diff --git a/README.md b/README.md
index 07ea68d..a8212b7 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,5 @@
 # SES2020spring
 
 ## Another day
+
+## The third day
\ No newline at end of file
diff --git a/pytorch_cancer_torch.py b/pytorch_cancer_torch.py
new file mode 100644
index 0000000..ab65dfe
--- /dev/null
+++ b/pytorch_cancer_torch.py
@@ -0,0 +1,85 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+import pandas as pd
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn import preprocessing
+
+#利用sklean自带的cancer数据集
+X,T= datasets.load_breast_cancer(return_X_y=True)
+#数据预处理，标准化
+for i in range(X.shape[1]):
+    mean = np.mean(X[:,i])
+    std = np.std(X[:,i])
+    X[:,i] = (X[:,i] - mean)/std
+#划分训练集和测试集，比例为7:3，交叉验证
+X_train,X_test,T_train,T_test = train_test_split(X,T,train_size=0.7,test_size=0.3)
+x_data = Variable(torch.Tensor(X_train))
+y_data = Variable(torch.Tensor(T_train))
+x_test = Variable(torch.Tensor(X_test))
+t_test = Variable(torch.Tensor(T_test))
+
+class Model(torch.nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        self.linear = torch.nn.Linear(x_data.shape[1], 1) # 30 in and 1 out
+        
+    def forward(self, x):
+        #y_pred = torch.sigmoid(self.linear(x))
+        y_pred = self.linear(x).sigmoid()
+        return y_pred
+    
+# Our model    
+model = Model()
+
+criterion = torch.nn.BCELoss(reduction="mean")
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+
+f = open("result.txt", 'w')
+# Training loop
+for epoch in range(1000):
+    # Forward pass: Compute predicted y by passing x to the model
+    y_pred = model(x_data)
+    pre=y_pred.ge(0.5).float()
+    correct = 0
+    for i in range(y_data.size(0)):
+        if pre[i] == y_data[i]:
+            correct += 1
+    acc = correct / y_data.size(0)  #计算Accuracy
+    print(epoch)
+    print('accuracy is',acc)
+    
+    # 计算损失
+    loss = criterion(y_pred, y_data)
+    print(' loss is', loss.data.item())
+    
+    # Zero gradients, perform a backward pass, and update the weights.
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+for f in model.parameters():
+    print('data is')
+    print(f.data)
+    print(f.grad)
+
+w = list(model.parameters())
+w0 = w[0].data.numpy()
+w1 = w[1].data.numpy()
+
+import matplotlib.pyplot as plt
+
+print("Final gradient descend:", w)
+# plot the data and separating line
+plt.scatter(x_data[:,0], x_data[:,1], c=y_data.reshape(len(x_data)), s=100, alpha=0.7)
+x_axis = np.linspace(-6, 6, 100)
+y_axis = -(w1[0] + x_axis*w0[0][0]) / w0[0][1]
+line_up, = plt.plot(x_axis, y_axis,'r--', label='gradient descent')
+plt.legend(handles=[line_up])
+plt.xlabel('X(1)')
+plt.ylabel('X(2)')
+plt.show()
+f.close() 
diff --git a/pytorch_cancer_torch_update.py b/pytorch_cancer_torch_update.py
new file mode 100644
index 0000000..81949ab
--- /dev/null
+++ b/pytorch_cancer_torch_update.py
@@ -0,0 +1,96 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+import pandas as pd
+from sklearn import datasets
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn import preprocessing
+
+#利用sklean自带的cancer数据集
+X,T= datasets.load_breast_cancer(return_X_y=True)
+#数据预处理，标准化
+for i in range(X.shape[1]):
+    mean = np.mean(X[:,i])
+    std = np.std(X[:,i])
+    X[:,i] = (X[:,i] - mean)/std
+#划分训练集和测试集，比例为7:3，交叉验证
+X_train,X_test,T_train,T_test = train_test_split(X,T,train_size=0.7,test_size=0.3)
+x_data = Variable(torch.Tensor(X_train))
+y_data = Variable(torch.Tensor(T_train))
+x_test = Variable(torch.Tensor(X_test))
+t_test = Variable(torch.Tensor(T_test))
+
+class Model(torch.nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+        #self.hidden = torch.nn.Linear(x_data.shape[1])
+        self.linear = torch.nn.Linear(x_data.shape[1], 1) # 30 in and 1 out
+        
+    def forward(self, x):
+        #y_pred = torch.sigmoid(self.linear(x))
+        y_pred = self.linear(x).sigmoid()
+        return y_pred
+    
+# Our model    
+model = Model()
+
+criterion = torch.nn.BCELoss(reduction="mean")
+optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+
+f = open("result.txt", 'w')
+# Training loop
+for epoch in range(1000):
+    # Forward pass: Compute predicted y by passing x to the model
+    y_pred = model(x_data)
+    pre=y_pred.ge(0.5).float()
+    correct = 0
+    for i in range(y_data.size(0)):
+        if pre[i] == y_data[i]:
+            correct += 1
+    acc = correct / y_data.size(0)  #计算Accuracy
+    print(epoch,file = f)
+    print('accuracy is',acc,file = f)
+    
+    # 计算损失
+    loss = criterion(y_pred, y_data)
+    print(' loss is', loss.data.item(),file = f)
+    
+    # Zero gradients, perform a backward pass, and update the weights.
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+for f in model.parameters():
+    print('data is')
+    print(f.data)
+    print(f.grad)
+
+w = list(model.parameters())
+w0 = w[0].data.numpy()
+w1 = w[1].data.numpy()
+
+y_pred1 = model(x_test)#预测测试集
+pre1=y_pred1.ge(0.5).float()
+correct1 = 0
+for i in range(t_test.size(0)):
+    if pre1[i] == t_test[i]:
+        correct1 += 1
+acc1 = correct1 / t_test.size(0)  #计算测试集的Accuracy
+print('test accuracy is',acc)
+
+import matplotlib.pyplot as plt
+
+print("Final gradient descend:", w)
+# plot the data and separating line
+plt.scatter(x_data[:,0], x_data[:,1], c=y_data.reshape(len(x_data)), s=100, alpha=0.7)
+x_axis = np.linspace(-6, 6, 100)
+y_axis = -(w1[0] + x_axis*w0[0][0]) / w0[0][1]
+line_up, = plt.plot(x_axis, y_axis,'r--', label='gradient descent')
+plt.legend(handles=[line_up])
+plt.xlabel('X(1)')
+plt.ylabel('X(2)')
+plt.show()
+
+
diff --git a/result.png b/result.png
new file mode 100644
index 0000000..e0173ca
Binary files /dev/null and b/result.png differ
diff --git a/result.txt b/result.txt
new file mode 100644
index 0000000..05257cf
--- /dev/null
+++ b/result.txt
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diff --git a/unit1/readme.md b/unit1/readme.md
index 3cff53f..8cb3e78 100644
--- a/unit1/readme.md
+++ b/unit1/readme.md
@@ -50,3 +50,9 @@ The same set of video could also be found on bilibili.com.
 
 1. Ian Sommerville, Software Engineering (10th Edition), 2015.
 2. Scott Chacon, Ben Straub, Pro Git (2nd Edition), 2014.
+
+<<<<<<< HEAD
+### Master change
+=======
+### NOTHING CHANGE
+>>>>>>> feature1
diff --git a/unit2/English-Redistributable-Intro-Scrum.pdf b/unit2/English-Redistributable-Intro-Scrum.pdf
new file mode 100644
index 0000000..8bcd906
Binary files /dev/null and b/unit2/English-Redistributable-Intro-Scrum.pdf differ
diff --git a/unit2/count_words.py b/unit2/count_words.py
new file mode 100644
index 0000000..a4adadf
--- /dev/null
+++ b/unit2/count_words.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Feb 25 09:39:33 2020
+
+@author: 85726
+"""
+
+import os.path
+
+def count_word(file):
+    with open(file, 'r') as f:
+        words = 0
+        for line in f:
+            for char in ('#', '\n'):
+                line = line.replace(char," ")
+            word = line.split()
+            words += len(word)
+        return words
+        
+file = './readme.md'
+
+if os.path.isfile(file):
+    words = count_word(file)
+    print('{} words in file'.format(words))
+else:
+    print('it is not a file')
diff --git a/unit2/python_intro.py b/unit2/python_intro.py
new file mode 100644
index 0000000..b046599
--- /dev/null
+++ b/unit2/python_intro.py
@@ -0,0 +1,128 @@
+# Variable
+
+x = 1
+x
+type(x)
+x = 1.1
+x
+type(x)
+1+1j
+x + 1
+x = 'abc'
+x
+x = "abc"
+x = """abc"""
+x = 'abc"abc"'
+x
+x + 'bcd'
+x = "   sfdsf   "
+x.strip()
+x
+dir(x)
+help(x.strip)
+help(help)
+help(dir)
+# this is a comment.
+print("Hello class!")
+
+# Control flow, branching
+x = 1
+if x == 1:
+    print("x is {}".format(x))
+else:
+    print("No way")
+
+if x == 1:
+    print('x==1')
+elif x == 0:
+    print('x == 0')
+else:
+    print('x')
+
+type(x==1)
+x == 1 and 2 == int('2')
+x == 1 or 2 == int('1')
+not x == 1
+a = [1, 2, 3]
+1 in a
+x = None
+x == 1
+x == 0
+type(x)
+x is None
+x is not None
+
+# List, tuple, and dict
+
+x = [1, 2, 3, 4, 5]
+x
+type(x)
+x[0]
+x[0] = -1
+x
+x[:2]
+type(x[:2])
+for elem in x:
+    print(str(elem + 1))
+
+for index in range(len(x)):
+    print(str(x[index]))
+
+[print(str(x[index])) for index in range(len(x))]
+x
+y = tuple(x)
+y
+y[0]
+y[0] = 1
+y = list(x)
+y
+z = dict()
+z = {}
+z[0] = 1
+z[1] = 2
+z
+z[0]
+for key, value in z.items():
+    print(str(key), str(value))
+
+
+
+dir(z)
+-1 in z
+1 in z
+
+# Control flow, loop
+
+for x in [1, 2, 3, 4, 5]:
+    if x == 2:
+        continue
+    elif x == 4:
+        break
+    else:
+        print(x)
+else:
+    print('no find')
+
+for x in [1, 2]:
+    print(x)
+else:
+    print('no find')
+
+i = 0
+while i < 5:
+    print(i)
+    i += 1
+
+# Function and module
+
+def count_char(fn,):
+    import os.path
+    if os.path.isfile(fn):
+        with open(fn, 'r') as fh:
+            total = 0
+            for line in fh:
+                total += len(line)
+            return total
+
+count_char('./unit2/readme.md')
+import os.path
diff --git a/unit2/readme.md b/unit2/readme.md
new file mode 100644
index 0000000..c55adf0
--- /dev/null
+++ b/unit2/readme.md
@@ -0,0 +1,67 @@
+This unit is about introduction to an agile development process called Scrum and introduction to a programming language called Python.
+
+Please go through the video and slides first.
+Please install the Python runtime before the official classtime.
+
+## Introduction to Scrum
+
+Scrum is an agile development process aiming to deliver business value over shortest time.
+It's widely used and often mixed with other agile development process.
+
+Video 1: Introduction to Scrum
+
+https://www.acfun.cn/v/ac13145403
+
+Video 2: The Sprint in Scrum
+
+https://www.acfun.cn/v/ac13145466
+
+Video 3: Roles, Meetings, and Documents in Scrum
+
+https://www.acfun.cn/v/ac13145616
+
+Slide: English-Redistributable-Intro-Scrum.pdf
+
+## Introduction to Python
+
+Install Python using miniconda
+
+https://docs.conda.io/en/latest/miniconda.html
+
+Video 1: Variables
+
+https://www.acfun.cn/v/ac13164403_1
+
+Video 2: Control flow, branching
+
+https://www.acfun.cn/v/ac13164403_2
+
+video 3: List, tuple, and dict
+
+https://www.acfun.cn/v/ac13164403_3
+
+Video 4: Control flow, loop
+
+https://www.acfun.cn/v/ac13164403_4
+
+Video 5: Function and Module
+
+https://www.acfun.cn/v/ac13164403_5
+
+
+### Exercise
+
+1. Write a Python code to count the words in this readme.
+2. Use Git to commit your code in unit2 directory and push to your repository. Also comment on the issue with the link to your code.
+
+## Reference
+
+1. Kenneth S. Rubin, Essential Scrum: A Practical Guide to the Most Popular Agile Process, 2012
+2. Eric Matthes, Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming  (2nd Edition), 2019
+3. Mark Pilgrim, Dive Into Python 3, 2009
+
+## Notes
+
+1. The same set of video could also be found on bilibili.com.
+2. Students without computers at your disposal are suggested to read till chapter 7 of "Dive Into Python 3", which is available online at https://diveintopython3.problemsolving.io, or section 1, 2, 3, 4, 5, 9 of Official Python tutorial at https://docs.python.org/3/tutorial/.
+3. This unit doesn't require students to team up.
diff --git a/unit3/FindMCircle.py b/unit3/FindMCircle.py
new file mode 100644
index 0000000..92a45fb
--- /dev/null
+++ b/unit3/FindMCircle.py
@@ -0,0 +1,140 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar  5 10:00:30 2020
+
+@author: 85726
+"""
+
+"""
+思想：保证每一个圆在当前可行域内都取得最大的半径，可取值为圆心到四边的距离和圆心到各个圆边的距离，取其中最小的一个
+就是当前最大半径
+
+实现：在保证新取的圆心处于可行域的范围内，使用scipy的优化器，在邻域内不断优化圆心的位置，直至找到最大的半径
+
+结果：
+m=10, sum of r^2 =1.14
+m=50, sum of r^2 =1.21
+m=100, sum of r^2 =1.23
+保存的图在unit3中
+"""
+
+import numpy as np
+import random
+import matplotlib.pyplot as plt
+from scipy.optimize import minimize
+
+#定义圆类
+class circle:
+    def __init__(self, radius = 0, x = 0, y = 0):
+        self.radius = radius
+        self.x = x
+        self.y = y
+        
+    def print_circle(self):
+        print('radius={}, coordinate=({},{})'.format(self.radius, self.x, self.y))
+    
+    #计算两圆心之间距离
+    def distance(self, c2):       
+        dis = ((self.x-c2.x)**2+(self.y-c2.y)**2)**0.5
+        return dis
+    
+    #判断新圆与现存圆是否相交，相交为0，全不相交为1 
+    def ifcross(self, c_list):
+        for i in range (len(c_list)):
+            c2 = c_list[i]
+            r1 = self.radius
+            r2 = c2.radius
+            rr = r1+r2
+            dis = self.distance(c2)
+            if dis < rr:
+                return 0
+        return 1
+        
+    #判断圆是否越界，越界为0，不越界为1 
+    def ifexcess(self):
+        r = self.radius
+        x = self.x
+        y = self.y         
+        if x + r > 1 or x - r < -1 or y + r > 1 or y - r < -1:
+            return 0
+        else:
+            return 1
+
+#找出可行的最大半径
+def MaxR(c1, c_list):
+    x = c1.x
+    y = c1.y
+    R_list = [1-x,1+x,1-y,1+y]
+    for i in range (len(c_list)):
+        c2 = c_list[i]
+        dis = c1.distance(c2)
+        R_list.append(dis-c2.radius)
+    return min(R_list)
+
+#需要优化的目标函数        
+def func(c_list):
+    return lambda x : 1 - MaxR(circle(x[0], x[1], x[2]), c_list)
+
+#找出最优圆心
+def opt_center(c, c_list):
+    r = c.radius
+    x = c.x
+    y = c.y
+    rxy = [r,x,y]
+    bd_r = (0, 1)
+    bd_x = (-1, 1)
+    bd_y = (-1, 1)
+    bds = (bd_r, bd_x, bd_y)       
+    res = minimize(func(c_list), rxy, method='SLSQP', bounds=bds)
+    c.x = res.x[1]
+    c.y = res.x[2]
+    c.radius = MaxR(c, c_list)
+    return c
+
+#找m个圆，使得每个圆在邻域内半径最大
+def FindMaxCircuit(m):
+    c_list = []
+    for i in range (m):
+        r = 0
+        x = random.uniform(-1, 1)
+        y = random.uniform(-1, 1)
+        c = circle(r, x, y)
+        while not c.ifcross(c_list):           
+            x = random.uniform(-1, 1)
+            y = random.uniform(-1, 1)
+            c = circle(r, x, y)
+        c = opt_center(c, c_list)
+        c_list.append(c)
+    return c_list
+
+def plot(c_list):
+    plt.figure()
+    plt.axes().set_aspect('equal')
+    plt.xlim([-1,1])
+    plt.ylim([-1,1])  
+    theta = np.linspace(0,2*np.pi,50)
+    for c in c_list:
+        plt.plot(c.x+c.radius*np.cos(theta),c.y+c.radius*np.sin(theta),'b')       
+    plt.show()
+    
+if __name__ == "__main__":
+    m = 10
+    c_list = FindMaxCircuit(m)   
+    RR = 0
+    for c in c_list:
+        RR += c.radius**2
+        c.print_circle()
+    print('for {} circles, the maximize sum of r^2 = {}'.format(m, RR))
+    
+    plot(c_list)
+    
+        
+        
+    
+        
+            
+    
+    
+    
+    
+    
\ No newline at end of file
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new file mode 100644
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new file mode 100644
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diff --git a/unit3/m=50.png b/unit3/m=50.png
new file mode 100644
index 0000000..b7b91d5
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