def starRegression(cache, dimensions, progressCallback=None, **args):
if len(cache.contAttributes) == 1:
return triangles1D(cache, True)
if not cache.points:
cache.points = orange.ExampleTable(
orange.Domain(cache.contAttributes, cache.data.domain.classVar),
cache.data).native(0)
points = cache.points
npoints = len(points)
if not cache.tri:
cache.tri = triangulate(cache, points)
tri = cache.tri
if not cache.stars:
cache.stars = [star(x, tri) for x in xrange(npoints)]
S = cache.stars
points = cache.points
if progressCallback:
nPoints = 100.0 / len(points)
for x, (S, p) in enumerate(zip(cache.stars, points)):
if S == []:
cache.deltas[x] = ['?' for i in dimensions]
continue
st = list(set(reduce(lambda x, y: x + y, S)))
A = [points[i][:-1] for i in st]
b = [[points[i][-1]] for i in st]
cache.deltas[x] = [i[0] for i in numpy.linalg.lstsq(A, b)[0]]
if progressCallback:
progressCallback(x * nPoints)
def starRegression(cache, dimensions, progressCallback=None, **args):
if len(cache.contAttributes) == 1:
return triangles1D(cache, True)
if not cache.points:
cache.points = orange.ExampleTable(
orange.Domain(cache.contAttributes, cache.data.domain.classVar), cache.data
).native(0)
points = cache.points
npoints = len(points)
if not cache.tri:
cache.tri = triangulate(cache, points)
tri = cache.tri
if not cache.stars:
cache.stars = [star(x, tri) for x in xrange(npoints)]
S = cache.stars
points = cache.points
if progressCallback:
nPoints = 100.0 / len(points)
for x, (S, p) in enumerate(zip(cache.stars, points)):
if S == []:
cache.deltas[x] = ["?" for i in dimensions]
continue
st = list(set(reduce(lambda x, y: x + y, S)))
A = [points[i][:-1] for i in st]
b = [[points[i][-1]] for i in st]
cache.deltas[x] = [i[0] for i in numpy.linalg.lstsq(A, b)[0]]
if progressCallback:
progressCallback(x * nPoints)
def Dpca(self):
self.pca()
return
if not self.deltas:
self.deltas = [[None] * len(self.contAttributes)
for x in xrange(len(self.data))]
dimensions = [d for d in self.dimensions if not self.deltas[0][d]]
if not dimensions:
return
if not self.points:
self.points = orange.ExampleTable(
orange.Domain(self.contAttributes, self.data.domain.classVar),
self.data).native(0)
points = self.points
npoints = len(points)
if not self.tri:
print self.dimension
self.tri = self.triangulate(points)
tri = self.tri
if not self.stars:
self.stars = [star(x, tri) for x in xrange(npoints)]
S = self.stars
points = import85
nPoints = 100.0 / len(points)
self.progressBarInit()
for x, (S, p) in enumerate(zip(self.stars, points)):
if S == []:
self.deltas[x] = ['?' for i in dimensions]
continue
st = list(set(reduce(lambda x, y: x + y, S)))
lenst = len(st)
avgy = sum([points[i][-1] for i in st]) / lenst
for di, d in enumerate(dimensions):
avgx = sum([points[i][di] for i in st]) / lenst
sxx2 = sum([(points[i][di] - avgx)**2 for i in st])
if sxx2:
sxx = sum([(points[i][di] - avgx) * (points[i][-1] - avgy)
for i in st])
b = sxx / sxx2
self.deltas[x][di] = b
else:
self.deltas[x][di] = '?'
self.progressBarSet(x * nPoints)
self.progressBarFinished()
def Dpca(self):
self.pca()
return
if not self.deltas:
self.deltas = [[None] * len(self.contAttributes) for x in xrange(len(self.data))]
dimensions = [d for d in self.dimensions if not self.deltas[0][d]]
if not dimensions:
return
if not self.points:
self.points = orange.ExampleTable(
orange.Domain(self.contAttributes, self.data.domain.classVar), self.data
).native(0)
points = self.points
npoints = len(points)
if not self.tri:
print self.dimension
self.tri = self.triangulate(points)
tri = self.tri
if not self.stars:
self.stars = [star(x, tri) for x in xrange(npoints)]
S = self.stars
points = import85
nPoints = 100.0 / len(points)
self.progressBarInit()
for x, (S, p) in enumerate(zip(self.stars, points)):
if S == []:
self.deltas[x] = ["?" for i in dimensions]
continue
st = list(set(reduce(lambda x, y: x + y, S)))
lenst = len(st)
avgy = sum([points[i][-1] for i in st]) / lenst
for di, d in enumerate(dimensions):
avgx = sum([points[i][di] for i in st]) / lenst
sxx2 = sum([(points[i][di] - avgx) ** 2 for i in st])
if sxx2:
sxx = sum([(points[i][di] - avgx) * (points[i][-1] - avgy) for i in st])
b = sxx / sxx2
self.deltas[x][di] = b
else:
self.deltas[x][di] = "?"
self.progressBarSet(x * nPoints)
self.progressBarFinished()
def starUnivariateRegression(cache, dimensions, progressCallback=None, **args):
if len(cache.contAttributes) == 1:
return triangles1D(cache, True)
if not cache.points:
cache.points = orange.ExampleTable(
orange.Domain(cache.contAttributes, cache.data.domain.classVar),
cache.data).native(0)
points = cache.points
npoints = len(points)
if not cache.tri:
cache.tri = triangulate(cache, points)
tri = cache.tri
if not cache.stars:
cache.stars = [star(x, tri) for x in xrange(npoints)]
S = cache.stars
points = cache.points
if progressCallback:
nPoints = 100.0 / len(points)
for x, (S, p) in enumerate(zip(cache.stars, points)):
if S == []:
cache.deltas[x] = ['?' for i in dimensions]
continue
st = list(set(reduce(lambda x, y: x + y, S)))
lenst = len(st)
avgy = sum([points[i][-1] for i in st]) / lenst
for d in dimensions:
avgx = sum([points[i][d] for i in st]) / lenst
sxx2 = sum([(points[i][d] - avgx)**2 for i in st])
if sxx2:
sxx = sum([(points[i][d] - avgx) * (points[i][-1] - avgy)
for i in st])
b = sxx / sxx2
cache.deltas[x][d] = b
else:
cache.deltas[x][d] = '?'
if progressCallback:
progressCallback(x * nPoints)
def starUnivariateRegression(cache, dimensions, progressCallback=None, **args):
if len(cache.contAttributes) == 1:
return triangles1D(cache, True)
if not cache.points:
cache.points = orange.ExampleTable(
orange.Domain(cache.contAttributes, cache.data.domain.classVar), cache.data
).native(0)
points = cache.points
npoints = len(points)
if not cache.tri:
cache.tri = triangulate(cache, points)
tri = cache.tri
if not cache.stars:
cache.stars = [star(x, tri) for x in xrange(npoints)]
S = cache.stars
points = cache.points
if progressCallback:
nPoints = 100.0 / len(points)
for x, (S, p) in enumerate(zip(cache.stars, points)):
if S == []:
cache.deltas[x] = ["?" for i in dimensions]
continue
st = list(set(reduce(lambda x, y: x + y, S)))
lenst = len(st)
avgy = sum([points[i][-1] for i in st]) / lenst
for d in dimensions:
avgx = sum([points[i][d] for i in st]) / lenst
sxx2 = sum([(points[i][d] - avgx) ** 2 for i in st])
if sxx2:
sxx = sum([(points[i][d] - avgx) * (points[i][-1] - avgy) for i in st])
b = sxx / sxx2
cache.deltas[x][d] = b
else:
cache.deltas[x][d] = "?"
if progressCallback:
progressCallback(x * nPoints)
def firstTriangle(cache, dimensions, progressCallback=None, **args):
if len(cache.contAttributes) == 1:
return triangles1D(cache, False)
if not cache.points:
cache.points = orange.ExampleTable(
orange.Domain(cache.contAttributes, cache.data.domain.classVar),
cache.data).native(0)
points = cache.points
npoints = len(points)
if not cache.tri:
cache.tri = triangulate(cache, points)
tri = cache.tri
if not cache.stars:
cache.stars = [star(x, tri) for x in xrange(npoints)]
S = cache.stars
if not cache.dts:
cache.dts = [
min([
min([
dist(points[x][:-1], points[v][:-1])
for v in simplex if v != x
]) for simplex in S[x]
]) * .1 for x in xrange(npoints)
]
if progressCallback:
nPoints = 100.0 / npoints
for x, (S, xp, dt, deltas) in enumerate(
zip(cache.stars, points, cache.dts, cache.deltas)):
for d in dimensions:
xn = xp[:-1]
DBG = 0
# if xn[0]>16 and xn[1]<44.5 and xn[1]>43:
# #if xn[0]>4.7 and xn[0]<4.9 and xn[1]<24.5 and xn[1]>23.5:
# DBG=1
# #print "DBG"
O = numpy.array(xp[:-1])
xn[d] += dt
swx = simplex_with_xn(cache, xn, S)
if swx:
# if DBG:
# print "iskanje cudnih trikotnikov"
# print swx
# print [points[k] for k in swx]
obrni = 1
# if DBG:
# print xp
# print swx
# print [points[k][-1] for k in swx]
# vecs = numpy.array([numpy.array(points[p][:-1])-O for p in swx if p!=x])
# vecs = vecs.transpose()
# XN = numpy.array(xn)-O
# coef = numpy.linalg.solve(vecs,XN)
# xnz = sum(coef*[numpy.array(points[p][-1]-xp[-1])for p in swx if p!=x])+xp[-1]
# dd = obrni * (xnz-xp[-1]) / dt
# print "xnz= ",xnz,"\tdd=",dd,"\trazlika (xnz-xp[-1])=",(xnz-xp[-1])
# print "-----------------"
else:
xn[d] = xp[d] - dt
swx = simplex_with_xn(cache, xn, S)
if swx:
obrni = -1
# if DBG:
# print xp
# print swx
# print "v levo\t",[points[k] for k in swx]
# print "----------------"
else:
# if DBG:
# print xp
# do = simplex_with_xnDBG(cache, xp, S, d)
# print do
do = simplex_with_xnDBG(cache, xp, S, d)
if do:
deltas[d] = do
else:
deltas[d] = "?"
continue
vecs = numpy.array(
[numpy.array(points[p][:-1]) - O for p in swx if p != x])
vecs = vecs.transpose()
XN = numpy.array(xn) - O
coef = numpy.linalg.solve(vecs, XN)
xnz = sum(
coef *
[numpy.array(points[p][-1] - xp[-1])
for p in swx if p != x]) + xp[-1]
deltas[d] = obrni * (xnz - xp[-1]) / dt
#print "DELTAS = ",deltas[d]
if progressCallback:
progressCallback(x * nPoints)
def firstTriangle(cache, dimensions, progressCallback=None, **args):
if len(cache.contAttributes) == 1:
return triangles1D(cache, False)
if not cache.points:
cache.points = orange.ExampleTable(
orange.Domain(cache.contAttributes, cache.data.domain.classVar), cache.data
).native(0)
points = cache.points
npoints = len(points)
if not cache.tri:
cache.tri = triangulate(cache, points)
tri = cache.tri
if not cache.stars:
cache.stars = [star(x, tri) for x in xrange(npoints)]
S = cache.stars
if not cache.dts:
cache.dts = [
min([min([dist(points[x][:-1], points[v][:-1]) for v in simplex if v != x]) for simplex in S[x]]) * 0.1
for x in xrange(npoints)
]
if progressCallback:
nPoints = 100.0 / npoints
for x, (S, xp, dt, deltas) in enumerate(zip(cache.stars, points, cache.dts, cache.deltas)):
for d in dimensions:
xn = xp[:-1]
DBG = 0
# if xn[0]>16 and xn[1]<44.5 and xn[1]>43:
# #if xn[0]>4.7 and xn[0]<4.9 and xn[1]<24.5 and xn[1]>23.5:
# DBG=1
# #print "DBG"
O = numpy.array(xp[:-1])
xn[d] += dt
swx = simplex_with_xn(cache, xn, S)
if swx:
# if DBG:
# print "iskanje cudnih trikotnikov"
# print swx
# print [points[k] for k in swx]
obrni = 1
# if DBG:
# print xp
# print swx
# print [points[k][-1] for k in swx]
# vecs = numpy.array([numpy.array(points[p][:-1])-O for p in swx if p!=x])
# vecs = vecs.transpose()
# XN = numpy.array(xn)-O
# coef = numpy.linalg.solve(vecs,XN)
# xnz = sum(coef*[numpy.array(points[p][-1]-xp[-1])for p in swx if p!=x])+xp[-1]
# dd = obrni * (xnz-xp[-1]) / dt
# print "xnz= ",xnz,"\tdd=",dd,"\trazlika (xnz-xp[-1])=",(xnz-xp[-1])
# print "-----------------"
else:
xn[d] = xp[d] - dt
swx = simplex_with_xn(cache, xn, S)
if swx:
obrni = -1
# if DBG:
# print xp
# print swx
# print "v levo\t",[points[k] for k in swx]
# print "----------------"
else:
# if DBG:
# print xp
# do = simplex_with_xnDBG(cache, xp, S, d)
# print do
do = simplex_with_xnDBG(cache, xp, S, d)
if do:
deltas[d] = do
else:
deltas[d] = "?"
continue
vecs = numpy.array([numpy.array(points[p][:-1]) - O for p in swx if p != x])
vecs = vecs.transpose()
XN = numpy.array(xn) - O
coef = numpy.linalg.solve(vecs, XN)
xnz = sum(coef * [numpy.array(points[p][-1] - xp[-1]) for p in swx if p != x]) + xp[-1]
deltas[d] = obrni * (xnz - xp[-1]) / dt
# print "DELTAS = ",deltas[d]
if progressCallback:
progressCallback(x * nPoints)
