From b76dffdc63a50ea3955068b3d28194bd12c26719 Mon Sep 17 00:00:00 2001
From: zthdsb <1278405208@qq.com>
Date: Sun, 26 Nov 2017 14:00:22 +0800
Subject: [PATCH] =?UTF-8?q?python=E4=BF=AE=E6=94=B9?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

张铁海  学号：201730310112  邮箱：1278405208@qq.com   初学python，代码修改还有欠缺，望老师指正~
---
 .../.gitignore"                               |   8 ++
 .../p1.py"                                    |   5 +
 .../p10.py"                                   |  84 +++++++++++
 .../p11.py"                                   | 136 ++++++++++++++++++
 .../p12.py"                                   |  16 +++
 .../p13.py"                                   |  31 ++++
 .../p13/compMean.py"                          |   6 +
 .../p13/compVariance.py"                      |  13 ++
 .../p13/p13.py"                               |  13 ++
 .../p14.py"                                   |  13 ++
 .../p15.py"                                   |  13 ++
 .../p16.py"                                   |  48 +++++++
 .../p2.py"                                    |  60 ++++++++
 .../p3.py"                                    |  65 +++++++++
 .../p4.py"                                    |  11 ++
 .../p5.py"                                    |  26 ++++
 .../p6.py"                                    |  38 +++++
 .../p7.py"                                    |  41 ++++++
 .../p8.py"                                    |  97 +++++++++++++
 .../p9.py"                                    | 101 +++++++++++++
 20 files changed, 825 insertions(+)
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/.gitignore"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p1.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p10.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p11.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p12.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13/compMean.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13/compVariance.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13/p13.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p14.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p15.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p16.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p2.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p3.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p4.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p5.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p6.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p7.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p8.py"
 create mode 100644 "python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p9.py"

diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/.gitignore" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/.gitignore"
new file mode 100644
index 0000000..2945e14
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/.gitignore"
@@ -0,0 +1,8 @@
+
+p13/__pycache__/__init__.cpython-35.pyc
+p13/__init__.py
+p13/__pycache__/p13.cpython-35.pyc
+p13/__pycache__/compVariance.cpython-35.pyc
+p13/__pycache__/compMean.cpython-35.pyc
+text.py
+泡泡.py
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p1.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p1.py"
new file mode 100644
index 0000000..1d4c553
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p1.py"
@@ -0,0 +1,5 @@
+n=10
+p=[]
+for i in range(n):
+    p.append(1/n)
+print(p)
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p10.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p10.py"
new file mode 100644
index 0000000..22b36e9
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p10.py"
@@ -0,0 +1,84 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+def getPositon(a):
+    raw, column = a.shape  # get the matrix of a raw and column
+    positon = np.argmax(a)  # get the index of max in the a
+    # print(positon)
+    r, c = divmod(positon , column)
+    return r,c
+
+def sence(p,z,world,pSenseCorrect):
+    # nRow=len(p[0])
+    # nCol=len(p[:0])
+    nRow,nCol=np.shape(p)
+    q_size=(nRow,nCol)
+    q=np.zeros(q_size)
+    for r in range(0,nRow):
+        for c in range(0,nCol):
+            if z== world[r][c]:
+                hit=1
+            else:
+                hit=0
+            q[r,c]=p[r,c]*(hit * pSenseCorrect + (1-hit) * (1-pSenseCorrect))
+    S=sum(q)
+    for i in range(len(q)):
+        q[i]=q[i]/sum(S)
+    return q
+
+world = (('red', 'green', 'green', 'red', 'red'),
+('red', 'red', 'green', 'red', 'red'),
+('red', 'red', 'green', 'green', 'red'),
+('red', 'red', 'red', 'red', 'red'))
+nRow = len(world)
+nCol = len(world[1])
+print(world[1][1])
+pStart = 0.7
+ones_size=(nRow,nCol)
+ones=np.ones(ones_size)
+p = (1 - pStart) / (nRow * nCol - 1) * ones
+pSenseCorrect=0.7
+p[2,1]=pStart
+measurements=['green']
+# print(measurements[0])
+q=sence(p,measurements[0],world,pSenseCorrect)
+print("The Prior:\n",p)
+print("The probability after sensing:\n",q)
+r,c=getPositon(q)
+print('The largest probability %s occurs at cell(%s,%s)\n'%(q[r,c],r,c))
+
+plt.ion()
+h=plt.figure(1)
+ax=Axes3D(h)
+ly= len(p[0])  #5            # Work out matrix dimensions
+lx= len(p[:,0])  #4
+xpos = np.arange(0,lx,1)    # Set up a mesh of positions
+ypos = np.arange(0,ly,1)
+xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25)
+xpos = xpos.flatten()   # Convert positions to 1D array
+ypos = ypos.flatten()
+zpos = np.zeros(lx*ly)
+
+dx = np.ones_like(zpos)
+dy = dx.copy()
+dz = p.T.flatten()
+ax.bar3d(xpos,ypos,zpos, dx, dy, dz)
+
+h1=plt.figure(2)
+ax=Axes3D(h1)
+ly= len(q[0])  #5            # Work out matrix dimensions
+lx= len(q[:,0])  #4
+xpos = np.arange(0,lx,1)    # Set up a mesh of positions
+ypos = np.arange(0,ly,1)
+xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25)
+xpos = xpos.flatten()   # Convert positions to 1D array
+ypos = ypos.flatten()
+zpos = np.zeros(lx*ly)
+
+dx = np.ones_like(zpos)
+dy = dx.copy()
+dz = q.T.flatten()
+ax.bar3d(xpos,ypos,zpos, dx, dy, dz)
+plt.ioff()
+plt.show()
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p11.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p11.py"
new file mode 100644
index 0000000..049ac6e
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p11.py"
@@ -0,0 +1,136 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+def getPositon(a):
+    raw, column = a.shape  # get the matrix of a raw and column
+    positon = np.argmax(a)  # get the index of max in the a
+    # print(positon)
+    r, c = divmod(positon , column)
+    return r,c
+
+def move(p,u,pMoveCorrect):
+    nRow=int(len(p))
+    nCol=int(len(p[0]))
+    q=np.zeros(ones_size)
+    for r in range(1,nRow+1):
+        for c in range(1,nCol+1):
+            q[r-1,c-1]=pMoveCorrect * p[np.mod(r-1-u[0],nRow),np.mod(c-1-u[1],nCol)]+ (1-pMoveCorrect)* p[r-1,c-1]
+        # return q
+    return q
+
+def sence(p,z,world,pSenseCorrect):
+    # nRow=len(p[0])
+    # nCol=len(p[:0])
+    nRow,nCol=np.shape(p)
+    q_size=(nRow,nCol)
+    q=np.zeros(q_size)
+    for r in range(0,nRow):
+        for c in range(0,nCol):
+            if z== world[r][c]:
+                hit=1
+            else:
+                hit=0
+            q[r,c]=p[r,c]*(hit * pSenseCorrect + (1-hit) * (1-pSenseCorrect))
+    S=sum(q)
+    for i in range(len(q)):
+        q[i]=q[i]/sum(S)
+    return q
+
+def compEntropy(p):
+    compEntropy = -sum(sum(p * np.log2(p)))
+    return compEntropy
+
+
+world = (('red', 'green', 'green', 'red', 'red'),
+('red', 'red', 'green', 'red', 'red'),
+('red', 'red', 'green', 'green', 'red'),
+('red', 'red', 'red', 'red', 'red'))
+nRow = len(world)
+nCol = len(world[1])
+print(nRow,nCol)
+step={
+    'stop':[0,0],
+    'right':[0,1],
+    'left':[0,-1],
+    'down':[1,0],
+    'up':[-1,0]
+}
+pMoveCorrect = 0.8
+pSenseCorrect=0.7
+ones_size=(nRow,nCol)
+ones=np.ones(ones_size)
+p = 1/ (nRow * nCol) * ones
+# compEntropy=-sum(sum(p * np.log2(p)))
+
+#First configuration
+motions=[step['stop'],step['right'],step['down'],step['down'],step['right']]
+measurements=['green','green','green','green','green']
+#Check the size
+if len(motions)!= len(measurements):
+    print("The variable 'motions' should be of the same size as 'measurements' ! ")
+entropy_size=(2,len(motions))
+entropy=np.zeros(entropy_size)
+#The main loop
+plt.ion()
+for i in range(0,len(motions)):
+    # plt.ion()
+    p=move(p,motions[i],pMoveCorrect)
+    p0=p
+    p=sence(p,measurements[i],world,pSenseCorrect)
+    #Compute entropy
+    entropy[:,i]=[compEntropy(p0),compEntropy(p)]
+    # Make figures
+    h=plt.figure(1)
+    ax1=h.add_subplot(2,1,1,projection='3d')
+    # ax = Axes3D(h)
+    plt.cla()
+    plt.title('Step %s\n The probability before sensing'%i)
+    ly = len(p0[0])  # 5            # Work out matrix dimensions
+    lx = len(p0[:, 0])  # 4
+    xpos = np.arange(0, lx, 1)  # Set up a mesh of positions
+    ypos=(1,2,3,4,5)
+    xpos, ypos = np.meshgrid(xpos, ypos)
+    xpos = xpos.flatten()  # Convert positions to 1D array
+    ypos = ypos.flatten()
+    zpos = np.zeros(lx * ly)
+
+    dx = np.ones_like(zpos)
+    dy = dx.copy()
+    dz = p0.T.flatten()
+    # ax1.set(color='r,b,g')
+    ax1.bar3d(xpos, ypos, zpos, dx, dy, dz,color='b',alpha=0.6)
+
+    ax2=h.add_subplot(2,1,2,projection='3d')
+    ly = len(p[0])  # 5            # Work out matrix dimensions
+    lx = len(p[:, 0])  # 4
+    xpos = np.arange(0, lx, 1)  # Set up a mesh of positions
+    ypos = np.arange(0, ly, 1)
+    xpos, ypos = np.meshgrid(xpos, ypos)
+    xpos = xpos.flatten()  # Convert positions to 1D array
+    ypos = ypos.flatten()
+    zpos = np.zeros(lx * ly)
+    dx = np.ones_like(zpos)
+    dy = dx.copy()
+    dz = p.T.flatten()
+    # plt.cla()
+    col=np.arange(30)
+    # plt.scatter(xpos,ypos,zpos,c=col)
+    ax2.bar3d(xpos, ypos, zpos, dx, dy, dz,color='g',alpha=0.6)
+    plt.pause(0.5)
+
+print('The Posterior:\n',p)
+r,c=getPositon(p)
+print('The largest probability %.4f occurs at cell (%d, %d)\n '%(p[r,c],r,c))
+
+h2=plt.figure(2)
+motions_size=len(motions)
+plt.plot(range(1,motions_size+1),entropy[0,:],'bh',linewidth=3,markersize=10)
+plt.plot(range(1,motions_size+1),entropy[1,:],'rx',linewidth=3,markersize=10)
+
+plt.xlim(0,(len(motions)+1))
+plt.xlabel('Step')
+plt.ylabel('Entropy')
+plt.legend(('After sensing', 'Before sensing'),loc='upper right')
+plt.ioff()
+plt.show()
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p12.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p12.py"
new file mode 100644
index 0000000..f2ae960
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p12.py"
@@ -0,0 +1,16 @@
+
+pCan=0.001
+pNon=0.999
+z='positive'
+# z='negative'
+pPosCan=0.8
+pPosNon=0.1
+if z=='positive':
+    p=[pPosCan*pCan, pPosNon*pNon]
+else:
+    p=[(1-pPosCan)*pCan,(1-pPosNon)*pNon]
+S=sum(p)
+for i in range(len(p)):
+    p[i]=p[i]/sum(p)
+print(p[0])
+print(p[1])
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13.py"
new file mode 100644
index 0000000..b32ade9
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13.py"
@@ -0,0 +1,31 @@
+from compMean import compMean
+from compVariance import compVariance
+import numpy as np
+
+def mean(x):  #计算均值的
+    S = np.sum(x)   #矩阵中每个元素求和必须用np.sum
+    shape = x.shape[:]
+    mean=S/(shape[0]*shape[1])
+    return mean
+
+def compVariance(x):   #计算方差的
+    mu=mean(x)
+    mu=np.mat(mu)
+    x2=np.square(x-mu)
+    sigma2=mean(x2)
+    return sigma2
+def compMean(x):   #计算均值的
+    S=np.sum(x)
+    shape=x.shape[:]
+    mu=S/(shape[0]*shape[1])
+    return mu
+
+x=[7,38,4,23,18]
+# x=[17,19,18,17,19]
+# x=[18,18,18,18,18]
+x=np.mat(x)
+mu=compMean(x)
+sigma2=compVariance(x)
+print('The Expectation / Mean:\t %s \n'%mu)
+print('The Variance is:\t\t %s \n'%sigma2)
+print('The Standard Deviation is:\t %s \n'%np.sqrt(sigma2))
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13/compMean.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13/compMean.py"
new file mode 100644
index 0000000..c63a2bf
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13/compMean.py"
@@ -0,0 +1,6 @@
+import numpy as np
+def compMean(x):   #计算均值的
+    S=np.sum(x)
+    shape=x.shape[:]
+    mu=S/(shape[0]*shape[1])
+    return mu
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13/compVariance.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13/compVariance.py"
new file mode 100644
index 0000000..e36fa8e
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13/compVariance.py"
@@ -0,0 +1,13 @@
+import numpy as np
+def mean(x):  #计算均值的
+    S = np.sum(x)   #矩阵中每个元素求和必须用np.sum
+    shape = x.shape[:]
+    mean=S/(shape[0]*shape[1])
+    return mean
+
+def compVariance(x):   #计算方差的
+    mu=mean(x)
+    mu=np.mat(mu)
+    x2=np.square(x-mu)
+    sigma2=mean(x2)
+    return sigma2
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13/p13.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13/p13.py"
new file mode 100644
index 0000000..203cdb6
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p13/p13.py"
@@ -0,0 +1,13 @@
+from compMean import compMean
+from compVariance import compVariance
+import numpy as np
+
+x=[7,38,4,23,18]
+# x=[17,19,18,17,19]
+# x=[18,18,18,18,18]
+x=np.mat(x)
+mu=compMean(x)
+sigma2=compVariance(x)
+print('The Expectation / Mean:\t %.2f \n'%mu)
+print('The Variance is:\t\t %.2f \n'%sigma2)
+print('The Standard Deviation is:\t %.2f \n'%np.sqrt(sigma2))
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p14.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p14.py"
new file mode 100644
index 0000000..e1d55ab
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p14.py"
@@ -0,0 +1,13 @@
+import numpy as np
+
+from p13 import compMean  #这种方式引用在使用函数时，前面必须加文件名  compMean.compMean(x)
+# import compMean    这种方式引用，使用函数时，前面不用加文件名
+a=2
+b=4
+x=[7,38,4,23,18]
+x=np.mat(x)
+y=a*x + b
+xmu=compMean.compMean(x)
+ymu=compMean.compMean(y)
+print('a * Xmu + b \t= %s \n'%(a * xmu + b))
+print('Ymu \t\t= %.2f \n'%ymu)
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p15.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p15.py"
new file mode 100644
index 0000000..7518a45
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p15.py"
@@ -0,0 +1,13 @@
+import numpy as np
+from p13 import compMean
+from p13 import compVariance
+
+x= [7, 38, 4, 23, 18]
+x=np.mat(x)
+x2= np.square(x)
+# print(x2)
+x2mu= compMean.compMean(x2)
+xmu = compMean.compMean(x)
+xvar= compVariance.compVariance(x)
+print('The Variance of X \t= %.2f \n'%xvar)
+print('E[X^2]-E[X]^2 \t\t= %.2f \n'%(x2mu - np.square(xmu)))
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p16.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p16.py"
new file mode 100644
index 0000000..0e99490
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p16.py"
@@ -0,0 +1,48 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import numpy.linalg
+from scipy.linalg import sqrtm
+
+#
+def getSigmaEllipse(x,c,nsig,n):
+    # if nargin==4:  #nargin 在matlab中表示函数输入参数的个数
+    inc=2 * np.pi/n
+    # else:
+    #     inc=np.pi/30
+    #     return inc
+    r=sqrtm(c)
+    phi=np.arange(0,(2*np.pi),inc)  #生成起始为0，终止为(2*np.pi)，步长为inc的矩阵，不包括终值
+    b=[2*np.pi,0]
+    phi = np.hstack((phi, b))  #按行合并
+    phi=np.mat(phi)
+    z = np.vstack((np.cos(phi), np.sin(phi)))
+    z=np.mat(z)
+    a=nsig * r * z
+    e=np.zeros_like(a)
+    e[0,:]=a[0,:] +x[0]
+    e[1,:]=a[1,:] +x[1]
+    return e
+
+mu=[0,0]
+sigmax=5
+sigmay=7
+rho=0.2
+sigmaxy=sigmax * sigmay * rho
+Sigma=[[np.square(sigmax),sigmaxy],[sigmaxy,np.square(sigmay)]]
+# print(Sigma)
+# Sigma=np.mat(Sigma)
+ellipsis=getSigmaEllipse(mu,Sigma,1,100)
+
+plt.figure('The 1-sigma ellipse')
+# plt.hold()
+plt.plot(ellipsis[0,:],ellipsis[1,:],'ro',linewidth=3,markersize=15)
+# plt.show()
+plt.plot(mu[0], mu[1], 'gx',linewidth=10,markersize=10)
+plt.xlabel('x')
+plt.ylabel('y')
+plt.grid(which='both')
+plt.minorticks_on()
+plt.title('σx = %.1f, σy = %.1f, ρ = %.1f'%(sigmax, sigmay, rho))
+plt.xlim(-5.5,5.5,0.5)
+plt.ylim(-5.5,5.5)
+plt.show()
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p2.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p2.py"
new file mode 100644
index 0000000..8df2cb0
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p2.py"
@@ -0,0 +1,60 @@
+import matplotlib.pyplot as plt
+import numpy as np
+# q=[0,0,0,0,0]
+def sense(p,world,pHit,pMiss):
+    q=np.zeros(len(p))
+    size=int(len(p))
+    obs = np.zeros(size)
+    for i in range(size):
+        if "red"==world[i]:
+            hit=1
+        else:
+            hit=0
+        obs[i]=hit*pHit+(1-hit)*pMiss
+        q[i]=p[i]*(hit*pHit+(1-hit)*pMiss)
+        # q.index(q[i])
+    S=sum(q)
+    for i in range(size):
+        q[i]=q[i]/S
+    print(q)
+
+    plt.figure('The Prior distribution')
+    plt.bar(x=(0,1,2,3,4),height=p,color='g')
+    plt.ylabel("Probability of being at the positon")
+    plt.xlabel("Position")
+    plt.ylim(0,0.5)
+
+    plt.figure('Observation model')
+    plt.bar(x=(0, 1, 2, 3, 4), height=obs, color='b')
+    plt.ylabel("Probability of being at the positon")
+    plt.xlabel("Position")
+    plt.ylim(0, 0.9)
+
+    plt.figure('The Posterior distribution')
+    plt.bar(x=(0, 1, 2, 3, 4), height=q, color='red')
+    plt.ylabel("Probability of being at the positon")
+    plt.xlabel("Position")
+    plt.ylim(0, 0.5)
+
+    plt.figure('Plot all')
+    plt.subplot(311)
+    plt.bar(x=(0,1,2,3,4),height=p,color='red')
+    plt.ylabel('Probability')
+    plt.ylim(0,0.5)
+    plt.subplot(312)
+    plt.bar(x=(0,1,2,3,4),height=obs)
+    plt.ylabel('Likelihood')
+    plt.ylim(0, 0.9)
+    plt.subplot(313)
+    plt.bar(x=(0,1,2,3,4),height=q)
+    plt.xlabel('Position')
+    plt.ylabel('Probability')
+    plt.ylim(0,0.5)
+    plt.show()
+
+world=["green","red","red","green","green"]
+p=[0.2,0.2,0.2,0.2,0.2]
+pHit=0.6
+pMiss=0.2
+sense(p,world,pHit,pMiss)
+
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p3.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p3.py"
new file mode 100644
index 0000000..b468d57
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p3.py"
@@ -0,0 +1,65 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+def sence(p,z,world,pHit,pMiss):
+    q=np.zeros(len(p))
+    for i in range(0,len(p)):
+        if z==world[i]:
+            hit=1
+        else:
+            hit=0
+        q[i]=p[i]* (hit * pHit+ (1-hit) * pMiss)
+    S=sum(q)
+    for i in range(len(q)):
+        q[i]=q[i]/S
+    return q
+
+def p3plot():
+    plt.ion()
+    plt.figure('Plot all')
+    plt.subplot(3, 1, 1)
+    plt.cla()
+    plt.bar(x=(1, 2, 3, 4, 5), height=p)
+    plt.ylabel('Prior')
+    plt.ylim(0, 0.5)
+    plt.title('Measurement=%s' % measurements[i])
+
+    plt.subplot(3, 1, 2)
+    obs = np.zeros(len(world))
+    for j in range(0, len(world)):
+        if measurements[i] == world[j]:
+            hit = 1
+        else:
+            hit = 0
+        obs[j] = hit * pHit + (1 - hit) * pMiss
+    plt.cla()
+    plt.bar(x=(1, 2, 3, 4, 5), height=obs)
+    plt.ylabel('Observation')
+    plt.ylim(0, 0.9)
+
+    plt.subplot(3, 1, 3)
+    plt.cla()
+    plt.bar(x=(1, 2, 3, 4, 5), height=q)
+    plt.xlabel('Posterior')
+    plt.ylabel('Posterior')
+    plt.ylim(0, 0.5)
+    # plt.pause(0.5)
+    plt.ioff()
+    plt.show()
+
+world=['green', 'red', 'red', 'green', 'green']
+p=[0.2,0.2,0.2,0.2,0.2]
+measurements=['red','red','green']
+pHit=0.6
+pMiss=0.2
+plt.ion()
+for i in range(0,len(measurements)):
+    q=sence(p,measurements[i],world,pHit,pMiss)
+    p3plot()
+    # plt.pause(0.5)
+    p=q
+print(p)
+p3plot()
+# plt.pause(0.5)
+# plt.ioff()
+# plt.show()
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p4.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p4.py"
new file mode 100644
index 0000000..9a81f38
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p4.py"
@@ -0,0 +1,11 @@
+p=[1/9, 1/3, 1/3, 1/9, 1/9]
+u=6
+def move(p,u):
+    n=len(p)# n=int(len(p))
+    q=[] #numpy.zeros(n)
+    for i in range(1,n+1):
+        x,y=divmod(i-1-u,n)
+        q.append(p[y])
+    print("Before movtion:\n",p)
+    print("After movtion:\n",q)
+move(p,u)
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p5.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p5.py"
new file mode 100644
index 0000000..70b4735
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p5.py"
@@ -0,0 +1,26 @@
+import numpy as np
+p=[0, 0.5, 0, 0.5, 0]
+u=2  #表示小车前进的格数
+pExact=0.8
+pOvershoot=0.1
+pUndershoot=0.1
+def move(p,u,pExact,pOvershoot,pUndershoot):
+    n=len(p)
+    q=np.zeros(n)
+    for i in range(1,n+1):
+        l=i-1
+        x,y=divmod(i-1-u,n)
+        # print(y)
+        q[l]=pExact*p[y]
+        # q.append(pExact*p[y])  #在正确的情况下，所在格子的概率
+        # print(q)
+        x,y=divmod(i-2-u,n)
+        q[l]=q[l]+pOvershoot*p[y]
+        # q.index(q[l]+pOvershoot*p[y])  #加上超过格子的误差
+        x, y = divmod(i - u, n)
+        q[l] = q[l] + pUndershoot * p[y]
+        # q.index(q[l]+pUndershoot*p[y])#加上不足格子的误差
+    print(p)
+    print(q)
+     # q.append(q)
+move(p,u,pExact,pOvershoot,pUndershoot)
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p6.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p6.py"
new file mode 100644
index 0000000..6c37d93
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p6.py"
@@ -0,0 +1,38 @@
+import numpy as np
+import matplotlib.pyplot as plt
+p=[1, 0, 0, 0, 0]
+u=1
+step=20
+pExact=0.8
+pOvershoot=0.1
+pUndershoot=0.1
+# n=len(p)
+# q = np.zeros(n)
+def move(p,u,pExact,pOvershoot,pUndershoot):
+    n=len(p)
+    q = np.zeros(n)
+    for i in range(1, n + 1):
+        l = i - 1
+        x, y = divmod(i - 1 - u, n)
+        q[l] = pExact * p[y]
+        x, y = divmod(i - 2 - u, n)
+        q[l] = q[l] + pOvershoot * p[y]
+        x, y = divmod(i - u, n)
+        q[l] = q[l] + pUndershoot * p[y]
+    return q
+fig1=plt.figure(figsize=(10,10),dpi=80)
+plt.ion()  #开启交互模式
+# q=move(p,u,pExact,pOvershoot,pUndershoot)
+for i in range(1,step+1):
+    print(i)
+    p=move(p,u,pExact,pOvershoot,pUndershoot)
+    print(p)
+    plt.cla()
+    plt.bar(x=(1,2,3,4,5),height=p)
+    plt.title('step=%s'%i)
+    plt.ylim(0,1)
+    plt.ylabel('Probability')
+    plt.pause(0.5)
+plt.ioff()#关闭交互模式，如果不关闭图像就会一闪而过，不会长留
+plt.show()
+
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p7.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p7.py"
new file mode 100644
index 0000000..3ee271e
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p7.py"
@@ -0,0 +1,41 @@
+import numpy as np
+import matplotlib.pyplot as plt
+p=[1, 0, 0, 0, 0]
+u=1
+step=100
+pExact=0.8
+pOvershoot=0.1
+pUndershoot=0.1
+entropy=np.zeros(step)
+def move(p,u,pExact,pOvershoot,pUndershoot):
+    n=len(p)
+    q = np.zeros(n)
+    for i in range(1, n + 1):
+        l = i - 1
+        x, y = divmod(i - 1 - u, n)
+        q[l] = pExact * p[y]
+        x, y = divmod(i - 2 - u, n)
+        q[l] = q[l] + pOvershoot * p[y]
+        x, y = divmod(i - u, n)
+        q[l] = q[l] + pUndershoot * p[y]
+    return q
+# q=move(p,u,pExact,pOvershoot,pUndershoot)
+plt.ion()
+for i in range(1,step+1):
+    p=move(p,u,pExact,pOvershoot,pUndershoot)
+    entropy[i-1]=-sum(p * np.log2(p))
+    print(i)
+    print(p)
+    # print(entropy)
+    # print(p)
+    # plt.cla()
+    # x=plt.xlim(1,step)
+y = entropy
+plt.plot(y,'r^',linewidth=0.5,markersize=6)#,shap='-',linewidth=2,color='g'
+plt.plot(y,'g',linewidth=2,markersize=2)#,shap=':',linewidth=1.5,color='r'
+    # plt.xlim(1,step,10)
+plt.xlabel('Motion step')
+plt.ylabel('Entropy')
+    # plt.pause(0.5)  #每次图表显示间隔的时间
+plt.ioff()
+plt.show()
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p8.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p8.py"
new file mode 100644
index 0000000..4821ce2
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p8.py"
@@ -0,0 +1,97 @@
+import numpy as np
+import matplotlib.pyplot as plt
+def sense(p,z,world,pHit,pMiss):
+    size=int(len(p))
+    # obs = np.zeros(size)
+    q=np.zeros(len(p))
+    for i in range(size):
+        if z==world[i]:
+            hit=1
+        else:
+            hit=0
+        # obs[i]=hit*pHit+(1-hit)*pMiss
+        q[i]=p[i]*(hit*pHit+(1-hit)*pMiss)
+        # q.index(q[i])
+    S=sum(q)
+    for i in range(size):
+        q[i]=q[i]/S
+    # print(q)
+    return q
+def move(p,u,pExact,pOvershoot,pUndershoot):
+    n=len(p)
+    q = np.zeros(n)
+    for i in range(1, n + 1):
+        l = i - 1
+        x, y = divmod(i - 1 - u, n)
+        q[l] = pExact * p[y]
+        x, y = divmod(i - 2 - u, n)
+        q[l] = q[l] + pOvershoot * p[y]
+        x, y = divmod(i - u, n)
+        q[l] = q[l] + pUndershoot * p[y]
+    return q
+p=[0.2, 0.2, 0.2, 0.2, 0.2]
+world=['green','red','red','green','green']
+#First configuration of meas.
+# measurements=['red','green']
+# motions=[1,1]
+#Sencond configuration
+measurements=['red','green','green']
+motions=[1,1,1]
+#Third configuration
+# measurements=['green','red']
+# motions=[1,1]
+#Fourth configuration
+# measurements=['green''green']
+# motions=[3,1]
+#Fifth configuration,It is impossible case
+# measurements=['red','red']
+# motions=[3,1]
+#Sixth configuration,It is impossible case
+# measurements=['red','red']
+# motions=[2,1]
+pHit=0.6
+pMiss=0.2
+pExact=0.8
+pOvershoot=0.1
+pUndershoot=0.1
+entropy=np.zeros((2,len(motions)))
+motions_size=len(motions)
+for i in range(len(measurements)):
+    #Updara by sensing
+    p=sense(p,measurements[i],world,pHit,pMiss)
+    entropy[0,i]=-sum(p*np.log2(p))
+    p=move(p,motions[i],pExact,pOvershoot,pUndershoot)
+    entropy[1,i]=-sum(p*np.log2(p))
+print('The final posterior:')
+print(p)
+print('The final entropy:')
+print(entropy[-1,-1])
+
+print(len(motions))
+
+plt.ion()
+h1=plt.figure(1)
+plt.cla()
+hAllBar=plt.bar(x=(1,2,3,4,5),height=p,width=1,facecolor='g',edgecolor='black')
+p=p.tolist()
+pMaxCell=max(p)
+iMaxCell=p.index(pMaxCell)   #提取不到最大值的位置
+pMax=np.zeros(len(p))
+pMax[iMaxCell]=pMaxCell
+hMaxBar=plt.bar(x=(1,2,3,4,5),height=pMax,width=1,facecolor='r',edgecolor='black')
+plt.xlabel('Index of cell')
+plt.ylabel('Posterior')
+plt.xlim(0,5.5,1)
+# plt.show(h1)
+
+h2=plt.figure(2)
+plt.plot(range(1,motions_size+1),entropy[0,:],'bh',linewidth=3,markersize=10)
+plt.plot(range(1,motions_size+1),entropy[1,:],'rx',linewidth=3,markersize=10)
+print(entropy[0,:])
+print(entropy[1,:])
+plt.xlim(-1,(len(motions)+1))
+plt.xlabel('Step')
+plt.ylabel('Entropy')
+plt.legend(('After sensing', 'Before sensing'),loc='upper right')
+plt.ioff()
+plt.show()
\ No newline at end of file
diff --git "a/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p9.py" "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p9.py"
new file mode 100644
index 0000000..1e46d50
--- /dev/null
+++ "b/python\344\277\256\346\224\271\345\274\240\351\223\201\346\265\267/p9.py"
@@ -0,0 +1,101 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+def getPositon(a):
+    raw, column = a.shape  # get the matrix of a raw and column
+    positon = np.argmax(a)  # get the index of max in the a
+    # print(positon)
+    r, c = divmod(positon , column)
+    return r,c
+
+def move(p,u,pMoveCorrect):
+    nRow=int(len(p))
+    nCol=int(len(p[0]))
+    q=np.zeros(ones_size)
+    for r in range(1,nRow+1):
+        for c in range(1,nCol+1):
+            q[r-1,c-1]=pMoveCorrect * p[np.mod(r-1-u[0],nRow),np.mod(c-1-u[1],nCol)]+ (1-pMoveCorrect)* p[r-1,c-1]
+        # return q
+    return q
+
+world = (('red', 'green', 'green', 'red', 'red'),
+('red', 'red', 'green', 'red', 'red'),
+('red', 'red', 'green', 'green', 'red'),
+('red', 'red', 'red', 'red', 'red'))
+# print(world)
+# print(len(world))
+# print(len(world[1]))
+nRow = len(world)
+nCol = len(world[1])
+ones_size=(nRow,nCol)
+ones=np.ones(ones_size)
+step={
+    'stop':[0,0],
+    'right':[0,1],
+    'left':[0,-1],
+    'down':[1,0],
+    'up':[-1,0]
+}
+pMoveCorrect = 0.8
+pStart = 0.7
+p = (1 - pStart) / (nRow * nCol - 1) * ones
+# Fisrt configuration
+# p[2,1]=pStart
+# motions=step['right']
+# Second configuration
+p[0,2]=pStart
+motions=step['up']
+
+q=move(p,motions,pMoveCorrect)
+print('The Prior:\n',p)
+print('The probability after moving:\n',q)
+r,c=getPositon(q)
+print('The largest probability %s occurs at cell(%s,%s)\n'%(q[r,c],r,c))
+
+plt.ion()
+h=plt.figure(1)
+ax=Axes3D(h)
+# x=np.arange(1,nRow+1,1)
+# y=np.arange(1,nCol+1,1)
+# z=p.flatten()
+# x=x.flatten()
+# y=y.flatten()
+# dx_size=(nRow,nCol)
+# dx=np.ones(dx_size)
+# dy=dx.copy()
+# dz=p.flatten()
+# ax.bar3d(x,y,z,dx,dy,dz)
+# plt.show()
+
+ly= len(p[0])  #5            # Work out matrix dimensions
+lx= len(p[:,0])  #4
+xpos = np.arange(0,lx,1)    # Set up a mesh of positions
+ypos = np.arange(0,ly,1)
+xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25)
+xpos = xpos.flatten()   # Convert positions to 1D array
+ypos = ypos.flatten()
+zpos = np.zeros(lx*ly)
+
+dx = np.ones_like(zpos)
+dy = dx.copy()
+dz = p.T.flatten()
+ax.bar3d(xpos,ypos,zpos, dx, dy, dz)
+
+h1=plt.figure(2)
+ax=Axes3D(h1)
+ly= len(q[0])  #5            # Work out matrix dimensions
+lx= len(q[:,0])  #4
+xpos = np.arange(0,lx,1)    # Set up a mesh of positions
+ypos = np.arange(0,ly,1)
+xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25)
+xpos = xpos.flatten()   # Convert positions to 1D array
+ypos = ypos.flatten()
+zpos = np.zeros(lx*ly)
+
+dx = np.ones_like(zpos)
+dy = dx.copy()
+dz = q.T.flatten()
+# color=plt.cm.
+ax.bar3d(xpos,ypos,zpos, dx, dy, dz)
+plt.ioff()
+plt.show()