25,0,1,0,1,0,0,5298,-1
0,23.33,1.5,0,0,0,1,1.415,0,1,0,1,0,422,200,-1
0,24.42,12.335,0,0,8,1,1.585,0,1,0,0,0,120,0,-1
0,42.17,5.04,0,0,8,1,12.75,0,1,0,0,0,92,0,-1
1,20.83,3,0,0,12,0,0.04,0,1,0,1,0,100,0,-1
0,23.08,11.5,0,0,9,1,2.125,0,0,11,0,0,290,284,-1
1,25.17,2.875,0,0,10,1,0.875,0,1,0,1,0,360,0,-1
0,43.08,0.375,1,1,0,0,0.375,0,0,8,0,0,300,162,-1
1,35.75,0.915,0,0,12,0,0.75,0,0,4,1,0,0,1583,-1
0,59.5,2.75,0,0,9,0,1.75,0,0,5,0,0,60,58,-1
0,21,3,1,1,1,0,1.085,0,0,8,0,0,160,1,-1
0,21.92,0.54,1,1,10,0,0.04,0,0,1,0,0,840,59,-1
1,65.17,14,0,0,13,7,0,0,0,11,0,0,0,1400,-1
1,20.33,10,0,0,0,1,1,0,0,4,1,0,50,1465,-1
0,32.25,0.165,1,1,0,1,3.25,0,0,1,0,0,432,8000,-1
0,30.17,0.5,0,0,0,0,1.75,0,0,11,1,0,32,540,-1
0,25.17,6,0,0,0,0,1,0,0,3,1,0,0,0,-1
0,39.17,1.625,0,0,0,0,1.5,0,0,10,1,0,186,4700,-1
0,39.08,6,0,0,6,0,1.29,0,0,5,0,0,108,1097,-1
0,31.67,0.83,0,0,10,0,1.335,0,0,8,0,0,303,3290,-1
0,41,0.04,0,0,11,0,0.04,1,0,1,1,2,560,0,-1
0,48.5,4.25,0,0,6,0,0.125,0,1,0,0,0,225,0,-1
0,32.67,9,1,1,9,1,5.25,0,1,0,0,0,154,0,-1
1,28.08,15,1,1,11,5,0,0,1,0,1,0,0,13212,-1
0,73.42,17.75,0,0,13,7,0,0,1,0,0,0,0,0,-1
0,64.08,20,0,0,10,1,17.5,0,0,9,0,0,0,1000,-1
0,51.58,15,0,0,0,0,8.5,0,0,9,1,0,0,0,-1
0,26.67,1.75,1,1,0,0,1,0,0,5,0,0,160,5777,-1
0,25.33,0.58,0,0,0,0,0.29,0,0,7,0,0,96,5124,-1
0,30.17,6.5,0,0,2,0,3.125,0,0,8,1,0,330,1200,-1
0,27,0.75,0,0,0,1,4.25,0,0,3,0,0,312,150,-1
0,34.17,5.25,0,0,9,0,0.085,1,1,0,0,0,290,6,-1
0,38.67,0.21,0,0,5,0,0.085,0,1,0,0,0,280,0,-1
0,25.75,0.75,0,0,0,2,0.25,0,1,0,1,0,349,23,-1
1,46.08,3,0,0,0,0,2.375,0,0,8,0,0,396,4159,-1
1,21.5,6,0,0,12,0,2.5,0,0,3,1,0,80,918,-1
0,20.5,2.415,0,0,0,0,2,0,0,11,0,0,200,3000,-1
1,29.5,0.46,0,0,5,0,0.54,0,0,4,1,0,380,500,-1
0,29.83,1.25,1,1,5,0,0.25,1,1,0,1,0,224,0,1
0,20.08,0.25,0,0,8,0,0.125,1,1,0,1,0,200,0,1
0,23.42,0.585,0,0,0,1,0.085,0,1,0,1,0,180,0,1
1,29.58,1.75,1,1,5,0,1.25,1,1,0,0,0,280,0,1
0,16.17,0.04,0,0,0,0,0.04,1,1,0,1,0,0,0,-1
0,32.33,3.5,0,0,5,0,0.5,1,1,0,0,0,232,0,1
0,47.83,4.165,0,0,10,2,0.085,1,1,0,0,0,520,0,1
0,20,1.25,1,1,5,0,0.125,1,1,0,1,0,140,4,1
0,27.58,3.25,1,1,8,1,5.085,1,0,2,0,0,369,1,1
0,22,0.79,0,0,9,0,0.29,1,0,1,1,0,420,283,1
0,19.33,10.915,0,0,0,2,0.585,1,0,2,0,0,200,7,1
1,38.33,4.415,0,0,0,0,0.125,1,1,0,1,0,160,0,1
0,29.42,1.25,0,0,0,1,0.25,1,0,2,0,0,400,108,1
0,22.67,0.75,0,0,3,0,1.585,1,0,1,0,0,400,9,1
0,32.25,14,1,1,13,7,0,1,0,2,1,0,160,1,1
0,29.58,4.75,0,0,6,0,2,1,0,1,0,0,460,68,1
0,18.42,10.415,1,1,12,0,0.125,0,1,0,1,0,120,375,1
0,22.17,2.25,0,0,3,0,0.125,1,1,0,1,0,160,10,1
0,22.67,0.165,0,0,0,3,2.25,1,1,0,0,2,0,0,-1
0,18.83,0,0,0,8,0,0.665,1,1,0,1,0,160,1,1
0,21.58,0.79,1,1,2,0,0.665,1,1,0,1,0,160,0,1
0,23.75,12,0,0,0,0,2.085,1,1,0,1,2,80,0,1
0,36.08,2.54,0,0,13,7,0,1,1,0,1,0,0,1000,1
0,29.25,13,0,0,1,1,0.5,1,1,0,1,0,228,0,1
1,19.58,0.665,0,0,9,0,1.665,1,1,0,1,0,220,5,1
1,22.92,1.25,0,0,8,0,0.25,1,1,0,0,0,120,809,1
1,27.25,0.29,0,0,6,1,0.125,1,0,1,0,0,272,108,1
1,38.75,1.5,0,0,13,7,0,1,1,0,1,0,76,0,1
0,32.42,2.165,1,1,5,7,0,1,1,0,1,0,120,0,1
1,23.75,0.71,0,0,9,0,0.25,1,0,1,0,0,240,4,1
0,18.17,2.46,0,0,0,4,0.96,1,0,2,0,0,160,587,1
0,40.92,0.5,1,1,6,0,0.5,1,1,0,0,0,130,0,1
0,19.5,9.585,0,0,12,0,0.79,1,1,0,1,0,80,350,1
0,28.58,3.625,0,0,12,0,0.25,1,1,0,0,0,100,0,1
0,35.58,0.75,0,0,5,0,1.5,1,1,0,0,0,231,0,1
0,34.17,2.75,0,0,3,2,2.5,1,1,0,0,0,232,200,1
0,31.58,0.75,1,1,12,0,3.5,1,1,0,0,0,320,0,1
1,52.5,7,0,0,12,1,3,1,1,0,1,0,0,0,1
0,36.17,0.42,1,1,9,0,0.29,1,1,0,0,0,309,2,1
0,37.33,2.665,0,0,2,0,0.165,1,1,0,0,0,0,501,1
1,20.83,8.5,0,0,0,0,0.165,1,1,0,1,0,0,351,1
0,24.08,9,0,0,12,0,0.25,1,1,0,0,0,0,0,1
0,25.58,0.335,0,0,5,1,3.5,1,1,0,0,0,340,0,1
1,35.17,3.75,0,0,13,7,0,1,0,6,1,0,0,200,1
0,48.08,3.75,0,0,3,2,1,1,1,0,1,0,100,2,1
1,15.83,7.625,0,0,8,0,0.125,1,0,1,0,0,0,160,1
1,22.5,0.415,0,0,3,0,0.335,1,1,0,0,2,144,0,1
0,21.5,11.5,0,0,3,0,0.5,0,1,0,0,0,100,68,1
1,23.58,0.83,0,0,8,0,0.415,1,0,1,0,0,200,11,1
1,21.08,5,1,1,13,7,0,1,1,0,1,0,0,0,1
0,25.67,3.25,0,0,0,1,2.29,1,0,1,0,0,416,21,1
1,38.92,1.665,0,0,12,0,0.25,1,1,0,1,0,0,390,1
1,15.75,0.375,0,0,0,0,1,1,1,0,1,0,120,18,1
1,28.58,3.75,0,0,0,0,0.25,1,0,1,0,0,40,154,1
0,22.25,9,0,0,12,0,0.085,1,1,0,1,0,0,0,1
0,29.83,3.5,0,0,0,0,0.165,1,1,0,1,0,216,0,1
1,23.5,1.5,0,0,9,0,0.875,1,1,0,0,0,160,0,1
0,32.08,4,1,1,2,0,1.5,1,1,0,0,0,120,0,1
0,31.08,1.5,1,1,9,0,0.04,1,1,0,1,2,160,0,1
0,31.83,0.04,1,1,6,0,0.04,1,1,0,1,0,0,0,1
1,21.75,11.75,0,0,0,0,0.25,1,1,0,0,0,180,0,1
1,17.92,0.54,0,0,0,0,1.75,1,0,1,0,0,80,5,1
0,30.33,0.5,0,0,1,1,0.085,1,1,0,0,2,252,0,1
0,51.83,2.04,1,1,13,7,1.5,1,1,0,1,0,120,1,1
0,47.17,5.835,0,0,9,0,5.5,1,1,0,1,0,465,150,1
0,25.83,12.835,0,0,2,0,0.5,1,1,0,1,0,0,2,1
1,50.25,0.835,0,0,12,0,0.5,1,1,0,0,0,240,117,1
1,37.33,2.5,0,0,3,1,0.21,1,1,0,1,0,260,246,1
1,41.58,1.04,0,0,12,0,0.665,1,1,0,1,0,240,237,1
1,30.58,10.665,0,0,8,1,0.085,1,0,12,0,0,129,3,1
0,19.42,7.25,0,0,6,0,0.04,1,0,1,1,0,100,1,1
1,17.92,10.21,0,0,13,7,0,1,1,0,1,0,0,50,1
1,20.08,1.25,0,0,0,0,0,1,1,0,1,0,0,0,1
0,19.5,0.29,0,0,5,0,0.29,1,1,0,1,0,280,364,1
0,27.83,1,1,1,1,1,3,1,1,0,1,0,176,537,1
0,17.08,3.29,0,0,3,0,0.335,1,1,0,0,0,140,2,1
0,36.42,0.75,1,1,1,0,0.585,1,1,0,1,0,240,3,1
0,40.58,3.29,0,0,6,0,3.5,1,1,0,0,2,400,0,1
0,21.08,10.085,1,1,11,1,1.25,1,1,0,1,0,260,0,1
1,22.67,0.75,0,0,0,0,2,1,0,2,0,0,200,394,1
1,25.25,13.5,1,1,13,7,2,1,0,1,0,0,200,1,1
0,17.92,0.205,0,0,12,0,0.04,1,1,0,1,0,280,750,1
0,35,3.375,0,0,0,1,8.29,1,1,0,0,0,0,0,1
credit-a.csv
本人小白一枚，请各位看客多多指教
深度学习之逻辑回归的实现 -- sigmoid的更多相关文章
- TensorFlow 深度学习笔记 逻辑回归 实践篇
Practical Aspects of Learning 转载请注明作者:梦里风林 Github工程地址:https://github.com/ahangchen/GDLnotes 欢迎star,有 ...
- 统计学习：逻辑回归与交叉熵损失（Pytorch实现）
1. Logistic 分布和对率回归 监督学习的模型可以是概率模型或非概率模型,由条件概率分布\(P(Y|\bm{X})\)或决 策函数(decision function)\(Y=f(\bm{X} ...
- 《神经网络和深度学习》系列文章三：sigmoid神经元
出处: Michael Nielsen的<Neural Network and Deep Leraning>,点击末尾“阅读原文”即可查看英文原文. 本节译者:哈工大SCIR硕士生 徐伟 ...
- 深度学习之softmax回归
前言 以下内容是个人学习之后的感悟,转载请注明出处~ softmax回归 首先,我们看一下sigmod激活函数,如下图,它经常用于逻辑回归,将一个real value映射到(0, ...
- 逻辑回归为什么用sigmoid函数
Logistic回归目的是从特征学习出一个0/1分类模型,而这个模型是将特性的线性组合作为自变量,由于自变量的取值范围是负无穷到正无穷. 因此,使用logistic函数(或称作sigmoid函数)将自 ...
- 【深度学习】softmax回归——原理、one-hot编码、结构和运算、交叉熵损失
1. softmax回归是分类问题 回归(Regression)是用于预测某个值为"多少"的问题,如房屋的价格.患者住院的天数等. 分类(Classification)不是问&qu ...
- 深度学习基础系列（三）| sigmoid、tanh和relu激活函数的直观解释
常见的激活函数有sigmoid.tanh和relu三种非线性函数,其数学表达式分别为: sigmoid: y = 1/(1 + e-x) tanh: y = (ex - e-x)/(ex + e-x) ...
- 动手学深度学习4-线性回归的pytorch简洁实现
导入同样导入之前的包或者模块 生成数据集 通过pytorch读取数据 定义模型 初始化模型 定义损失函数 定义优化算法 训练模型 小结 本节利用pytorch中的模块,生成一个更加简洁的代码来实现同样 ...
- 深度学习在美团点评推荐平台排序中的应用&& wide&&deep推荐系统模型--学习笔记
写在前面:据说下周就要xxxxxxxx, 吓得本宝宝赶紧找些广告的东西看看 gbdt+lr的模型之前是知道怎么搞的,dnn+lr的模型也是知道的,但是都没有试验过 深度学习在美团点评推荐平台排序中的运 ...
随机推荐
- 神经网络AI加速器技术
神经网络AI加速器技术 能够直接加速卷积神经网络,还能够直接运行常见的网络框架,如TensorFlow.Caffe.PyTorch,DarkNet等,支持用户定制化的网络和计算类型. 功能特点: ● ...
- Kaggle上的犬种识别（ImageNet Dogs）
Kaggle上的犬种识别(ImageNet Dogs) Dog Breed Identification (ImageNet Dogs) on Kaggle 在本节中,将解决在Kaggle竞赛中的犬种 ...
- 利用NVIDIA NGC的TensorRT容器优化和加速人工智能推理
利用NVIDIA NGC的TensorRT容器优化和加速人工智能推理 Optimizing and Accelerating AI Inference with the TensorRT Contai ...
- python2向python3移植问题
问题: payload = "A"*140 # padding ropchain = p32(puts_plt) ropchain += p32(entry_point) ropc ...
- QT Dialog模态与非模态
模态 // 创建对话框窗口 TestDialog* dlg = new TestDialog(this); // 阻塞程序的运行 dlg->exec(); 这样的话,当运行对话窗口的时候,会阻塞 ...
- 在VScode 中使用RT-Thread Studio初体验
前言 工欲善其事,必先利其器,VScode是什么东东,想必大家都非常熟悉了,丰富的插件,有好的开发界面,是很多程序开发者的不二之选,RT-Thread竟然也开发了Vscode插件,真的是非常的nice ...
- Nginx为什么快到根本停不下来？
Nginx 是一个免费的,开源的,高性能的 HTTP 服务器和反向代理,以及 IMAP / POP3 代理服务器. 图片来自 Pexels Nginx 以其高性能,稳定性,丰富的功能,简单的配置和低资 ...
- 【Linux进阶】使用grep、find、sed以及awk进行文本操作
目录 一.元字符 二.grep命令 1. 过滤出包含某字符串的行 2. 过滤出以某字符串开头(结尾)的行 3. 过滤出包含某字符串及其相邻的行 4. 过滤出不包含某关键字的行 5. 过滤出包含多个字符 ...
- HTTP头部POST表单详解
2 POST /hello/checkUser.html?opt=xxx HTTP/1.1 方法的声明,Get,Post,Delete等 3 Accept: */* 4 Referer: http:/ ...
- 微信获取信息发生错误（两个access_token的区别），错误代码：40001，说明：invalid credential, access_token is invalid or not latest hints
微信有两个access_token,一个是基础access_token,一个是网页授权access_token. 想要获取不带unionid的用户信息(以下链接)使用基础access_token ht ...