def __init__(self, parent, data):
self.data = data
self.tool = Mytools()
self.childNodes = []
self.parent = parent
self.childTagName = None
pass
def rangos(self, Hessii, Etot, n_interv):
""" 0.5*k*DQ^2 = E_tot"""
my.hess_check(Hessii)
d = [dict() for n in range(len(Hessii))]
DQ = [sqrt(2.0 * Etot / hii) for hii in Hessii]
n1 = 0
for bond in self.bonds:
n2 = 0
low = self.b0[n1] - DQ[n1]
interv = 2. * DQ[n1] / float(n_interv)
for i in range(n_interv):
rango = (low + i * interv, low + (i + 1) * interv)
d[n1][rango] = 0
n1 += 1
n1 = len(self.b0)
for ang in self.angles:
low = self.a0[n1 - len(self.b0)] - DQ[n1]
interv = 2. * DQ[n1] / float(n_interv)
for i in range(n_interv):
rango = (low + i * interv, low + (i + 1) * interv)
d[n1][rango] = 0
n1 += 1
n1 = len(self.b0) + len(self.a0)
cte = len(self.b0) + len(self.a0)
for dieh in self.dihedrals:
low = self.d0[n1 - cte] - DQ[n1]
interv = 2. * DQ[n1] / float(n_interv)
for i in range(n_interv):
rango = (low + i * interv, low + (i + 1) * interv)
d[n1][rango] = 0
n1 += 1
return d
def main():
datapath = '../TestData/'
predictionDir = '../Predictions/'
if not os.path.isdir(predictionDir):
os.mkdir(predictionDir)
Single = True
nbc = 128
binhalfwins = [2, 3, 4]
nbc = 128
lenthresh = 10
halfwins = [8, 17, 25]
alpha = 3
shiftstart = -4
shiftend = -1
fileList = os.listdir(datapath)
siz = 1
thresh = 0
samplelist = [files for files in fileList if files.startswith("Sample")]
for sample in samplelist:
if Single:
QuadDir = '{}{}/SpecificQuadDescriptors/'.format(datapath, sample)
else:
QuadDir = '{}{}/AllQuadDescriptors/'.format(datapath, sample)
print 'saving classifction number for', sample
smp = GestureSample(datapath, sample, training=False)
Numberofframe = smp.data['numFrames']
OutDir = '{}ClassifierOutputs/'.format(QuadDir)
allBinaryLabels = np.zeros((1, Numberofframe))
ss1 = 0
for binhalfwin in binhalfwins:
BinClfFile = BinClfFile = '{}binary_halfwin_{}.mat'.format(
OutDir, str(binhalfwin))
BinaryLabels = sio.loadmat(BinClfFile)['frame_label']
allBinaryLabels += BinaryLabels
ss1 += 1
allprobs = np.zeros((20, Numberofframe))
ss = 0
for halfwin in halfwins:
ss += 1
inFile = '{}Classfication_nbc_{}_halfwin{}.mat'.format(
OutDir, str(nbc), str(halfwin))
probabilties = 1 - np.transpose(sio.loadmat(inFile)['frame_probs'])
allprobs += probabilties
(r, c) = np.shape(probabilties)
M = np.zeros((r + 1, c))
M[:20, :] = allprobs / ss
# M[20,:] = BinaryLabels
BinaryLabels = mytools.myfilter(allBinaryLabels / ss1, siz, thresh)
# M[20,:] = binprob
M[20, :] = BinaryLabels
(p, D) = dpEM(M, alpha)
(labels, starts, ends) = getLabels(p)
FinalLabels = refineLabes(labels, starts, ends, M, lenthresh,
Numberofframe, shiftstart, shiftend)
tempfilename = '../Predictions/{}_prediction.csv'.format(sample)
mytools.exportPredictions(FinalLabels, tempfilename)
def internas_dihe(self, conex, ndih):
try:
self.qt_1 = self.dihedrals
self.dihedrals = [my.dihedros(self.cart[conex[i][0]-1], self.cart[conex[i][1]-1], \
self.cart[conex[i][2]-1], self.cart[conex[i][3]-1],self.qt_1[i]) for i in range(ndih)]
except AttributeError:
self.qt_1 = [0.0 for w in range(ndih)]
self.dihedrals = [my.dihedros(self.cart[conex[i][0]-1], self.cart[conex[i][1]-1], \
self.cart[conex[i][2]-1], self.cart[conex[i][3]-1],self.qt_1[i]) for i in range(ndih)]
def main():
datapath = '../TestData/'
predictionDir = '../Predictions/'
if not os.path.isdir(predictionDir):
os.mkdir(predictionDir)
Single =True;
nbc =128
binhalfwins = [2,3,4];
nbc =128
lenthresh = 10
halfwins = [8,17,25];
alpha = 3
shiftstart = -4
shiftend = -1
fileList = os.listdir(datapath)
siz = 1;thresh = 0;
samplelist=[files for files in fileList if files.startswith("Sample")]
for sample in samplelist:
if Single:
QuadDir = '{}{}/SpecificQuadDescriptors/'.format(datapath,sample)
else:
QuadDir = '{}{}/AllQuadDescriptors/'.format(datapath,sample)
print 'saving classifction number for',sample
smp=GestureSample(datapath,sample,training = False);
Numberofframe = smp.data['numFrames'];
OutDir = '{}ClassifierOutputs/'.format(QuadDir)
allBinaryLabels = np.zeros((1,Numberofframe))
ss1=0
for binhalfwin in binhalfwins:
BinClfFile = BinClfFile = '{}binary_halfwin_{}.mat'.format(OutDir,str(binhalfwin))
BinaryLabels = sio.loadmat(BinClfFile)['frame_label']
allBinaryLabels += BinaryLabels
ss1+=1
allprobs = np.zeros((20,Numberofframe))
ss =0
for halfwin in halfwins:
ss+=1
inFile = '{}Classfication_nbc_{}_halfwin{}.mat'.format(OutDir,str(nbc),str(halfwin))
probabilties = 1-np.transpose(sio.loadmat(inFile)['frame_probs'])
allprobs += probabilties
(r,c) = np.shape(probabilties);
M = np.zeros((r+1,c));
M[:20,:] = allprobs/ss
# M[20,:] = BinaryLabels
BinaryLabels = mytools.myfilter(allBinaryLabels/ss1,siz,thresh)
# M[20,:] = binprob
M[20,:] = BinaryLabels;
(p,D) = dpEM(M,alpha)
(labels,starts,ends) = getLabels(p)
FinalLabels = refineLabes(labels,starts,ends,M,lenthresh,Numberofframe,shiftstart,shiftend)
tempfilename = '../Predictions/{}_prediction.csv'.format(sample)
mytools.exportPredictions(FinalLabels,tempfilename)
def calc_grad_ext(F_ext, atom1, atom2):
v1 = my.attr_val(atom1)
v2 = my.attr_val(atom2)
# vector = [v1[i]-v2[i] for i in range(3)]
vector = [v2[i] - v1[i] for i in range(3)]
norm = np.linalg.norm(vector)
U = [x / norm for x in vector]
g_ext = [F_ext * x for x in U]
return g_ext
class MyNode:
def __init__(self, parent, data):
self.data = data
self.tool = Mytools()
self.childNodes = []
self.parent = parent
self.childTagName = None
pass
def parser(self):
self.name = self.tool.getTagName(self.data)
self.attr = self.tool.getAttributes(self.data)
if not self.tool.IsChildrenExist(self.data):
self.text = self.tool.GetText(self.data)
pass
def getChildren(self, tag_name):
if self.tool.IsChildrenExist(self.data):
childdatas = self.tool.getChildrenData(self.data)
self.childTagName = self.tool.getTagName(childdatas)
subdatas = self.tool.splitChildrenData(childdatas,
self.childTagName)
for subdata in subdatas:
node = MyNode(self, subdata)
node.parser()
self.childNodes.append(node)
return self.childNodes
pass
def saveFVs(QuadDir,NumberofFrame,gmm,nbc,halfWindows):
savefilename = "{}FVS/FVsnbc_{}_halfwin_{}.mat".format(QuadDir,str(nbc),str(halfWindows[-1]))
# if not os.path.isfile(savefilename):
XX = []
print "Saving Framwise FVS for ", QuadDir;
for numFrame in range(1,NumberofFrame+1):
filename = '{}desc{}.mat'.format(QuadDir,str(numFrame).zfill(5))
Quads = sio.loadmat(filename)['QuadDescriptors']
XX.append(Quads)
num = np.shape(XX)[0]
# fvs = np.zeros((NumberofFrame,nbc*13))
Allfvs = [np.zeros((NumberofFrame,nbc*13)) for k in range(len(halfWindows)) ]
# del fvs
print np.shape(Allfvs),' one ',np.shape(Allfvs[0])
for numFrame in xrange(1,NumberofFrame+1):
wincount = -1
for halfwin in halfWindows:
wincount+=1
XXtemp = []
for fnum in np.arange(max(0,numFrame-halfwin-1),min(numFrame+halfwin,NumberofFrame),1):
Quads = XX[fnum]
if np.shape(Quads)[0]>1:
XXtemp.extend(Quads)
num = np.shape(XXtemp)[0]
if num>0:
Allfvs[wincount][numFrame-1,:] = mytools.fisher_vector(XXtemp, gmm)
wincount = -1
for halfwin in halfWindows:
wincount+=1
savefilename = "{}FVS/FVsnbc_{}_halfwin_{}.mat".format(QuadDir,str(nbc),str(halfwin))
fvs = Allfvs[wincount]
sio.savemat(savefilename,mdict = {'fvs':fvs})
def saveClfOut(QuadDir,OutDir,halfwins,Numberofframe,svmclf,nbc):
for halfwin in halfwins:
outFile = '{}Classfication_nbc_{}_halfwin{}.mat'.format(OutDir,str(nbc),str(halfwin))
# if not os.path.isfile(outFile):
FVsFile = "{}FVS/FVsnbc_{}_halfwin_{}.mat".format(QuadDir,str(nbc),str(halfwin))
fvs = sio.loadmat(FVsFile)['fvs']
vecAllfvs = np.zeros((Numberofframe,nbc*13))
isFrame_labeled = np.zeros(Numberofframe)
i = 0;
for fnum in xrange(Numberofframe):
fvsum = np.sum(fvs[fnum])
if abs(fvsum)>0:
vecAllfvs[i,:] = fvs[i,:]
isFrame_labeled[fnum] = 1
i+=1
vecAllfvs = vecAllfvs[:i,:]
vecAllfvs = mytools.power_normalize(vecAllfvs,0.2)
frame_probs = svmclf.predict_proba(vecAllfvs)
frame_label = svmclf.predict(vecAllfvs)
frame_probstemp = np.zeros((Numberofframe,20))
frame_probstemp[isFrame_labeled>0,:] = frame_probs
frame_labelstemp = np.zeros(Numberofframe)
frame_labelstemp[isFrame_labeled>0]=frame_label
print 'saving to ' , outFile
sio.savemat(outFile,mdict={'frame_probs':frame_probstemp, 'frame_label':frame_labelstemp,
'isFrame_labeled':isFrame_labeled})
def saveClfOut(sample,QuadDir,OutDir,halfwin,Numberofframe,svmclf,nbc):
outFile = '{}binary_halfwin_{}.mat'.format(OutDir,str(halfwin))
# if not os.path.isfile(outFile):
FVsFile = "{}FVS/FVsnbc_{}_halfwin_{}.mat".format(QuadDir,str(nbc),str(halfwin))
fvs = sio.loadmat(FVsFile)['fvs']
vecAllfvs = np.zeros((Numberofframe,nbc*13))
isFrame_labeled = np.zeros(Numberofframe)
i = 0;
for fnum in xrange(Numberofframe):
fvsum = np.sum(fvs[fnum])
if abs(fvsum)>0:
vecAllfvs[i,:] = fvs[i,:]
isFrame_labeled[fnum] = 1
i+=1
vecAllfvs = vecAllfvs[:i,:]
vecAllfvs = mytools.power_normalize(vecAllfvs,0.4)
frame_label = svmclf.predict(vecAllfvs)
frame_labelstemp = np.zeros((Numberofframe))
frame_labelstemp[isFrame_labeled>0]=frame_label
print 'saving to ' , outFile
sio.savemat(outFile,mdict={'frame_label':frame_labelstemp,'isFrame_labeled':isFrame_labeled})
def flat_points(lists_points):
L = []
for val in lists_points:
x, y, z = my.attr_val(val)
L.append(x)
L.append(y)
L.append(z)
return L
def saveQuads(saveDir, isAll, numFrame, Joints3D, Single, combs):
savefilename = '{}desc{}.mat'.format(saveDir, str(numFrame).zfill(5))
QuadDescriptors = []
# AllQuadDescriptors = []
if np.sum(Joints3D[0, :]) > 0.05:
for combination in combs:
quadrupleJoints = Joints3D[combination - 1]
QuadDescriptor = mytools.ComputeQuadDescriptor(
quadrupleJoints, Single, isAll)
if isAll:
QuadDescriptors.extend(QuadDescriptor)
else:
QuadDescriptors.append(QuadDescriptor)
QuadDescriptors = checkDescs4NAN(QuadDescriptors)
sio.savemat(savefilename, mdict={'QuadDescriptors': QuadDescriptors})
def saveFVs(QuadDir, NumberofFrame, gmm, nbc, halfWindows):
savefilename = "{}FVS/FVsnbc_{}_halfwin_{}.mat".format(
QuadDir, str(nbc), str(halfWindows[-1]))
# if not os.path.isfile(savefilename):
XX = []
print "Saving Framwise FVS for ", QuadDir
for numFrame in range(1, NumberofFrame + 1):
filename = '{}desc{}.mat'.format(QuadDir, str(numFrame).zfill(5))
Quads = sio.loadmat(filename)['QuadDescriptors']
XX.append(Quads)
num = np.shape(XX)[0]
# fvs = np.zeros((NumberofFrame,nbc*13))
Allfvs = [
np.zeros((NumberofFrame, nbc * 13)) for k in range(len(halfWindows))
]
# del fvs
print np.shape(Allfvs), ' one ', np.shape(Allfvs[0])
for numFrame in xrange(1, NumberofFrame + 1):
wincount = -1
for halfwin in halfWindows:
wincount += 1
XXtemp = []
for fnum in np.arange(max(0, numFrame - halfwin - 1),
min(numFrame + halfwin, NumberofFrame), 1):
Quads = XX[fnum]
if np.shape(Quads)[0] > 1:
XXtemp.extend(Quads)
num = np.shape(XXtemp)[0]
if num > 0:
Allfvs[wincount][numFrame - 1, :] = mytools.fisher_vector(
XXtemp, gmm)
wincount = -1
for halfwin in halfWindows:
wincount += 1
savefilename = "{}FVS/FVsnbc_{}_halfwin_{}.mat".format(
QuadDir, str(nbc), str(halfwin))
fvs = Allfvs[wincount]
sio.savemat(savefilename, mdict={'fvs': fvs})
"""
Created on 2011-10-6
@author: huangkan
"""
import Mytools
ten10 = 10 ** 10
l = list(Mytools.int2(7830457, 2))
ans = 1
for i in l:
if i == "0":
ans = ans * ans % ten10
else:
ans = ans * ans * 2 % ten10
ans = (ans * 28433 + 1) % ten10
print(ans)
print(l)
newdes = des
if NANs.any() > 0:
print 'There is NAN case', np.shape(des)
newdes = des[not NANs]
print 'sahpe after', np.shape(newdes)
return newdes
if __name__ == '__main__':
# main()
# Path which contains sample files in .zip format
datapaths = ['../TestData/']
# Keep an copy the the sample files We delete what is not required
# _depth.mp4 _video.mp4 _user.mp4 and sampleXXXXX.zip files will be deteted
mytools.UnzipAllfiles(datapaths[0])
kk = 0
isAll = False
Single = True
for datapath in datapaths:
# Get the list of training samples
fileList = os.listdir(datapath)
print datapath
# Filter input files (only ZIP files)
samplelist = [
files for files in fileList if files.startswith("Sample")
]
for sample in samplelist:
#print("\t Processing file " + sample)
'''
Created on 2011-4-30
@author: huangkan
'''
import Mytools
i=144
while 1:
o=Mytools.hexagonal(i)
if Mytools.Ispentagon(o) and Mytools.Istriangle(o):
print(o)
break
i+=1
'''
Created on 2011-10-1
@author: huangkan
'''
import Mytools
import math
l=50
ans=l*l*3
for x in range(1,l+1):
for y in range(1,l+1):
g=Mytools.gcd(x, y)
x1=x/g
y1=y/g
k=min(math.floor((l-x)/y1),math.floor(y/x1))
ans+=k*2
print(ans)
def cinetica(self):
k = 0.5 * my.cuad(self.vel, self.vel) * self.mass
return k
return L
########### READING DATA ########################
# READING DATA FROM THE INPUT FILE
input = open('input.dat')
files = [line for line in input.read().split()]
#reading of the Cartesian Cordinates, number of Atoms, type of Atoms,
#Masses and Energy
file_st1 = files[0]
cord, numat, typ, symb, mass, st1_energy, st1_grad_cart, st1_hess_cart = \
my.initial_data(file_st1,'state1')
# READING CONECTIVITY
#my.mtx_conect('conex.dat')
conexion = list(open('internas.dat'))
nbond = int(conexion[0])
nang = int(conexion[nbond + 1])
ndih = int(conexion[nbond + nang + 2])
bond = [tuple([int(m) for m in w.split()]) for w in conexion[1:nbond + 1]]
ang = [
tuple([int(m) for m in w.split()])
for w in conexion[nbond + 2:nbond + nang + 2]
]
dih = [
tuple([int(m) for m in w.split()])
'''
Created on 2011-8-7
@author: huangkan
'''
limit=50*10**6
flag=[False]*(limit+1)
import Mytools
ps=Mytools.primes(int(limit**.5)+1)
for i in ps:
sum=i*i
for j in ps:
sum=i*i+j*j*j
if sum>limit:
break
else:
for k in ps:
sum=i**2+j**3+k**4
if sum>limit:break
else: flag[sum]=True
ans=0
for i in flag:
if i:ans+=1
print(ans)
'''
Created on 2011-7-31
@author: huangkan
'''
import math
import Mytools
sum=0
print(int(Mytools.mysqrt(2*10**200)))
for i in range(2,100):
if i**.5!=int(i**.5):
t=int(math.sqrt(i*10**200))
#print(len(str(t)))
ints=list(str(t))
for j in ints[1:]:
sum+=int(j)
print(sum)
def internas_bonds(self, conex, nbond):
self.bonds = [my.enlaces(self.cart[conex[i][0]-1], self.cart[conex[i][1]-1]) \
for i in range(nbond)]
def internas_ang(self, conex, nang):
self.angles = [my.angulos(self.cart[conex[i][0]-1], self.cart[conex[i][1]-1], \
self.cart[conex[i][2]-1]) for i in range(nang)]
'''
Created on 2011-7-9
@author: huangkan
'''
def cont(i,j):
return int(str(i)+str(j))
#from Mytools import primes
from Mytools import Isprime
import Mytools
from itertools import combinations
estimate=1000000
limit=100000
pr=Mytools.primes_shelter(limit)
p1=[i for i in range(limit) if pr[i] and i%3==1 and i>673]
t=[3,7,109,673]
Oracle=Mytools.PrimeOracle()
i=673
while True:
if Mytools.Isprime(i):
for j in t:
if not Mytools.Isprime(cont(i,j)) or not Mytools.Isprime(cont(j,i)):
break
else:
print(i)
break
i+=6
"""
Created on 2011-7-30
@author: huangkan
"""
import Mytools
limit = 100
l = [0] * (limit + 1)
for i in range(0, limit + 1):
if i % 2 == 0:
l[i] = 1
p = Mytools.primes(limit)
for i in p[1:]:
tl = l[:]
for j in range(1, int(limit / i) + 1):
for k in range(limit + 1):
if k + i * j < limit:
l[k + i * j] += tl[k]
else:
break
print(l)
for i in range(limit):
if l[i] > 5000:
print(i)
break
