def merge(hrus, dominant_hrus, nodata_value):
"""
Non-dominant HRUs are merged into neighboring dominant HRUs
using a Euclidean allocation method.
Parameters
----------
hrus: array
original hru1 raster pre-threshold
dominant_hrus: array
dominant hrus obtained from hru1.shp post-threshold
nodata_value: float
nodata_value for hru1.tif (carried over from original raster)
Returns
-------
hrus: array
hru raster with non-dominant hrus merged into dominant hrus
"""
# inside_watershed will have values for which merging step is carried
inside_watershed_indexes = nonzero(hrus != nodata_value)
outside_watershed_indexes = nonzero(hrus == nodata_value)
# copy hru values
hrus_test = hrus.copy()
# change the default nodata value to 1
hrus_test[outside_watershed_indexes] = 1
# set type increases following loop's performance
dominant_hrus_set = set(dominant_hrus)
# set dominant hrus to 0 and non-dominant to 1
for i in range(0, len(inside_watershed_indexes[0])):
if hrus_test[inside_watershed_indexes[0][i]][
inside_watershed_indexes[1][i]] in dominant_hrus_set:
hrus_test[inside_watershed_indexes[0][i]][
inside_watershed_indexes[1][i]] = 0
else:
hrus_test[inside_watershed_indexes[0][i]][
inside_watershed_indexes[1][i]] = 1
# perform eclidean allocation, returns the indexes
# of the nearest dominant hru
indexes = ndimage.distance_transform_edt(hrus_test, return_indices=True)[1]
# rows and columns of the indexes
rows = indexes[0]
cols = indexes[1]
# use indexes to update non-dominant hrus with nearest dominant hru
for i in range(0, len(rows)):
for j in range(0, len(rows[0])):
hrus[i][j] = hrus[rows[i][j]][cols[i][j]]
# reset nodata now that merging is complete
hrus[outside_watershed_indexes] = nodata_value
return hrus
def extract(condition, arr):
"""Return the elements of ravel(arr) where ravel(condition) is True
(in 1D).
Equivalent to compress(ravel(condition), ravel(arr)).
"""
return _nx.take(ravel(arr), nonzero(ravel(condition))[0])
def extract(condition, arr):
"""Return the elements of ravel(arr) where ravel(condition) is True
(in 1D).
Equivalent to compress(ravel(condition), ravel(arr)).
"""
return _nx.take(ravel(arr), nonzero(ravel(condition))[0])
def _mate_parents(self,p1,p2):
size = self.graph.vcount
child_tour = np.zeros(self.graph.vcount) + p2.tour
while(1) :
a = np.random.randint(0, size, 2)
if(a[0]!=a[1]):
temp = min(a[0],a[1])
a[1] = a[0]+a[1] - temp
a[0] = temp
break;
index_list = []
for i in range(a[0],a[1] + 1):
temp = nonzero(p2.tour == p1.tour[i] )
index_list.append(temp[0][0])
index_list.sort()
for i in range(index_list.__len__()):
temp = a[0] + i
child_tour[index_list[i]] = p1.tour[temp]
child = Tour()
child.tour = child_tour
child.tour_cost = self.graph._get_tour_cost(child_tour)
return child
def selectJ(i,oS,Ei):
maxK = -1;maxDeltaE = 0;Ej = 0
oS.eCache[i] = [1,Ei]
validEcacheList = nonzero(oS.eCache[:,0].A)[0]
if(len(validEcacheList)) > 1:
for k in validEcacheList:
if k == i:continue
Ek = calcEk(oS, k)
deltaE = abs(Ei - Ek)
if(deltaE > maxDeltaE):
maxK = k;maxDeltaE = deltaE;Ej = Ek
return maxK,Ej
else:
j = selectJrand(i, oS.m)
Ej = calcEk(oS, j)
return j,Ej
def smoP(dataMatIn,classLabels,C,toler,maxIter,kTup=('lin',0)):
oS = optStruct(mat(dataMatIn),mat(classLabels).transpose(),C,toler,kTup)
iter = 0
entireSet = True;alphaPairsChanged = 0
while(iter < maxIter) and ((alphaPairsChanged > 0) or (entireSet)):
alphaPairsChanged = 0
if entireSet:
for i in range(oS.m):
alphaPairsChanged += innerL(i,oS)
print "fullSet, iter: %d i:%d,pairs changed %d" % (iter,i,alphaPairsChanged)
iter += 1
else:
nonBoundIs = nonzero((oS.alphas.A > 0) * (oS.alphas.A < C))[0]
for i in nonBoundIs:
alphaPairsChanged += innerL(i,oS)
print "non-bound, iter: %d i:%d,pairs changed %d" % (iter,i,alphaPairsChanged)
iter += 1
if entireSet:entireSet = False;
elif (alphaPairsChanged == 0):entireSet = True;
print "iteration number:% d" % iter
return oS.b,oS.alphas
def KMeans(dataSet, k, distMeas=disEclud, createCen=randCent):
m = np.shape(dataSet)[0]
clusterAssment = np.mat(np.zeros(m, 2))
centroids = randCent(dataSet, k)
clusterChanged = True
while clusterChanged:
clusterChanged = False
for i in range(m):
minDist = inf
minIndex = -1
for j in range(k):
distJI = distMeas(centroids[j, :], dataSet[i, :])
if distJI < minDist:
minDist = distJI
minIndex = j
if clusterAssment[i, :] != minIndex:
clusterAssment = True
clusterAssment[i, :] = minIndex, minDist**2
print(centroids)
for cent in range(k):
pstInClust = dataSet([nonzero(clusterAssment[:0]).A == cent][0])
centroids[cent, :] = mean(pstInClust, axis=0)
return centroids, clusterAssment
def testDigits(kTup=('rbf',10)):
dataArr,labelArr = loadImages('trainingDigits')
b,alphas = smoP(dataArr,labelArr,200,0.0001,10000,kTup)
datMat = mat(dataArr);labelMat = mat(labelArr).transpose()
svInd=nonzero(alphas.A >0)[0]
sVs = datMat[svInd]
labelSV = labelMat[svInd]
print "there are %d support vectors" % shape(sVs)[0]
m,n = shape(datMat)
errorCount = 0
for i in range(m):
kernelEval = kernelTrans(sVs, datMat[i,:], kTup)
predict = kernelEval.T * multiply(labelSV,alphas[svInd]) + b
if sign(predict) != sign(labelArr[i]):errorCount += 1
print "the training error rate is: %f" % (float(errorCount) / m)
dataArr,labelArr = loadImages('testDigits')
errorCount = 0
datMat = mat(dataArr);labelMat=mat(labelArr).transpose()
m,n = shape(datMat)
for i in range(m):
kernelEval = kernelTrans(sVs, datMat[i,:], kTup)
predict = kernelEval.T * multiply(labelSV,alphas[svInd]) + b
if sign(predict) != sign(labelArr[i]):errorCount += 1
print "the test error rate is:%f" % (float(errorCount)/m)
def testRbf(k1=0.9):
dataArr,labelArr = loadDataSet('testSetRBF.txt')
b,alphas = smoP(dataArr,labelArr,200,0.0001,10000,('rbf',k1))
datMat = mat(dataArr);labelMat = mat(labelArr).transpose()
svInd = nonzero(alphas.A > 0)[0]
sVs = datMat[svInd]
labelSV = labelMat[svInd]
print "there are %d Support Vectors" % shape(sVs)[0]
m,n = shape(datMat)
errorCount = 0
for i in range(m):
kernelEval = kernelTrans(sVs, datMat[i,:], ('rbf',k1))
predict = kernelEval.T * multiply(labelSV,alphas[svInd]) +b
if sign(predict) != sign(labelArr[i]):errorCount += 1
print "the training error rate is:%f" % (float(errorCount) / m)
dataArr,labelArr = loadDataSet('testSetRBF2.txt')
errorCount = 0
datMat = mat(dataArr);labelMat = mat(labelArr).transpose()
m,n = shape(datMat)
for i in range(m):
kernelEval = kernelTrans(sVs, datMat[i,:], ('rbf',k1))
predict = kernelEval.T * multiply(labelSV,alphas[svInd]) + b
if sign(predict) != sign(labelArr[i]):errorCount += 1
print "the test error rate is: %f" % (float(errorCount)/m)
def ChangeGreenToWhite(I, Gthr):
R, G, B = cv2.split(I)
I2 = nonzero((G >= Gthr)) # Zwraca indexy wszystkich elementow dla ktorych condition is true
I[I2] = [255, 255, 255]
return I
def ChangeGreenScreenRGB(I, Ibcg, Gthr):
R, G, B = cv2.split(I)
I2 = nonzero((G >= Gthr))
I[I2] = Ibcg[I2]
return I
def ChangeGreenToWhite(I, Gthr):
R,G,B = cv2.split(I)
I2 = nonzero((G>=Gthr)) #Zwraca indexy wszystkich elementow dla ktorych condition is true
I[I2] = [255, 255, 255]
return I
def ChangeGreenScreenRGB(I, Ibcg, Gthr):
R,G,B = cv2.split(I)
I2 = nonzero((G>=Gthr))
I[I2] = Ibcg[I2]
return I
def replaceNanWithMean(self):
datMat = self.loadDataSet('secom.data', ' ')
numFeat = shape(datMat)[1]
for i in range(numFeat):
meanVal = mean(datMat[nonzero(~isnan(datMat[:, i].A))[0], i])
datMat[nonzero(isnan(datMat[:, i].A))[0], i] = meanVal
