def analystTestData2():
ds = dataset.DataSet()
ds.getTrainData()
dt = datadeal.DataTrain()
idxs = []
mouses = []
goals = []
for i in range(1000):
idx, mouse, goal, _ = ds.readTestFile()
mouses.append(mouse)
goals.append(goal)
# print goals
with open('./data/BDC1236_20170607.txt', 'r') as f:
while True:
idx = f.readline()
idx = int(idx)
if idx > 1000:
break
idxs.append(idx)
idxs = np.array(idxs)
print idxs
# print mouses[0]
dw = datadraw.DataDraw('3d')
colors = dw.getColorsValue()
start = 10
def main():
ds=dataset.DataSet()
ds.getTrainData()
mouses=ds.train["mouses"]
goals=ds.train["goals"]
labels=ds.train["labels"]
n=ds.train["size"]
vector=[]
idxs=[]
# print n
for i in range(n):
cnum=dealmouse(mouses[i])
if cnum>0:
print i,cnum
idxs.append(i)
print len(idxs)
dw=datadraw.DataDraw(typex='3d')
# dw.draw3dline(mouses[2600],c='r')
# dw.draw3dline(mouses[2700],c='g')
dw.draw3dline(mouses[2800],c='b')
dw.draw3dline(mouses[2870],c='g')
dw.draw3dline(mouses[2900],c='y')
# dw.draw3dline(mouses[2999],c='r')
# print goals.shape
# dw.drawgoal([mse[:2600,0],mse[:2600,1]],'b')
# dw.drawgoal([mse[2600:,0],mse[2600:,1]],'r')
# for i in range(2600):
# dw.draw3dgoal(mse[i],c='b')
# for i in range(2600,3000):
# dw.draw3dgoal(mse[i],c='r')
plt.show()
def axist():
ds=dataset.DataSet()
ds.getTrainData()
dw=datadraw.DataDraw('2d')
mouses=ds.train["mouses"]
goals=ds.train["goals"]
labels=ds.train["labels"]
n=ds.train["size"]
def getvecotr(mouse,goal):
tn=len(mouse[2])
t=mouse[2]
if tn==1:
return 0
return t[tn-1]-t[0]
vector=[]
for i in range(n):
vector.append(getvecotr(mouses[i],goals[i]))
vector=np.array(vector,dtype=np.float)
# dw.drawline([range(2600),vector[:2600]],c='b')
# dw.drawline([range(2600,3000),vector[2600:]],c='r')
# print vector[:2600].mean()
# print vector[2600:].mean()
# dw.drawbatchgoal(np.array([vector[:2600],labels[:2600]]).T,c='b')
# dw.drawbatchgoal(np.array([vector[2600:],labels[2600:]]).T,c='r')
# plt.show()
# vector=np.array(vector)
dt=datadeal.DataTrain()
# # clf = MLPClassifier(alpha=1e-3, hidden_layer_sizes=(2,2), random_state=1)
clf = SVC()
dt.trainTest(clf,vector.reshape([3000,1]),labels)
def startendvector():
ds=dataset.DataSet()
ds.getTrainData()
dw=datadraw.DataDraw('2d')
mouses=ds.train["mouses"]
goals=ds.train["goals"]
labels=ds.train["labels"]
n=ds.train["size"]
def getvecotr(mouse,goal):
n=len(mouse[0])
ex=mouse[0][n-1]
ey=mouse[1][n-1]
et=mouse[2][n-1]
bx=mouse[0][0]
by=mouse[1][0]
bt=mouse[2][0]
if n>1:
tmp=(bx-ex)**2+(by-ey)**2+(by-ey)**2
else:
tmp=(bx)**2+(by)**2+(by)**2
bx=by=bt=0.0
tmp=tmp**0.5
if tmp<1e-3:
tmp=1.0
return [(ex-bx)/tmp,(ey-by)/tmp,(et-bt)/tmp]
vector=[]
for i in range(n):
vector.append(getvecotr(mouses[i],goals[i]))
vector=np.array(vector)
dt=datadeal.DataTrain()
# clf = MLPClassifier(alpha=1e-3, hidden_layer_sizes=(2,2), random_state=1)
clf = SVC()
dt.trainTest(clf,vector,labels)
def main():
ds=dataset.DataSet()
ds.getTrainData()
mouses=ds.train["mouses"]
goals=ds.train["goals"]
labels=ds.train["labels"]
n=ds.train["size"]
vector=[]
idxs=[]
# print n
dw=datadraw.DataDraw(typex='3d')
path='./data/pic/'
for i in range(n):
if i<2000:
continue
# if i>3000:
# break
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
dw.draw3dline(mouses[i],ax,c='g')
plt.savefig(path+"%d.png"%i)
plt.clf()
plt.close()
# if i >3:
# break
if i%20==0:
print i
# break
print "over"
def analystTestData():
ds = dataset.DataSet()
ds.getTrainData()
dt = datadeal.DataTrain()
idxs = []
mouses = []
goals = []
for i in range(1000):
idx, mouse, goal, _ = ds.readTestFile()
if mouse[1][0] < 0:
mouse[1][0] = 2100
if mouse[0][0] > 1000:
mouse[0][0] -= 400
mouses.append([mouse[0][0], mouse[1][0]])
goals.append(goal)
with open('./data/BDC1236_20170607.txt', 'r') as f:
while True:
idx = f.readline()
idx = int(idx)
if idx > 1000:
break
idxs.append(idx)
idxs = np.array(idxs)
idxs = idxs - 1
# print sum(idxs)
mouses = np.array(mouses)
dw = datadraw.DataDraw('2d')
dw.drawbatchgoal(mouses, c='b')
dw.drawbatchgoal(mouses[idxs], c='r')
plt.show()
def main():
ds=dataset.DataSet()
ds.getTrainData()
mouses=ds.train["mouses"]
goals=ds.train["goals"]
labels=ds.train["labels"]
n=ds.train["size"]
# config["borders"]=get_borders(mouses)
machine_list=[]
mouse_start=ds.getPosOfMouse(-1)
mclass=[[],[],[]]
xyt=0
for i in range(3000):
xyt=0
tmpx=mouse_start[i,xyt]-goals[i][xyt]
xyt=1
tmpy=mouse_start[i,xyt]-goals[i][xyt]
tmp=(tmpx**2+tmpy**2)**0.5
if tmp>3000:
tmp=3000
# if tmp<-60:
# tmp=-60
machine_list.append(tmp)
value=np.array(machine_list)
dw=datadraw.DataDraw(typex='2d')
# dw.draw3dline(mouses[1802])
# print goals.shape
dw.drawgoal([range(2600),value[:2600]],'b')
dw.drawgoal([range(2600,3000),value[2600:]],'r')
plt.show()
print value.min()
print value.max()
print value.mean()
def notgetanalyst():
ds = dataset.DataSet()
ds.getTrainData()
dt = datadeal.DataTrain()
y = ds.train["labels"]
mouses = ds.train["mouses"]
goals = ds.train["goals"]
labels = ds.train["labels"]
with open('./data/notget.txt', 'r') as f:
s = f.read()
wrongs = s.split()
idxw = np.array(wrongs, dtype='int') - 1
# print len(idxw)
mousew = [mouses[i] for i in idxw]
# print len(mousew)
# mousew=mouses[]
# print idxw
dw = datadraw.DataDraw('3d')
colors = dw.getColorsValue()
count = 0
dw.draw3dline(mouses[0], c=colors[count % 10])
for i in range(len(mousew)):
count += 1
if count > 10:
break
dw.draw3dline(mousew[i], c=colors[count % 10])
# goal=goals[i]
# dw.draw3dgoal([goal[0],goal[1]+2000,0],c=colors[count%10])
plt.show()
def test():
ds=dataset.DataSet()
ds.getTrainData()
dw=datadraw.DataDraw('3d')
mouses=ds.train["mouses"]
goals=ds.train["goals"]
labels=ds.train["labels"]
n=ds.train["size"]
vector=[]
idxs=[1,100,1000,2000,2600,2700,2800,2800]
for i in idxs:
tmp=getspeed(mouses[i])
tmpstr=""
for v in tmp:
tmpstr+="%5.2f "%v
print tmpstr
# x=vector[1]
# xx=vector[1000]
# y=vector[2700]
# z=vector[2800]
# for i in range(len(vector[0])):
# # print i,x[i],xx[i],y[i],z[i]
# if abs(x[i])!=0 and abs(abs(x[i])-abs(y[i]))/abs(x[i])>0.2:
# print i,x[i],y[i]
# # print len(vector[0])
exit()
exit()
def endgoalvector():
ds=dataset.DataSet()
ds.getTrainData()
dw=datadraw.DataDraw('2d')
mouses=ds.train["mouses"]
goals=ds.train["goals"]
labels=ds.train["labels"]
n=ds.train["size"]
def getvecotr(mouse,goal):
n=len(mouse[0])
ex=mouse[0][n-1]
ey=mouse[1][n-1]
gx=goal[0]
gy=goal[1]
tmp=(gx-ex)**2+(gy-ey)**2
tmp=tmp**0.5
return [(gx-ex)/tmp,(gy-ey)/tmp]
vector=[]
for i in range(n):
vector.append(getvecotr(mouses[i],goals[i]))
vector=np.array(vector)
dt=datadeal.DataTrain()
# clf = MLPClassifier(alpha=1e-3, hidden_layer_sizes=(2,2), random_state=1)
clf = SVC()
dt.trainTest(clf,vector,labels)
def drawline3d2():
ds=dataset.DataSet()
ds.getTrainData()
dw=datadraw.DataDraw('3d')
mouses=ds.train["mouses"]
goals=ds.train["goals"]
# x,y,t=mouses[0]
colors=dw.getColorsValue()
for i in range(10):
dw.draw3dline(mouses[2600+i*40],c=colors[i%10])
goal=goals[2600+i*40]
dw.draw3dgoal([goal[0],goal[1]+2000,0],c=colors[i%10])
plt.show()
def drawline():
ds=dataset.DataSet()
ds.getTrainData()
dw=datadraw.DataDraw('2d')
mouses=ds.train["mouses"]
goals=ds.train["goals"]
# x,y,t=mouses[0]
colors=dw.getColorsValue()
for i in range(10):
# if i==2:
dw.drawline(mouses[i*160],c=colors[i%10])
goal=goals[i*160]
dw.drawgoal([goal[0],goal[1]+2000],c=colors[i%10])
plt.show()
def drawScatter():
ds=dataset.DataSet()
ds.getTrainData()
mouses=ds.train["mouses"]
goals=ds.train["goals"]
dw=datadraw.DataDraw("2d")
mouses_start=ds.getPosOfMouse(1)
dw.drawbatchgoal(mouses_start[:2600],'y')
dw.drawbatchgoal(mouses_start[2600:],'b')
dw.drawbatchgoal(goals[:2600],'y')
dw.drawbatchgoal(goals[2600:],'b')
plt.show()
def draw2d():
ds=dataset.DataSet()
ds.getTrainData()
mouses=ds.train["mouses"]
goals=ds.train["goals"]
dw=datadraw.DataDraw("2d")
START=2700
PAIRS=2
colors=['b','r','g','y','c','k','m']
for i in range(PAIRS):
dw.drawline(mouses[i])
dw.drawline(mouses[START+i])
# dw.drawgoal([goals[i][0],goals[i][1],i],c=colors[i%7])
# dw.drawgoal([goals[START+i][0],goals[START+i][1]],c=colors[(i+3)%7])
plt.show()
def axisy():
ds=dataset.DataSet()
ds.getTrainData()
dw=datadraw.DataDraw('2d')
mouses=ds.train["mouses"]
goals=ds.train["goals"]
labels=ds.train["labels"]
n=ds.train["size"]
def getvecotr(mouse,goal):
yn=len(mouse[1])
y=mouse[1]
if yn==1:
return 0
for i in range(yn)[-1:0:-1]:
y[i]=y[i]-y[i-1]
flag=1
state=y[0]
ychange=0
for i in range(1,yn):
if state*y[i]<0:
ychange+=1
state=y[i]
return ychange
vector=[]
for i in range(n):
vector.append(getvecotr(mouses[i],goals[i]))
vector=np.array(vector,dtype=np.float)
# dw.drawline([range(2600),vector[:2600]],c='b')
# dw.drawline([range(2600,3000),vector[2600:]],c='r')
# print vector[:2600].mean()
# print vector[2600:].mean()
# dw.drawbatchgoal(np.array([vector[:2600],labels[:2600]]).T,c='b')
# dw.drawbatchgoal(np.array([vector[2600:],labels[2600:]]).T,c='r')
# plt.show()
# vector=np.array(vector)
dt=datadeal.DataTrain()
# # clf = MLPClassifier(alpha=1e-3, hidden_layer_sizes=(2,2), random_state=1)
clf = SVC()
dt.trainTest(clf,vector.reshape([3000,1]),labels)
def assemble():
ds = dataset.DataSet()
ds.getTrainData()
dw = datadraw.DataDraw('2d')
mouses = ds.train["mouses"]
goals = ds.train["goals"]
labels = ds.train["labels"]
n = ds.train["size"]
vector = []
# print mouses[492],goals[492]
for i in range(n):
vector.append(getvector(1, mouses[i], goals[i], 1))
vector = np.array(vector)
# print vector.shape
dt = datadeal.DataTrain()
# clf = MLPClassifier(alpha=1e-3, hidden_layer_sizes=(40), random_state=1)
clf = SVC()
dt.trainTest(clf, vector, labels)
def main():
ds=dataset.DataSet()
ds.getTrainData()
mouses=ds.train["mouses"]
goals=ds.train["goals"]
labels=ds.train["labels"]
n=ds.train["size"]
dw=datadraw.DataDraw("2d")
mouses_start=get_start(mouses)
# print mouses_start.shape
# dw.draw3dgoal
# drawbatch(mouses_start[0:2600].T,dw,'y')
# drawbatch(mouses_start[2650:2800].T,dw,'g')
# drawbatch(mouses_start[2800:2900].T,dw,'r')
# drawbatch(mouses_start[2900:3000].T,dw,'b')
# print mouses_start[0]
# exit()
dw.drawbatchgoal(mouses_start[2650:2700],'g')
# dw.drawbatchgoal(mouses_start[2700:2800],'b')
dw.drawbatchgoal(mouses_start[2800:2900],'y')
dw.drawbatchgoal(mouses_start[2900:3000],'r')
dw.drawbatchgoal(mouses_start[2600:2650],'y')
# dw.draw3dgoal(mouses_start[0:2600],c='y')
# dw.draw3dgoal(mouses_start[2650:2850],c='g')
# dw.draw3dgoal(mouses_start[2850:2950],c='g')
# dw.draw3dgoal(mouses_start[2850:3000],c='y')
# dw.draw3dgoal(mouses_start[2600:],c='g')
# dw.draw3dgoal(mouses_start[2600:],c='b')
# borders=get_borders(mouses)
# for i in range(4):
# drawrectangle(borders[i])
# print borders
plt.show()
def main_start():
ds=dataset.DataSet()
ds.getTrainData()
mouses=ds.train["mouses"]
goals=ds.train["goals"]
labels=ds.train["labels"]
n=ds.train["size"]
dw=datadraw.DataDraw("2d")
# mouses_start=ds.getPosOfMouse(0)
# mouses_start=mouses_start.T
# print mouses_start.shape
# exit()
# print mouses_start[:2600].shape
# print mouses_start[:,0].shape
# exit()
# print mouses_start[:,0].T.shape
# mouses_start[:,0].T
mouses_start=get_start(mouses)
# print data.shape
# plt.scatter([1,2],[2,3],c='y')
# print mouses_start[:10]
# exit()
# path="./data/start_pic/"
# for i in range(2650,3001,50):
# dw.drawbatchgoal(mouses_start[:2600],'y')
# dw.drawbatchgoal(mouses_start[i-50:i],'r')
# plt.title("%d.png"%i)
# plt.savefig(path+"%d.png"%i)
# plt.clf()
# plt.close()
# print i
dw.drawbatchgoal(mouses_start[2650:2850],'g')
dw.drawbatchgoal(mouses_start[2850:2950],'r')
dw.drawbatchgoal(mouses_start[2850:3000],'b')
dw.drawbatchgoal(mouses_start[2600:],'y')
dw.drawbatchgoal(mouses_start[2600:],'p')
def main2d():
ds=dataset.DataSet()
ds.getTrainData()
mouses=ds.train["mouses"]
goals=ds.train["goals"]
labels=ds.train["labels"]
n=ds.train["size"]
dw=datadraw.DataDraw("2d")
mouses_start=get_start(mouses)
# print mouses_start.shape
dw.drawbatchgoal(mouses_start[0:2600],'y')
dw.drawbatchgoal(mouses_start[2650:2850],'g')
dw.drawbatchgoal(mouses_start[2850:2950],'g')
dw.drawbatchgoal(mouses_start[2850:3000],'y')
dw.drawbatchgoal(mouses_start[2600:],'g')
dw.drawbatchgoal(mouses_start[2600:],'b')
borders=get_borders(mouses)
for i in range(4):
drawrectangle(borders[i])
# print borders
plt.show()
def analystnoget():
tmp = np.loadtxt('./data/tmp0619.txt')
tmp = np.array(tmp, dtype='int')
ds = dataset.DataSet()
ds.getTrainData()
dw = datadraw.DataDraw('3d')
mouses = ds.train["mouses"]
goals = ds.train["goals"]
labels = ds.train["labels"]
n = ds.train["size"]
# vector=[]
colors = dw.getColorsValue()
count = -1
dw.draw3dline(mouses[0], c=colors[0])
dw.draw3dline(mouses[2700], c=colors[15])
for i in tmp:
count += 1
if count == 6:
continue
dw.draw3dline(mouses[i], c=colors[count % 10 + 1])
plt.show()