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modified: GPy/util/visualize.py

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Added the functionality of showing a mosaic of NxN reconstructed images
when the size of the number of elements in the feature vector greater
than dimensions[0]*dimensions[1].
This commit is contained in:
Teo de Campos 2013-05-16 17:09:58 +01:00
parent 70459be588
commit ea34f8c949
1 changed files with 20 additions and 5 deletions
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25
GPy/util/visualize.py
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@ -193,7 +193,7 @@ class lvm_dimselect(lvm):
class image_show(data_show): class image_show(data_show):
"""Show a data vector as an image.""" """Show a data vector as an image."""
def __init__(self, vals, axes=None, dimensions=(16,16), transpose=False, invert=False, scale=False, palette=[], presetMean = 0., presetSTD = -1., selectImage = 0): def __init__(self, vals, axes=None, dimensions=(16,16), transpose=False, invert=False, scale=False, palette=[], presetMean = 0., presetSTD = -1., selectImage=0):
data_show.__init__(self, vals, axes) data_show.__init__(self, vals, axes)
self.dimensions = dimensions self.dimensions = dimensions
self.transpose = transpose self.transpose = transpose
@ -214,15 +214,30 @@ class image_show(data_show):
def modify(self, vals): def modify(self, vals):
self.set_image(vals) self.set_image(vals)
self.handle.set_array(self.vals) self.handle.set_array(self.vals)
self.axes.figure.canvas.draw() # Teo - original line: plt.show() self.axes.figure.canvas.draw()
def set_image(self, vals): def set_image(self, vals):
dim = self.dimensions[0] * self.dimensions[1] dim = self.dimensions[0] * self.dimensions[1]
self.vals = np.reshape(vals[0,dim*self.selectImage+np.array(range(dim))], self.dimensions, order='F') nImg = np.sqrt(vals[0,].size/dim)
if nImg > 1 and nImg.is_integer(): # Show a mosaic of images
nImg = np.int(nImg)
self.vals = np.zeros((self.dimensions[0]*nImg, self.dimensions[1]*nImg))
for iR in range(nImg):
for iC in range(nImg):
currImgId = iR*nImg + iC
currImg = np.reshape(vals[0,dim*currImgId+np.array(range(dim))], self.dimensions, order='F')
firstRow = iR*self.dimensions[0]
lastRow = (iR+1)*self.dimensions[0]
firstCol = iC*self.dimensions[1]
lastCol = (iC+1)*self.dimensions[1]
self.vals[firstRow:lastRow, firstCol:lastCol] = currImg
else:
self.vals = np.reshape(vals[0,dim*self.selectImage+np.array(range(dim))], self.dimensions, order='F')
if self.transpose: if self.transpose:
self.vals = self.vals.T.copy() self.vals = self.vals.T.copy()
if not self.scale: # if not self.scale:
self.vals = self.vals # self.vals = self.vals
if self.invert: if self.invert:
self.vals = -self.vals self.vals = -self.vals