def pick(event):
global data
global X
global Y
global numpy_container
if event.key == 'q':
if len(X) == 0: return
if not numpy_container:
data = VectorDataSet(X)
else:
data = PyVectorDataSet(numpy.array(X))
data.attachLabels(Labels(Y))
X = []
Y = []
print 'done creating data. close this window and use the decisionSurface function'
pylab.disconnect(binding_id)
if event.key == '1' or event.key == '2':
if event.inaxes is not None:
print 'data coords', event.xdata, event.ydata
X.append([event.xdata, event.ydata])
Y.append(event.key)
pylab.plot([event.xdata], [event.ydata],
plotStr[int(event.key) - 1])
pylab.draw()
def pick(event) :
global data
global X
global Y
global numpy_container
if event.key == 'q' :
if len(X) == 0 : return
if not numpy_container :
data = VectorDataSet(X)
else :
data = PyVectorDataSet(numpy.array(X))
data.attachLabels(Labels(Y))
X = []
Y = []
print 'done creating data. close this window and use the decisionSurface function'
pylab.disconnect(binding_id)
if event.key =='1' or event.key == '2' :
if event.inaxes is not None:
print 'data coords', event.xdata, event.ydata
X.append([event.xdata, event.ydata])
Y.append(event.key)
pylab.plot([event.xdata], [event.ydata],
plotStr[int(event.key) - 1])
pylab.draw()
def adjacent(self, i, j):
xi = numpy.array(self.data.getPattern(i))
xj = numpy.array(self.data.getPattern(j))
stepSize = 1.0 / (self.lineSampleSize + 1)
lambdas = numpy.arange(0, 1, stepSize)
X = []
for l in lambdas[1:]:
X.append((xi * l + xj * (1 - l)).tolist())
testdata = VectorDataSet(X)
for i in range(len(testdata)):
f = self.decisionFunc(testdata, i)
if f < 0:
return False
return True
def decisionSurface(classifier, fileName=None, **args):
global data
global numpy_container
classifier.train(data)
numContours = 3
if 'numContours' in args:
numContours = args['numContours']
title = None
if 'title' in args:
title = args['title']
markersize = 5
fontsize = 'medium'
if 'markersize' in args:
markersize = args['markersize']
if 'fontsize' in args:
fontsize = args['fontsize']
contourFontsize = 10
if 'contourFontsize' in args:
contourFontsize = args['contourFontsize']
showColorbar = False
if 'showColorbar' in args:
showColorbar = args['showColorbar']
show = True
if fileName is not None:
show = False
if 'show' in args:
show = args['show']
# setting up the grid
delta = 0.01
if 'delta' in args:
delta = args['delta']
x = arange(xmin, xmax, delta)
y = arange(ymin, ymax, delta)
Z = numpy.zeros((len(x), len(y)), numpy.float)
gridX = numpy.zeros((len(x) * len(y), 2), numpy.float)
n = 0
for i in range(len(x)):
for j in range(len(y)):
gridX[n][0] = x[i]
gridX[n][1] = y[j]
n += 1
if not numpy_container:
gridData = VectorDataSet(gridX)
gridData.attachKernel(data.kernel)
else:
gridData = PyVectorDataSet(gridX)
results = classifier.test(gridData)
n = 0
for i in range(len(x)):
for j in range(len(y)):
Z[i][j] = results.decisionFunc[n]
n += 1
#pylab.figure()
im = pylab.imshow(numpy.transpose(Z),
interpolation='bilinear',
origin='lower',
cmap=pylab.cm.gray,
extent=(xmin, xmax, ymin, ymax))
if numContours == 1:
C = pylab.contour(numpy.transpose(Z), [0],
origin='lower',
linewidths=(3),
colors='black',
extent=(xmin, xmax, ymin, ymax))
elif numContours == 3:
C = pylab.contour(numpy.transpose(Z), [-1, 0, 1],
origin='lower',
linewidths=(1, 3, 1),
colors='black',
extent=(xmin, xmax, ymin, ymax))
else:
C = pylab.contour(numpy.transpose(Z),
numContours,
origin='lower',
linewidths=2,
extent=(xmin, xmax, ymin, ymax))
pylab.clabel(C, inline=1, fmt='%1.1f', fontsize=contourFontsize)
# plot the data
scatter(data, markersize=markersize)
xticklabels = pylab.getp(pylab.gca(), 'xticklabels')
yticklabels = pylab.getp(pylab.gca(), 'yticklabels')
pylab.setp(xticklabels, fontsize=fontsize)
pylab.setp(yticklabels, fontsize=fontsize)
if title is not None:
pylab.title(title, fontsize=fontsize)
if showColorbar:
pylab.colorbar(im)
# colormap:
pylab.hot()
if fileName is not None:
pylab.savefig(fileName)
if show:
pylab.show()
def decisionSurface(classifier, fileName = None, **args) :
global data
global numpy_container
classifier.train(data)
numContours = 3
if 'numContours' in args :
numContours = args['numContours']
title = None
if 'title' in args :
title = args['title']
markersize=5
fontsize = 'medium'
if 'markersize' in args :
markersize = args['markersize']
if 'fontsize' in args :
fontsize = args['fontsize']
contourFontsize = 10
if 'contourFontsize' in args :
contourFontsize = args['contourFontsize']
showColorbar = False
if 'showColorbar' in args :
showColorbar = args['showColorbar']
show = True
if fileName is not None :
show = False
if 'show' in args :
show = args['show']
# setting up the grid
delta = 0.01
if 'delta' in args :
delta = args['delta']
x = arange(xmin, xmax, delta)
y = arange(ymin, ymax, delta)
Z = numpy.zeros((len(x), len(y)), numpy.float)
gridX = numpy.zeros((len(x) *len(y), 2), numpy.float)
n = 0
for i in range(len(x)) :
for j in range(len(y)) :
gridX[n][0] = x[i]
gridX[n][1] = y[j]
n += 1
if not numpy_container :
gridData = VectorDataSet(gridX)
gridData.attachKernel(data.kernel)
else :
gridData = PyVectorDataSet(gridX)
results = classifier.test(gridData)
n = 0
for i in range(len(x)) :
for j in range(len(y)) :
Z[i][j] = results.decisionFunc[n]
n += 1
#pylab.figure()
im = pylab.imshow(numpy.transpose(Z),
interpolation='bilinear', origin='lower',
cmap=pylab.cm.gray, extent=(xmin,xmax,ymin,ymax) )
if numContours == 1 :
C = pylab.contour(numpy.transpose(Z),
[0],
origin='lower',
linewidths=(3),
colors = 'black',
extent=(xmin,xmax,ymin,ymax))
elif numContours == 3 :
C = pylab.contour(numpy.transpose(Z),
[-1,0,1],
origin='lower',
linewidths=(1,3,1),
colors = 'black',
extent=(xmin,xmax,ymin,ymax))
else :
C = pylab.contour(numpy.transpose(Z),
numContours,
origin='lower',
linewidths=2,
extent=(xmin,xmax,ymin,ymax))
pylab.clabel(C,
inline=1,
fmt='%1.1f',
fontsize=contourFontsize)
# plot the data
scatter(data, markersize=markersize)
xticklabels = pylab.getp(pylab.gca(), 'xticklabels')
yticklabels = pylab.getp(pylab.gca(), 'yticklabels')
pylab.setp(xticklabels, fontsize=fontsize)
pylab.setp(yticklabels, fontsize=fontsize)
if title is not None :
pylab.title(title, fontsize=fontsize)
if showColorbar :
pylab.colorbar(im)
# colormap:
pylab.hot()
if fileName is not None :
pylab.savefig(fileName)
if show :
pylab.show()
